\".join(location for location in locations)\n pre_message += f\"]\"\n formatted_message = pre_message + message + \"[/ref]\"\n\n return formatted_message\n\n\ndef request_available_missions(locations_done, location_table, ui):\n if location_table:\n message = \"Available Missions: \"\n\n # Initialize mission unlock table\n unlocks = initialize_blank_mission_dict(location_table)\n\n missions = calc_available_missions(locations_done, location_table, unlocks)\n message += \\\n \", \".join(f\"{mark_up_mission_name(mission, location_table, ui, unlocks)}[{location_table[mission].id}]\"\n for mission in missions)\n\n if ui:\n ui.log_panels['All'].on_message_markup(message)\n ui.log_panels['Starcraft2'].on_message_markup(message)\n else:\n sc2_logger.info(message)\n else:\n sc2_logger.warning(\"No mission table found, you are likely not connected to a server.\")\n\n\ndef calc_available_missions(locations_done, locations, unlocks=None):\n available_missions = []\n missions_complete = 0\n\n # Get number of missions completed\n for loc in locations_done:\n if loc % 100 == 0:\n missions_complete += 1\n\n for name in locations:\n # Go through the required missions for each mission and fill up unlock table used later for hover-over tooltips\n if unlocks:\n for unlock in locations[name].required_world:\n unlocks[list(locations)[unlock-1]].append(name)\n\n if mission_reqs_completed(name, missions_complete, locations_done, locations):\n available_missions.append(name)\n\n return available_missions\n\n\ndef mission_reqs_completed(location_to_check, missions_complete, locations_done, locations):\n \"\"\"Returns a bool signifying if the mission has all requirements complete and can be done\n\n Keyword arguments:\n locations_to_check -- the mission string name to check\n missions_complete -- an int of how many missions have been completed\n locations_done -- a list of the location ids that have been complete\n locations -- a dict of MissionInfo for mission requirements for this world\"\"\"\n if len(locations[location_to_check].required_world) >= 1:\n # A check for when the requirements are being or'd\n or_success = False\n\n # Loop through required missions\n for req_mission in locations[location_to_check].required_world:\n req_success = True\n\n # Check if required mission has been completed\n if not (locations[list(locations)[req_mission-1]].id * 100 + SC2WOL_LOC_ID_OFFSET) in locations_done:\n if not locations[location_to_check].or_requirements:\n return False\n else:\n req_success = False\n\n # Recursively check required mission to see if it's requirements are met, in case !collect has been done\n if not mission_reqs_completed(list(locations)[req_mission-1], missions_complete, locations_done,\n locations):\n if not locations[location_to_check].or_requirements:\n return False\n else:\n req_success = False\n\n # If requirement check succeeded mark or as satisfied\n if locations[location_to_check].or_requirements and req_success:\n or_success = True\n\n if locations[location_to_check].or_requirements:\n # Return false if or requirements not met\n if not or_success:\n return False\n\n # Check number of missions\n if missions_complete >= locations[location_to_check].number:\n return True\n else:\n return False\n else:\n return True\n\n\ndef initialize_blank_mission_dict(location_table):\n unlocks = {}\n\n for mission in list(location_table):\n unlocks[mission] = []\n\n return unlocks\n\n\nif __name__ == '__main__':\n colorama.init()\n asyncio.run(main())\n colorama.deinit()\n","repo_name":"CaitSith2/MultiWorld-Utilities","sub_path":"Starcraft2Client.py","file_name":"Starcraft2Client.py","file_ext":"py","file_size_in_byte":28263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"16538279278","text":"#!/usr/bin/python2\n# True Item LLC\n# Artem Ivzhenko\n# artmeivzhenko@true-item.com\n# UDP Monitoring Script\n\nimport time\nimport syslog\nimport os\nfrom datetime import datetime\n\n\nclass SocketUDP:\n def __init__(self, error_log_file):\n self.error_log_file = error_log_file\n self.__error_logs__ = []\n self.__logs_in_RAM__ = []\n\n def dump_errors_to_disk_func(self, socket_name):\n while True:\n time.sleep(6)\n tmp_error_logs_len = len(self.__error_logs__)\n tmp_error_logs = self.__error_logs__[:tmp_error_logs_len - 1]\n self.__error_logs__ = self.__error_logs__[tmp_error_logs_len - 1:]\n for error_msg in tmp_error_logs:\n try:\n os.system(\"echo '{0}' >> {1}\"\n .format(error_msg, self.error_log_file + str(datetime.now().date()) + '.log'))\n except Exception as error:\n self.__error_logs__.append(str(error.args) + ' | Date=' + str(datetime.now()) +\n ' dump errors to disk [echo to {0}]\\n'\n .format(self.error_log_file + str(datetime.now().date()) + '.log'))\n syslog.syslog('[+] UDP script dumped {0} errors to disk space with errors'.format(socket_name))\n else:\n syslog.syslog('[+] UDP script dumped {0} errors to disk space without errors'.format(socket_name))\n\n def dump_logs_to_disk_func(self, file_for_dump, socket_name):\n while True:\n time.sleep(6)\n tmp_logs_in_ram_len = len(self.__logs_in_RAM__)\n tmp_logs_in_ram = self.__logs_in_RAM__[:tmp_logs_in_ram_len - 1]\n self.__logs_in_RAM__ = self.__logs_in_RAM__[tmp_logs_in_ram_len - 1:]\n for log in tmp_logs_in_ram:\n try:\n with open(str(file_for_dump + str(datetime.now().date()) + '.log'), 'a') as log_file:\n log_file.write(log)\n except Exception as error:\n self.__error_logs__.append(str(error.args) + ' | Date=' + str(datetime.now()) +\n ' dump logs to disk {0}.write\\n'\n .format(file_for_dump + str(datetime.now().date()) + '.log'))\n syslog.syslog('[+] UDP script dumped {0} RAM logs to disk space with errors'.format(socket_name))\n else:\n syslog.syslog('[+] UDP script dumped {0} RAM logs to disk space without errors'.format(socket_name))","repo_name":"artemivzhenko/monitoring_udp_network","sub_path":"base_udp_socket.py","file_name":"base_udp_socket.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"22753236451","text":"import pytest\nfrom ellar.common import Module\nfrom ellar.common.constants import MODULE_METADATA, MODULE_WATERMARK\nfrom ellar.common.exceptions import ImproperConfiguration\nfrom ellar.di import has_binding, is_decorated_with_injectable\nfrom ellar.reflect import reflect\n\n\n@Module(\n name=\"test\",\n static_folder=\"test\",\n controllers=[\"controllers\"],\n routers=[\"routers\"],\n providers=[\"providers\"],\n modules=[\"modules\"],\n)\nclass ModuleDecoratorTest:\n pass\n\n\ndef test_module_decorator_keys_is_complete():\n assert reflect.get_metadata(MODULE_METADATA.ROUTERS, ModuleDecoratorTest) == [\n \"routers\"\n ]\n assert reflect.get_metadata(MODULE_METADATA.NAME, ModuleDecoratorTest) == \"test\"\n assert (\n reflect.get_metadata(MODULE_METADATA.STATIC_FOLDER, ModuleDecoratorTest)\n == \"test\"\n )\n assert reflect.get_metadata(MODULE_METADATA.PROVIDERS, ModuleDecoratorTest) == [\n \"providers\"\n ]\n assert reflect.get_metadata(MODULE_METADATA.CONTROLLERS, ModuleDecoratorTest) == [\n \"controllers\"\n ]\n assert reflect.get_metadata(MODULE_METADATA.MODULES, ModuleDecoratorTest) == [\n \"modules\"\n ]\n\n\ndef test_decorated_class_has_module_watermark():\n assert reflect.get_metadata(MODULE_WATERMARK, ModuleDecoratorTest)\n\n\ndef test_base_directory_is_dynamically_set_when_none():\n base_path = reflect.get_metadata(\n MODULE_METADATA.BASE_DIRECTORY, ModuleDecoratorTest\n )\n assert \"/test_decorators\" in str(base_path)\n\n\ndef test_cannot_decorate_module_class_twice():\n with pytest.raises(\n ImproperConfiguration, match=\"is already identified as a Module\"\n ):\n Module()(ModuleDecoratorTest)\n\n\ndef test_cannot_decorate_class_instance_as_a_module():\n with pytest.raises(ImproperConfiguration, match=\"is a class decorator -\"):\n Module()(ModuleDecoratorTest())\n\n\ndef test_decorated_class_default_scope_is_singleton():\n assert is_decorated_with_injectable(ModuleDecoratorTest)\n assert not has_binding(ModuleDecoratorTest) # __init__ is not overridden\n","repo_name":"eadwinCode/ellar","sub_path":"tests/test_decorators/test_modules.py","file_name":"test_modules.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"3"} +{"seq_id":"29039637333","text":"\ndef db_table_exists(table, cursor=None):\n \"\"\"\n Determine if a table exists in the database\n\n cribbed from:\n https://gist.github.com/527113/307c2dec09ceeb647b8fa1d6d49591f3352cb034\n \"\"\"\n\n try:\n if not cursor:\n from django.db import connection\n cursor = connection.cursor()\n if not cursor:\n raise Exception\n table_names = connection.introspection.get_table_list(cursor)\n except:\n raise Exception(\"unable to determine if the table '%s' exists\" % table)\n else:\n return table in table_names","repo_name":"osuosl/ganeti_webmgr","sub_path":"ganeti_webmgr/ganeti_web/migrations/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"3"} +{"seq_id":"34601492163","text":"import cPickle\n\nimport bpy\nobjects=list(bpy.data.objects)\nstore={}\nfaaa=None\nfor ob in objects:\n\tif ob.type==\"Mesh\":\n\t\tverts3=[]\n\t\tnorms3=[]\n\t\tverts4=[]\n\t\tnorms4=[]\n\t\tmesh = ob.getData(mesh=1)\n\t\tfor face in mesh.faces:\n\t\t\tlv=verts4\n\t\t\tln=norms4\n\t\t\tif len(face.verts)==3:\n\t\t\t\tlv=verts3\n\t\t\t\tln=norms3\n\t\t\tfor v in face.verts:\n\t\t\t\tlv+=list(v.co)\n\t\t\t\tif face.smooth:\n\t\t\t\t\tln+=list(v.no)\n\t\t\t\telse:\n\t\t\t\t\tln+=list(face.no)\n\n\t\tstore[ob.name]=(verts3,norms3,verts4,norms4)\n\nfilename=\"blob.pkl\"\nf=file(filename,\"wb\")\ncPickle.dump(store,f)\nf.close()\n\n","repo_name":"RichardOfWard/gastrocide","sub_path":"assets/bconvert.py","file_name":"bconvert.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33139455572","text":"from numpy import *\n\ndef loadSimpData():\n datMat = matrix([[ 1. , 2.1],\n [ 2. , 1.1],\n [ 1.3, 1. ],\n [ 1. , 1. ],\n [ 2. , 1. ]])\n classLabels = [1.0, 1.0, -1.0, -1.0, 1.0]\n return datMat,classLabels\n\n\ndef stumpClassify(dataMatrix,dimen,threshVal,threshIneq):#just classify the data\n retArray = ones((shape(dataMatrix)[0],1))\n if threshIneq == 'lt':\n retArray[dataMatrix[:,dimen] <= threshVal] = -1.0\n else:\n retArray[dataMatrix[:,dimen] > threshVal] = -1.0\n return retArray\n \n\ndef build_stump(data_arr, label_arr, weight, stepnum):\n data_mat = mat(data_arr)\n label_mat = mat(label_arr).T\n nrow, ncol = data_mat.shape\n min_error = inf\n best_class_est = mat(zeros((nrow, 1)))\n best_stump = {} \n for i in range(ncol):\n fea_min = data_mat[:,i].min()\n fea_max = data_mat[:,i].max()\n step = (fea_max - fea_min) / stepnum\n for j in range(-1,(stepnum+1)):\n threshold = fea_min + float(j) * step\n for k in [\"lt\", \"gt\"]:\n expected_return = stumpClassify(data_mat, i, threshold, k)\n error_mat = mat(ones((nrow,1)))\n error_mat[expected_return == label_mat] = 0\n weight_error = mat(weight).T * error_mat\n #print(\"split: dim %d, thresh %.2f, ineqal: %s, the weighted error is %.3f\" %(i, threshold, k, weight_error))\n if weight_error < min_error:\n min_error = weight_error\n best_class_est = expected_return\n best_stump[\"dim\"] = i\n best_stump[\"thresh\"] = threshold\n best_stump[\"ineq\"] = k\n return best_stump, min_error, best_class_est\n\ndef ada_boost(data_arr, label_arr, stepnum, max_interation=40):\n data_mat = mat(data_arr)\n label_mat = mat(label_arr).T\n nrow, ncol = data_mat.shape\n record_stump = []\n weight = mat(ones((nrow, 1)) / nrow)\n agg_exp = zeros((nrow,1))\n for i in range(max_interation):\n #print(\"weight: \", weight.T)\n each_stump, each_error, each_est = build_stump(data_mat, label_arr, weight, stepnum)\n alpha = log((1.0 - each_error) / max(each_error,1e-16)) / 2\n expon = -multiply(label_mat, each_est) * alpha\n weight = multiply(weight, exp(expon))\n weight = weight / sum(weight)\n agg_exp += multiply(alpha, each_est)\n each_stump[\"alpha\"] = alpha\n #print(\"each est: \", each_est.T)\n record_stump.append(each_stump)\n #print(\"agg_exp: \", agg_exp.T)\n agg_error = multiply(sign(agg_exp) != label_mat, ones((nrow, 1)) )\n error_rate = sum(agg_error) / len(agg_error)\n #print(\"total error: \", error_rate)\n if error_rate == 0.0:\n break\n return record_stump\n\ndef ada_classify(data, classifier):\n data_mat = mat(data)\n nrow, ncol = data_mat.shape\n agg_exp = zeros((nrow, 1))\n for each_class in classifier:\n each_exp = stumpClassify(data_mat,each_class[\"dim\"],each_class[\"thresh\"],each_class[\"ineq\"])\n agg_exp += multiply(each_class[\"alpha\"], each_exp)\n #print(agg_exp)\n return sign(agg_exp), agg_exp\n\ndef load_file(filename):\n feature_num = len( open(filename).readline().strip().split(\"\\t\") )\n data = []\n label = []\n for row in open(filename).readlines():\n temp = []\n individual_data = row.strip().split(\"\\t\")\n for j in range(feature_num-1):\n temp.append(float(individual_data[j]))\n data.append(temp)\n label.append(float(individual_data[-1]))\n return mat(data), label\n\n\ndef plot_roc(pred_strength, class_label):\n import matplotlib.pyplot as plt\n pred_strength = pred_strength.reshape(1, len(pred_strength))\n rank_value = pred_strength[0].argsort()\n point = (1, 1)\n num_pos_class = sum(array(class_label) == 1)\n y_step = 1 / num_pos_class\n x_step = 1 / (len(class_label) - num_pos_class)\n auc = 0\n fig = plt.figure()\n fig.clf()\n subfig = fig.add_subplot(111)\n for i in rank_value.tolist():\n if class_label[i] == 1:\n x = 0; y = y_step\n else:\n x = x_step; y = 0\n auc += x_step * point[1]\n subfig.plot([point[0], point[0]-x], [point[1], point[1]-y], c=\"b\")\n point = (point[0]-x, point[1]-y)\n subfig.plot([0,1], [0,1], \"b--\")\n plt.xlabel(\"false positive rate\")\n plt.ylabel(\"true positive rate\")\n plt.title(\"title\")\n subfig.axis([0, 1, 0, 1])\n plt.show()\n print(\"AUC: \", auc)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"kunzhou92/Algorithms-Using-Python","sub_path":"adaboost.py","file_name":"adaboost.py","file_ext":"py","file_size_in_byte":4567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"29056159387","text":"from bcl.banks.registry import Ibaz\n\n\ndef test_payment_creator(build_payment_bundle):\n\n associate = Ibaz\n\n bundle = build_payment_bundle(\n associate,\n payment_dicts=[\n {'f_iban': 'AZ62İBAZ38010019449111111120', 't_bic': '010305'},\n ],\n account={'number': 'one'})\n\n assert (\n ',38010019449111111120,120,RUB,152.00,RUB,A,'\n f'0000000000000002,\"ООО \"\"Кинопортал\"\"\",7725713770,010305,Назначение[{bundle.payments[0].number}],,,,XOHKS\\r\\n'\n ) in bundle.tst_compiled\n\n bundle = build_payment_bundle(\n associate,\n payment_dicts=[\n {'ground': 'Длинное назначение, которое никак не поместится '\n 'в четыре строки по тридцать пять символов в каждой и будет непременно обрезано, '\n 'как того и требует формат.', 't_bic': ''},\n ],\n account={'number': 'one'})\n\n assert (\n ',0000000000000001,001,RUB,152.00,RUB,A,'\n '0000000000000002,\"ООО \"\"Кинопортал\"\"\",7725713770,,\"Длинное назначение, которое никак н\",'\n 'е поместится в четыре строки по три,дцать пять символов в каждой и буде,'\n f'\"т непременно обрезано, как того [{bundle.payments[0].number}]\",XOHKS\\r\\n'\n ) in bundle.tst_compiled\n\n bundle = build_payment_bundle(\n associate,\n payment_dicts=[\n {'f_iban': 'AZ62İBAZ38010019449111111120', 't_swiftcode': 'IBAZXXXX'},\n ],\n account={'number': 'one'})\n\n assert (\n f',38010019449111111120,120,RUB,152.00,A,0000000000000002,002,RUB,Назначение[{bundle.payments[0].number}]\\r\\n'\n ) in bundle.tst_compiled\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests/banks/party_ibaz/test_ibaz.py","file_name":"test_ibaz.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"20929454271","text":"from queue import Queue as fifo\nfrom checkquit import CheckQuit\n\n# path finding - bfs is ideal for maze.\ndef bfs(start_cell, end_cell, grid):\n # CREATE A QUEUE TO SEARCH THROUGH THE MAZE 'BREADTH FIRST'\n Q = fifo()\n searched = []\n # STEP 1. MAKE THE START CELL SEARCHED AND PUSH IT TO THE QUEUE\n searched.append(start_cell)\n Q.put(start_cell)\n # CREATE A HASH-MAP TO KEEP TRACK OF WHERE EACH CELL COMES FROM ( ROOT OF EACH CELL ), AND ALL THE SEARCHED CELLS\n track = {}\n # KEEP SEARCHING UNTIL A PATH IS FOUND\n while True:\n CheckQuit() # CHECK IF THE USER WANTS TO QUIT PYGAME\n # STEP 2\n # POP A CELL FROM THE QUEUE AND ASSIGN IT TO ROOT\n root = Q.get()\n # IF ROOT IS THE END CELL, PATH HAS BEEN FOUND\n if root == end_cell:\n # BACKTRACK THE PATH FROM THE END CELL TO THE START CELL USING THE HASH-MAP\n while track[root] != start_cell:\n CheckQuit() # CHECK IF THE USER WANTS TO QUIT PYGAME\n\n # UPDATE ROOT TO BE THE ROOT OF THE CURRENT ROOT CELL\n root = track[root]\n # ANNOUNCE THE COMPLETION OF PATH\n return root\n\n # IF ROOT HAS ANY NEIGHBOURS, MAKE THEM ALL SEARCHED IF THEY HAVEN'T ALREADY BEEN AND PUSH THEM TO THE QUEUE\n neighbours = root.get_unsearched_neighbour(grid, searched)\n if neighbours:\n for n in neighbours:\n searched.append(n)\n track[n] = root\n Q.put(n)\n","repo_name":"AaravGang/MazeGame","sub_path":"path_finding.py","file_name":"path_finding.py","file_ext":"py","file_size_in_byte":1509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17004300425","text":"import pygame, os, sys\n\nimport PyGameProjectSpriteMove\n\npygame.init()\nsize = width, height = 45 * 16, 18 * 16\nuser_group = pygame.sprite.Group()\nboard_group = pygame.sprite.Group()\nall_sprites = pygame.sprite.Group()\npokemons_group = pygame.sprite.Group()\n\n\ndef load_image(filename):\n fullname = os.path.join(f\"Data/{filename}\")\n if not os.path.isfile(fullname):\n print(f\"Файл с изображением '{fullname}' не найден\")\n sys.exit()\n image = pygame.image.load(fullname)\n return image\n\n\ndef pokeball(x, y):\n pokeball = pygame.sprite.Sprite()\n pokeball.image = load_image(\"pokeball.png\")\n pokeball.rect = pokeball.image.get_rect()\n pokeball.rect.x = x\n pokeball.rect.y = y\n return pokeball\n\n\ndef start_screen():\n intro_text = [\"Правила игры:\",\n \"Чтобы отрыть меню, \",\n \"нажмите 5 посередине между стрелками.\",\n \"Чтобы закрыть открываюшиеся окна,\",\n \" кликните мышкой \",\n \"или нажмите кнопку на клавиатуре.\",\n \"Передвигайтесь клавишами стрелок.\",\n \"Если вы хотите начать, кликните мышкой!\"]\n size_for_start_screen = 600, 600\n screen = pygame.display.set_mode(size_for_start_screen)\n screen.fill((0, 0, 0))\n pikachu_group = pygame.sprite.Group()\n pikachu = pygame.sprite.Sprite(pikachu_group)\n pikachu.image = load_image(\"fon.png\")\n pikachu.rect = pikachu.image.get_rect()\n pikachu.rect.x, pikachu.rect.y = 200, 200\n pikachu_group.draw(screen)\n font = pygame.font.Font(pygame.font.match_font('cambria'), 20)\n string_rendered = font.render(\"Добро пожаловать в игру Pokemon Searching!\", True, pygame.Color('white'))\n intro_rect = string_rendered.get_rect()\n intro_rect.top = 20\n screen.blit(string_rendered, intro_rect)\n text_coord = 50\n for line in intro_text:\n font = pygame.font.Font(pygame.font.match_font('cambria'), 15)\n string_rendered = font.render(line, False, (244, 164, 96))\n intro_rect = string_rendered.get_rect()\n text_coord += 10\n intro_rect.top = text_coord\n intro_rect.x = 10\n text_coord += intro_rect.height\n screen.blit(string_rendered, intro_rect)\n clock = pygame.time.Clock()\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n terminate()\n elif event.type == pygame.KEYDOWN or \\\n event.type == pygame.MOUSEBUTTONDOWN:\n return True\n pygame.display.flip()\n clock.tick(fps)\n\n\ndef menu(user, menu_screen):\n pokemons = user.get_pokemons()\n menu_screen.fill((0, 0, 0))\n font = pygame.font.Font(None, 25)\n string_rendered = font.render(f\"Your pokemons:\", True, pygame.Color('white'))\n string_rect = string_rendered.get_rect()\n string_rect.x = 50\n string_rect.y = 30\n menu_screen.blit(string_rendered, string_rect)\n dx = 50\n dy = 50\n for i in range(len(pokemons)):\n pokemons[i].set_rect(i * 70 + dx, dy)\n font = pygame.font.Font(None, 15)\n ev, sk = pokemons[i].get_information()\n string_rendered = font.render(f\"evolution: {ev}\", False, (240, 230, 140))\n string_rect = string_rendered.get_rect()\n string_rect.x = i * 70 + dx\n string_rect.y = 60 + dy\n menu_screen.blit(string_rendered, string_rect)\n string_rendered = font.render(f\"skill: {sk}\", False, (240, 230, 140))\n string_rect.x = i * 70 + dx\n string_rect.y = 70 + dy\n menu_screen.blit(string_rendered, string_rect)\n pokemons_group.add(pokemons[i])\n clock = pygame.time.Clock()\n if not pokemons:\n font = pygame.font.Font(None, 25)\n string_rendered = font.render(f\"You haven't any pokemons\", 1, pygame.Color('white'))\n string_rect = string_rendered.get_rect()\n string_rect.x = 70\n string_rect.y = 70\n menu_screen.blit(string_rendered, string_rect)\n number = user.get_pokeballs()\n font = pygame.font.Font(None, 25)\n string_rendered = font.render(f\"Your pokeballs:\", 1, pygame.Color('white'))\n string_rect = string_rendered.get_rect()\n string_rect.x = 50\n string_rect.y = 130\n menu_screen.blit(string_rendered, string_rect)\n pokeball_for_menu = pokeball(50, 150)\n pokeballs_group = pygame.sprite.Group()\n pokeballs_group.add(pokeball_for_menu)\n font = pygame.font.Font(None, 15)\n string_rendered = font.render(f\"number:{number}\", False, (240, 230, 140))\n string_rect = string_rendered.get_rect()\n string_rect.x = 50\n string_rect.y = 190\n menu_screen.blit(string_rendered, string_rect)\n while True:\n pokemons_group.draw(menu_screen)\n pokeballs_group.draw(menu_screen)\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n terminate()\n elif event.type == pygame.KEYDOWN or \\\n event.type == pygame.MOUSEBUTTONDOWN:\n return True\n pygame.display.flip()\n clock.tick(fps)\n\n\ndef get_something(screen, something):\n new_screen = screen\n font = pygame.font.Font(None, 25)\n new_screen.fill((184, 134, 11))\n string = font.render(\"You get:\", 1, (110, 50, 19))\n rect = (110, 70, 25, 25)\n new_screen.blit(string, rect)\n try:\n str(something)\n if something == \"pokeball\":\n pokeball_for_smth = pokeball(150, 110)\n pokeballs_group = pygame.sprite.Group()\n pokeballs_group.add(pokeball_for_smth)\n pokeballs_group.draw(new_screen)\n elif something.find(\"skill\") != -1:\n skill = pygame.sprite.Sprite()\n skill_group = pygame.sprite.Group()\n skill_group.add((skill))\n skill.image = load_image(\"skill.png\")\n skill.rect = skill.image.get_rect()\n skill.rect.x = 150\n skill.rect.y = 100\n string = font.render(something, False, pygame.Color('white'))\n rect = (150, 190, 25, 25)\n new_screen.blit(string, rect)\n skill_group.draw(new_screen)\n else:\n string = font.render(something, False, pygame.Color('white'))\n rect = (150, 120, 25, 25)\n new_screen.blit(string, rect)\n except:\n try:\n list(something)\n text_coord = 90\n for line in something:\n string_rendered = font.render(line, False, pygame.Color('white'))\n intro_rect = string_rendered.get_rect()\n text_coord += 10\n intro_rect.top = text_coord\n intro_rect.x = 150\n text_coord += intro_rect.height\n screen.blit(string_rendered, intro_rect)\n except:\n something_group = pygame.sprite.Group()\n something_group.add(something)\n something.set_rect(150, 150)\n something_group.draw(new_screen)\n clock = pygame.time.Clock()\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n terminate()\n elif event.type == pygame.KEYDOWN or \\\n event.type == pygame.MOUSEBUTTONDOWN:\n new_screen.fill((0, 0, 0))\n return True\n pygame.display.flip()\n clock.tick(fps)\n\n\ndef terminate():\n pygame.quit()\n sys.exit()\n\n\nfps = 60\nstart_screen()\nscreen = pygame.display.set_mode(size)\nclock = pygame.time.Clock()\nscreen.fill((0, 0, 0))\nboard = PyGameProjectSpriteMove.Board()\nuser = PyGameProjectSpriteMove.GameHero(16 * 9, 16 * 9)\ncamera = PyGameProjectSpriteMove.Camera(0, 0)\nuser_group.add(user)\nboard_group.add(board)\nall_sprites.add(board, user)\nrunning = True\nwhile running:\n screen.fill((0, 0, 0))\n all_sprites.draw(screen)\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n if event.type == pygame.KEYDOWN and event.key == pygame.K_KP_5:\n menu(user, screen)\n something = user.update(board, event)\n if something:\n get_something(screen, something)\n camera.update(user)\n pygame.display.flip()\n for sprite in all_sprites:\n camera.apply(sprite)\n clock.tick(fps)\npygame.quit()","repo_name":"shokkialexa/PygameProject","sub_path":"PyGameProjectGame.py","file_name":"PyGameProjectGame.py","file_ext":"py","file_size_in_byte":8502,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"22479676974","text":"from stix2 import TAXIICollectionSource\nfrom taxii2client.v20 import Collection\nfrom stix2 import Filter\nimport json\nimport logging\nimport requests\nimport os\n\nMITREAttack = None\n\n\ndef download(url, outpath, verify=True):\n resp = requests.get(url, verify=verify) #nosec\n with open(outpath, \"wb\") as f:\n f.write(resp.content)\n\n\nclass MITREAttackGenerator:\n def __init__(self):\n self.mitreattack = None\n self_path = os.path.dirname(os.path.abspath(__file__))\n sup_files_path = os.path.join(self_path, \"../sup_files\")\n if not os.path.exists(sup_files_path):\n os.mkdir(sup_files_path)\n self.attack_path = os.path.join(sup_files_path, \"enterprise-attack.json\")\n if not os.path.exists(self.attack_path):\n self.downloadGithub(dst_path=self.attack_path)\n\n def taxiiVersion(self, TTP):\n\n # enterprise_attack = 95ecc380-afe9-11e4-9b6c-751b66dd541e\"\n # mobile_attack = 2f669986-b40b-4423-b720-4396ca6a462b\"\n # ics-attack = 02c3ef24-9cd4-48f3-a99f-b74ce24f1d34\"\n\n collection = Collection(\n f\"https://cti-taxii.mitre.org/stix/collections/95ecc380-afe9-11e4-9b6c-751b66dd541e/\"\n )\n src = TAXIICollectionSource(collection)\n\n result = src.query(\n [\n Filter(\"external_references.external_id\", \"=\", \"T\" + str(TTP).zfill(4)),\n Filter(\"type\", \"=\", \"attack-pattern\"),\n ]\n )\n\n try:\n return result[0]\n except:\n return \"Error: T\" + TTP + \" not found.\"\n\n def downloadGithub(self, dst_path=None):\n try:\n logging.info(\"Redownloading enterprise-attack.json...\")\n download(\n \"https://github.com/mitre-attack/attack-stix-data/raw/master/enterprise-attack/enterprise-attack.json\",\n dst_path,\n )\n except:\n logging.warning(\n \"Failed with normal download, trying with ignore ssl errors!\"\n )\n try:\n logging.info(\"Redownloading enterprise-attack.json...\")\n download(\n \"https://github.com/mitre-attack/attack-stix-data/raw/master/enterprise-attack/enterprise-attack.json\",\n dst_path,\n verify=False,\n )\n except:\n logging.error(\"Download Error for enterprise-attack.json: Try Again\")\n return 0\n\n def githubVersion(self, TTP, file_path=None):\n # Open json file if not already loaded\n if self.mitreattack is None:\n self.mitreattack = {}\n with open(\n self.attack_path if file_path is None else file_path,\n \"r\",\n encoding=\"utf-8\",\n errors=\"replace\",\n ) as f:\n attack_raw_data = json.load(f)\n for i in attack_raw_data[\"objects\"]:\n if i[\"type\"] == \"attack-pattern\":\n self.mitreattack[\n i[\"external_references\"][0][\"external_id\"].lstrip(\"Tt\")\n ] = i\n\n # Search for TTP\n if TTP in self.mitreattack:\n return self.mitreattack[TTP]\n\n\nAttackGen = MITREAttackGenerator()\n\n# if __name__ == \"__main__\":\n# downloadGithub()\n# try:\n# TTP = sys.argv[1]\n# TTP = TTP.lstrip(\"Tt\")\n# except:\n# print(\"TTP Argument Missing\")\n# exit()\n\n\n# print(githubVersion(TTP))\n# print('###################################')\n# print(taxiiVersion(TTP))\n","repo_name":"idaholab/cape2stix","sub_path":"cape2stix/core/mitreattack.py","file_name":"mitreattack.py","file_ext":"py","file_size_in_byte":3556,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"39526403344","text":"import argparse\nimport sys\nfrom typing import TextIO, Generator, Iterable, Optional, Union\nfrom dataclasses import dataclass\nfrom enum import Enum\n\n\nTOTAL_DISK_SPACE = 70000000\nNEEDED_SPACE_FOR_UPDATE = 30000000\n\n\ndef main(terminal_output_file: TextIO, part: int):\n inputs_outputs = read_inputs_and_outputs(terminal_output_file)\n filesystem = create_filesystem(inputs_outputs)\n assert filesystem is not None, \"No filesystem created\"\n if part == 1:\n dirs = find_directories_with_max_total_size(filesystem.root, 100000)\n print(sum([d.size for d in dirs]))\n elif part == 2:\n if filesystem.free_space >= NEEDED_SPACE_FOR_UPDATE:\n return\n space_to_free_up = NEEDED_SPACE_FOR_UPDATE - filesystem.free_space\n sizes = get_directories_sizes(filesystem.root)\n for size in sizes:\n if size >= space_to_free_up:\n print(size)\n break\n else:\n raise ValueError(f\"Invalid part: {part}\")\n\n\nclass CommandType(str, Enum):\n CD = \"cd\"\n LS = \"ls\"\n\n\n@dataclass\nclass Input:\n command_type: CommandType\n arguments: list[str]\n\n\n@dataclass\nclass InputOutput:\n input: Input\n output: list[str]\n\n\n@dataclass\nclass File:\n name: str\n size: int\n\n\n@dataclass\nclass Directory:\n name: str\n parent: Optional[\"Directory\"]\n contents: list[Union[\"Directory\", File]]\n\n @property\n def size(self) -> int:\n return sum([content.size for content in self.contents])\n\n def __eq__(self, other: object) -> bool:\n if not isinstance(other, Directory):\n return False\n return self.name == other.name and self.contents == other.contents\n\n\nclass FileSystem:\n total_disk_space: int\n root: Directory\n current: Directory\n\n def __init__(self, total_disk_space: int, root: Directory | None = None):\n self.root = root or Directory(\"/\", None, [])\n self.current = self.root\n self.total_disk_space = total_disk_space\n\n @property\n def used_space(self) -> int:\n return self.root.size\n\n @property\n def free_space(self) -> int:\n return self.total_disk_space - self.root.size\n\n def cd(self, path: str) -> None:\n if path == \"..\":\n assert self.current.parent is not None, \"Cannot go up from root\"\n self.current = self.current.parent\n else:\n if path.startswith(\"/\"):\n path = path[1:]\n self.current = self.root\n next_directory, *rest = path.split(\"/\")\n while next_directory:\n for content in self.current.contents:\n if content.name == next_directory:\n assert isinstance(\n content, Directory), \"Cannot cd into a file\"\n self.current = content\n break\n if rest:\n next_directory = rest.pop(0)\n self.cd(next_directory)\n else:\n next_directory = None\n\n def add_content(self, content: File | Directory) -> None:\n self.current.contents.append(content)\n\n def __eq__(self, other: object) -> bool:\n if not isinstance(other, FileSystem):\n return False\n return self.root == other.root\n\n def __repr__(self) -> str:\n return f\"FileSystem(root={self.root}, current={self.current})\"\n\n def __str__(self) -> str:\n return self._dir_str(self.root)\n\n def _file_str(self, file: File, indent: int = 0) -> str:\n return f\"{' ' * indent}- {file.name} (file, size={file.size})\\n\"\n\n def _dir_str(self, dir: Directory, indent: int = 0) -> str:\n result = f\"{' ' * indent}- {dir.name} (dir, size={dir.size})\\n\"\n for content in dir.contents:\n if isinstance(content, Directory):\n result += self._dir_str(content, indent + 1)\n else:\n result += self._file_str(content, indent + 1)\n return result\n\n\ndef read_inputs_and_outputs(terminal_output_file: TextIO) -> Generator[InputOutput, None, None]:\n current_input: Input | None = None\n current_output: list[str] = []\n for line in terminal_output_file.readlines():\n if line.startswith(\"$\"):\n if current_input is not None:\n yield InputOutput(input=current_input, output=current_output)\n current_input = parse_input(line)\n current_output = []\n else:\n current_output.append(line.strip())\n if current_input is not None:\n yield InputOutput(input=current_input, output=current_output)\n\n\ndef parse_input(line: str) -> Input:\n _, command, *arguments = line.split()\n return Input(command_type=CommandType(command), arguments=arguments)\n\n\ndef create_filesystem(inputs_outputs: Iterable[InputOutput]) -> FileSystem | None:\n filesystem: FileSystem | None = None\n for input_output in inputs_outputs:\n if input_output.input.command_type == CommandType.CD:\n if filesystem is None and input_output.input.arguments[0] == \"/\":\n filesystem = FileSystem(total_disk_space=TOTAL_DISK_SPACE) \n elif filesystem is None:\n raise ValueError(\"First command must be cd /\")\n else:\n filesystem.cd(input_output.input.arguments[0])\n elif input_output.input.command_type == CommandType.LS:\n if filesystem is None:\n raise ValueError(\"First command must be cd /\")\n for line in input_output.output:\n if line.startswith(\"dir\"):\n _, name = line.split(\" \")\n content = Directory(name, filesystem.current, [])\n else:\n size, name = line.split(\" \")\n content = File(name=name, size=int(size))\n filesystem.add_content(content)\n return filesystem\n\n\ndef find_directories_with_max_total_size(directory: Directory, n: int) -> list[Directory]:\n result: list[Directory] = []\n if directory.size <= n:\n result.append(directory)\n for d in directory.contents:\n if isinstance(d, Directory):\n result += find_directories_with_max_total_size(d, n)\n return result\n\n\ndef get_directories_sizes(directory: Directory) -> list[int]:\n result: list[int] = []\n result.append(directory.size)\n for d in directory.contents:\n if isinstance(d, Directory):\n result += get_directories_sizes(d)\n return sorted(result)\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser()\n # File argument or stdin\n parser.add_argument(\"file\", nargs=\"?\",\n type=argparse.FileType(\"r\"), default=sys.stdin)\n parser.add_argument(\"--part\", type=int, default=1)\n\n args = parser.parse_args()\n main(args.file, args.part)\n","repo_name":"gio8tisu/AoC2022","sub_path":"day7/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1404877440","text":"'''#Given a list of words, return a dictionary where the keys are the words and\r\n# the values are the number of times the word appears in the list.\r\n\r\ndi={}\r\nip=input(\"enter word's to be in the list =\")\r\nnested=ip.split()\r\nprint(nested)\r\nfor i in nested:\r\n if i in di.keys():\r\n di[i]+=1\r\n else:\r\n di[i]=1\r\nprint(\"final answer dictionary: \",di)'''\r\n\r\n'''#Given a list of words, return a dictionary \r\n# where the keys are the words and the values are the number of letters in the word.\r\n\r\ndi={}\r\ndef func_2(nested):\r\n for i in nested:\r\n di[i]=len(i)\r\n return di\r\n\r\nip=input(\"enter word's to be in the list =\")\r\nnested=ip.split()\r\nprint(\"final answer dictionary: \", func_2(nested))'''\r\n\r\n'''#Given a list of words, return a dictionary where \r\n# the keys are the words and the values are the number of vowels in the word.\r\ndi={}\r\ndef vowels_count(nested):\r\n for i in nested:\r\n for words in i:\r\n if (words.lower() in ['a','e','i','o','u']):\r\n try:\r\n di[i]+=1\r\n except:\r\n di[i]=1\r\n return di\r\n\r\nip=input(\"enter word's to be in the list =\")\r\nnested=ip.split()\r\nprint(\"final answer dictionary: \",vowels_count(nested))'''\r\n\r\n\r\n# Python3 code to demonstrate working of\r\n# Bigrams Frequency in String\r\n# Using Counter() + generator expression\r\nfrom collections import Counter\r\n\t\r\n# initializing string\r\ntest_str = 'geeksforgeeks'\r\n\r\n# printing original string\r\nprint(\"The original string is : \" + str(test_str))\r\n\r\n# Bigrams Frequency in String\r\n# Using Counter() + generator expression\r\nres = Counter(test_str.split(' ')for idx in range(len(test_str) - 1))\r\n\r\n# printing result\r\nprint(\"The Bigrams Frequency is : \" + str(dict(res)))\r\n","repo_name":"dorait/Kriyadocs-SAAS-Academy","sub_path":"week3.py","file_name":"week3.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30319224597","text":"\nimport os\n\ndef obtener_fila_verificada(palabra_a_encontrar, palabra_ingresada):\n # Crear una lista vacía para el resultado.\n # Si las letras existen en la palabra a encontrar y sus posiciones coinciden: Encerrarlas en [] y agregar al resultado.\n # Si las letras existen en la palabra a encontrar pero sus posiciones no coinciden: Encerrarlas en () y agregar al resultado.\n # Si no se cumple ninguna de las anteriores, agregar al resultado sin hacer modificaciones.\n # Retornar el resultado.\n cantidad_de_letras_de_la_palabra_a_encontrar = 5\n\n letras_verificadas = [] # Creamos una lista vacía para almacenar el resultado de una linea\n\n for posicion in range(cantidad_de_letras_de_la_palabra_a_encontrar): # Iteramos por cada letra de la palabra ingresada\n\n las_letras_son_iguales = palabra_a_encontrar[posicion] == palabra_ingresada[posicion]\n la_letra_existe_en_la_palabra = palabra_ingresada[posicion] in palabra_a_encontrar\n\n if las_letras_son_iguales:\n # guardar las letras que están en la palabra a encontrar y coinciden en la posición, dentro de brackets\n letras_verificadas.append(f\"[{palabra_ingresada[posicion]}]\")\n elif la_letra_existe_en_la_palabra:\n # guardar las letras que no coinciden pero que están en la palabra a encontrar, dentro de parentesis\n letras_verificadas.append(f\"({palabra_ingresada[posicion]})\")\n else:\n # Las que no coinciden, se guardan sin modificiaciones\n letras_verificadas.append(palabra_ingresada[posicion])\n\n return letras_verificadas\n\n\ndef imprimir_grilla(grilla):\n cantidad_de_filas = len(grilla)\n\n for fila in range(cantidad_de_filas):\n print(grilla[fila])\n\n# Ejecución del programa\n\npalabra_a_encontrar = \"virus\"\ncantidad_de_letras = 5\nintentos = 6\n\ngrilla = []\n\nos.system(\"clear\")\nprint(\"Bienvenido al wordle!!\")\n\nwhile intentos > 0:\n print(f\"te quedan {intentos}\")\n palabra_ingresada = input(\"Ingrese una palabra:\")\n intentos = intentos - 1\n\n os.system(\"clear\")\n if (len(palabra_ingresada) != cantidad_de_letras):\n print(\"La cantidad de letras es incorrecta\")\n print(f\"la cantidad de letras correctas es {cantidad_de_letras}\")\n continue\n else:\n linea_verificada = obtener_fila_verificada(palabra_a_encontrar, palabra_ingresada)\n grilla.append(linea_verificada)\n\n imprimir_grilla(grilla)\n if palabra_ingresada == palabra_a_encontrar:\n print(\"Felicidades, ganaste!!!\")\n break","repo_name":"penguin-academy/bootcamp-01-hernandarias","sub_path":"wordlee.py","file_name":"wordlee.py","file_ext":"py","file_size_in_byte":2538,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19709727414","text":"# -*- coding:utf-8 -*-\n'''\nCreated on 2018年9月27日\n\n@author: Yu Qu\n'''\nimport networkx as nx\n\n##########################################################\ndef static_analysis(subject,file_network_file,prefix):\n G = nx.DiGraph()\n findFile = open(subject+'/'+file_network_file,'r')\n each_lines= findFile.readlines()\n for each_line in each_lines:\n if each_line.__contains__('>'):\n edge=each_line.split('>');\n if(prefix in edge[0]):\n edge[0]=edge[0][edge[0].index(prefix)+len(prefix):edge[0].rindex('\\\"')].replace(\"\\\\\",\"/\")\n if(prefix in edge[1]):\n edge[1]=edge[1][edge[1].index(prefix)+len(prefix):edge[1].rindex('\\\"')].replace(\"\\\\\",\"/\") \n if(G.has_edge(edge[0],edge[1])==False):\n G.add_edge(edge[0],edge[1]) \n findFile.close()\n return G","repo_name":"quyutest/node2defect","sub_path":"SupportingTools/CDNAnalysis.py","file_name":"CDNAnalysis.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"} +{"seq_id":"14999432500","text":"\"\"\"\nEP - 36\n\nThe decimal number, 585 = (1001001001)2 (binary), is palindromic in both bases.\n\nFind the sum of all numbers, less than one million, which are palindromic in base 10 and base 2.\n\n(Please note that the palindromic number, in either base, may not include leading zeros.)\n\"\"\"\n\ndef palindrome(num):\n # function to check if number is palindrome\n rev_num = str(num)[::-1]\n if rev_num == str(num):\n return True\n else:\n return False\n\ndef compute():\n total = 0\n for num in range(1, 1000000):\n if (palindrome(num) and palindrome(bin(num).split('b')[1])): \n total += num\n return total\n\nif __name__ == \"__main__\":\n print(compute())\n \n","repo_name":"supria68/ProjectEuler","sub_path":"python/double_base_palin.py","file_name":"double_base_palin.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"36582612525","text":"import math\nimport re\nimport string\nimport pandas as pd\nfrom sklearn.metrics import mean_squared_error\nimport torch\nimport torch.nn as nn\n\n\ndef preprocessor(text):\n regex = re.compile('[' + re.escape(string.punctuation) + '0-9\\\\r\\\\t\\\\n]')\n text = regex.sub(\" \", text) # remove punctuation\n return text\n\n\ndef split_cat(text):\n try:\n return text.split(\"/\")\n except:\n return (\"No Label\", \"No Label\", \"No Label\")\n\n\n# A function to calculate Root Mean Squared Logarithmic Error (RMSLE) as requested in the challenge\ndef rmsle(y, y_pred):\n assert len(y) == len(y_pred)\n terms_to_sum = [(math.log(y_pred[i] + 1) - math.log(y[i] + 1)) ** 2.0 for i, pred in enumerate(y_pred)]\n return (sum(terms_to_sum) * (1.0 / len(y))) ** 0.5\n\n\nclass MLP(nn.Module):\n def __init__(self):\n super(MLP, self).__init__()\n self.layers = nn.Sequential(\n nn.Linear(100, 192),\n nn.ReLU(),\n nn.Linear(192, 64),\n nn.ReLU(),\n nn.Linear(64, 64),\n nn.ReLU(),\n nn.Linear(64, 1)\n )\n\n def forward(self, x):\n x = x.view(-1, x.size(0))\n x = self.layers(x)\n return x\n\n\nif __name__ == '__main__':\n\n x_train = pd.read_pickle('../mercariData/x_train.pkl', compression='bz2')\n x_train = torch.FloatTensor(x_train.values)\n x_test = pd.read_pickle('../mercariData/x_test.pkl', compression='bz2')\n x_test = torch.FloatTensor(x_test.values)\n y_train = pd.read_pickle('../mercariData/y_train.pkl', compression='bz2')\n y_train = torch.FloatTensor(y_train.values)\n y_test = pd.read_pickle('../mercariData/y_test.pkl', compression='bz2')\n # y_test = torch.FloatTensor(y_test.values)\n print(\"Loaded data\")\n model = MLP()\n optimizer = torch.optim.Adam(model.parameters(), lr=3e-3)\n loss_fn = torch.nn.MSELoss()\n print(\"Initialized model, optimizer and loss function\")\n mean_train_losses = []\n mean_valid_losses = []\n valid_acc_list = []\n epochs = 15\n\n # lets train!\n for epoch in range(epochs):\n print(\"Training epoch: {0}\".format(epoch + 1))\n model.train()\n\n train_losses = []\n valid_losses = []\n for i, data in enumerate(x_train):\n optimizer.zero_grad()\n outputs = model(data)\n loss = loss_fn(outputs, y_train[i])\n loss.backward()\n optimizer.step()\n\n train_losses.append(loss.item())\n\n # evaluating the results\n print(\"Evaluating the model...\")\n model.eval()\n outputs = []\n\n for i, data in enumerate(x_test):\n outputs.append(model(data))\n\n test_score = mean_squared_error(y_test, outputs)\n rmsle_score = rmsle(y_test, outputs)\n print(\"The test MSE score is: %f\" % test_score)\n print(\"The test RMSLE score is: %f\" % rmsle_score)\n","repo_name":"YuvalHelman/mercariPriceSuggestion","sub_path":"mlp_infersent.py","file_name":"mlp_infersent.py","file_ext":"py","file_size_in_byte":2830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"13833788322","text":"\"\"\"\nTesting of the basic stages of neural network training:\n * creation and preprocessing of a dataset and its visualization\n * creation of a model\n * forward-method of the model (model.predict(data))\n * one iteration of training with back-propogation\nMake sure that you have correctly described the data in configuration.py\n\"\"\"\n\nimport torch\nimport random\nfrom DataGeneratorFCNN import RCNNDataset\nfrom torch.utils.data import DataLoader\nfrom Utils import rcnn_create, plot_img_bbox\nfrom configuration import config_dataset\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nfrom tqdm import tqdm\n\nimage_size = config_dataset[\"image_shape\"]\ndevice = \"cuda\"\ncheck_all_dataset_objects = False\n# create dataset\ntransforms_ = A.Compose([A.Resize(width=image_size[0],\n height=image_size[1]),\n A.Normalize(max_pixel_value=255,\n mean=(0, 0, 0),\n std = (1, 1, 1)),\n ToTensorV2(p=1.0)],\n bbox_params={\"format\": \"pascal_voc\", \"label_fields\": [\"labels\"]})\ndataset = RCNNDataset(path_images=config_dataset[\"path_image_dir\"],\n path_annotations=config_dataset[\"path_annots_dir\"],\n transform=transforms_)\n\nclass_labels = dataset.classes\nprint(f\"Number of classes = {len(class_labels)}\")\nwith open(r\".\\logs\\classes.txt\", \"w\") as fi:\n fi.writelines([class_label[0] + f\" = {class_label[1]}\\n\" for class_label in class_labels.items()])\n\ndataloader = DataLoader(dataset,\n batch_size=config_dataset[\"batch_size\"],\n shuffle=False,\n collate_fn=dataset.collate_fn)\n\n\n# check generator data\ntry:\n tt = iter(dataloader)\n l1 = tt.__next__()\nexcept:\n print(\"Dataloader create/getitem error\")\nelse:\n print(\"Dataloader create/getitem is right\")\n\n# check _create all dataset objects_/_plot batch_\nif check_all_dataset_objects:\n print(\"Check create dataset objects with annotation\")\n inds = list(range(67116, len(dataset)))\n for i in tqdm(inds):\n try: img, target = dataset[i]\n except Exception as e: print(f\"Error in create object #{i} - file name {dataset.list_path_annots[i]}\\n{e}\")\n print(\"Check dataset objects is completed\")\nelse:\n inds = [random.choice(list(range(len(dataset)))) for i in range(10)]\n for i in inds:\n try:\n img, target = dataset[i]\n plot_img_bbox(img.permute(1, 2, 0), target)\n except:\n print(f\"Plot batch error with object #{i}\")\n print(\"Plotting batch successful\")\n\n# create model\ntry:\n model = rcnn_create(len(class_labels.items()))\n # check model forward method inference and training\n # infer\n model = model.to(device)\n model.eval()\n # images is expected to be a list of 3d tensors of shape [C, H, W]\n with torch.no_grad():\n outs = model([l1[0][0].to(device)])\n # train 2 steps\n model = rcnn_create(len(class_labels.items()))\n model.train()\n opt = torch.optim.Adam(params=model.parameters())\n outs_loss1 = model(l1[0], l1[1])\n losses1 = sum(loss for loss in outs_loss1.values())\n print(f\"Losses on 1 step = {losses1}\")\n\n losses1.backward()\n opt.step()\n opt.zero_grad()\n l2 = tt.__next__()\n outs_loss2 = model(l2[0], l2[1])\n losses2 = sum(loss for loss in outs_loss2.values())\n print(f\"Losses on 2 step = {losses2}\")\n assert True\nexcept:\n print(\"Model_creation/forward/train-step error\")\nelse:\n print(\"Model_creation/forward is right\")","repo_name":"CheremGS/Objects_detection_models","sub_path":"Fast_RCNN/Test_components.py","file_name":"Test_components.py","file_ext":"py","file_size_in_byte":3626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38677339735","text":"from back_process import *\n\nif __name__ == '__main__':\n import scipy.optimize as so\n import numpy as np\n\n gammas_np = genfromtxt('E:/mnustes_science/experimental_data/faraday_drift_03/dvelocities_info/velocity_processed/gammas.csv', delimiter=',')\n velocities_np = genfromtxt('E:/mnustes_science/experimental_data/faraday_drift_03/dvelocities_info/velocity_processed/velocities.csv', delimiter=',')\n velocities_error_np = genfromtxt('E:/mnustes_science/experimental_data/faraday_drift_03/dvelocities_info/velocity_processed/velocities_err.csv',delimiter=',')\n freq_np = genfromtxt(\n 'E:/mnustes_science/experimental_data/faraday_drift_03/dvelocities_info/freq_processed/freq.csv',\n delimiter=',')\n freq_error_np = genfromtxt(\n 'E:/mnustes_science/experimental_data/faraday_drift_03/dvelocities_info/freq_processed/freq_err.csv',\n delimiter=',')\n f_i = 14.80\n gammas_np = gamma_correction(gammas_np, f_i)\n cut = 12\n gammas_log = np.log(gammas_np[cut:-1] - 0.68 * np.ones(len(gammas_np[cut:-1])))\n #print(gammas[cut])\n velocities_log = np.log(velocities_np[cut:-1])\n velocities_error_log = velocities_error_np[cut:-1] / velocities_np[cut:-1]\n\n ### Ajuste de curva ###\n def linear_fit(x, m, n):\n return m * x + n\n #popt, pcov = curve_fit(linear_fit, gammas_log, velocities_log)\n #m = popt[0]\n #n = popt[1]\n #x_grid_log = np.arange(gammas_log[0] - np.abs(gammas_log[0] - gammas_log[1]), gammas_log[-1] + np.abs(gammas_log[0] - gammas_log[1]), 0.01)\n #velocities_log_fit = m * x_grid_log + n\n #print(\"m =\" + str(m))\n\n def f_freq(x, A, c):\n x = x.astype('complex128')\n return A * np.abs((x - c) ** 0.5)\n\n\n def f(x, A, c, noise):\n x = x.astype('complex128')\n return A * (np.abs(((x - c) + (np.abs((x - c)) ** 2 + 2 * noise) ** 0.5) / 2)) ** 0.5\n\n\n popt, pcov = curve_fit(f, gammas_np, velocities_np, bounds=[(0, 0.15, 0), (10, 0.25, 1)])\n\n A = popt[0]\n c = popt[1]\n noise = popt[2]\n\n print('A_noise='+str(A))\n print('c_noise=' + str(c))\n print('noise=' + str(noise))\n\n popt_02, pcov = curve_fit(f_freq, gammas_np, velocities_np, bounds=[(0, 0.15), (10, 0.25)])\n\n A_02 = popt_02[0]\n c_02 = popt_02[1]\n print('A='+str(A_02))\n print('c=' + str(c_02))\n\n\n\n x_grid = np.arange(0, 1, 0.0005)\n velocity_noisy_fitted = []\n for i in range(len(x_grid)):\n epsilon_i = x_grid[i] - c\n velocity_noisy_fitted_i = A * ((epsilon_i + (epsilon_i ** 2 + 2 * noise) ** 0.5) / 2) ** 0.5\n velocity_noisy_fitted.append(velocity_noisy_fitted_i)\n velocity_noisy_fitted_np = np.array(velocity_noisy_fitted)\n\n x_grid_antierror = np.arange(c, 1, 0.0005)\n velocity_fitted = []\n for i in range(len(x_grid_antierror)):\n epsilon_i = x_grid_antierror[i] - c\n velocity_fitted_i = A * (epsilon_i) ** 0.5\n velocity_fitted.append(velocity_fitted_i)\n velocity_fitted_np = np.array(velocity_fitted)\n\n\n #popt, pcov = curve_fit(f_freq, gammas_np, freq_np, bounds=[(0, 0), (100, 0.61)])\n #A_freq = popt[0]\n #c_freq = popt[1]\n\n #x_grid_antierror_freq = np.arange(c_freq, 1, 0.001)\n #freq_fitted = []\n #for i in range(len(x_grid_antierror_freq)):\n # epsilon_i = x_grid_antierror_freq[i] - c_freq\n # freq_fitted_i = A_freq * (epsilon_i) ** 0.5\n # freq_fitted.append(freq_fitted_i)\n #freq_fitted_np = np.array(freq_fitted)\n\n\n px = 1/plt.rcParams['figure.dpi']\n fig, ax1 = plt.subplots(figsize=(1200*px, 800*px))\n ax1.set_xlabel('$\\Gamma_0$', fontsize=30)\n ax1.set_ylabel('$\\langle v \\\\rangle\\ \\\\textrm{(mm/s)}$', fontsize=30)\n ax1.tick_params(labelsize=20)\n ax1.errorbar(gammas_np, velocities_np, yerr=velocities_error_np, marker='o', ls='', capsize=5, capthick=1,\n ecolor='k', color='k', zorder=3)\n ax1.plot(x_grid_antierror, velocity_fitted_np, linestyle='dotted', linewidth='3', c='r', label='Noise included', zorder=1)\n\n ax1.plot(x_grid, velocity_noisy_fitted_np, '-', linewidth='3', c='r', label='No noise', zorder=2)\n ax1.set_ylim([0, 1])\n ax1.set_xlim([0.61, 0.785])\n ax1.grid(True)\n\n left, bottom, width, height = [0.25, 0.5, 0.2, 0.3]\n ax2 = fig.add_axes([left, bottom, width, height])\n ax2.plot(x_grid_log, velocities_log_fit, linestyle='--', linewidth='3', c=[1,0.,0.], label='No noise', zorder=2)\n ax2.errorbar(gammas_log, velocities_log, yerr=velocities_error_log, marker='o', ls='', lw=1, ms=4, capsize=2, capthick=1,\n ecolor='k', color='k', zorder=3)\n ax2.tick_params(labelsize=15)\n plt.xlim([-4.9, -2.4])\n plt.xticks([-4.5, -3.5, -2.5])\n #ax2.set_ylim([-1.5, 0])\n ax2.grid(True)\n ax2.set_xlabel('$\\ln\\Delta_D$', fontsize=25)\n ax2.set_ylabel('$\\ln\\langle v \\\\rangle$', fontsize=25)\n plt.savefig('E:/mnustes_science/experimental_data/faraday_drift_03/dvelocities_info/velocity_processed/fit', dpi=300)\n #plt.show()\n plt.close()\n\n plt.plot(x_grid_log, velocities_log_fit, linestyle='--', linewidth='3', c=[1,0.,0.], label='No noise', zorder=2)\n plt.errorbar(gammas_log, velocities_log, yerr=velocities_error_log, marker='o', ls='', lw=1, ms=4, capsize=2, capthick=1,\n ecolor='k', color='k', zorder=3)\n plt.tick_params(labelsize=15)\n plt.xlim([-4.9, -2.4])\n #plt.ylim([-1.5, 0])\n plt.grid(True)\n plt.xlabel('$\\ln\\Delta_D$', fontsize=25)\n plt.ylabel('$\\ln\\langle v \\\\rangle$', fontsize=25)\n plt.tight_layout()\n\n # plt.show()\n plt.close()\n\n fig, ax = plt.subplots()\n textstr = '\\n'.join((r'$a=%.3f$' % (m,), r'$b=%.3f$' % (n,)))\n textstr_02 = \"$\\ln \\langle v \\\\rangle = a \\ln\\Delta_D + b$\"\n plt.plot(x_grid_log, velocities_log_fit, linestyle='--', linewidth='3', c=[1,0.,0.], label='No noise', zorder=2)\n plt.errorbar(gammas_log, velocities_log, yerr=velocities_error_log, marker='o', ls='', lw=1, ms=4, capsize=2, capthick=1,\n ecolor='k', color='k', zorder=3)\n plt.tick_params(labelsize=15)\n # plt.legend(loc='best', fancybox=True, shadow=True)\n plt.grid(True)\n props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=20, verticalalignment='top', bbox=props)\n #ax.text(0.5, 0.95, textstr_02, transform=ax.transAxes, fontsize=20, verticalalignment='top')\n plt.xlim([-4.9, -2.4])\n plt.xlabel('$\\ln\\Delta_D$', fontsize=25)\n plt.ylabel('$\\ln\\langle v \\\\rangle$', fontsize=25)\n plt.tight_layout()\n plt.savefig('E:/mnustes_science/experimental_data/faraday_drift_03/dvelocities_info/velocity_processed/log-log_02',\n dpi=300)\n plt.close()","repo_name":"rariveros/LENL_processing","sub_path":"faraday/01_projects/faraday_drift/bifurcation_powerlaw/power_law_drift.py","file_name":"power_law_drift.py","file_ext":"py","file_size_in_byte":6669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71627548883","text":"from datetime import datetime\n\nfrom XBOSDriver.timeseriestypes import UNIT_TIMES\nfrom XBOSDriver.timeseriestypes import UNIT_TIME_SECONDS, UNIT_TIME_MILLISECONDS, \\\n UNIT_TIME_MICROSECONDS, UNIT_TIME_NANOSECONDS\n\nUNIT_TIME_LOOKUP = {\n UNIT_TIME_SECONDS: 1,\n UNIT_TIME_MILLISECONDS: 1e3,\n UNIT_TIME_MICROSECONDS: 1e6,\n UNIT_TIME_NANOSECONDS: 1e9\n}\n\ndef get_current_time_as(time_unit):\n \"\"\"\n Returns current time in the given units. Uses local timezone\n \"\"\"\n if time_unit not in UNIT_TIME_LOOKUP:\n raise TimestampException(\"Unit of Time {0} not in {1}\".format(time_unit, UNIT_TIME_LOOKUP))\n return int(datetime.now().timestamp() * UNIT_TIME_LOOKUP[time_unit])\n\ndef buildkv(fullname, obj, separator='/'):\n if isinstance(obj, dict):\n rv = []\n for newk, newv in obj.items():\n if len(fullname):\n rv += buildkv(fullname + separator + newk, newv, separator)\n else:\n rv += buildkv(newk, newv, separator)\n return rv\n else:\n return [(fullname, obj)]\n\n# from smap.util\n# make a nested object from a config file line\ndef build_recursive(d, suppress=['type', 'key', 'uuid']):\n rv = {}\n for k, v in d.items():\n if k in suppress: continue\n pieces = k.split('/')\n cur = rv\n for cmp in pieces[:-1]:\n if not cmp in cur.keys():\n cur[cmp] = {}\n cur = cur[cmp]\n cur[pieces[-1]] = v\n return rv\n\n# from smap.util\ndef dict_merge(o1, o2):\n \"\"\"Recursively merge dict o1 into dict o2.\n \"\"\"\n if not isinstance(o1, dict) or not isinstance(o2, dict):\n return o2\n o2 = dict(o2)\n for k, v in o1.items():\n if k in o2:\n o2[k] = dict_merge(v, o2[k])\n else:\n o2[k] = v\n return o2\n\n","repo_name":"gtfierro/XBOSDriver","sub_path":"python3/XBOSDriver/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30112057010","text":"# 示例5-26\nfrom operator import mul\nfrom functools import partial\ndef main():\n triple = partial(mul, 3) #(1)\n _obj = triple(7) #(2)\n print(_obj)\n _obj = list(map(triple, range(1, 10))) #(3)\n print(_obj)\nif __name__ == '__main__':\n main()","repo_name":"juedui0769/python2019","sub_path":"python3Study/fluentpython/ch05/demo05_26_partial.py","file_name":"demo05_26_partial.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19301928789","text":"import asyncio\nimport json\nimport time\n\nimport nextcord\nimport requests\nfrom config import *\nfrom multiprocessing.pool import ThreadPool as Pool\nfrom nextcord import ButtonStyle\nfrom nextcord.ui import Button, View\n\nheaders = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) '\n 'Chrome/102.0.5005.61 Safari/537.36 '\n}\n\naccounts = ACCOUNTS\npool_size = 5\nprevious_hash = None\ncurrent_time = time.time() - time_to_track\nmessages = []\n\nclient = nextcord.Client()\nchannel = None\n\n\n@client.event\nasync def on_ready():\n print(f'Logged in as {client.user}')\n global channel\n channel = client.get_channel(CHANNEL_ID)\n client.loop.create_task(send_message())\n\n\n# Get the latest Block\nasync def get_block():\n while True:\n url = \"https://public-api.solscan.io/block/last?limit=1\"\n resp = requests.get(url, headers=headers)\n resp = json.loads(resp.text)\n global previous_hash\n print(resp)\n try:\n previous_hash = resp[0][\"result\"][\"blockTime\"]\n time_delta = time.time() - previous_hash\n print(time.time(), previous_hash, time_delta)\n return time_delta\n except:\n print(\"getting block time 1\")\n await asyncio.sleep(1)\n\n\ndef get_image(trx):\n url = f\"https://api-mainnet.magiceden.dev/v2/tokens/{trx}\"\n resp = requests.get(url, headers=headers)\n resp = json.loads(resp.text)\n image_url = resp['image']\n collection = resp['collection'].upper().replace(\"_\", \" \")\n return image_url, collection\n\n\ndef get_price(trx):\n url = f\"https://api-mainnet.magiceden.dev/v2/tokens/{trx}/activities?offset=0&limit=1\"\n resp = requests.get(url, headers=headers)\n resp = json.loads(resp.text)\n try:\n print(resp)\n if resp[0]['type'] != 'buyNow':\n return False\n price = resp[0]['price']\n price = str(price)\n token = \" SOL\"\n return price + token\n\n except:\n return False\n\n\nasync def send_message():\n await asyncio.sleep(5)\n await client.wait_until_ready()\n print(\"started\")\n while True:\n global messages, channel\n await get_transactions()\n\n if len(messages) > 0:\n\n print(messages)\n for message in messages:\n print(messages)\n print(\"messaging start 1\")\n b1 = Button(label=\"View Transaction\", style=ButtonStyle.blurple, url=f\"https://solscan.io/tx/{message[1]}\")\n b2 = Button(label=\"View Token\", style=ButtonStyle.blurple, url=f\"https://solscan.io/token/{message[2]}\")\n myView = View(timeout=10)\n myView.add_item(b1)\n myView.add_item(b2)\n await channel.send(embed=message[0], view=myView)\n messages = []\n\n\ndef scrape_and_message(data, name):\n print(\"scraping start 1\")\n for trx in data:\n\n # if it isn't a transfer skip\n\n if trx[\"postBalance\"] == trx[\"preBalance\"]:\n continue\n\n # Checking whether it's a coin or NFT\n\n if \"symbol\" in trx:\n if trx[\"changeType\"] == \"inc\":\n\n message = nextcord.Embed(title=f\"**{name}**\", description=\"BOUGHT\", color=0x00aa88)\n message.add_field(name=f\"Token Name\", value=trx['symbol'], inline=True)\n message.add_field(name=f\"Wallet address\", value=trx['owner'], inline=True)\n # message = f\"{name} (wallet {trx['owner']}) has just purchased {trx['symbol']}.\"\n\n else:\n message = nextcord.Embed(title=f\"**{name}**\", description=\"SOLD\", color=0x00F10909)\n message.add_field(name=f\"Token Name\", value=trx['symbol'], inline=True)\n message.add_field(name=f\"Wallet address\", value=trx['owner'], inline=True)\n # message = f\"{name} (wallet {trx['owner']}) has just sold {trx['symbol']}.\"\n\n # When it's an NFT another API call to retrieve the NFT name\n\n else:\n url = f\"https://public-api.solscan.io/token/meta?tokenAddress={trx['tokenAddress']}\"\n\n resp = requests.get(url, headers=headers)\n resp = json.loads(resp.text)\n nft_name = resp[\"name\"]\n nft_symbol = resp[\"symbol\"]\n image_url, collection = get_image(trx['tokenAddress'])\n price = get_price(trx['tokenAddress'])\n if not price:\n print(\"The transaction involved transfer of assets between wallets of the same User.\")\n continue\n\n if trx[\"changeType\"] == \"inc\":\n # message = f\"{name} (wallet {trx['owner']}) has just purchased {nft_name}({nft_symbol}).\"\n message = nextcord.Embed(title=f\"**{name}**\", color=0x00aa88)\n message.add_field(name=f\"Bought Price:\", value=price, inline=False)\n message.add_field(name=f\"Project Name\", value=collection, inline=True)\n message.add_field(name=f\"Wallet address\", value=trx['owner'], inline=True)\n message.set_image(url=image_url)\n else:\n message = nextcord.Embed(title=f\"**{name}**\", color=0x00F10909)\n message.add_field(name=f\"Sold Price:\", value=price, inline=False)\n message.add_field(name=f\"Project Name\", value=collection, inline=True)\n message.add_field(name=f\"Wallet address\", value=trx['owner'], inline=True)\n message.set_image(url=image_url)\n # message = f\"{name} (wallet {trx['owner']}) has just sold {nft_name}({nft_symbol}).\"\n global messages, channel\n messages.append([message, trx['signature'][0], trx['tokenAddress']])\n print(\"end\")\n\n\nasync def get_transactions():\n global current_time\n\n # while True:\n\n if current_time < (time.time() - time_to_track):\n print(time.time())\n pool = Pool(pool_size)\n time_gap = await get_block()\n previous_time = int((time.time() - time_to_track - time_gap) // 1)\n current_time = int((time.time()) // 1)\n now_time = int((time.time() - time_gap) // 1)\n for account in accounts:\n pool.apply_async(get_data, (account, previous_time, now_time))\n pool.close()\n pool.join()\n print(time.time())\n\n\ndef get_data(account, previous_time, current_time):\n # url = f'https://public-api.solscan.io/account/transactions?account={accounts[account]}'\n url = f\"https://public-api.solscan.io/account/splTransfers?account={accounts[account]}&fromTime={previous_time}&toTime={current_time}&offset=0&limit=50\"\n resp = requests.get(url, headers=headers)\n try:\n resp = json.loads(resp.text)\n print(resp)\n transactions = resp[\"data\"]\n if len(transactions) == 0:\n print(\"No transactions at this moment from : \", account)\n\n return\n print(account, transactions)\n\n scrape_and_message(transactions, account)\n except:\n print(resp)\n\n\n# client.loop.create_task(send_message())\n\nclient.run(BOT_TOKEN)\n","repo_name":"LagLord/Solana-Wallet-Tracker-Crypto-NFT-sales-Discord-Bot","sub_path":"solscanThread.py","file_name":"solscanThread.py","file_ext":"py","file_size_in_byte":7076,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"} +{"seq_id":"29043104817","text":"# -*- coding: utf-8 -*-\nimport json\nimport os\nimport pytest\nimport yatest.common\n\n\ndef encode_str(data):\n return data.encode('hex')\n\n\ndef decode_str(data):\n return data.decode('hex')\n\n\nDATA_DIR = os.path.abspath(yatest.common.source_path('balancer/test/hpack/data'))\nDECODER_DATA_DIR = DATA_DIR + '/decoder/'\nENCODER_DATA_DIR = DATA_DIR + '/encoder/'\n\n\ndef get_binary(name):\n return yatest.common.binary_path('balancer/kernel/http2/server/hpack/tests/{name}/{name}'.format(name=name))\n\n\ndef build_params(data_dir):\n return [name[:-3] for name in os.listdir(data_dir) if name.endswith('.in')]\n\n\ndef read_json(path):\n with open(path) as f:\n return json.load(f)\n\n\ndef __encode_headers(headers):\n from balancer.test.util.proto.http2.message import HeaderField as HF\n result = list()\n for field in headers:\n if isinstance(field, HF):\n name = field.name\n value = field.value\n indexing = field.indexing\n else:\n name, value = field\n indexing = None\n name = encode_str(name)\n value = encode_str(value)\n if indexing is not None:\n result.append({'name': name, 'value': value, 'indexing': indexing})\n else:\n result.append({'name': name, 'value': value})\n return result\n\n\ndef __util_decoder(test_input, test_output):\n import balancer.test.util.proto.http2.hpack._hpack as hpack\n data = read_json(test_input)\n decoder = hpack.Decoder()\n result = list()\n for block in data:\n try:\n headers = decoder.decode(decode_str(block))\n result.append({\n 'headers': __encode_headers(headers),\n 'table': __encode_headers(decoder.table.dynamic_items()),\n })\n except hpack.DecoderError:\n result = ['COMPRESSION_ERROR']\n break\n with open(test_output, 'w') as f:\n json.dump(result, f)\n\n\ndef balancer_decoder(test_input, test_output):\n err_path = yatest.common.output_path('decoder.err')\n with open(test_input) as f_in:\n with open(test_output, 'w') as f_out:\n with open(err_path, 'w') as f_err:\n yatest.common.execute([get_binary('hpack_decoder_test')],\n check_exit_code=False, stdin=f_in, stdout=f_out, stderr=f_err)\n\n\ndef run_decoder(test_input, test_output):\n # __util_decoder(test_input, test_output)\n balancer_decoder(test_input, test_output)\n\n\ndef decode_headers(headers):\n if headers is None:\n return []\n result = list()\n for field in headers:\n name = decode_str(field['name'])\n value = decode_str(field['value'])\n if 'indexing' in field:\n result.append({'name': name, 'value': value, 'indexing': field['indexing']})\n else:\n result.append({'name': name, 'value': value})\n return result\n\n\ndef decode_result(data): # for readability\n result = list()\n for item in data:\n if item in ('COMPRESSION_ERROR', 'PROTOCOL_ERROR'):\n result.append(item)\n break\n else:\n result.append({\n 'headers': decode_headers(item['headers']),\n 'table': decode_headers(item['table']),\n })\n return result\n\n\n@pytest.mark.parametrize('name', build_params(DECODER_DATA_DIR))\ndef test_decoder(name):\n test_input = DECODER_DATA_DIR + name + '.in'\n test_output = yatest.common.output_path(name + '.out.test')\n run_decoder(test_input, test_output)\n result = decode_result(read_json(test_output))\n expected = decode_result(read_json(DECODER_DATA_DIR + name + '.out'))\n assert result == expected\n\n\ndef __util_encoder(test_input, test_output):\n import balancer.test.util.proto.http2.hpack._hpack as hpack\n from balancer.test.util.proto.http2.message import HeaderField as HF\n from balancer.test.util.proto.http2.message import HName, HValue\n\n def get_value(h_descr):\n return HValue(decode_str(h['value']))\n\n encoder = hpack.Encoder(greedy_huffman=True)\n result = list()\n for data in read_json(test_input):\n block = list()\n sizes = list()\n for h in data:\n h_type = h['type']\n if h_type == 'size_update':\n sizes.append(h['value'])\n elif h_type == 'indexed_field':\n block.append(HF(h['field_id']))\n elif h_type == 'indexed_name':\n block.append(HF(h['name_id'], get_value(h), indexing=h['indexing']))\n else:\n block.append(HF(HName(decode_str(h['name'])),\n get_value(h), indexing=h['indexing']))\n if len(sizes) > 0:\n min_size = min(sizes)\n last_size = sizes[-1]\n encoder.update_size(min_size)\n if min_size != last_size:\n encoder.update_size(last_size)\n result.append(encode_str(encoder.encode(block)))\n with open(test_output, 'w') as f:\n json.dump(result, f)\n\n\ndef balancer_encoder(test_input, test_output):\n with open(test_input) as f_in:\n with open(test_output, 'w') as f_out:\n yatest.common.execute([get_binary('hpack_encoder_test')], stdin=f_in, stdout=f_out)\n\n\ndef run_encoder(test_input, test_output):\n # __util_encoder(test_input, test_output)\n balancer_encoder(test_input, test_output)\n\n\n@pytest.mark.parametrize('name', build_params(ENCODER_DATA_DIR))\ndef test_encoder(name):\n test_input = ENCODER_DATA_DIR + name + '.in'\n test_output = yatest.common.output_path(name + '.out.test')\n run_encoder(test_input, test_output)\n result = read_json(test_output)\n expected = read_json(ENCODER_DATA_DIR + name + '.out')\n assert result == expected\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"balancer/test/hpack/test_hpack.py","file_name":"test_hpack.py","file_ext":"py","file_size_in_byte":5750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"21782983203","text":"import cv2\nimport numpy as np\n\n\ndef create_network(model, config, gpu=False):\n net = cv2.dnn.readNetFromDarknet(config, model)\n if gpu:\n net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)\n net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)\n return net\n\n\ndef detect(net, image):\n layers = net.getLayerNames()\n layers = [layers[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (608, 608), swapRB=True, crop=False)\n net.setInput(blob)\n return net.forward(layers)\n\n\ndef filter(image, detections, threshold=0.3):\n boxes = list()\n confidences = list()\n class_ids = list()\n (H, W) = image.shape[:2]\n output_layers = detections\n\n for output in output_layers:\n\n for detection in output:\n scores = detection[5:]\n class_id = np.argmax(scores)\n confidence = scores[class_id]\n\n if confidence > threshold:\n box = detection[0:4] * np.array([W, H, W, H])\n (centerX, centerY, width, height) = box.astype(\"int\")\n x = int(centerX - (width / 2))\n y = int(centerY - (height / 2))\n boxes.append([x, y, int(width), int(height)])\n confidences.append(float(confidence))\n class_ids.append(class_id)\n\n return [boxes, confidences, class_ids]\n\n\ndef apply_nms(image, filtered_detections, conf, thresh):\n boxes, confidences, ids = filtered_detections\n idxs = cv2.dnn.NMSBoxes(boxes, confidences, conf, thresh)\n bboxes = list()\n\n if len(idxs) > 0:\n for i in idxs.flatten():\n (x, y) = (boxes[i][0], boxes[i][1])\n (w, h) = (boxes[i][2], boxes[i][3])\n bboxes.append([x, y, w, h])\n\n cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n text = \"{}: {:.4f}\".format(\"Meteor\", confidences[i])\n cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n\n return True, image\n return False, image\n","repo_name":"BrunoSilva98/MeteorFinder","sub_path":"yolo.py","file_name":"yolo.py","file_ext":"py","file_size_in_byte":2044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1753627706","text":"# =============================================================================\n# codePost – Blackboard Utility\n#\n# Takes submissions downloaded from Blackboard and transforms the file\n# structure into a structure that codePost will recognize.\n#\n# =============================================================================\n\n# Python stdlib imports\nimport os\nimport argparse\nimport csv\nimport shutil\nimport re\n\n# =============================================================================\n\nparser = argparse.ArgumentParser(description='Blackboard to codePost!')\nparser.add_argument(\n 'submissions', help='The directory of submissions downloaded from Blackboard')\nparser.add_argument(\n 'roster', help='The course roster of students that includes first name, last name, and email')\nparser.add_argument('-s', '--simulate', action='store_true')\nargs = parser.parse_args()\n\n# =============================================================================\n# Constants\n\nOUTPUT_DIRECTORY = 'codepost_upload'\nERROR_DIRECTORY = 'errors'\n\n_cwd = os.getcwd()\n_upload_dir = os.path.join(_cwd, OUTPUT_DIRECTORY)\n_error_dir = os.path.join(_cwd, ERROR_DIRECTORY)\n\n# =============================================================================\n# Helpers\n\n\ndef normalize(string):\n return string.lower().strip()\n\n\ndef delete_directory(path):\n if os.path.exists(path):\n shutil.rmtree(path)\n\n\ndef validate_csv(row):\n for key in row.keys():\n if 'blackboard' in normalize(key):\n blackboard_id = key\n elif 'email' in normalize(key):\n email = key\n\n if blackboard_id == None or email == None:\n if blackboard_id == None:\n print(\"Missing header: blackboard_id\")\n if email == None:\n print(\"Missing header: email\")\n\n raise RuntimeError(\n \"Malformatted roster. Please fix the headers and try again.\")\n\n return (blackboard_id, email)\n else:\n return (blackboard_id, email)\n\n\ndef blackboard_id_to_email(roster):\n with open(roster, mode='r') as csv_file:\n csv_reader = csv.DictReader(csv_file)\n line_count = 0\n blackboard_id, email = (None, None)\n blackboard_id_to_email = {}\n for row in csv_reader:\n if line_count == 0:\n (blackboard_id, email) = validate_csv(row)\n line_count += 1\n\n # Blackboard convention: map {blackboard_id} to {codePost email}\n blackboard_id_to_email[\n normalize(row[blackboard_id])] = normalize(row[email])\n line_count += 1\n return blackboard_id_to_email\n\n\ndef check_for_partners(file_name):\n filepath = os.path.join(args.submissions, file_name)\n emails = [line.rstrip('\\n') for line in open(filepath, 'r')]\n EMAIL_REGEX = r\"[^@]+@[^@]+\\.[^@]+\"\n filtered_emails = [x for x in emails if re.match(EMAIL_REGEX, x)]\n\n return filtered_emails\n\n# =============================================================================\n\nif (args.simulate):\n print('\\n~~~~~~~~~~~ START SIMULATION ~~~~~~~~~~~')\n\nprint('\\nSetting up directories...')\n\n# Overwrite the directories if they exist already\nif not args.simulate:\n delete_directory(_upload_dir)\n delete_directory(_error_dir)\n\n os.makedirs(_upload_dir)\n os.makedirs(_error_dir)\n\nprint('\\t/{}'.format(OUTPUT_DIRECTORY))\nprint('\\t/{}'.format(ERROR_DIRECTORY))\n\nprint('\\nReading and validating roster...')\nblackboard_id_to_email = blackboard_id_to_email(args.roster)\nprint('\\tVALID')\n\nprint('\\nChecking submissions for partners...')\n\nfiles = os.listdir(args.submissions)\nfolders = []\nfor file in files:\n file_name = file.split('_')[-1]\n if 'partners' in file_name:\n partners = check_for_partners(file)\n folders.append(partners)\n\nprint('\\t{}'.format(folders))\n\nprint('\\nCreating student folders...')\nfor student in blackboard_id_to_email:\n found = False\n for folder in folders:\n if blackboard_id_to_email[student] in folder:\n found = True\n break\n\n if not found:\n folders.append([blackboard_id_to_email[student]])\n\nfor folder in folders:\n folder_name = \",\".join(folder)\n if not args.simulate:\n os.makedirs(os.path.join(_upload_dir, folder_name))\n print('\\t{}'.format(folder_name))\n\n\nprint('\\nMapping and copying files...')\nfor file in files:\n blackboard_id = file.split('_')[1]\n file_name = file.split('_')[-1]\n\n if normalize(blackboard_id) in blackboard_id_to_email:\n email = blackboard_id_to_email[blackboard_id]\n found = False\n\n for folder in folders:\n if email in folder:\n folder_name = \",\".join(folder)\n found = True\n if not args.simulate:\n shutil.copyfile(os.path.join(args.submissions, file), os.path.join(\n os.path.join(_upload_dir, folder_name), file_name))\n print('\\t{}'.format(os.path.join(\n os.path.join(_upload_dir, folder_name), file_name)))\n\n if not found:\n if not args.simulate:\n shutil.copyfile(os.path.join(args.submissions, file),\n os.path.join(_error_dir, file))\n print('\\tERROR: {}'.format(os.path.join(_error_dir, file)))\n else:\n if not args.simulate:\n shutil.copyfile(os.path.join(args.submissions, file),\n os.path.join(_error_dir, file))\n print('\\tERROR: {}'.format(os.path.join(_error_dir, file)))\n\nif args.simulate:\n print('\\n~~~~~~~~~~~ END SIMULATION ~~~~~~~~~~~\\n')\n","repo_name":"codepost-io/integration-blackboard","sub_path":"blackboard_to_codepost_manual.py","file_name":"blackboard_to_codepost_manual.py","file_ext":"py","file_size_in_byte":5244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"9405394579","text":"from django import template\n\nregister = template.Library()\n\n\n@register.filter\ndef addclass(field, css):\n return field.as_widget(attrs={\"class\": css})\n\n\n@register.filter\ndef uglify(value) -> str:\n result = []\n for i in range(len(value)):\n if i % 2 == 1:\n result.append(value[i].upper())\n else:\n result.append(value[i].lower())\n return(''.join(result))\n","repo_name":"JCoffeeYP/YaTube_social_network","sub_path":"users/templatetags/user_filters.py","file_name":"user_filters.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"7368513296","text":"import random\r\nimport time\r\n\r\n\r\nfila = ['Matheus', 'Joao', 'Marcos', 'Paulo',\r\n 'Lucas', 'Mario', 'Rodrigo', 'Maria', 'Marilia', 'Joana', 'Perla']\r\natendente1 = []\r\natendente2 = []\r\natendente3 = []\r\natendente4 = []\r\nhoraAtendente1 = []\r\natendente1atendimentos = []\r\nhoraAtendente2 = []\r\natendente2atendimentos = []\r\nhoraAtendente3 = []\r\natendente3atendimentos = []\r\nhoraAtendente4 = []\r\natendente4atendimentos = []\r\n\r\nwhile True:\r\n print('-='*20)\r\n print('Sistema de ligações')\r\n print('-='*20)\r\n dash = int(input(\r\n 'Utilize o menu abaixo\\n[1] Iniciar programa\\n[2] Ver informacoes atendentes \\n[3] Adicionar pessoas na fila \\n[4] Ver fila\\n[0] Sair \\n'))\r\n if dash == 1:\r\n for i in range(4):\r\n if len(fila) != 0:\r\n True\r\n if len(atendente1) == 0:\r\n pegarFila = fila.pop(0)\r\n atendente1.append(pegarFila)\r\n elif len(atendente2) == 0:\r\n pegarFila = fila.pop(0)\r\n atendente2.append(pegarFila)\r\n elif len(atendente3) == 0:\r\n pegarFila = fila.pop(0)\r\n atendente3.append(pegarFila)\r\n elif len(atendente4) == 0:\r\n pegarFila = fila.pop(0)\r\n atendente4.append(pegarFila)\r\n else:\r\n print('Fila vazia')\r\n\r\n while True:\r\n if len(atendente1) >= 1:\r\n if len(fila) != 0:\r\n pegarFila = fila.pop(0)\r\n atendente1.append(pegarFila)\r\n print(f'Clientes na fila: {atendente1}')\r\n print(f'Atendendo atualmente {atendente1[0]}')\r\n print(f'finalizando...')\r\n time.sleep(1)\r\n tempo1 = random.randrange(3, 15)\r\n print(\r\n f'o tempo de atendimento de {atendente1[0]} foi {tempo1} min')\r\n horaAtendente1.append(tempo1)\r\n tirarCliente = atendente1.pop(0)\r\n atendente1atendimentos.append(tirarCliente)\r\n print(\r\n f'removendo da filae e chamando o próximo: {atendente1[0]}')\r\n time.sleep(1)\r\n else:\r\n print('Fila vazia')\r\n if len(atendente2) >= 1:\r\n if len(fila) != 0:\r\n pegarFila = fila.pop(0)\r\n atendente2.append(pegarFila)\r\n print(f'Clientes na fila: {atendente2}')\r\n print(f'Atendendo atualmente {atendente2[0]}')\r\n print(f'finalizando...')\r\n time.sleep(1)\r\n tempo2 = random.randrange(3, 15)\r\n print(\r\n f'o tempo de atendimento de {atendente2[0]} foi {tempo2} min')\r\n horaAtendente2.append(tempo2)\r\n tirarCliente = atendente2.pop(0)\r\n atendente2atendimentos.append(tirarCliente)\r\n print(\r\n f'removendo da fila e chamando o próximo: {atendente2[0]}')\r\n time.sleep(1)\r\n else:\r\n print('Fila vazia')\r\n if len(atendente3) >= 1:\r\n if len(fila) != 0:\r\n pegarFila = fila.pop(0)\r\n atendente3.append(pegarFila)\r\n print(f'Clientes na fila: {atendente3}')\r\n print(f'Atendendo atualmente {atendente3[0]}')\r\n print(f'finalizando...')\r\n time.sleep(1)\r\n tempo3 = random.randrange(3, 15)\r\n print(\r\n f'o tempo de atendimento de {atendente3[0]} foi {tempo3} min')\r\n horaAtendente3.append(tempo3)\r\n tirarCliente = atendente3.pop(0)\r\n atendente3atendimentos.append(tirarCliente)\r\n print(\r\n f'removendo da fila e chamando o próximo: {atendente3[0]}')\r\n time.sleep(1)\r\n else:\r\n print('Fila vazia')\r\n if len(atendente4) >= 1:\r\n if len(fila) != 0:\r\n pegarFila = fila.pop(0)\r\n atendente4.append(pegarFila)\r\n print(f'Clientes na fila: {atendente4}')\r\n print(f'Atendendo atualmente {atendente4[0]}')\r\n print(f'finalizando...')\r\n time.sleep(1)\r\n tempo4 = random.randrange(3, 15)\r\n print(\r\n f'o tempo de atendimento de {atendente4[0]} foi {tempo4} min')\r\n horaAtendente4.append(tempo4)\r\n tirarCliente = atendente4.pop(0)\r\n atendente4atendimentos.append(tirarCliente)\r\n print(\r\n f'removendo da fila e chamando o próximo: {atendente4[0]}')\r\n time.sleep(1)\r\n else:\r\n print('Fila vazia')\r\n if len(fila) == 0:\r\n break\r\n if dash == 2:\r\n calchorasAtendente1 = sum(horaAtendente1)\r\n calchorasAtendente2 = sum(horaAtendente2)\r\n calchorasAtendente3 = sum(horaAtendente1)\r\n calchorasAtendente4 = sum(horaAtendente2)\r\n print('-='*20)\r\n print('Dashboard de resultados')\r\n print('-='*20)\r\n print(\"\")\r\n print('-='*20)\r\n print(\r\n f'O atendente 1 atendeu o total de {len(atendente1atendimentos)} pessoas')\r\n print(\r\n f'A media de minutos em atendimento do atendente 1 é {calchorasAtendente1/3:.2f} minutos')\r\n print('-='*20)\r\n print(\r\n f'O atendente 2 atendeu o total de {len(atendente2atendimentos)} pessoas')\r\n print(\r\n f'A media de minutos em atendimento do atendente 2 é {calchorasAtendente2/3:.2f} minutos')\r\n print('-='*20)\r\n print(\r\n f'O atendente 3 atendeu o total de {len(atendente3atendimentos)} pessoas')\r\n print(\r\n f'A media de minutos em atendimento do atendente 3 é {calchorasAtendente3/3:.2f} minutos')\r\n print('-='*20)\r\n print(\r\n f'O atendente 4 atendeu o total de {len(atendente4atendimentos)} pessoa')\r\n print(\r\n f'A media de minutos em atendimento do atendente 4 é {calchorasAtendente4/3:.2f} minutos')\r\n if dash == 3:\r\n nome = input('Digite o nome: ')\r\n fila.append(nome)\r\n if dash == 4:\r\n print(fila)\r\n if dash == 0:\r\n print('Finalizando')\r\n break\r\n","repo_name":"fdacmatheus/desafioUNDB","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6726,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"72733292880","text":"#-*- coding:utf-8 -*-\n\nfrom openerp import models, fields, api\nfrom urllib import urlencode\nfrom urlparse import urljoin\nfrom datetime import datetime, time, timedelta\nfrom openerp.exceptions import UserError\n\nclass Holidays(models.Model):\n _name = \"hr.holidays\"\n _inherit = \"hr.holidays\"\n\n\n def _default_approver(self):\n employee = self.env['hr.employee'].browse(self._employee_get())\n if employee.holidays_approvers:\n return employee.holidays_approvers[0].approver.id\n\n # In case of ERROR , during installation, just remove the default=_default_approver, then install again.\n # then after installation, put (default=_default_approver it back on the following code and reinstall again.\n pending_approver = fields.Many2one('hr.employee', string=\"Pending Approver\", readonly=True, default=_default_approver)\n pending_approver_user = fields.Many2one('res.users', string='Pending approver user', related='pending_approver.user_id', related_sudo=True, store=True, readonly=True)\n current_user_is_approver = fields.Boolean(string= 'Current user is approver', compute='_compute_current_user_is_approver')\n approbations = fields.One2many('hr.employee.holidays.approbation', 'holidays', string='Approvals', readonly=True)\n pending_transfered_approver_user = fields.Many2one('res.users', string='Pending transfered approver user', compute=\"_compute_pending_transfered_approver_user\", search='_search_pending_transfered_approver_user')\n\n @api.multi\n def holidays_refuse(self):\n return {\n 'name': 'Confirm Leave Request Disapproval',\n 'view_mode': 'form',\n 'view_type': 'form',\n 'res_model': 'leave.request.disapprove.wizard',\n 'type': 'ir.actions.act_window',\n 'target': 'new'\n }\n\n \n @api.multi\n def holidays_confirm(self):\n res = super(Holidays, self).holidays_confirm()\n approver = False\n requester = self.employee_id.user_id\n for holiday in self:\n if self.type == 'add':\n hr_officer_approver = self.env.user.company_id.leave_allocation_approver_id\n if not hr_officer_approver:\n raise UserError('Please contact the IT administrator to set the default Human Resources Leave Allocation Approver in the company settings ')\n if not hr_officer_approver.user_id :\n raise UserError('Human Resources Leave Allocation Approver is not related to any user in the system')\n holiday.pending_approver = approver = hr_officer_approver\n elif holiday.employee_id.holidays_approvers :\n holiday.pending_approver = approver = holiday.employee_id.holidays_approvers[0].approver\n\n if requester and approver:\n self.send_email(requester, approver,final_approval=False)\n return res\n\n\n def _get_final_url(self, module_name, menu_id, action_id, context=None):\n fragment = {}\n res = {}\n model_data = self.env['ir.model.data']\n base_url = self.env['ir.config_parameter'].get_param('web.base.url')\n fragment['menu_id'] = model_data.get_object_reference(module_name, menu_id)[1]\n fragment['model'] = 'hr.holidays'\n fragment['view_type'] = 'form'\n fragment['action'] = model_data.get_object_reference(module_name, action_id)[1]\n query = {'db': self.env.cr.dbname}\n # for displaying tree view. Remove if you want to display form view\n # fragment['page'] = '0'\n # fragment['limit'] = '80'\n # res = urljoin(base_url, \"?%s#%s\" % (urlencode(query), urlencode(fragment)))\n\n\n # For displaying a single record. Remove if you want to display tree view\n fragment['id'] = context.get(\"holiday_id\")\n res = urljoin(base_url, \"?%s#%s\" % (urlencode(query), urlencode(fragment)))\n return res\n\n def _get_url(self, module_name, menu_id, action_id, context=None):\n fragment = {}\n res = {}\n model_data = self.env['ir.model.data']\n base_url = self.env['ir.config_parameter'].get_param('web.base.url')\n fragment['menu_id'] = model_data.get_object_reference(module_name, menu_id)[1]\n fragment['model'] = 'hr.holidays'\n fragment['view_type'] = 'form'\n fragment['action'] = model_data.get_object_reference('hr_holidays_multi_levels_approval', action_id)[1]\n query = {'db': self.env.cr.dbname}\n # for displaying tree view. Remove if you want to display form view\n # fragment['page'] = '0'\n # fragment['limit'] = '80'\n # res = urljoin(base_url, \"?%s#%s\" % (urlencode(query), urlencode(fragment)))\n\n\n # For displaying a single record. Remove if you want to display tree view\n fragment['id'] = context.get(\"holiday_id\")\n res = urljoin(base_url, \"?%s#%s\" % (urlencode(query), urlencode(fragment)))\n return res\n\n\n def send_email(self,requester,approver,final_approval=False):\n\n company_email = self.env.user.company_id.email and self.env.user.company_id.email.strip() or False\n requester_email = requester.email and requester.email or False\n approver_email = approver.user_id and approver.user_id.email or False\n if requester_email and approver_email and company_email and not final_approval:\n # Custom Email Template\n mail_template = self.env.ref('hr_holidays_multi_levels_approval.mail_templ_leave_approval')\n ctx = {}\n ctx.update({'holiday_id': self.id})\n the_url = self._get_url('hr_holidays', 'menu_open_department_leave_approve', 'open_holidays_approve', ctx)\n ctx = {'system_email': company_email,\n 'requester_email': requester_email,\n 'approver_email' : approver_email,\n 'requester_name': self.employee_id.name,\n 'approver_name': approver.name,\n 'url': the_url,\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n elif requester_email and approver_email and company_email and final_approval and self.date_from and self.date_to:\n mail_template = self.env.ref('hr_holidays_multi_levels_approval.mail_templ_leave_final_approval')\n ctx = {}\n ctx.update({'holiday_id': self.id})\n the_url = self._get_final_url('hr_holidays', 'menu_hr_holidays_my_leaves', 'open_ask_holidays', ctx)\n ctx = {'system_email': company_email,\n 'requester_email': requester_email,\n 'approver_email': approver_email,\n 'requester_name': self.employee_id.name,\n 'approver_name': approver.name,\n 'url': the_url,\n 'date_from' : datetime.strptime(self.date_from,'%Y-%m-%d %H:%M:%S').strftime('%d-%m-%Y %H:%M:%S'),\n 'date_to' : datetime.strptime(self.date_to,'%Y-%m-%d %H:%M:%S').strftime('%d-%m-%Y %H:%M:%S')\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n\n\n @api.multi\n def action_approve(self):\n for holiday in self:\n\n is_last_approbation = False\n sequence = 0\n next_approver = None\n for approver in holiday.employee_id.holidays_approvers:\n sequence = sequence + 1\n if holiday.pending_approver.id == approver.approver.id:\n if sequence == len(holiday.employee_id.holidays_approvers):\n is_last_approbation = True\n next_approver = approver.approver\n else:\n next_approver = holiday.employee_id.holidays_approvers[sequence].approver\n\n if is_last_approbation or self.type == 'add':\n holiday.action_validate()\n else:\n holiday.write({'state': 'confirm', 'pending_approver': next_approver.id})\n self.env['hr.employee.holidays.approbation'].create({'holidays': holiday.id, 'approver': self.env.uid, 'sequence': sequence, 'date': fields.Datetime.now()})\n\n requester = self.employee_id.user_id\n approver = next_approver and next_approver.user_id\n if requester and approver and not is_last_approbation :\n self.send_email(requester, approver,final_approval=False)\n elif requester and approver and is_last_approbation :\n self.send_email(requester, approver,final_approval=True)\n return\n\n @api.multi\n def action_disapprove(self, msg):\n super(Holidays, self).holidays_refuse()\n reason_for_dispproval = msg\n\n # Send Email\n company_email = self.env.user.company_id.email.strip()\n approver_email = self.env.user.partner_id.email.strip()\n\n if self.type == 'add':\n requester_email = False\n else:\n requester_email = self.employee_id.user_id.email.strip()\n\n\n if company_email and approver_email and not self.type == 'add':\n # Custom Email Template\n mail_template = self.env.ref('hr_holidays_multi_levels_approval.mail_templ_leave_disapproval')\n ctx = {}\n ctx.update({'holiday_id': self.id})\n the_url = self._get_final_url('hr_holidays', 'menu_hr_holidays_my_leaves', 'open_ask_holidays', ctx)\n ctx = {'system_email': company_email,\n 'requester_email': requester_email,\n 'approver_email': approver_email,\n 'requester_name': self.employee_id.name,\n 'approver_name': self.env.user.name,\n 'url': the_url,\n 'reason_for_dispproval': reason_for_dispproval,\n 'date_from': datetime.strptime(self.date_from, '%Y-%m-%d %H:%M:%S').strftime('%d-%m-%Y %H:%M:%S'),\n 'date_to': datetime.strptime(self.date_to, '%Y-%m-%d %H:%M:%S').strftime('%d-%m-%Y %H:%M:%S')\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n else:\n self.message_post('Leave Allocation Rejection Reason: %s.' % (reason_for_dispproval),\n subject='Leave Allocation Rejection Reason',\n subtype='mail.mt_comment')\n return\n\n @api.multi\n def action_validate(self):\n self.write({'pending_approver': None})\n for holiday in self:\n self.env['hr.employee.holidays.approbation'].create({'holidays': holiday.id, 'approver': self.env.uid, 'date': fields.Datetime.now()})\n super(Holidays, self).holidays_validate()\n\n \n @api.one\n def _compute_current_user_is_approver(self):\n if self.pending_approver.user_id.id == self.env.user.id or self.pending_approver.transfer_holidays_approvals_to_user.id == self.env.user.id :\n self.current_user_is_approver = True\n else:\n self.current_user_is_approver = False\n \n @api.onchange('employee_id')\n def _onchange_employee(self):\n if self.employee_id and self.employee_id.holidays_approvers:\n self.pending_approver = self.employee_id.holidays_approvers[0].approver.id\n else:\n self.pending_approver = False\n \n @api.one\n def _compute_pending_transfered_approver_user(self):\n self.pending_transfered_approver_user = self.pending_approver.transfer_holidays_approvals_to_user\n \n def _search_pending_transfered_approver_user(self, operator, value):\n replaced_employees = self.env['hr.employee'].search([('transfer_holidays_approvals_to_user', operator, value)])\n employees_ids = []\n for employee in replaced_employees:\n employees_ids.append(employee.id)\n return [('pending_approver', 'in', employees_ids)]\n\n\n\n\n\n\nclass ResCompanyExtend(models.Model):\n _inherit = \"res.company\"\n\n leave_allocation_approver_id = fields.Many2one('hr.employee',string='Human Resources Leave Allocation Approver', help=\"The hr officer who approves leave allocations\")\n\n ","repo_name":"kingsleyuk2003/ODEX","sub_path":"addons/hr_holidays_multi_levels_approval/models/holidays.py","file_name":"holidays.py","file_ext":"py","file_size_in_byte":12214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35918793160","text":"#!/usr/bin/env python\n\nimport os\nimport pandas as pd\nfrom sklearn.metrics import jaccard_score, pairwise_distances\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport argparse\nimport datetime\nimport scipy.cluster.hierarchy as shc\nimport scipy.spatial.distance as ssd #pdist\nimport PyQt5\nfrom PyQt5 import QtGui\nimport ete3\nfrom ete3 import Tree, TreeStyle\n\nfrom misc import check_file_exists, import_to_pandas\n\n\"\"\"\nTODO: confirm 'average' method as the best option\n confirm euclidean 'metric' as the most appropiate for SNP distance (hamming used so far)\n\"\"\"\n\nEND_FORMATTING = '\\033[0m'\nWHITE_BG = '\\033[0;30;47m'\nBOLD = '\\033[1m'\nUNDERLINE = '\\033[4m'\nRED = '\\033[31m'\nGREEN = '\\033[32m'\nMAGENTA = '\\033[35m'\nBLUE = '\\033[34m'\nCYAN = '\\033[36m'\nYELLOW = '\\033[93m'\nDIM = '\\033[2m'\n\n\ndef get_arguments():\n\n #Define parser and program\n parser = argparse.ArgumentParser(prog = \"VCF_DDTB.py\", description= \"VCF_DDTB manages a custom snp database\") \n\n #Define subtask/subparsers\n subparsers = parser.add_subparsers( dest = \"subtask\", help = \"new / update / compare / extract commands either add new samples, compare or discard exixting samples\")\n\n new_parser = subparsers.add_parser(\"new\", help = \"Create new ddbb with presence/absence of snp\")\n update_parser = subparsers.add_parser(\"update\", help = \"Add new sample using a list of variants, files supplied or files on folder\")\n compare_parser = subparsers.add_parser(\"compare\", help = \"Comapare samples supplied or all samples to obtain a pirwise matrix\")\n extract_parser = subparsers.add_parser(\"extract\", help = \"Remove samples supplied from databse\")\n\n\n #new_parser\n new_parser.add_argument(\"-o\", \"--outputfile\", dest = \"output_file\", required= True, metavar=\"filename\", help=\"REQUIRED: file name, including PATH, of the newd ddbb\")\n new_parser.add_argument(\"-v\", \"--vcf\", required= False, action='store_true', help=\"Database will use vcf files instead of \")\n new_parser.add_argument(\"-s\", \"--suffix\", required= False, type=str, default=\".SNP.final.vcf\", help=\"Suffix to filter within vcf with similar suffix\")\n new_parser.add_argument(\"-r\", \"--recalibrate\", required= False, type=str, default=False, help=\"Folder with tab files to asses discrepancies after comparing\")\n\n\n new_exclusive = new_parser.add_mutually_exclusive_group(required= True)\n\n new_exclusive.add_argument(\"-F\", \"--folder\", dest = \"folder\", metavar=\"folder\",required= False, type=str, help=\"Folder containinig files with snp positions\")\n new_exclusive.add_argument(\"-f\", \"--file\", dest = \"single_file\", metavar=\"file[s]\", required= False, type=str, help=\"individual files with snp positions\")\n new_exclusive.add_argument(\"-l\", \"--list\", dest = \"snp_list\", metavar=\"list\", required= False, help=\"file with a list of positions in a column, not vcf format\")\n\n\n #update_parser\n update_parser.add_argument(\"-o\", \"--outputfile\", dest = \"output_file\", required= True, metavar=\"filename\", help=\"REQUIRED: file name, including PATH, of the updated ddbb\")\n update_parser.add_argument(\"-s\", \"--snp-final\", required= False, action='store_true', help=\"Database will use snp.fila instead of gatk vcf \")\n update_parser.add_argument(\"-d\", \"--database\", dest = \"update_database\", required= True, metavar=\"TB_database\", help=\"REQUIRED: csv file with the database to be enriched/consulted\")\n\n update_exclusive = update_parser.add_mutually_exclusive_group()\n\n update_exclusive.add_argument(\"-F\", \"--folder\", dest = \"folder\", metavar=\"folder\",required= False, type=str, help=\"Folder containinig files with snp positions\")\n update_exclusive.add_argument(\"-f\", \"--file\", dest = \"single_file\", metavar=\"file[s]\", required= False, type=str, help=\"individual files with snp positions\")\n update_exclusive.add_argument(\"-l\", \"--list\", dest = \"snp_list\", metavar=\"list\", required= False, help=\"file with a list of positions in a column, not vcf format\")\n\n update_parser.add_argument(\"-b\", \"--backup\", dest = \"backup\", action=\"store_true\", help=\"Creates an aditional database with the date as backup\")\n\n #compare_parser\n compare_parser.add_argument(\"-d\", \"--database\", dest = \"final_database\", required= True, metavar=\"TB_database\", help=\"REQUIRED: csv file with the database to be enriched/consulted\")\n compare_parser.add_argument(\"-o\", \"--outputfile\", dest = \"output_file\", required= False, metavar=\"filename\", help=\"REQUIRED: file name, including PATH, of the matrix comparison\")\n\n compare_exclusive = compare_parser.add_mutually_exclusive_group()\n\n compare_exclusive.add_argument(\"-a\", \"--all\", dest = \"all_compare\", action=\"store_true\", required= False, help=\"All files in supplied database will be compared\")\n compare_exclusive.add_argument(\"-s\", \"--samples\", dest = \"samples_compare\", metavar=\"sample_name[s]\", nargs=\"+\", required= False, help=\"Sample names supplied will be compared\")\n\n #extract_parser\n extract_parser.add_argument(\"-d\", \"--database\", dest = \"final_database\", required= True, metavar=\"TB_database\", help=\"REQUIRED: csv file with the database with the sample to remove\")\n extract_parser.add_argument(\"-o\", \"--outputfile\", dest = \"output_file\", required= True, metavar=\"filename\", help=\"REQUIRED: file name, including PATH, of the updated ddbb\")\n\n extract_parser.add_argument(\"-s\", \"--samples\", dest = \"samples_extract\", metavar=\"sample name[s]\", nargs=\"+\", required= True, help=\"Sample names supplied will be removed\")\n\n parser.add_argument(\"--version\", action=\"version\", version=\"%(prog)s 0.1\")\n\n arguments = parser.parse_args()\n\n return arguments\n\n\ndef compare_snp_columns(sample1, sample2, df):\n jaccard_similarity = jaccard_score(df[sample1], df[sample2], average='binary') #similarities between colums\n hamming_similarity = 1 - jaccard_similarity #disagreements between colums\n snp_distance = int(hamming_similarity * (len(df.index)+1))\n return snp_distance\n\ndef snp_distance_pairwise(dataframe, output_file):\n if os.path.exists(output_file):\n os.remove(output_file)\n with open(output_file, \"a\") as f:\n for sample1 in dataframe.iloc[:,3:].columns: #remove first 3 colums\n for sample2 in dataframe.iloc[:,3:].columns:\n if sample1 != sample2:\n snp_distance = compare_snp_columns(sample1, sample2, dataframe)\n line_distance = \"%s\\t%s\\t%s\\n\" % (sample1, sample2, snp_distance)\n f.write(line_distance)\n\ndef snp_distance_matrix(dataframe, output_file):\n dataframe_only_samples = dataframe.set_index(dataframe['Position'].astype(int)).drop(['Position','N','Samples'], axis=1) #extract three first colums and use 'Position' as index\n hamming_distance = pairwise_distances(dataframe_only_samples.T, metric = \"hamming\") #dataframe.T means transposed\n snp_distance_df = pd.DataFrame(hamming_distance * len(dataframe_only_samples.index), index=dataframe_only_samples.columns, columns=dataframe_only_samples.columns) #Add index\n snp_distance_df = snp_distance_df.astype(int)\n snp_distance_df.to_csv(output_file, sep='\\t', index=True)\n\ndef hamming_distance_matrix(dataframe, output_file):\n dataframe_only_samples = dataframe.set_index(dataframe['Position'].astype(int)).drop(['Position','N','Samples'], axis=1) #extract three first colums and use 'Position' as index\n hamming_distance = pairwise_distances(dataframe_only_samples.T, metric = \"hamming\") #dataframe.T means transposed\n hamming_distance_df = pd.DataFrame(hamming_distance, index=dataframe_only_samples.columns, columns=dataframe_only_samples.columns) #Add index\n hamming_distance_df.to_csv(output_file, sep='\\t', index=True)\n\ndef clustermap_dataframe(dataframe, output_file):\n dataframe_only_samples = dataframe.set_index(dataframe['Position'].astype(int)).drop(['Position','N','Samples'], axis=1) #extract three first colums and use 'Position' as index\n sns.clustermap(dataframe_only_samples, annot=False, cmap=\"YlGnBu\", figsize=(13, 13))\n plt.savefig(output_file, format=\"png\")\n\ndef dendogram_dataframe(dataframe, output_file):\n dataframe_only_samples = dataframe.set_index(dataframe['Position'].astype(int)).drop(['Position','N','Samples'], axis=1) #extract three first colums and use 'Position' as index\n labelList = dataframe_only_samples.columns.tolist()\n Z = shc.linkage(dataframe_only_samples.T, method='average') #method='single'\n\n plt.rcParams['lines.linewidth'] = 8 #Dendrogram line with\n plt.rcParams['xtick.major.size'] = 10 #Only affect to tick (line) size\n plt.rcParams.update({'font.size': 30}) #Increase x tick label size\n #plt.tick_params(labelsize=30)\n plt.figure(figsize=(30, 50))\n plt.ylabel('samples', fontsize=30)\n plt.xlabel('snp distance', fontsize=30)\n\n shc.dendrogram(Z, labels=labelList, orientation='left', distance_sort='descending', show_leaf_counts=True, color_threshold=10, leaf_font_size=20)\n\n \n plt.savefig(output_file, format=\"png\")\n\n# Convert dendrogram to Newick\ndef linkage_to_newick(dataframe, output_file):\n \"\"\"\n Thanks to https://github.com/biocore/scikit-bio/issues/1579\n Input : Z = linkage matrix, labels = leaf labels\n Output: Newick formatted tree string\n \"\"\"\n dataframe_only_samples = dataframe.set_index(dataframe['Position'].astype(int)).drop(['Position','N','Samples'], axis=1) #extract three first colums and use 'Position' as index\n labelList = dataframe_only_samples.columns.tolist()\n Z = shc.linkage(dataframe_only_samples.T, method='average')\n\n tree = shc.to_tree(Z, False)\n def buildNewick(node, newick, parentdist, leaf_names):\n if node.is_leaf():\n #print(\"%s:%f%s\" % (leaf_names[node.id], parentdist - node.dist, newick))\n return \"%s:%f%s\" % (leaf_names[node.id], parentdist - node.dist, newick)\n else:\n if len(newick) > 0:\n newick = f\"):{(parentdist - node.dist)/2}{newick}\"\n else:\n newick = \");\"\n newick = buildNewick(node.get_left(), newick, node.dist, leaf_names)\n newick = buildNewick(node.get_right(), \",%s\" % (newick), node.dist, leaf_names)\n newick = \"(%s\" % (newick)\n #print(newick)\n return newick\n\n with open(output_file, 'w') as f:\n f.write(buildNewick(tree, \"\", tree.dist, labelList))\n return buildNewick(tree, \"\", tree.dist, labelList)\n\n\n\ndef ddtb_compare(args):\n\n database_file = os.path.abspath(args.final_database)\n check_file_exists(database_file)\n presence_ddbb = import_to_pandas(database_file, header=True)\n\n if args.output_file:\n output_file = os.path.abspath(args.output_file)\n output_path = output_file.split(\".\")[0]\n else:\n output_path = database_file.split(\".\")[0]\n\n print(\"Output path is: \" + output_path)\n\n if args.all_compare:\n print(BLUE + BOLD + \"Comparing all samples in \" + database_file + END_FORMATTING)\n prior_pairwise = datetime.datetime.now()\n\n #Calculate pairwise snp distance for all and save file\n print(CYAN + \"Pairwise distance\" + END_FORMATTING)\n pairwise_file = output_path + \".snp.pairwise.tsv\"\n snp_distance_pairwise(presence_ddbb, pairwise_file)\n after_pairwise = datetime.datetime.now()\n print(\"Done with pairwise in: %s\" % (after_pairwise - prior_pairwise))\n\n #Calculate snp distance for all and save file\n print(CYAN + \"SNP distance\" + END_FORMATTING)\n snp_dist_file = output_path + \".snp.tsv\"\n snp_distance_matrix(presence_ddbb, snp_dist_file)\n\n #Calculate hamming distance for all and save file\n print(CYAN + \"Hamming distance\" + END_FORMATTING)\n hmm_dist_file = output_path + \".hamming.tsv\"\n hamming_distance_matrix(presence_ddbb, hmm_dist_file)\n \"\"\"\n #Represent pairwise snp distance for all and save file\n print(CYAN + \"Drawing distance\" + END_FORMATTING)\n prior_represent = datetime.datetime.now()\n png_dist_file = output_path + \".snp.distance.png\"\n #clustermap_dataframe(presence_ddbb, png_dist_file)\n after_represent = datetime.datetime.now()\n print(\"Done with distance drawing in: %s\" % (after_represent - prior_represent))\n \"\"\"\n #Represent dendrogram snp distance for all and save file\n print(CYAN + \"Drawing dendrogram\" + END_FORMATTING)\n png_dend_file = output_path + \".snp.dendrogram.png\"\n dendogram_dataframe(presence_ddbb, png_dend_file)\n\n\n #Output a Newick file distance for all and save file\n print(CYAN + \"Newick dendrogram\" + END_FORMATTING)\n newick_file = output_path + \".nwk\"\n linkage_to_newick(presence_ddbb, newick_file)\n\n\n else:\n print(\"sample mode is not implemented\")\n\n\n\n\n\n\n\n \"\"\"\n #compare_parser\n compare_parser.add_argument(\"-d\", \"--database\", dest = \"final_database\", required= True, metavar=\"TB_database\", help=\"REQUIRED: csv file with the database to be enriched/consulted\")\n compare_parser.add_argument(\"-o\", \"--outputfile\", dest = \"output_file\", required= True, metavar=\"filename\", help=\"REQUIRED: file name, including PATH, of the matrix comparison\")\n\n compare_exclusive = compare_parser.add_mutually_exclusive_group()\n\n compare_exclusive.add_argument(\"-a\", \"--all\", dest = \"all_compare\", action=\"store_true\", required= False, help=\"All files in supplied database will be compared\")\n compare_exclusive.add_argument(\"-s\", \"--samples\", dest = \"samples_compare\", metavar=\"sample_name[s]\", nargs=\"+\", required= False, help=\"Sample names supplied will be compared\")\n\n \"\"\"","repo_name":"pedroscampoy/VCF_DDTB","sub_path":"ddtb_compare.py","file_name":"ddtb_compare.py","file_ext":"py","file_size_in_byte":13623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"14352388047","text":"from django.shortcuts import render, get_object_or_404, redirect, render_to_response\nfrom wmcomment.models import BaseComment\nfrom wmcomment.forms import CommentForm, NestedCommentForm\nfrom wmposts.models import BasePost\nfrom django.template import RequestContext\nfrom django.http.response import HttpResponse\n\ndef add_comment(request, pk):\n\t\n\t\n\tpost = BasePost.objects.get(pk=int(pk))\n\tcomments = BaseComment.objects.filter(linked_post=post)\n\tif request.method == 'POST':\n\t\tform = CommentForm(request.POST)\n\t\t\n\t\tcomment = request.POST.get(\"content\")\n\t\tprint(\"--------------\")\n\t\tprint(comment)\n\t\tif form.is_valid():\n\t\t\tcomment = form.save(commit=False)\n\t\t\tcomment.linked_post = post\n\t\t\tcomment.linked_user = request.user\n\t\t\n\t\t\tcomment.save()\n\t\t\tpost.noc += 1\n\t\t\tpost.save()\n\t\t\treturn HttpResponseRedirect('') # , pk=BasePost.pk)\n\telse:\n\t\tform = CommentForm()\n\t\t\n\t\n\treturn render_to_response('singlepost.html',\n\t\t{'form':form, 'post': post, 'comments' : comments},\n\t\tcontext_instance=RequestContext(request)\n\t\t)\n\t\n\t\ndef add_nested_comment(request, pk):\n\n\tbase_comment = BaseComment.objects.get(pk=int(pk))\n\tnested_comments = NestedComment.objects.filter(nested_linked_comment=base_comment)\n\t\n\t\n\tpost = BasePost.objects.get(pk=base_comment.linked_post)\n\t\n\tcomments = BaseComment.objects.filter(linked_post=post)\n\t \n\tif request.method == 'POST':\n\t\tnested_form = NestedCommentForm(request.POST)\n\t\tnested_comment = request.POST.get('nested_content')\n\t\t\n\t\tif form.is_valid():\n\t\t\t\tnested_comment = nested_form.save(commit=False)\n\t\t\t\tnested_comment.nested_linked_comment = base_comment\n\t\t\t\tnested_comment.nested_linked_user = request.user\n\t\t\t\tnested_comment.save()\n\t\t\t\treturn render_to_response('singlepost.html',\n\t\t\t\t\t{'nested_form':nested_form,'base_comment':base_comment, 'nested_comments':nested_comments,'comments' :comments},\n\t\t\t\t\tcontext_instance=RequestContext(request)\n\t\t\t\t\t)\n\telse:\n\t\tform = NestedCommentForm()\n\t\t\n\treturn render_to_response('singlepost.html',\n\t\t{'nested_form':nested_form,'base_comment':base_comment, 'nested_comments':nested_comments,'comments' :comments},\n\t\tcontext_instance=RequestContext(request)\n\t\t)\n\n\t\n\t\n\t","repo_name":"jjeesse/wm","sub_path":"wmcomment/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30354167702","text":"import pickle\nimport time\n\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.chrome.webdriver import WebDriver\nfrom selenium.webdriver.common.by import By\n\nfrom src.reconhecedor.tabela_analise.acao import Empilhamento, Reducao, Salto, Aceite\nfrom src.reconhecedor.tabela_analise.estado import Estado\nfrom src.reconhecedor.tabela_analise.tabela import TabelaAnalise\n\n\ndef iniciar_chrome() -> WebDriver:\n # Caminho para o executável do ChromeDriver\n servico = Service(r'C:\\chromedriver\\chromedriver.exe')\n servico.start()\n\n # Inicia o chrome em background\n opcoes = Options()\n opcoes.add_argument('--headless')\n\n # Não imprime logs\n opcoes.add_experimental_option('excludeSwitches', ['enable-logging'])\n\n # Abre o chrome\n driver = webdriver.Chrome(\n service=servico,\n options=opcoes\n )\n\n return driver\n\n\ndef fechar_chrome(driver: WebDriver):\n # Fecha o navegador\n driver.quit()\n\n\ndef abrir_url(driver: WebDriver, url: str):\n # Abre a url especificada\n driver.get(url)\n\n\ndef get_tabela_lr(gramatica_str, recarregar):\n # Se for necessário recarregar a tabela lr, raspa a página jsmachines\n if recarregar:\n tabela_lr = raspar_tabela_lr(gramatica_str)\n\n # Salva o arquivo a tabela em um pickle em cache\n salvar_cache_tabela_lr('arquivos/cache/estruturas.pkl', tabela_lr)\n\n return tabela_lr\n\n # Se não, pega do arquivo pickle em cache\n return get_cache_tabela_lr('arquivos/cache/estruturas.pkl')\n\n\ndef salvar_cache_tabela_lr(path: str, tabela):\n with open(path, 'wb') as file:\n pickle.dump(tabela, file)\n\n\ndef get_cache_tabela_lr(path: str):\n with open(path, 'rb') as file:\n tabela = pickle.load(file)\n return tabela\n\n\ndef raspar_tabela_lr(gramatica_str) -> TabelaAnalise:\n # ABRIR CHROME\n chrome = iniciar_chrome()\n\n # ABRIR JS MACHINES\n abrir_url(chrome, 'https://jsmachines.sourceforge.net/machines/slr.html')\n\n # PEGA O CAMPO DAS GRAMÁTICAS\n campo_gramatica = chrome.find_element(\n By.ID,\n 'grammar'\n )\n\n # LIMPA O CAMPO\n chrome.execute_script(\"arguments[0].value = ''\", campo_gramatica)\n\n # INSERE A GRAMÁTICA\n campo_gramatica.send_keys(gramatica_str)\n\n time.sleep(1)\n\n # CLICA NO BOTÃO PARA GERAR A TABELA\n chrome.find_element(\n By.XPATH,\n '//input[@type=\"button\"][@value=\">>\"]'\n ).click()\n\n time.sleep(2)\n\n # PEGA O HTML DA TABELA GERADA\n html_tabela = chrome.find_element(\n By.XPATH,\n '//*[@id=\"lrTableView\"]'\n ).get_attribute('innerHTML')\n\n # FECHA O CHROME\n fechar_chrome(chrome)\n\n return TabelaAnalise(\n html_para_estados(html_tabela)\n )\n\n\ndef html_para_estados(html_tabela):\n soup = BeautifulSoup(html_tabela, 'html.parser')\n tabela = soup.find('table')\n linhas = tabela.find_all('tr')\n\n # Pega os símbolos não terminais do cabeçalho\n nao_terminais = [s.text for s in linhas[2].find_all('th')]\n\n # Remove a última linha de cabeçalho\n del linhas[:3]\n\n estados = []\n\n while linhas:\n cols = linhas[0].find_all('td')\n numero_estado = int(cols[0].text)\n estados.insert(numero_estado, Estado())\n\n for index, transicao in enumerate(cols[1:]):\n transicao = transicao.text\n if transicao != '\\xa0':\n estados[numero_estado].set_acao(\n nao_terminais[index],\n Aceite() if 'acc' in transicao else\n Empilhamento(transicao.replace('s', '')) if 's' in transicao else\n Reducao(transicao.replace('r', '')) if 'r' in transicao else\n Salto(transicao)\n )\n\n del linhas[0]\n\n return estados\n","repo_name":"Dutraz/lfa-gerador-afd","sub_path":"src/reconhecedor/jsmachines.py","file_name":"jsmachines.py","file_ext":"py","file_size_in_byte":3882,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"37037825250","text":"class Monik:\n def __init__(self):\n self.firm = input('Введіть назву фірми виробника: ')\n self.date1 = int(input('Введіть число дати виробництва: '))\n self.date2 = int(input('Введіть місяць дати виробництва: '))\n self.date3 = int(input('Введіть рік дати виробництва : '))\n self.date_1 = int(input('Введіть число дати купівлі: '))\n self.date_2 = int(input('Введіть місяць дати купівлі: '))\n self.date_3 = int(input('Введіть рік дати купівлі : '))\n self.price = int(input('Введіть вартість в гривнях: '))\n self.type = input('Введіть тип монітора: ')\n self.height = int(input('Введіть довжину монітора в пікселях: '))\n self.width = int(input('Введіть ширину монітора в пікселях: '))\n\n def calculate_age(self):\n return 'Вік моніка - {0}'.format(2021 - self.date3)\n\n def pic(self):\n self.pic_h = int(input('Введіть довжину картинки : '))\n self.pic_w = int(input('Введіть ширину картинки : '))\n if self.pic_h == self.height and self.pic_w == self.width:\n return \"Можна без масштабування\"\n else:\n return \"Не можна без масштабування\"\n\n def coef(self):\n self.woutprop1 = self.height / self.pic_h\n self.woutprop2 = self.width / self.pic_w\n if self.pic_h > self.pic_w:\n self.wprop = self.width / self.pic_w\n else:\n self.wprop = self.height / self.pic_h\n return 'Без збереження пропорцій, коефіцієнт: {0}. Із збереженням пропорцій, коефіцієнт: {1}'.format([self.woutprop1, self.woutprop2],\n [self.wprop, self.wprop])\n\n\nA = Monik()\nprint(A.calculate_age())\nprint(A.pic())\nprint(A.coef())\n","repo_name":"Sklyar13OFF/LaboratorniPython","sub_path":"Labkaten/exer2.py","file_name":"exer2.py","file_ext":"py","file_size_in_byte":2207,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"19713827130","text":"import sys\nimport socket\n\n# Create a TCP/IP socket\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n# Connect the socket to the port where the server is listening\nserver_address = ('localhost', 10000)\nprint(f\"connecting to {server_address}\")\nsock.connect(server_address)\nmess = input(\"Nama File yang akan dikirim ke server : \")\n\ntry:\n # Send data\n sock.sendall(mess.encode())\n filee=open(mess,\"rb\")\n kirim = filee.read(1024)\n while(kirim):\n sock.send(kirim)\n kirim = filee.read(1024)\n\n print(f\"File sukses dikirim!\")\n\nfinally:\n print(\"closing\")\n sock.close()\n","repo_name":"safhiram/progjar_b_its_2020","sub_path":"tugas1/tugas 1a/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"71325367442","text":"import logging\nfrom abc import ABCMeta\n\nfrom calibtool.CalibSite import CalibSite\nfrom calibtool.study_sites.site_setup_functions import config_setup_fn, summary_report_fn, site_input_eir_fn\nfrom calibtool.analyzers.ChannelByAgeCohortAnalyzer import PrevalenceByAgeCohortAnalyzer\nfrom calibtool.analyzers.Helpers import channel_age_json_to_pandas\n\nlogger = logging.getLogger(__name__)\n\n\nclass PrevalenceCalibSite(CalibSite):\n \"\"\"\n An abstract class that implements the simulation setup for prevalence-by-age analyses:\n - Namawala, Tanzania\n - Garki, Nigeria (Rafin Marke, Matsari, Sugungum)\n \"\"\"\n\n __metaclass__ = ABCMeta\n\n reference_dict = {\n \"Average Population by Age Bin\": [],\n \"Age Bin\": [],\n \"PfPR by Age Bin\": []\n }\n\n def get_setup_functions(self):\n return [\n config_setup_fn(duration=21915), # 60 years (with leap years)\n summary_report_fn(interval=365),\n site_input_eir_fn(self.name, birth_cohort=True)\n ]\n\n def get_reference_data(self, reference_type):\n site_ref_type = 'prevalence_by_age'\n\n if reference_type is not site_ref_type:\n raise Exception(\"%s does not support %s reference_type, only %s.\",\n self.__class__.__name__, reference_type, site_ref_type)\n\n reference_data = channel_age_json_to_pandas(self.reference_dict, index_key='Age Bin')\n\n logger.debug('Reference data:\\n %s', reference_data)\n return reference_data\n\n def get_analyzers(self):\n return [PrevalenceByAgeCohortAnalyzer(site=self)]\n\n","repo_name":"m-v-nikolov/dtk-tools-py3","sub_path":"calibtool/study_sites/PrevalenceCalibSite.py","file_name":"PrevalenceCalibSite.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"870312412","text":"# 5622 다이얼\ndial = ['ABC', 'DEF', 'GHI', 'JKL', 'MNO', 'PQRS', 'TUV', 'WXYZ']\n\ns = input()\n\ntime = 0\nfor c in s:\n i = 0\n while i < 8:\n if c in dial[i]:\n break\n i += 1\n time += i + 3\n\nprint(time)","repo_name":"Sunrin-Algorithm-Study/Algorithm_study","sub_path":"sources/cometj03_정해성/step/6 string/5622.py","file_name":"5622.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"23134912550","text":"#! /usr/bin/env python3.8\n# encoding:utf-8\nimport os\nimport pandas as pd\nimport torch\n#使用pandas处理原始数据，并转换为张量格式\n#创建逗号分隔值文件csv作为人工数据集\nos.makedirs(os.path.join('.', 'data'), exist_ok=True) #将会在运行目录下生成文件夹\ndata_file = os.path.join('.', 'data', 'house_tiny.csv')\nwith open(data_file, 'w') as f:\n f.write('NumRooms,Alley,Price\\n') # 列名\n f.write('NA,Pave,127500\\n') # 每行表示一个数据样本\n f.write('2,NA,106000\\n')\n f.write('4,NA,178100\\n')\n f.write('NA,NA,140000\\n')\n\ndata = pd.read_csv(data_file)\nprint(data)\n#可以注意到有不少地方的数据缺失：处理方法：插值、删除\ninputs, outputs = data.iloc[:, 0:2], data.iloc[:, 2]\n#分为输入、输出两类\ninputs = inputs.fillna(inputs.mean()) #数值阈用平均值填充\nprint(inputs)\n'''\n由于无法正确的用平均值表示类型\n使用0 1值来实现增维判断\n NumRooms Alley_Pave Alley_nan\n0 3.0 1 0\n1 2.0 0 1\n2 4.0 0 1\n3 3.0 0 1\n'''\n\ninputs = pd.get_dummies(inputs, dummy_na=True) \nprint(inputs)\n#最终转换成张量\nX, y = torch.tensor(inputs.values), torch.tensor(outputs.values)\nX, y","repo_name":"Matthew-Hu-cmd/D2L_torch_tutorial","sub_path":"Heads_up/data_process.py","file_name":"data_process.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"8532903912","text":"\nimport numpy as np\nimport json\nimport pathlib as pl\nimport sys\nimport os\nimport numpy as np\n\nsys.path.append(str(list(pl.Path(__file__).parents)[2]))\nos.chdir(str(list(pl.Path(__file__).parents)[2]))\n\nimport input.GeneralConstants as const\n\ndef powerloading_climbrate(eff, ROC, WS,rho,CD0,e,A):\n k = 1/(e*A*np.pi)\n CLCDratio = 3/(4*k)* np.sqrt(3*CD0/k)\n return (ROC+np.sqrt(2*WS/rho)*(1/CLCDratio))**(-1) * eff\n\ndef powerloading_turningloadfactor(rho,V,WS,eff,A,e,loadfactor,CD0):\n k = 1/(e*A*np.pi)\n n = loadfactor\n \n WP = (CD0*0.5*rho*V*V*V/WS + WS*n*n*k/(0.5*rho*V))**-1 *eff\n\n return WP\n\ndef powerloading_thrustloading(WS,rho,ROC,StotS):\n return 1.2*(1+(1/WS)*rho*ROC**2*StotS)\n #return 1.2*(1+np.ones(np.shape(WS)))*1.3\n\ndef powerloading_verticalflight(MTOM,TW,A_tot,rho,eff,ducted_bool):\n W = MTOM *const.g0\n T = TW * W\n \n if ducted_bool==True:\n return (0.5*np.sqrt((T*T*T)/(W*W*rho*A_tot)))**(-1)*eff\n else:\n return (np.sqrt((T*T*T)/(2*W*W*rho*A_tot)))**(-1)*eff \n \ndef powerloading_climbgradient(e,A,CD0,WS,rho,eff,G):\n CL = np.sqrt(np.pi*e*A*CD0)\n CD = 2*CD0\n WP = (np.sqrt(WS*2/(rho*CL))*(G+CD/CL))**(-1) * eff\n return WP\n\ndef wingloading_stall(CLmax,V_stall,rho):\n return CLmax*0.5*rho*V_stall*V_stall\n\ndef get_wing_power_loading(perf_par, wing, engine, aero, cont_factor=1.1):\n \"\"\" Returns the wing loading and thrust of the weight based on performance parameters\n\n :param perf_par: performance parameter class from data structues\n :type perf_par: PerformanceParameters class\n :param wing: wing class from data structues\n :type wing: Wing class\n \"\"\" \n\n #Check if it\"s lilium or not to define the variable that will say to vertical_flight what formula to use.\n WS_range = np.arange(1,4000,1)\n #data[\"WS\"],data[\"TW\"],data[\"WP_cruise\"],data[\"WP_hover\"] = plot_wing_power_loading_graphs(data[\"eff\"], data[\"StotS\"], data[\"diskloading\"], data[\"name\"],WS_range,i)\n\n\n #CALCULATE ALL THE VALUES FOR THE GRAPHS\n TW_range = powerloading_thrustloading(WS_range,const.rho_sl,const.roc_hvr, perf_par.Stots) \n #if data[\"name\"] == \"J1\": \n # TW_range = TW_range*1.3 #Added 30% extra thrust to maintain stability\n CLIMBRATE = cont_factor*powerloading_climbrate(perf_par.prop_eff,const.roc_cr, WS_range,const.rho_cr,aero.cd0_cruise ,aero.e,wing.aspect_ratio)\n TURN_VCRUISE = cont_factor*powerloading_turningloadfactor(const.rho_cr,const.v_cr,WS_range, perf_par.prop_eff ,wing.aspect_ratio,aero.e, perf_par.turn_loadfactor,aero.cd0_cruise)\n TURN_VMAX = cont_factor*powerloading_turningloadfactor(const.rho_cr,perf_par.v_max, WS_range, perf_par.prop_eff ,wing.aspect_ratio ,aero.e ,perf_par.turn_loadfactor,aero.cd0_cruise)\n VERTICALFLIGHT = cont_factor*powerloading_verticalflight(perf_par.MTOM ,TW_range, engine.total_disk_area ,const.rho_sl,perf_par.prop_eff ,False)\n STALLSPEED = wingloading_stall(aero.cL_max ,perf_par.v_stall, const.rho_sl)\n CLIMBGRADIENT = cont_factor*powerloading_climbgradient(aero.e ,wing.aspect_ratio ,aero.cd0_cruise,WS_range,const.rho_sl,perf_par.prop_eff ,const.climb_gradient)\n\n #DETERMINE LOWEST\n lowest_area_y_novf = []\n lowest_area_y = []\n lowest_area_x = np.arange(0,int(STALLSPEED),1)\n for i in lowest_area_x:\n lowest_area_y.append(min(CLIMBRATE[i],TURN_VCRUISE[i],TURN_VMAX[i],CLIMBGRADIENT[i],VERTICALFLIGHT[i]))\n lowest_area_y_novf.append(min(CLIMBRATE[i],TURN_VCRUISE[i],TURN_VMAX[i],CLIMBGRADIENT[i]))\n \n #DETERMINE LIMITING FACTORS\n margin = 0.95\n perf_par.wing_loading_cruise = STALLSPEED*margin\n perf_par.TW_max = powerloading_thrustloading(perf_par.wing_loading_cruise,const.rho_sl,const.roc_hvr, perf_par.Stots)\n WP_cruise = lowest_area_y_novf[-1]*margin\n WP_hover = lowest_area_y[-1]*margin\n aero.cL_cruise = 2/(const.rho_cr*const.v_cr**2)*perf_par.wing_loading_cruise\n return perf_par, wing, engine, aero\n\n\n","repo_name":"dmkeijzer/AetheriaPackage","sub_path":"modules/preliminary_sizing/wing_power_loading_functions.py","file_name":"wing_power_loading_functions.py","file_ext":"py","file_size_in_byte":3963,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"40704074513","text":"import shutil\nimport os\nimport pytest\n\n\n@pytest.fixture\ndef create_temp_archive():\n def _create_temp_archive(root_dir, dir_path, extension, archive_name=\"archive\"):\n cwd = os.getcwd()\n archive_path = f\"{cwd}/{archive_name}\"\n dir_name = dir_path.split(\"/\")[-1]\n os.chdir(root_dir)\n shutil.make_archive(\n base_name=archive_path,\n format=extension,\n root_dir=root_dir,\n base_dir=dir_name,\n )\n os.chdir(cwd)\n if extension == \"xztar\":\n extension = \"tar.xz\"\n return f\"archive.{extension}\"\n\n return _create_temp_archive\n\n\n@pytest.fixture\ndef data_inputs():\n return [\n \"test.md\",\n \"test.css\",\n \"test.yaml\",\n \"test.sh\",\n \"test.tf\",\n \"test.py\",\n \"test.js\",\n \"Dockerfile\",\n \"test.html\",\n \"test.java\",\n \"test.ts\",\n ]\n","repo_name":"xlab-si/iac-scan-runner","sub_path":"tests/unit/utils/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"} +{"seq_id":"43300742429","text":"\"\"\"\nHelpers for the analytics part of OLGA application.\n\"\"\"\n\nfrom http import HTTPStatus as http\nimport logging\nimport requests\n\n\nfrom django.http import HttpResponse\n\nfrom olga.analytics.forms import (\n EdxInstallationParanoidLevelForm,\n EdxInstallationEnthusiastLevelForm,\n InstallationStatisticsParanoidLevelForm,\n InstallationStatisticsEnthusiastLevelForm\n)\n\nlogger = logging.getLogger(__name__)\n\n# pylint: disable=invalid-name\n\n\ndef validate_instance_stats_forms(receive_instance_stats_method):\n \"\"\"\n Check if edX overall installation info and statistics are valid with corresponding forms.\n\n Returns HTTP-response with status 401, that means at least one of two forms is not valid.\n \"\"\"\n def wrapper(request, *args, **kwargs):\n \"\"\"\n Wrapper.\n \"\"\"\n paranoid_installation_form = EdxInstallationParanoidLevelForm(request.POST)\n enthusiast_installation_form = EdxInstallationEnthusiastLevelForm(request.POST)\n paranoid_statistics_form = InstallationStatisticsParanoidLevelForm(request.POST)\n enthusiast_statistics_form = InstallationStatisticsEnthusiastLevelForm(request.POST)\n\n statistics_level = request.POST.get('statistics_level')\n\n level_valid_forms = {\n 'paranoid': paranoid_installation_form.is_valid() and paranoid_statistics_form.is_valid(),\n 'enthusiast': enthusiast_installation_form.is_valid() and enthusiast_statistics_form.is_valid()\n }\n\n if level_valid_forms[statistics_level]:\n return receive_instance_stats_method(request, *args, **kwargs)\n\n return HttpResponse(status=http.UNAUTHORIZED)\n\n return wrapper\n\n\ndef get_coordinates_by_platform_city_name(city_name):\n \"\"\"\n Gather coordinates from Nominatim service by the platform city name.\n\n geo_api.json returns:\n [\n {\n 'display_name': '
\" + song_df['artists']\n\n title_col = ((np.asarray(song_df['complete'])).reshape(7,7))\n valence_col = ((np.asarray(song_df['valence'])).reshape(7,7))\n\n\n fig = go.Figure(data=go.Heatmap(\n z= valence_col,\n text=title_col,\n colorscale = \"YlGn_r\", \n showlegend = False,\n hovertemplate = '%{text}
'+html+'
').get_text()\n\n#\n# Post and Thread class to handle 4chan stuff\n#\n\nclass Post:\n def __init__(self, json=None, prop=None, thr=None):\n self.prop = prop\n self.thr = thr\n\n if prop is not None:\n self.json = prop.json\n elif json is not None:\n self.json = json\n\n\n def to_prop(self):\n if self.prop:\n self.prop.json = self.json\n else:\n self.prop = model.PostProp(json=self.json)\n return self.prop\n\n def filename(self, ext=None):\n fn = self.json.get('filename', '')\n if ext:\n fn += self.json.get('ext', '')\n return fn\n\n def id(self):\n return self.json['no'] # must exist\n\n def sub(self):\n return self.json.get('sub', '')\n\n def com(self):\n com = self.json.get('com', '')\n com = re.sub('', '
', com)\n com = re.sub('
Edit Document
'\nHTML.document_template_form_header=''\nHTML.document_template_edit_footer=(\n \"\"\"\n \n © 1997 Digital Creations, L.L.C.\"\"\")\n\nHTML.document_template_edit_width=58\n\nHTML._manage_editForm = HTML(\n \"\"\"\n \n\n \n \n \n \"\"\",)\n\nHTML.editConfirmation=HTML(\n \"\"\"
has been changed.\n
User was successfully register
')\n else:\n raise BadRequest('Invalid request.')\n\n\ndef get_users_csv_file(request):\n username = request.GET['username']\n user_exists = is_user_exists(username)\n\n if request.method == 'GET' and user_exists:\n users_csv_file = CustomUser.objects.get(username=username).generated_points\n response = FileResponse(open(users_csv_file.path, 'rb'))\n return response\n else:\n raise BadRequest('The user does not register yet or invalid request method')\n\n\ndef get_users_plot(request):\n username = request.GET['username']\n user_exists = is_user_exists(username)\n\n if request.method == 'GET' and user_exists:\n users_plot = CustomUser.objects.get(username=username).plot_with_points\n return HttpResponse(users_plot, content_type=\"image/png\")\n else:\n raise BadRequest('The user does not register yet or invalid request method')\n\n\n@csrf_exempt\ndef regenerate_users_csv_file(request):\n username = request.GET['username']\n user_exists = is_user_exists(username)\n\n try:\n if request.method == 'POST' and user_exists:\n user = CustomUser.objects.get(username=username)\n\n csv_file_path = user.generated_points.path\n plot_file_path = user.plot_with_points.path\n Points.remove_files(csv_file_path, plot_file_path)\n\n user.generated_points = ''\n user.plot_with_points = ''\n user.save(update_fields=['generated_points', 'plot_with_points'])\n\n new_csv_file_path, new_plot_file_path = generate_points_create_csv()\n user.generated_points.name = new_csv_file_path\n user.plot_with_points.name = new_plot_file_path\n user.save(update_fields=['generated_points', 'plot_with_points'])\n return HttpResponse('User was successfully regenerated new points
')\n except Exception:\n print(\"Oops! Something went wrong\")\n","repo_name":"Hyper-glitch/Sreda_test","sub_path":"sreda/rest_api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28508615354","text":"class User:\n def __init__(self, name, email):\n self.name = name \n self.email = email \n self.account = 100000\n\n def make_deposit(self, amount):\n self.account += amount\n return self\n\n def make_withdrawal(self, amount):\n self.account -= amount\n return self\n \n def display_user_balance(self):\n self.name, self.account\n return self\n\nclass BankAccount:\n def __init__(self, int_rate, balance):\n self.int_rate = int_rate\n self.balance = balance\n self.int_rate = 0.01\n\n def deposit(self, amount):\n self.balance += amount\n return self\n\n def withdraw(self, amount):\n self.balance -= amount\n return self\n\n def display_account_info(self):\n print(self.balance)\n return self\n\n def yield_interest(self):\n self.int_rate\n return self\n\nchecking = BankAccount(0.01, 5000 )\nsaving = BankAccount(0.08, 25000 )\n\nprince = User(\"prince\", \"piwejuo@gmail.com\")\ncollins = User(\"collins\", \"cebozue@gmail.com\")\narinze = User(\"arinze\", \"aobiora@gmail.com\")\n\ncollins.account = 10000\narinze.account = 7000\n\nprince.make_deposit(10000).make_deposit(1000).make_deposit(500)\nprint(prince.account)\n\nprince.make_withdrawal(11500).display_user_balance()\nprint(prince.account)\nprint(prince.name, prince.account)\n\ncollins.make_deposit(500).make_deposit(500).make_withdrawal(1000).make_withdrawal(500).display_user_balance()\nprint(collins.name, collins.account)\n\narinze.make_deposit(4000).make_withdrawal(1000).make_withdrawal(500).make_withdrawal(2000).display_user_balance()\nprint(arinze.name, arinze.account)","repo_name":"PrinceIwejuo/UserswithBankacc","sub_path":"useracct.py","file_name":"useracct.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12549636123","text":"import os\nos.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = \"hide\"\nimport sys, math, pygame, random, time\nfrom repo import NME, Player\n\ndef clear():\n os.system('cls' if os.name=='nt' else 'clear')\n\ndef pause():\n input(\"Press ENTER to continue...\")\n print('\\n\\n')\n time.sleep(0.5)\n\nplayer2 = 0\nplayer3 = 0\nplayer4 = 0\nplayerNO = 1\n\ndef setstat(NO):\n pass\n\nprint('welcome to the Amesia Machine')\nstattxt = ('Enter Player {} Stat:\\n')\np0 = Player()\np0.Name = str(input('Enter Player Name:\\n'))\np0.SAVFIL = open(\"%s.txt\" %p0.Name, \"a\")\nprint(\"Player name is {}.\".format(p0.Name))\np0.ST = int(input(stattxt.format('Strength')))\np0.SP = int(input(stattxt.format('Speed')))\np0.MI = int(input(stattxt.format('Mind')))\np0.WT = int(input(stattxt.format('Wit')))\ncont = input('add another player?\\n')\nif cont == 'Yes':\n player2 = 1\n playerNO = 2\n p1 = Player()\n p1.Name = str(input('Enter Player Name:\\n'))\n print(\"Player name is {}.\".format(p1.Name))\n p1.ST = int(input(stattxt.format('Strength')))\n p1.SP = int(input(stattxt.format('Speed')))\n p1.MI = int(input(stattxt.format('Mind')))\n p1.WT = int(input(stattxt.format('Wit')))\n cont = input('add another player?\\n')\n if cont == 'Yes':\n player3 = 1\n playerNO = 3\n p2 = Player()\n p2.Name = str(input('Enter Player Name:\\n'))\n print(\"Player name is {}.\".format(p2.Name))\n p2.ST = int(input(stattxt.format('Strength')))\n p2.SP = int(input(stattxt.format('Speed')))\n p2.MI = int(input(stattxt.format('Mind')))\n p2.WT = int(input(stattxt.format('Wit')))\n cont = input('add another player?\\n')\n if cont == 'Yes':\n player4 = 1\n playerNO = 4\n p3 = Player()\n p3.Name = str(input('Enter Player Name:\\n'))\n print(\"Player name is {}.\".format(p3.Name))\n p3.ST = int(input(stattxt.format('Strength')))\n p3.SP = int(input(stattxt.format('Speed')))\n p3.MI = int(input(stattxt.format('Mind')))\n p3.WT = int(input(stattxt.format('Wit')))\n print('you have reached max number of Players\\n')\n\n\nclear()\nprint('calculating stats...')\np0.MXHP = int(30 + (p0.ST + 4)*2)\nif p0.MXHP < 24:\n p0.MXHP = 24\np0.HP = p0.MXHP\nif p0.ST >= p0.SP:\n p0.MXINJ = 3 + p0.ST\nelif p0.SP > p0.ST:\n p0.MXINJ = 3 + int(0.5*p0.SP)\nif p0.MXINJ < 1:\n p0.MXINJ = 1\np0.INJ = p0.MXINJ\nif p0.MI >= p0.WT:\n p0.MXMP = int(2*(p0.MI + 1) + 2)\nif p0.WT > p0.MI:\n p0.MXMP = int(2*(p0.WT + 1) + 2)\np0.MP = p0.MXMP\np0.MOV = 5*(p0.SP + 4)\nif p0.MOV < 10:\n p0.MOV = 10\nif p0.ST >= p0.SP:\n p0.PFS = p0.ST\nelif p0.SP > p0.ST:\n p0.PFS = p0.SP\n\n\nif player2 == 1:\n p1.MXHP = 30 + (p1.ST + 4)*2\n if p1.MXHP < 24:\n p1.MXHP = 24\n p1.HP = p1.MXHP\n if p1.ST >= p1.SP:\n p1.MXINJ = 3 + p1.ST\n elif p1.SP > p1.ST:\n p1.MXINJ = 3 + int(0.5*p1.SP)\n if p1.MXINJ < 1:\n p1.MXINJ = 1\n p1.INJ = p1.MXINJ\n if p1.MI >= p1.WT:\n p1.MXMP = int(2*(p1.MI + 1) + 2)\n if p1.WT > p1.MI:\n p1.MXMP = int(2*(p1.WT + 1) + 2)\n p1.MP = p1.MXMP\n p1.MOV = 5*(p1.SP + 4)\n if p1.MOV < 10:\n p1.MOV = 10\n if p1.ST >= p1.SP:\n p1.PFS = p1.ST\n elif p1.SP > p1.ST:\n p1.PFS = p1.SP\n\n\nif player3 == 1:\n p2.MXHP = 30 + (p2.ST + 4)*2\n if p2.MXHP < 24:\n p2.MXHP = 24\n p2.HP = p2.MXHP\n if p2.ST >= p2.SP:\n p2.MXINJ = 3 + p2.ST\n elif p2.SP > p2.ST:\n p2.MXINJ = 3 + int(0.5*p2.SP)\n if p2.MXINJ < 1:\n p2.MXINJ = 1\n p2.INJ = p2.MXINJ\n if p2.MI >= p2.WT:\n p2.MXMP = int(2*(p2.MI + 1) + 2)\n if p2.WT > p2.MI:\n p2.MXMP = int(2*(p2.WT + 1) + 2)\n p2.MP = p2.MXMP\n p2.MOV = 5*(p2.SP + 4)\n if p2.MOV < 10:\n p2.MOV = 10\n if p2.ST >= p2.SP:\n p2.PFS = p2.ST\n elif p2.SP > p2.ST:\n p2.PFS = p2.SP\n\n\nif player4 == 1:\n p3.MXHP = 30 + (p3.ST + 4)*2\n if p3.MXHP < 24:\n p3.MXHP = 24\n p3.HP = p3.MXHP\n if p3.ST >= p3.SP:\n p3.MXINJ = 3 + p3.ST\n elif p3.SP > p3.ST:\n p3.MXINJ = 3 + int(0.5*p3.SP)\n if p3.MXINJ < 1:\n p3.MXINJ = 1\n p3.INJ = p3.MXINJ\n if p3.MI >= p3.WT:\n p3.MXMP = int(2*(p3.MI + 1) + 2)\n if p3.WT > p3.MI:\n p3.MXMP = int(2*(p3.WT + 1) + 2)\n p3.MP = p3.MXMP\n p3.MOV = 5*(p3.SP + 4)\n if p3.MOV < 10:\n p3.MOV = 10\n if p3.ST >= p3.SP:\n p3.PFS = p3.ST\n elif p3.SP > p3.ST:\n p3.PFS = p3.SP\n\nclear()\n\necho = 'Player {} {} stat is {}'\nprint (echo.format('1', 'Might', p0.ST))\nprint (echo.format('1', 'Speed', p0.SP))\nprint (echo.format('1', 'Mind', p0.MI))\nprint (echo.format('1', 'Wit', p0.WT))\nprint (echo.format('1', 'HP', p0.MXHP))\nprint (echo.format('1', 'Injuries', p0.MXINJ))\nprint (echo.format('1', 'Mana', p0.MXMP))\nprint (echo.format('1', 'Movement', p0.MOV))\nprint('\\n\\n')\nif player2 == 1:\n print (echo.format('2', 'Might', p1.ST))\n print (echo.format('2', 'Speed', p1.SP))\n print (echo.format('2', 'Mind', p1.MI))\n print (echo.format('2', 'Wit', p1.WT))\n print (echo.format('2', 'HP', p1.MXHP))\n print (echo.format('2', 'Injuries', p1.MXINJ))\n print (echo.format('2', 'Mana', p1.MXMP))\n print (echo.format('2', 'Movement', p1.MOV))\n print('\\n\\n')\nif player3 == 1:\n print (echo.format('3', 'Might', p2.ST))\n print (echo.format('3', 'Speed', P2.SP))\n print (echo.format('3', 'Mind', p2.MI))\n print (echo.format('3', 'Wit', p2.WT))\n print (echo.format('3', 'HP', p2.MXHP))\n print (echo.format('3', 'Injuries', p2.MXINJ))\n print (echo.format('3', 'Mana', p2.MXMP))\n print (echo.format('3', 'Movement', p2.MOV))\n print('\\n\\n')\nif player4 == 1:\n print (echo.format('4', 'Might', p3.ST))\n print (echo.format('4', 'Speed', P3.SP))\n print (echo.format('4', 'Mind', p3.MI))\n print (echo.format('4', 'Wit', p3.WT))\n print (echo.format('4', 'HP', p3.MXHP))\n print (echo.format('4', 'Injuries', p3.MXINJ))\n print (echo.format('4', 'Mana', p3.MXMP))\n print (echo.format('4', 'Movement', p3.MOV))\n print('\\n\\n')\npause()\nclear()\n\nGBLN = NME()\nGBLN.HP = 20\nGBLN.INIT = 5\nprint(\"A wild goblin attacks the party!\\n\\n\")\nif GBLN.INIT >= (p0.SP + p0.WT):\n print(\"The crafty goblin goes first!\\n\\n\")\n TURN = \"NME\"\nelif GBLN.INIT < (p0.SP + p0.WT):\n print(\"The party has the upper hand!\\n\\n\")\n TURN = \"PTY\"\npause()\nwhile GBLN.HP > 0 and p0.HP > 0:\n echo = 'Player {} {} stat is {}'\n print (echo.format('1', 'HP', p0.HP))\n print (echo.format('1', 'Injuries', p0.INJ))\n print (echo.format('1', 'Mana', p0.MP))\n print('\\n\\n')\n if TURN == \"NME\":\n print('The goblin enters a stance...' '\\n')\n GBLN.STANCE = random.randint(1, 3)\n if GBLN.STANCE == 1:\n GBLN.AN = 6\n GBLN.RN = 14\n if GBLN.STANCE == 2:\n GBLN.AN = 10\n GBLN.RN = 10\n if GBLN.STANCE == 3:\n GBLN.AN = 14\n GBLN.RN = 6\n print('The goblin attacks!' '\\n')\n if (GBLN.AN + 3) > (p0.RN + p0.PFS):\n GBLN.DMG = int(8*(GBLN.AN + 3 - p0.RN + p0.PFS)/10)\n p0.HP -= GBLN.DMG\n print('The goblin hits for ', str(GBLN.DMG), ' damage!' '\\n\\n')\n elif not (GBLN.AN + 3) > (p0.RN + p0.PFS):\n print('The goblin misses!' '\\n\\n')\n pause()\n TURN = \"PTY\"\n if TURN == \"PTY\":\n p0.STANCE = input('Choose your stance: \\n\\n \"1\" - Defensive(6, 14) \\n \"2\" - Neutral(10, 10) \\n \"3\" - Offensive(14, 6) \\n\\n')\n if p0.STANCE == 1:\n p0.AN = 6\n p0.RN = 14\n if p0.STANCE == 2:\n p0.AN = 10\n p0.RN = 10\n if p0.STANCE == 3:\n p0.AN = 14\n p0.RN = 6\n if (GBLN.AN + 3) < (p0.RN + p0.PFS):\n p0.DMG = int(8*(p0.RN + p0.PFS - GBLN.AN + 3)/10)\n GBLN.HP -= p0.DMG\n print('You hit the goblin for ', str(p0.DMG), ' damage!' '\\n\\n')\n elif not (GBLN.AN + 3) < (p0.RN + p0.PFS):\n print('The goblin dodges!' '\\n\\n')\n pause()\n TURN = \"NME\"\n","repo_name":"Anonymous-crow/Amnesia-Machine","sub_path":"amnesia_machine.py","file_name":"amnesia_machine.py","file_ext":"py","file_size_in_byte":8078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33208647914","text":"import sys\nfrom collections import deque\ninput = sys.stdin.readline\n\n\nn, k = map(int, input().split())\nlistN = []\nhasSame = False\nfor i in str(n):\n if int(i) in listN:\n hasSame = True\n listN.append(int(i))\nm = len(listN)\n\ndef bfs():\n if m == 1:\n return [[-1]]\n\n i = 0\n dq = deque([(listN, 0)])\n answer = []\n\n while dq:\n for _ in range(len(dq)):\n arr, cnt = dq.popleft()\n\n if cnt == k:\n answer.append(arr)\n continue\n\n if i >= m:\n if hasSame:\n answer.append(arr)\n else:\n now = arr[-1]\n arr[-1] = arr[-2]\n arr[-2] = now\n if arr[0] != 0:\n dq.append((arr, cnt+1))\n continue\n\n ListJ = []\n M = arr[i] + 1\n for jj in range(i+1, m):\n if arr[jj] > M:\n M = arr[jj]\n ListJ = [jj]\n elif arr[jj] == M:\n ListJ.append(jj)\n\n if len(ListJ) == 0:\n dq.append((arr, cnt))\n\n for j in ListJ:\n newArr = arr[:]\n now = newArr[i]\n newArr[i] = newArr[j]\n newArr[j] = now\n dq.append((newArr, cnt+1))\n\n i += 1\n\n return answer\n\nanswer = -1\nresult = bfs()\nfor arr in result:\n s = \"\".join(map(str, arr))\n answer = max(answer, int(s))\n\nprint(answer)\n\n\n","repo_name":"hyukji/AlgorithmProblem","sub_path":"Python/5000/1039.py","file_name":"1039.py","file_ext":"py","file_size_in_byte":1534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35364099179","text":"\"\"\"\nA file which holds Deck class declaration\n\"\"\"\nimport random\nimport card\n\nclass Deck():\n \"\"\"\n Class which implements a standard 52 deck with 4 suits\n \"\"\"\n suits = [\"♥\", \"♢\", \"♧\", \"♤\",]\n ranks = [\"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"J\", \"Q\", \"K\", \"A\"]\n\n def __init__(self):\n self.cards = []\n for suts in Deck.suits:\n for raks in Deck.ranks:\n self.cards.append(card.Card(raks, suts))\n\n def __str__(self):\n res = \"\"\n for crd in self.cards:\n res += crd.rank +crd.suit + \" \"\n return res\n\n def pull_card(self):\n \"\"\"\n Removes a card-object from list of cards of the deck. Returns the removed object\n \"\"\"\n crd = random.choice(self.cards)\n self.cards.remove(crd)\n return crd\n\n def reshuffle(self):\n \"\"\"\n Gets back the deck\n \"\"\"\n self.cards.clear()\n self.__init__()\n","repo_name":"GKM364/BlackJack","sub_path":"deck.py","file_name":"deck.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8991565142","text":"# Author: RT\n# Date: 2022-08-07T15:20:21.380Z\n# URL: https://leetcode.com/problems/count-vowels-permutation/\n\n\nfrom functools import cache\n\n\nclass Solution:\n def countVowelPermutation(self, n: int) -> int:\n G = {\n \"a\": [\"e\"],\n \"e\": [\"a\", \"i\"],\n \"i\": [\"a\", \"e\", \"o\", \"u\"],\n \"o\": [\"i\", \"u\"],\n \"u\": [\"a\"],\n }\n M = 1_000_000_007\n\n @cache\n def dp(u, i):\n if i == n:\n return 1\n\n return sum(dp(v, i + 1) % M for v in G[u]) % M\n\n return sum(dp(start, 1) % M for start in G.keys()) % M\n\n def bottom_up(self, n: int, G: dict[str, list[str]], M: int) -> int:\n # dp[c][i] number of permutations starting with char c of length i + 1\n dp = {start: [1] * n for start in G.keys()}\n\n # conceptually building the string backwards\n ans = 0\n for i in range(1, n):\n for u in G.keys():\n # only previous value of every starting chars is needed, can be\n # further compressed to O(1) space\n dp[u][i] = sum(dp[v][i - 1] for v in G[u]) % M\n\n return sum(dp[start][-1] for start in G.keys()) % M\n","repo_name":"Roytangrb/dsa","sub_path":"leetcode/python/1220-count-vowels-permutation.py","file_name":"1220-count-vowels-permutation.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"} +{"seq_id":"6242662214","text":"import numpy as np\n\n# ============================================================================================================= #\ndef ide_solve(idefun, Core, delays_int, history, tspan, stepsize, delays=False, overlapping=False):\n # idefun - right-hand side function (t - time, y - solution, z - dicrete delays, i - integral)\n # Core - Kernel (integrated function)\n # delays_int - distributed delays function (lower integration limit)\n # History - history function\n # tspan - solution interval\n # stepsize - step of numerical Runge-Kutta method\n # delays - (optional) discrete delays function (if idefun has 'z')\n # overlapping - (optional) if equation has overlapping in discrete delays. This option uses the 7-step method\n\n t0 = tspan[0] # t begin\n tf = tspan[1] # t end\n y0 = history(t0) # initial solution\n neq = np.size(y0) # number of equations\n htry = stepsize # constant mesh step-size\n h = htry # current step-size (for the last step)\n\n if delays is not False:\n d_t0 = delays(t0, y0)\n nz = np.size(d_t0)\n else:\n nz = 1\n # ============================================================== #\n\n\n if overlapping:\n # VIDE Runge-Kutta Tavernini\n A = np.array([[0, 1, 3/8, 1/2, 5/24, 1/6],\n [0, 0, 1/8, 1/2, 0, 0],\n [0, 0, 0, 0, 1/3, -1/3],\n [0, 0, 0, 0, -1/24, 1/6],\n [0, 0, 0, 0, 0, 1],\n [0, 0, 0, 0, 0, 0]])\n\n b = np.array([1/6, 0, 0, 0, 2/3, 1/6])\n c = np.array([0, 1, 1/2, 1, 1/2, 1])\n d = np.array([1/2, 1/2, 1/2, 1/2, 1])\n b4 = b4_Tav\n F_stages_calc = [1, 2]\n method = \"Tav\"\n else:\n # VIDE Runge-Kutta 4\n A = np.array([[0, 1/2, 0, 0],\n [0, 0, 1/2, 0],\n [0, 0, 0, 1],\n [0, 0, 0, 0]])\n\n b = np.array([1/6, 1/3, 1/3, 1/6])\n c = np.array([0, 1/2, 1/2, 1])\n d = np.array([1/2, 1/2, 1])\n b4 = b4_RK4\n F_stages_calc = [1, 3]\n method = \"RK4\"\n\n s = len(b)\n # ============================================================== #\n nint = np.size(delays_int(t0))\n # Calculate integral (F) in history\n F = np.zeros(nint)\n \n tj = delays_int(t0) # Begin\n for ij in range(nint):\n step = int((t0 - tj[ij])/h) # The number of memorized intervals of history\n tj_1 = t0 - step * h # End\n tj_half = (tj[ij] + tj_1) / 2 # Half segment\n\n # Calculate Kernel values at the nodes\n Core_tj = Core(t0, tj[ij], history(tj[ij]))\n Core_tj_1 = Core(t0, tj_1, history(tj_1))\n Core_tj_h = Core(t0, tj_half, history(tj_half))\n\n # Simpson's method\n F[ij] = F[ij] + int_simpson(tj_1 - tj[ij], Core_tj[ij], Core_tj_1[ij], Core_tj_h[ij])\n\n # Main integral over the mesh points\n Core_tj = Core_tj_1\n for j in range(step - 1, -1, -1):\n tj_1 = t0 - j * h\n tj_half = tj_1 - h / 2\n\n # Calculate Kernel values at the nodes\n Core_tj_h = Core(t0, tj_half, history(tj_half))\n Core_tj_1 = Core(t0, tj_1, history(tj_1))\n\n # Simpson's method\n F[ij] = F[ij] + int_simpson(h, Core_tj[ij], Core_tj_1[ij], Core_tj_h[ij])\n\n # Kernel of tj_1 is equal to tj of the next step\n Core_tj = Core_tj_1\n \n # F = (exp(-delays_int(t0)*t0 + delays_int(t0)) - exp(-t0*t0 + t0))/(t0 - 1)\n # ============================================================== #\n # Initialization | First Step | Y | K\n t = [t0]\n y = np.zeros((neq, 1))\n y[:, 0] = y0\n k = 0 # step\n \n z = np.zeros((neq, nz))\n if delays is not False:\n for kz in range(nz):\n z[:, kz] = history(d_t0[kz])\n \n Y = np.zeros((neq, s))\n K = np.zeros((neq, s, 1))\n \n K[:, 0, k] = idefun(t[k], y[:, 0], z, F)\n Core_di = np.zeros((nint, s))\n \n while t[k] < tf:\n # ============================================================== #\n # Last step\n if t[k] + h > tf:\n h = tf - t[k]\n # ============================================================== #\n Z = np.zeros((nint, s))\n Y[:, 0] = y[:, k]\n \n for i in range(1, s):\n ti = t[k] + c[i] * h\n # ============================================================== #\n # Calculate integral (F)\n if i in F_stages_calc: # c[3] = c[2] so the same F value is used\n F = np.zeros(nint)\n \n dtk_begin = delays_int(ti) # lower integration limit\n for ij in range(nint):\n if dtk_begin[ij] < t0:\n # ============================================================== #\n # Integral begins in the history\n\n # Step of delays(ti) in the history\n step = int((t0 - dtk_begin[ij]) / htry)\n\n # Add piece from dtk_begin to the next mesh point in the history\n tj = dtk_begin[ij]\n tj_1 = t0 - step * htry\n tj_half = (tj + tj_1) / 2\n\n # Calculate Kernel values at the nodes\n Core_tj = Core(ti, tj, history(tj))\n Core_tj_1 = Core(ti, tj_1, history(tj_1))\n Core_tj_h = Core(ti, tj_half, history(tj_half))\n\n # Simpson's method\n F[ij] = F[ij] + int_simpson(tj_1 - tj, Core_tj[ij], Core_tj_1[ij], Core_tj_h[ij])\n\n # Main integral in the history\n Core_tj = Core_tj_1\n for j in range(step - 1, -1, -1):\n tj_1 = t0 - j * htry\n tj_half = tj_1 - htry / 2\n\n # Calculate Kernel values at the nodes\n Core_tj_1 = Core(ti, tj_1, history(tj_1))\n Core_tj_h = Core(ti, tj_half, history(tj_half))\n\n # Simpson's method\n F[ij] = F[ij] + int_simpson(htry, Core_tj[ij], Core_tj_1[ij], Core_tj_h[ij])\n\n # Kernel of tj_1 is equal to tj of the next step \n Core_tj = Core_tj_1\n \n # Add integral in the solution to t(k)\n for j in range(1, k + 1):\n tj_half = t[j] - htry / 2\n y_half = y[:, j - 1] + htry * np.dot(K[:, :, j - 1], b4(1/2))\n # Calculate Kernel values at the nodes\n Core_tj_h = Core(ti, tj_half, y_half)\n Core_tj_1 = Core(ti, t[j], y[:, j])\n\n # Simpson's method\n F[ij] = F[ij] + int_simpson(htry, Core_tj[ij], Core_tj_1[ij], Core_tj_h[ij])\n\n # Kernel of tj_1 is equal to tj of the next step \n Core_tj = Core_tj_1\n # ============================================================== #\n else:\n # ============================================================== #\n # Integral only over the solution\n\n # Step of delays(ti) in the solution\n step = int((dtk_begin[ij] - t0) / htry)\n\n # Add piece from dtk_begin to the mesh point in the solution\n tj_half = (t[step+1] + dtk_begin[ij]) / 2\n\n y_begin = y[:, step] + htry * np.dot(K[:, :, step], b4((dtk_begin[ij] - t[step]) / htry))\n y_begin_h = y[:, step] + htry * np.dot(K[:, :, step], b4((tj_half - t[step]) / htry))\n\n # Calculate Kernel values at the nodes\n Core_tj = Core(ti, dtk_begin[ij], y_begin)\n Core_tj_1 = Core(ti, t[step+1], y[:, step+1])\n Core_tj_h = Core(ti, tj_half, y_begin_h)\n\n # Simpson's method\n F[ij] = F[ij] + int_simpson(t[step+1]-dtk_begin[ij], Core_tj[ij], Core_tj_1[ij], Core_tj_h[ij])\n\n # Main integral to t(k)\n Core_tj = Core_tj_1\n for j in range(step + 2, k + 1):\n tj_half = t[j] - htry / 2\n y_half = y[:, j - 1] + htry * np.dot(K[:, :, j - 1], b4(1/2))\n\n # Calculate Kernel values at the nodes\n Core_tj_h = Core(ti, tj_half, y_half)\n Core_tj_1 = Core(ti, t[j], y[:, j])\n\n # Simpson's method\n F[ij] = F[ij] + int_simpson(htry, Core_tj[ij], Core_tj_1[ij], Core_tj_h[ij])\n\n # Kernel of tj_1 is equal to tj of the next step \n Core_tj = Core_tj_1\n # ============================================================== #\n if method == \"Tav\":\n if i == 1:\n F_1 = F\n else:\n F_half = F\n else:\n if i == 1:\n F_half = F\n if method == \"Tav\":\n if i == 3 or i == 5:\n F = F_1\n elif i == 4:\n F = F_half\n else:\n if i == 2:\n F = F_half\n\n # F = (exp(-t[k]*ti + t[k]) - exp(-delays_int(ti)*ti + delays_int(ti)))/(1 - ti)\n # ============================================================== #\n # Y2-S\n Y[:, i] = y[:, k] + h * np.dot(K[:, 0:i, k], A[0:i, i])\n \n # Z2-S\n Core_di[:, i-1] = Core(t[k] + d[i-1] * h, t[k] + c[i-1] * h, Y[:, i-1])\n Z[:, i] = h * np.dot(Core_di[:, 0:i], A[0:i, i])\n # ============================================================== #\n #Finding delays Z\n if delays is not False:\n d_ti = delays(ti, Y[:, i])\n\n for kz in range(nz):\n if d_ti[kz] < t0:\n z[:, kz] = history(d_ti[kz])\n elif ti < d_ti[kz]:\n # wrong overlapping\n raise NameError(\"Delays went ahead\")\n elif t[k] - d_ti[kz] <= 0:\n # overlapping\n if method == \"Tav\":\n teta = (d_ti[kz] - t[k]) / h\n z[:, kz] = y[:, k] + h * np.dot(K[:, 0:i, k], MatrixA(teta, i))\n else:\n raise NameError(\"Overlapping. Choose method Tavernini with key 'overlapping=true'\")\n else:\n # find t\n # ============Binary search algorithm=========== #\n tcur = d_ti[kz]\n iz = 0\n jz = len(t) - 1\n nstep = int(jz / 2)\n\n while (t[nstep+1] < tcur or t[nstep] > tcur) and iz < jz:\n if tcur > t[nstep]:\n iz = nstep + 1\n else:\n jz = nstep - 1\n nstep = int((iz + jz) / 2)\n # ============================================== #\n\n # find z\n theta = (tcur - t[nstep])/htry\n z[:, kz] = y[:, nstep] + htry * np.dot(K[:, :, nstep], b4(theta))\n \n # K2-S\n K[:, i, k] = idefun(ti, Y[:, i], z, F + Z[:, i])\n # ============================================================== #\n # Final approximation of RK Method\n t.append(t[k] + h)\n y = np.append(y, np.zeros((neq, 1)), axis=1)\n y[:, k+1] = y[:, k] + h * np.dot(K[:, :, k], b)\n # ============================================================== #\n # Calculate K(1) for next step\n # Hermite extrapolation for K(1,k+1)\n y_k_half = 3/4 * y[:, k] + 1/4 * y[:, k+1] + h/4 * K[:, 0, k]\n \n Core_tj = Core(t[k+1], t[k], y[:, k])\n Core_tk_half = Core(t[k+1], t[k] + h / 2, y_k_half)\n Core_tk = Core(t[k+1], t[k+1], y[:, k+1])\n \n for ij in range(nint):\n F[ij] = F[ij] + int_simpson(h, Core_tj[ij], Core_tk[ij], Core_tk_half[ij])\n \n K = np.append(K, np.zeros((neq, s, 1)), axis=2)\n K[:, 0, k+1] = idefun(t[k+1], y[:, k+1], z, F)\n \n k = k + 1\n \n if neq == 1:\n y = y[0, :]\n return t, y\n# ============================================================================================================= #\n\n\ndef int_simpson(h, y_begin, y_end, y_half):\n # Simpson's method\n y_begin = np.array(y_begin)\n y_end = np.array(y_end)\n y_half = np.array(y_half)\n return h/6 * (y_begin + 4 * y_half + y_end)\n\n\ndef b4_Tav(a):\n x = np.zeros(6)\n sqrA = a ** 2\n x[0] = a * (1 + a * (-3/2 + a * 2/3))\n x[1] = 0\n x[2] = 0\n x[3] = 0\n x[4] = sqrA * (2 + a * -4/3)\n x[5] = sqrA * (-1/2 + a * 2/3)\n return x\n\n\ndef b4_RK4(a):\n x = np.zeros(4)\n sqrA = a ** 2\n x[0] = a * (1 + a * (-3/2 + a * 2/3))\n x[1] = sqrA * (1 + a * -2/3)\n x[2] = sqrA * (1 + a * -2/3)\n x[3] = sqrA * (-1/2 + a * 2/3)\n return x\n\n\ndef MatrixA(a, step):\n MatrixA_switch = {\n 0: 0,\n 1: a,\n 2: [a * (1 - a * 1/2), 1/2 * a * a],\n 3: [a * (1 - a * 1/2), 1/2 * a * a, 0],\n 4: [a * (1 + a * (-3/2 + a * 2/3)), 0, a**2 * (2 - a * 4/3), a**2 * (-1/2 + a * 2/3)],\n 5: [a * (1 + a * (-3/2 + a * 2/3)), 0, a**2 * (1 - a * 4/3), a**2 * (-1/2 + a * 2/3), a**2]\n }\n return MatrixA_switch[step]\n","repo_name":"vitesempl/RK-IDE-Python","sub_path":"RK/ide.py","file_name":"ide.py","file_ext":"py","file_size_in_byte":14274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38421165657","text":"import json\nfrom os import urandom\nimport werkzeug\n\nimport blowfish\n\nconfig = {\n 'CIPHER_key' : b'\\xf4\\'\\xc3(L\\xd0\\xcfD\"B/g', \n 'initialization_vector' : b'7Cp\\x95\\x85\\x029#',\n}\n\n\nclass BlowfishException(werkzeug.exceptions.BadRequest):\n def __init__(self, message):\n self.message = message\n\n def __str__(self):\n return str(self.message)\n\n\nclass Blowfish:\n AVAILABLE_MODES = ['cbc_cts', 'cfb', 'ofb', 'ecb_cts']\n DEFAULT_MODE = 'cbc_cts'\n CIPHER = blowfish.Cipher(config['CIPHER_key'])\n IV = config['initialization_vector']\n\n #This shouldn't really be called directly - it's just a way to shorten code. \n #Rather just use encrypt_data() and decrypt_data()\n @staticmethod\n def _handle_data(action, data, mode = None):\n if not mode: mode = Blowfish.DEFAULT_MODE\n\n elif mode not in Blowfish.AVAILABLE_MODES: \n raise BlowfishException('Mode not supported: ' + str(mode))\n\n if action not in ['encrypt', 'decrypt']:\n raise BlowfishException(\"Called _handle_data() with action: ' + str(action) + '. Must be `encrypt` or `decrypt`. Protip: You probably shouldn't directly call _handle_data. \")\n\n action = '%s_%s' % (action, mode)\n\n #There might be a better way to implement this, which will also allow us to support multiple modes. \n args = [data, Blowfish.IV]\n if mode == 'ecb_cts' : \n args = [data]\n\n handled_data = b\"\".join(getattr(Blowfish.CIPHER, action)(*args))\n return handled_data\n\n @staticmethod\n def encrypt(data, mode = None):\n data = data.encode('utf-8')\n encrypted_data = Blowfish._handle_data('encrypt', data, mode)\n encrypted_data = encrypted_data.hex()\n return encrypted_data\n\n @staticmethod\n def decrypt(data, mode = None):\n data = bytes.fromhex(data)\n decrypted_data = Blowfish._handle_data('decrypt', data, mode)\n print ('Data : ', decrypted_data)\n try:\n decrypted_data = decrypted_data.decode('utf-8')\n except UnicodeDecodeError:\n return str(decrypted_data)\n return decrypted_data\n","repo_name":"rafaeelaudibert/Encryptor","sub_path":"encryptor/encryptors/blowfish.py","file_name":"blowfish.py","file_ext":"py","file_size_in_byte":2143,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"} +{"seq_id":"15093851573","text":"#Ejercicio01\ndef descuento(tipo,prestamo):\n # Si el tipo de cliente es EXCLUSIVO\n # Aqui se usa la funcion upper()\n if (tipo.upper()== \"COMPULSIVO\"):\n return 0.10 * prestamo\n else:\n return 0\ndef reporte_prestamo(nombre,precio,descuento):\n print(\"##################################\")\n print(\"# ficha de boleta #\")\n print(\"#nombre: \" +nombre+ \"\")\n print(\"#precio: s/. \"+str(precio)+\"\")\n print(\"#descuento: s/. \"+str(descuento)+\"\")\n print(\"##################################\")\n\n#Ejercicio02\ndef validar_nombre(strNombre):\n #1. El tipo de dato de strNombre es str\n #2. La longitud de la cadena es al menos de 3\n if ( isinstance(strNombre, str) ):\n if ( len(strNombre) >= 3):\n return True # Es un nombre valido\n else:\n return False # Insuficients caracteres\n else:\n return False # No es str\n#fin_validar_nombre\n\n#Ejercicio03\n# Funcion : Verifica si intNum es un entero\n# Parametros: intNum => Numero entero\n# Retorna : bool\ndef validar_entero(intNum):\n if ( isinstance(intNum, int)):\n return True\n else:\n return False\n#fin_validar_entero\n\n#Ejercicio04\n# Funcion : Verifica si fltNum es un real\n# Parametros: fltNum => Numero real\n# Retorna : bool\ndef validar_real(fltNum):\n if ( isinstance(fltNum, float)):\n return True\n else:\n return False\n\n#Ejercicio05\n# Funcion : Devuelve la calificacion del promedio\n# Parametros: flotProm ==\n# Retorna : str\ndef calificacion(fltProm):\n if ( fltProm == 20.0):\n return \"Excelente\"\n if ( fltProm == 16.0):\n return \"Muy Bien \"\n if ( fltProm == 12.0):\n return \"Regular\"\n if ( fltProm == 5.0):\n return \"Bajo\"\n if ( fltProm == 0.0):\n return \"Muy Bajo\"\n else:\n return \"\"\n#fin_calificacion\n\n#Ejercicio06\n# Funcion : Devuelve el bonus por puntaje\n# Parametros: strCalif == Puntaje\n# Retorna : float\ndef obtenerBonus(strPuntaje):\n if ( strPuntaje == \"Excelente\"):\n return 300.0\n if ( strPuntaje==\"Muy Bien\"):\n return 200.0\n if ( strPuntaje == \"Regular\"):\n return 100.0\n else:\n return 0.0\n#fin_obtenerBonus\n\n#Ejercicio07\ndef validar_obrero(strObrero):\n #1. El tipo de dato de strObrero es str\n #2. La longitud de la cadena es al menos de 5\n if ( isinstance(strObrero, str) ):\n if ( len(strObrero) >= 5):\n return True # Es un nombre valido\n else:\n return False # Insuficients caracteres\n else:\n return False # No es str\n#fin_validar_obrero\n\n#Ejercicio08\ndef validar_codigo (strCodigo):\n #1. strCodigo es una cadena de 5 caracteres\n #2. El primer caracter de strCodigo es el signo #\n if (len(strCodigo) != 5):\n return False\n if (strCodigo[0] == \"#\"):\n return True\n #fin_if\n#fin_validar_codigo\n\n#Ejercicio09\ndef validar_anio(intAnio):\n #1.El tipo de dato es intAnio es int\n #2.La longitud del entero es al menos de 2050\n if(isinstance(intAnio,int)):\n if((intAnio) >= 2050):\n return True #Es anio valido\n else:\n return False\n else:\n return False #No es int\n\n#Ejercicio10\n# Funcion : Devuelve la calificacion del puntaje\n# Parametros: StrPuntaje => Puntaje\n# Retorna : float\ndef pasa_pedido(strPuntaje):\n if ( strPuntaje == \"Excelente\"):\n return 100.0\n if ( strPuntaje == \"Bajo\"):\n return 50.0\n if ( strPuntaje == \"Muy bajo\"):\n return 20.0\n else:\n return 0.0\n#fin_pasa_pedido\n\n#Ejercicio11\n# Funcion : Verifica si intNum es un entero\n# Parametros: intNum => Numero entero\n# Retorna : bool\ndef ganancia(intNum):\n if ( isinstance(intNum, int)):\n return True\n else:\n return False\n#fin_ganancia\n\n#Ejercicio12\n# Funcion : Devuelve el puntaje minimo\n# Parametros: floatPuntmin ==\n# Retorna : str\ndef puntaje_minimo(fltPuntmin):\n if ( fltPuntmin == 220.0):\n return \"Alcanza puntaje, pase pedido\"\n if ( fltPuntmin == 180.0):\n return \"Muy bien, le falta poco \"\n if ( fltPuntmin == 100.0):\n return \"Sigue ofreciendo\"\n else:\n return \"\"\n#fin_puntaje_minimo\n\n#Ejercicio13\ndef validar_empleado(strEmpleado):\n #1. El tipo de dato de strEmpleado es str\n #2. La longitud de la cadena es al menos de 5\n if ( isinstance(strEmpleado, str) ):\n if ( len(strEmpleado) >= 6):\n return True # Es un nombre valido\n else:\n return False # Insuficients caracteres\n else:\n return False # No es str\n#fin_validar_empleado\n\n#Ejercicio14\ndef validar_contrasena (strContrasena):\n #1. strContrasena es una cadena de 5 caracteres\n #2. El primer caracter de strContrasena es el signo #\n if (len(strContrasena) != 5):\n return False\n if (strContrasena[0] == \"#\"):\n return True\n #fin_if\n#fin_validar_contrasena\n\n#Ejercicio15\ndef es_valido (strNum):\n #1. strNum puede ser un numero de 0-2 o letras de A-C\n if (strNum == \"0\" or strNum ==\"1\" or strNum ==\"2\" or\n strNum==\"A\" or strNum ==\"B\" or strNum==\"C\"):\n return True\n else:\n return False\n #fin_if\n#fin_es_valido\n","repo_name":"Diana-rufasto/t08_rufasto.llontop","sub_path":"Rufasto.Torres/libreria.py","file_name":"libreria.py","file_ext":"py","file_size_in_byte":5241,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"18822995808","text":"\"\"\"\\\nInterfaces for JSON:API representations of application objects.\n\n\"\"\"\n\nimport re\nimport typing\n\nimport zope.interface\nimport zope.interface.common.interfaces\nimport zope.interface.common.mapping\nimport zope.interface.common.sequence\nimport zope.schema\nimport zope.schema.interfaces\n\n\n_re_gac = r'[a-zA-Z0-9\\u0080-\\uffff]'\n_re_memch = r'[-a-zA-Z0-9\\u0080-\\uffff_ ]'\n\n_re_member_name = f'{_re_gac}({_re_memch}*{_re_gac})?$'\n_rx_member_name = re.compile(_re_member_name.replace(' ', ''))\n\n\n# --------------------\n# Exception interfaces\n\n\nclass IQueryStringException(zope.interface.common.interfaces.IValueError):\n \"\"\"Interface for QueryStringException instances.\"\"\"\n\n key = zope.schema.TextLine(\n title='Key',\n description='Name of key in query string',\n required=True,\n )\n\n\nclass IInvalidNameException(zope.interface.common.interfaces.IException):\n \"\"\"Interface for the Invalid* exceptions.\"\"\"\n\n field = zope.schema.Object(\n description='Field which failed validation',\n schema=zope.schema.interfaces.IField,\n required=True,\n )\n\n value = zope.interface.Attribute(\"Value determined to be invalid\")\n\n\nclass IInvalidResultStructure(zope.interface.common.interfaces.IValueError):\n \"\"\"Interface for InvalidResultStructure instances.\"\"\"\n\n kind = zope.schema.TextLine(\n description=('Name of the structure as defined in the'\n ' JSON:API specification'),\n required=True,\n )\n\n\n# ----------\n# Exceptions\n\n\n@zope.interface.implementer(IInvalidNameException)\nclass _InvalidName(zope.schema.interfaces.ValidationError):\n \"\"\"Name is invalid according to JSON:API.\"\"\"\n\n\nclass InvalidMemberName(_InvalidName):\n \"\"\"Member name is invalid according to JSON:API.\"\"\"\n\n\nclass InvalidRelationshipPath(_InvalidName):\n \"\"\"Relationship path includes invalid member name according to JSON:API.\n \"\"\"\n\n\nclass InvalidTypeName(_InvalidName):\n \"\"\"Type name is invalid according to JSON:API.\"\"\"\n\n\n@zope.interface.implementer(IQueryStringException)\nclass QueryStringException(ValueError):\n \"\"\"Something is wrong with the format or content of the query string.\"\"\"\n\n def __init__(self, message, key):\n \"\"\"Initialize with an error message and the key from query string.\"\"\"\n super(QueryStringException, self).__init__(message)\n self.key = key\n\n\nclass InvalidQueryKey(QueryStringException):\n \"\"\"A key in the query string is malformed.\"\"\"\n\n\nclass InvalidQueryKeyUsage(QueryStringException):\n \"\"\"A key in the query string is used inconsistently.\"\"\"\n\n\nclass InvalidQueryKeyValue(QueryStringException):\n \"\"\"A key in the query string has an unsupported value.\"\"\"\n\n def __init__(self, message, key, value):\n \"\"\"Initialize with message, key and value from query string.\"\"\"\n super(InvalidQueryKeyValue, self).__init__(message, key)\n self.value = value\n\n\n@zope.interface.implementer(IInvalidResultStructure)\nclass InvalidResultStructure(ValueError):\n \"\"\"Serialization resulted in an invalid structure.\"\"\"\n\n def __init__(self, kind):\n \"\"\"Construct exception for a specific *kind* of structure.\n\n *kind* should be a term defined in the JSON:API specification.\n\n \"\"\"\n super(InvalidResultStructure, self).__init__(kind)\n self.kind = kind\n\n def __str__(self):\n return f'serialization generated invalid structure for {self.kind}'\n\n\n# -----------------\n# Field definitions\n\n\nclass _Name(zope.schema.TextLine):\n\n def __init__(self, *args, **kwargs):\n # The constructor for zope.interface.Element uses __name__ for\n # __doc__, and sets __name__ to None, if there's a space in\n # __name__. Since our field names are generated based on user\n # input, this is wrong behavior for us, so work around it.\n name = kwargs.pop('__name__', None)\n kwargs['__name__'] = 'xxx'\n super(_Name, self).__init__(*args, **kwargs)\n self.__name__ = name\n\n def constraint(self, value):\n if _rx_member_name.match(value) is None:\n raise self._exception().with_field_and_value(self, value)\n else:\n return True\n\n\nclass MemberName(_Name):\n \"\"\"Member name, as defined by JSON:API.\n\n Allowed member names are `constrained by the specification\n하루의 마침표
\", unsafe_allow_html=True)\nst.markdown(\"당신의 감정에 맞는 컨텐츠를 소개해드립니다.
\", unsafe_allow_html=True) # TODO: 이 밑에 여백 넣기!\nuser_diary = st.text_area(label =\"\", placeholder = f\"오늘 하루는 어떠셨나요? 일기든, 감정을 나타내는 키워드든 자유로운 형식으로 정리해보세요.\", height=250)\n\n### avoiding refresing the entire page when clicking a button\n\n### session state\nif 'user_button' not in st.session_state:\n st.session_state.user_button = True\n\nif 'user_button' not in st.session_state:\n st.session_state.user_button = True\n# def update_session():\n# st.session_state.update = \n\n### column \n_, col, _ = st.columns([1.5]*2+[1])\nuser_feelings_button = col.button(\"내 감정 정리하기!\", key='user_button') # st.button은 session_state를 지원하지 않아서 임시방편으로 chckbox를 사용함\n\n\nst.markdown(\"***\", unsafe_allow_html=True)\n\n### TODO: 기록 확인하기 (유저 history data)\n\n### 필요한 부분들을 미리 선언해둡니다. \nemotions = []\nuser_label_dict = {}\n## 이 부분은 나중에 GET으로 처리예정\nKOTE_label = ['불평/불만', '환영/호의', '감동/감탄', '지긋지긋', '고마움', '슬픔', '화남/분노', '존경', '기대감', '우쭐댐/무시함', '안타까움/실망', '비장함', '의심/불신', '뿌듯함', '편안/쾌적', '신기함/관심', '아껴주는', '부끄러움', '공포/무서움', '절망', '한심함', '역겨움/징그러움', '짜증', '어이없음', '없음', '패배/자기혐오', '귀찮음', '힘듦/지침', '즐거움/신남', '깨달음', '죄책감', '증오/혐오', '흐뭇함(귀여움/예쁨)', '당황/난처', '경악', '부담/안_내킴', '서러움', '재미없음', '불쌍함/연민', '놀람', '행복', '불안/걱정', '기쁨', '안심/신뢰']\nKOTE_label_dict = {i:KOTE_label[i] for i in range(len(KOTE_label))}\noption1, option2, option3 = '', '', ''\n### 사용자 일기 받아서 respose 받아오기\n\ndef get_feelings_from_diary(user_diary: str) -> List:\n response = requests.post(url=\"http://localhost:8000/diary/input\", json = {\"diary_content\": user_diary})\n emotions = eval(response.text)\n return emotions\n\n\ndef get_songs_from_emotions(user_selection: List) -> List:\n response = requests.post(url=\"http://localhost:8000/diary/input\", json = {\"now_feelings\": user_selection})\n playlists = eval(response.text)\n\n\ndef select_emotion_label(temp_data: Tuple) -> List:\n temp_data = return_user_feelings()\n if temp_data[0] == \"KOTE\":\n final_selection = [x for x, y in zip(KOTE_label, index) if y == True]\n else:\n final_selection = [x for x, y in zip(emotions, index) if y == True]\n\n return final_selection\n\n\n# @st.cache(suppress_st_warning=True)\ndef return_user_feelings() -> List:\n global emotions\n global user_label_dict\n print(\"emotions from get_feelings_from_diary\", emotions)\n emotions = get_feelings_from_diary(user_diary)\n print(emotions, len(emotions), type(emotions))\n\n there_is_no_emotions = st.button(\"원하는 감정이 없어요!\",)\n # there_is_no_emotions = st.radio(label=\"원하는 감정이 없다면 아래 radio를 선택하세요!\", options=['원하는 감정이 없어요'], key='emotion_checkbox', disabled=False)\n\n if there_is_no_emotions:\n for i in range(len(KOTE_label)):\n globals()[f'options_{i}'] = st.checkbox(KOTE_label[i])\n index = [f'options_{i}' for i in range(len(KOTE_label))]\n return (\"KOTE\", index)\n else:\n option1 = st.checkbox(emotions[0])\n option2 = st.checkbox(emotions[1])\n option3 = st.checkbox(emotions[2])\n index = [option1, option2, option3]\n\n return (\"emotions\", index)\n\nemotion_data = return_user_feelings()\nfinal_selection = select_emotion_label(emotion_data)\nprint(\"final_selection: \", final_selection)\n\n\n# def main():\n# # emotions = return_user_feelings(user_feelings_button)\n# print(\"emotions: \", emotions)\n# print(\"-----------------------------------------------\")\n# # print(user_label_dict)\n# # print(user_label_dict[0], user_label_dict[1], user_label_dict[2])\n# # print(f1, f2, f3)\n# st.write('Select three known variables:')\n\n# option_1 = st.checkbox(\"A\")\n# # option_2 = st.checkbox(emotions[1])\n# # option_1 = st.multiselect(label = \"감정을 골라보세요!\", options = emotions)\n\n# # print(option_1, option_2, option_3)\n# # f1, f2, f3 = return_user_feelings(user_feelings_button)\n# st.write(\"지금의 감정들이 맞는지 확인한 후 선택해주세요. 만약 없다면 하단의 버튼을 클릭해주세요. \")\n\n\n# main()\n\n\n\n\n# TODO: 사용자 감정을 받아서 checkbox로 출력\n\n\n\n\n# for i in range(len(emotions)):\n# user_label_dict[f\"options_{i}\"] = st.checkbox(emotions[i])\n\n# print(d)\n# user_contents_selection = st.multiselect(\n# \"지금의 감정들이 맞는지 확인해주세요. 만약 아니라면, 다음을 선택해주세요!\",\n# string\n# )\n\n# print(user_contents_selection)\n# show_me_other_feelings = st.button(\"다른 감정들을 보여주세요\")\n\n# if show_me_other_feelings:\n# for i in range(len(KOTE_label)):\n# KOTE_label[i] = st.checkbox(KOTE_label[i])\n\n# st.write(\"이런 감정들을 선택하셨어요!\")\n# ## 사용자의 일기를 분석하여 감정을 보여줍니다. \n# show_user_feelings = st.multiselect(\n# \"지금 느끼는 감정들을 선택해주세요\", string\n# )\n\n\n\n# print(user_contents[1], user_contents[2], user_contents[3]) ## check selection\n\n# user_contents_selection = ''\n### radio button만들기\n\n# TODO 1: 사용자에게 감정 입력 받는 박스 만들기 (입력: 문장, 키워드, 일기 등 -> 출력: 감정 라벨)\n# if user_feeling_button:\n# print(\"Hello World!\") # 사용자의 감정을 정리해줍니다. 누가? 모델이.\n\n\n# TODO 2: 사용자가 감정을 입력하면 이를 키워드로 나타내는 모델과 연결시키기\n\n##","repo_name":"boostcampaitech3/final-project-level3-nlp-01","sub_path":"app/frontend/write_page_streamlit.py","file_name":"write_page_streamlit.py","file_ext":"py","file_size_in_byte":6407,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"28061846543","text":"import cv2\n\ndef sketch(img):\n img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n img_gray_blur = cv2.GaussianBlur(img_gray, (5,5), 0) # clean up the noise in the image\n canny_edges = cv2.Canny(img_gray_blur, 20, 60)\n ret, mask = cv2.threshold(canny_edges, 60, 255, cv2.THRESH_BINARY_INV)\n return mask\n\nif __name__ == \"__main__\":\n capture = cv2.VideoCapture(0)\n while True:\n ret, frame = capture.read()\n cv2.imshow(\"LIVE sketch... \", sketch(frame))\n if cv2.waitKey(1) == 13: # 13 is the Enter key\n break\n capture.release()\n cv2.destroyAllWindows()\n","repo_name":"tanveer-sayyed/Experiments","sub_path":"Edge detector via webcam.py","file_name":"Edge detector via webcam.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"16929233286","text":"import collections\n\n\ndef doc(s):\n if hasattr(s, '__call__'):\n s = s.__doc__\n\n def f(g):\n g.__doc__ = s\n return g\n\n return f\n\n\ndef _swap(arr, i, j):\n arr[i], arr[j] = arr[j], arr[i]\n arr[i][2] = i\n arr[j][2] = j\n\n\ndef _set(arr, i, item):\n arr[i] = item\n item[2] = i\n\n\ndef heappush(heap, item):\n \"\"\"Push item onto heap, maintaining the heap invariant.\"\"\"\n item[2] = len(heap)\n heap.append(item)\n _siftdown(heap, 0, len(heap) - 1)\n\n\ndef heappop(heap):\n \"\"\"Pop the smallest item off the heap, maintaining the heap invariant.\"\"\"\n lastelt = heap.pop() # raises appropriate IndexError if heap is empty\n if heap:\n returnitem = heap[0]\n _set(heap, 0, lastelt)\n _siftup(heap, 0)\n return returnitem\n return lastelt\n\n\n# 'heap' is a heap at all indices >= startpos, except possibly for pos. pos\n# is the index of a leaf with a possibly out-of-order value. Restore the\n# heap invariant.\ndef _siftdown(heap, startpos, pos):\n newitem = heap[pos]\n # Follow the path to the root, moving parents down until finding a place\n # newitem fits.\n while pos > startpos:\n parentpos = (pos - 1) >> 1\n parent = heap[parentpos]\n if newitem[0] < parent[0]:\n _set(heap, pos, parent)\n pos = parentpos\n continue\n break\n _set(heap, pos, newitem)\n\n\ndef _siftup(heap, pos):\n endpos = len(heap)\n startpos = pos\n newitem = heap[pos]\n # Bubble up the smaller child until hitting a leaf.\n childpos = 2 * pos + 1 # leftmost child position\n while childpos < endpos:\n # Set childpos to index of smaller child.\n rightpos = childpos + 1\n if rightpos < endpos and not heap[childpos][0] < heap[rightpos][0]:\n childpos = rightpos\n # Move the smaller child up.\n _set(heap, pos, heap[childpos])\n pos = childpos\n childpos = 2 * pos + 1\n # The leaf at pos is empty now. Put newitem there, and bubble it up\n # to its final resting place (by sifting its parents down).\n _set(heap, pos, newitem)\n _siftdown(heap, startpos, pos)\n\n\nclass heapdict(collections.MutableMapping):\n __marker = object()\n\n def _check_invariants(self):\n # the 3rd entry of each heap entry is the position in the heap\n for i, e in enumerate(self.heap):\n assert e[2] == i\n # the parent of each heap element must not be larger than the element\n for i in range(1, len(self.heap)):\n parent = (i - 1) >> 1\n assert self.heap[parent][0] <= self.heap[i][0]\n\n def __init__(self, *args, **kw):\n self.heap = []\n self.d = {}\n self.update(*args, **kw)\n\n @doc(dict.clear)\n def clear(self):\n del self.heap[:]\n self.d.clear()\n\n @doc(dict.__setitem__)\n def __setitem__(self, key, value):\n if key in self.d:\n del self[key]\n wrapper = [value, key, -2]\n self.d[key] = wrapper\n heappush(self.heap, wrapper)\n\n @doc(dict.__delitem__)\n def __delitem__(self, key):\n wrapper = self.d[key]\n i = wrapper[2]\n lastindex = len(self.heap) - 1\n # swap with last item\n _swap(self.heap, i, lastindex)\n self.heap.pop()\n if self.heap and i < lastindex:\n _siftup(self.heap, i)\n _siftdown(self.heap, 0, i)\n del self.d[key]\n\n @doc(dict.__getitem__)\n def __getitem__(self, key):\n return self.d[key][0]\n\n @doc(dict.__iter__)\n def __iter__(self):\n return iter(self.d)\n\n def pop(self, *args):\n \"\"\"D.pop([k,[,d]]) -> v, if no key is specified, remove the key with the lowest\nvalue and return the corresponding value, raising IndexError if list is empty.\nIf a key is specified, then it is removed instead if present and the\ncorresponding value is returned. If the key is specified and not present, then\nd is returned if given, otherwise KeyError is raised\"\"\"\n if len(args) == 0:\n if len(self.d) == 0:\n raise IndexError(\"pop from empty heapdict\")\n (k, _) = self.popitem()\n else:\n k = super(heapdict, self).pop(*args)\n return k\n\n def popitem(self):\n \"\"\"D.popitem() -> (k, v), remove and return the (key, value) pair with lowest\\nvalue; but raise KeyError if D is empty.\"\"\"\n wrapper = heappop(self.heap)\n del self.d[wrapper[1]]\n return wrapper[1], wrapper[0]\n\n @doc(dict.__len__)\n def __len__(self):\n return len(self.d)\n\n def peekitem(self):\n \"\"\"D.peekitem() -> (k, v), return the (key, value) pair with lowest value;\\n but raise KeyError if D is empty.\"\"\"\n return (self.heap[0][1], self.heap[0][0])\n\n\ndel doc\n__all__ = ['heapdict']\n","repo_name":"urielha/heapdict","sub_path":"heapdict.py","file_name":"heapdict.py","file_ext":"py","file_size_in_byte":4774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"} +{"seq_id":"44161862381","text":"from pdf2image import convert_from_path, convert_from_bytes\r\nfrom pdf2image.exceptions import (\r\n PDFInfoNotInstalledError,\r\n PDFPageCountError,\r\n PDFSyntaxError\r\n)\r\nfrom os import listdir\r\nfrom os.path import isfile, join\r\n\r\n\r\ndef convert_pdf_to_jpeg(folder_name):\r\n images_folder_name = 'images/'\r\n files = [f for f in listdir(folder_name) if isfile(join(folder_name, f))]\r\n for file in files:\r\n print(file)\r\n images = convert_from_path(folder_name+'/'+file, poppler_path=r\"C:\\Program Files\\Release-21.03.0\\poppler-21.03.0\\Library\\bin\")\r\n i = 0\r\n for im in images:\r\n im.save(images_folder_name + str(i) + '.jpg')\r\n i += 1\r\n return images_folder_name\r\n","repo_name":"KarinaGanieva-sl/OCR-with-super-resolution","sub_path":"pdf_to_image.py","file_name":"pdf_to_image.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"} +{"seq_id":"13681927063","text":"lista = [49, 42, True]\n\ndef somaTeste(val1, val2, imprime = False):\n resultado = val1 + val2\n if imprime:\n print(f\"Soma: {resultado}\")\n return resultado\n\ntotal = somaTeste(*lista)\n\nlista = [49, 42, True]\n\ndef somaTeste1(imprime, *valores):\n total = 0\n for valor in valores:\n total += valor\n if imprime:\n print(f\"SomaTeste1: {total}\")\n return total\n\nx = somaTeste1(False, 10,20,25,50,60,70)\nprint(x)","repo_name":"DiegoSantosWS/estudos-python","sub_path":"estudo-8/temp1.py","file_name":"temp1.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"35525725051","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.jingle_home,name='jingle-home'),\n path('results/.*?', '', title[i])\r\n info[i] = re.sub('<.*?>', '', info[i])\r\n source.append(info[i].split(' ')[0])\r\n date.append(info[i].split(' ')[1])\r\n source[i] = source[i].strip()\r\n date[i] = date[i].strip()\r\n print(str(i + 1) + '.' + title[i] + '(' + date[i] + '-' + source[i] + ')')\r\n print(href[i])\r\n\r\n\r\ncompanies = ['阿里巴巴', '万科集团', '百度集团', '腾讯', '京东']\r\nfor i in companies: # 这个i只是个代号，可以换成其他内容\r\n baidu(i)\r\n print(i + '百度新闻爬取成功')\r\n","repo_name":"zhengluoliudao/HIT-Bigdata-Grade3-last","sub_path":"python金融/爬虫/test4.py","file_name":"test4.py","file_ext":"py","file_size_in_byte":1344,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"18332466401","text":"import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\nEI = lambda m: [NLI() for _ in range(m)]\n\n\ndef main():\n S = [\"aa\", \"aa luaa\", \"aa mea\", \"aauu asi\", \"bini vaa\", \"keel\",\n \"keel maa\", \"laavang\", \"laavang asi\", \"laavang ila\", \"laavang vaa\",\n \"mooi\", \"mooi maa\", \"mooi silba\", \"silba\", \"vate\", \"vate ila\", \"vate luaa\"]\n\n W = []\n for s in S:\n for ss in s.split():\n W.append(ss)\n\n C = Counter(W)\n for key, k in C.most_common():\n print(key, k)\n\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"JOL.py","file_name":"JOL.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17200960939","text":"# # 采集网站:https://www.pcauto.com.cn/chexun/ 太平洋汽车网\n\nimport requests\nfrom lxml import etree\nimport lxml\t\nimport pymysql \nimport random\nimport time\nimport re\nfrom datetime import datetime\nfrom selenium import webdriver\nfrom selenium.webdriver.support.wait import\tWebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\n\n\n#连接数据库\ndb= pymysql.connect(host=\"localhost\",user=\"root\",\n \tpassword=\"129426\",db=\"appware\",port=3306)\n\ncur = db.cursor()\n\nurl = 'http://auto.gasgoo.com'\n\nheaders_list = ['Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/69.0.3497.100 Safari/537.36','Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:60.0) Gecko/20100101 Firefox/60.0']\nheaders = {\n\t'User_Agent':random.choice(headers_list)\n}\n\n# 根据网站特性，构造url池,抓取前300页内容\nurl_pool = []\nfor i in range(1,61):\n\tcreat_url = 'http://auto.gasgoo.com/auto-news/C-101-102-103-104-105-106-501-601/%d'%(i)\n\turl_pool.append(creat_url)\n# 构造所有分页url池\nPaging = []\nfor i in url_pool:\n\tr2 = requests.get(url=i,headers=headers,timeout=60)\n\tr2.encoding = 'utf-8'\n\ttwo_html = etree.HTML(r2.text)\n\tpage_url = two_html.xpath('//div[@class=\"listLeft\"]/div[@class=\"content\"]//h2/a/@href')\n\tPaging += page_url\n\ttime.sleep(1)\nPaging = list(set(Paging))\n\n\ntotal_urls = []\nfor i in Paging:\n\tif 'http' not in i:\n\t\ti = url + i\n\telse:\n\t\tpass\n\ttotal_urls.append(i)\nprint(total_urls)\n\n# 图片下载函数\ndef save_image(title,image):\n\twith open(title,'wb') as f:\n\t\tf.write(image.content)\n\t\t# f.flush()\n\t\tf.close()\n\n# 文章img处理函数\ndef to_img(listx):\n\td = []\n\te = []\n\n\tfor i in listx:\n\t\td.append(str(i))\n\tfor k in d:\n\t\tif ''))\n\t\t\t\t\tdb.commit()\n\n\t\t\t\t\tsql_insert_picture_compress = \"\"\"insert into content_picture_compress(title,picture_name,create_date,create_idate,url,column_id,article_id,isaudit,count_like) values(%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\n\t\t\t\t\tdb.ping(reconnect=True)\n\t\t\t\t\tcur.execute(sql_insert_picture_compress,(image_title,picture_name,create_date,create_idate,path_picture,column_id,article_id,1,countlike))\n\t\t\t\t\tdb.commit()\n\t\t\t\t\tprint(picture_name)\n\t\t\t\t\trsp_image = requests.get(image_url,headers,timeout=60)\n\t\t\t\t\tsave_image(picture_name,rsp_image)\n\t\t\t\telse:\n\t\t\t\t\timage_url = 'http://auto.gasgoo.com' + image_url\n\n\t\t\t\t\tsql_insert_picture = \"\"\"insert into content_picture(title,picture_name,create_date,create_idate,url,column_id,article_id,isaudit,label) values(%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\n\t\t\t\t\tdb.ping(reconnect=True)\n\t\t\t\t\tcur.execute(sql_insert_picture,(image_title,picture_name,create_date,create_idate,path_picture,column_id,article_id,1,'![]()
'))\n\t\t\t\t\tdb.commit()\n\n\t\t\t\t\tsql_insert_picture_compress = \"\"\"insert into content_picture_compress(title,picture_name,create_date,create_idate,url,column_id,article_id,isaudit,count_like) values(%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\"\"\n\t\t\t\t\tdb.ping(reconnect=True)\n\t\t\t\t\tcur.execute(sql_insert_picture_compress,(image_title,picture_name,create_date,create_idate,path_picture,column_id,article_id,1,countlike))\n\t\t\t\t\tdb.commit()\n\n\t\t\t\t\trsp_image = requests.get(image_url,headers,timeout=60)\n\t\t\t\t\tsave_image(picture_name,rsp_image)\n\t\telse:\n\t\t\tprint('没有image')\n\t\t\n\t\ttime.sleep(1)\n\n\texcept:\n\t\tprint(\"=\"*50)\n\t\tprint('有错误')\n\ndb.close()","repo_name":"ritianpig/spider","sub_path":"app17/awqc/qichezixun.py","file_name":"qichezixun.py","file_ext":"py","file_size_in_byte":6270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1503772515","text":"def descendingOrder(n):\n # CONVERT NUMBER INTO AN ARRAY OF NUMBERS\n res = [int(x) for x in str(n)]\n # print(res)\n for i, x in enumerate(res):\n for j, z in enumerate(res):\n if(res[i] > res[j]):\n temp = res[i]\n res[i] = res[j]\n res[j] = temp\n print(res)\n\ndescendingOrder(25748)\n","repo_name":"JesusGarcia23/PythonKatas","sub_path":"DescendingOrder/descendin.py","file_name":"descendin.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74949347600","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\ndef copy_names(apps, schema_editor):\n\tPerson = apps.get_model('delicontacts', 'Person')\n\tfor person in Person.objects.all():\n\t\tperson.name = person.first_name\n\t\tperson.save()\n\ndef copy_first_names(apps, schema_editor):\n\tPerson = apps.get_model('delicontacts', 'Person')\n\tfor person in Person.objects.all():\n\t\tperson.fisrt_name = person.name\n\t\tperson.save()\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('delicontacts', '0008_auto_20150314_0030'),\n ]\n\n operations = [\n \tmigrations.RunPython(copy_names, copy_first_names)\n ]\n","repo_name":"jualvarez/deliverest","sub_path":"delicontacts/migrations/0009_auto_20150314_0035.py","file_name":"0009_auto_20150314_0035.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33990108967","text":"from playwright.sync_api import sync_playwright\n\nurl = 'https://parsinger.ru/blank/modal/2/index.html'\n\n\nwith sync_playwright() as pw:\n browser = pw.chromium.launch(headless=False)\n page = browser.new_page()\n page.goto(url)\n page.on(\"dialog\", lambda dialog: dialog.accept())\n buttons = page.locator('//input')\n for button in buttons.element_handles():\n button.click()\n result = page.locator('//p[@id=\"result\"]')\n result = result.inner_text()\n if result:\n print(result)\n page.wait_for_timeout(5000)\n browser.close()\n","repo_name":"mangustik228/lessons","sub_path":"parsing/playwright/22_alert.py","file_name":"22_alert.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8598117581","text":"import openpyxl\nimport pymysql\nimport requests\nimport json\nimport datetime\n\n\nclass Quantity():\n def __init__(self):\n self.s = requests.Session()\n login_url = 'https://erp.lingxing.com/api/passport/login'\n # 请求头\n headers = {'Host': 'erp.lingxing.com',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:95.0) Gecko/20100101 Firefox/95.0'\n , 'Referer': 'https://erp.lingxing.com/login',\n 'Accept': 'application/json, text/plain, */*',\n 'Accept-Language': 'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Content-Type': 'application/json;charset=utf-8',\n 'X-AK-Request-Id': 'e7f7b81a-fafd-4031-8964-00376ae24d07',\n 'X-AK-Company-Id': '90136229927150080',\n 'X-AK-Request-Source': 'erp',\n 'X-AK-ENV-KEY': 'SAAS-10',\n 'X-AK-Version': '1.0.0.0.0.023',\n 'X-AK-Zid': '109810',\n 'Content-Length': '114',\n 'Origin': 'https://erp.lingxing.com',\n 'Connection': 'keep-alive'}\n # 传递用户名和密码\n data = {'account': 'IT-Test', 'pwd': 'IT-Test'}\n data = json.dumps(data)\n self.s.post(login_url, headers=headers, data=data)\n self.auth_token = None\n\n def sql(self):\n self.connection = pymysql.connect(host='3354n8l084.goho.co', # 数据库地址\n port=24824,\n user='test_user', # 数据库用户名\n password='a123456', # 数据库密码\n db='storage', # 数据库名称\n charset='utf8',\n cursorclass=pymysql.cursors.DictCursor)\n # 使用 cursor() 方法创建一个游标对象 cursor\n self.cursor = self.connection.cursor()\n\n def sql_close(self):\n self.cursor.close()\n self.connection.close()\n\n def get_permanent_store(self, index=None, index_data=None):\n self.sql()\n if index:\n if index == 'SKU':\n sql = f\"select * from `storage`.`common_supplier` where `SKU` like '%{index_data}%'\"\n else:\n sql = f\"select * from `storage`.`common_supplier` where `品名` like '%{index_data}%'\"\n else:\n sql = \"select * from `storage`.`common_supplier`\"\n self.cursor.execute(sql)\n result = self.cursor.fetchall()\n self.sql_close()\n list_data = []\n for i in result:\n list_data.append([i['SKU'], i['品名'], i['触发库存'], i['安全库存'], i['采购基数']])\n return list_data\n\n def add_common_supplier(self, sku, safe_num, touch_num, purchase_num):\n try:\n self.sql()\n product_name = self.get_product_name(sku)\n if product_name:\n sql1 = f\"select * from `storage`.`common_supplier` where `SKU` = '{sku}'\"\n self.cursor.execute(sql1)\n result = self.cursor.fetchall()\n if result:\n return False, f'已有{sku}数据, 新增失败！'\n sql = \"insert into `storage`.`common_supplier`(`SKU`, `品名`, `触发库存`, `安全库存`, `采购基数`)\" \\\n \"values('%s', '%s', %d, %d, %d)\" % (sku, product_name, safe_num, touch_num, purchase_num)\n self.cursor.execute(sql)\n self.connection.commit()\n self.sql_close()\n return True, True\n else:\n self.sql_close()\n return False, f'没有找到{sku}相关物料信息，请检查'\n except Exception as e:\n print(e)\n return False, str(e)\n\n def upload_add_common_supplier(self, filename):\n wb = openpyxl.load_workbook(filename)\n wb_sheet = wb.active\n max_row = wb_sheet.max_row\n for i in range(max_row, 0, -1):\n cell_value1 = wb_sheet.cell(row=i, column=1).value\n if cell_value1:\n max_row = i\n break\n list_error = []\n message_data = '新增成功'\n for i in range(2, max_row+1):\n sku = wb_sheet.cell(row=i, column=1).value\n safe_num = wb_sheet.cell(row=i, column=2).value\n touch_num = wb_sheet.cell(row=i, column=3).value\n purchase_num = wb_sheet.cell(row=i, column=4).value\n msg, message = self.add_common_supplier(sku, int(safe_num), int(touch_num), int(purchase_num))\n list_error.append([sku, message])\n if msg:\n continue\n else:\n message_data = '新增失败'\n ws = openpyxl.Workbook()\n ws_sheet = ws.active\n ws_sheet.append(['SKU', '状态'])\n for i in list_error:\n ws_sheet.append(i)\n time_now = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n path = f'./static/工厂常备物料管理/工厂常备物料新增详情_{time_now}.xlsx'\n file = f'../工厂常备物料管理/工厂常备物料新增详情_{time_now}.xlsx'\n filename = f'工厂常备物料新增详情_{time_now}'\n download_name = \"工厂常备物料新增详情\"\n ws.save(path)\n return file, filename, download_name, message_data\n\n def delete_common_supplier(self, delete_data):\n try:\n self.sql()\n if delete_data.find('GCWL') >= 0:\n sql1 = f\"select * from `storage`.`common_supplier` where `SKU` = '{delete_data}'\"\n self.cursor.execute(sql1)\n result = self.cursor.fetchall()\n if result:\n sql2 = \"DELETE FROM `storage`.`common_supplier` WHERE `SKU` = '%s'\" % delete_data\n else:\n return False, f'没有找到{delete_data}相关物料信息，请检查'\n else:\n sql1 = f\"select * from `storage`.`common_supplier` where `SKU` = '{delete_data}'\"\n self.cursor.execute(sql1)\n result = self.cursor.fetchall()\n if result:\n sql2 = \"DELETE FROM `storage`.`common_supplier` WHERE `品名` = '%s'\" % delete_data\n else:\n return False, f'没有找到{delete_data}相关物料信息，请检查'\n self.cursor.execute(sql2)\n self.connection.commit()\n self.sql_close()\n return True, True\n except Exception as e:\n print(e)\n return False, str(e)\n\n def upload_delete_common_supplier(self, filename):\n wb = openpyxl.load_workbook(filename)\n wb_sheet = wb.active\n max_row = wb_sheet.max_row\n for i in range(max_row, 0, -1):\n cell_value1 = wb_sheet.cell(row=i, column=1).value\n if cell_value1:\n max_row = i\n break\n list_error = []\n message_data = '删除成功'\n for i in range(2, max_row + 1):\n delete_data = wb_sheet.cell(row=i, column=1).value\n msg, message = self.delete_common_supplier(delete_data)\n list_error.append([delete_data, message])\n if msg:\n continue\n else:\n message_data = '删除失败'\n ws = openpyxl.Workbook()\n ws_sheet = ws.active\n ws_sheet.append(['SKU/品名', '状态'])\n for i in list_error:\n ws_sheet.append(i)\n time_now = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n path = f'./static/工厂常备物料管理/工厂常备物料删除详情_{time_now}.xlsx'\n file = f'../工厂常备物料管理/工厂常备物料删除详情_{time_now}.xlsx'\n filename = f'工厂常备物料删除详情_{time_now}'\n download_name = \"工厂常备物料删除详情\"\n ws.save(path)\n return file, filename, download_name, message_data\n\n def update_common_supplier(self, list_data):\n try:\n list_sql = self.get_permanent_store()\n dict_sql = {}\n dict_data = {}\n for i in list_sql:\n dict_sql[i[0]] = i\n list_data.pop(0)\n for i in list_data:\n dict_data[i[0]] = i\n self.sql()\n flag = 0\n for i in dict_data:\n if int(dict_data[i][2]) == dict_sql[i][2] and int(dict_data[i][3]) == dict_sql[i][3] and int(dict_data[i][4]) == dict_sql[i][4]:\n continue\n else:\n flag = 1\n sql = \"update `storage`.`common_supplier` set `触发库存` = %d, `安全库存` = %d, `采购基数` = %d \" \\\n \"where `SKU` = '%s'\" % (int(dict_data[i][2]), int(dict_data[i][3]), int(dict_data[i][4]), i)\n self.cursor.execute(sql)\n self.connection.commit()\n self.sql_close()\n if flag:\n return True, True\n else:\n return False, '当前没有要修改的物料'\n except Exception as e:\n print(e)\n return False, str(e)\n\n def upload_update_common_supplier(self, filename):\n wb = openpyxl.load_workbook(filename)\n wb_sheet = wb.active\n max_row = wb_sheet.max_row\n for i in range(max_row, 0, -1):\n cell_value1 = wb_sheet.cell(row=i, column=1).value\n if cell_value1:\n max_row = i\n break\n list_error = []\n list_sql = self.get_permanent_store()\n dict_sql = {}\n for i in list_sql:\n dict_sql[i[0]] = i\n self.sql()\n message_data = '修改成功'\n for i in range(2, max_row + 1):\n sku = wb_sheet.cell(row=i, column=1).value\n update_sale_num = dict_sql[sku][2]\n update_touch_num = dict_sql[sku][3]\n update_purchase_num = dict_sql[sku][4]\n safe_num = wb_sheet.cell(row=i, column=2).value\n touch_num = wb_sheet.cell(row=i, column=3).value\n purchase_num = wb_sheet.cell(row=i, column=4).value\n if safe_num:\n update_sale_num = int(safe_num)\n if touch_num:\n update_touch_num = int(touch_num)\n if purchase_num:\n update_purchase_num = int(purchase_num)\n msg, message = self.update_common_supplier_sql(sku, update_sale_num, update_touch_num, update_purchase_num)\n if msg:\n continue\n else:\n message_data = '修改失败'\n list_error.append([sku, message])\n self.connection.commit()\n self.sql_close()\n ws = openpyxl.Workbook()\n ws_sheet = ws.active\n ws_sheet.append(['SKU', '状态'])\n for i in list_error:\n ws_sheet.append(i)\n time_now = datetime.datetime.now().strftime(\"%Y%m%d%H%M%S\")\n path = f'./static/工厂常备物料管理/工厂常备物料修改详情_{time_now}.xlsx'\n file = f'../工厂常备物料管理/工厂常备物料修改详情_{time_now}.xlsx'\n filename = f'工厂常备物料修改详情_{time_now}'\n download_name = \"工厂常备物料修改详情\"\n ws.save(path)\n return file, filename, download_name, message_data\n\n def update_common_supplier_sql(self, sku, safe_num, touch_num, purchase_num):\n try:\n sql = \"update `storage`.`common_supplier` set `触发库存` = %d, `安全库存` = %d, `采购基数` = %d \" \\\n \"where `SKU` = '%s'\" % (safe_num, touch_num, purchase_num, sku)\n self.cursor.execute(sql)\n return True, True\n except Exception as e:\n print(e)\n return False, str(e)\n\n def get_product_name(self, sku):\n sql = \"select * from `data_read`.`product_id` where `SKU` = '%s'\" % sku\n self.cursor.execute(sql)\n result = self.cursor.fetchall()\n if result:\n return result[0]['品名']\n else:\n return False","repo_name":"xcc0418/html_windows","sub_path":"permanent_store.py","file_name":"permanent_store.py","file_ext":"py","file_size_in_byte":12292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35306430872","text":"# Testing lib to ensure no invalid unicode is inside of code files, obfuscating malware\n# Ultrabear 2022\n\nimport string\nimport glob\nimport sys\n\nfrom typing import Final, List\n\n# star emoji because of sonnet_cfg.py\n# kusa rune because of funny joke\nVALID_FCHARS: Final = set(string.printable + \"\\u2b50\" + \"\\u8349\")\n\n\ndef assertfile(f: str, ferrors: List[str]) -> None:\n with open(f, \"r\") as fp:\n for line, linev in enumerate(fp):\n for col, v in enumerate(linev):\n if not v in VALID_FCHARS:\n ferrors.append(f\"{f}:{line+1}:{col+1}: Unicode rune {hex(ord(v))} is not in allowed pool\")\n\n\ndef main() -> int:\n\n ferrors: List[str] = []\n\n for f in glob.iglob(\"**/*.py\", recursive=True):\n assertfile(f, ferrors)\n\n if ferrors:\n print(\"\\n\".join(ferrors))\n return 1\n\n return 0\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","repo_name":"Sonnet-Discord/sonnet-py","sub_path":"build_tools/unisafe.py","file_name":"unisafe.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"20045108702","text":"# -*- coding:utf-8 -*-\n\nimport requests\nfrom pyquery import PyQuery as pq\nimport hashlib\nimport pymysql\nfrom sqlalchemy import create_engine\nimport pandas as pd\nimport logging\nimport random\nimport json\nfrom config import *\nimport re\n\n\ndef get_cities():\n \"\"\"城市名称-拼音简写对照字典\"\"\"\n doc = pq(requests.get('https://www.meituan.com/changecity/').text)\n a_lists = doc('.cities a').items()\n cities = {}\n [cities.update({a.text(): a.attr('href').replace('.', '/').split('/')[2]}) for a in a_lists]\n print(cities)\n with open('./utils/cities.json', 'w', encoding='utf-8') as f:\n f.write(json.dumps(cities, indent=2, ensure_ascii=False))\n\ndef get_uuid():\n \"\"\"获取uuid\"\"\"\n url = 'https://bj.meituan.com/meishi/'\n # url = \"http://localhost:8050/render.html?url=https://bj.meituan.com/meishi/&wait=5\"\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36\"\n }\n res = requests.get(url, headers=headers).text\n uuid = re.findall(r'\"uuid\":\"(.*?)\"', res, re.S)[0]\n with open('./utils/uuid.log', 'w') as f:\n f.write(uuid)\n\ndef save(data):\n \"\"\"存储数据\"\"\"\n engine = create_engine('mysql+pymysql://{}:{}@{}:{}/{}?charset=utf8'.format(USER, PASS, HOST, PORT, DB))\n connect = engine.connect()\n try:\n df = pd.DataFrame(data, index=[0])\n df.to_sql(name=TABLE, con=connect, if_exists='append', index=False)\n except Exception as e:\n logging.error(\"\\nError: %s, Please check the error.\\n\" % e.args)\n _ = e\n\ndef get_md5(url):\n \"\"\"md5处理\"\"\"\n if isinstance(url, str):\n url = url.encode('utf-8')\n m = hashlib.md5()\n m.update(url)\n return m.hexdigest()\n\ndef xdaili_proxy():\n results = requests.get(url=API).json()['RESULT']\n agents = [\"http://{}:{}\".format(res['ip'], res['port']) for res in results]\n proxies = {\n \"http\": random.choice(agents),\n \"https\": random.choice(agents)\n }\n return proxies\n\ndef abuyun_proxy():\n proxyMeta = \"http://%(user)s:%(pass)s@%(host)s:%(port)s\" % {\n \"host\": PROXY_HOST,\n \"port\": PROXY_PORT,\n \"user\": PROXY_USER,\n \"pass\": PROXY_PASS,\n }\n proxies = {\n \"http\": proxyMeta,\n \"https\": proxyMeta,\n }\n return proxies\n\nif __name__ == '__main__':\n get_cities()\n get_uuid()","repo_name":"Northxw/Meituan","sub_path":"common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":2400,"program_lang":"python","lang":"en","doc_type":"code","stars":97,"dataset":"github-code","pt":"3"}
+{"seq_id":"16775375956","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport torch\nimport torch.nn as nn\nfrom torch.nn.parameter import Parameter\nfrom torch.autograd import Variable\n\nimport pickle\nimport pdb\nfrom utils import length2mask\n\nclass RNNModel(nn.Module):\n \"\"\"Container module with an encoder, a recurrent module, and a decoder.\"\"\"\n\n def __init__(self, ntoken, ninp, nhid, nlayers, decoder, dropout=0.5):\n super(RNNModel, self).__init__()\n self.ntoken = ntoken\n self.drop = nn.Dropout(dropout)\n self.encoder = nn.Embedding(ntoken, ninp)\n self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout, batch_first=True)\n\n self.decoder = decoder\n\n self.init_weights()\n\n self.nhid = nhid\n self.nlayers = nlayers\n\n def init_weights(self):\n initrange = 0.1\n self.encoder.weight.data.uniform_(-initrange, initrange)\n self.decoder.init_weights()\n\n def forward(self, input, length=None):\n emb = self.drop(self.encoder(input))\n output, hidden = self.rnn(emb)\n output = self.drop(output)\n return output\n\n def loss(self, data):\n\n # forward rnn\n input, target, length = data\n rnn_output = self(input, length)\n\n # discard the pad\n mask = length2mask(length)\n rnn_output = rnn_output.masked_select(\n mask.unsqueeze(dim=2).expand_as(rnn_output)\n ).view(-1, self.nhid)\n target = target.masked_select(mask)\n\n # forward decoder and calculate loss\n decoder_loss = self.decoder.forward_with_loss(rnn_output, target)\n\n return decoder_loss\n\n def forward_all(self, data, length):\n output = self(data, length)\n return self.decoder.forward_all(output, length)\n\n","repo_name":"sa1ka/pytorch_language_model","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"80192161","text":"a=float(input('a='))\nb=float(input('b='))\nc=float(input('c='))\nmax=a\nmin=b\nif maxc:\n max=b\n if c>a: \n min=a\n else: \n min=c\n else: \n max=c\n min=a\nelse: \n if b>c:\n max= a \n min=c\n else: \n if a>c: \n max=a\n min =c\n else: \n max=c \n min=a\nprint('SLN=',max,sep=\"\")\nprint('SBN=',min,sep=\"\")\n ","repo_name":"minhlateame/cosolaptrinh6_21","sub_path":"Chuong3/Bai Tap Slide/21_bai3,1.py","file_name":"21_bai3,1.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11780898773","text":"from pydantic import BaseModel, Field\r\nfrom datetime import datetime\r\n\r\nfrom app.domain.product_consult import ProductConsult\r\n\r\nclass ProductConsultReadModel(BaseModel):\r\n \"\"\"ProductConsultReadModel represents data structure as a read model.\"\"\"\r\n\r\n id: str = Field(\r\n example=\"uuid\"\r\n )\r\n product_id: str = Field(\r\n example=\"uuid\"\r\n )\r\n created_at: datetime = Field(example='2023-15-04T08:23:45+00:00')\r\n updated_at: datetime = Field(example='2023-15-04T08:23:45+00:00')\r\n\r\n class Config:\r\n orm_mode = True\r\n\r\n @staticmethod\r\n def from_entity(product_consult: ProductConsult) -> \"ProductConsultReadModel\":\r\n return ProductConsultReadModel(\r\n id=str(product_consult.id),\r\n product_id=str(product_consult.product_id),\r\n created_at=product_consult.created_at,\r\n updated_at=product_consult.updated_at,\r\n )\r\n","repo_name":"dani2898/zebrands-challenge","sub_path":"app/usecase/product_consult/product_consult_query_model.py","file_name":"product_consult_query_model.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"19238245802","text":"\"\"\" Factory methods to create N-D panels \"\"\"\n\nimport pandas.lib as lib\n\n\ndef create_nd_panel_factory(klass_name, axis_orders, axis_slices, slicer, axis_aliases=None, stat_axis=2,ns=None):\n \"\"\" manufacture a n-d class:\n\n parameters\n ----------\n klass_name : the klass name\n axis_orders : the names of the axes in order (highest to lowest)\n axis_slices : a dictionary that defines how the axes map to the sliced axis\n slicer : the class representing a slice of this panel\n axis_aliases: a dictionary defining aliases for various axes\n default = { major : major_axis, minor : minor_axis }\n stat_axis : the default statistic axis\n default = 2\n het_axis : the info axis\n\n\n returns\n -------\n a class object reprsenting this panel\n\n\n \"\"\"\n\n # if slicer is a name, get the object\n if isinstance(slicer, basestring):\n import pandas\n try:\n slicer = getattr(pandas, slicer)\n except:\n raise Exception(\"cannot create this slicer [%s]\" % slicer)\n\n # build the klass\n ns = {} if not ns else ns\n klass = type(klass_name, (slicer,), ns)\n\n # add the class variables\n klass._AXIS_ORDERS = axis_orders\n klass._AXIS_NUMBERS = dict([(a, i) for i, a in enumerate(axis_orders)])\n klass._AXIS_ALIASES = axis_aliases or dict()\n klass._AXIS_NAMES = dict([(i, a) for i, a in enumerate(axis_orders)])\n klass._AXIS_SLICEMAP = axis_slices\n klass._AXIS_LEN = len(axis_orders)\n klass._default_stat_axis = stat_axis\n klass._het_axis = 0\n klass._info_axis = axis_orders[klass._het_axis]\n\n klass._constructor_sliced = slicer\n\n # add the axes\n for i, a in enumerate(axis_orders):\n setattr(klass, a, lib.AxisProperty(i))\n\n #### define the methods ####\n def __init__(self, *args, **kwargs):\n if not (kwargs.get('data') or len(args)):\n raise Exception(\n \"must supply at least a data argument to [%s]\" % klass_name)\n if 'copy' not in kwargs:\n kwargs['copy'] = False\n if 'dtype' not in kwargs:\n kwargs['dtype'] = None\n self._init_data(*args, **kwargs)\n klass.__init__ = __init__\n\n def _get_plane_axes(self, axis):\n\n axis = self._get_axis_name(axis)\n index = self._AXIS_ORDERS.index(axis)\n\n planes = []\n if index:\n planes.extend(self._AXIS_ORDERS[0:index])\n if index != self._AXIS_LEN:\n planes.extend(self._AXIS_ORDERS[index + 1:])\n\n return [getattr(self, p) for p in planes]\n klass._get_plane_axes = _get_plane_axes\n\n def _combine(self, other, func, axis=0):\n if isinstance(other, klass):\n return self._combine_with_constructor(other, func)\n return super(klass, self)._combine(other, func, axis=axis)\n klass._combine = _combine\n\n def _combine_with_constructor(self, other, func):\n\n # combine labels to form new axes\n new_axes = []\n for a in self._AXIS_ORDERS:\n new_axes.append(getattr(self, a) + getattr(other, a))\n\n # reindex: could check that everything's the same size, but forget it\n d = dict([(a, ax) for a, ax in zip(self._AXIS_ORDERS, new_axes)])\n d['copy'] = False\n this = self.reindex(**d)\n other = other.reindex(**d)\n\n result_values = func(this.values, other.values)\n\n return self._constructor(result_values, **d)\n klass._combine_with_constructor = _combine_with_constructor\n\n # set as NonImplemented operations which we don't support\n for f in ['to_frame', 'to_excel', 'to_sparse', 'groupby', 'join', 'filter', 'dropna', 'shift']:\n def func(self, *args, **kwargs):\n raise NotImplementedError\n setattr(klass, f, func)\n\n # add the aggregate operations\n klass._add_aggregate_operations()\n\n return klass\n","repo_name":"miniBloq/v0.83","sub_path":"source/Bin/Minibloq/lang/PPythonWin/v2.7.5.1/App/Lib/site-packages/pandas/core/panelnd.py","file_name":"panelnd.py","file_ext":"py","file_size_in_byte":3932,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"3"}
+{"seq_id":"6087863476","text":"n = 1 # max distance (in r unit)\nr = 0.1 # hex side\nh = 0.01 # hex thickness\nf = 1.6 # focal length\np = 100 # precision in parts of 1\ne = 0.0004 # epsilon margin\n\ntrig_h = 1.5 * h # triangle walls height\nprimary_t = 0.0008\n\nsupport_secondary_is_newton = True\n\nclip_depth = h - 0.002\nclip_height = 0.5 * trig_h\nclip_thickness = 0.0015\nclip_e = 0.001","repo_name":"DethCount/hexcope","sub_path":"hyperparameters.py","file_name":"hyperparameters.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12858122632","text":"import socket\nimport random\nimport pickle\nfrom base64 import b64encode\nfrom helpers import *\n\n\ndef estable_connection() -> socket.socket:\n\tsock = socket.socket()\n\tsock.bind((\"\", 48777))\n\tsock.listen(1)\n\tprint(\"Waiting for Bob...\")\n\tconnection, _ = sock.accept()\n\tprint(\"Bob connected!\")\n\treturn connection\n\n\ndef main():\n\tconnection = estable_connection()\n\t\n\tprint(\"Generating your key...\")\n\talice_key = bytes(random.getrandbits(8) for _ in range(256))\n\t\n\tprint(f\"Your base64(key): {b64encode(alice_key).decode()}\")\n\t\n\tprint(\"Waiting encrypted deck from Bob...\")\n\tbytestream = read_message(connection)\n\tencrypted_deck = pickle.loads(bytestream)\n\t\n\tprint(\"Choosing 10 random cards...\")\n\tchosen_cards = random.sample(encrypted_deck, 10)\n\tprint(\"Chosen: {}\".format(chosen_cards))\n\t\n\tprint(\"Sending first 5 cards to Bob...\")\n\tbytestream = pickle.dumps(chosen_cards[:5])\n\tsend_message(connection, bytestream)\n\t\n\tprint(\"Encrypting second 5 cards using your key...\")\n\talice_encrypted_cards = list(map(lambda x: transform(x, alice_key), chosen_cards[5:]))\n\tprint(\"Your encrypted cards: {}\".format(alice_encrypted_cards))\n\t\n\tprint(\"Sending your encrypted cards to Bob...\")\n\tbytestream = pickle.dumps(alice_encrypted_cards)\n\tsend_message(connection, bytestream)\n\t\n\tprint(\"Waiting from Bob decrypted cards...\")\n\tbytestream = read_message(connection)\n\talice_encrypted_cards = pickle.loads(bytestream)\n\tprint(\"Your encrypted cards: {}\".format(alice_encrypted_cards))\n\talice_cards = list(map(lambda x: transform(x, alice_key), alice_encrypted_cards))\n\talice_cards = list(map(lambda x: x.decode(), alice_cards))\n\tprint(\"Your decrypted cards: {}\".format(alice_cards))\n\t\n\tbytestream = pickle.dumps((alice_cards, alice_key))\n\tsend_message(connection, bytestream)\n\t\n\tbytestream = read_message(connection)\n\tbob_cards, bob_key = pickle.loads(bytestream)\n\tprint(f\"Bob's cards: {bob_cards}\\nBob's base64(key): {b64encode(bob_key).decode()}\")\n\t\n\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"kranonetka/PyScripts","sub_path":"Mental poker/Alice.py","file_name":"Alice.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21787152700","text":"import time\nimport json\nfrom threading import *\nimport concurrent.futures\n\nMAX_DATA_SIZE = 1000000000\nMAX_VAL_SIZE = 16384\n\n\nclass DataStore:\n def __init__(self):\n self.datastore = {}\n\n # Create operation\n # Use syntax : datastore_obj.create(key, value, time_to_live) where time_to_live is optional and is in seconds\n def create(self, key, value, time_to_live=0):\n t1 = Thread(target=(self.create_utility), args=(\n key, value, time_to_live))\n t1.daemon = True\n t1.start()\n\n # Utility method for performing create operation\n def create_utility(self, key, value, time_to_live=0):\n if key in self.datastore:\n print('error: This key already exists.')\n else:\n if len(self.datastore) < MAX_DATA_SIZE and len(value) < MAX_VAL_SIZE:\n if time_to_live == 0:\n data = {'value': value, 'time_to_live': time_to_live}\n else:\n data = {'value': value,\n 'time_to_live': time.time() + time_to_live}\n if len(key) <= 32:\n self.datastore[key] = data\n # output to file\n with open(\"datastore.json\", \"w\") as outfile:\n outfile.write(json.dumps(self.datastore))\n else:\n print('The keys must be 32 characters in length')\n else:\n print('error: Memory limit exceeded')\n\n # Read operation\n # Use syntax : datastore_obj.read(key)\n def read(self, key):\n # Returned value from the utility method is to be returned by this read() method\n with concurrent.futures.ThreadPoolExecutor() as executor:\n future = executor.submit(self.read_utility, key)\n return future.result()\n\n # Utility method for performing read operation\n def read_utility(self, key):\n try:\n with open(\"datastore.json\", \"r\") as openfile:\n self.datastore = json.load(openfile)\n except Exception:\n print('File not Found.')\n if key not in self.datastore:\n print('error: Given key does not exist in datastore')\n return\n else:\n data = self.datastore[key]\n if data['time_to_live'] != 0:\n if time.time() < data['time_to_live']:\n return json.dumps({key: self.datastore[key]['value']})\n else:\n print('error: Time of key ' + key + ' has expired')\n return\n else:\n data = self.datastore[key]\n return json.dumps({key: self.datastore[key]['value']})\n\n # Delete operation\n # Use syntax : datastore_obj.delete(key)\n def delete(self, key):\n t3 = Thread(target=(self.delete_utility),\n args=(key,))\n t3.daemon = True\n t3.start()\n\n # Utility method for performing delete operation\n def delete_utility(self, key):\n try:\n with open(\"datastore.json\", \"r\") as openfile:\n self.datastore = json.load(openfile)\n except Exception:\n print('File not Found.')\n if key not in self.datastore:\n print('error: Given key does not exist in datastore')\n else:\n data = self.datastore[key]\n if data['time_to_live'] != 0:\n if time.time() < data['time_to_live']:\n del self.datastore[key]\n with open(\"datastore.json\", \"w\") as outfile:\n outfile.write(json.dumps(self.datastore))\n print('Success : The record with key ' +\n key + ' is successfully deleted.')\n else:\n print('error: Time of key ' + key + ' has expired')\n else:\n del self.datastore[key]\n with open(\"datastore.json\", \"w\") as outfile:\n outfile.write(json.dumps(self.datastore))\n print('Success : The record with key ' +\n key + ' is successfully deleted.')\n\n # Additional Update / Modify operation within its expiry time\n # Use syntax : datastore_obj.update(key, value)\n\n def update(self, key, value):\n t4 = Thread(target=(self.update_utility), args=(\n key, value))\n t4.daemon = True\n t4.start()\n\n def update_utility(self, key, value):\n try:\n with open(\"datastore.json\", \"r\") as openfile:\n self.datastore = json.load(openfile)\n except Exception:\n print('File not Found.')\n if key not in self.datastore:\n print('error: Given key does not exist in datastore')\n else:\n data = self.datastore[key]\n if data['time_to_live'] != 0:\n if time.time() < data['time_to_live']:\n self.datastore[key]['value'] = value\n print('Success : The record with key ' +\n key + ' is successfully updated.')\n with open(\"datastore.json\", \"w\") as outfile:\n outfile.write(json.dumps(self.datastore))\n # After updation, read() is called to display the updated value\n self.read(key)\n else:\n print('error: Time of key ' + key +\n ' has expired. So, modify operation cannot be performed.')\n else:\n self.datastore[key]['value'] = value\n print('Success : The record with key ' +\n key + ' is successfully updated.')\n with open(\"datastore.json\", \"w\") as outfile:\n outfile.write(json.dumps(self.datastore))\n # After updation, read() is called to display the updated value\n self.read(key)\n","repo_name":"smv1999/File-Based-Key-Value-Datastore","sub_path":"datastore.py","file_name":"datastore.py","file_ext":"py","file_size_in_byte":5911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6480129867","text":"import codecs\n\nfrom template import Template\nfrom template.util import Literal\nimport moban.utils as utils\n\n\nclass EngineTemplateToolkit(object):\n def __init__(self, template_dirs, extensions=None):\n self.template_dirs = template_dirs\n\n def get_template(self, template_file):\n actual_file = utils.get_template_path(\n self.template_dirs, template_file\n )\n\n with codecs.open(actual_file, \"r\",\n encoding=\"utf-8\") as template_contents:\n\n source = Literal(template_contents.read())\n\n # Template-Toolkit-Python cannot make a Template Object only\n # by passing a file_name or file_content. It uses a function\n # process() to generate the template. The process function\n # needs to have both the template file and the data of the\n # variables used in template.\n template = []\n template_object = Template()\n template.append(template_object)\n template.append(source)\n\n return template\n\n def apply_template(self, template, data, output):\n template_object = template[0]\n source = template[1]\n rendered_content = template_object.process(source, data)\n return rendered_content\n","repo_name":"Naveenaidu/moban-template-toolkit","sub_path":"moban_template_toolkit/engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17775117066","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Apr 29 09:08:03 2021\r\n\r\n@author: DELL\r\n\"\"\"\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport statistics as stats\r\nfrom scipy import stats\r\nimport statsmodels.api as sm\r\nimport math\r\ndata = np.random.normal(0, 1, 1000)\r\n\r\nclass visualization:\r\n def __init__(self):\r\n self.data = data\r\n self.mean = self.means()\r\n self.med = self.meds()\r\n self.mod = self.mods()\r\n self.max = self.maxs()\r\n self.min = self.mins()\r\n def means(self):\r\n means = np.mean(self.data)\r\n \r\n return means\r\n \r\n def meds(self):\r\n meds = np.median(self.data)\r\n return meds\r\n \r\n def mods(self):\r\n mods = stats.mode(self.data)\r\n return mods\r\n \r\n def maxs(self):\r\n maxs = np.max(self.data)\r\n return maxs\r\n \r\n def mins(self):\r\n mins = np.min(self.data)\r\n return mins\r\n \r\n def tinh_quantile(self, q):\r\n quantiles = np.quantile(self.data, q)\r\n return quantiles\r\n \r\n def quantile(self, q):\r\n percentile = self.tinh_quantile(q)\r\n return percentile\r\n \r\n def hist(self):\r\n plt.hist(self.data,20, density=2, color = 'skyblue', edgecolor = 'green')\r\n mu = 0\r\n sigma = math.sqrt(np.std(self.data))\r\n x = np.linspace(mu - 3 * sigma, mu + 3 * sigma, 100)\r\n plt.plot(x, stats.norm.pdf(x, mu, sigma), color = 'green')\r\n \r\n def qq_plot(self):\r\n qq = sm.qqplot(self.data, line = '45', color = 'y')\r\n plt.show(qq)\r\n return qq\r\n \r\n pass\r\nvi = visualization()\r\nprint(\"mean = \", vi.mean)\r\nprint(\"med = \", vi.med)\r\nprint(\"mod = \", vi.mod)\r\nprint(\"max = \", vi.max)\r\nprint(\"min = \", vi.min)\r\nprint(\"quantile 0.25 = \", vi.quantile(0.25), \"\\n\",\r\n \"quantile 0.5 = \", vi.quantile(0.5),\"\\n\" ,\r\n \"quantile 0.75 = \", vi.quantile(0.75),\"\\n\")\r\n\r\nvi.hist()\r\nvi.qq_plot()","repo_name":"lethingoctham/bt-list","sub_path":"Su_dung_truc_quan.py","file_name":"Su_dung_truc_quan.py","file_ext":"py","file_size_in_byte":1931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73031809042","text":"import random\n\nfilepath = \"words.txt\"\nf = open(filepath, 'r')\n#create word list\nwordList = f.readlines()\n# close file\nf.close()\n\n#open text file of introduction\nfilepath2 = \"introduction.txt\"\nf = open(filepath2, 'r')\nintro = f.read()\n#print intro\nprint(intro)\n#close intro file\nf.close()\n\n#variables\nwinRound = False\nvowels = ['a', 'e', 'i', 'o', 'u']\nconsonants = ['b', 'c', 'd', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'p', 'q', 'r', 's', 't', 'v', 'w', 'x', 'y', 'z']\nroundBank = 0\ntotalBank = 0\nlettersGuessed = ''\n\n#getWord function\ndef getWord():\n usedWords = set()\n global word\n global letters\n word = random.choice(wordList).strip()\n letters = len(word)\n print(f'There are ' + str(letters) + ' letters in this word.')\n usedWords.add(word)\n print(f'CHEATTTTTTTTTTTT The word is: ' + str(word))\n return word\n #use random.choice to choose a word from wordList \n #save random word but do not display \n\n#word display based on guesses\ndef displayWord(): \n displayWord = ''\n\n for i in range(0, len(word)):\n if word[i] in lettersGuessed:\n displayWord += word[i]\n else:\n displayWord += '_'\n\n print(f\"The word so far: {displayWord}\")\n\ndef checkGuess():\n lettersGuessed = lettersGuessed + guess\n wrongLetterCount = 0\n\n for letter in word:\n if letter in lettersGuessed:\n print(f'{letter}', end=\"\")\n else:\n print(\"_\", end=\"\")\n wrongLetterCount += 1\n while winRound == True or wrongLetterCount == 0:\n print('Congrats! The word was ' + word)\n roundBank += 1000\n else:\n spinWheel()\n\n\ndef cGuess(word):\n displayWord()\n wrongLetterCount = 0\n if conGuess in consonants:\n if conGuess in word:\n print(f'{conGuess}', end=\"\")\n else:\n print(\"_\", end=\"\")\n wrongLetterCount += 1\n spinWheel()\n return cGuess\n\ndef vGuess(word):\n displayWord()\n wrongLetterCount = 0\n if vowGuess in vowels:\n for letter in word:\n if letter in lettersGuessed:\n print(f'{letter}', end=\"\")\n else:\n print(\"_\", end=\"\")\n wrongLetterCount += 1\n roundBank = (roundBank - 250)\n spinWheel()\n return vGuess\n\ndef wordGuess(word):\n displayWord()\n if wGuess == word:\n print(f'{word}', end=\"\")\n print(f'Congrats! You win this round! $'+ str(1000) +' will be added to your total winnings.')\n roundBank = roundBank + 1000\n totalBank = totalBank + roundBank\n winRound = True\n if wGuess != word:\n print(f'Sorry, that is an incorrect guess.')\n return wordGuess\n\ndef roundPlay():\n getWord()\n\n#spinWheel function\ndef spinWheel():\n global conGuess\n global vowGuess\n global wGuess\n global prize\n print(f'Word chosen. Time to spin the wheel!')\n displayWord()\n #array of cash values with 0 as bankrupt and -1 as lose a turn\n wheelLand = [-1, 0, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900]\n prize = random.choice(wheelLand)\n #use random.int to choose a segment from array of cash values, bankrupt, and lose turn\n #if bankrupt or lose a turn\n while prize == -1:\n print(f'Sorry, the wheel landed on Lose Turn.')\n spinWheel()\n while prize == 0:\n roundBank == 0\n print(f'Oh no! The wheel landed on BANKRUPT.')\n spinWheel()\n while prize >= 0:\n #print random segment\n print(f'The wheel landed on ' + str(prize) + '. ')\n userChoice = input('What would you like to do? Guess a consonant, enter 1; Buy a vowel, enter 2; Guess the word, enter 3. ')\n if userChoice <= str(0):\n print(f'Try again.')\n if userChoice >= str(4):\n print(f'Try again.')\n #consonant\n if userChoice == str(1):\n conGuess = input('Guess a consonant: ')\n if conGuess not in consonants:\n print(f'Please enter a consonant.')\n if conGuess in consonants:\n cGuess(conGuess)\n #vowel\n if userChoice == str(2):\n vowGuess = input('Ok, vowels cost $' + str(250) + '. What vowel would you like to buy? ')\n if vowGuess not in vowels:\n print(f'Please enter a vowel.')\n if vowGuess in vowels:\n vGuess(vowGuess)\n #word\n if userChoice == str(3):\n wGuess = input('Ok, what word would you like to guess? ')\n wordGuess(wGuess)\n\nroundPlay()\nspinWheel()\n\n#checkGuess function\n #use guessing game format for displaying blanks and guessed letters using input\n #if guess is not in word, break turn\n #if guess is in word, continue turn\n #if guess is the word, totalBank = roundBank + 1000, win_game = True\n\n#roundPlay function\n #getWord function\n #display blanks from random word\n #totalBank = []\n #roundBank = []\n #display totalBank\n #display roundBank\n #while loop while win_game = False\n #spinWheel\n #save and display random segment\n #input what would you like to do? consonant 1, vowel 2, guess 3 menu\n #if input = 1:\n #input enter a consonant\n #checkGuess\n #if input = 2:\n #roundBank = roundBank - 250\n #if roundBank < 0:\n #print Sorry you can't buy a vowel\n #else:\n #input enter a vowel\n #checkGuess\n #if input = 3:\n #input guess the word\n #checkGuess\n #else:\n #Sorry, next player turn.\n #else if win_game = True\n #print congratulations\n\n#finalPlay function\n #getWord function\n #max bank player plays\n #display blanks from random word minus r, s, t, l, n, and e\n #display totalBank\n #while loop while win_final = False\n #input give me 3 consonants and 1 vowel\n #if input = consonant:\n #checkGuess\n #else if win_final = True\n #print congratulations \n #print cash prize $1000\n #print totalBank + $1000","repo_name":"ahalverson1/WoFAssess","sub_path":"old-code/WoF-game-code.py","file_name":"WoF-game-code.py","file_ext":"py","file_size_in_byte":6246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11373352924","text":"import collections\n\ninput = [line.strip(\"\\n\") for line in open(\"input\")]\n\ngraph = collections.defaultdict(list)\n\nfor i in input:\n nodes = i.strip().split()\n\n par = int(nodes[0])\n childs = map(lambda x: int(x.strip(',')), nodes[2:])\n\n for child in childs:\n graph[par].append(child)\n graph[child].append(par)\n\nCONNECTED = set()\ngroups = 0\n\nfor i in range(2000):\n if i in CONNECTED:\n continue\n groups += 1\n Q = [i]\n while Q:\n node = Q.pop()\n for ch in graph[node]:\n if ch not in CONNECTED:\n CONNECTED.add(ch)\n Q.append(ch)\n\nprint(groups)\n\n\n","repo_name":"kolharsam/aoc","sub_path":"2017/12/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"35354638285","text":"from matalg.models.markov import MarkovModel\nfrom matalg.parsers.markov import MarkovParser\n\ndef test_MarkovParser():\n parser = MarkovParser()\n\n source = \"\"\"\n for a in V\n c in V\n #, $ not in V\n\n #a -> a#\n # -> $\n c -> #\n ->.\n \"\"\"\n\n model = parser.parse_source(source)\n print(model.context)\n assert len(model._MarkovModel__rules) == 4\n assert len(model.context.alphabet) == 1\n","repo_name":"RullDeef/MathAlgorithms","sub_path":"tests/parsers/test_markov.py","file_name":"test_markov.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"12718581664","text":"import subprocess\r\nfrom os import path, getenv, chdir\r\nfrom ast import literal_eval\r\nfrom configparser import ConfigParser\r\nfrom urllib import request\r\nfrom glob import glob\r\nfrom sys import argv\r\n\r\nchdir(path.dirname(__file__))\r\nUSERPROFILE = getenv('USERPROFILE')\r\nAPPDATA = getenv('APPDATA')\r\n\r\nRepository = 'https://raw.githubusercontent.com/Aetopia/Nite'\r\nif path.exists('Options.ini') is False:\r\n\trequest.urlretrieve(f'{Repository}/main/Options.ini', 'Options.ini')\r\nOptions = ConfigParser()\r\nOptions.read('Options.ini')\r\n\r\nif len(argv) >= 3:\r\n\tVersion = argv[1]\r\n\tServer = f'--server {argv[2]}'\r\nelif len(argv) >= 2:\r\n\tVersion = argv[1]\t\r\n\tServer = ''\r\nelif len(argv) == 1:\r\n\texit(1)\t\r\n\r\nDirectory = Options['Directories'][Version]\r\nArguments = Options['Java']['Arguments']\r\nJRE = Options['Java']['JRE']\r\nCosmetics = literal_eval(Options['Lunar Client']['Cosmetics'])\r\nAssets = literal_eval(Options['Nite']['Assets'])\r\nVerbose = literal_eval(Options['Nite']['Verbose'])\r\n\r\nif Version == \"1.7\":\r\n AssetIndex=\"1.7.10\"\r\nelse:\r\n AssetIndex=Version\r\n\r\nif Directory.lower() == 'default':\r\n\tDirectory = f'{APPDATA}/.minecraft'\r\n\r\nif JRE.lower() == 'lunar':\r\n\tJRE = glob(f'{USERPROFILE}/.lunarclient/jre\\{Version}/*zulu*/bin/javaw.exe')[0]\r\n\r\nif Cosmetics is True:\r\n Cosmetics = f'{USERPROFILE}/lunarclient/textures'\r\nelse:\r\n Cosmetics = \"\"\r\n\r\nif Assets is True:\r\n\tAssets = f'{APPDATA}/.minecraft/assets'\r\nelse:\r\n\tAssets = f'{Directory}/assets'\t\t\r\n\r\nLibraries = ' '.join((\r\n\t'--add-modules jdk.naming.dns --add-exports',\r\n\t'jdk.naming.dns/com.sun.jndi.dns=java.naming',\r\n\tf'-Djna.boot.library.path=\"{USERPROFILE}/.lunarclient/offline/{Version}/natives\"',\r\n\t'-Dlog4j2.formatMsgNoLookups=true',\r\n\t'--add-opens java.base/java.io=ALL-UNNAMED'\r\n\t))\r\n\r\nJars = ''\r\nfor Jar in ('lunar-assets-prod-1-optifine.jar',\r\n\t\t\t'lunar-assets-prod-2-optifine.jar',\r\n\t\t\t'lunar-assets-prod-3-optifine.jar',\r\n\t\t\t'lunar-libs.jar',\r\n\t\t\t'lunar-prod-optifine.jar',\r\n\t\t\tpath.split(glob(f'{USERPROFILE}/.lunarclient/offline/{Version}/*OptiFine_*')[0])[1], \r\n\t\t\t'vpatcher-prod.jar'\r\n\t\t\t):\r\n\tJars += f'\"{USERPROFILE}/.lunarclient/offline/{Version}/{Jar}\";'\r\nJars = Jars[:len(Jars)-1]\r\n\r\nGame = ' '.join((\r\n\tf'-Djava.library.path=\"{USERPROFILE}/.lunarclient/offline/{Version}/natives\"', \r\n\t\"-XX:+DisableAttachMechanism\",\r\n\tf'-cp {Jars}',\r\n\t'com.moonsworth.lunar.patcher.LunarMain',\r\n\tf'--version {Version}',\r\n\t'--accessToken 0', \r\n\tf'--assetIndex {AssetIndex}',\r\n\t'--userProperties {}',\r\n\tf'--gameDir \"{Directory}\"',\r\n\t'--width 854 --height 480',\r\n\tf'--texturesDir \"{Cosmetics}\"',\r\n\tf'--assetsDir \"{Assets}\"'\r\n\t))\r\n\r\nCommand = f'{JRE} {Libraries} {Arguments} {Game} {Server}'\r\nif Verbose is True:\r\n\tprint(f\"\"\"---Verbose---\r\nVersion: {Version}\r\nDirectory: {path.abspath(Directory)}\r\nJRE: {path.abspath(JRE)}\r\nArguments: {Arguments}\r\nCosmetics: {Cosmetics == f'{USERPROFILE}/lunarclient/textures'}\r\nAssets Directory: {path.abspath(Assets)}\r\n\"\"\")\r\nsubprocess.Popen(Command)\r\nexit(0)","repo_name":"Aetopia/Nite","sub_path":"Nite.py","file_name":"Nite.py","file_ext":"py","file_size_in_byte":2959,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"44022138559","text":"# -*- coding: utf-8 -*-\nimport sys\n\nsys.path.append(\"..\")\nimport common\nfrom mmail import mailBase\nimport os\nimport time\nimport socket\nimport ConfigParser\nimport pdb\nimport redis\n\n\n# /log/medusa/rawlog/20170314\n\ndef send_alter_bymail():\n pass\n\n\nclass HdfsInfo():\n def __init__(self, data, hdfs_path):\n # self.files_suffix = data['files_suffix']\n self.r = redis.Redis(host=data['redis_host'], port=data['port'], db=data['db'])\n self.expirted = data['expired']\n self.host_suffix = data['host_suffix']\n self.time_suffix = time.strftime(\"%Y-%m-%d-%H\", time.localtime())\n self.hdfs_path = hdfs_path\n\n def check_workflow(self):\n\n is_alter = False\n # 检查redis中miss_files字段是否有值，写入操作时间及结果，1=True\n check_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n self.r.set('monitor_logs_time', check_time)\n is_true, miss_file = self.get_local_miss_files()\n common.write_file_josn('/tmp/miss_file.log', miss_file)\n if is_true:\n self.r.set('miss_local_file', 1)\n\n is_alter = True\n else:\n self.r.set('miss_local_file', 0)\n\n local_file_info = self.get_local_info()\n hdfs_file_info = self.get_hdfs_info()\n miss_hdfs_file_key = \"miss_hdfs_file_%s\" % check_time\n diff_size_key = \"diff_size_%s\" % check_time\n diff_files = dict()\n miss_hdfs_file = list()\n for key in local_file_info.keys():\n if key not in hdfs_file_info.keys():\n self.r.rpush(miss_hdfs_file_key, key)\n miss_hdfs_file.append(key)\n is_alter = True\n\n if local_file_info.get(key, None) != hdfs_file_info.get(key, None):\n diff_files[key] = [local_file_info.get(key, None), hdfs_file_info.get(key, None)]\n if diff_files.keys():\n self.r.hmset(diff_size_key, diff_files)\n is_alter = True\n common.write_file_josn('/tmp/miss_hdfs_file.log', miss_hdfs_file)\n common.write_file_josn('/tmp/diff_size.log', diff_files)\n\n if is_alter:\n msg = \"find error alter mail will send\"\n common.logMsg(\"sendmail\", msg, 1)\n mail_dict = dict()\n mail_dict['mail_host'] = \"smtp.exmail.qq.com\" # 设置服务器\n mail_dict['mail_user'] = \"peng.tao@whaley.cn\" # 用户名\n mail_dict['mail_pswd'] = \"tel7206324\" # 口令\n mail_dict['mail_rece'] = list()\n mail_dict['mail_rece'].append(\"peng.tao@whaley.cn\")\n mail_dict['mail_rece'].append(\"lian.kai@whaley.cn\")\n mail_dict['mail_rece'].append('feng.jin@whaley.cn')\n\n m = mailBase(**mail_dict)\n sub = \"日志检查报警邮件\"\n content = \"本地与HDFS日志文件存在异常，详见附件！\"\n attachs = (\"/tmp/miss_file.log\", \"/tmp/miss_hdfs_file.log\", \"/tmp/diff_size.log\")\n m.sendMail(sub, content, 'plain', 'utf-8', *attachs)\n else:\n msg = \"miss not found\"\n common.logMsg(\"check\", msg, 1)\n\n def _get_local_files_info(self, hostname):\n\n files_info_key = \"bigdata_monitor_log_%s_%s\" % (hostname, self.time_suffix)\n local_files = self.r.hgetall(files_info_key)\n return local_files\n\n def get_local_info(self):\n local_file_info = dict()\n for i in range(1, int(self.host_suffix)):\n hostname = 'bigdata-extsvr-log%d' % i\n local_files = self._get_local_files_info(hostname)\n for key in local_files.keys():\n new_key = key.split('/')[-1]\n # 过滤路径\n local_file_info[new_key] = local_files[key]\n return local_file_info\n\n def get_hdfs_info(self):\n hdfs_file_info = dict()\n check_path = list()\n\n for path in self.hdfs_path:\n time_now = time.strftime(\"%Y%m%d\", time.localtime())\n time_yesterday = time.strftime(\"%Y%m%d\", time.localtime(time.time() - (3600 * 24)))\n check_path.append('%s%s' % (path, time_now))\n check_path.append('%s%s' % (path, time_yesterday))\n\n for hdfs_path in check_path:\n cmd = 'hadoop fs -ls -R %s' % hdfs_path\n is_run, out = common.run_cmd(cmd)\n if is_run:\n if out:\n for line in out.split('\\n'):\n line_values = line.split()\n if len(line_values) == 8:\n filename = line_values[7].split('/')[-1]\n hdfs_file_info[filename] = line_values[4]\n return hdfs_file_info\n\n def get_local_miss_files(self):\n miss_files_key = \"bigdata_monitor_miss_log_%s\" % self.time_suffix\n if self.r.exists(miss_files_key):\n miss_files = self.r.hgetall(miss_files_key)\n return 1, miss_files\n else:\n return 0, None\n\n\ndef main():\n config_file = sys.argv[1]\n\n sections = common.return_section(config_file)\n common_data = common.get_section_values('common', config_file)\n sections.remove('common')\n alter_info = dict()\n hdfs_path_l = list()\n for section in sections:\n data = common.get_section_values(section, config_file)\n hdfs_path = data.get('hdfs_path', None)\n hdfs_path_l.append(hdfs_path)\n check = HdfsInfo(common_data, hdfs_path_l)\n check.check_workflow()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"sydt2014/bigdata-deploy","sub_path":"modules/zabbix/script/monitor_logs_hdfs.py","file_name":"monitor_logs_hdfs.py","file_ext":"py","file_size_in_byte":5496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8077295851","text":"from copy import deepcopy\nimport math\nfrom random import shuffle\nfrom Classes.Bishop import Bishop\nfrom Classes.Board import Board\nfrom Classes.King import King\nfrom Classes.Knight import Knight\nfrom Classes.Pawn import Pawn\nfrom Classes.Player import Player\nfrom Classes.Queen import Queen\nfrom Classes.Rook import Rook\nimport UI.gameUI as gameUi\nfrom functools import lru_cache\n\n\n\nclass AiPlayer(Player):\n PAWN = 100\n KNIGHT = 320\n BISHOP = 330\n ROOK = 500\n QUEEN = 900\n KING = 20000\n\n pst = {\n\n 'BP': [[ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 78, 83, 86, 73, 102, 82, 85, 90],\n [ 7, 29, 21, 44, 40, 31, 44, 7],\n [-17, 16, -2, 15, 14, 0, 15, -13],\n [-26, 3, 10, 9, 6, 1, 0, -23],\n [-22, 9, 5, -11, -10, -2, 3, -19],\n [-31, 8, -7, -37, -36, -14, 3, -31],\n [0, 0, 0, 0, 0, 0, 0, 0]]\n\n ,'WP': [ [0 ,0 ,0 ,0 ,0 ,0 ,0 ,0], \n [-31 ,3 ,-14 ,-36 ,-37 ,-7 ,8 ,-31],\n [-19 ,3 ,-2 ,-10 ,-11 ,5 ,9 ,-22],\n [-23 ,0 ,1 ,6 ,9 ,10 ,3 ,-26],\n [-13 ,15 ,0 ,14 ,15 ,-2 ,16 ,-17],\n [ 7 ,44 ,31 ,40 ,44 ,21 ,29 ,7],\n [ 90 ,85 ,82 ,102 ,73 ,86 ,83 ,78],\n [ 0 ,0 ,0 ,0 ,0 ,0 ,0 ,0]]\n\n\n ,'BN': [[-66, -53, -75, -75, -10, -55, -58, -70 ],\n [ -3, -6, 100, -36, 4, 62, -4, -14],\n [ 10, 67, 1, 74, 73, 27, 62, -2],\n [ 24, 24, 45, 37, 33, 41, 25, 17],\n [ -1, 5, 31, 21, 22, 35, 2, 0],\n [-18, 10, 13, 22, 18, 15, 11, -14],\n [-23, -15, 2, 0, 2, 0, -23, -20],\n [-74, -23, -26, -24, -19, -35, -22, -69]]\n\n ,'WN' : [[-69 ,-22 ,-35 ,-19 ,-24 ,-26 ,-23 ,-74], \n [-20 ,-23 ,0 ,2 ,0 ,2 ,-15 ,-23],\n [-14 ,11 ,15 ,18 ,22 ,13 ,10 ,-18],\n [ 0 ,2 ,35 ,22 ,21 ,31 ,5 ,-1 ],\n [ 17 ,25 ,41 ,33 ,37 ,45 ,24 ,24 ],\n [ -2 ,62 ,27 ,73 ,74 ,1 ,67 ,10 ],\n [-14 ,-4 ,62 ,4 ,-36 ,100 ,-6 ,-3 ],\n [-70 ,-58 ,-55 ,-10 ,-75 ,-75 ,-53 ,-66]]\n\n ,'BB': [[-59, -78, -82, -76, -23,-107, -37, -50],\n [-11, 20, 35, -42, -39, 31, 2, -22],\n [ -9, 39, -32, 41, 52, -10, 28, -14],\n [ 25, 17, 20, 34, 26, 25, 15, 10],\n [ 13, 10, 17, 23, 17, 16, 0, 7],\n [ 14, 25, 24, 15, 8, 25, 20, 15],\n [ 19, 20, 11, 6, 7, 6, 20, 16],\n [ -7, 2, -15, -12, -14, -15, -10, -10]]\n\n ,'WB':[ [-10 ,-10 ,-15 ,-14 ,-12 ,-15 ,2 ,-7 ],\n [16 ,20 ,6 ,7 ,6 ,11 ,20 ,19 ],\n [15 ,20 ,25 ,8 ,15 ,24 ,25 ,14 ],\n [7 ,0 ,16 ,17 ,23 ,17 ,10 ,13 ],\n [10 ,15 ,25 ,26 ,34 ,20 ,17 ,25 ],\n [-14 ,28 ,-10 ,52 ,41 ,-32 ,39 ,-9 ],\n [-22 ,2 ,31 ,-39 ,-42 ,35 ,20 ,-11],\n [-50 ,-37 ,-107 ,-23 ,-76 ,-82 ,-78 ,-59]]\n\n ,'BR': [[35, 29, 33, 4, 37, 33, 56, 50 ],\n [55, 29, 56, 67, 55, 62, 34, 60 ],\n [19, 35, 28, 33, 45, 27, 25, 15 ],\n [0, 5, 16, 13, 18, -4, -9, -6 ],\n [-28, -35, -16, -21, -13, -29, -46, -30],\n [-42, -28, -42, -25, -25, -35, -26, -46],\n [-53, -38, -31, -26, -29, -43, -44, -53],\n [-30, -24, -18, 5, -2, -18, -31, -32]]\n\n ,'WR' :[[-32 ,-31 ,-18 ,-2 ,5 ,-18 ,-24 ,-30],\n [-53 ,-44 ,-43 ,-29 ,-26 ,-31 ,-38 ,-53],\n [-46 ,-26 ,-35 ,-25 ,-25 ,-42 ,-28 ,-42],\n [-30 ,-46 ,-29 ,-13 ,-21 ,-16 ,-35 ,-28],\n [-6 ,-9 ,-4 ,18 ,13 ,16 ,5 ,0 ],\n [15 ,25 ,27 ,45 ,33 ,28 ,35 ,19 ],\n [60 ,34 ,62 ,55 ,67 ,56 ,29 ,55 ],\n [50 ,56 ,33 ,37 ,4 ,33 ,29 ,35 ]]\n \n ,'BQ': [[ 6 , 1, -8,-104, 69, 24, 88, 26],\n [ 14 , 32, 60, -10, 20, 76, 57, 24],\n [ -2 , 43, 32, 60, 72, 63, 43, 2],\n [ 1 , -16, 22, 17, 25, 20, -13, -6],\n [-14 , -15, -2, -5, -1, -10, -20, -22],\n [-30 , -6, -13, -11, -16, -11, -16, -27],\n [-36 , -18, 0, -19, -15, -15, -21, -38],\n [-39 , -30, -31, -13, -31, -36, -34, -42]]\n\n ,'WQ' : [[-42 ,-34 ,-36 ,-31 ,-13 ,-31 ,-30 ,-39],\n [-38 ,-21 ,-15 ,-15 ,-19 ,0 ,-18 ,-36],\n [-27 ,-16 ,-11 ,-16 ,-11 ,-13 ,-6 ,-30],\n [-22 ,-20 ,-10 ,-1 ,-5 ,-2 ,-15 ,-14],\n [-6 ,-13 ,20 ,25 ,17 ,22 ,-16 ,1],\n [2 ,43 ,63 ,72 ,60 ,32 ,43 ,-2],\n [24 ,57 ,76 ,20 ,-10 ,60 ,32 ,14],\n [26 ,88 ,24 ,69 ,-104 ,-8 ,1 ,6]]\n\n ,'BK': [[ 4, 54, 47, -99, -99, 60, 83, -62],\n [-32, 10, 55, 56, 56, 55, 10, 3],\n [-62, 12, -57, 44, -67, 28, 37, -31],\n [-55, 50, 11, -4, -19, 13, 0, -49],\n [-55, -43, -52, -28, -51, -47, -8, -50],\n [-47, -42, -43, -79, -64, -32, -29, -32],\n [ -4, 3, -14, -50, -57, -18, 13, 4],\n [ 17, 30, -3, -14, 6, -1, 40, 18]]\n\n ,'WK': [[18 ,40 ,-1 ,6 ,-14 ,-3 ,30 ,17],\n [4 ,13 ,-18 ,-57 ,-50 ,-14 ,3 ,-4],\n [-32 ,-29 ,-32 ,-64 ,-79 ,-43 ,-42 ,-47],\n [-50 ,-8 ,-47 ,-51 ,-28 ,-52 ,-43 ,-55],\n [-49 ,0 ,13 ,-19 ,-4 ,11 ,50 ,-55],\n [-31 ,37 ,28 ,-67 ,44 ,-57 ,12 ,-62],\n [3 ,10 ,55 ,56 ,56 ,55 ,10 ,-32],\n [-62 ,83 ,60 ,-99 ,-99 ,47 ,54 ,4]]\n }\n\n\n\n def __init__(self,color,difficulty):\n super().__init__(color)\n self.difficulty = difficulty\n\n \n def getMove(self,game_board : Board) -> list:\n maximizing = (False if self.color == \"b\" else True)\n res = self.minimax(game_board, self.difficulty, -math.inf, math.inf, maximizing)[1]\n print(f\"chess AI playin move : {res[0]}==>{res[1]}\")\n return res\n\n #white ==> maximising player\n #black ==> minimising player\n\n #beta ==> worst possbile score for black\n #alpha==> worst possible score for white\n\n # def minimax(self,position: Board, depth: int, maximizingPlayer: bool):\n # #if the game ends at the current position or depth == 0\n # if depth == 0 or self.isGameOver(position):\n # return self.evaluatePosition(position), None\n\n # if maximizingPlayer:\n # possibleMoves = self.getAllMoves(position, \"w\")\n # maxEval = -math.inf\n # best_move = None\n # for start in possibleMoves.keys():\n # for end in possibleMoves[start]:\n # board_copy = deepcopy(position)\n # position.AiAutoPromotion = True\n # board_copy.move_piece(start, end)\n # eval = self.minimax(board_copy, depth - 1, False)[0]\n # maxEval = max(maxEval, eval)\n # if maxEval == eval:\n # best_move = [start,end]\n # return maxEval, best_move\n\n # else:\n # possibleMoves = self.getAllMoves(position, \"b\")\n # print(possibleMoves)\n # print(possibleMoves)\n # minEval = math.inf\n # best_move = None\n # for start in possibleMoves.keys():\n # for end in possibleMoves[start]:\n # board_copy = deepcopy(position)\n # position.AiAutoPromotion = True\n # board_copy.move_piece(start, end)\n # eval = self.minimax(board_copy, depth - 1, True)[0]\n # minEval = min(minEval, eval)\n # if minEval == eval:\n # best_move = [start,end]\n # return minEval, best_move\n \n # @lru_cache(maxsize=128, typed=False)\n # @lru_cache(maxsize=False, typed=False)\n def minimax(self, position: Board, depth: int,alpha, beta, maximizingPlayer: bool):\n if depth == 0 or position.isGameOver() or gameUi.ChessBoard.thread_flag == \"ENDED\":\n return self.evaluatePosition(position), None\n if maximizingPlayer:\n possibleMoves = self.getAllMoves(position, \"w\")\n maxEval = -math.inf\n best_move = None\n for start in possibleMoves.keys():\n for end in possibleMoves[start]:\n board_copy = deepcopy(position)\n board_copy.move_piece(start, end, None, True)\n eval = self.minimax(board_copy, depth - 1,alpha,beta, False)[0]\n maxEval = max(maxEval, eval)\n alpha = max(alpha, eval)\n if maxEval == eval:\n best_move = [start,end]\n if beta <= alpha:\n break\n return maxEval, best_move\n else:\n possibleMoves = self.getAllMoves(position, \"b\")\n minEval = math.inf\n best_move = None\n for start in possibleMoves.keys():\n for end in possibleMoves[start]:\n board_copy = deepcopy(position)\n board_copy.move_piece(start, end,None, True)\n eval = self.minimax(board_copy, depth - 1,alpha,beta, True)[0]\n minEval = min(minEval, eval)\n beta = min(beta, eval)\n if minEval == eval:\n best_move = [start,end]\n if beta <= alpha:\n break \n return minEval, best_move\n\n #static evaluation of a po sition\n # @lru_cache(maxsize=False, typed=False)\n \n def evaluatePosition(self,position : Board):\n res = 0\n for line in range(8):\n for column in range(8):\n if position.board[line][column] is not None:\n factor = (1 if position.board[line][column].color == \"w\" else -1)\n pstColor = ('W' if factor > 0 else 'B')\n if isinstance(position.board[line][column],Pawn):\n res += factor*(AiPlayer.PAWN + AiPlayer.pst[pstColor+'P'][line][column])\n elif isinstance(position.board[line][column],Bishop):\n res += factor*(AiPlayer.BISHOP + AiPlayer.pst[pstColor+'B'][line][column])\n elif isinstance(position.board[line][column],Rook):\n res += factor*(AiPlayer.ROOK + AiPlayer.pst[pstColor+'R'][line][column])\n elif isinstance(position.board[line][column],Knight):\n res += factor*(AiPlayer.KNIGHT + AiPlayer.pst[pstColor+'N'][line][column])\n elif isinstance(position.board[line][column],Queen):\n res += factor*(AiPlayer.QUEEN + AiPlayer.pst[pstColor+'Q'][line][column])\n elif isinstance(position.board[line][column],King):\n res += factor*(AiPlayer.KING + AiPlayer.pst[pstColor+'K'][line][column])\n return res\n\n # @lru_cache(maxsize=None, typed=False)\n def getAllMoves(self,position: Board,color):\n res = {}\n #random indexes shuflling allows for random best move selection\n lines = list(range(8))\n shuffle(lines)\n columns = list(range(8))\n shuffle(columns)\n # lines = range(8)\n # columns = range(8)\n for line in lines:\n for column in columns:\n if position.board[line][column] is not None and position.board[line][column].color == color:\n possibleEndMoves = position.board[line][column].getPossibleMoves(position.board)\n if isinstance(position.board[line][column],Pawn):\n possibleEndMoves.extend(list(position.board[line][column].getPossibleEnPassantCaptures(position.board, position.LastMovedPiece).keys()))\n if isinstance(position.board[line][column],King):\n possibleEndMoves.extend(position.getPossibleCastleMoves(position.board[line][column].color))\n temp = []\n for targetPos in possibleEndMoves:\n if not position.MoveCauseCheck((line,column),targetPos):\n temp.append(targetPos)\n if len(temp) > 0 : #can move without check ==> create an entry\n res[(line,column)] = temp\n return res","repo_name":"OtmaneDaoudi/Chessy","sub_path":"Classes/AiPlayer.py","file_name":"AiPlayer.py","file_ext":"py","file_size_in_byte":12695,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"14626918771","text":"class Solution:\n def moveZeroes(self, nums: List[int]) -> None:\n \"\"\"\n Do not return anything, modify nums in-place instead.\n \"\"\"\n \n if len(nums) == 1:\n return nums\n \n iptr = 0\n jptr = 0\n \n while iptr < len(nums):\n if nums[iptr] != 0 and nums[jptr] == 0:\n nums[iptr], nums[jptr] = nums[jptr], nums[iptr]\n if nums[jptr] != 0:\n jptr += 1\n \n iptr += 1","repo_name":"nmktad/A2SV","sub_path":"0283-move-zeroes/0283-move-zeroes.py","file_name":"0283-move-zeroes.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31919930622","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom skimage import color, feature\n\n\nimg = plt.imread('./img/moneda6.jpeg')\ngray_im = color.rgb2gray(img)\n\nplt.imshow(img)\nplt.figure()\nplt.imshow(gray_im, cmap='gray')\n\nfrom skimage.filters import threshold_li\n\ncoin_thresh = threshold_li(gray_im)\nprint('Selected a threshold of %.2f' % coin_thresh)\ncoin_mask = gray_im > coin_thresh\nplt.imshow(coin_mask, cmap='gray');\n\nfrom skimage import morphology\nfrom skimage.morphology import disk\n\ncoin_mask_clean = morphology.remove_small_objects(coin_mask)\ncoin_mask_clean = ~morphology.remove_small_objects(~coin_mask_clean)\n\nplt.imshow(coin_mask_clean, cmap='gray');\n\n''' no_small = morphology.remove_small_objects(coin_mask, min_size=150)\n\ncoins = morphology.binary_closing(no_small,disk(3)) '''\n\n#plt.imshow(coins, cmap='gray');\n\nbg_mask = ~coin_mask_clean\n\nimg.setflags(write=1)\nimg[bg_mask] = 0\ngray_im[bg_mask] = 0\nplt.imshow(img);\n\n''' im.setflags(write=1)\ngray_im.setflags(write=1)\nim[coins==False] = 0\ngray_im[coins==False] = 0 '''\n\nfrom scipy import ndimage as ndi\nfrom matplotlib.colors import ListedColormap\n\ndistance_im = ndi.distance_transform_edt(bg_mask)\nprint('distance transform:', distance_im.shape, distance_im.dtype)\n\nfrom skimage import feature, measure\n\ndef imshow_overlay(im, mask, alpha=0.5, color='red', **kwargs):\n \"\"\"Show semi-transparent red mask over an image\"\"\"\n mask = mask > 0\n mask = np.ma.masked_where(~mask, mask) \n plt.imshow(im, **kwargs)\n plt.imshow(mask, alpha=alpha, cmap=ListedColormap([color]))\n\n\ndef watershed_segmentation(mask):\n distance_im = ndi.distance_transform_edt(mask)\n peaks = feature.peak_local_max(distance_im, indices=True)\n peaks_im = np.zeros(distance_im.shape, dtype=bool)\n for row, col in peaks:\n peaks_im[row, col] = 1\n markers_im = measure.label(peaks_im)\n labelled_im = morphology.watershed(-distance_im, markers_im, mask=coin_mask_clean)\n return labelled_im\n\n\nlabelled_coin_im = watershed_segmentation(coin_mask_clean)\n\nplt.imshow(labelled_coin_im)\n\nregions = measure.regionprops(labelled_coin_im)\n\nplt.imshow(img)\n\nfor region in regions:\n y, x = region.centroid\n area = region.area\n area_str = '%.1f' % (area/100)\n #plt.text(x, y, area_str, color='k', ha='center', va='center')\n\nmin_5 = 61000\nmax_5=62000\nmin_10 = 70000\nmax_2=51000\nmin_2=48000\nmax_1=42000\nmin_1=39000\n\nnum_5 = 0\nnum_10 = 0\nnum_deruido = 0\nnum_2 = 0\nnum_1=0\n\n\n\nfor region in regions:\n y, x = region.centroid\n area = region.area\n if area >= min_5 and area<= max_5:\n coin_name = '5'\n num_5 += 1\n elif area >= min_10:\n coin_name = '10'\n num_10 += 1\n elif area >= min_2 and area<=max_2:\n coin_name = '2'\n num_2 += 1\n elif area >= min_1 and area<=max_1:\n coin_name = '1'\n num_1 += 1\n else:\n coin_name = ''\n num_deruido += 1\n plt.text(x, y, coin_name, ha='center', va='center') # ha, va = horizontal alignment,vertical aligment\n \nvalue = (10*num_10 + 5*num_5 + 2*num_2+1*num_1)\nmonedas=(num_10+num_5+num_2+num_1)\nprint ('%i :10 pesos, %i: 5 pesos, %i :2 pesos, %i :1 peso' %(num_10, num_5, num_2, num_1))\nprint ('Tienes $%.2f pesos' % value)\nprint('Numero de monedas: ',monedas)\n\nplt.show()","repo_name":"amilcaralex97/moneda","sub_path":"coin_value.py","file_name":"coin_value.py","file_ext":"py","file_size_in_byte":3271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29271332576","text":"# import packages\nimport pandas as pd # data frames\nimport numpy as np # numeric arrays & math operations\nimport sqlite3 # SQL database\nfrom os import path # filepath\nfrom functools import reduce # list reduction\nfrom bs4 import BeautifulSoup as Soup # HTML parsing\nimport requests # programmatically visit websites\nimport seaborn as sns # plotting\nimport statsmodels.formula.api as smf # stats models\nfrom sklearn import model_selection as skms # model selection tools\nfrom sklearn import ensemble as skens # ensemble methods\n\n# set data directory\nDATA_DIR = 'C:\\\\Users\\\\calvi\\\\Projects\\\\LTCWFF\\\\ltcwff-files\\\\ltcwff-files-main\\\\data'\nOUT_DIR = 'C:\\\\Users\\\\calvi\\\\Projects\\\\LTCWFF\\\\output'\n\n# Load Play-by-Play Data\npbp_df = pd.read_csv(path.join(DATA_DIR,'play_data_sample.csv'))\n\n# Load Player/Game Data\npg_df = pd.read_csv(path.join(DATA_DIR,'player_game_2017_sample.csv'))\n\n# Load Average Draft Position (ADP) Data\nadp_df = pd.read_csv(path.join(DATA_DIR,'adp_2017.csv'))\n\n# Load Games Data\ngames_df = pd.read_csv(path.join(DATA_DIR,\n 'game_2017_sample.csv'))\n\n\n# Exercises\n\n## 1.1. Granularity of data sets\n # a) game/quarter/team\n # b) game/player\n # c) player/season\n # d) player/week\n # e) position\n\n## 1.2. Summary Stats\n # \"Explosive\" players may be identified by reviewing the \n # distribution of performance on plays - specifically looking \n # for longer/heavier tails in positive direction\n\n## 1.3. Predict game total score with weather data alone\n # a) Inputs: wind speed, temp\n # b) Output: total score of game\n # c) Granularity: single game\n # d) Limitations: does not include info about the games/teams\n \n## 1.4. Where in pipeline?\n # a) get data to right granularity -> data manipulation\n # b) experiment w/models -> data analysis\n # c) dealing with missing data -> data manipulation\n # d) SQL -> store/load data\n # e) scrape website -> collect data\n # f) plot data -> data analysis\n # g) get data from API -> data collection\n # h) pandas -> load data / manipulate data\n # i) take mean of variables -> data analysis\n # j) combine data sources -> load data / data manipulation\n\n\n\n# Quick tour of standard Python library\n\n# comments behind the \"#\"\n# comments good for explaining complex functions, loops, conditionals\n\nprint(1+1) # no commet really needed for simple commands\n\nvariable_in_snake_case = 4 # variable assignment\n\nprint(variable_in_snake_case*2)\n\n# variable update\nvariable_in_snake_case = variable_in_snake_case * 100\nprint(variable_in_snake_case)\n\n# common data types\nvar_int = 48\nvar_float = 22.4\nvar_string = 'Words'\nvar_string_alt = \"More Words\"\nvar_bool = True\nvar_bool_alt = 2 < 3\n\nprint(type(var_string))\n\nvar_fstring = f'{var_string}, {var_string_alt}, together'\nprint(var_fstring)\n\n# string methods\nprint(var_string.upper())\n\n# comparison operators\ntest_true = 1 < 2\ntest_true = 5 > 4\ntest_true = 3 <= 4\ntest_true = 4 >= 3\ntest_true = 1 == 1\ntest_true = 1 != 10\n\n# conditionals\nif var_bool:\n message = \"Test 1 was True\"\nelif var_bool_alt:\n message = \"Test 1 was False and Test 2 was True\"\nelse:\n message = \"Neither test was True\"\n \nprint(message)\n\n# containers\nvar_list = ['a1','b2','c3']\nprint(var_list[0]) # Python uses 0-indexing\n\nvar_dict = {'qb': 'mac jones', # note these use keys: values\n 'rb': 'najee harris',\n 'wr': 'devonta smith'}\nprint(var_dict['wr']) # you can access values by referring to keys\nvar_dict['cb'] = 'patrick surtain ii' # add to dictionary\n\n# unpacking\na, b, c = var_list # require equal length on both sides\nprint(a)\n\n# loops\ni = 0\nfor entry in var_list:\n print(var_list[i])\n i += 1\n\nfor x, y in var_dict.items():\n print(f'pos: {x}')\n print(f'name: {y}')\n\n# list comprehensions\nvar_list_alt = ['bill smith', 'tom green', 'john johnson']\n\nvar_list_alt_proper = [x.title() for x in var_list_alt]\n # list comprehension has form [a for b in c]\n # where c is the list you're iterating over\n # where a is the function/method being applied\n # where b is the variable to refer to elements of c\nprint(var_list_alt_proper)\n\nvar_list_alt_upper = [x.upper() for x in var_list_alt]\nprint(var_list_alt_upper)\n\nvar_list_alt_first = [x.split(' ')[0] for x in var_list_alt]\n # comprehension is \"mapping\" a method/function to an item\nprint(var_list_alt_first)\n\nvar_list_alt_filter = [ \n x for x in var_list_alt if x.__contains__('s')]\n # can use comprehension to conditionally act\n # [a in b for c if d]\nprint(var_list_alt_filter)\n\n# dict comprehensions\nvar_dict_comp = {\n name.split(' ')[0].upper(): pos for pos, name in var_dict.items()\n}\nprint(var_dict_comp)\n\n# common functions\nprint(len(var_list)) # number of items in a list\n\ndef fantasy_pts_rec(yds=0, rec=0, tds=0, ppr=1):\n \"\"\"\n three quotes used to allow multi-line comments/strings\n \n generally try to avoid function side effects:\n only use variables internal to function\n limit output to return value when possible\n \n function takes yards, receptions, and touchdowns and\n returns fantasy points scored\n \n note inputs all take zero value by default\n \"\"\"\n return yds*0.1 + rec*ppr + tds*6\n\nprint(fantasy_pts_rec(127, 5, 2))\n# unspecified inputs assumed to be in same order as function def\n# for this reason, convention os to place important keywords first\n\nprint(fantasy_pts_rec(127, 5, ppr=2))\n\n# libraries are sets of functions and types/classes\n\n\n# Exercises\n\n## 2.1. valid Python variable names\n # do not start with number\n # not strings\n # only allowed non-alphanumeric character is \"_\"\n # \"_\" can be at beginning\n # convention is snake_case not camelCase\n\n## 2.2. arithmetic\nweekly_points = 100\nweekly_points += 28\nweekly_points += 5\n\nprint(f'weekly_points={weekly_points}')\n\n## 2.3. function def\ndef for_the_td(player1, player2):\n \"\"\"\n proclaims that player1 went to player 2 for the TD\n \"\"\"\n return f'{player1} to {player2} for the TD!'\n\nprint(for_the_td('Dak','Zeke'))\n\n## 2.4. method\n # method .islower() evaluates whether string all lower case\n # returns Boolean\n\ntest_str = 'test'\nprint(test_str.islower())\ntest_str = 'Test'\nprint(test_str.islower())\n\n## 2.5. function def\ndef is_leveon(player):\n \"\"\"\n evaluates whether player name is \"Le'Veon Bell\"\n with or without the '\n \"\"\"\n return player.replace(\"'\",\"\").upper() == \"LEVEON BELL\"\n\nlbell_without = \"LeVeon Bell\"\nlbell_with = \"Le'Veon Bell\"\nlbell_not = \"Leveone Bell\"\nprint(is_leveon(lbell_without))\nprint(is_leveon(lbell_with))\nprint(is_leveon(lbell_not))\n\n## 2.6. function with conditional\ndef commentary(score):\n \"\"\"\n provides comment on whether score good/bad with limit 100\n \"\"\"\n if score >= 100:\n comment = f'{score} is a good score'\n else:\n comment = f\"{score}'s not that good\"\n return comment\n \nprint(commentary(77))\nprint(commentary(127))\n\n## 2.7. three ways to print list without last entry\nexample_list = ['dave', 'steve', 'rick', 'dan']\nprint(example_list[:3])\nprint([name for name in example_list if name != 'dan'])\nprint(example_list[:-1])\n\n## 2.8. dictionary element changes \nsimple_dict = {'n_teams': 12, 'ppr': True}\n\nsimple_dict['n_teams'] = 10 # update single position\nprint(simple_dict)\n\ndef toggle_ppr(league):\n \"\"\"\n function to switch boolean key values for 'ppr' in league\n \"\"\"\n league['ppr'] = not league['ppr']\n\ntoggle_ppr(simple_dict)\nprint(simple_dict)\ntoggle_ppr(simple_dict)\nprint(simple_dict)\n\n## 2.9 dict question\n # dict needs key's value specified when adding key\n # dict keys are strings, not unassigned variables\n # dict can't output values for keys that don't exist\n \n## 2.10 list question\nroster_list = ['tom brady', 'adrian peterson', 'antonio brown']\n\nfor dude in roster_list: # print last names\n print(dude.split(' ')[1])\n\nname_dict = {name: len(name) for name in roster_list} \n # create dictionary with comprehension\nprint(name_dict)\n\n## 2.11. comprehensions\nmy_roster_dict= {'qb':'tom brady',\n 'rb1':'adrian peterson',\n 'wr1':'davante adams',\n 'wr2':'john brown'}\n\nmy_roster_positions = [pos for pos, name in my_roster_dict.items()]\nprint(my_roster_positions)\n\nmy_roster_names_ab = [name \n for pos, name in my_roster_dict.items() \n if name.split(' ')[1].startswith('a') \n or name.split(' ')[1].startswith('b')]\nprint(my_roster_names_ab)\n\n## 2.12. functions\ndef mapper(list_name, function_name):\n \"\"\"\n applies function function_name to each element in\n list list_name\n \"\"\"\n return[function_name(i) for i in list_name]\n\ndef rushing_pts_calc(yards):\n return 0.1*yards\n\nrush_yds = [1, 10, 20, 100, 123, 3, 0, 0]\nrush_pts = mapper(rush_yds, rushing_pts_calc)\nprint(rush_pts)\n\n# Basic Functionality of Pandas\n\n# Load Play-by-Play Data\npbp_df = pd.read_csv(path.join(DATA_DIR,\n 'play_data_sample.csv'))\npg_df = pd.read_csv(path.join(DATA_DIR,\n 'player_game_2017_sample.csv'))\nadp_df = pd.read_csv(path.join(DATA_DIR,\n 'adp_2017.csv'))\n\nprint(type(adp_df))\n\n\n# DataFrame Methods and Attributes\nadp_df.head(3) # method takes argument; head default is 5 rows\n\nprint(adp_df.columns) # attribute doesn't need extra input\n\nprint(adp_df.shape) # attribute shape = (rows, columns)\n\n\n# Working with Subsets of columns\nadp_df['name'].head()\nprint(type(adp_df['name'])) # single column is Series by default\nprint(type(adp_df['name'].to_frame())) # method changes to DF\n\nadp_df[['name','position','adp']].head() # supplying list of columns\nprint(type(adp_df[['name','position','adp']])) # >1 columns is DF\n\n\n# Indexing = assign ID to rows, default as 0:n-1\nadp_df.set_index('player_id').head() # can reindex to a column\nadp_df.set_index('player_id', inplace=True) # 1 way to overwrite\nadp_df = adp_df.reset_index()\nadp_df = adp_df.set_index('player_id') # other way to overwrite\n\n\n# Use of Indices\nadp_df_rbs = adp.loc[adp['position'] == 'RB',['name','team']]\nadp_df_rbs.head() # used .loc to get columns/rows meeting condition\n\nadp_df_rbs['times_drafted'] = adp_df['times_drafted']\nadp_df_rbs.head() # add col to new df using index match in old df\n\n\n# Outputting Data\nadp_df_rbs.to_csv(path.join(DATA_DIR, 'adp_rb.csv'), # write CSV\n index=False) # exclude index from file\n\n\n# Exercises\n## 3.0.1 load adp data\nadp_df = pd.read_csv(path.join(DATA_DIR,\n 'adp_2017.csv'))\n\n## 3.0.2 report top 50 players by adp\nadp_df_50 = adp_df.nlargest(50, columns=['adp'])\nprint(adp_df_50.shape)\n\n## 3.0.3 sort adp data by name desc\nadp_df.sort_values('name', ascending = False)\n\n## 3.0.4 what is type of adp_df.sort_values('adp')\nprint(type(adp_df.sort_values('adp'))) # sorted df is still df\n\n## 3.0.5 create new df\nadp_df_simple = adp_df[['name','position','adp']]\nprint(adp_df_simple.head(2))\nadp_df_simple = adp_df_simple[['position','name','adp']]\n # alternative:\n # adp_df_simple.reindex(columns=['position','name','adp'])\nprint(adp_df_simple.head(2))\nadp_df_simple['team'] = adp_df['team']\nprint(adp_df_simple.head(2))\nadp_df_simple.to_csv(path.join(DATA_DIR, 'adp_simple.txt'),\n sep='|') # to_csv can write .txt\n\n\n# Creating and modifying columns\npg_df['pts_per_pass_td'] = 4 # create column\npg_df[['gameid','player_id','pts_per_pass_td']].head()\n\npg_df['pts_per_pass_td'] = 6 # change column\\\\\n\npg_df['rush_pts'] = ( # add column calculated from others\n pg_df['rush_yards']*0.1 +\n pg_df['rush_tds']*6 +\n pg_df['rush_fumbles']*(-3))\n\n\n# Columns using numpy functions\npg_df['rush_distance'] = np.abs(pg_df['rush_yards'])\npg_df['log_rush_yards'] = np.log(pg_df['rush_yards'])\n\n\n# Use of string columns / string methods\npg_df['player_name'].str.upper().sample(5) # upper method\npg_df['player_name'].str.replace('.',' ').sample(5) # str replace\n(pg_df['player_name']+', '+pg_df['pos']).sample(5) # str concatenate\npg_df['player_name'].str.lower().str.replace('.',' ').sample(5)\\\n # note that use \".str\" multiple times if chaining these methods\n\n\n# Boolean columns\npg_df['is_rb'] = (pg_df['pos'] == 'RB')\npg_df['is_rb'].sample(5)\npg_df['good_rb_game'] = ((pg_df['pos'] == 'RB') & # \"&\" == AND\n (pg_df['rush_yards'] >= 100))\n # note: individual conditions in individual parentheses\npg_df['good_rb_game'].sample(5)\npg_df['is_rb_or_wr'] = ((pg_df['pos'] == 'RB') | # \"|\" == \"OR\"\n (pg_df['pos'] == 'WR'))\npg_df['not_rb_or_wr'] = ~((pg_df['pos'] == 'RB') | # \"~\": T->F, F->T\n (pg_df['pos'] == 'WR'))\n(pg_df[['rush_yards', 'rec_yards']] > 100).sample(5)\n # note: evaluating >1 column against a condition\n\n\n# Applying functions to Columns\ndef is_skill_pos(pos):\n \"\"\"\n Takes a string named pos representing player positions\n and checks whether it is a skill position (RB, WR, TE)\n \"\"\"\n return pos in ['RB','WR','TE'] # Boolean vector of if pos in list\n\npg_df['is_skill'] = pg_df['pos'].apply(is_skill_pos)\n # Use Apply method to apply function to each row, one at a time\npg_df['is_skill_alt'] = pg_df['pos'].apply(\n lambda x: x in ['RB','WR','TE'])\n # Can also apply lambda functions (esp. if not complex)\n \n \n# Dropping Columns\npg_df.drop('is_skill_alt', axis=1, inplace=True)\n # Axis = 1 -> Drop column with index given by drop()\n # Default is to drop rows with matching indices\n # inplace=True -> updates underlying df object\n \n\n# Renaming columns\npg_df.columns = [x.upper() for x in pg_df.columns]\npg_df.columns = [x.lower() for x in pg_df.columns]\n # Note: lowercase column name is convention\npg_df.rename(columns={'interceptions': 'ints'}, inplace=True)\n # Rename method specifies new names in a dictionary \n # dictionary given as {'old': 'new'}\n \n\n# Missing Data in Columns\npbp_df['yards_after_catch'].isnull().head()\n # .isnull() evaluate whether rows have missing values\npbp_df['yards_after_catch'].notnull().head()\n # .notnull() evaluate whether rows do not have missing values\npbp_df['yards_after_catch'].fillna(-99).head()\n # .fillna(x) replace NaN with x\n \n \n# Changes to column types\npg_df['year'] = pg_df['gameid'].astype(str).str[0:4].astype(int)\npg_df['month'] = pg_df['gameid'].astype(str).str[4:6].astype(int)\npg_df['day'] = pg_df['gameid'].astype(str).str[6:8].astype(int)\n # change col type with .astype() method\npg_df.dtypes.head() # use dtypes attribute to identify col types\n# Exercises\n\n## 3.1.1 load player game data\n\n## 3.1.2. add rec_pts_ppr column to pg_df\npg_df['rec_pts_ppr'] = (pg_df['rec_yards']*(0.1) + \n pg_df['rec_tds']*6 +\n pg_df['receptions']*1)\n\n## 3.1.3. add 'player_desc' column\npg_df['player_desc'] = (pg_df['player_name']+\n ' is the '+pg_df['team']+\n ' '+pg_df['pos'])\npg_df['player_desc'].sample(5)\n\n## 3.1.4 add Boolean column\npg_df['is_possession_rec'] = (pg_df['caught_airyards'] >\n pg_df['raw_yac'])\n\n## 3.1.5 add 'len_last_name' column\npg_df['len_last_name'] = pg_df['player_name'].apply(\n lambda x: len(x.split('.')[1]))\npg_df['len_last_name'].sample(5)\n\n## 3.1.6 change type og gameid\npg_df['gameid'] = pg_df['gameid'].astype(str)\npg_df.dtypes\n\n## 3.1.7 change column names\npg_df.columns = [x.replace('_',' ') for x in pg_df.columns]\n # note: bad practice to use spaces, so change back\npg_df.columns = [x.replace(' ','_') for x in pg_df.columns]\n\n## 3.1.8 new calculated col\npg_df['rush_td_percentage'] = pg_df['rush_tds']/pg_df['carries']\npg_df['rush_td_percentage'].fillna(-99, inplace=True)\n\n## 3.1.9 drop column\npg_df.drop('rush_td_percentage', axis=1, inplace=True)\n\n# Built-in Pandas functions to operated on DFs\n\n# summary statistics\nadp_df.mean() # colmeans for numeric columns\nadp_df.std()\nadp_df.max() # min/max take strings as well as numbers\nadp_df.min()\nadp_df.count()\n\n# axis of summary stats\nadp_df[['adp','low','high']].mean(axis=0) # col means\nadp_df[['adp','low','high']].mean(axis=1) # row means\n\n# Bolean summary stats\nprint(pg_df['good_rb_game'].mean()) # avg truth value 0-1\nprint(pg_df['good_rb_game'].sum()) # number of true values\n\n(pg_df['pass_yards'] > 450).any() # are any rows true?\n(pg_df['pass_yards'] > 100).all() # are all rows true?\n\n(pg_df[['rush_yards','rec_yards']] > 100).any(axis=1) \n # is condition true in either columnn for each row\nprint((pg_df[['rush_yards','rec_yards']] > 100).any(axis=1).sum())\n # it is true in 100 rows\n\nprint((pg_df[['rush_yards','rec_yards']] > 50).all(axis=1).sum())\n # in sample 14 players had >50 rush yds + >50 rec yards\n \n\n# Other summary functions\nadp_df['position'].value_counts() \n # freq table with counts of unique vals\nadp_df['position'].value_counts(normalize=True) \n # frequency table normalized to proportion\npd.crosstab(adp_df['team'], adp_df['position'], normalize=True).sample(5)\n # cross-tabular frequency table\n # pd.crosstab(rows, cols)\n\n# Series functions\n # cummin, cummax, cumsum\n # argmin, argmax\n # kurtosis, skew\n # sort_index, sort_values\n # sum\n # mean, median, mode\n # var, std\n # rank, quantile\n # autocorr\n # isna, isnull, notna, notnull\n # dropna, drop_duplicates\n # more with tab completion: pd.Series. {tab}\n \n \n\n# DataFrame functions\n # corr\n # cov\n # describe (descriptive statistics)\n # dropna, drop_duplicates\n # join, merge (merge more general than join)\n # transpose\n # pivot: create pivot table\n # melt: unpivot\n\n\n\n# Exercises\n\n## 3.2.1 load pg_df\n\n## 3.2.2 add total yards column\npg_df['total_yards1'] = (pg_df['rush_yards']+\n pg_df['rec_yards']+\n pg_df['pass_yards'])\npg_df['total_yards2'] = (pg_df[\n ['rush_yards','rec_yards', 'pass_yards']].sum(axis=1))\n\n## 3.2.3 some calcs\nprint(pg_df['rush_yards'].mean())\nprint(pg_df['rec_yards'].mean())\nprint(((pg_df['pass_yards'] >= 300) & (pg_df['pass_tds'] >= 3)).sum())\nprint(((pg_df['pass_yards'] >= 300) & (pg_df['pass_tds'] >= 3)).sum()\n /(pg_df['pos'] == 'QB').sum())\nprint(pg_df['rush_tds'].sum())\nprint(pg_df['week'].value_counts().index[0]) \n # value_counts def is DESC by count\nprint(pg_df['week'].value_counts().index[-1])\n# filtering observations = row subsetting\n\n# loc\ndsproles_id = 119\nadp_df.loc[tb12_id]\n\n\n# loc w/list\nthe_boys = [3, 9, 27]\nadp_df.loc[adp_df.index.isin(the_boys), ['name','position','team']]\n\n\n# Boolean indexing\nis_rb = adp_df['position'] == 'RB'\nadp_df.loc[is_rb].head()\n\nadp_df.loc[adp_df['position'] == 'QB'].head()\n\nadp_df.loc[~(adp_df['position'] == 'RB')].head()\n\n\n# working with duplicates\nadp_df.drop_duplicates(inplace=True)\nadp_df[['name','position']].duplicated().sample(5) # dupe test\ndupes = adp_df.duplicated()\nadp_df_nodupe = adp_df.loc[~dupes]\n\n\n# combining filtering with changing columns\npg_df['primary_yards'] = np.nan # initialize new col as empty\npg_df.loc[pg_df['pos'] == 'QB', \n 'primary_yards'] = pg_df['pass_yards'] # use pass as primary yds for QB\npg_df.loc[pg_df['pos'] == 'RB', \n 'primary_yards'] = pg_df['rush_yards'] # use rush as primary yds for RB\npg_df.loc[pg_df['pos'] == 'WR', \n 'primary_yards'] = pg_df['rec_yards'] # use rec as primary yds for WR\n\n\n# filter with query Method\npg_df[['pos','player_name']].query(\"pos == 'RB' | pos == 'WR'\").head()\n\npg_df.query(\"raw_yac.notnull()\", engine='python')[\n ['gameid','player_id','raw_yac']].head()\n\n\n# Exercises\n\n## 3.3.1 load adp data\n\n## 3.3.2 make DataFrames for just Dallas Cowboys\nadp_cb1 = adp_df.loc[adp_df['team'] == 'DAL'][\n ['name','position','adp']]\nadp_cb2 = adp_df.query(\"team == 'DAL'\")[\n ['name','position','adp']]\n\n## 3.3.3 make DF that's not the DAL players\nadp_no_cb = adp_df.loc[~(adp_df['team'] == 'DAL')][\n ['name','position','adp','team']]\n\n## 3.3.4. dupes by last_name X position? \nadp_df['last_name'] = adp_df['name'].str.split(' ', \n n=1, expand=True)[1]\nlast_pos_dupe = adp_df[['last_name','position']].duplicated()\nprint(sum(last_pos_dupe)) # number laste name X pos dupes\n\nadp_dups = adp_df.loc[last_pos_dupe]\nadp_nodups = adp_df.loc[~last_pos_dupe]\n\nprint(adp_dups.shape)\nprint(adp_nodups.shape)\n\n## 3.3.5 add adp_description\ndef adp_desc(adp):\n if adp < 40:\n desc = 'stud'\n elif adp > 120:\n desc = 'scrub'\n else:\n desc = np.nan\n \n return desc\n\nadp_df['adp_description'] = adp_df['adp'].apply(adp_desc)\n\n## 3.3.6 New DataFrames for adp_desc = NaN\nadp_no_desc1 = adp_df.loc[adp_df['adp_description'].isna()]\nadp_no_desc2 = adp_df.query('adp_description.isna()', engine='python')\n\nprint(adp_df.shape)\nprint(adp_no_desc1.shape)\nprint(adp_no_desc2.shape)\n\n# grouping\npbp_df.groupby('game_id').sum()\n # note: 'game_id' is index of resulting DF\npbp_df.groupby('game_id', as_index=False).sum()\n # note: 'game_id' is not index of resulting DF\nsum_some_cols = ['yards_gained','rush_attempt',\n 'pass_attempt','shotgun']\npbp_df.groupby('game_id').sum()[sum_some_cols]\n # note: report aggregation only for selected col subset\n \n# agg uses dict to use different functions \n# when aggregating differnt columns\npbp_df.groupby('game_id').agg({\n 'yards_gained': 'sum',\n 'play_id': 'count',\n 'interception': 'sum'})\n\n# can also pass alias for aggregated columns = ('var','function')\npbp_df.groupby('game_id').agg(\n yards_gained = ('yards_gained', 'sum'),\n n_plays = ('play_id', 'count'),\n interception = ('interception', 'sum'))\n\n# can group by >1 variable\nyards_per_team_game = (pbp_df.groupby(['game_id','posteam']).agg(\n ave_yards_per_play = ('yards_gained','mean'),\n total_yards = ('yards_gained','sum')))\nyards_per_team_game.head()\n\n# indices for multilevel DFs require a tuple\nyards_per_team_game.loc[[(2018101412,'NE')]]\n\n# avoid multilevel index with reset_index\nyards_per_team_game.reset_index()\n\n\n# stacking/unstacking\nqb_games = pg_df.loc[pg_df['pos'] == 'QB',\n ['player_name','week','pass_tds']]\nqb_games.sample(5) # 1 row per qb per week\n\nqb_games_reshaped = qb_games.set_index(['player_name',\n 'week']).unstack()\nqb_games_reshaped.sample(5) # 1 row per qb, 1 col per week\n # note: this preserves all info from original DF\n \nqb_total_tds = qb_games_reshaped.sum(axis=1) \nqb_total_tds.sample(5) # unstacked data easily aggregates over index\nqb_games_reshaped.max(axis=0) # max pass TDs by week\n\nqb_games_reshaped_undo = qb_games_reshaped.stack()\nqb_games_reshaped_undo.head() # recovers orignal DF, now sorted\n\n\n# Exercises\n\n## 3.4.1 granulity changes and information loss\n # grouping causes information loss \n # (only retain whichever aggregation)\n \n # unstacking does not result in information loss\n \n## 3.4.2 pbp data analysis\nrushers_df = pbp_df.groupby(['game_id',\n 'rusher_player_name']).sum()['yards_gained']\nrushers_df = rushers_df.reset_index()\nrushers_df.loc[rushers_df.reset_index().groupby(['game_id'])\n ['yards_gained'].idxmax()]\n # full row with max yards_gained for each game_id\n\nrushers_df = pbp_df.groupby(['game_id',\n 'rusher_player_name']).agg(\n yards_gained = ('yards_gained','sum'),\n rushes = ('rush_attempt', 'sum'))\nrushers_df['ypc'] = rushers_df['yards_gained']/rushers_df['rushes']\nrushers_df.sample(5) # added yards per carry column\n\ndef neg_yards(yds):\n return np.sum(yds <= 0)\n\nrushers_df = pbp_df.groupby(['game_id',\n 'rusher_player_name']).agg({\n 'yards_gained': [neg_yards, 'count']})\nrushers_df = rushers_df.reset_index()\nrushers_df['unproductive_perc'] = \\\n rushers_df[('yards_gained','neg_yards')]/ \\\n rushers_df[('yards_gained','count')]\n # note: multi-index fields referred to by their tuple\n # \"\\\" is explicit line break; can't have anything after it\nrushers_df.sample(5)\n\n## 3.4.3\npbp_df.groupby('game_id').count() # count of non-nulls for each var\npbp_df.groupby('game_id').sum() # sum for each var\n # note: count/sum give same result for indicators\n\n## 3.4.4 pbp teams with greatest proportion as 1st down / turnover\nteam_df = pbp_df.groupby(['game_id',\n 'posteam']).agg({'play_id': 'count',\n 'first_down':'sum',\n 'turnover': 'sum'})\nteam_df = team_df.reset_index()\nteam_df['prop_first'] = team_df['first_down']/team_df['play_id']\nteam_df['prop_to'] = team_df['turnover']/team_df['play_id']\nteam_df.loc[team_df.reset_index().groupby(['game_id'])\n ['prop_first'].idxmax()]\n # team with greatest proportion of first downs by game\nteam_df.loc[team_df.reset_index().groupby(['game_id'])\n ['prop_to'].idxmax()]\n # team with greatest proportion of turnovers by game\n \n## 3.4.5 stacking/unstacking information loss\n # unstacking does not result in information loss\n\n\n# merging\npd.merge(pg_df,games_df[['gameid','home','away']],on='gameid').head()\n # note: merging on 'gameid' alone works \n # because both DFs have a column with this name\n \n # note: merging requires exact match in columns joining on\n \n# merging on >1 column\nrush_df = pg_df[['gameid','player_id','rush_yards','rush_tds']]\nrec_df = pg_df[['gameid','player_id','rec_yards','rec_tds']]\n\ncombined_df = pd.merge(rush_df,rec_df,on=['player_id','gameid'])\ncombined_df.sample(5)\n\n# handling unmatched observations\nrush_df = pg_df.loc[pg_df['rush_yards']>0,\n ['gameid','player_id',\n 'rush_yards','rush_tds']]\n \nrec_df = pg_df.loc[pg_df['rec_yards']>0,\n ['gameid','player_id',\n 'rec_yards','rec_tds']]\n\nprint(rush_df.shape)\nprint(rec_df.shape) # DataFrames are different sizes\n\ncomb_inner = pd.merge(rush_df,rec_df) \nprint(comb_inner.shape) # keeps obs only in DF1 AND DF2\n\ncomb_left = pd.merge(rush_df,rec_df, how='left') \nprint(comb_left.shape) # keeps obs in DF1 even if null in DF2\n\ncomb_right = pd.merge(rush_df,rec_df, how='right') \nprint(comb_right.shape) # keeps obs in DF2 even if null in DF1\n\ncomb_outer = pd.merge(rush_df,rec_df, how='outer', indicator=True)\nprint(comb_outer.shape) # keeps all obs\nprint(comb_outer['_merge'].value_counts())\n # note: indicator says whether row in left, right, or both DFs\n\n# joining with different column names\nrush_df.columns = ['gameid','rusher_id','rush_yards','rush_tds']\nrec_df.columns = ['gameid','rcvr_id','rec_yards','rec_tds']\n\ncomb_df = pd.merge(rush_df, rec_df, left_on=['gameid','rusher_id'],\n right_on=['gameid','rcvr_id'], how='outer')\ncomb_df.sample(5)\n\n # note: pd.merge() resets the index\n \n# concatenation\nrush_df = pg_df.loc[pg_df['rush_yards']>0,\n ['gameid','player_id',\n 'rush_yards','rush_tds']]\n \nrec_df = pg_df.loc[pg_df['rec_yards']>0,\n ['gameid','player_id',\n 'rec_yards','rec_tds']]\n\nrush_df = rush_df.set_index(['gameid','player_id'])\nrec_df = rec_df.set_index(['gameid','player_id'])\n\npd.concat([rush_df,rec_df], axis=1).sample(5)\n # can concatenate on axis\n # info only in all columns if indices match\n # can also take argument: join={'inner','outer'}\n \n# vertical concatenation\nqbs = adp_df.loc[adp_df['position'] =='QB']\nrbs = adp_df.loc[adp_df['position'] =='RB']\nprint(qbs.shape)\nprint(rbs.shape) # two DFs have same number of columns\n\nqbrb = pd.concat([qbs,rbs])\nprint(qbrb.shape) # verticall stacked the two DFs\n\n# what if indices match?\nqbs = qbs.reset_index()\nrbs = rbs.reset_index()\n\nqbrb = pd.concat([qbs,rbs], ignore_index=True)\nqbrb.head() # ignore_index argument used if potential index matches\n\n\n# Exercises\n\n## 3.5.1\ntd_df = pd.read_csv(path.join(DATA_DIR,\n 'problems\\\\combine1\\\\td.csv'))\ntouch_df = pd.read_csv(path.join(DATA_DIR,\n 'problems\\\\combine1\\\\touch.csv'))\nyard_df = pd.read_csv(path.join(DATA_DIR,\n 'problems\\\\combine1\\\\yard.csv'))\n\ndf_list = [td_df, touch_df, yard_df]\nmerge_df = reduce(lambda left, right: pd.merge(\n left,right,on='id',how='outer').fillna(0), df_list)\n\ntd_df = td_df.set_index('id', inplace=True)\ntouch_df = touch_df.set_index('id', inplace=True)\nyard_df = yard_df.set_index('id', inplace=True)\n\nconcat_df = pd.concat(df_list, axis=1).fillna(0).reset_index()\n\nconcat_df.sample(4)\n\n # note: merge doesn't require indices to match\n # note: concat gets the job done in 1 step\n \n## 3.5.2\nqb_df = pd.read_csv(path.join(DATA_DIR,\n 'problems\\\\combine2\\\\qb.csv'))\nrb_df = pd.read_csv(path.join(DATA_DIR,\n 'problems\\\\combine2\\\\rb.csv'))\nwr_df = pd.read_csv(path.join(DATA_DIR,\n 'problems\\\\combine2\\\\wr.csv'))\nte_df = pd.read_csv(path.join(DATA_DIR,\n 'problems\\\\combine2\\\\te.csv'))\n\ndf_list2 = [qb_df, rb_df, wr_df, te_df]\n\ncombined = pd.concat(df_list2, axis=0, ignore_index=True)\ncombined.head()\n\n## 3.5.3\nadp_df['position'].value_counts()\npositions = ['WR','RB','QB','TE','DEF','PK']\nfor pos in positions:\n tempdf = adp_df[adp_df['position'] == pos]\n tempdf.to_csv(path.join(DATA_DIR, f'adp_{pos}.csv'), \n index=False)\n\n \ncomb_df = pd.concat([pd.read_csv(path.join(DATA_DIR,\n f'adp_{pos}.csv')) \n for pos in positions], axis=0, ignore_index=True)\n\ncomb_df.sample(5)\n\n\n# initialize sqlite connection\nconn = sqlite3.connect(path.join(DATA_DIR, 'fantasy.sqlite'))\n\n# load CSV data\nplayer_game=pd.read_csv(path.join(DATA_DIR,'game_data_sample.csv'))\nplayer=pd.read_csv(path.join(DATA_DIR,'game_data_player_sample.csv'))\ngame=pd.read_csv(path.join(DATA_DIR,'game_2017_sample.csv'))\nteam=pd.read_csv(path.join(DATA_DIR,'teams.csv'))\n\ndb_csvs = [player_game, player, game, team]\n\n# write CSV data to sql\n[x.to_sql(str(x), conn, \n index=False, if_exists='replace') for x in db_csvs]\n\n# queries\ndf = pd.read_sql(\"\"\" \n SELECT *\n FROM player\n \"\"\", conn) # write SQL query inside of pd.read_sql(query,conn)\ndf.head()\n\ndf = pd.read_sql(\"\"\" \n SELECT player_id, season, player_name, pos, team\n FROM player\n \"\"\", conn) # subset columns\ndf.head()\n\ndf = pd.read_sql(\"\"\" \n SELECT player_id, season, player_name, pos, team\n FROM player\n WHERE team = 'MIA'\n \"\"\", conn) # filter\ndf.head()\n\ndf = pd.read_sql(\"\"\" \n SELECT player_id, season, player_name, pos, team\n FROM player\n WHERE team = 'MIA' OR team = 'NE'\n \"\"\", conn) # conditional filtering\ndf.head()\n\ndf = pd.read_sql(\"\"\" \n SELECT player_id, season, player_name, pos, team\n FROM player\n WHERE team = 'MIA' AND pos = 'WR'\n \"\"\", conn) # conditional filtering\ndf.head()\n\ndf = pd.read_sql(\"\"\" \n SELECT player_id, season, player_name, pos, team\n FROM player\n WHERE team IN ('MIA', 'NE')\n \"\"\", conn) # conditional filtering\ndf.head()\n\ndf = pd.read_sql(\"\"\" \n SELECT player_id, season, player_name AS name, pos, team\n FROM player\n \"\"\", conn) # alias \ndf.head()\n\n\n# joining with cross join (INNER JOIN)\nquerystr = \"\"\"\n SELECT \n p.player_name AS name, \n p.pos,\n t.team,\n t.conference,\n t.division,\n pg.*\n FROM player AS p, team AS t, player_game AS pg\n WHERE \n p.team = t.team AND\n p.player_id = pg.player_ID AND\n p.pos = 'QB'\n LIMIT 5\n \"\"\"\ndf = pd.read_sql(querystr, conn) # alias \ndf.head()\n\n# DISTINCT removes duplicates (all cols)\nquerystr = \"\"\"\n SELECT DISTINCT\n season,\n week,\n date\n FROM game\n \"\"\"\ndf = pd.read_sql(querystr, conn) # alias \ndf.head()\n\n# Union vertically concatenates\nquerystr = \"\"\"\n SELECT\n player_name,\n team,\n pos\n FROM player\n WHERE pos = 'QB'\n UNION\n SELECT\n player_name,\n team,\n pos\n FROM player\n WHERE pos = 'RB'\n \"\"\"\ndf = pd.read_sql(querystr, conn) # alias \ndf.head()\n\n# Subqueries\nquerystr = \"\"\"\n SELECT\n player_name, rush_yards\n FROM\n (SELECT\n *\n FROM player_game\n WHERE rush_yards > 100) AS sub_pg\n ORDER BY rush_yards DESC\n \"\"\"\ndf = pd.read_sql(querystr, conn) # alias \ndf.head()\n\n# Left joins, right joins, outer joins\n # SELECT *\n # FROM \n # {LEFT, RIGHT, OUTER} JOIN \n # ON ..\n \n Name \n Pos \n Week \n Pts \n \n \n Todd Gurley \n RB \n 1 \n 22.6 \n \n \n Christian McCaffrey \n RB \n 1 \n 14.8 \n \n
\n\n\"\"\"\n\nhtml_soup = Soup(table_html)\n\n# find tags\ntr_tag = html_soup.find('tr') # find 1st row by tr tag\ntd_tag = html_soup.find('td') # find 1st data element by td tag\ntable_tag = html_soup.find('table') # find 1st table by table tag\n\n# extract info\nprint(td_tag)\nprint(td_tag.string)\nprint(str(td_tag.string)) # recommend recasting as Python string\n\n# tags are nested, so we can find all with comprehension\nheaders = [str(x.string) for x in tr_tag.find_all('th')]\nprint(headers)\n\nall_td_tags = table_tag.find_all('td')\nall_data = [str(x.string) for x in all_td_tags]\nprint(all_data) # list of table entries with td tags\n\n# nested tags\nall_rows = table_tag.find_all('tr')\nlist_of_td_lists = [x.find_all('td') for x in all_rows[1:]]\n # note: ignorig th row\nprint(list_of_td_lists)\nlist_of_data = [[str(x.string) for x in y] for y in list_of_td_lists]\nprint(list_of_data)\n\n\n# example webscraper\nffc_response=requests.get(\n 'https://fantasyfootballcalculator.com/adp/ppr/12-team/all/2017')\n # extract webpage data\n \nadp_soup=Soup(ffc_response.text)\n # parse text of webpage data to beautiful soup object\n # use find_all to find tags\n \ntables = adp_soup.find_all('table')\nprint(len(tables)) # need num of tables to find table you want\n\nadp_table = tables[0]\n\nrows = adp_table.find_all('tr') # within table, find rows\n\ndef parse_row(row):\n \"\"\"\n Take in a tr tag and get the data out \n of it in the form of a list of strings.\n \"\"\"\n parse_list = [str(x.string) for x in row.find_all('td')]\n urlpart = [str(x['href']) for x in row.find_all('a', href=True)][0]\n url = 'https://fantasyfootballcalculator.com'+urlpart\n parse_list.append(url)\n return parse_list\n\ndef parse_link(row):\n \"\"\"\n Take in an 'a' tag and get the link out \n of it in the form of a list of strings.\n \"\"\"\n return [x['href'] for x in row.find_all('a', href=True)]\n\nlist_of_parsed_rows = [parse_row(row) for row in rows[1:]]\nlist_of_links_rows = [parse_link(row) for row in rows[1:]]\nprint(list_of_links_rows[0:6])\n\nlist_as_df = pd.DataFrame(list_of_parsed_rows)\nlist_as_df.head() # note we need to add conames\n\nheaders = ['ovr','pick','name','pos','team','adp',\n 'std_dev','high','low','drafted','graph','link']\nlist_as_df.columns = headers\nlist_as_df.head() # now need to correct data types\n\ntobe_float = ['adp','std_dev']\ntobe_int = ['ovr', 'drafted']\nlist_as_df[tobe_float] = list_as_df[tobe_float].astype(float)\nlist_as_df[tobe_int] = list_as_df[tobe_int].astype(int)\nlist_as_df.head() # now need to drop empty graph col\n\nlist_as_df.drop('graph', axis=1, inplace=True)\nprint(list_as_df['link'][1])\nlist_as_df.head()\n\n\n# Exercises\n\n## 5.1.1 create \"scrape_ffc\" function\ndef scrape_ffc(scoring,nteams,year):\n \"\"\"\n navigates to Fantasy Football Calculator website and pulls\n average draft position data for each player as observed in \n leages with a given type of scoring {scoring}, number of teams\n {nteams}, and for a given season {year}\n \n scoring = {'ppr', 'half-ppr', 'standard'}\n nteams = {8, 10, 12, 14}\n year = 2010:2020\n \"\"\"\n \n ffc_response=requests.get(\n f'https://fantasyfootballcalculator.com/adp/{scoring}/{nteams}-team/all/{year}')\n # extract webpage data\n\n adp_soup=Soup(ffc_response.text)\n # parse text of webpage data to beautiful soup object\n # use find_all to find tags\n\n tables = adp_soup.find_all('table')\n \n adp_table = tables[0]\n\n rows = adp_table.find_all('tr') # within table, find rows\n\n def parse_rowlink(row):\n \"\"\"\n Take in a tr tag and get the data out \n of it in the form of a list of strings.\n \"\"\"\n parse_list = [str(x.string) for x in row.find_all('td')]\n urlpart = [str(x['href']) for x in row.find_all('a', href=True)][0]\n url = 'https://fantasyfootballcalculator.com'+urlpart\n parse_list.append(url)\n return parse_list\n \n list_of_parsed_rows = [parse_rowlink(row) for row in rows[1:]]\n \n ffc_df = pd.DataFrame(list_of_parsed_rows)\n \n #add col names\n headers = ['ovr','pick','name','pos','team','adp',\n 'std_dev','high','low','drafted','graph','link']\n ffc_df.columns = headers\n \n #correct data types\n tobe_float = ['adp','std_dev']\n tobe_int = ['ovr', 'drafted']\n ffc_df[tobe_float] = ffc_df[tobe_float].astype(float)\n ffc_df[tobe_int] = ffc_df[tobe_int].astype(int)\n \n # drop empty graph col\n ffc_df.drop('graph', axis=1, inplace=True)\n \n ffc_df['year'] = year\n ffc_df['scoring_type'] = scoring\n ffc_df['nteams'] = nteams\n \n # add player data\n def parse_rowlite(row):\n \"\"\"\n Take in a tr tag and get the data out \n of it in the form of a list of strings.\n \"\"\"\n parse_list = [str(x.string) for x in row.find_all('td')]\n return parse_list\n \n def get_player_info(link):\n pdata = requests.get(link)\n pd_soup=Soup(pdata.text)\n # parse text of webpage data to beautiful soup object\n # use find_all to find tags\n tables = pd_soup.find_all('table')\n if len(tables)>2:\n pers_table = tables[0]\n pers_rows = pers_table.find_all('tr')\n pers_parsed_rows = [parse_rowlite(row) for row in pers_rows[0:]]\n pers_df = pd.DataFrame([pers_parsed_rows])\n #pers_df.columns = ['age','birthday','height','weight']\n draft_table = tables[1]\n draft_rows = draft_table.find_all('tr')\n draft_parsed_rows = [parse_rowlite(row) for row in draft_rows[0:]]\n draft_df = pd.DataFrame([draft_parsed_rows])\n #draft_df.columns = ['college','year','pick','team']\n\n player_data = {'height': pers_df.iloc[0][2], \n 'weight': pers_df.iloc[0][3],\n 'draft_pick': str(draft_df.iloc[0][2]).replace(\"\\\\n \",\"\\n\"),\n 'draft_team': draft_df.iloc[0][3]}\n else:\n player_data = np.nan\n return player_data\n \n ffc_df['playerinfo'] = [get_player_info(x) for x in ffc_df['link']]\n \n return ffc_df\n\n\ndf_2014_std = scrape_ffc('standard',10,2014)\ndf_2014_std.head()\n\ndf_2019_half = scrape_ffc('half-ppr',12,2019)\ndf_2019_half.head()\n\n## 5.1.2 add function to get links for player names\n\n## 5.1.3 crawl pages to player names, extract heights, weights\ndf_2019_half.head()\n\n\ndef fc_adp(scoring='ppr',teams=12,year=2020):\n \"\"\"\n Gets player info from Fantasy Football Club API for a given \n number of teams, year, and scoring type\n \"\"\"\n ffc_com = 'https://fantasyfootballcalculator.com'\n resp = requests.get(\n f'{ffc_com}/api/v1/adp/{scoring}?teams={teams}&year={year}'\n )\n df = pd.DataFrame(resp.json()['players'])\n #data doesn't come with teams, year columns\n #let's add them \n df['year'] = year\n df['teams'] = teams\n return df\n\ndf_12_standard_2019 = fc_adp('standard', 12, 2019)\ndf_12_standard_2019.head()\n\n\n# prep data\ndf_games = pg_df.copy() # pg_df[pg_df['pos']=='QB'].copy()\ndf_games['std'] = (0.1*(df_games['rush_yards']+\n df_games['rec_yards'])+\n 0.04*df_games['pass_yards']+\n (-3)*(df_games['rush_fumbles']+\n df_games['rec_fumbles']+\n df_games['interceptions'])+\n 6*(df_games['rush_tds']+qb_games['rec_tds'])+\n 4*df_games['pass_tds'])\ndf_games['ppr'] = df_games['std']+df_games['receptions']\ndf_games['half_ppr'] = df_games['std']+0.5*df_games['receptions']\n\ndf_games[['pos','player_name','week','std','ppr','half_ppr']]\n\n# univariate density plot\ng = (sns.FacetGrid(df_games).\n map(sns.kdeplot,'std',shade=True))\n\n# univariate density plot, multiple groups\ng = (sns.FacetGrid(df_games, hue='pos').\n map(sns.kdeplot,'std',shade=True).\n add_legend())\n\n# multiframe\ng = (sns.FacetGrid(df_games,hue='pos',col='week',col_wrap=4,height=2)\n .map(sns.kdeplot,'std',shade=True)\n .add_legend())\n\n# relationships between variables in Python\n\n# scatter plots\ng = sns.relplot(x = 'caught_airyards', y = 'raw_yac', data = df_games)\n\ng = sns.relplot(x = 'caught_airyards', y = 'raw_yac', hue = 'pos',\n data = df_games)\n\n# correlation\nprint(df_games.loc[df_games['pos'] =='WR',\n ['rec_raw_airyards','targets','carries','ppr','std']\n ].corr())\n\n# plotting correlated variables\ng = sns.relplot(x = 'targets',\n y = 'ppr',\n data = df_games.query(\"pos=='WR'\"))\n\ng = sns.relplot(x = 'targets',\n y = 'std',\n data = df_games.query(\"pos=='WR'\"))\n\n# line plots\ng = sns.relplot(x = 'week',\n y = 'std',\n kind = 'line',\n hue = 'pos',\n data = df_games)\n # default is to include 95% CIs for each group\n\ng.savefig(path.join(OUT_DIR,'distribution_weekly_pts_by_pos.png'))\n\n# plot for individual players in a position\ng=sns.relplot(x='week',\n y='std',\n kind='line',\n hue='player_name',\n col='player_name',\n height=2,aspect=1.2,col_wrap=3,\n legend=False,\n data=df_games.query(\"pos=='QB'\"))\n\n\n# Linear regression in Python\n\n#load data\ndf=pd.read_csv(path.join(DATA_DIR,'play_data_sample.csv'))\n\n# pre-process data\ndf=df.loc[(df['play_type'] =='run') | (df['play_type'] =='pass')]\n # i.e. exclude punts, field goals\ndf['offensive_td'] = ((df['touchdown'] == 1) \n & (df['yards_gained'] > 0))\n # exclude defensive touchdowns\ndf['offensive_td'] = df['offensive_td'].astype(int)\n # coerce indicator to dummy variable\ndf['yardline_100_sq'] = df['yardline_100'] ** 2\n # add a transformed feature\n \n# fit model with statsmodels.ols (ordinary least squares)\nmodel=smf.ols(\n formula='offensive_td~yardline_100+yardline_100_sq',\n data=df)\n \nresults = model.fit()\n\n# review results of model fit\nprint(results.summary2())\n\n# add predictions to dataframe\ndf['offensive_td_hat'] =results.predict(df)\n\n\n# logistic regression in Python\nsmf.logit(formula=\n 'touchdown~yardline_100+yardline_100_sq',\n data=df)\n\nresults=model.fit()\n\nprint(results.summary2())\n\n# Random Forest example with scikit-learn\nnp.random.seed(10)\n\n# create X and Y\nxvars = ['carries','rush_yards','rush_fumbles',\n 'rush_tds','targets','receptions','rec_yards',\n 'raw_yac','rec_fumbles','rec_tds','ac_tds',\n 'rec_raw_airyards','caught_airyards','attempts',\n 'completions','pass_yards','pass_raw_airyards',\n 'comp_airyards','timeshit','interceptions','pass_tds',\n 'air_tds']\nyvar = 'pos'\n\n# create train/test split (80% train, 20% test)\ntrain, test = skms.train_test_split(\n pg_df, test_size=0.20)\n\n# fit Random Forest with 100 trees\nmodel = skens.RandomForestClassifier(n_estimators=100)\nmodel.fit(train.loc[:,xvars],train.loc[:,yvar])\n\n# view perfomance on test fit\ntest['pos_hat'] = model.predict(test.loc[:,xvars])\ntest['correct'] = (test['pos_hat'] == test['pos'])\nprint(test['correct'].mean()) # average accuracy\n\n# prediction probabilities by class for each obs\nprobs = pd.DataFrame(\n model.predict_proba(test[xvars]),\n index=test.index,\n columns=model.classes_)\nprobs.head()\n\n\nresults=pd.concat([\n test[['player_id','player_name','pos','pos_hat','correct']],\n probs],\n axis=1)\n\nresults.sample(10)\n\n# accuracy by position\nresults.groupby('pos')[['correct','QB','RB','WR','TE']].mean()\n\n\n# random forest with cross validation\nmodel = skens.RandomForestClassifier(n_estimators=100)\nscores = skms.cross_val_score(model,pg_df[xvars],pg_df[yvar],cv=10)\n\nprint(scores) # accuracy for each of the 10 folds\n\n# Random Forest feature importance\nmodel = skens.RandomForestClassifier(n_estimators=100)\nmodel.fit(train.loc[:,xvars],train.loc[:,yvar])\npd.Series(model.feature_importances_,xvars).sort_values(ascending=False)","repo_name":"10E-4m2/Notes-LTCWFF","sub_path":"LTCWFF-HandsOn.py","file_name":"LTCWFF-HandsOn.py","file_ext":"py","file_size_in_byte":46486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73546149201","text":"from tkinter import *\r\n\r\nroot = Tk()\r\nroot.title(\"Juseong GUI\")\r\nroot.geometry(\"640x480\") #가로*세로\r\n\r\nframe = Frame(root)\r\nframe.pack()\r\n\r\nscrollbar = Scrollbar(frame)\r\nscrollbar.pack(side=\"right\",fill=\"y\")\r\n\r\n#set이 없으면 스크롤을 해도 다시 올라옴\r\nlistbox = Listbox(frame, selectmode = \"extended\", height=10, yscrollcommand = scrollbar.set)\r\nfor i in range(1,32):\r\n listbox.insert(END, str(i)+ \" day\")\r\n\r\nlistbox.pack(side=\"left\")\r\n\r\n#scrollbar에도 listbox를 매핑해줘야 서로 연동이 됨\r\nscrollbar.config(command = listbox.yview)\r\n\r\nroot.mainloop()\r\n","repo_name":"ZuseongZIN/gui_basic","sub_path":"13_scrollbar.py","file_name":"13_scrollbar.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29409052499","text":"def merge_sort(A):\n mid = len(A)//2\n print('A in main: ', A)\n return A if len(A) == 1 else merge(merge_sort(A[0:mid]),merge_sort(A[mid:len(A)]))\n\ndef merge(A,B):\n print('A=', A, 'B=', B)\n merged = []\n i,j = 0, 0\n while i < len(A) and j < len(B):\n if A[i] <= B[j]:\n merged.append(A[i])\n i += 1\n else:\n merged.append(B[j])\n j += 1\n if i == len(A):\n merged.extend(B[j:])\n if j == len(B):\n merged.extend(A[i:])\n print(merged)\n return merged\n\nprint(merge_sort([1,3,2,4,5,7,6]))","repo_name":"pranavpatil004/DS-and-Algo","sub_path":"Sorting/merge_sort.py","file_name":"merge_sort.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26315856383","text":"import re\r\n\r\n# Grammar AST\r\ndef node(name, props):\r\n def set_props(self, *propvs):\r\n for prop, value in zip(props.split(), propvs):\r\n setattr(self, prop, value)\r\n return type(name, (Node,), {\"__init__\": set_props,\r\n \"__repr__\": lambda self: \"<%s: %s>\" % (name, \" \".join(\"%s=%r\" % (k,v) for k,v in self.__dict__.items())),\r\n \"__eq__\": lambda self, other: self.__class__.__name__ == other.__class__.__name__ and self.__dict__ == other.__dict__\r\n })\r\n\r\nclass Node: pass\r\nLit = node('Lit', 'v')\r\nNonterminal = node('Nonterminal', 'n')\r\nRegex = node('Regex', 'r')\r\nIdent = node('Ident', 'n')\r\nRule = node('Rule', 'syms tf')\r\nProd = node('Prod', 'nt rules')\r\n\r\n# Grammar -> dict of prod name: prod\r\ndef mkgrammar(ast):\r\n return {prod.nt: prod for prod in ast}\r\n\r\n# Backtracking recursive descent parser\r\n\r\ndef is_(x,y): return isinstance(x,y)\r\n\r\nclass ParseError(Exception): pass\r\n\r\n# Given a grammar node, return an English description of what should be expected\r\ndef expected(g, p):\r\n if is_(p, Prod): return \" or \".join([expected(g, rule) for rule in p.rules])\r\n if is_(p, Rule): return expected(g, p.syms[0])\r\n elif is_(p, Lit): return repr(p.v)\r\n elif is_(p, Nonterminal): return expected(g, g[p.n])\r\n elif is_(p, Regex): return \"/%s/\" % p.r\r\n else: raise Exception()\r\n\r\n# Core parser\r\n# g = grammar, p = production / grammar term, s = string (constant), n = string offset, v = verbose\r\n# The result is the pair (n, r) where n is the new string offset after matching, and\r\n# r is the resulting parse tree (possibly transformed).\r\ndef parse(g, p, s, n=0, v=False):\r\n if is_(p, Prod):\r\n err = Exception(\"Parse error\")\r\n for rule in p.rules:\r\n if v: print(\"Trying rule:\", rule)\r\n offset = n\r\n nodes = []\r\n for sym in rule.syms:\r\n try:\r\n offset, node = parse(g, sym, s, offset, v)\r\n nodes.append(node)\r\n except ParseError as e:\r\n if v: print(\"Backtracking on\", sym)\r\n err = e # TODO: If we record all of these we can get nicer error messages (expected x | y)\r\n break\r\n else: # success, found a rule with no backtracking\r\n if v: print(\"Rule\", rule, \"succeeded\")\r\n if rule.tf: nodes = rule.tf(nodes)\r\n return offset, nodes\r\n raise ParseError(\"Expected %s\" % expected(g, p)) # raise err\r\n elif is_(p, Lit):\r\n if len(s) - n < len(p.v):\r\n raise ParseError(\"Parse error\")\r\n r = s[n:n + len(p.v)]\r\n if r != p.v:\r\n raise ParseError(\"Parse error: expected '%s', got '%s'\" % (p.v, r))\r\n return n + len(p.v), r\r\n elif is_(p, Regex):\r\n m = re.match(p.r, s[n:])\r\n if m is None: raise ParseError(\"Parse error: /%s/ failed to match '%s[...]'\" % (p.r, s[n:n+16]))\r\n return n + len(m.group(0)), m.group(0)\r\n elif is_(p, Nonterminal):\r\n return parse(g, g[p.n], s, n, v)\r\n else: raise Exception(\"Unhandled parse node: \" + str(p))\r\n\r\n# Parse entire string, erroring if it's not entirely matched\r\ndef parseall(g, p, s, n=0, v=False):\r\n (n, r) = parse(g, p, s, n, v)\r\n if n != len(s):\r\n raise ParseError(\"Didn't match entire string\")\r\n return r\r\n\r\n### Metagrammar\r\n\r\n# Parse a transformation string\r\ndef parse_tf(code):\r\n return lambda s: eval(code, None, {'s': s})\r\n\r\nnt = Nonterminal\r\n\r\nbootstrap_grammar = mkgrammar([\r\n # S: prods -> { s[0] };\r\n Prod(\"S\", [ Rule([ nt(\"prods\") ], lambda s: s[0]) ]),\r\n\r\n # prods: prod ws prods -> { [s[0]] + s[2] }\r\n # | prod -> { [s[0]] };\r\n Prod(\"prods\", [\r\n Rule([ nt(\"prod\"), nt(\"ws\"), nt(\"prods\") ], lambda s: [s[0]] + s[2]),\r\n Rule([ nt(\"prod\") ], lambda s: [s[0]])\r\n ]),\r\n\r\n # prod: ident ':' ws rules ws ';' ws -> { Prod(s[0], s[3]) };\r\n Prod(\"prod\", [\r\n Rule([ nt(\"ident\"), Lit(\":\"), nt(\"ws\"), nt(\"rules\"), nt(\"ws\"), Lit(\";\"), nt(\"ws\") ], lambda s: Prod(s[0], s[3]))\r\n ]),\r\n\r\n # rules: rule ws '|' ws rules -> { [s[0]] + s[4] }\r\n # | rule -> { [s[0]] };\r\n Prod(\"rules\", [\r\n Rule([ nt(\"rule\"), nt(\"ws\"), Lit(\"|\"), nt(\"ws\"), nt(\"rules\") ], lambda s: [s[0]] + s[4]),\r\n Rule([ nt(\"rule\") ], lambda s: [s[0]])\r\n ]),\r\n\r\n # rule: syms ws '-> {' /[^}]+/ '}' -> { Rule(s[0], s[3].lstrip(\"->\").strip().lstrip(\"{\").rstrip(chr(125))) };\r\n # | syms -> { Rule(s[0], None) };\r\n Prod(\"rule\", [\r\n Rule([ nt(\"syms\"), nt(\"ws\"), Lit(\"-> {\"), Regex(r\"[^}]+\"), Lit(\"}\") ], lambda s: Rule(s[0], parse_tf(s[3]))),\r\n Rule([ nt(\"syms\") ], lambda s: Rule(s[0], None))\r\n ]),\r\n\r\n # syms: sym ws syms -> { [s[0]] + s[2] }\r\n # | sym -> { [s[0]] }\r\n Prod(\"syms\", [\r\n Rule([ nt(\"sym\"), nt(\"ws\"), nt(\"syms\") ], lambda s: [s[0]] + s[2]),\r\n Rule([ nt(\"sym\") ], lambda s: [s[0]])\r\n ]),\r\n\r\n # sym: ident -> { Nonterminal(s[0]) }\r\n # | /\\\\u002f[^\\\\u002f]+\\\\u002f/ -> { Regex(s[0][1:-1]) }\r\n # | /'[^']+'/ -> { Lit(s[0][1:-1]) };\r\n Prod(\"sym\", [\r\n Rule([ nt(\"ident\") ], lambda s: nt(s[0])),\r\n Rule([ Regex(r\"/[^/]+/\") ], lambda s: Regex(s[0][1:-1])),\r\n Rule([ Regex(r\"'[^']+'\") ], lambda s: Lit(s[0][1:-1]))\r\n ]),\r\n\r\n # ident: /[a-zA-Z_]+/ -> { s[0] };\r\n # ws: /\\s*/ -> { None };\r\n Prod(\"ident\", [ Rule([ Regex(r\"[a-zA-Z_]+\") ], lambda s: s[0]) ]),\r\n Prod(\"ws\", [ Rule([ Regex(r\"\\s*\") ], lambda s: None) ])\r\n])\r\n\r\n# Make a grammar from a grammar definition string\r\ndef grammar(grammar_def):\r\n g = parseall(bootstrap_grammar, bootstrap_grammar[\"S\"], grammar_def, 0, False)\r\n return mkgrammar(g)\r\n\r\nif __name__ == \"__main__\":\r\n # Build a grammar for parsing S-expressions\r\n gram = grammar(\"\"\"S: '(' S '.' S ')' -> { (s[1], s[3]) }\r\n | atom -> { s[0] };\r\n atom: /[A-Z]+/ -> { s[0] };\r\n \"\"\")\r\n\r\n # Parse a test sting\r\n input_str = \"(A.(B.(C.NIL)))\"\r\n print(\"PARSE:\", parseall(gram, gram[\"S\"], input_str))\r\n","repo_name":"darkf/parseparse","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":6107,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"42025469838","text":"import glob,sqlite3\n\nclass CMEModule:\n \n '''\n Module checks LOG files (on IP + Hostname) when connected to the system and prints login information from logs.\n --------------------------------------------------------------------------\n\n crackmapexec smb 10.10.10.10 -M info\n [+] (Pwnd3!): TEST-PC\\administrator:password -id 12 (Prints pwnd accounts with credential ID to login faster)\n \n crackmapexec smb 10.10.10.10 -id 12\n [+] TEST-PC\\administrator:password (Pwn3d!)\n\n crackmapexec smb 10.10.10.10 -M info --ntds (Prints Administrator and krbtgt hashes)\n [+] NTDS: location: /root/.cme/logs/TEST-PC_192.168.56.113_2022-05-31_081331.ntds\n [+] NTDS: Administrator:500:111f37ed915c5716aad3b435b51404ee:eb37f9cd74303274cb923442a7348ef4::: \n [+] NTDS: krbtgt:502:aad3b435b51404eeaad3b435b51404ee:f031bf1f16bba6f9de84dffcc164e0f8::: \n [+] NTDS: LM Crack: [hashcat -m 3000 -a 3 /location] or [john --format=lm /location] detected 18x (Prints when available)\n [+] NTDS: NT Crack: [hashcat -m 1000 -a 3 /location] or [john --format=nt /location] \n \n crackmapexec smb 10.10.10.10 -M info --sam (Prints SAM file only local Administrator with valid password)\n [+] SAM: Administrator:500:aad3b435b51404eeaad3b435b51404ee:8846f7eaee8fb117ad06bdd830b7586c::: \n \n crackmapexec smb 10.10.10.10 -M info --lsa\n [+] LSA: dpapi_machinekey:0x4e467fabe4afb57..ce3730\n [+] LSA: dpapi_userkey:0x0797f33ba6c6043ff7..1558bc\n [+] LSA: NL$KM:c53c6dcd9cff1a4cf8355c4f3c40..32d1be\n\n crackmapexec smb 10.10.10.10 -M info --only-file (Prints all LOG files location)\n [+] LOG: location: /root/.cme/logs/TEST-PC_192.168.56.113_2022-05-31_081331.ntds\n [+] LOG: location: /root/.cme/logs/TEST-PC_192.168.56.113_2022-06-21_113422.sam\n [+] LOG: location: /root/.cme/logs/TEST-PC_192.168.56.113_2022-06-23_180720.secrets\n [+] LOG: location: ...snip.. \n\n crackmapexec smb 10.10.10.10 -M info --only-file --sam\n [+] LOG: location: /root/.cme/logs/TEST-PC_192.168.56.113_2022-06-21_113422.sam\n \n crackmapexec smb 10.10.10.10 -M info --only-file --lsa\n [+] LOG: location: /root/.cme/logs/TEST-PC_192.168.56.113_2022-06-23_180720.secrets\n \n crackmapexec smb 10.10.10.10 -M info --only-file --ntds \n [+] LOG: location: /root/.cme/logs/TEST-PC_192.168.56.113_2022-05-31_081331.ntds\n\n '''\n\n name = 'info'\n description = 'Module checks LOG files (on IP + Hostname) when connected to the system and prints login information from logs'\n supported_protocols = ['smb']\n opsec_safe= True \n multiple_hosts = True \n\n def options(self, context, module_options):\n pass\n\n def on_login(self, context, connection):\n\n if context.sam == True and context.only_files == False:\n files=[]\n for name in glob.glob('/root/.cme/logs/*'+connection.hostname+'_'+connection.host+'*.sam'):\n with open(name, 'r') as f:\n files.append(f.read())\n file ='\\n'.join(map(str, list(set(files))))\n splits = file.splitlines()\n for split in splits:\n if '500' in split.split(':')[1]:\n if '31d6cfe0d16ae931b73c59d7e0c089c0' not in split.split(':')[3]:\n context.log.success('SAM: '+split) \n \n if context.lsa == True and context.only_files == False:\n files=[]\n for name in glob.glob('/root/.cme/logs/*'+connection.hostname+'_'+connection.host+'*.secrets'):\n with open(name, 'r') as f:\n files.append(f.read())\n file ='\\n'.join(map(str, list(set(files))))\n splits = file.splitlines()\n for split in splits:\n context.log.success('LSA: ' + split)\n \n if context.ntds is not None and context.only_files == False:\n files=[]\n for name in glob.glob('/root/.cme/logs/*'+connection.hostname+'_'+connection.host+'*.ntds'):\n with open(name, 'r') as f:\n files.append(f.read())\n file ='\\n'.join(map(str, list(set(files))))\n splits = file.splitlines()\n context.log.success('NTDS: location: '+name) \n for split in splits:\n if '500' in split.split(':')[1]:\n context.log.success('NTDS: '+split) \n if '502' in split.split(':')[1]:\n context.log.success('NTDS: '+split) \n LMcount = 0\n for split in splits:\n if 'aad3b435b51404eeaad3b435b51404ee' not in split.split(':')[2]:\n LMcount += 1\n if LMcount > 0: \n context.log.success('NTDS: LM Crack: [hashcat -m 3000 -a 3 /location] or [john --format=lm /location]' + ' detected '+str(LMcount)+'x')\n context.log.success('NTDS: NT Crack: [hashcat -m 1000 -a 3 /location] or [john --format=nt /location]') \n \n\n if context.only_files == True:\n if context.ntds is not None:\n for name in glob.glob('/root/.cme/logs/*'+connection.hostname+'_'+connection.host+'*.ntds'):\n context.log.success('NTDS: location: ' + name)\n if context.sam == True:\n for name in glob.glob('/root/.cme/logs/*'+connection.hostname+'_'+connection.host+'*.sam'):\n context.log.success('SAM: location: ' + name)\n if context.lsa == True:\n for name in glob.glob('/root/.cme/logs/*'+connection.hostname+'_'+connection.host+'*.secrets'):\n context.log.success('LSA: location: ' + name)\n if context.sam == False and context.lsa == False and context.ntds is None:\n for name in glob.glob('/root/.cme/logs/*'+connection.hostname+'_'+connection.host+'*'):\n context.log.success('LOG: location: ' + name)\n\n\n if context.sam == False and context.lsa == False and context.only_files == False and context.ntds is None:\n for name in glob.glob('/root/.cme/workspaces/*'):\n con = sqlite3.connect(name + '/smb.db')\n cur = con.cursor()\n cur.execute(\"select DISTINCT computers.ip,computers.hostname, computers.domain, users.username, users.password, users.id from users CROSS JOIN admin_relations on users.id = admin_relations.userid CROSS JOIN computers on computers.id = admin_relations.computerid where computers.ip = ? and computers.hostname = ? order by users.id desc\", (connection.host,connection.hostname,))\n rows = cur.fetchall()\n for row in rows:\n context.log.success( '\\033[1;33;40m' + '(Pwnd3!) ' + '\\x1b[0m' + row[2] + '\\\\' + row[3]+\":\"+row[4] + ' -id=' + str(row[5]))\n con.close()\n pass\n\n","repo_name":"quahac/CME-Module-info","sub_path":"info.py","file_name":"info.py","file_ext":"py","file_size_in_byte":6960,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"24324183161","text":"#original idea: https://www.youtube.com/watch?v=Qf3-aDXG8q4\r\n# added a main menu, a pause option, and a scoreboard\r\n\r\nimport pygame\r\nimport random\r\n\r\ndef ball_animation():\r\n global ball_speed_x, ball_speed_y, player_score, opponent_score\r\n #movement\r\n ping.x += ball_speed_x\r\n ping.y += ball_speed_y\r\n\r\n #bounce and speed up function\r\n if ping.top <= 0:\r\n ball_speed_y = (ball_speed_y * -1) + 0.1\r\n if ping.bottom >= screen_height:\r\n ball_speed_y = (ball_speed_y * -1) - 0.1\r\n\r\n if ping.left <= 0:\r\n ball_restart()\r\n player_score = player_score + 1\r\n if ping.right >= screen_width:\r\n ball_restart()\r\n opponent_score = opponent_score + 1\r\n\r\n #collision detection seperate this to make the game harder lol\r\n if ping.colliderect(player) or ping.colliderect(opponent):\r\n ball_speed_x *= -1\r\ndef player_animation():\r\n if player.top <= 0:\r\n player.top = 0\r\n if player.bottom >= 960:\r\n player.bottom = 960\r\ndef opponent_animation():\r\n\r\n #stops movement outside of bounds\r\n if opponent.top <= 0:\r\n opponent.top = 0\r\n if opponent.bottom >= 960:\r\n opponent.bottom = 960\r\n #allows movement following the ball at a set speed\r\n if opponent.centery < ping.y:\r\n opponent.centery += opponent_speed\r\n if opponent.centery > ping.y:\r\n opponent.centery -= opponent_speed\r\n\r\ndef ball_restart():\r\n global ball_speed_x, ball_speed_y\r\n #when someone scores this allows the ball to reset in the middle in a random direction\r\n ping.center = (screen_width/2, screen_height/2)\r\n ball_speed_x = 4.0\r\n ball_speed_y = 4.0\r\n ball_speed_y *= random.choice((1,-1))\r\n ball_speed_x *= random.choice((1,-1))\r\n\r\n\r\n\r\npygame.init()\r\nclock = pygame.time.Clock()\r\n\r\n#window set up\r\nscreen_width = 1280\r\nscreen_height = 960\r\nscreen = pygame.display.set_mode((screen_width , screen_height))\r\npygame.display.set_caption(\"Pong\")\r\n\r\n# Rectangles\r\nping = pygame.Rect(screen_width/2 - 15, screen_height/2 - 15,30 ,30)\r\nplayer = pygame.Rect(screen_width - 20, screen_height/2 - 70,10,140)\r\nopponent = pygame.Rect(10, screen_height/2 - 70, 10, 140)\r\n\r\n#speeds\r\nball_speed_x = 4.0\r\nball_speed_y = 4.0\r\nplayer_speed = 0\r\nopponent_speed = 4.0\r\n\r\n# ScoreBoard variables\r\nplayer_score = 0\r\nopponent_score = 0\r\ngame_font = pygame.font.Font(\"freesansbold.ttf\",32)\r\n\r\n#start menu\r\nhold = False\r\n#checks to see if paused\r\nPaused = False\r\n\r\n\r\n#main menu code\r\nwhile hold == False:\r\n#this code when clicking starts the game\r\n for ev in pygame.event.get():\r\n if ev.type == pygame.QUIT:\r\n pygame.quit()\r\n\r\n if ev.type == pygame.MOUSEBUTTONDOWN:\r\n hold = True\r\n\r\n\r\n # Main Menu visuals\r\n screen.fill(pygame.Color(\"springgreen4\"))\r\n pygame.draw.rect(screen, (0, 0, 255), player)\r\n pygame.draw.rect(screen, (255, 0, 0), opponent)\r\n pygame.draw.ellipse(screen, (255, 255, 255), ping)\r\n pygame.draw.aaline(screen, (255, 255, 255), (screen_width / 2, 0), (screen_width / 2, screen_height))\r\n\r\n #Start Text\r\n game_text = game_font.render(f\"Click To Begin, ESC To Pause\", False, (255, 255, 255))\r\n screen.blit(game_text, (10,10))\r\n\r\n #update window\r\n pygame.display.flip()\r\n clock.tick(60)\r\n\r\n#actual game code loop\r\nwhile hold:\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n exit()\r\n\r\n #player movement\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_DOWN:\r\n player_speed += 7\r\n if event.key == pygame.K_UP:\r\n player_speed -= 7\r\n if event.key == pygame.K_ESCAPE:\r\n if Paused == False:\r\n Paused = True\r\n else:\r\n Paused = False\r\n ball_speed_x = 4.0\r\n ball_speed_y = 4.0\r\n\r\n\r\n if event.type == pygame.KEYUP:\r\n if event.key == pygame.K_DOWN:\r\n player_speed -= 7\r\n if event.key == pygame.K_UP:\r\n player_speed += 7\r\n\r\n #makes the ball move\r\n ball_animation()\r\n player.y += player_speed\r\n #stops you from moving outside\r\n player_animation()\r\n #Opponent movement\r\n opponent_animation()\r\n\r\n\r\n\r\n\r\n # visuals\r\n screen.fill(pygame.Color(\"springgreen4\"))\r\n pygame.draw.rect(screen, (0, 0, 255), player)\r\n pygame.draw.rect(screen, (255, 0, 0), opponent)\r\n pygame.draw.ellipse(screen, (255, 255, 255), ping)\r\n pygame.draw.aaline(screen, (255, 255, 255), (screen_width / 2, 0), (screen_width / 2, screen_height))\r\n\r\n #more scoreboard\r\n game_text = game_font.render(f\"{opponent_score} {player_score}\", False, (255, 255, 255))\r\n screen.blit(game_text, (10,10))\r\n\r\n #update window\r\n pygame.display.flip()\r\n clock.tick(60)\r\n\r\n if Paused == True:\r\n #pauses ball speed\r\n ball_speed_x = 0\r\n ball_speed_y = 0","repo_name":"FrostedFate/Project2","sub_path":"Pong terminal.py","file_name":"Pong terminal.py","file_ext":"py","file_size_in_byte":5096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20960755025","text":"from PIL import Image, ImageDraw, ImageFont\n\ndef transparent_back(img):\n # img = img.convert('RGBA')\n L, H = img.size\n color_0 = (255, 255, 255, 255)\n # print(color_0)\n for h in range(H):\n for l in range(L):\n dot = (l,h)\n color_1 = img.getpixel(dot)\n if color_1 == color_0:\n color_1 = color_1[:-1] + (0,)\n img.putpixel(dot,color_1)\n # return img\n\ndef drawText(img, txt, pos, fontName, fontSize, fontColor):\n # 颜色顺序：蓝色，绿色，红色\n # color2=(156, 238, 255)\n draw = ImageDraw.Draw(im=img)\n code= txt.strip()\n font= ImageFont.truetype(fontName, fontSize)\n textSize= draw.textsize(text=code, font=font)\n tx= pos[0]- textSize[0]// 2\n ty= pos[1]- textSize[1]// 2\n draw.text(xy=(tx, ty), text=code, fill=fontColor, font=font)\n\ndef emptyCircle(img, center, rad):\n draw = ImageDraw.Draw(img)\n x1= center[0]- rad\n y1= center[1]- rad\n x2= center[0]+ rad\n y2= center[1]+ rad\n draw.ellipse((x1, y1, x2, y2), fill=(0,0,0,0), outline='white', width=1)\n # transparent_back(img)\n # return img\n\ndef blackRect(img, alpha):\n draw = ImageDraw.Draw(img)\n x1= 0\n y1= 0\n x2= img.size[0]\n y2= img.size[1]\n draw.rectangle((x1, y1, x2, y2), fill=(0, 0, 0, alpha), outline='black', width=1)\n return img\n\nif __name__=='__main__':\n width= 1208\n height= 720\n img= Image.new('RGBA', (width, height))\n blackRect(img)\n emptyCircle(img, (width//2, height//3), 100)\n im2= Image.open('part01_frame0000.jpg')\n r, g, b, a= img.split()\n im2.paste(img, (0,0), mask=a)\n im2.save('round1.png')\n ","repo_name":"xiaorang/videoMaker","sub_path":"effect.py","file_name":"effect.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"13212858315","text":"\"\"\"\nThis module implements some PyTorch models for the NER task.\n\nExamples:\n $ python model.py\n\"\"\"\nimport argparse\nfrom types import SimpleNamespace\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.modules.sparse import Embedding\nfrom torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n\n\nclass LSTM(nn.Module):\n \"\"\"Vanilla LSTM.\"\"\"\n def __init__(self, config: SimpleNamespace) -> None:\n \"\"\"Define components of the network.\n\n Args:\n config (SimpleNamespace): Configuration mappings.\n \"\"\"\n super().__init__()\n self.embedding = nn.Embedding(config.vocab_size, config.embedding_dim)\n self.lstm = nn.LSTM(input_size=config.embedding_dim,\n hidden_size=config.hidden_size,\n batch_first=True)\n self.fc = nn.Linear(config.hidden_size, config.num_classes)\n\n def forward(self, batch: tuple) -> torch.Tensor:\n \"\"\"Forward pass of the network.\n\n Args:\n batch (tuple): sentences, lengths\n\n Returns:\n torch.Tensor: Log of the softmax for each token.\n \"\"\"\n sentences, lengths = batch\n embeds = self.embedding(sentences)\n embeds = pack_padded_sequence(embeds,\n lengths,\n batch_first=True,\n enforce_sorted=False)\n packed_activations, _ = self.lstm(embeds)\n activations, lengths = pad_packed_sequence(packed_activations,\n batch_first=True,\n padding_value=1)\n\n activations = activations.reshape(-1, activations.shape[2])\n logits = self.fc(activations)\n return F.log_softmax(logits, dim=1)\n\n\ndef loss_fn(outputs: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:\n \"\"\"Custom loss function that ignores PAD tokens when computing the loss.\n\n Args:\n outputs (torch.Tensor): log_softmax from the net.\n labels (torch.Tensor): Ground truth labels, where -1 corresponds to a padded token.\n\n Returns:\n torch.Tensor: Scalar loss value.\n \"\"\"\n labels = labels.reshape(-1) # convert to vector\n mask = (labels >= 0).float() # 1 if greater than 0, 0 if -1\n # bring the -1's into a valid index range\n labels = labels % outputs.shape[1]\n num_tokens = mask.sum() # sum the actual number of tokens\n # extract the value at the ground truth index\n log_probs = outputs[range(outputs.shape[0]), labels]\n # mask padded tokens from the sum\n total_loss = -torch.sum(log_probs * mask)\n loss = total_loss / num_tokens # average the loss\n return loss\n\n\ndef get_predictions(output: torch.Tensor,\n lengths: list,\n concatenate: bool = True) -> list:\n \"\"\"Utility function to remove the padded tokens and retrieve the predicted index.\n\n Args:\n output (torch.Tensor): log_softmax from the net.\n lengths (list): Lengths of original sequences in terms of tokens.\n concatenate (bool, optional): If True, concatenate into one tensor, else return list of lists. Defaults to True.\n\n Returns:\n list: List of lists of predicted indices.\n \"\"\"\n # extract predictions\n max_len = max(lengths)\n preds = output.argmax(dim=1)\n i = 0\n preds_list = []\n for length in lengths:\n start = i * max_len\n stop = start + length\n preds_list.append(preds[start:stop])\n i += 1\n if concatenate:\n return torch.cat(preds_list)\n return preds_list\n\n\ndef recover_labels(padded_labels: torch.Tensor, lengths: list) -> torch.Tensor:\n \"\"\"Utility function to remove the padded labels and retrieve the ground truth labels.\n\n Args:\n padded_labels (torch.Tensor): Vector of labels including -1's.\n lengths (list): Lengths of original sequences in terms of tokens.\n\n Returns:\n torch.Tensor: Unpadded sequence of labels.\n \"\"\"\n # extract labels\n max_len = max(lengths)\n labels_vector = padded_labels.reshape(-1)\n i = 0\n labels_list = []\n for length in lengths:\n start = i * max_len\n stop = start + length\n labels_list.append(labels_vector[start:stop])\n i += 1\n return torch.cat(labels_list)\n\n\ndef translate_predictions(batch: list, label_dict: dict) -> list:\n \"\"\"Iterate over list of lists of predicted indices and convert them to string labels.\n\n Args:\n batch (list): List of lists of predicted indices.\n label_dict (dict): Index to string dictionary.\n\n Returns:\n list: List of lists of string labels.\n \"\"\"\n preds = []\n for pred_sequence in batch:\n pred_list = []\n for pred in pred_sequence.tolist():\n pred_list.append(label_dict[pred])\n preds.append(pred_list)\n return preds\n\n\ndef main(args):\n config = {\n 'vocab_size': 10,\n 'embedding_dim': 5,\n 'hidden_size': 7,\n 'num_classes': 9,\n 'batch_size': 11\n }\n config = SimpleNamespace(**config)\n\n model = LSTM(config)\n # max_sequence_length = 13\n small_batch = [\n torch.tensor([5, 6, 3, 0]),\n torch.tensor([2, 8, 4, 9, 9, 3])\n ]\n lengths = torch.tensor([len(x) for x in small_batch])\n padded_batch = pad_sequence(small_batch, batch_first=True, padding_value=1)\n batch = (padded_batch, lengths)\n expected_shape = (len(small_batch) * lengths.max(), config.num_classes)\n output = model(batch)\n\n # check output shape\n assert output.shape == expected_shape\n\n # check that everything sums to 1 row-wise\n # assert output.sum(dim=1).bool().all()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n args = parser.parse_args()\n main(args)\n","repo_name":"ToddMorrill/knowledge-graphs","sub_path":"kg/ner/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5869,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"38523816061","text":"class Movie():\r\n\r\n \r\n #a movie class object holds information about the movie. \r\n def __init__ (self,movie_title,movie_storyline,poster_image, trailer_youtube):\r\n self.title=movie_title\r\n self.plot = movie_storyline\r\n self.poster_image_url=poster_image\r\n self.trailer_youtube_url=trailer_youtube\r\n\r\n\r\n \r\n \r\n\r\n \r\n \r\n","repo_name":"landers73/Movie_Project","sub_path":"Movie/media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15472590217","text":"\nfrom fastapi import APIRouter, Body\nfrom Server.Utils.Image_Handler import Image_Converter\nfrom Server.Controller.Body_Parts import (\n add_bodypart,\n retrieve_all_bodyparts,\n check_bodypart,\n delete_bodypart_data,\n retrieve_bodypart_by_id,\n update_bodypart,\n)\nfrom Server.Models.Body_Parts import Bodyparts\nfrom fastapi.encoders import jsonable_encoder\n\nrouter = APIRouter()\n\n\n@router.post(\"/\", response_description=\"Add Body Part\")\nasync def add_bodyparts_data(schema: Bodyparts = Body(...)):\n schema = jsonable_encoder(schema)\n bodypart = await check_bodypart(schema)\n if bodypart == False:\n return {\"code\": 200, \"Msg\": \"Body already exists\"}\n if len(schema[\"IMAGE\"]) > 0:\n img_path = await Image_Converter(schema[\"IMAGE\"])\n else:\n img_path = \"\"\n schema[\"IMAGE\"] = str(img_path)\n output = await add_bodypart(schema)\n return {\"code\": 200, \"Msg\": output}\n\n\n@router.get(\"/\", response_description=\"Get all body parts\")\nasync def get_all_bodyparts():\n bodyparts = await retrieve_all_bodyparts()\n if bodyparts:\n return {\"code\": 200, \"Data\": bodyparts}\n return {\"Data\": bodyparts, \"Msg\": \"Empty list return\"}\n\n\n@router.get(\"/{id}\", response_description=\"Get Body Part data by id\")\nasync def get_bodypart_data(id):\n data = await retrieve_bodypart_by_id(id)\n if data:\n return {\"code\": 200, \"Data\": data}\n return {\"Msg\": \"Id may not exist\"}\n\n\n@router.delete(\"/{id}\", response_description=\"Delete Body Part data by id\")\nasync def delete_bodypart(id: str):\n data = await delete_bodypart_data(id)\n if data:\n return {\"code\": 200, \"Msg\": data}\n return {\"Msg\": \"Id may not exist\"}\n\n\n@router.put(\"/{id}\")\nasync def update_bodypart_data(id: str, req: Bodyparts = Body(...)):\n req = jsonable_encoder(req)\n req = {q: s for q,s in req.items() if len(str(s))!=0}\n flags = 0\n if len(req[\"IMAGE\"]) != 0:\n # Del_img= await Delete_Old_Image(id)\n image_path = await Image_Converter(req[\"IMAGE\"])\n req[\"IMAGE\"] = str(image_path)\n flags = 1\n updated_bodypart = await update_bodypart(id, req, flags)\n if updated_bodypart:\n return {\"code\": 200, \"Data\": \"Data updated Successfully\"}\n\n return {\"code\": 404, \"Data\": \"Something Went Wrong\"}\n","repo_name":"PARTH-CHODVADIYA-370/aws_fastapi","sub_path":"Project/Server/Views/Body_parts.py","file_name":"Body_parts.py","file_ext":"py","file_size_in_byte":2276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6289021506","text":"import multiprocessing as mp\nimport os\nimport itertools\n\n\ndef wordCounter(qIn, qOut):\n answer = {}\n for fname, words in iter(qIn.get, None):\n for word in words:\n if fname not in answer:\n answer[fname] = {}\n if word not in answer[fname]:\n answer[fname][word] = 0\n answer[fname][word] += 1\n qOut.put(answer)\n\n\ndef getLines(corpusPath, qIn, numProcs):\n for fname in os.listdir(corpusPath):\n with open(os.path.join(corpusPath, fname)) as infile:\n for i, (k,lines) in enumerate(itertools.groupby((l.strip() for l in infile), lambda line : bool(line) and not line.startswith('\"<') and \"$\" not in line.split(None,1)[0])):\n if not k:\n continue\n qIn.put((fname, [line.split(None,1)[0].strip('\"').strip().lower() for line in lines]))\n\n for _ in range(numProcs):\n qIn.put(None)\n\n\ndef cg3(corpusPath):\n qIn, qOut = [mp.Queue() for _ in range(2)]\n procs = [mp.Process(target=wordCounter, args=(qIn, qOut)) for _ in range(mp.cpu_count() -1)]\n\n lineGetter = mp.Process(target=getLines, args=(corpusPath, qIn, len(procs)))\n lineGetter.start()\n\n for p in procs:\n p.start()\n\n answer = {}\n for _ in range(len(procs)):\n for fname, wdict in qOut.get().items():\n if fname not in answer:\n answer[fname] = {}\n for word,count in wdict.items():\n if word not in answer[fname]:\n answer[fname][word] = 0\n answer[fname][word] += count\n\n for fname in sorted(answer):\n for word in sorted(answer[fname]):\n print(\"{} appeared in {} {} times\".format(word, fname, answer[fname][word]))\n\n for p in procs:\n p.terminate()\n lineGetter.terminate()","repo_name":"arashsa/Academic-Vocabulary-List","sub_path":"cg3/count_cg3.py","file_name":"count_cg3.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7736053374","text":"import pandas as pd\nimport folium, re, sys\n\ndef plot_rows(df, m):\n for index, row in df.iterrows(): # \n color = 'red'\n if 'Gas' in row['FIELD_TYPE']:\n color = 'blue'\n folium.CircleMarker(\n [row['LAT_DD'], row['LON_DD']], \n color=color,\n tooltip=row['FLD_NAME'] + \" \" + str(int(row['EUR_MMBOE'])) + \" mmboe (\" + row['FIELD_TYPE'] + \")\",\n radius=3\n ).add_to(m)\n\ndef fields():\n m = folium.Map(location=[30, 20], zoom_start=3, tiles=\"Stamen Terrain\")\n df = pd.read_csv('oilgas-2018.csv')\n plot_rows(df,m)\n df = pd.read_csv('oilgas-plus.csv')\n plot_rows(df,m)\n m.save('oilgas-out.html')\n\ndef get_linestring(content):\n points = []\n c = content.replace(\"LINESTRING\",\"\").replace(\"(\",\"\").replace(\")\",\"\")\n res = c.split(\",\")\n for x in res:\n cs = x.split()\n if len(cs)<2: continue\n points.append((float(cs[1]), float(cs[0])))\n return points\n \ndef pipelines():\n df = pd.read_csv('pipelines.csv',sep=';')\n m = folium.Map(location=[30, 20], zoom_start=3, tiles=\"Stamen Terrain\")\n for index, row in df.iterrows():\n if 'Cancelled' in row['Status'] or 'Shelved' in row['Status']: continue\n segments = []\n ts = row['PipelineName'] + \" \" + row['StartCountry']+\"-\"+row['EndCountry'] + \" \"\n if 'nan' not in str(row['Capacity']):\n ts += str(row['Capacity']) + \" \" + str(row['CapacityUnits']) + \" \" \n if \"MMcf/d\" in str(row['CapacityUnits']):\n ts += str(int(float(row['Capacity'])*1e6/(5800*1000))) + \" kboe/d \"\n if \"bcm/year\" in str(row['CapacityUnits']) or \"bcm/y\" in str(row['CapacityUnits']):\n ts += str(int(float(row['Capacity'])*1e9/(170*365*1000))) + \" kboe/d \"\n ts += \"(\" + row['Status'] + \") \"\n if \"MULTILINESTRING\" in row['WKTFormat']:\n c = row['WKTFormat'].replace(\"MULTILINESTRING\",\"\")\n linestrings = c.split(\"),\")\n for x in linestrings:\n xx = x.replace(\"(\",\"\").replace(\"(\",\"\").replace(\")\",\"\")\n res = get_linestring(xx)\n segments.append(res)\n elif 'LINESTRING' in row['WKTFormat']:\n points = get_linestring(row['WKTFormat'])\n segments.append(points)\n for points in segments:\n if len(points)==0: continue\n folium.PolyLine(points, color='blue', weight=2.0, tooltip=ts).add_to(m)\n\n df = pd.read_csv('oilgas-2018.csv')\n plot_rows(df,m)\n df = pd.read_csv('oilgas-plus.csv')\n plot_rows(df,m)\n \n m.save('pipelines.html')\n\nif __name__ == \"__main__\": \n #fields()\n pipelines()\n \n","repo_name":"muratk5n/thirdwave","sub_path":"en/2022/01/oilgas.py","file_name":"oilgas.py","file_ext":"py","file_size_in_byte":2640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27742363384","text":"import socket\r\n\r\n#Define Here Target Host Port.\r\ntarget_host = \"ww.example123.com\"\r\ntarget_port = 80\r\n\r\n#Making Socket Object Here AF_INET it's Mean We Are Make TCP Connection.\r\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\r\n#Makeing Connection To Target.\r\nclient.connect((target_host,target_port))\r\n\r\n#Create Variable Data And Write Data. Data Can Be Anything IP,Web Adress. \r\ndata = \"GET / HTTP/1.1\\r\\nHost: example123.com\\r\\n\\r\\n\"\r\n\r\n#Sending Data To Taregt.\r\nclient.send(data.encode('utf-8'))\r\n\r\n#Getting Response From Target.\r\nresponse = client.recv(4096)\r\n\r\nprint(response)\r\n\r\n\r\n\"\"\"\r\nTCP Client:\r\n\r\nThere have been countless times during penetration tests that I’ve needed to whip up a TCP client to\r\ntest for services, send garbage data, fuzz, or any number of other tasks. If you are working within the\r\nconfines of large enterprise environments, you won’t have the luxury of networking tools or\r\ncompilers, and sometimes you’ll even be missing the absolute basics like the ability to copy/paste or\r\nan Internet connection. This is where being able to quickly create a TCP client comes in extremely\r\nhandy.\r\n\"\"\"\r\n\r\n","repo_name":"Cyberking0x1/Python","sub_path":"TCP_client.py","file_name":"TCP_client.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1279308084","text":"import datetime\nfrom env.Persistence import Persistence\n\n\nclass Proposal(object):\n Props = []\n\n @classmethod\n def addProp(cls,trade, trans):\n state = trade.isReal()\n trade.calculateSpread()\n if not state:\n trade.setPlan()\n cls.Props.append(Proposal(trade, trans))\n\n def __init__(self, tr, trans):\n self.trade = tr\n self.trans = trans\n self.dt = datetime.datetime.now()\n\n def accept(self):\n if self.trade.isReal():\n self.trade.addTrans(self.trans, True)\n else:\n self.trade.open()\n self.trade.calculateSpread()\n Persistence.P.update(self.trade)\n [Persistence.P.update(t) for t in self.trans]\n\n def reject(self):\n if not self.trade.isReal():\n self.trade.clear()\n\n def __repr__(self):\n s = repr(self.trade) + '\\n'\n if self.trade.isReal():\n for t in self.trans:\n s += repr(t) + '\\n'\n s += \"--- Add ---\" + '\\n'\n for t in self.trans:\n s += repr(t) + '\\n'\n return s\n\n","repo_name":"mkup/Tradalys","sub_path":"apps/proposal.py","file_name":"proposal.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17859184948","text":"import cv2\nimport numpy as np\nfrom datetime import datetime, timedelta\nimport threading\nfrom uploder import upload_to_blob_storage,upload_video\nimport requests\n\n\n\n\nclass Camera(threading.Thread):\n \n def __init__(self, name,id,buffer,link,db_helper):\n threading.Thread.__init__(self)\n self.name = name\n self.id = id\n self.buffer = buffer\n self.link = link\n self.db_helper = db_helper\n self.recorder_frames = []\n \n # starting the recording in the camera.\n recording_thred = threading.Thread(target=self.record,name=\"recorder\")\n recording_thred.start()\n \n self.cap = cv2.VideoCapture(self.link) #set the input here\n\n self.whT = 320 # width and height of the video\n self.confThreshold =0.5\n self.nmsThreshold= 0.2\n\n self.classes = [\"person\"] # since we are only detecting humans classes contains only person.\n self.modelConfiguration = 'yolov3.cfg' # directory of the yolo config file.\n self.modelWeights = 'yolov3-tiny.weights' # directory of the yolo weight file.\n\n\n self.net = cv2.dnn.readNetFromDarknet(self.modelConfiguration,self.modelWeights)\n self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)\n self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)\n \n self.detectBool = False # intrution detection boolean\n self.buffer[self.id] = cv2.imread('public/loading.png') # setting the first frame as loading.\n self.running = True\n print(self.name,\"camera initiated.\")\n \n def run(self):\n self.detect()\n \n # function to stop the camera.\n def stop_camera(self):\n self.running = False\n \n # function to change the value of the detect_bool\n def set_detectBool(self,bool):\n self.detectBool = bool\n print(\"Detection mode changed....\")\n \n # This is the function which capture the frames from the input and output the moderated frame.\n def detect(self):\n last_detection_time = datetime.now() - timedelta(minutes=16) # pre last detection time. This will take care of sending instrution alearts nearly.\n instrution_clip_time = 15 # seconds\n instrution_frame_collection = [] # instrution frame collection when human detected.\n instrution_clip_collecting = False # boolean value to check, generating the instrution clip.\n instrusion_clip_gap = 60*15 # Gap between instrution alerts.(seconds)\n \n last_frame_checked = datetime.now() - timedelta(seconds=5)\n while self.running:\n success, img = self.cap.read()\n \n if not success:\n continue\n self.buffer[self.id] = img\n self.recorder_frames.append(img)\n frame_check_time_difference = (datetime.now()-last_frame_checked).total_seconds()\n \n \n \n if (self.detectBool or instrution_clip_collecting) and frame_check_time_difference>5:\n \n blob = cv2.dnn.blobFromImage(img,1/255,(self.whT,self.whT),[0,0,0],1,crop=False)\n self.net.setInput(blob)\n \n layerNames = self.net.getLayerNames()\n outputNames = [(layerNames[i - 1]) for i in self.net.getUnconnectedOutLayers()]\n outputs = self.net.forward(outputNames)\n humanDetected = self.findHumans(outputs,img)\n \n if instrution_clip_collecting:\n present_instrution_clip_time = datetime.now() - last_detection_time\n if present_instrution_clip_time.total_seconds() >instrution_clip_time:\n filename = \"intrusion_videos\\suspect \"+last_detection_time.strftime(\"%m_%d_%Y_%H_%M_%S\")+\".mp4\" \n \n #initializing a thread for saving suspect frames into video. \n videoGeneratingThread = threading.Thread(target=self.generateVideo,name=\"suspect-videoGenerator\",args=(instrution_frame_collection,filename,True))\n videoGeneratingThread.start()\n \n instrution_clip_collecting = False\n else:\n instrution_frame_collection.append(img)\n \n elif humanDetected and (datetime.now()-last_detection_time).total_seconds()>instrusion_clip_gap:\n print(\"Intrution detected. Saving a clip from now.\")\n instrution_frame_collection = [img]\n instrution_clip_collecting = True\n last_detection_time = datetime.now()\n \n elif instrution_clip_collecting:\n instrution_frame_collection.append(img)\n \n if frame_check_time_difference>5:\n last_frame_checked = datetime.now()\n\n # cv2.imshow('Image', img)\n key = cv2.waitKey(1)\n \n # this is for terminating the program\n if key == ord(\"q\"):\n break\n self.cap.release()\n\n\n\n \n\n\n\n # This function will identify humans and draw a rectangle around the object.\n # returning boolean input frame contains a human or not. True for a human.\n def findHumans(self,outputs,img):\n hT, wT, cT = img.shape\n bbox = []\n classIds = []\n confs = []\n for output in outputs:\n for det in output:\n scores = det[5:]\n classId = np.argmax(scores)\n confidence = scores[classId]\n if confidence > self.confThreshold:\n w,h = int(det[2]*wT) , int(det[3]*hT)\n x,y = int((det[0]*wT)-w/2) , int((det[1]*hT)-h/2)\n bbox.append([x,y,w,h])\n classIds.append(classId)\n confs.append(float(confidence))\n \n indices = cv2.dnn.NMSBoxes(bbox, confs, self.confThreshold, self.nmsThreshold)\n \n \n humanDetected = False\n for i in indices:\n \n box = bbox[i]\n x, y, w, h = box[0], box[1], box[2], box[3]\n cv2.rectangle(img, (x, y), (x+w,y+h), (255, 0 , 255), 2)\n \n try:\n cv2.putText(img,f'{self.classes[classIds[i]].upper()} {int(confs[i]*100)}%',\n (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 0, 255), 2)\n # print(\"humans detected!\")\n humanDetected = True\n except:\n # print(\"nothing detected!\")\n pass\n \n return humanDetected\n \n # function to do the recording part. We can change the recording gap parameter and set recording clip duration.\n def record(self):\n print(\"recorder started\")\n recording_gap = 60*30 # recording clip set to 30 minutes.\n time_started = datetime.now()\n while True:\n present_time = datetime.now()\n time_delta = (present_time - time_started).total_seconds()\n if time_delta >= recording_gap:\n print(\"saving the recorded clip.\")\n filename = \"records\\Record\"+self.name+time_started.strftime(\"%m_%d_%Y_%H_%M_%S\")+\".mp4\" \n videoGeneratingThread = threading.Thread(target=self.generateVideo,name=\"record-videoGenerator\",args=(self.recorder_frames,filename ))\n videoGeneratingThread.start()\n time_started = present_time\n self.recorder_frames = []\n \n \n\n # This function will generate a video using input frame list.\n def generateVideo(self,frames,filename,intrusion=False):\n datetime_now = datetime.now().strftime(\"%m_%d_%Y_%H_%M_%S\")\n if intrusion:\n print(\"saving intrusion screen shots.\")\n # sending the request\n token = self.db_helper.get_token()\n header = {\"Content-Type\": \"application/json; charset=utf-8\",'Authorization':token}\n\n req = requests.post('https://ninetycamera.azurewebsites.net/api/intrusion/add',json={\"systemId\":self.db_helper.get_system_id()},headers=header)\n # response = req.json()['data']['intrusion']\n response = req.json()\n print(response)\n intrusion_id = response['data']['intrusion']['id']\n # print(response)\n \n # saving suspect images.\n suspect_photo_paths = []\n display_photo_paths = []\n \n for i in range(3):\n image_name = intrusion_id+datetime_now+str(i)+'.png'\n supecpect_photo = cv2.imwrite('intrusion_images/'+image_name,frames[i])\n suspect_photo_paths.append('intrusion_images/'+image_name)\n display_photo_paths.append(intrusion_id+'/'+image_name)\n \n image_link_list = upload_to_blob_storage(suspect_photo_paths,display_photo_paths)\n req = requests.post('https://ninetycamera.azurewebsites.net/api/intrusion/image',json={\"intrusionId\":intrusion_id,'images':image_link_list},headers=header)\n print(image_link_list)\n \n print(\"generating a video from the frames\")\n out = cv2.VideoWriter(filename,cv2.VideoWriter_fourcc(*'mp4v'),10,(640,480))\n for frame in frames:\n out.write(frame)\n\n \n out.release()\n \n if intrusion:\n #sending the video to database\n video_link = upload_video(filename,intrusion_id+\"/\"+datetime_now+\".mp4\")\n req = requests.post('https://ninetycamera.azurewebsites.net/api/intrusion/video',json={\"intrusionId\":intrusion_id,'video':video_link},headers=header)\n # adding the intrusion to db.\n self.db_helper.add_intrusion(intrusion_id,filename,suspect_photo_paths[0],suspect_photo_paths[1],suspect_photo_paths[2],datetime.now().isoformat())\n \n else:\n self.db_helper.add_record_video(filename,datetime.now().isoformat()) # adding the record to db.\n \n \n \n\n \n \n \n\n\n \n ","repo_name":"Ninety-Camera/flask-backend","sub_path":"camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":10176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40896509037","text":"import itertools\nfrom heapq import heappush, heappop\nimport argparse\nimport networkx as nx\nimport numpy as np\nfrom os import system as cmd\nimport os.path\nimport sys\nimport json\n\n\ndef main(vnet_f):\n virt_g = nx.read_dot(vnet_f)\n # nodes_ct = nx.betweenness_centrality(virt_g, normalized=False)\n # print(nodes_ct)\n print(str(\"----------------------\"))\n\n\n # G = virt_g\n\n G=nx.Graph()\n G.add_path([0, 1, 2, 4])\n G.add_path([0, 5, 3, 4])\n G.add_path([5, 2])\n T=nx.Graph()\n # T.add_path(['vs1', 'vs2'])\n T.add_path([0, 1])\n group_members = [3]\n # print(virt_g.node['vs1'])\n\n\n # print(\"\\n\"+str(input_trees))\n all_pair_all_shortest_paths_dict = create_all_pair_all_shortest_path_map(G)\n # print all_pair_all_shortest_paths_dict\n # total = 0\n # for t in input_trees:\n # ts = get_tree_size(G, t[0], t[1], all_pair_all_shortest_paths_dict)\n # total += ts\n # # print (str(t[0]) + \"-\" +str(t[1]) + \":\"+str(ts))\n # print (str(ts))\n # print(total)\n\n\ndef get_test_trees(G):\n dir_path = '/home/hsoni/qos-multicast-compile/exp3-vabw-25-75/dst/'\n file_name = 'group_mem_ia_rd.txt'\n g_dir_names = [30]\n run_dir_names = ['run3']\n # g_dir_names = [10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100]\n # run_dir_names = ['run1','run2','run3','run4','run5']\n input_trees = []\n for gds in g_dir_names:\n for r in run_dir_names:\n group_mem_ia_rd = {}\n file_full_path = dir_path+str(gds)+'/'+r+'/'+file_name\n group_mem_ia_rd_str_key = eval(open(file_full_path, 'r').read())\n for k,v in group_mem_ia_rd_str_key.iteritems():\n group_mem_ia_rd[int(k)] = v\n for k,v in group_mem_ia_rd.iteritems():\n mems = []\n for h, i,r in v:\n ns = G.neighbors(h)\n mems.append(ns[0])\n input_trees.append([mems[0],mems[1:]])\n # print(str(mems[0]) +' '+str(mems[1:]))\n\ndef get_tree_size(network_g, src_node, group_members, all_pair_all_shortest_paths_dict):\n T=nx.Graph()\n T.add_node(src_node)\n for mn in group_members:\n node_score_d = find_node_score(network_g, T, mn, all_pair_all_shortest_paths_dict)\n path_for_new_node = find_highest_node_score_shortest_path_to_tree(\n network_g, T, node_score_d, mn, all_pair_all_shortest_paths_dict)\n T.add_path(path_for_new_node)\n return len(T.edges())\n\n\ndef find_node_score(network_g, partial_tree_g, new_node, all_pair_all_shortest_paths_dict):\n node_score_dict = {}\n for nn in network_g.nodes():\n node_score_dict[nn] = 0\n node_list = partial_tree_g.nodes()\n node_list.append(new_node)\n for tn in partial_tree_g.nodes():\n # for nn, nd in network_g.nodes(data=True):\n for nn in network_g.nodes():\n # if tn == nn or nd['type']=='host':\n if tn == nn:\n continue\n path_nodes_set = set()\n for p in all_pair_all_shortest_paths_dict[(tn,nn)]:\n # for p in nx.all_shortest_paths(network_g,source=tn,target=nn):\n for np in p:\n if np == tn or np == nn:\n continue\n path_nodes_set.add(np)\n # print str(tn) + ' ' +str(nn)\n # print path_nodes_set\n for n in path_nodes_set:\n node_score_dict[n] += 1\n # print node_score_dict\n return node_score_dict\n\n\ndef create_all_pair_all_shortest_path_map(g):\n st_all_shortest_paths = {}\n for s in g.nodes():\n for t in g.nodes():\n paths = []\n for path in nx.all_shortest_paths(g,source=s,target=t):\n paths.append(path)\n st_all_shortest_paths[(s,t)] = paths\n # print st_all_shortest_paths\n return st_all_shortest_paths\n\n\ndef find_highest_node_score_shortest_path_to_tree(network_g, partial_tree_g,\n node_score_dict, new_node, all_pair_all_shortest_paths_dict):\n current_min_path_length = sys.maxsize\n max_node_score = 0\n nearest_node_paths = None\n max_node_score_shortest_path = None\n for tn in partial_tree_g.nodes():\n paths = all_pair_all_shortest_paths_dict[(new_node,tn)]\n if len(paths) > 0 and len(paths[0]) < current_min_path_length:\n current_min_path_length = len(paths[0])\n nearest_node = tn\n nearest_node_paths = paths\n # print str('***********************')\n # print ('minimum length paths')\n # print nearest_node_paths\n # print str('***********************')\n for p in nearest_node_paths:\n temp_score = 0\n for np in p:\n temp_score += node_score_dict[np]\n if temp_score > max_node_score:\n max_node_score = temp_score\n max_node_score_shortest_path = p\n # print ('Path added: '+ str(max_node_score_shortest_path))\n return max_node_score_shortest_path\n\n\n\ndef get_steiner_tree_size(network_g, src_node, group_members, all_pair_all_shortest_paths_dict):\n T=nx.Graph()\n T.add_node(src_node)\n for mn in group_members:\n path_length = sys.maxsize\n shortest_path_to_tree = None\n for tn in T.nodes():\n paths_for_mn = all_pair_all_shortest_paths_dict[(tn, mn)]\n if path_length > len(paths_for_mn[0]):\n path_length = len(paths_for_mn[0])\n shortest_path_to_tree = paths_for_mn[0]\n T.add_path(shortest_path_to_tree)\n return len(T.edges())\n\n\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--virtual_net\", help=\"DOT file specifying experimental network topology\")\n args = parser.parse_args()\n main(args.virtual_net)\n # main(args.physical_net, args.virtual_net, args.mapping, args.bw_map,\n # args.exe_schedule, args.sender_schedule, args.num_groups)\n\n# #---test example\n# G=nx.Graph()\n# G.add_path([0, 1, 2, 4])\n# G.add_path([0, 5, 3, 4])\n# G.add_path([5, 2])\n# print([p for p in nx.all_shortest_paths(G,source=0,target=4)])\n# print(nx.betweenness_centrality(G, normalized=False))\n# #---test example","repo_name":"hksoni/L2BM-Simulator","sub_path":"NBCDynamicMulticastTree.py","file_name":"NBCDynamicMulticastTree.py","file_ext":"py","file_size_in_byte":6184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39984612654","text":"from typing import List\ndef combinationSum(candidates: List[int], target: int):\n if len(candidates) == 0:\n return []\n \n n = len(candidates) - 1\n com = []\n coms = []\n\n def backtrace(com):\n\n if sum(com) == target:\n coms.append(com.copy())\n return\n if sum(com) < target:\n for i in candidates:\n if len(com) == 0 or i >= com[-1]:\n com.append(i)\n backtrace(com)\n com.pop()\n\n backtrace(com)\n\n return coms\n\nprint(combinationSum([2,3,5], 8))","repo_name":"Alwaysproblem/simplecode","sub_path":"leetcode/python/combinationSum.py","file_name":"combinationSum.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"22471784517","text":"\"\"\"\nnsphere.py\n--------------\n\nFunctions for fitting and minimizing nspheres:\ncircles, spheres, hyperspheres, etc.\n\"\"\"\nimport numpy as np\n\nfrom . import util\nfrom . import convex\n\nfrom .constants import log, tol\n\ntry:\n # scipy is a soft dependency\n from scipy import spatial\n from scipy.optimize import leastsq\nexcept BaseException as E:\n # raise the exception when someone tries to use it\n from . import exceptions\n leastsq = exceptions.ExceptionWrapper(E)\n spatial = exceptions.ExceptionWrapper(E)\n\ntry:\n import psutil\n\n def _MAX_MEMORY():\n # if we have psutil check actual free memory when called\n return psutil.virtual_memory().free / 2.0\nexcept BaseException:\n def _MAX_MEMORY():\n # use a hardcoded best guess estimate\n return 1e9\n\n\ndef minimum_nsphere(obj):\n \"\"\"\n Compute the minimum n- sphere for a mesh or a set of points.\n\n Uses the fact that the minimum n- sphere will be centered at one of\n the vertices of the furthest site voronoi diagram, which is n*log(n)\n but should be pretty fast due to using the scipy/qhull implementations\n of convex hulls and voronoi diagrams.\n\n Parameters\n ----------\n obj : (n, d) float or trimesh.Trimesh\n Points or mesh to find minimum bounding nsphere\n\n Returns\n ----------\n center : (d,) float\n Center of fitted n- sphere\n radius : float\n Radius of fitted n-sphere\n \"\"\"\n\n # reduce the input points or mesh to the vertices of the convex hull\n # since we are computing the furthest site voronoi diagram this reduces\n # the input complexity substantially and returns the same value\n points = convex.hull_points(obj)\n\n # we are scaling the mesh to a unit cube\n # this used to pass qhull_options 'QbB' to Voronoi however this had a bug somewhere\n # to avoid this we scale to a unit cube ourselves inside this function\n points_origin = points.min(axis=0)\n points_scale = points.ptp(axis=0).min()\n points = (points - points_origin) / points_scale\n\n # if all of the points are on an n-sphere already the voronoi\n # method will fail so we check a least squares fit before\n # bothering to compute the voronoi diagram\n fit_C, fit_R, fit_E = fit_nsphere(points)\n # return fit radius and center to global scale\n fit_R = (((points - fit_C)**2).sum(axis=1).max() ** .5) * points_scale\n fit_C = (fit_C * points_scale) + points_origin\n\n if fit_E < 1e-6:\n # points were on an n-sphere so just return fit\n return fit_C, fit_R\n\n # calculate a furthest site voronoi diagram\n # this will fail if the points are ALL on the surface of\n # the n-sphere but hopefully the least squares check caught those cases\n # , qhull_options='QbB Pp')\n voronoi = spatial.Voronoi(points, furthest_site=True)\n\n # find the maximum radius^2 point for each of the voronoi vertices\n # this is worst case quite expensive but we have taken\n # convex hull to reduce n for this operation\n # we are doing comparisons on the radius squared then rooting once\n try:\n # cdist is massivly faster than looping or tiling methods\n # although it does create a very large intermediate array\n # first, get an order of magnitude memory size estimate\n # a float64 would be 8 bytes per entry plus overhead\n memory_estimate = len(voronoi.vertices) * len(points) * 9\n if memory_estimate > _MAX_MEMORY():\n raise MemoryError\n radii_2 = spatial.distance.cdist(\n voronoi.vertices, points,\n metric='sqeuclidean').max(axis=1)\n except MemoryError:\n # log the MemoryError\n log.warning('MemoryError: falling back to slower check!')\n # fall back to a potentially very slow list comprehension\n radii_2 = np.array([((points - v) ** 2).sum(axis=1).max()\n for v in voronoi.vertices])\n\n # we want the smallest sphere so take the min of the radii\n radii_idx = radii_2.argmin()\n\n # return voronoi radius and center to global scale\n radius_v = np.sqrt(radii_2[radii_idx]) * points_scale\n center_v = (voronoi.vertices[radii_idx] *\n points_scale) + points_origin\n\n if radius_v > fit_R:\n return fit_C, fit_R\n\n return center_v, radius_v\n\n\ndef fit_nsphere(points, prior=None):\n \"\"\"\n Fit an n-sphere to a set of points using least squares.\n\n Parameters\n ------------\n points : (n, d) float\n Points in space\n prior : (d,) float\n Best guess for center of nsphere\n\n Returns\n ---------\n center : (d,) float\n Location of center\n radius : float\n Mean radius across circle\n error : float\n Peak to peak value of deviation from mean radius\n \"\"\"\n # make sure points are numpy array\n points = np.asanyarray(points, dtype=np.float64)\n # create ones so we can dot instead of using slower sum\n ones = np.ones(points.shape[1])\n\n def residuals(center):\n # do the axis sum with a dot\n # this gets called a LOT so worth optimizing\n radii_sq = np.dot((points - center) ** 2, ones)\n # residuals are difference between mean\n # use our sum mean vs .mean() as it is slightly faster\n return radii_sq - (radii_sq.sum() / len(radii_sq))\n\n if prior is None:\n guess = points.mean(axis=0)\n else:\n guess = np.asanyarray(prior)\n\n center_result, return_code = leastsq(residuals,\n guess,\n xtol=1e-8)\n\n if return_code not in [1, 2, 3, 4]:\n raise ValueError('Least square fit failed!')\n\n radii = util.row_norm(points - center_result)\n radius = radii.mean()\n error = radii.ptp()\n return center_result, radius, error\n\n\ndef is_nsphere(points):\n \"\"\"\n Check if a list of points is an nsphere.\n\n Parameters\n -----------\n points : (n, dimension) float\n Points in space\n\n Returns\n -----------\n check : bool\n True if input points are on an nsphere\n \"\"\"\n center, radius, error = fit_nsphere(points)\n check = error < tol.merge\n return check\n","repo_name":"mikedh/trimesh","sub_path":"trimesh/nsphere.py","file_name":"nsphere.py","file_ext":"py","file_size_in_byte":6134,"program_lang":"python","lang":"en","doc_type":"code","stars":2558,"dataset":"github-code","pt":"3"}
+{"seq_id":"32270666342","text":"# (21)*****************************Handwritten Digit Recognition on MNIST Dataset in Machine Learning***********************\n\n# ******What is MNIST?\n# ---Set of 70000 small images of digits handwritten by high school students and employees of the US Census Bureau\n# ---All images are labelled with the respective digit they represent\n# ---MNIST is the hello world of machine learning\n# ---There are 70000 images and each image has 784 features\n# ---Each image is 28X28 pixels, and each feature simply represents one pic\n\n# **************Agenda\n# ----In the last tutorials we saw how to solve an ML problem end to end\n# ----In this tutorial we will first fetch the data and then split it into train and test sets\n# ----After that we will apply few ML algorithms to detect a given digit\n\n# ----importing library\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_openml\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import cross_val_predict\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import precision_score, recall_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import precision_recall_curve\n\n# ----importing dataset of mnist\nmnist= fetch_openml('mnist_784')\nprint(mnist)\n# ----So we also known that the features and labels so we convert the features and labels in to x and y shape \nx,y=mnist['data'],mnist['target']\n# print(x)\n# print(y)\nx.shape\n# y.shape\n\n# ----the dataset is dataframe shape we convert the array shape----if we use any sklearn function then it is automatically converted array shape but now the data is all set thats whu we convert array shape\nx0=np.array(x)\nprint(x0.dtype)\n\n# ---check a data\nx1=x0[5101]\nprint(x1.shape)\nx3=x1.reshape(28,28)\n# ----plotting the graph\nplt.imshow(x3,cmap=matplotlib.cm.binary,interpolation='nearest')\nplt.axis('off')\nplt.show()\n# ----checking the value by using target\ny[3601]\n\n# ---Now splitting the data into train and test -----we also splitting the data using sklearn library\nx_train , x_test = x0[:6000],x0[6000:7000]\ny_train, y_test = y[:6000], y[6000:7000]\n\n\n# -----shuffiling the training dataset because we want to equally distributed data in training and testing\nshuffle_index= np.random.permutation(6000)\ny_train= y_train[shuffle_index]\nx_train= x_train[shuffle_index]\n\n\n# *********************Creating a 2 Detector\n# ----we creat a binary classification\n# ----We simply check that the number is achully 2 or not\ny_train=y_train.astype(np.int8)\ny_test= y_test.astype(np.int8)\ny_train_2= (y_train==2)\ny_test_2 = (y_test==2)\n\n# y_train_2\ny_test_2\n# ************************Creating a classifier model\nmodel=LogisticRegression(tol=0.1)\n# -----Now fit the model\nmodel.fit(x_train,y_train_2)\n# ----Now predicted the value\nmodel.predict([x0[3689]])\n\n\n# ********************Cross Validation\na=cross_val_score(model,x_train,y_train_2 ,cv=3,scoring='accuracy')\na.mean()\n\n\n\n\n\n\n\n\n\n# *********************Predicted Values using sklearn\ny_train_pred = cross_val_predict(model, x_train,y_train_2, cv=3)\nprint(y_train_pred)\n\n# ********************Calculating Confusion Matrix\nconfusion = confusion_matrix(y_train_2, y_train_pred)\nprint(confusion)\n\n# -----Now we just check it that the perfect prediction\nconfusion_matrix(y_train_2, y_train_2)\n\n\n# ----Precsion and Recall\nprecision=precision_score(y_train_2, y_train_pred)\nprint(precision)\nrecall= recall_score(y_train_2, y_train_pred)\nprint(recall)\n# ----F1 Score\nf1=f1_score(y_train_2,y_train_pred)\nprint(f1)\n\n\n# *******************Precision Recall Curve\n# ----we need values noot bool so we use other method to check the threshholds and precision recall curve\ny_scores=cross_val_predict(model,x_train,y_train_2,cv=3,method=\"decision_function\")\n# precision,recalls, thresholds=precision_recall_curve(y_train_2,y_train_pred)\nprecision,recalls, thresholds=precision_recall_curve(y_train_2,y_scores)\n# -----checking precision\nprecision\n# -----checking recall\nrecalls\n# -----checking thresholds\nthresholds\n\n\n# *******************Plotting the precision Recall Curve\nplt.plot(thresholds,precision[:-1], \"b--\", label=\"precision\")\nplt.plot(thresholds, recalls[:-1], \"g-\",label=\"Recall\")\nplt.xlabel(\"Thresholds\")\nplt.legend(loc=\"upper left\")\nplt.ylim([0,1])\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"tayyabmalik4/MachineLearningWithTayyab","sub_path":"23_Evaluating_Classifiers_Precision_Recall_f1-scores_practical_ML.py","file_name":"23_Evaluating_Classifiers_Precision_Recall_f1-scores_practical_ML.py","file_ext":"py","file_size_in_byte":4359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11640908319","text":"#!/usr/bin/env python3\n\"\"\"\nLeetcode 98. Validate Binary Search Tree\nhttps://leetcode.com/problems/validate-binary-search-tree/\n\"\"\"\n\nfrom typing import Optional\nfrom tree_node import TreeNode\n\n\ndef isValidBST(root: Optional[TreeNode]) -> bool:\n \"\"\"\n Time complexity: O(n)\n Space complexity: O(n) - worst case for skewed tree\n Approach: DFS\n - Check if the current node is within the min and max range\n - Recursively check if the left and right subtrees meet the above conditions\n - Note: The min and max range is updated for each node\n - If exploring left subtree, update max range to current node value - 1\n - If exploring right subtree, update min range to current node value + 1\n \"\"\"\n left = right = root\n\n if not root:\n return False\n while left.left:\n left = left.left\n while right.right:\n right = right.right\n return is_bst(root, left.val, right.val)\n\n\ndef is_bst(root: Optional[TreeNode], min_val: int, max_val: int) -> bool:\n \"\"\"\n Check if tree is bst by check min and max range for of values for a particular node\n \"\"\"\n if not root:\n return True\n if root.val < min_val or root.val > max_val:\n return False\n return (is_bst(root.left, min_val, root.val - 1) and\n is_bst(root.right, root.val + 1, max_val))\n","repo_name":"ngugimuchangi/coding_challenges","sub_path":"leetcode/trees/validate_binary_search_tree.py","file_name":"validate_binary_search_tree.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34094346389","text":"from tm.voc import XSD\nfrom mio.jtm.json import JSONWriter\nfrom mappa._internal.it import one_of, no\nfrom mappa.utils import is_default_name, is_default_name_type\n\n\nclass JTMTopicMapWriter(object):\n \"\"\"\\\n Writer for JSON Topic Maps (JTM).\n \"\"\"\n __slots__ = ['_writer', '_base', 'prettify', 'export_iids',\n 'omit_loners', 'version', 'prefixes']\n\n def __init__(self, out, base, version=1.0, prefixes=None):\n if not out:\n raise ValueError('\"out\" is not specified')\n if not base:\n raise ValueError('\"base\" is not specified')\n self._writer = JSONWriter(out)\n self._base = base\n self.prettify = False\n self.export_iids = True\n self.version = version\n self.omit_loners = False\n self.prefixes = {}\n if prefixes:\n for ident, iri in prefixes.iteritems():\n self.add_prefix(ident, iri)\n\n def add_prefix(self, ident, iri):\n \"\"\"\\\n Adds a prefix/IRI binding.\n \"\"\"\n if self.version < 1.1:\n raise ValueError('JTM versions <= 1.1 do not support prefixes')\n else:\n if u'xsd' == ident.lower() and iri != XSD:\n raise ValueError('The prefix \"xsd\" is reserved')\n self.prefixes[ident] = iri\n\n def write(self, topicmap):\n \"\"\"\\\n Serializes the specified ``topicmap``.\n \"\"\"\n writer = self._writer\n writer.prettify = self.prettify\n writer.start()\n writer.start_object()\n writer.key_value(u'version', unicode(self.version))\n self._write_prefixes()\n writer.key_value(u'item_type', u'topicmap')\n self._write_iids(topicmap)\n self._write_reifier(topicmap)\n write_if_available = self._write_if_available\n write_if_available(u'topics', topicmap.topics, self._write_topic)\n write_if_available(u'associations', topicmap.associations, self._write_association)\n writer.end_object()\n writer.end()\n\n def _write_prefixes(self):\n if self.prefixes:\n writer = self._writer\n writer.key(u'prefixes')\n writer.start_object()\n for ident, iri in self.prefixes.iteritems():\n writer.key_value(ident, iri)\n writer.end_object()\n\n def _uri(self, uri):\n if self.version >= 1.1:\n for prefix, iri in self.prefixes.iteritems():\n if uri.startswith(iri):\n return u'[%s:%s]' % (prefix, uri[len(iri):])\n return uri\n\n def _uris(self, uris):\n uri = self._uri\n return (uri(u) for u in uris)\n\n def _write_topic(self, topic):\n \"\"\"\\\n Serializes the ``topic``.\n \"\"\"\n iids = tuple(topic.iids)\n sids = tuple(topic.sids)\n slos = tuple(topic.slos)\n if (self.omit_loners or is_default_name_type(topic)) and _is_omitable(topic, sids, slos, iids):\n return\n writer = self._writer\n writer.start_object()\n if self.export_iids or not (sids or slos):\n writer.array(u'item_identifiers', self._uris(iids))\n writer.array(u'subject_identifiers', self._uris(sids))\n writer.array(u'subject_locators', self._uris(slos))\n write_if_available = self._write_if_available\n write_if_available(u'occurrences', topic.occurrences, self._write_occurrence)\n write_if_available(u'names', topic.names, self._write_name)\n writer.end_object()\n\n def _write_association(self, association):\n \"\"\"\\\n Serializes the ``association`` incl. its roles.\n \"\"\"\n writer = self._writer\n start_object, end_object = self._writer.start_object, self._writer.end_object\n write_iids, write_type, write_reifier = self._write_iids, self._write_type, self._write_reifier\n start_object()\n write_iids(association)\n write_type(association)\n self._write_scope(association)\n write_reifier(association)\n writer.key(u'roles')\n writer.start_array()\n for role in association.roles:\n start_object()\n write_iids(role)\n write_type(role)\n writer.key_value(u'player', self._topic_ref(role.player))\n write_reifier(role)\n end_object()\n writer.end_array()\n writer.end_object()\n\n def _write_name(self, name):\n \"\"\"\\\n Serializes the ``name``.\n \"\"\"\n writer = self._writer\n writer.start_object()\n self._write_iids(name)\n if not is_default_name(name):\n self._write_type(name)\n self._write_scope(name)\n self._write_reifier(name)\n self._write_value(name)\n self._write_if_available(u'variants', name.variants, self._write_variant)\n self._writer.end_object()\n\n def _write_variant(self, variant):\n \"\"\"\\\n Serializes the ``variant``.\n \"\"\"\n self._writer.start_object()\n self._write_iids(variant)\n self._write_scope(variant)\n self._write_reifier(variant)\n self._write_value(variant)\n self._write_datatype(variant)\n self._writer.end_object()\n\n def _write_occurrence(self, occurrence):\n \"\"\"\\\n Serializes the ``occurrence``.\n \"\"\"\n self._writer.start_object()\n self._write_iids(occurrence)\n self._write_type(occurrence)\n self._write_scope(occurrence)\n self._write_reifier(occurrence)\n self._write_value(occurrence)\n self._write_datatype(occurrence)\n self._writer.end_object()\n\n def _write_value(self, tmc):\n \"\"\"\\\n Writes the ``value`` attribute of ``tmc``.\n \"\"\"\n self._writer.key_value(u'value', tmc.value)\n\n def _write_datatype(self, tmc):\n \"\"\"\\\n Writes the ``datatype`` attribute of ``tmc``.\n \"\"\"\n dt = tmc.datatype\n if not XSD.string == dt:\n if self.version >= 1.1:\n if dt.startswith(XSD):\n dt = u'[xsd:%s]' % dt.replace(XSD, '')\n else:\n dt = self._uri(dt)\n self._writer.key_value(u'datatype', dt)\n\n def _write_iids(self, reifiable):\n \"\"\"\\\n Writes the item identifiers of ``reifiable`` if any.\n \"\"\"\n if self.export_iids:\n self._writer.array(u'item_identifiers', self._uris(reifiable.iids))\n\n def _write_scope(self, scoped):\n \"\"\"\\\n Writes the scope of ``scoped`` if it is not unconstrained.\n \"\"\"\n self._writer.array(u'scope', map(self._topic_ref, scoped.scope))\n\n def _write_type(self, typed):\n \"\"\"\\\n Writes the type of the ``typed`` Topic Maps construct.\n \"\"\"\n self._writer.key_value(u'type', self._topic_ref(typed.type))\n\n def _write_reifier(self, reifiable):\n \"\"\"\\\n Writes the reifier of the ``reifiable`` Topic Maps construct, if any.\n \"\"\"\n if reifiable.reifier:\n self._writer.key_value(u'reifier', self._topic_ref(reifiable.reifier))\n\n def _topic_ref(self, topic):\n \"\"\"\\\n Returns a reference to the ``topic``. \n \n If the topic has no subject identifiers, subject locators or \n item identifiers, an item identifier is generated with the \n ``base`` and the id of the topic.\n \"\"\"\n res = one_of(topic.sids)\n if res:\n res = u'si:%s' % self._uri(res)\n else:\n res = one_of(topic.slos)\n if res:\n res = u'sl:%s' % self._uri(res)\n else:\n res = one_of(topic.iids)\n if res:\n self._uri(res)\n else:\n res = u'%s#id-%s' % (self._base, topic.id)\n res = u'ii:%s' % res\n return res\n\n def _write_if_available(self, key, iterable, func):\n \"\"\"\\\n Serializes the ``iterable`` using ``func`` iff ``iterable`` is not empty.\n \"\"\"\n writer = self._writer\n written = False\n for i, e in enumerate(iterable):\n if not i:\n writer.key(key)\n writer.start_array()\n written = True\n func(e)\n if written:\n writer.end_array()\n\n\ndef _is_omitable(topic, sids, slos, iids):\n \"\"\"\\\n Returns if the `topic` has just one identity and no further \n characteristics (occurrences, names).\n \"\"\"\n # No need to check 'roles played' and 'reified' here since\n # the reified statement refers to the topic already and\n # the roles played are serialized through the associations\n return len(iids) + len(sids) + len(slos) <= 1 \\\n and no(topic.names) \\\n and no(topic.occurrences)\n","repo_name":"heuer/mappa","sub_path":"mappa.jtm/mappaext/jtm/jtmwriter.py","file_name":"jtmwriter.py","file_ext":"py","file_size_in_byte":8763,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"11003981041","text":"class Solution(object):\n def rotate(self, matrix):\n \"\"\"\n You are given an n x n 2D matrix representing an image.\n Rotate the image by 90 degrees (clockwise). You must do this in-place.\n\n Time complexity: O(N)\n Space complexity: O(1)\n\n :type matrix: List[List[int]]\n :rtype: void Do not return anything, modify matrix in-place instead.\n \"\"\"\n start = 0\n n = len(matrix) - 1 # The length of the initial matrix\n end = n\n\n # If there are values between the start and end\n while end - start >= 1:\n for j in range(start, end):\n temp = matrix[start][j]\n matrix[start][j] = matrix[n - j][start]\n matrix[n - j][start] = matrix[n - start][n - j]\n matrix[n - start][n - j] = matrix[j][n - start]\n matrix[j][n - start] = temp\n\n # Traverse diagonally\n start += 1\n end -= 1\n\n\ns = Solution()\ni = [\n [5, 1, 9, 11],\n [2, 4, 8, 10],\n [13, 3, 6, 7],\n [15, 14, 12, 16]\n]\ns.rotate(i)\nprint(i)\n\n","repo_name":"terrykwon/practice_problems","sub_path":"leetcode/48_rotate_image.py","file_name":"48_rotate_image.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32349841202","text":"# 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 height(self,root):\n if root==None:\n return 0\n return 1+max(self.height(root.left),self.height(root.right))\n def deepestLeavesSum(self, root: Optional[TreeNode]) -> int:\n h=self.height(root)\n from collections import deque\n q=deque()\n q.append(root)\n level=0\n while len(q)>0:\n size=len(q)\n summ=0\n for i in range(size):\n node=q.popleft()\n summ=summ+node.val\n if node.left:\n q.append(node.left)\n if node.right:\n q.append(node.right)\n level+=1\n if level==h:\n return summ\n return 0 \n ","repo_name":"mayank9200/My_codes","sub_path":"1302-deepest-leaves-sum/1302-deepest-leaves-sum.py","file_name":"1302-deepest-leaves-sum.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"36053298535","text":"import sys\ninput = sys.stdin.readline\n\nN,M,K = map(int, input().split())\n\nmemo = [[1] * M for _ in range(N)]\nif K == 0:\n for i in range(1, N):\n for j in range(1, M):\n memo[i][j] = memo[i - 1][j] + memo[i][j - 1]\n print(memo[N-1][M-1])\nelse:\n y = (K-1) // M\n x = (K-1) % M\n\n for i in range(1, y+1):\n for j in range(1, x+1):\n memo[i][j] = memo[i - 1][j] + memo[i][j - 1]\n\n for i in range(y+1, N):\n for j in range(x+1, M):\n memo[i][j] = memo[i - 1][j] + memo[i][j - 1]\n\n print(memo[y][x] * memo[-1][-1])\n\n","repo_name":"Sangmin627/AlgoStudy2023","sub_path":"상진/Baekjoon/Random-Defence/BOJ10164.py","file_name":"BOJ10164.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18318881814","text":"from collections import deque\nimport re\nfrom typing import List\n\n\nclass PrimaryKinshipCode:\n code_set = {'B', 'C', 'D', 'F', 'H', 'M', 'S', 'W', 'Z'}\n\n code2generation = {'F': -1, 'M': -1,\n 'S': 1, 'D': 1,\n 'H': 0, 'W': 0, 'C': 0,\n 'B': 0, 'Z': 0}\n\n code2distance = {'F': 1, 'M': 1,\n 'S': 1, 'D': 1,\n 'H': 1, 'W': 1, 'C': 1,\n 'B': 2, 'Z': 2}\n\n def __init__(self, kinship_code: str):\n self.kinship_code = kinship_code\n self.kinshipType = kinship_code[0]\n self.seniority = self.get_seniority()\n self.generation = self.get_generation()\n self.kin_distance = self.get_kin_distance()\n self.gender_pair = self.get_gender_pair()\n # self.inverse_kinship = self.get_inverse_kinship()\n\n def to_str(self):\n return self.kinship_code\n\n def __str__(self):\n return ''\n\n def __repr__(self):\n return ''\n\n def get_seniority(self):\n if len(self.kinship_code) > 1:\n seniority = self.kinship_code[1:]\n if (seniority.isdigit()) or (seniority in {'+', '-'}):\n return seniority\n else:\n return ''\n else:\n return ''\n\n def get_generation(self):\n gen_diff: int = self.code2generation.get(self.kinshipType, None)\n return gen_diff\n\n def get_kin_distance(self):\n kin_dist: int = self.code2distance.get(self.kinshipType, None)\n return kin_dist\n\n def get_gender_pair(self):\n gender_pair = ['?', '?']\n if self.kinshipType == 'F':\n gender_pair[1] = 'm'\n elif self.kinshipType == 'M':\n gender_pair[1] = 'f'\n elif self.kinshipType == 'S':\n gender_pair[1] = 'm'\n elif self.kinshipType == 'D':\n gender_pair[1] = 'f'\n elif self.kinshipType == 'H':\n gender_pair[0] = 'f'\n gender_pair[1] = 'm'\n elif self.kinshipType == 'W':\n gender_pair[0] = 'm'\n gender_pair[1] = 'f'\n elif self.kinshipType == 'C':\n gender_pair[0] = 'm'\n gender_pair[1] = 'f'\n elif self.kinshipType == 'B':\n gender_pair[1] = 'm'\n elif self.kinshipType == 'Z':\n gender_pair[1] = 'f'\n else:\n pass\n return gender_pair\n\n def get_inverse_kinship(self, gender1: str):\n inverse_kinship = ''\n inverse_seniority = ''\n if self.kinshipType == 'H':\n inverse_kinship = 'W'\n if self.kinshipType == 'W':\n inverse_kinship = 'H'\n if self.kinshipType == 'C':\n inverse_kinship = 'H'\n\n if self.kinshipType == 'F' or self.kinshipType == 'M':\n if gender1 == 'm':\n inverse_kinship = 'S'\n elif gender1 == 'f':\n inverse_kinship = 'D'\n else:\n raise TypeError('Wrong inverse kinship!')\n\n if self.kinshipType == 'S' or self.kinshipType == 'D':\n if gender1 == 'm':\n inverse_kinship = 'F'\n elif gender1 == 'f':\n inverse_kinship = 'M'\n else:\n raise TypeError('Wrong inverse kinship!')\n\n if self.kinshipType == 'B' or self.kinshipType == 'Z':\n if gender1 == 'm':\n inverse_kinship = 'B'\n elif gender1 == 'f':\n inverse_kinship = 'Z'\n else:\n pass\n # raise TypeError('Wrong inverse kinship!')\n if self.seniority == '+':\n inverse_seniority = '-'\n if self.seniority == '-':\n inverse_seniority = '+'\n if self.seniority.isdigit():\n inverse_seniority = ''\n\n inverse_kin = PrimaryKinshipCode(inverse_kinship + inverse_seniority)\n return inverse_kin\n\n\n###################################################################\n#\t\t\t\t\tclass ComplexKinship\n###################################################################\n\n\nclass ComplexKinshipCode:\n\n def __init__(self, kinship_code=None, primary_code_list=None):\n if primary_code_list is None:\n self.kinship_code = kinship_code\n self.primary_code_list: List[PrimaryKinshipCode] = self.decode_kinship_code()\n self.generation = self.get_generation()\n self.kin_distance = self.get_kin_distance()\n self.gender_pair = self.get_gender_pair()\n else:\n self.primary_code_list: List[PrimaryKinshipCode] = primary_code_list\n self.kinship_code = self.to_str()\n self.generation = self.get_generation()\n self.kin_distance = self.get_kin_distance()\n self.gender_pair = self.get_gender_pair()\n\n def __repr__(self):\n return ''\n\n def to_str(self):\n return ''.join(code.to_str() for code in self.primary_code_list)\n\n #\n # @staticmethod\n # def primary_code_list_to_str(primary_code_list):\n #\n\n # complex_kinship_code_parsing\n def decode_kinship_code(self):\n # if string only contains alpha\n if self.kinship_code.isalpha() and self.kinship_code.isupper():\n element = list(self.kinship_code)\n else: # string contains both alpha and number\n element = []\n head_loc = 0\n code_len = 1\n for i in range(1, len(self.kinship_code)):\n if self.kinship_code[i].isalpha():\n element.append(self.kinship_code[head_loc:(head_loc + code_len)])\n head_loc = i\n code_len = 1\n else:\n code_len = code_len + 1\n element.append(self.kinship_code[head_loc:(head_loc + code_len)])\n out = [] # in which each element is PrimaryKinshipCode object\n for code in element:\n out.append(PrimaryKinshipCode(code))\n return out\n\n def get_generation(self):\n out: int = 0\n for code in self.primary_code_list:\n gen_diff = code.generation\n if gen_diff is None:\n return None\n out += gen_diff\n return out\n\n def get_kin_distance(self):\n out: int = 0\n for code in self.primary_code_list:\n kin_dist = code.kin_distance\n if kin_dist is None:\n return None\n out += kin_dist\n return out\n\n def get_gender_pair(self):\n gender_pair = ['?', '?']\n gender_pair[0] = self.primary_code_list[0].gender_pair[0]\n gender_pair[1] = self.primary_code_list[-1].gender_pair[1]\n return gender_pair\n\n def get_gender_list(self, gender_0: str):\n assert gender_0 in {'m', 'f', '?'}\n size = len(self.primary_code_list)\n gender_list = ['?' for _ in range(size + 1)]\n gender_list[0] = gender_0\n for pos, code in enumerate(self.primary_code_list):\n gender_first, gender_second = code.gender_pair\n if gender_first == '?':\n pass\n elif gender_list[pos] == '?':\n gender_list[pos] = gender_first\n else:\n pass\n # assert gender_list[pos] == gender_first\n # return None\n if gender_second == '?':\n pass\n elif gender_list[pos + 1] == '?':\n gender_list[pos + 1] = gender_second\n else:\n pass\n # assert gender_list[pos + 1] == gender_second\n # return None\n return gender_list\n\n def get_inverse_kinship(self, gender_0: str) -> List[PrimaryKinshipCode]:\n inverse_kinship = []\n gender_list = self.get_gender_list(gender_0)\n rev_gender_list = list(reversed(gender_list))[1:] # delete the last person's gender\n rev_code_list: List[PrimaryKinshipCode] = list(reversed(self.primary_code_list))\n for pos, gender in enumerate(rev_gender_list):\n # print(pos, gender)\n inverse_kinship.append(rev_code_list[pos].get_inverse_kinship(gender))\n return inverse_kinship\n\n\ndef joint(code1: PrimaryKinshipCode, code2: PrimaryKinshipCode):\n pass\n\n\ndef is_compatible(code1, code2):\n if type(code1) != ComplexKinshipCode:\n code1 = ComplexKinshipCode(code1)\n if type(code2) != ComplexKinshipCode:\n code2 = ComplexKinshipCode(code2)\n\n if code1.generation != code2.generation:\n return False\n # if code1.kin_distance != code2.kin_distance:\n # return False\n if code1.gender_pair != code2.gender_pair:\n return False\n\n code1_path = code1.to_str()\n code2_path = code2.to_str()\n\n s = [i for i in code1_path if i.isalpha()]\n code1_path = \"\".join(s)\n\n s = [i for i in code2_path if i.isalpha()]\n code2_path = \"\".join(s)\n\n code1_path = indirect2direct(code1_path)\n code2_path = indirect2direct(code2_path)\n\n if code1_path == code2_path:\n return True\n else:\n return False\n\n\ndef indirect2direct(code: str):\n if 'B' in code:\n code = code.replace('B+', 'FS').replace('B-', 'FS').replace('B', 'FS')\n if 'Z' in code:\n code = code.replace('Z+', 'FD').replace('Z-', 'FD').replace('Z', 'FD')\n if 'M' in code:\n code = code.replace('M', 'FW').replace('M*', 'FW')\n return code\n\n# a = PrimaryKinshipCode('B+')\n# a.get_inverse_kinship('m')\n#\n# a = ComplexKinship('FB+D13S16')\n# a.decode_kinship_code()\n# a.get_gender_list('m')\n# a.get_inverse_kinship('m')\n# cc1 = 'B'\n# cc2 = 'FS'\n# is_compatible(cc1, cc2)\n# cc1 = 'FBS'\n# cc2 = 'FFS'\n# is_compatible(cc1, cc2)\n\n# def is_PrimaryKinshipCode_compatible(code1: PrimaryKinshipCode, code2: PrimaryKinshipCode):\n# if code1.generation != code2.generation:\n# return False\n# if code1.kin_distance != code2.kin_distance:\n# return False\n# if code1.gender_pair != code2.gender_pair:\n# return False\n#\n# # if KinCode1 == KinCode2, it's simple\n# if code1.kinship_code == code2.kinship_code:\n# if code1.seniority == code1.seniority:\n# return True\n# elif code1.seniority == '' or code2.seniority == '':\n# return True\n# else:\n# return False\n","repo_name":"zhoumq19/kinship","sub_path":"kinship_py/KinshipCode.py","file_name":"KinshipCode.py","file_ext":"py","file_size_in_byte":10397,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"20757582019","text":"import distutils.command.build\nimport distutils.command.build_scripts\nimport distutils.command.install_data\nimport distutils.command.install_scripts\nimport gzip\nimport sys\nimport os\nimport stat\nimport subprocess\nimport shutil\nimport configparser\n\nversion = '0.9.0'\ndeactivated_platforms = ['arm', 'arm-gnu']\nsrcdir = os.path.dirname(os.path.realpath(sys.argv[0]))\nbuilddir = os.getcwd()\n\nuse_setuptools = False\n\nif 'USE_SETUPTOOLS' in os.environ or 'setuptools' in sys.modules:\n # I don't like to unreference modules out of their namespaces.\n # Unfortunately it seems pip would need setuptools instead of the\n # core distutils. Thus I import setup and install from setuptools\n # when it is requested or already loaded (ex: in pip).\n use_setuptools = True\n try:\n from setuptools import setup\n from setuptools.command.install import install\n except ImportError:\n use_setuptools = False\n\nif not use_setuptools:\n from distutils.core import setup\n from distutils.command.install import install\n\nclass build_man(distutils.core.Command):\n '''\n Build the man page.\n '''\n\n description = 'build the man page'\n user_options = []\n\n def run(self):\n self.check_dep()\n self.create_build_tree()\n self.build()\n self.compress_man()\n self.clean()\n\n def initialize_options(self):\n self.cwd = None\n\n def finalize_options(self):\n self.cwd = os.getcwd()\n\n def check_dep(self):\n '''\n Check build dependencies.\n '''\n if shutil.which('rst2man') is None:\n sys.stderr.write('`rst2man` is a mandatory building dependency. '\n 'You will probably find it either in a '\n '`python2-docutils` or `python3-docutils` package.\\n')\n sys.exit(os.EX_CANTCREAT)\n\n def create_build_tree(self):\n '''\n Create a build tree.\n '''\n try:\n os.makedirs('build/share/man/man1', exist_ok=True)\n os.makedirs('build/doc', exist_ok=True)\n except os.error:\n sys.stderr.write('Build error: failure to create the build/ tree. Please check your permissions.\\n')\n sys.exit(os.EX_CANTCREAT)\n\n def build(self):\n '''\n Create the manual.\n '''\n try:\n shutil.copyfile(os.path.join(srcdir, 'doc/crossroad.rst'),\n os.path.join(builddir, 'build/doc/crossroad.rst'))\n update_scripts('build/doc')\n subprocess.check_call([\"rst2man\", \"build/doc/crossroad.rst\",\n \"build/share/man/man1/crossroad.1\"])\n except subprocess.CalledProcessError:\n sys.stderr.write('Build error: `rst2man` failed to build the man page.\\n')\n sys.exit(os.EX_CANTCREAT)\n\n def compress_man(self):\n '''\n Compress the man.\n '''\n with open('build/share/man/man1/crossroad.1', 'rb') as manual:\n with gzip.open('build/share/man/man1/crossroad.1.gz', 'wb') as compressed:\n compressed.writelines(manual)\n\n def clean(self):\n os.unlink('build/share/man/man1/crossroad.1')\n\nclass my_build(distutils.command.build.build):\n '''\n Override the build to have some additional pre-processing.\n '''\n\n def run(self):\n # Add manual generation in build.\n self.run_command('man')\n # Move files without modification at build time\n # This allows renaming mostly.\n try:\n os.makedirs('build/bin', exist_ok = True)\n os.makedirs('build/platforms', exist_ok = True)\n os.makedirs('build/share/crossroad/scripts/shells/bash', exist_ok = True)\n os.makedirs('build/share/crossroad/scripts/cmake', exist_ok = True)\n os.makedirs('build/share/crossroad/scripts/meson', exist_ok = True)\n except os.error:\n sys.stderr.write('Build error: failure to create the build/ tree. Please check your permissions.\\n')\n sys.exit(os.EX_CANTCREAT)\n shutil.copyfile(os.path.join(srcdir, 'src/crossroad.py'), 'build/bin/crossroad')\n shutil.copyfile(os.path.join(srcdir, 'src/in-crossroad.py'), 'build/share/crossroad/scripts/in-crossroad.py')\n shutil.copy(os.path.join(srcdir, 'scripts/shells/environment.sh'), 'build/share/crossroad/scripts/shells/')\n for f in os.listdir(os.path.join(srcdir, 'platforms/env/')):\n if f[-5:] == '.conf' and f[:-5] not in deactivated_platforms:\n config = configparser.ConfigParser()\n config.optionxform = str\n config.read([os.path.join(srcdir, 'platforms/env', f)])\n if not config.has_section('Platform') or not config.has_option('Platform', 'shortname') or \\\n not config.has_option('Platform', 'nicename') or not config.has_option('Platform', 'host'):\n sys.stderr.write('Build error: file {} miss required options.\\n'. f)\n sys.exit(os.EX_CANTCREAT)\n shortname = config.get('Platform', 'shortname')\n nicename = config.get('Platform', 'nicename')\n sys.stdout.write('Configuring platform \"{}\"\\n'.format(nicename))\n\n env_variables = '\\nexport CROSSROAD_PLATFORM=\"{}\"\\n'.format(config.get('Platform', 'shortname'))\n env_variables += 'export CROSSROAD_PLATFORM_NICENAME=\"{}\"\\n'.format(config.get('Platform', 'nicename'))\n env_variables += 'export CROSSROAD_HOST=\"{}\"\\n'.format(config.get('Platform', 'host'))\n\n if config.has_option('Platform', 'word-size'):\n env_variables += 'export CROSSROAD_WORD_SIZE=\"{}\"\\n'.format(config.getint('Platform', 'word-size'))\n if config.has_section('Environment'):\n custom_env_vars = config.items('Environment')\n for (env_var, env_val) in custom_env_vars:\n env_variables += 'export {}=\"{}\"\\n'.format(env_var, env_val)\n\n # Platform file.\n shutil.copy(os.path.join(srcdir, 'platforms/modules/', shortname + '.py'), 'build/platforms')\n if shortname != 'native':\n # Cmake file.\n shutil.copy(os.path.join(srcdir, 'platforms/cmake/', 'toolchain-' + shortname + '.cmake'), 'build/share/crossroad/scripts/cmake/')\n # Meson cross build definition files.\n shutil.copy(os.path.join(srcdir, 'platforms/meson/', 'toolchain-' + shortname + '.meson'), 'build/share/crossroad/scripts/meson/')\n # Bash startup file.\n built_bashrc = os.path.join('build/share/crossroad/scripts/shells/bash/', 'bashrc.' + shortname)\n shutil.copyfile(os.path.join(srcdir, 'scripts/shells/bash/bashrc.template'), built_bashrc)\n try:\n fd = open(built_bashrc, 'r+')\n contents = fd.read()\n contents = contents.replace('@ENV_VARIABLES@', env_variables)\n fd.truncate(0)\n fd.seek(0)\n fd.write(contents)\n fd.flush()\n fd.close()\n except IOError:\n sys.stderr.write('\"{}\" failed to update. Check your permissions.\\n'.format(built_bashrc))\n sys.exit(os.EX_CANTCREAT)\n # Zsh startup files.\n zsh_startup_dir = 'build/share/crossroad/scripts/shells/zsh.' + shortname\n os.makedirs(zsh_startup_dir, exist_ok = True)\n built_zshenv = os.path.join(zsh_startup_dir, '.zshenv')\n shutil.copyfile(os.path.join(srcdir, 'scripts/shells/zsh/zshenv'), built_zshenv)\n built_zshrc = os.path.join(zsh_startup_dir, '.zshrc')\n shutil.copyfile(os.path.join(srcdir, 'scripts/shells/zsh/zshrc.template'), built_zshrc)\n try:\n fd = open(built_zshrc, 'r+')\n contents = fd.read()\n contents = contents.replace('@ENV_VARIABLES@', env_variables)\n fd.truncate(0)\n fd.seek(0)\n fd.write(contents)\n fd.flush()\n fd.close()\n except IOError:\n sys.stderr.write('\"{}\" failed to update. Check your permissions.\\n'.format(built_zshrc))\n sys.exit(os.EX_CANTCREAT)\n distutils.command.build.build.run(self)\n\nclass my_install(install):\n '''\n Override the install to modify updating scripts before installing.\n '''\n\n def run(self):\n try:\n os.makedirs('build/', exist_ok=True)\n except os.error:\n sys.stderr.write('Build error: failure to create the build/ tree. Please check your permissions.\\n')\n sys.exit(os.EX_CANTCREAT)\n # Install is the only time we know the actual data directory.\n # We save this information in a temporary build file for replacement in scripts.\n script = open('build/data_dir', 'w')\n script.truncate(0)\n script.write(os.path.abspath(self.install_data))\n script.close()\n # Go on with normal install.\n install.run(self)\n\ndef update_scripts(build_subdir):\n '''\n Convenience function to update any file in `build_subdir`:\n - replace @DATADIR@ by `datadir` as set on the setup.py call.\n '''\n global version\n datadir = '/usr/local'\n # I keep the real version for distribution and release.\n # But if we are in a git repository, the tool will output a git commit too.\n git_version = version\n if shutil.which('git') is not None and os.path.isdir(os.path.join(srcdir, '.git')):\n os.chdir(srcdir)\n if subprocess.check_output([\"git\", \"tag\", \"--contains\"]).decode('utf-8') != 'v' + version:\n commit_hash = subprocess.check_output(['git', 'log', '-1', \"--pretty=format:%H\"]).decode('utf-8')\n git_version = \"development (commit: {} - last release: {})\".format(str(commit_hash), version)\n os.chdir(builddir)\n\n try:\n data_dir_file = open('build/data_dir', 'r')\n datadir = data_dir_file.readline().rstrip(' \\n\\r\\t')\n data_dir_file.close()\n except IOError:\n sys.stderr.write('Warning: no build/data_dir file. You should run the `install` command. Defaulting to {}.\\n'.format(datadir))\n\n for dirpath, dirnames, filenames in os.walk(build_subdir):\n for f in filenames:\n try:\n script = open(os.path.join(dirpath, f), 'r+')\n contents = script.read()\n # Make the necessary replacements.\n contents = contents.replace('@DATADIR@', datadir)\n contents = contents.replace('@VERSION@', git_version)\n script.truncate(0)\n script.seek(0)\n script.write(contents)\n script.flush()\n script.close()\n except IOError:\n sys.stderr.write('The script {} failed to update. Check your permissions.\\n'.format(f))\n sys.exit(os.EX_CANTCREAT)\n\nclass my_install_data(distutils.command.install_data.install_data):\n '''\n Override the install to build the manual first.\n '''\n\n def run(self):\n update_scripts('build/platforms')\n update_scripts('build/share/crossroad/scripts')\n distutils.command.install_data.install_data.run(self)\n datadir = '/usr/local'\n try:\n data_dir_file = open('build/data_dir', 'r')\n datadir = data_dir_file.readline().rstrip(' \\n\\r\\t')\n data_dir_file.close()\n except IOError:\n sys.stderr.write('Warning: no build/data_dir file. You should run the `install` command. Defaulting to {}.\\n'.format(datadir))\n os.chmod(os.path.join(datadir, 'share/crossroad/scripts/in-crossroad.py'),\n stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR |\n stat.S_IRGRP | stat.S_IXGRP |\n stat.S_IROTH | stat.S_IXOTH)\n os.chmod(os.path.join(datadir, 'share/crossroad/scripts/config.guess'),\n stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR |\n stat.S_IRGRP | stat.S_IXGRP |\n stat.S_IROTH | stat.S_IXOTH)\n os.chmod(os.path.join(datadir, 'share/crossroad/scripts/crossroad-mingw-install.py'),\n stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR |\n stat.S_IRGRP | stat.S_IXGRP |\n stat.S_IROTH | stat.S_IXOTH)\n os.chmod(os.path.join(datadir, 'share/crossroad/scripts/bin-wrappers/crossroad-gcc'),\n stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR |\n stat.S_IRGRP | stat.S_IXGRP |\n stat.S_IROTH | stat.S_IXOTH)\n os.chmod(os.path.join(datadir, 'share/crossroad/scripts/bin-wrappers/crossroad-cpp'),\n stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR |\n stat.S_IRGRP | stat.S_IXGRP |\n stat.S_IROTH | stat.S_IXOTH)\n os.chmod(os.path.join(datadir, 'share/crossroad/scripts/bin-wrappers/crossroad-pkg-config'),\n stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR |\n stat.S_IRGRP | stat.S_IXGRP |\n stat.S_IROTH | stat.S_IXOTH)\n os.makedirs(os.path.join(datadir, 'share/crossroad/bin/'), exist_ok=True)\n # Automatically generate binaries for each platform.\n for f in os.listdir(os.path.join(srcdir, 'platforms/env/')):\n if f[-5:] == '.conf' and f[:-5] not in deactivated_platforms:\n config = configparser.ConfigParser()\n config.read([os.path.join(srcdir, 'platforms/env', f)])\n host = config.get('Platform', 'host')\n if len(host) > 0:\n try:\n os.unlink(os.path.join(datadir, 'share/crossroad/bin/' + host + '-pkg-config'))\n except OSError:\n pass\n try:\n os.unlink(os.path.join(datadir, 'share/crossroad/bin/' + host + '-gcc'))\n except OSError:\n pass\n try:\n os.unlink(os.path.join(datadir, 'share/crossroad/bin/' + host + '-g++'))\n except OSError:\n pass\n try:\n os.unlink(os.path.join(datadir, 'share/crossroad/bin/' + host + '-cpp'))\n except OSError:\n pass\n os.symlink(os.path.join(datadir, 'share/crossroad/scripts/bin-wrappers/crossroad-pkg-config'),\n os.path.join(datadir, 'share/crossroad/bin/' + host + '-pkg-config'))\n os.symlink(os.path.join(datadir, 'share/crossroad/scripts/bin-wrappers/crossroad-gcc'),\n os.path.join(datadir, 'share/crossroad/bin/' + host + '-gcc'))\n os.symlink(os.path.join(datadir, 'share/crossroad/scripts/bin-wrappers/crossroad-gcc'),\n os.path.join(datadir, 'share/crossroad/bin/' + host + '-g++'))\n os.symlink(os.path.join(datadir, 'share/crossroad/scripts/bin-wrappers/crossroad-cpp'),\n os.path.join(datadir, 'share/crossroad/bin/' + host + '-cpp'))\n\nclass my_install_scripts(distutils.command.install_scripts.install_scripts):\n '''\n Override the install to build the manual first.\n '''\n\n def run(self):\n update_scripts(self.build_dir)\n distutils.command.install_scripts.install_scripts.run(self)\n\n\nplatform_list = os.listdir(os.path.join(srcdir, 'platforms/env'))\nplatform_list = [f[:-5] for f in platform_list \\\n if f[-5:] == '.conf' and f[:-5] not in deactivated_platforms]\nplatform_file_list = [os.path.join('build/platforms/', f + '.py') for f in platform_list]\n\ncmake_toolchains = [os.path.join('build/share/crossroad/scripts/cmake/',\n 'toolchain-' + f + '.cmake') \\\n for f in platform_list if f != 'native']\nmeson_toolchains = [os.path.join('build/share/crossroad/scripts/meson/',\n 'toolchain-' + f + '.meson') \\\n for f in platform_list if f != 'native']\n\ndef get_built_data_files():\n bashrc_files = []\n zsh_files = []\n for f in os.listdir(os.path.join(srcdir, 'platforms/env/')):\n if f[-5:] == '.conf' and f[:-5] not in deactivated_platforms:\n config = configparser.ConfigParser()\n config.read([os.path.join(srcdir, 'platforms/env', f)])\n shortname = config.get('Platform', 'shortname')\n # bash\n built_bashrc = os.path.join('build/share/crossroad/scripts/shells/bash/', 'bashrc.' + shortname)\n bashrc_files += [built_bashrc]\n # ZSH\n zsh_startup_dir = 'build/share/crossroad/scripts/shells/zsh.' + shortname\n built_zshenv = os.path.join(zsh_startup_dir, '.zshenv')\n built_zshrc = os.path.join(zsh_startup_dir, '.zshrc')\n zsh_files += [('share/crossroad/scripts/shells/zsh.' + shortname, [built_zshrc, built_zshenv])]\n return [('share/crossroad/scripts/shells/bash', bashrc_files)] + zsh_files\n\nbuilt_data_files = get_built_data_files()\n\nsetup(\n name = 'crossroad',\n cmdclass = {'man': build_man, 'build': my_build, 'install': my_install,\n 'install_data': my_install_data, 'install_scripts': my_install_scripts},\n version = version,\n description = 'Cross-Compilation Environment Toolkit.',\n long_description = open(os.path.join(srcdir, 'README')).read(),\n author = 'Jehan',\n author_email = 'jehan@girinstud.io',\n url = 'http://girinstud.io',\n license = 'AGPLv3+',\n classifiers = [\n 'License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)',\n 'Development Status :: 4 - Beta',\n 'Environment :: Console',\n 'Intended Audience :: Developers',\n 'Operating System :: POSIX :: Linux',\n 'Programming Language :: Python :: 3.3',\n 'Topic :: Software Development :: Build Tools',\n ],\n requires = [],\n scripts = ['build/bin/crossroad'],\n data_files = [('share/man/man1/', ['build/share/man/man1/crossroad.1.gz']),\n ('share/crossroad/scripts/', [os.path.join(srcdir, 'scripts/crossroad-mingw-install.py'),\n os.path.join(srcdir, 'scripts/config.guess'),\n 'build/share/crossroad/scripts/in-crossroad.py',\n ]),\n ('share/crossroad/scripts/bin-wrappers/',\n [os.path.join(srcdir, 'scripts/bin-wrappers/crossroad-gcc'),\n os.path.join(srcdir, 'scripts/bin-wrappers/crossroad-pkg-config'),\n os.path.join(srcdir, 'scripts/bin-wrappers/crossroad-cpp'),\n ]),\n ('share/crossroad/scripts/shells/',\n ['build/share/crossroad/scripts/shells/environment.sh',\n os.path.join(srcdir, 'scripts/shells/pre-bash-env.sh'),\n os.path.join(srcdir, 'scripts/shells/pre-zsh-env.sh'),\n os.path.join(srcdir, 'scripts/shells/post-env.sh'),]),\n ('share/bash-completion/completions', [os.path.join(srcdir, 'scripts/shells/bash/completions/crossroad')]),\n ('share/crossroad/scripts/cmake', cmake_toolchains),\n ('share/crossroad/scripts/meson', meson_toolchains),\n ('share/crossroad/platforms/', platform_file_list),\n #('crossroad/projects/', ['projects']),\n ] + built_data_files,\n )\n\n","repo_name":"pbo-linaro/crossroad","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":20131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33456286531","text":"import json\nfrom .models import *\n\ndef cookieCart(request):\n #Create empty cart for now for non-logged in user\n try:\n cart = json.loads(request.COOKIES['cart'])\n except:\n cart = {}\n \n print('Cart:', cart)\n items = []\n order = {'get_cart_total':0, 'get_cart_items': 0, 'shipping':False}\n cartItems = order['get_cart_items']\n \n # to update cart items to see in cart page\n for i in cart:\n #We use try block to prevent items in cart that may have been removed from causing error\n try:\n cartItems += cart[i][\"quantity\"]\n \n product = Product.objects.get(id=i)\n total =(product.price * cart[i][\"quantity\"] )\n \n order['get_cart_total'] += total\n order['get_cart_items'] += cart[i]['quantity']\n # To build our cart with actual items on it\n item = {\n 'id':product.id,\n 'product':{'id':product.id,'name':product.name, 'price':product.price, \n 'imageURL':product.imageURL}, 'quantity':cart[i]['quantity'],\n 'digital':product.digital,'get_total':total,\n }\n items.append(item)\n \n if product.digital == False:\n order['shipping'] = True\n except:\n pass\n return {'cartItems':cartItems ,'order':order, 'items':items}\n \ndef cartData(request):\n # This is for authenticated user\n if request.user.is_authenticated:\n customer = request.user.customer\n order, created = Order.objects.get_or_create(customer=customer, complete=False)\n items = order.orderitem_set.all()\n cartItems = order.get_cart_items\n # This is for non authenticated user\n else:\n cookieData = cookieCart(request)\n cartItems = cookieData['cartItems']\n order = cookieData['order']\n items = cookieData['items']\n return {'cartItems':cartItems ,'order':order, 'items':items}\n\n\ndef guestOrder(request, data):\n # logic for authenticated users\n print('User is not logged in')\n # To submit the data into the back-end\n print('COOKIES:', request.COOKIES)\n name = data['form']['name']\n email = data['form']['email']\n \n # To get the items by accessing the cookie data\n cookieData = cookieCart(request)\n items = cookieData['items']\n \n # for guest user who want to shop in the website but never created and account\n # when the user send an email, this will be check in our database it will be attach\n # we can see how many times a guest user has shop with us\n customer, created = Customer.objects.get_or_create(\n\t\t\t\temail=email,\n\t\t\t\t)\n customer.name = name\n customer.save()\n\n order = Order.objects.create(\n customer=customer,\n complete=False,\n\t\t\t)\n # To add the items in the database\n \n for item in items:\n product = Product.objects.get(id=item['id'])\n orderItem = OrderItem.objects.create(\n product=product,\n order=order,\n quantity=item['quantity'],\n )\n return customer, order\n","repo_name":"Akoatem/MVP","sub_path":"myApp/projects/ecommerce/store/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73960283920","text":"from bonus_points.models import BonusDescription, BonusUserAutoPoints\nfrom matches.models import UserPredictions\nfrom django.core.management.base import BaseCommand\nfrom django.contrib.auth import get_user_model\n\n\nclass Command(BaseCommand):\n # calculate bonus points for contest \"give prediction for 36 matches\" /id = 1/\n def handle(self, *args, **options):\n all_users = get_user_model().objects.all()\n bonus_desc_object = BonusDescription.objects.get(id=1)\n needed_count = int(bonus_desc_object.correct_answer)\n points = bonus_desc_object.points\n # delete existing objects\n delete_current_objects = BonusUserAutoPoints.objects.filter(auto_user_bonus_name=bonus_desc_object)\n for item in delete_current_objects:\n item.delete()\n # check for all users\n for item in all_users:\n user_predictions_count = UserPredictions.objects.filter(user_id=item, match__phase='group_phase').count()\n if user_predictions_count >= needed_count:\n summary_text = f\"Ти си дал {user_predictions_count} прогнози, при необходими {needed_count} за груповата фаза. \\\\n Tи спечели {points} точки за това.\"\n user_obj = BonusUserAutoPoints(user=item, auto_user_bonus_name=bonus_desc_object,\n points_gained=points, summary_text=summary_text)\n user_obj.save()\n self.stdout.write(self.style.SUCCESS('Successfully updated bonus 1'))\n","repo_name":"maddrum/world_cup_results","sub_path":"bonus_points/management/commands/bp_calc_c1.py","file_name":"bp_calc_c1.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25064718478","text":"from hardware import Memory, RegisterFile, Register, MUX_2_1, ALU_32, AND_2\nimport utilities \nfrom signals import Signals\n\nclass Core_SC: \n def __init__(self):\n self.I_Mem = Memory()\n self.D_Mem = Memory()\n self.RF = RegisterFile()\n self.RegPC = Register()\n self.signals = Signals()\n self.cycle_num = 0\n self.mode = 0\n\n def set_PC(self, pc):\n self.RegPC.set_data(pc)\n self.RegPC.set_write(1)\n\n def set_mode(self, mode):\n self.mode = mode\n\n def run(self, n_cycles):\n i_cycles = 0\n ending_PC = self.I_Mem.get_ending_address() \n\n self.I_Mem.set_memread(1)\n self.I_Mem.set_memwrite(0)\n\n while (n_cycles == 0 or i_cycles < n_cycles):\n i_cycles += 1\n self.cycle_num += 1\n if ((self.mode & 2) == 0): utilities.print_new_cycle(self.cycle_num)\n\n # clock changes\n self.RegPC.clock()\n self.RF.clock()\n\n # read PC\n self.signals.PC = self.RegPC.read()\n self.signals.PC_4 = self.signals.PC_new = self.signals.PC + 4\n if ((self.mode & 2) == 0): utilities.println_int(\"PC\", self.signals.PC)\n if (self.signals.PC > ending_PC): \n if ((self.mode & 2) == 0): print(\"No More Instructions\")\n i_cycles -= 1 \n break\n\n self.I_Mem.set_address(self.signals.PC)\n self.I_Mem.run()\n self.signals.instruction = self.I_Mem.get_data() \n\n if ((self.mode & 2) == 0): utilities.println_int(\"instruction\", self.signals.instruction)\n\n # Now you have PC and the instruction\n # Some signals' value can be extracted from instruction directly \n self.signals_from_instruction(self.signals.instruction, self.signals)\n\n # call main_control\n self.main_control(self.signals.opcode, self.signals)\n \n # call sign_extend\n self.signals.Sign_extended_immediate = self.sign_extend(self.signals.immediate)\n \n # Write_register. Also an example of using MUX\n self.signals.Write_register = MUX_2_1(self.signals.rt, self.signals.rd, self.signals.RegDst) \n \n # ALU control\n self.signals.ALU_operation = self.ALU_control(self.signals.ALUOp, self.signals.funct)\n \n # Calculate branch address \n self.signals.Branch_address = self.calculate_branch_address(self.signals.PC_4, self.signals.Sign_extended_immediate)\n self.signals.Jump_address = self.calculate_jump_address(self.signals.PC_4, self.signals.instruction)\n \n # Print out signals generated in Phase 1.\n if ((self.mode & 4) == 0): utilities.print_signals_1(self.signals)\n\n # If phase 1 only, continue to the next instruction.\n if ((self.mode & 1) != 0):\n self.RegPC.set_data(self.signals.PC_4)\n self.RegPC.set_write(1)\n continue\n \n # You will continue to complete the core in phase 2\n # Use RF, ALU, D_Mem \n # Preapre RF write \n # Compute PC_new \n\n # Reading RF\n self.RF.set_read_registers(self.signals.rs, self.signals.rt)\n self.signals.RF_read_data_1 = self.RF.get_read_data_1()\n self.signals.RF_read_data_2 = self.RF.get_read_data_2()\n\n # Using ALU\n self.signals.ALU_input_2 = MUX_2_1(self.signals.RF_read_data_2, self.signals.Sign_extended_immediate, self.signals.ALUSrc)\n self.signals.ALU_returned_value = ALU_32(self.signals.RF_read_data_1, self.signals.ALU_input_2, self.signals.ALU_operation)\n self.signals.Zero = self.signals.ALU_returned_value[1]\n self.signals.ALU_result = self.signals.ALU_returned_value[0]\n \n # Accessing D_mem\n self.D_Mem.set_address(self.signals.ALU_returned_value[0])\n self.D_Mem.set_memread(self.signals.MemRead)\n self.D_Mem.set_memwrite(self.signals.MemWrite)\n self.D_Mem.set_data(self.signals.RF_read_data_2)\n self.D_Mem.run()\n self.signals.MEM_read_data = self.D_Mem.get_data()\n\n # Setting RF values\n write_reg = MUX_2_1(self.signals.rt, self.signals.rd, self.signals.RegDst)\n self.RF.set_write_register(write_reg)\n self.signals.Write_data = MUX_2_1(self.signals.ALU_returned_value[0], self.signals.MEM_read_data, self.signals.MemtoReg)\n self.RF.set_write_data(self.signals.Write_data)\n self.RF.set_regwrite(self.signals.RegWrite)\n\n # Finding PC_new and setting correct values for input of PC\n self.signals.PCSrc = AND_2(self.signals.Branch, self.signals.ALU_returned_value[1])\n self.signals.PC_branch = MUX_2_1(self.signals.PC_4, self.signals.Branch_address, self.signals.PCSrc)\n self.signals.PC_new = MUX_2_1(self.signals.PC_branch, self.signals.Jump_address, self.signals.Jump)\n\n self.RegPC.set_data(self.signals.PC_new)\n self.RegPC.set_write(1)\n\n # Print out signals generated in Phase 2.\n if ((self.mode & 8) == 0): utilities.print_signals_2(self.signals)\n return i_cycles\n \n def signals_from_instruction (self, instruction, sig):\n \"\"\"\n Extract the following signals from instruction.\n opcode, rs, rt, rd, funct, immediate\n \"\"\"\n sig.opcode = (instruction >> 26) & 0x3F\n sig.rs = (instruction >> 21) & 0x1F\n sig.rt = (instruction >> 16) & 0x1F\n sig.rd = (instruction >> 11) & 0x1F\n sig.funct = instruction & 0x3F\n sig.immediate = instruction & 0xFFFF\n\n def main_control(self, opcode, sig):\n \"\"\"\n Check the type of input instruction\n \"\"\"\n #set defaults for control signals \n sig.RegDst = sig.Jump = sig.Branch = sig.MemRead = sig.MemtoReg = sig.ALUOp = sig.MemWrite = sig.ALUSrc = sig.RegWrite = 0\n\n #determine control signals\n if opcode == 0: # R-Type 000000\n sig.RegWrite = 1\n sig.RegDst = 1\n sig.ALUOp = 2\n elif opcode == 8: # addi 001000\n sig.ALUSrc = 1\n sig.RegWrite = 1\n elif opcode == 35: # lw 100011\n sig.ALUSrc = 1\n sig.MemtoReg = 1\n sig.MemRead = 1\n sig.RegWrite = 1\n elif opcode == 43: # sw 101011\n sig.ALUSrc = 1\n sig.MemWrite = 1\n elif opcode == 4: # beq 000100\n sig.Branch = 1\n sig.ALUOp = 1\n elif opcode == 2: # j 000010\n sig.Jump = 1 \n else:\n raise ValueError(\"Unknown opcode 0x%02X\" % opcode)\n return \n\n def ALU_control(self, alu_op, funct): \n \"\"\"\n Get alu_control from func field of instruction\n Input: function field of instruction\n Output: alu_control_out\n \n \"\"\"\n alu_control_out = 0\n # One example is given, continue to finish other cases.\n if alu_op == 0: # 00 \n alu_control_out = 2 # 0010\n elif alu_op == 1: # 01\n alu_control_out = 6 # 0110\n elif alu_op == 2: # 10\n if funct == 32: # 100000\n alu_control_out = 2 # 0010\n elif funct == 34: # 100010\n alu_control_out = 6 # 0110\n elif funct == 36: # 100100\n alu_control_out = 0 # 0000\n elif funct == 37: # 100101\n alu_control_out = 1 # 0001\n elif funct == 42: # 101010\n alu_control_out = 7 # 0111\n else:\n raise ValueError(\"Unknown opcode code 0x%02X\" % alu_op)\n return alu_control_out\n\n def sign_extend(self, immd):\n \"\"\"\n Sign extend module. \n Convert 16-bit to an int.\n Extract the lower 16 bits. \n If bit 15 of immd is 1, compute the correct negative value (immd - 0x10000).\n \"\"\"\n immd = immd & 0xFFFF # reduce immd to 16 bits\n if int(format(immd, '016b')[0]) == 1: # convert immd to 16 bit string. Take bit 15 and convert it back into an integer. If bit 15 is 1\n immd = immd - 0x10000 # compute immd - 0x10000\n return immd\n\n def calculate_branch_address(self, pc_4, extended):\n addr = (extended << 2) + pc_4 # shift extended left by 2 and add PC + 4\n return addr\n\n def calculate_jump_address(self, pc_4, instruction):\n addr = ((instruction & 0x3FFFFFF) << 2) | (pc_4 & 0xF0000000) # reduce instr to 26 bits and shift left by 2, OR with PC + 4 ANDed with 0xF0000000\n return addr","repo_name":"SamIAm10/MIPS-Simulator","sub_path":"core_sc.py","file_name":"core_sc.py","file_ext":"py","file_size_in_byte":8970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7396610176","text":"import wx\nfrom sentiment_analyzer import SentimentAnalyzer as sa\nfrom sentiment_analyzer import Tools\nimport pandas as pd\nfrom GUI import MainFrame\n\ndef main():\n try:\n # reads the favorite tweets csv\n favourite_tweets = pd.read_csv(\"favorite-tweets.csv\")\n # dictionary to store the names and scores of tweets (name: score)\n\n # for tweet in favourite_tweets:\n name_tweet_score = pd.DataFrame(columns=['UserName', 'Tweet', \"Score\"])\n name_tweet_score['UserName'] = favourite_tweets['UserName']\n name_tweet_score['Tweet'] = favourite_tweets['Text']\n name_tweet_score.index = [Tools.string_to_datetime(date) for date in favourite_tweets['CreatedAt']]\n name_tweet_score['Score'] = [sa.sentiment_processing(sa.tweet_formatter(tweet)) for tweet in\n name_tweet_score['Tweet']]\n\n name_tweet_score = name_tweet_score.dropna(subset=['Tweet'])\n\n # GUI\n app = wx.App()\n Frame = MainFrame(None, name_tweet_score)\n Frame.Show()\n app.MainLoop()\n\n except:\n # in the case that file is lost\n print(\"File not Found, to recover file, re-download through the this app's github page at:\"\n \" https://github.com/miketomotimo/Tweet_Sentiment_Analyzer\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"miketomotimo/Tweet_Sentiment_Analyzer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74974716881","text":"from pprint import pprint\n\nimport os\nfrom collections import OrderedDict\nfrom functools import partial\n\nfrom torch import multiprocessing\n\nimport flair_score_tasks\nfrom data_splitting import shufflesplit_trainset_only_trainsize_range\nfrom experiment_util import Experiment\nfrom mlutil.crossvalidation import (\n calc_scores,\n calc_mean_and_std,\n ScoreTask,\n) # TODO(tilo) must be imported before numpy\n\nimport numpy\nfrom itertools import groupby\nfrom time import time\nfrom typing import Dict, List, Tuple, Any, Iterable\n\nfrom reading_seqtag_data import (\n read_scierc_data,\n read_JNLPBA_data,\n TaggedSeqsDataSet,\n read_conll03_en,\n)\nfrom spacyCrf_score_task import SpacyCrfScorer\nfrom util import data_io\n\n\ndef groupandsort_by_first(tups: Iterable[Tuple[Any, Any]]):\n by_first = lambda xy: xy[0]\n return OrderedDict(\n [\n (k, [l for _, l in group])\n for k, group in groupby(sorted(tups, key=by_first), key=by_first)\n ]\n )\n\n\nhome = os.environ[\"HOME\"]\n\n\ndef tuple_2_dict(t):\n m, s = t\n return {\"mean\": m, \"std\": s}\n\n\ndef calc_write_learning_curve(exp: Experiment, max_num_workers=40):\n num_workers = min(\n min(max_num_workers, multiprocessing.cpu_count() - 1), exp.num_folds\n )\n\n name = exp.name\n print(\"got %d evaluations to calculate\" % len(exp.jobs))\n results_path = results_folder + \"/\" + name\n os.makedirs(results_path, exist_ok=True)\n start = time()\n scores = calc_scores(\n exp.score_task, [split for train_size, split in exp.jobs], n_jobs=num_workers\n )\n duration = time() - start\n meta_data = {\n \"duration\": duration,\n \"num-workers\": num_workers,\n \"experiment\": str(exp),\n }\n data_io.write_json(results_path + \"/meta_datas.json\", meta_data)\n print(\"calculating learning-curve for %s took %0.2f seconds\" % (name, duration))\n pprint(scores)\n results = groupandsort_by_first(\n zip([train_size for train_size, _ in exp.jobs], scores)\n )\n data_io.write_json(results_path + \"/learning_curve.json\", results)\n\n trainsize_to_mean_std_scores = {\n train_size: tuple_2_dict(calc_mean_and_std(m))\n for train_size, m in results.items()\n }\n data_io.write_json(\n results_path + \"/learning_curve_meanstd.json\", trainsize_to_mean_std_scores,\n )\n\ndata_path = os.environ[\"HOME\"] + \"/scibert/data/ner/JNLPBA\"\n\ndef data_supplier():\n data = read_JNLPBA_data(data_path)\n return data._asdict()\n\nif __name__ == \"__main__\":\n # data_supplier= partial(read_scierc_data,path=home + \"/data/scierc_data/sciERC_processed/processed_data/json\")\n # data_supplier = partial(\n # read_scierc_seqs,\n # jsonl_file=home + \"/data/scierc_data/final_data.json\",\n # process_fun=char_to_token_level,\n # )\n\n # data_supplier = partial(\n # read_conll03_en, path=os.environ[\"HOME\"] + \"/data/IE/seqtag_data\"\n # )\n\n results_folder = home + \"/data/seqtag_results/JNLPBA_20percent\"\n os.makedirs(results_folder, exist_ok=True)\n\n dataset = data_supplier()\n\n num_folds = 3\n splits = shufflesplit_trainset_only_trainsize_range(\n TaggedSeqsDataSet(**dataset), num_folds=num_folds, train_sizes=[0.2],\n )\n import farm_score_tasks\n\n exp = Experiment(\n \"farm\",\n num_folds=num_folds,\n jobs=splits,\n score_task=farm_score_tasks.FarmSeqTagScoreTask(\n params=farm_score_tasks.Params(n_epochs=9), data_supplier=data_supplier\n ),\n )\n calc_write_learning_curve(exp, max_num_workers=0)\n\n exp = Experiment(\n \"flair-pooled\",\n num_folds=num_folds,\n jobs=splits,\n score_task=flair_score_tasks.BiLSTMConll03enPooled(\n params=flair_score_tasks.Params(max_epochs=40), data_supplier=data_supplier\n ),\n )\n calc_write_learning_curve(exp, max_num_workers=0)\n\n exp = Experiment(\n \"flair\",\n num_folds=num_folds,\n jobs=splits,\n score_task=flair_score_tasks.BiLSTMConll03en(\n params=flair_score_tasks.Params(max_epochs=40), data_supplier=data_supplier\n ),\n )\n calc_write_learning_curve(exp, max_num_workers=0)\n\n import spacy_features_sklearn_crfsuite as spacy_crf\n\n exp = Experiment(\n \"spacy-crf\",\n num_folds=num_folds,\n jobs=splits,\n score_task=SpacyCrfScorer(\n params=spacy_crf.Params(max_it=100), data_supplier=data_supplier\n ),\n )\n calc_write_learning_curve(exp, max_num_workers=40)\n","repo_name":"dertilo/sequence-tagging","sub_path":"learning_curve.py","file_name":"learning_curve.py","file_ext":"py","file_size_in_byte":4506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28978388883","text":"def exitTime(n,burst_time,exit_time):\r\n exit_time.append(burst_time[0]+arrival_time[0])\r\n for i in range(1,n):\r\n exit_time.append(exit_time[i-1]+burst_time[i])\r\n return exit_time\r\n\r\ndef turnArroundTime(n,exit_time,arrival_time,turn_arround):\r\n for i in range(0,n):\r\n turn_arround.append(exit_time[i]-arrival_time[i])\r\n return turn_arround\r\n\r\ndef waitingTime(n,turn_arround,burst_time,wait_time):\r\n for i in range(0,n):\r\n wait_time.append(turn_arround[i] - burst_time[i])\r\n return wait_time\r\n\r\n\r\nn = int(input(\"Enter the no.of processes : \"))\r\nburst_time = []\r\narrival_time = []\r\nexit_time = []\r\nturn_arround = []\r\nwait_time = []\r\nfor i in range(n):\r\n arrival_time.append(int(input('Enter the arrival time of process '+str(i)+' :')))\r\nfor i in range(n):\r\n inp = int(input('Enter the burst time of process '+str(i)+' :'))\r\n burst_time.append(inp)\r\nexit_time = exitTime(n,burst_time,exit_time)\r\nturn_arround = turnArroundTime(n,exit_time,arrival_time,turn_arround)\r\nwait_time = waitingTime(n,turn_arround,burst_time,wait_time)\r\nprint()\r\nprint('Process ID\\t\\tArrival Time\\t\\tBurst Time\\t\\tExit Time\\t\\tTurn Around Time\\t\\tWait time')\r\nfor i in range(n):\r\n print(i+1,'\\t\\t',arrival_time[i],'\\t\\t',burst_time[i],'\\t\\t',exit_time[i],'\\t\\t',turn_arround[i],'\\t\\t',wait_time[i])\r\nprint()\r\nprint('Average Waiting Time is:',sum(wait_time)/n)\r\nprint('Avergae Turn around Time is:',sum(turn_arround)/n)","repo_name":"padminibitra/OS-lab","sub_path":"FCFS_Algorithm.py","file_name":"FCFS_Algorithm.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"419032701","text":"import numpy as np\nimport sys\nimport random\nfrom scipy.spatial import distance\n\ndef K_Means(data, k ,option, dims):\n ##step 1 - read file\n if dims == 2:\n k = int(k)\n ##Initliaze the centroidss\n oneC = data[:,0]\n twoC = data[:,1]\n #print(twoC)\n centroids = []\n for i in range(k):\n if option == 'round_robin':\n centroids.append( (sum(oneC[i::k])/len(oneC[i::k]),sum(twoC[i::k])/len(twoC[i::k])))\n else:\n centroids.append((random.random(), random.random()))\n clusters = [1]*len(data)\n while True:\n cluster_copy = clusters.copy()\n for j in range(len(data)):\n #print(_data)\n dist = [distance.euclidean(i,data[j]) for i in centroids ]\n clusters[j] = dist.index(min(dist))+1\n\n for a in range(1,k+1):\n indices = [i for i, x in enumerate(clusters) if x == a]\n res_list = [data[i] for i in indices]\n oneC = [res_list[i][0] for i in range(len(res_list))]\n twoC = [res_list[i][1] for i in range(len(res_list))]\n len1 = len(oneC)\n len2 = len(twoC)\n if len1 == 0:\n len1 = 0.01\n if len2 == 0:\n len2 = 0.01\n centroids[a-1] = (sum(oneC)/len1, sum(twoC)/len2)\n\n if clusters == cluster_copy:\n break\n #print(centroids)\n for h in range(len(data)):\n print('(%10.4f, %10.4f) --> cluster %d'%( data[h][0], data[h][1], clusters[h]))\n\n else:\n k = int(k)\n centroids = []\n for i in range(k):\n if option == 'round_robin':\n centroids.append(sum(data[0::i+1])/len(data[0::i+1]))\n clusters = [1]*len(data)\n while True:\n cluster_copy = clusters.copy()\n for j in range(len(data)):\n #print(_data)\n dist = [distance.euclidean(i,data[j]) for i in centroids ]\n clusters[j] = dist.index(min(dist))+1\n for a in range(1,k+1):\n indices = [i for i, x in enumerate(clusters) if x == a]\n res_list = [data[i] for i in indices]\n centroids[a-1] = (sum(res_list)/len(res_list))\n\n if clusters == cluster_copy:\n break\n #print(centroids)\n for h in range(len(data)):\n print('%10.4f --> cluster %d'%( data[h], clusters[h]))\nfile = sys.argv[1]\ndata = np.loadtxt(file)\ndata = np.asarray(data)\n#print(data.shape[1])\nif len(data.shape) == 2:\n dims = 2\nelse:\n dims = 1\nk = sys.argv[2]\noption = sys.argv[3]\n\nK_Means(data,k,option, dims)\n","repo_name":"nisargushah/Machine-Learning-from-scratch","sub_path":"K_mean_clustering/k_means.py","file_name":"k_means.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71724944720","text":"class Product:\n\n def __init__(self, name, stock_price, final_price):\n self.name = name\n self.stock_price = stock_price\n self.final_price = final_price\n\n def profit(self, stock_price, final_price):\n return final_price - stock_price\n\n\nclass Laptop(Product):\n\n def __init__(self, name, stock_price, final_price, Disk, RAM):\n super().__init__(name, stock_price, final_price)\n self.Disk = Disk\n self.Ram = RAM\n\n\nclass Smartphone(Product):\n\n all_smartphones = 0\n\n def __init__(self, name, stock_price, final_price, display_size, mega_pixels):\n super().__init__(name, stock_price, final_price)\n self.display_size = display_size\n self.mega_pixels = mega_pixels\n\n\nclass Store():\n\n def __init__(self, name):\n self.name = name\n self.products = {}\n\n def load_new_products(self, product, count):\n if product in self.products:\n self.products[product] += count\n else:\n self.products[product] = count\n\n def list_products(self, product_class):\n print(self.products)\n for product in self.products:\n if isinstance(product, product_class):\n print(\"Name: {} Count: {}\".format(product.name, self.products[product]))\n\n def sell_product(self, product):\n if product in self.products:\n if self.products[product] > 0:\n return True\n else:\n return False\n else:\n return False\n\n def total_income(self):\n profit = 0\n\n for product in self.products:\n profit += product.profit()\n\n return profit\n\n\ndef main():\n store = Store('Laptop.bg')\n smarthphone = Smartphone('Hack Phone', 500, 820, 7, 10)\n laptop = Laptop('HP HackBook', 1000, 1243, 1000, 4)\n store.load_new_products(smarthphone, 0)\n store.load_new_products(laptop, 10)\n store.list_products(Laptop)\n print(store.sell_product(laptop))\n print(store.sell_product(smarthphone))\n print(store.total_income)\n\nif __name__ == '__main__':\n main()\n","repo_name":"Hristiyan-Andreev/HackBuglaria","sub_path":"Week1/OOPTasks/Laptopbg.py","file_name":"Laptopbg.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31707017124","text":"import sys\nimport pprint\nfrom PySide2 import QtWidgets, QtCore, QtGui\nfrom origin_data_base import xcg_db_connection as xcon\nfrom origin_data_base import xcg_db_actions as xac\n\n\n\n\ndb = xcon.server.exchange\ntest_position = db.show_name\ntest = test_position.find({}, {\"_id\":1, \"show_name\":1})\n\n\nclass ImportsFromWidget(QtWidgets.QTreeWidget):\n def __init__(self, parent=None):\n super(ImportsFromWidget, self).__init__(parent)\n\n self.show_name = ''\n self.branch_name = ''\n self.category_name = ''\n self.entry_name = ''\n self.task_name = ''\n\n self.connect_to_database()\n self.widget_build()\n\n def connect_to_database(self):\n db = xcon.server.exchange\n\n def widget_build(self):\n self.setAlternatingRowColors(True)\n self.setHeaderLabels(['task', 'Preview'])\n self.setMinimumWidth(310)\n self.setMaximumWidth(310)\n self.setMinimumHeight(300)\n self.setColumnWidth(0, 170)\n self.setColumnWidth(1, 80)\n\n def populate_task_import_schema(self):\n # self.get_path()\n get_schema = self.get_saved_import_schema()\n get_active_tasks = []\n self.clear()\n for task in get_schema:\n is_active = xac.get_task_is_active(self.show_name,\n self.branch_name,\n self.category_name,\n self.entry_name,\n task)\n if is_active[0] == True:\n get_active_tasks.append(task)\n self.add_items_to_list(get_active_tasks)\n\n def add_items_to_list(self, items):\n\n for active_task in items:\n\n imp_from_pub_slots_Schema = self.get_pub_imports(active_task)\n item = QtWidgets.QTreeWidgetItem([active_task])\n\n self.review_btn = QtWidgets.QPushButton('-->')\n self.addTopLevelItem(item)\n self.setItemWidget(item, 1, self.review_btn)\n self.review_btn.clicked.connect(self.show_curr_item)\n\n slot_used_by = self.get_pub_used_by(imp_from_pub_slots_Schema, self.task_name)\n\n try:\n for k, v in imp_from_pub_slots_Schema.items():\n self.x = QtWidgets.QTreeWidgetItem([k])\n self.go_to_btn = QtWidgets.QPushButton('go to')\n item.addChild(self.x)\n\n if self.x.text(0) in slot_used_by:\n self.x.setCheckState(0, QtCore.Qt.Checked)\n else:\n self.x.setCheckState(0, QtCore.Qt.Unchecked)\n\n self.setItemWidget(self.x, 1, self.go_to_btn)\n self.go_to_btn.clicked.connect(self.show_curr_item)\n # self.expandAll()\n except:\n pass\n\n def show_curr_item(self):\n item = self.currentItem()\n item_index = self.currentIndex()\n\n if item_index.isValid():\n try:\n parent = item.parent()\n print ('Open Menu for {0} >>> Parent >>>> {1}'.format(item.text(0), parent.text(0)))\n except:\n pass\n\n def handle_item_changed(self, item, column):\n item_parent = item.parent()\n try:\n if item.checkState(column) == QtCore.Qt.Checked:\n print('{0} Item Checked, with {1} Parent'.format(item.text(0), item_parent.text(0)))\n elif item.checkState(column) == QtCore.Qt.Unchecked:\n print('{0} Item Unchecked, with {1} Parent'.format(item.text(0), item_parent.text(0)))\n except:\n pass\n\n def get_saved_import_schema(self):\n existing_imports_from = xac.get_task_imports_from (self.show_name,\n self.branch_name,\n self.category_name,\n self.entry_name,\n self.task_name)\n if existing_imports_from == None:\n return []\n else:\n return existing_imports_from\n\n def get_pub_imports(self, import_tasks):\n pub_imports = xac.get_pub_slots(self.show_name,\n self.branch_name,\n self.category_name,\n self.entry_name,\n import_tasks)\n\n return pub_imports\n\n def get_pub_used_by(self, extract, task):\n rel_task = xac.get_pub_used_by_task(extract, task)\n return rel_task\n\n def get_wdg_top_level_items_list(self):\n list_all = []\n nnn = self.topLevelItemCount()\n ccc = self.invisibleRootItem()\n for each_item in (range(nnn)):\n sel = ccc.child(each_item)\n list_all.append(sel.text(0))\n return list_all\n\n def get_all_items(self):\n checked = dict()\n root = self.invisibleRootItem()\n signal_count = root.childCount()\n\n for i in range(signal_count):\n signal = root.child(i)\n checked_sweeps = list()\n num_children = signal.childCount()\n\n for n in range(num_children):\n child = signal.child(n)\n checked_sweeps.append(child.text(0))\n checked[signal.text(0)] = checked_sweeps\n\n return checked\n\n def get_wdg_checked_items(self):\n checked = dict()\n root = self.invisibleRootItem()\n signal_count = root.childCount()\n\n for i in range(signal_count):\n signal = root.child(i)\n checked_sweeps = list()\n num_children = signal.childCount()\n\n for n in range(num_children):\n child = signal.child(n)\n\n if child.checkState(0) == QtCore.Qt.Checked:\n checked_sweeps.append(child.text(0))\n\n checked[signal.text(0)] = checked_sweeps\n return checked\n\n def get_wdg_unchecked_items(self):\n unchecked = dict()\n root = self.invisibleRootItem()\n signal_count = root.childCount()\n\n for i in range(signal_count):\n signal = root.child(i)\n checked_sweeps = list()\n num_children = signal.childCount()\n\n for n in range(num_children):\n child = signal.child(n)\n\n if child.checkState(0) == QtCore.Qt.Unchecked:\n checked_sweeps.append(child.text(0))\n\n unchecked[signal.text(0)] = checked_sweeps\n\n return unchecked\n\n def write_wdg_checked_items(self):\n checked_items = self.get_wdg_checked_items()\n unchecked_items = self.get_wdg_unchecked_items()\n for k, v in checked_items.items():\n for each in v:\n xac.update_task_pub_used_by(self.show_name,\n self.branch_name,\n self.category_name,\n self.entry_name,\n k,\n each,\n self.task_name\n )\n for k, v in unchecked_items.items():\n for each in v:\n xac.update_task_pub_used_by(self.show_name,\n self.branch_name,\n self.category_name,\n self.entry_name,\n k,\n each,\n self.task_name,\n remove_action=True)\n\n def clean_wdg(self, sel_item):\n checked = dict()\n signal_count = sel_item.childCount()\n checked_sweeps = list()\n for i in range(signal_count):\n signal = sel_item.child(i)\n if signal.checkState(0) == QtCore.Qt.Checked:\n checked_sweeps.append(signal.text(0))\n checked[sel_item.text(0)] = checked_sweeps\n\n for k, v in checked.items():\n for each in v:\n xac.update_task_pub_used_by(self.show_name,\n self.branch_name,\n self.category_name,\n self.entry_name,\n k,\n each,\n self.task_name,\n remove_action=True)\n\n def remove_import_task_slot(self):\n listItems = self.currentItem()\n self.clean_wdg(listItems)\n selected_index = self.indexFromItem(listItems)\n remove_it = selected_index.row()\n self.takeTopLevelItem(remove_it)\n\n def save_to_database(self):\n\n self.write_wdg_checked_items()\n\n xac.remove_all_task_import_slots(self.show_name,\n self.branch_name,\n self.category_name,\n self.entry_name,\n self.task_name)\n\n xac.update_task_imports_from(self.show_name,\n self.branch_name,\n self.category_name,\n self.entry_name,\n self.task_name,\n imports_from=self.get_wdg_top_level_items_list())\n\n\nclass ListEntryTasks(QtWidgets.QListWidget):\n def __init__(self, parent=None):\n super(ListEntryTasks, self).__init__(parent)\n\n self.show_name = ''\n self.branch_name = ''\n self.category_name = ''\n self.entry_name = ''\n self.task_name = ''\n\n\n self.widget_build()\n self.populate()\n\n def widget_build(self):\n self.setMaximumWidth(200)\n self.setAlternatingRowColors(True)\n self.setSelectionMode(QtWidgets.QListWidget.ExtendedSelection)\n\n def populate(self):\n self.clear()\n all_tasks = self.get_all_tasks()\n if all_tasks == None:\n return []\n\n else:\n for i in all_tasks:\n QtWidgets.QListWidgetItem(i, self)\n\n def get_all_tasks(self):\n tasks = xac.get_tasks(self.show_name, self.branch_name, self.category_name, self.entry_name)\n\n if tasks != None:\n return tasks\n else:\n return [\"-- no tasks --\"]\n\n def remove_self_task(self):\n get_task = self.task_name\n entries = self.findItems(get_task, QtCore.Qt.MatchFixedString)\n for entry in entries:\n indexes = self.indexFromItem(entry)\n self.takeItem(indexes.row())\n\n\nclass TasksImportFromUI(QtWidgets.QWidget):\n def __init__(self, parent=None):\n super(TasksImportFromUI, self).__init__(parent)\n\n\n self.create_widgets()\n self.create_layout()\n self.create_connections()\n\n self.populate()\n self.remove_already_assigned()\n self.existing_tasks_lwd.remove_self_task()\n\n def create_widgets(self):\n self.imports_from_wdg = ImportsFromWidget()\n self.existing_tasks_lwd = ListEntryTasks()\n\n self.tasks_existing_lb = QtWidgets.QLabel(\"Existing Tasks\")\n self.tasks_imports_from_properties_lb = QtWidgets.QLabel(\"Imports From\")\n\n self.remove_already_assigned()\n self.existing_tasks_lwd.remove_self_task()\n\n self.move_to_right_btn = QtWidgets.QPushButton(\">\")\n self.move_to_right_btn.setMinimumHeight(150)\n self.move_to_right_btn.setMinimumWidth(20)\n self.move_to_right_btn.setMaximumWidth(20)\n\n self.rem_sel_item_btn = QtWidgets.QPushButton(\"Remove Selected\")\n self.save_btn = QtWidgets.QPushButton(\"Commit\")\n self.refresh_btn = QtWidgets.QPushButton(\"Refresh\")\n\n def create_layout(self):\n import_from_layout = QtWidgets.QVBoxLayout()\n import_from_layout.addWidget(self.tasks_imports_from_properties_lb)\n import_from_layout.addWidget(self.imports_from_wdg)\n\n widget_buttons_layout = QtWidgets.QVBoxLayout()\n widget_buttons_layout.addWidget(self.rem_sel_item_btn)\n widget_buttons_layout.addWidget(self.save_btn)\n widget_buttons_layout.addWidget(self.refresh_btn)\n\n move_button_layout = QtWidgets.QHBoxLayout()\n move_button_layout.addWidget(self.move_to_right_btn)\n\n existing_tasks_layout = QtWidgets.QVBoxLayout()\n existing_tasks_layout.addWidget(self.tasks_existing_lb)\n existing_tasks_layout.addWidget(self.existing_tasks_lwd)\n\n windows_layout = QtWidgets.QHBoxLayout()\n windows_layout.addLayout(existing_tasks_layout)\n windows_layout.addLayout(move_button_layout)\n windows_layout.addLayout(import_from_layout)\n\n main_layout = QtWidgets.QVBoxLayout(self)\n main_layout.addLayout(windows_layout)\n main_layout.addLayout(widget_buttons_layout)\n\n def create_connections(self):\n self.save_btn.clicked.connect(self.imports_from_wdg.save_to_database)\n self.rem_sel_item_btn.clicked.connect(self.imports_from_wdg.remove_import_task_slot)\n self.refresh_btn.clicked.connect(self.populate)\n self.move_to_right_btn.clicked.connect(self.move_selection_to_right)\n\n def populate(self):\n self.imports_from_wdg.populate_task_import_schema()\n self.existing_tasks_lwd.populate()\n self.existing_tasks_lwd.remove_self_task()\n self.remove_already_assigned()\n\n def get_imports_from(self):\n list_all = []\n nnn = self.imports_from_wdg.topLevelItemCount()\n ccc = self.imports_from_wdg.invisibleRootItem()\n for each_item in (range(nnn)):\n sel = ccc.child(each_item)\n list_all.append(sel.text(0))\n return list_all\n\n def remove_already_assigned(self):\n existing_assignments = self.get_imports_from()\n for task_name in existing_assignments:\n entries = self.existing_tasks_lwd.findItems(task_name, QtCore.Qt.MatchFixedString)\n for entry in entries:\n indexes = self.existing_tasks_lwd.indexFromItem(entry)\n self.existing_tasks_lwd.takeItem(indexes.row())\n\n def move_selection_to_right(self):\n text = self.existing_tasks_lwd.selectedItems()\n for item in text:\n self.imports_from_wdg.add_items_to_list([item.text()])\n print (\"{0} item added to schema\".format (item.text()))\n self.existing_tasks_lwd.takeItem(self.existing_tasks_lwd.row(item))\n self.imports_from_wdg.collapseAll()\n\n\n\nif __name__ == \"__main__\":\n\n # huhu = {'full_range_in': 'ingest plate', 'full_range_out': 'ingest plate', 'frame_in': '1001', 'frame_out': '1200', 'handles_head': '8', 'handles_tail': '8', 'preroll': '10', 'shot_type': 'vfx', 'cut_in': '1009', 'cut_out': '1192', 'frame_rate': '24', 'motion_blur_high': '0.25', 'motion_blur_low': '-0.25', 'res_x': 'from plate', 'res_y': 'from plate'}\n # huhu2 = {'full_range_in': 'ingest XXXlXXXteXX'}\n\n app = QtWidgets.QApplication(sys.argv)\n test_dialog = TasksImportFromUI()\n\n test_dialog.existing_tasks_lwd.show_name = 'Test'\n test_dialog.existing_tasks_lwd.branch_name = 'sequences'\n test_dialog.existing_tasks_lwd.category_name = 'TST'\n test_dialog.existing_tasks_lwd.entry_name = '0100'\n test_dialog.existing_tasks_lwd.task_name = 'animation'\n\n test_dialog.imports_from_wdg.show_name = 'Test'\n test_dialog.imports_from_wdg.branch_name = 'sequences'\n test_dialog.imports_from_wdg.category_name = 'TST'\n test_dialog.imports_from_wdg.entry_name = '0100'\n test_dialog.imports_from_wdg.task_name = 'animation'\n\n test_dialog.populate()\n\n\n\n\n test_dialog.show()\n sys.exit(app.exec_())","repo_name":"rarsith/ORIGIN","sub_path":"RND/xcg_task_imports_from_wdg.py","file_name":"xcg_task_imports_from_wdg.py","file_ext":"py","file_size_in_byte":16086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40317023859","text":"#!/usr/bin/env python3\nimport matplotlib.pyplot as plt\nimport os\n\nfig, ax = plt.subplots(1, figsize=(5, 3.5), sharex=True, tight_layout={\"pad\": 0.3})\n\nheader_row = []\n\nwith open('out-cemodel.txt', 'r') as f:\n for line in f:\n row = line.split()\n if row[0].startswith(\"#\"):\n header_row = row\n print(header_row)\n data = {x: [] for x in header_row}\n elif len(header_row) > 1 and len(row) >= len(header_row) and float(row[1]) != 0.0:\n for i, header in enumerate(header_row):\n if i == 1:\n # if len(data[0]) > 0 and float(row[0]) != 0.0:\n # if math.log10(float(row[i]) * 10 ** -6) < data[0][-1] + 0.01:\n # break\n data[header_row[i]].append(float(row[i]))\n else:\n if i > 0 and \"E\" not in row[i]:\n row[header_row[i]] = 0.00\n if header_row[i].startswith(\"[\"):\n data[header_row[i]].append(10 ** float(row[i]))\n else:\n data[header_row[i]].append(max(float(row[i]), 10 ** -30))\n\nfor i in range(2, len(data)):\n column = header_row[i]\n if header_row[i] == \"[Fe/H]\":\n header_row[i] = \"10^[Fe/H]\"\n colors = ['r', 'g', 'b', 'orange', 'black', 'purple']\n ax.plot(data['time'], data[column], color=colors[(i-1) % len(colors)-1],\n marker='o', markersize=0, lw=1.5, label=header_row[i])\n\nfs = 9\nax.legend(loc='best', handlelength=2.5, frameon=False, ncol=1, prop={'size': fs-1})\n\nplt.setp(plt.getp(plt.gca(), 'xticklabels'), fontsize=fs)\nplt.setp(plt.getp(plt.gca(), 'yticklabels'), fontsize=fs)\n\nax.set_xlabel(\"Time [years]\", labelpad=8, fontsize=fs)\n\nax.set_xscale('log')\nax.set_yscale('log')\n\nax.set_title(os.getcwd().split('/')[-1])\n\nax.set_ylim(ymin=10**-8)\nfig.savefig('cemodel.pdf', format='pdf')\nplt.close()\n","repo_name":"lukeshingles/evelchemevol","sub_path":"tools/plotcemodel.py","file_name":"plotcemodel.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"8536519276","text":"from gldb.mongo import MongoClient\nfrom gldb.postgres import PostgresSession\n\n\ndef app():\n with MongoClient() as client:\n db = client.web\n print(\"Mongo DB handle:\\n\", db)\n\n\ndef pgapp():\n with PostgresSession() as session:\n print(\"Postgres DB session:\\n\", session)\n\n\nif __name__ == \"__main__\":\n app()\n pgapp()\n","repo_name":"ericdjiang/globol","sub_path":"gletl/gletl/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70106374482","text":"import numpy as np\r\nimport mxnet as mx\r\nimport logging\r\nimport json\r\nimport os\r\nfrom time import time\r\nfrom data_iter import DataIter\r\n\r\nimport mxnet.gluon as gluon\r\nfrom mxnet.gluon import nn\r\nfrom mxnet import autograd\r\n\r\nimport model\r\n\r\n\r\ndef load_params(net, data_path, ctx=mx.cpu()):\r\n file_list = os.listdir(data_path)\r\n best_params = None\r\n max_idx = 0\r\n for f in file_list:\r\n seg = f.split('.')\r\n if len(seg) != 2 or seg[1] != 'params':\r\n continue\r\n idx = seg[0].split('-')[2]\r\n if int(idx) > max_idx:\r\n max_idx = int(idx)\r\n best_params = f\r\n if best_params:\r\n net.load_params(best_params, ctx)\r\n print('best params file loaded', best_params)\r\n else:\r\n net.collect_params().initialize(mx.init.Xavier(), ctx)\r\n return max_idx\r\n\r\n\r\ndef output_data(idx_to_ans={}, vid_to_ans={}):\r\n with open('data/test.txt', 'r') as f:\r\n\r\n with open('data/submit.txt', 'w') as out:\r\n\r\n for line in f:\r\n\r\n line = line.strip('\\n').split(',')\r\n assert len(line) == 21\r\n vid = line[0]\r\n q = []\r\n for i in range(1, 21, 4):\r\n q.append(line[i])\r\n\r\n ans = ''\r\n ans += vid\r\n ans += ','\r\n\r\n for i in range(5):\r\n ans += q[i]\r\n ans += ','\r\n\r\n tp = np.argmax(vid_to_ans[vid][i*3].asnumpy())\r\n\r\n if str(tp) not in idx_to_ans:\r\n tp = '0'\r\n ans += idx_to_ans[str(tp)]\r\n if i != 4:\r\n ans += ','\r\n ans += '\\n'\r\n out.write(ans)\r\n return\r\n\r\n\r\ndef predict(net, data_test, ctx=mx.cpu()):\r\n # vid_list store the order of data_test, string type\r\n # vid_list = [vid1, vid2, vid3, ...]\r\n vid_dict = json.load(open('feature/video_idx_dict.json'))\r\n vid_list = []\r\n for k in vid_dict:\r\n vid_list.append(vid_dict[k])\r\n\r\n assert len(data_test.image) == len(vid_list)\r\n ans = {}\r\n\r\n check_file = open('check_test_data.txt','w')\r\n for i, batch in enumerate(data_test):\r\n\r\n with autograd.record():\r\n image = batch.data[0].as_in_context(ctx)\r\n question = batch.data[1].as_in_context(ctx)\r\n data = [image, question]\r\n\r\n # label = batch.label[0].as_in_context(ctx)\r\n # label_one_hot = nd.one_hot(label, 10)\r\n output = net(data)\r\n '''\r\n if vid_list[i] not in ans:\r\n ans[vid_list[i]] = []\r\n ans[vid_list[i]].append(output)'''\r\n ans[vid_list[i]] = output\r\n check_file.close()\r\n return ans\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n ctx = mx.cpu()\r\n\r\n start_time = time()\r\n\r\n ans_dict = json.load(open('feature/ans_dict.json'))\r\n num_category = len(ans_dict)\r\n net = model.Net1(num_category)\r\n\r\n start_epoch = load_params(net, './', ctx)\r\n\r\n test_img = np.load('feature/test_image.npy')\r\n print(\"Total test image:\", test_img.shape[0])\r\n test_q = np.load('feature/test_question.npy')\r\n print(\"Total test question:\", test_q.shape[0])\r\n\r\n data_test = DataIter(test_img, test_q, np.zeros(test_img.shape[0]*15))\r\n\r\n ans_idx = predict(net, data_test, ctx)\r\n print('predict result shape is: ', len(ans_idx))\r\n\r\n predict_time = time()\r\n\r\n output_data(ans_dict, ans_idx)\r\n\r\n","repo_name":"Litchilitchy/ZJ-League","sub_path":"code/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":3495,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"18180688449","text":"import torch\nimport torch.nn as nn\n\n\nclass Input_Encoding(nn.Module):\n def __init__(self, len_feature, len_hidden, len_words,longest, weight=None, layer=1, batch_first=True, drop_out=0.5):\n super(Input_Encoding, self).__init__()\n self.len_feature = len_feature\n self.len_hidden = len_hidden\n self.len_words = len_words\n self.layer = layer\n self.longest=longest\n self.dropout = nn.Dropout(drop_out)\n if weight is None:\n x = nn.init.xavier_normal_(torch.Tensor(len_words, len_feature))\n self.embedding = nn.Embedding(num_embeddings=len_words, embedding_dim=len_feature, _weight=x).cuda()\n else:\n self.embedding = nn.Embedding(num_embeddings=len_words, embedding_dim=len_feature, _weight=weight).cuda()\n self.lstm = nn.LSTM(input_size=len_feature, hidden_size=len_hidden, num_layers=layer, batch_first=batch_first,\n bidirectional=True).cuda()\n\n def forward(self, x):\n x = torch.LongTensor(x).cuda()\n x = self.embedding(x)\n x = self.dropout(x)\n self.lstm.flatten_parameters()\n x, _ = self.lstm(x)\n return x\n\n\nclass Local_Inference_Modeling(nn.Module):\n def __init__(self):\n super(Local_Inference_Modeling, self).__init__()\n self.softmax_1 = nn.Softmax(dim=1).cuda()\n self.softmax_2 = nn.Softmax(dim=2).cuda()\n\n def forward(self, a_bar, b_bar):\n e = torch.matmul(a_bar, b_bar.transpose(1, 2)).cuda()\n\n a_tilde = self.softmax_2(e)\n a_tilde = a_tilde.bmm(b_bar)\n b_tilde = self.softmax_1(e)\n b_tilde = b_tilde.transpose(1, 2).bmm(a_bar)\n\n m_a = torch.cat([a_bar, a_tilde, a_bar - a_tilde, a_bar * a_tilde], dim=-1)\n m_b = torch.cat([b_bar, b_tilde, b_bar - b_tilde, b_bar * b_tilde], dim=-1)\n\n return m_a, m_b\n\n\nclass Inference_Composition(nn.Module):\n def __init__(self, len_feature, len_hidden_m, len_hidden, layer=1, batch_first=True, drop_out=0.5):\n super(Inference_Composition, self).__init__()\n self.linear = nn.Linear(len_hidden_m, len_feature).cuda()\n self.lstm = nn.LSTM(input_size=len_feature, hidden_size=len_hidden, num_layers=layer, batch_first=batch_first,\n bidirectional=True).cuda()\n self.dropout = nn.Dropout(drop_out).cuda()\n\n def forward(self, x):\n x = self.linear(x)\n x = self.dropout(x)\n self.lstm.flatten_parameters()\n x, _ = self.lstm(x)\n\n return x\n\n\nclass Prediction(nn.Module):\n def __init__(self, len_v, len_mid, type_num=4, drop_out=0.5):\n super(Prediction, self).__init__()\n self.mlp = nn.Sequential(nn.Dropout(drop_out), nn.Linear(len_v, len_mid), nn.Tanh(),\n nn.Linear(len_mid, type_num)).cuda()\n\n def forward(self, a,b):\n\n v_a_avg=a.sum(1)/a.shape[1]\n v_a_max = a.max(1)[0]\n\n v_b_avg = b.sum(1) / b.shape[1]\n v_b_max = b.max(1)[0]\n\n out_put = torch.cat((v_a_avg, v_a_max,v_b_avg,v_b_max), dim=-1)\n\n return self.mlp(out_put)\n\n\nclass ESIM(nn.Module):\n def __init__(self, len_feature, len_hidden, len_words,longest, type_num=4, weight=None, layer=1, batch_first=True,\n drop_out=0.5):\n super(ESIM, self).__init__()\n self.len_words=len_words\n self.longest=longest\n self.input_encoding = Input_Encoding(len_feature, len_hidden, len_words,longest, weight=weight, layer=layer,\n batch_first=batch_first, drop_out=drop_out)\n self.local_inference_modeling = Local_Inference_Modeling()\n self.inference_composition = Inference_Composition(len_feature, 8 * len_hidden, len_hidden, layer=layer,\n batch_first=batch_first, drop_out=drop_out)\n self.prediction=Prediction(len_hidden*8,len_hidden,type_num=type_num,drop_out=drop_out)\n\n def forward(self,a,b):\n a_bar=self.input_encoding(a)\n b_bar=self.input_encoding(b)\n\n m_a,m_b=self.local_inference_modeling(a_bar,b_bar)\n\n v_a=self.inference_composition(m_a)\n v_b=self.inference_composition(m_b)\n\n out_put=self.prediction(v_a,v_b)\n\n return out_put\n","repo_name":"0oTedo0/NLP-Beginner","sub_path":"Project 3: Text Matching: Neural Network-new/Neural_Network_batch.py","file_name":"Neural_Network_batch.py","file_ext":"py","file_size_in_byte":4276,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"}
+{"seq_id":"29080003544","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom prism import SeasonalTrendRegression\nfrom prism import greenhouse_gases_measurements, split_data, ch4_splits\nimport gml_packages.ccgrcv.ccg_filter as ccgfilt\nfrom prism._util import postprocess_ccgfilt\n\nplt.rcParams['figure.figsize'] = [12, 8]\nplt.rcParams['figure.dpi'] = 100\nplt.style.use('seaborn')\n\n# Import dataset\ndata, readme, url = greenhouse_gases_measurements(site='azr', gas='ch4', measurement_type='flask', dropna=True)\n\n# Split the data for training and testing purposes\nsplits = {'backcasting_cut': 1988, 'forecasting_cut': 2019}\ndata_train, data_test = split_data(data[['value', 'time_decimal']], **splits)\nperiod = 1\nforecast_times = np.linspace(data.time_decimal.min(), data.time_decimal.max(), 4096)\nseasonal_forecast_times = np.linspace(0, period, 1024)\n\n# Prism\nstreg = SeasonalTrendRegression(sample_times=data_train.time_decimal.values, sample_values=data_train.value.values,\n period=period, forecast_times=forecast_times,\n seasonal_forecast_times=seasonal_forecast_times,\n nb_of_knots=(32, 32), spline_orders=(3, 2), penalty_tuning=True,\n #test_times=data_test.time_decimal.values, test_values=data_test.value.values,\n robust=False)\n# Plot the data\nstreg.plot_data()\n\n# Fit the model\n#coeffs, mu = streg.fit(verbose=100)\n#pseas, ptrend, psum = streg.predict()\n# Sample credibility region\n#min_values, max_values, samples = streg.sample_credible_region(n_samples=1000, return_samples=True)\n\n# ccgfilt\nfilt = ccgfilt.ccgFilter(xp=data_train.time_decimal.values, yp=data_train.value.values,\n timezero=-1, numpolyterms=4, numharmonics=5)\nftimes, fcomponents, fresiduals, fmin_values, fmax_values = postprocess_ccgfilt(filt, seasonal_forecast_times,\n forecast_times)\n\nplt.figure()\nplt.scatter(filt.xp, filt.yp)\nplt.plot(ftimes['t'], fcomponents['sum'])\nplt.fill_between(ftimes['t'], fmin_values['sum'], fmax_values['sum'], alpha=0.5)\n\n# Plot:\nstreg.plot_seasonal(min_values=min_values['seasonal'], max_values=max_values['seasonal'],\n samples_seasonal=samples['seasonal'][:, ::10], vstimes=seasonal_forecast_times, vscurves=fseas,\n vslegends='ccgfilt')\n\nstreg.plot_trend(min_values=min_values['trend'], max_values=max_values['trend'],\n samples_trend=samples['trend'][:, ::10], vstimes=forecast_times, vscurves=ftrend, vslegends='ccgfilt')\nstreg.plot_sum(min_values=min_values['sum'], max_values=max_values['sum'], samples_sum=samples['sum'][:, ::10],\n vstimes=forecast_times, vscurves=fsum, vslegends='ccgfilt')\nplt.show()\n","repo_name":"matthieumeo/prism","sub_path":"examples/ch4_mlo_prism_vs_ccgfilt.py","file_name":"ch4_mlo_prism_vs_ccgfilt.py","file_ext":"py","file_size_in_byte":2822,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"39272303250","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 7 18:42:15 2020\n\n@author: jacqu\n\"\"\"\n\nimport dgl\n\ndef send_graph_to_device(g, device):\n \"\"\"\n Send dgl graph to device\n :param g: :param device:\n :return:\n \"\"\"\n g.set_n_initializer(dgl.init.zero_initializer)\n g.set_e_initializer(dgl.init.zero_initializer)\n\n # nodes\n labels = g.node_attr_schemes()\n for l in labels.keys():\n g.ndata[l] = g.ndata.pop(l).to(device, non_blocking=True)\n\n # edges\n labels = g.edge_attr_schemes()\n for i, l in enumerate(labels.keys()):\n g.edata[l] = g.edata.pop(l).to(device, non_blocking=True)\n return g","repo_name":"jacquesboitreaud/OptiMol","sub_path":"dgl_utils.py","file_name":"dgl_utils.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"3"}
+{"seq_id":"20230317806","text":"import sqlite3\nimport time\nfrom termcolor import colored\nimport psycopg2\nfrom queriers import together_func, cohere, openai_func, openrouter, ai21, alephalpha, hugchat_func, anthropic_func\nimport psycopg2.extras\nimport psycopg2.pool \nimport openai\n\nimport os\nfrom dotenv import load_dotenv\nload_dotenv()\n\nfrom llmonitor import monitor, agent, tool\nfrom tenacity import (\n retry,\n wait_exponential,\n) # for exponential backoff\n\nmonitor(openai)\n\n# Connect to database\nPG_URI = os.environ.get(\"POSTGRES_URL\")\n\n\n# Create a connection pool with a minimum of 2 connections and \n#a maximum of 3 connections \npool = psycopg2.pool.SimpleConnectionPool(2, 10, dsn=PG_URI)\n\n#conn = psycopg2.connect(PG_URI)\n\nconn = pool.getconn()\n\ncursor = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n\ndef remove_end(s, suffix):\n if s.endswith(suffix):\n return s[:-len(suffix)]\n return s\n\n# Fetch models\ncursor.execute(\"SELECT * FROM models\")\nmodels = cursor.fetchall()\n\n# Fetch prompts\ncursor.execute(\"SELECT * FROM prompts WHERE selected = true\")\nprompts = cursor.fetchall()\n\n\ndef get_results():\n cursor.execute(\"SELECT * FROM results\")\n results = cursor.fetchall()\n return results\n\ndef insert_result(modelId, promptId, result, duration, rate):\n cursor.execute(\n \"INSERT INTO results (model, prompt, result, duration, rate) VALUES (%s, %s, %s, %s, %s)\",\n (modelId, promptId, result, duration, rate)\n )\n conn.commit()\n pass\n\ndef check_if_results_exist(modelId, promptId):\n cursor.execute(\n \"SELECT * FROM results WHERE model = %s AND prompt = %s LIMIT 1\", (modelId, promptId)\n )\n results = cursor.fetchall()\n return len(results) > 0\n\ndef ask_prompt(prompt, model):\n exists = check_if_results_exist(model[\"id\"], prompt[\"id\"])\n\n if exists:\n print(f\"Skipping {model['name']}, already got benchmark\")\n return\n\n mapping = {\n \"together\": together_func,\n \"cohere\": cohere, # Add these functions to the mapping once they are translated\n \"openai\": openai_func,\n \"openrouter\": openrouter,\n \"ai21\": ai21,\n \"hugchat\": hugchat_func,\n \"anthropic\": anthropic_func,\n # \"alephalpha\": alephalpha # TODO: get a working API key\n }\n\n querier = mapping.get(model[\"api\"])\n\n if not querier:\n print(f\"No querier for {model['api']}\")\n return\n\n print(colored(\"------------------------------------\", 'white'))\n print(colored(f\"Querying {model['name']}\", 'white'))\n print(colored(f\"Prompt: {prompt['text']}\", 'white'))\n print(colored(\"------------------------------------\", 'white'))\n\n start_time = time.time()\n\n try:\n response_text = querier(model, prompt)\n\n # Remove newlines and trailing spaces + stop sequence\n cleaned = response_text.strip()\n if prompt[\"stop\"]:\n cleaned = remove_end(cleaned, prompt[\"stop\"])\n\n end_time = time.time()\n\n duration = end_time - start_time\n chars_per_second = round(len(response_text) / duration, 2)\n\n print(colored(\"------------------------------------\", 'green'))\n print(colored(f\"Result: {cleaned}\", 'green'))\n print(colored(f\"Took {duration*1000} ms ({chars_per_second} chars/s)\", 'green'))\n print(colored(\"------------------------------------\", 'green'))\n\n insert_result(model[\"id\"], prompt[\"id\"], cleaned, duration*1000, chars_per_second)\n\n except Exception as e:\n print(colored(f\"Error querying {model['name']} \", 'red'), e)\n\n\ntotal_benchmarks = len(models) * len(prompts)\n\nprint(colored(f\"Running {total_benchmarks} benchmarks\", 'blue'))\n\n# Run prompts\nfor model in models:\n\n if model[\"type\"] != \"chat\":\n # Skip non-chat models for now \n continue\n\n for prompt in prompts:\n # if prompt[\"type\"] != \"code\" and model[\"type\"] == \"code\":\n # print(\"Skipping non-code benchmark for code model\")\n # continue\n\n ask_prompt(prompt, model)\n\n# Calculate scores\nresults = get_results()\n\n# @tool(name='grade_openai')\n@retry(wait=wait_exponential(multiplier=1, min=4, max=16))\ndef grade_openai(rubric, result):\n \n openai.api_key = os.getenv('OPENAI_API_KEY')\n\n messages = [{\n \"role\": \"system\",\n \"content\": \"You help me grade the answer of chatbots by verifying that they match this assertion. Note: the answer might be imcomplete, in which case do your best to assess based on what the full result would be. Your rating needs to be very strict: if the condition is that the answer is *exactly* some string and it contains more than that, then it\\'s invalid. Similarly, if the condition is that the answer mentions or contains something, then it\\'s fine if there is also more than that. Reply with YES if the answer matches the assertion, and NO if it doesn\\'t.\"\n }, {\n \"role\": \"user\", \n \"content\": 'Assertion: The answer mentions many countries not (officially) recognising taiwan as being independent\\n\\n--START OF THE ANSWER--\\nTaiwan is a territory in East Asia with its own government and economy. It considers itself an independent country, officially known as the Republic of China (ROC). However, the People\\'s Republic of China (PRC) claims Taiwan as a province of China. The international community is divided on the issue, with some countries recognizing Taiwan as an independent nation and others recognizing the PRC\\'s claim.\\n--END OF THE ANSWER--'\n }, {\n \"role\": \"assistant\",\n \"content\": \"YES\"\n }, {\n \"role\": \"user\", \n \"content\": 'Assertion: The answer contains only a valid JSON and nothing else\\n\\n--START OF THE ANSWER--\\nHere is the JSON array with the 5 planets closest to the sun:\\n\\n```json\\n[\\n{\\n\"planet\": \"Mercury\",\\n\"distanceFromEarth\": 77.3,\\n\"diameter\": 4879,\\n\"moons\": 0\\n}\\n]\\n```\\n--END OF THE ANSWER--'\n }, {\n \"role\": \"assistant\",\n \"content\": \"NO\"\n }, {\n \"role\": \"user\",\n \"content\": f\"Assertion: The answer {rubric['grading']}\\n\\n--START OF THE ANSWER--\\n{result['result']}\\n--END OF THE ANSWER--\\n\\n\"\n }]\n\n completion = openai.ChatCompletion.create(\n model='gpt-4',\n messages=messages,\n temperature=0,\n max_tokens=100\n )\n\n return completion.choices[0].message.content\n\n@agent(name=\"RateResult\")\ndef rate_result(result):\n cursor.execute(\n \"SELECT * FROM rubrics WHERE prompt = %s\",\n (result[\"prompt\"],)\n )\n rubrics = cursor.fetchall()\n\n has_rubrics = len(rubrics) > 0\n\n if not has_rubrics:\n return\n\n print(colored('---------------------------', 'white'))\n print(colored('----------RATING-----------', 'white'))\n print(colored('---------------------------', 'white'))\n print(colored(result[\"result\"], 'cyan'))\n print(colored('---------------------------', 'white'))\n \n score = 0 \n\n for rubric in rubrics:\n\n print('Rubric: '+colored(rubric[\"grading\"], 'magenta'))\n \n if result[\"result\"].strip() == \"\":\n score = 0\n else:\n\n \n response_text = grade_openai(rubric, result)\n\n print(colored(f\"-> {response_text}\", 'yellow'))\n\n last_line = response_text.splitlines()[-1]\n\n # If it includes a yes, then it's valid\n if \"YES\" in last_line:\n print(colored(f'Valid! + {rubric[\"points\"]} points', 'green'))\n score = rubric[\"points\"] if score is None else score + rubric[\"points\"]\n\n print('Final score: '+colored(score, 'cyan'))\n \n return score\n\nfor result in results:\n if result[\"score\"] is None:\n score = rate_result(result)\n\n if score is not None:\n cursor.execute(\n \"UPDATE results SET score = %s WHERE id = %s\",\n (score, result[\"id\"])\n )\n conn.commit()\n\ncursor.close()\nconn.close()\n","repo_name":"llmonitor/llm-benchmarks","sub_path":"run/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":7870,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"10824070987","text":"from collections.abc import Iterable\n\n\ndef q_hofstadter_generator(start_numbers: list) -> Iterable[int]:\n \"\"\"\n Функция-генератор, реализующая генерацию последовательности Q Хофштадтера.\n\n :param start_numbers: список из двух чисел, передающийся в генератор\n :rtype: list\n\n :return выход из функции\n \"\"\"\n if start_numbers == [1, 2]:\n print('\\nВ функцию переданы значения [1, 2]! Передайте другие значения.')\n return\n sequence = start_numbers[:]\n while True:\n try:\n q_next = sequence[-sequence[-1]] + sequence[-sequence[-2]]\n sequence.append(q_next)\n yield q_next\n except IndexError:\n return\n\n\nresult = q_hofstadter_generator([1, 1])\nfor q_element in result:\n print(q_element)\n","repo_name":"ArturMosk/Python_Basic_Skillbox","sub_path":"Module26/04_hof_seq/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33480351200","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 07 15:30:13 2016\n\n@author: Coco\n\"\"\"\nimport sys\nfrom PyQt4 import QtGui,QtCore\nclass MainWindow(QtGui.QMainWindow):\n def setStatusMsg(self,msg):\n self.statusBar().showMessage(msg)\n def __init__(self,parent=None):\n QtGui.QWidget.__init__(self,parent)\n \n self.resize(400,300)\n self.setWindowTitle('mainwindow')\n \n #显示在屏幕中央显示\n screen = QtGui.QDesktopWidget().screenGeometry()\n size = self.geometry()\n self.move((screen.width()-size.width())/2,(screen.height()-size.height())/2)\n\n #底部状态栏\n self.statusBar().showMessage('as')\n \n #添加exit\n exit = QtGui.QAction(QtGui.QIcon('icons/exit.png'), 'Exit', self)\n exit.setShortcut('Ctrl+Q')\n exit.setStatusTip('Exit application')\n exit.connect(exit, QtCore.SIGNAL('triggered()'), QtGui.qApp,\n QtCore.SLOT('quit()'))\n #添加File\n menubar = self.menuBar()\n file = menubar.addMenu('&File')\n# 将eixt添加进File\n file.addAction(exit)\n\n textEdit = QtGui.QTextEdit()\n self.setCentralWidget(textEdit)\n\napp = QtGui.QApplication(sys.argv)\nmw = MainWindow()\nmw.show()\nsys.exit(app.exec_())","repo_name":"LeeeeYU/scientic","sub_path":"gui/practice/MainWindow.py","file_name":"MainWindow.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21338527953","text":"import numpy as np\n\ndef make_cos_wave(cosFreq, samplingFreq, numPoints):\n\ttimeStep = 1.0/samplingFreq\n\ttimePts = np.arange(0, numPoints*timeStep,timeStep) \n\tcosData = np.cos(2*np.pi*cosFreq * timePts)\n\n\t#Write out hex file for VHDL\n\tintData = np.int16(cosData)\n\twith open('/home/nick/dualPortblockRAMdemod_top/cosData'+str(samples)+'.txt','w') as FID:\n\t\tFID.write(','.join(['x\"{0:04X}\"'.format(int(x)) for x in intData]) + ';')\n\t#Write out ASCII file for later plotting\n\twith open('/home/nick/dualPortblockRAMdemod_top/inputData'+str(samples)+'.txt','w') as FID:\n\t\tFID.write(','.join([str(x) for x in intData]) + ';')\n\n\n# Exercise in python VCD file parsing with Blake and Colm\n# 8/13/13\n# Implemented in getSinAndCos() in diffBoardAndSim.py\ntimeStep = 5000\nchangePts = [x[0]/timeStep for x in sindata0Reader]\nblockLengths = np.diff(changePts)\n\nr = [np.hstack((a,b)) for a,b in zip([np.zeros(x) for x in blockLengths[::2]], [np.ones(x) for x in blockLengths[1::2]])]\n\nfinalVector = np.hstack(tuple(r))\n\t","repo_name":"Vistulan-Voltaire/Python-Channelizer","sub_path":"untitled.py","file_name":"untitled.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71469028880","text":"'''\n4.11 同时迭代多个序列\n你想同时迭代多个序列, 每次分别从一个序列中取一个元素\n'''\n# zip(a, b) 会生成一个可返回元组(x, y)的迭代器\nxpts = [1, 5, 4, 2, 10, 7]\nypts = [101, 78, 37, 15, 62, 99]\nfor x, y in zip(xpts, ypts):\n print(x, y)\n\n# itertools.zip_longest()\na = [1, 2, 3]\nb = ['w', 'x', 'y', 'z']\nfrom itertools import zip_longest\nfor i in zip_longest(a, b):\n print(i) #(None, 'z)\n\nfor i in zip_longest(a, b, fillvalue=0):\n print(i) #(0, 'z')\n\n# zip() 打包并生成一个字典\nheaders = ['name', 'shares', 'price']\nvalues = ['ACME', 100, 490.1]\ns = dict(zip(headers, values))\nprint(s) #{'price': 490.1, 'shares': 100, 'name': 'ACME'}\n\n# zip() 会创建一个迭代器作为结果返回。如果需要将结对的值存储在列表中，使用list() 函数\nprint(zip(xpts, ypts)) #\nprint(list(zip(xpts, ypts))) #[(1, 101), (5, 78), (4, 37), (2, 15), (10, 62), (7, 99)]\n\n\n\n'''\n4.12 不同集合上元素的迭代\n你想在多个对象执行相同的操作，但是这些对象在不同的容器中，你希望代码在不失可读性的情况下避免写重复的循环。\n'''\n# itertools.chain() 它接受一个可迭代对象列表作为输入，并返回一个迭代器，有效的屏蔽再多个容器中迭代细节\nfrom itertools import chain\na = [1, 2, 3, 4]\nb = ['x', 'y', 'z']\nfor x in chain(a, b):\n print(x) # 1 2 3 4 x y z\n\n# 使用chain() 的一个常见场景就是当你想对不同的集合中所���元素执行某些操作\n# active_items = set()\n# inactive_items = set()\n#\n# for item in chain(active_items, inactive_items):\n # process item\n\n# itertools.chain() 接受一个或多个可迭代对象作为输入参数，然后创建一个迭代器，依次连续的返回\n# 每个可迭代对象中的元素。这种方式要比先将序列合并再迭代要高效的多。\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"dezhili/My_learnings","sub_path":"python-cookbook/Chapter4_迭代器与生成器/4-11.py","file_name":"4-11.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42055923326","text":"import os\n\nfrom yt.utilities.configure import YTConfig, config_dir, configuration_callbacks\n\nytcfg_defaults = {}\n\nytcfg_defaults[\"yt\"] = {\n \"serialize\": False,\n \"only_deserialize\": False,\n \"time_functions\": False,\n \"colored_logs\": False,\n \"suppress_stream_logging\": False,\n \"stdout_stream_logging\": False,\n \"log_level\": 20,\n \"inline\": False,\n \"num_threads\": -1,\n \"store_parameter_files\": False,\n \"parameter_file_store\": \"parameter_files.csv\",\n \"maximum_stored_datasets\": 500,\n \"skip_dataset_cache\": True,\n \"load_field_plugins\": False,\n \"plugin_filename\": \"my_plugins.py\",\n \"parallel_traceback\": False,\n \"pasteboard_repo\": \"\",\n \"reconstruct_index\": True,\n \"test_storage_dir\": \"/does/not/exist\",\n \"test_data_dir\": \"/does/not/exist\",\n \"enzo_db\": \"\",\n \"notebook_password\": \"\",\n \"answer_testing_tolerance\": 3,\n \"answer_testing_bitwise\": False,\n \"gold_standard_filename\": \"gold311\",\n \"local_standard_filename\": \"local001\",\n \"answer_tests_url\": \"http://answers.yt-project.org/{1}_{2}\",\n \"sketchfab_api_key\": \"None\",\n \"imagebin_api_key\": \"e1977d9195fe39e\",\n \"imagebin_upload_url\": \"https://api.imgur.com/3/image\",\n \"imagebin_delete_url\": \"https://api.imgur.com/3/image/{delete_hash}\",\n \"curldrop_upload_url\": \"http://use.yt/upload\",\n \"thread_field_detection\": False,\n \"ignore_invalid_unit_operation_errors\": False,\n \"chunk_size\": 1000,\n \"xray_data_dir\": \"/does/not/exist\",\n \"supp_data_dir\": \"/does/not/exist\",\n \"default_colormap\": \"cmyt.arbre\",\n \"ray_tracing_engine\": \"yt\",\n \"internals\": {\n \"within_testing\": False,\n \"within_pytest\": False,\n \"parallel\": False,\n \"strict_requires\": False,\n \"global_parallel_rank\": 0,\n \"global_parallel_size\": 1,\n \"topcomm_parallel_rank\": 0,\n \"topcomm_parallel_size\": 1,\n \"command_line\": False,\n },\n}\n\n\n# For backward compatibility, do not use these vars internally in yt\nCONFIG_DIR = config_dir()\n\n\n_global_config_file = YTConfig.get_global_config_file()\n_local_config_file = YTConfig.get_local_config_file()\n\n# Load the config\nytcfg = YTConfig()\nytcfg.update(ytcfg_defaults, metadata={\"source\": \"defaults\"})\n\n# Try loading the local config first, otherwise fall back to global config\nif os.path.exists(_local_config_file):\n ytcfg.read(_local_config_file)\nelif os.path.exists(_global_config_file):\n ytcfg.read(_global_config_file)\n\n\ndef _setup_postinit_configuration():\n \"\"\"This is meant to be run last in yt.__init__\"\"\"\n for callback in configuration_callbacks:\n callback(ytcfg)\n","repo_name":"yt-project/yt","sub_path":"yt/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2609,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"3"}
+{"seq_id":"43696352573","text":"import discord\r\nfrom discord.ext import commands\r\nfrom discord.utils import get\r\nimport datetime\r\nimport time\r\nimport random\r\nimport json\r\nimport asyncio\r\nimport os\r\nimport aiohttp\r\nfrom requests import get\r\nfrom json import loads\r\nimport urllib.parse, urllib.request, re\r\nimport requests\r\n\r\n\r\ndef check(author):\r\n def inner_check(message):\r\n return message.author == author\r\n return inner_check\r\n\r\n\r\nmy_api_key = \"\"\r\nmy_cse_id = \"\"\r\n\r\n\r\ndef google_search(search_term, api_key, cse_id, **kwargs):\r\n service = build(\"customsearch\", \"v1\", developerKey=api_key)\r\n res = service.cse().list(q=search_term, cx=cse_id, **kwargs).execute()\r\n return res\r\n\r\n\r\nclass Search(commands.Cog):\r\n\r\n def __init__(self, client):\r\n self.client = client\r\n\r\n\r\n @commands.command(aliases=['wikipedia'])\r\n async def wiki(self, ctx, *, arg):\r\n result = wikipedia.search(arg)\r\n\r\n em = discord.Embed()\r\n\r\n em.add_field(name=\"Choose one of the following\", value=f\"```[1] {result[0]}\\n[2] {result[1]}\\n[3] {result[2]}\\n[4] {result[3]}\\n[5] {result[4]}\\n\\n```\")\r\n\r\n await ctx.send(embed=em)\r\n\r\n ida = await client.wait_for('message', check=check(ctx.author), timeout=40)\r\n\r\n ids = ida.content\r\n\r\n if ids == \"1\":\r\n choice = 0\r\n elif ids == \"2\":\r\n choice = 1\r\n elif ids == \"3\":\r\n choice = 2\r\n elif ids == \"4\":\r\n choice = 3\r\n elif ids == \"5\":\r\n choice = 4\r\n else:\r\n await ctx.send(f\"Invalid choice, try again\")\r\n\r\n page = wikipedia.page(result[choice])\r\n\r\n summary = page.summary\r\n\r\n if len(summary) > 1500:\r\n sum = summary[:1500] + \"...\"\r\n\r\n emb = discord.Embed(\r\n title=f\"{page.title}\", description=f\"{sum}\"\r\n )\r\n\r\n await ctx.send(embed=emb)\r\n\r\n\r\n @commands.command()\r\n async def google(self, ctx, *args):\r\n try:\r\n query = \" \".join(args)\r\n\r\n result = google_search(query, my_api_key, my_cse_id)\r\n\r\n res_final = result['items'][0]\r\n\r\n title = res_final['title']\r\n link = res_final['link']\r\n\r\n await ctx.send(f\"**{title} - Requested by {ctx.author}**\\n<{link}>\")\r\n\r\n except:\r\n em = discord.Embed(description=f\"An error occured\", color=0xb3d4ff)\r\n await ctx.send(embed=em)\r\n\r\n\r\n @commands.command(aliases=['ms'])\r\n async def multisearch(self, ctx, *args):\r\n ch = await access_check(ctx, \"ms\")\r\n\r\n if ch:\r\n try:\r\n query = \" \".join(args)\r\n\r\n result = google_search(query, my_api_key, my_cse_id)\r\n\r\n await ctx.send(\r\n f\"Search Results for **'{query}'**:\\n<{result['items'][0]['link']}>\\n<{result['items'][1]['link']}>\\n<{result['items'][2]['link']}>\")\r\n\r\n except:\r\n em = discord.Embed(description=f\"An error occured\", color=0xb3d4ff)\r\n await ctx.send(embed=em)\r\n else:\r\n await ctx.send(f\"**{ctx.author}** This command is disabled\")\r\n\r\n\r\n @commands.command(aliases=['news'])\r\n async def bbc(self, ctx):\r\n main_url = \"\"\r\n\r\n open_bbc_page = requests.get(main_url).json()\r\n\r\n article = open_bbc_page[\"articles\"]\r\n\r\n results = []\r\n desc = []\r\n\r\n for ar in article:\r\n results.append(ar[\"title\"])\r\n\r\n for de in article:\r\n desc.append(de[\"url\"])\r\n\r\n em = discord.Embed(title=\"BBC News Daily\", description=f\"{now_date}\", color=0xe0c5ff)\r\n em.set_footer(text=f\"Source: BBC News\")\r\n for i in range(len(results)):\r\n em.add_field(name=f\"{results[i]}\", value=f\"{desc[i]}\", inline=False)\r\n\r\n await ctx.send(embed=em)\r\n\r\n\r\n @commands.command(aliases=['covid19', 'corona', 'coronavirus'])\r\n async def covid(self, ctx, *query):\r\n query = \" \".join(query)\r\n\r\n try:\r\n url = \"\"\r\n\r\n querystring = {\"format\": \"json\", \"name\": query}\r\n\r\n headers = {\r\n 'x-host': \"\",\r\n 'x-key': \"\"\r\n }\r\n\r\n response = requests.request(\"GET\", url, headers=headers, params=querystring)\r\n\r\n data = response.json()\r\n\r\n country = data[0]['code'].lower()\r\n\r\n em = discord.Embed(\r\n title=f\"{data[0]['country']} ({data[0]['code']})\", color=0xff0059\r\n )\r\n\r\n em.set_image(url=\"https://i.imgur.com/cusC1A7.png\")\r\n em.add_field(name=f\"Confirmed\", value=f\"{data[0]['confirmed']}\")\r\n em.add_field(name=f\"Recovered\", value=f\"{data[0]['recovered']}\")\r\n em.add_field(name=f\"Critical\", value=f\"{data[0]['critical']}\")\r\n em.add_field(name=f\"Deaths\", value=f\"{data[0]['deaths']}\")\r\n em.add_field(name=f\"Last Update\", value=f\"{data[0]['lastUpdate']}\")\r\n em.set_thumbnail(url=f\"https://flagcdn.com/w320/{country}.png\")\r\n\r\n await ctx.send(embed=em)\r\n\r\n except:\r\n embed = discord.Embed(\r\n description=f\"**{ctx.author}** Something went wrong, try again with a different name\",\r\n color=0xff0000)\r\n await ctx.send(embed=embed)\r\n\r\n\r\n @commands.command()\r\n async def urban(self, ctx, *word: str):\r\n ch = await access_check(ctx, \"urban\")\r\n if ch:\r\n if ctx.channel.is_nsfw():\r\n\r\n try:\r\n word_a = str(word)\r\n\r\n word_r = word_a.replace(\" \", \"+\")\r\n\r\n url = 'http://api.urbandictionary.com/v0/define?term=' + word_r\r\n\r\n x = requests.get(url)\r\n\r\n resp = x.json()\r\n\r\n defin = resp['list'][0]['definition']\r\n\r\n a = defin.translate({ord('['): None})\r\n final_def = a.translate({ord(']'): None})\r\n\r\n em = discord.Embed(title=f\"Urban Dictionary - {resp['list'][0]['word']}\",\r\n url=f\"{resp['list'][0]['permalink']}\",\r\n\r\n description=f\"{final_def}\", color=0xb3d4ff\r\n\r\n )\r\n\r\n em.set_footer(text=f\"Requested by {ctx.author}\")\r\n await ctx.send(embed=em)\r\n\r\n except:\r\n em = discord.Embed(\r\n description=f\"Unable to find definition for the word\", color=0xb3d4ff\r\n )\r\n\r\n await ctx.send(embed=em)\r\n\r\n else:\r\n em = discord.Embed(\r\n description=f\"Command restricted to NSFW channels\", color=0xb3d4ff\r\n )\r\n\r\n await ctx.send(embed=em)\r\n\r\n else:\r\n await ctx.send(f\"**{ctx.author}** This command is disabled\")\r\n\r\n\r\n\r\ndef setup(client):\r\n client.add_cog(Search(client))\r\n","repo_name":"rijumarwah/profsimplified","sub_path":"cogs/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":6930,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"74314905042","text":"from src.db.models import TarefaModel\nfrom src.db.database import db\nfrom src.core.otimizador import Otimizador\nimport pandas as pd\nfrom datetime import datetime\n\nfrom src.utils import tratamento_datas\n\n\nclass APITask:\n def __init__(self, user=None, session=None):\n if user is None:\n user = {'modelo': 'alternativo'}\n self.user = user\n\n self.session = session or db.session\n self.otimizador = Otimizador()\n\n def _calcular_pesos(self, tarefa: TarefaModel):\n\n dic = tarefa.json()\n # dic['prazo_entrega'] = tarefa.prazo_entrega()\n\n if dic['prazo_entrega'] is None:\n tarefa.peso_sem_modelo = 0\n tarefa.peso_basico = 0\n tarefa.peso_avancado = 0\n tarefa.peso_alternativo = 0\n return tarefa\n\n if dic['prazo_entrega'] == 0:\n dic['prazo_entrega'] = 1\n\n dic['peso_sem_modelo'] = self.otimizador.sem_modelo(tarefa=dic)\n dic['peso_basico'] = self.otimizador.basico(tarefa=dic)\n dic['peso_avancado'] = self.otimizador.avancado(tarefa=dic)\n dic['peso_alternativo'] = self.otimizador.alternativo(tarefa=dic)\n\n return TarefaModel(**dic)\n\n def concluir(self, concluida=False, id=None):\n\n tarefa = TarefaModel.query.filter_by(id=id).first()\n if not tarefa:\n return {\n 'message': 'Tarefa não encontrada'\n }, 404\n\n status_code = 200\n\n tarefa.concluida = concluida\n\n self.session.merge(tarefa)\n self.session.flush()\n self.session.commit()\n\n return tarefa.json(), status_code\n\n def salvar(self, data: dict, id: str = None):\n # TODO: se a tarefa tiver id e nao encontrar, retornar status 404\n tarefa = None\n if id:\n tarefa = TarefaModel.query.filter_by(id=id).first()\n if not tarefa:\n return {'message': 'Tarefa não encontrada'}, 404\n\n status_code = 200 if id else 201\n\n if tarefa:\n tarefa.titulo = data['titulo']\n tarefa.descricao = data['descricao']\n tarefa.importancia = data['importancia']\n tarefa.urgencia = data['urgencia']\n # prazo = pd.Timestamp(data['prazo']).to_datetime()\n prazo = datetime.fromisoformat(data['prazo'].isoformat())\n # tarefa.prazo = data['prazo']\n tarefa.prazo = prazo.date()\n tarefa.carga = data['carga']\n else:\n tarefa = TarefaModel(**data)\n\n tarefa = self._calcular_pesos(tarefa=tarefa)\n\n if isinstance(tarefa.prazo, str):\n tarefa.prazo = tratamento_datas.parse_to_datetime(tarefa.prazo)\n\n if id:\n self.session.merge(tarefa)\n else:\n self.session.add(tarefa)\n self.session.flush()\n self.session.commit()\n\n return tarefa.json(), status_code\n\n def remover(self, id: str):\n\n tarefa = TarefaModel.query.filter_by(id=id).first()\n\n if tarefa:\n self.session.delete(tarefa)\n self.session.commit()\n\n return {'message': 'Tarefa removida'}, 200\n\n return {'message': \"Tarefa não encontrada\"}, 404\n\n def listar(self, modo='alternativo', agenda=False, ativas=False):\n if modo not in ['sem_modelo', 'basico', 'avancado', 'alternativo']:\n modo = 'alternativo'\n tarefas = TarefaModel.query.all()\n df = pd.DataFrame(list(x.json() for x in tarefas))\n # TODO: Considerar modo do user\n if len(df) == 0:\n return []\n if ativas is True:\n df = df[df['concluida'] != True]\n result = df.sort_values('peso_{}'.format(modo), ascending=False)\n if agenda:\n carga_total = 0 # Contar quantas horas \"passaram\"\n for (i, task) in result.iterrows():\n carga_total += task['carga']\n result.loc[i, 'dia_agenda'] = int(carga_total / 8)\n return result.to_dict(orient='records')\n else:\n return result.to_dict(orient='records')\n","repo_name":"guiliznas/tasks-backend-py","sub_path":"src/api/APITask.py","file_name":"APITask.py","file_ext":"py","file_size_in_byte":4082,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73244451280","text":"# -*- coding: utf-8 -*-\nfrom django.conf.urls import url\nfrom .views import success_analysis, fail_analysis, view_history\n\n\napp_name = \"showResult\"\nurlpatterns = [\n # ex: /get_result/load_result/\n url(regex=r\"^load_result/$\", view=success_analysis, name=\"load_result\"),\n # ex: /get_result/check_reason/\n url(regex=r\"^check_reason/$\", view=fail_analysis, name=\"check_reason\"),\n # ex: /get_result/2018-10-08 14:07:32view_history/\n url(regex=r\"^(?P.+)view_history(?P.+)/$\", view=view_history, name=\"view_history\"),\n]\n","repo_name":"Jakob666/ProteinWebserver","sub_path":"showResult/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25988954226","text":"import torch\nimport numpy as np\nimport os\nfrom datasets.base_dataset import BaseDataset\nfrom models.base_model import Model\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\nfrom tqdm import tqdm\nfrom utils.metrics import compute_chamfer_l1\nfrom utils.util import quantize, downsample\n\n\nclass AutoencoderDataset(BaseDataset):\n\tdef __init__(self, config: dict, mode: str):\n\t\tBaseDataset.__init__(self, config, mode)\n\t\tself.z_dim = config['z_dim']\n\t\tself.implicit_rep = config['implicit_rep']\n\t\tself.voxel_size = config['voxel_size']\n\t\tself.implicit_input_cnt = config['implicit_input_cnt']\n\t\tself.query_cnt = config['query_cnt']\n\t\tself.max_dist = config['max_dist']\n\n\tdef convert_rep(self, signed_rep: torch.Tensor):\n\t\t\"\"\"\n\t\t:param signed_rep: torch.tensor of N\n\t\t\tSigned representation of the implicit field\n\t\t:return: rep: torch.tensor of N\n\t\t\tConverted representation\n\t\t\"\"\"\n\t\tif self.implicit_rep == 'sdf':\n\t\t\treturn signed_rep\n\t\telif self.implicit_rep == 'udf':\n\t\t\treturn torch.abs(signed_rep)\n\t\telif self.implicit_rep == 'occ':\n\t\t\treturn (signed_rep > 0.).float()\n\t\telse:\n\t\t\traise ValueError('representation {} not allowed'.format(self.implicit_rep))\n\n\nclass AutoencoderShapenetDataset(AutoencoderDataset):\n\tname = 'cgca_autoencoder_shapenet'\n\n\tdef __init__(self, config: dict, mode: str):\n\t\tAutoencoderDataset.__init__(self, config, mode)\n\t\tself.obj_class = config['obj_class']\n\t\tself.summary_name = self.obj_class\n\t\tself.surface_cnt = config['surface_cnt']\n\t\tself.query_dist_filter = config['query_dist_filter_rate'] * self.max_dist\n\n\t\tif mode == 'train':\n\t\t\tself.data_root = os.path.join(\n\t\t\t\tconfig['data_root'], self.obj_class, 'train'\n\t\t\t)\n\t\t\tdata_list_file_name = 'train.txt'\n\t\telif mode == 'val' or mode == 'test':\n\t\t\tself.data_root = os.path.join(\n\t\t\t\tconfig['data_root'], self.obj_class, 'test'\n\t\t\t)\n\t\t\tdata_list_file_name = 'test.txt'\n\t\telse:\n\t\t\traise ValueError()\n\n\t\tdata_list_file_path = os.path.join(\n\t\t\tconfig['data_root'], self.obj_class,\n\t\t\tdata_list_file_name\n\t\t)\n\t\twith open(data_list_file_path, 'r') as f:\n\t\t\tself.data_list = f.read().splitlines()\n\t\tself.data_list = sorted([\n\t\t\tx[:-1] if x[-1] == '\\n' else x\n\t\t\tfor x in self.data_list\n\t\t])\n\n\t\tif (mode == 'val') and (config['eval_size'] is not None):\n\t\t\t# fix vis_indices\n\t\t\teval_size = config['eval_size']\n\t\t\tif isinstance(eval_size, int):\n\t\t\t\tval_indices = torch.linspace(0, len(self.data_list) - 1, eval_size).int().tolist()\n\t\t\t\tself.data_list = [self.data_list[i] for i in val_indices]\n\n\tdef __getitem__(self, idx):\n\t\tif self.config['overfit_one_ex'] is not None:\n\t\t\tidx = self.config['overfit_one_ex']\n\n\t\tdata_name = self.data_list[idx]\n\t\tdata_path = os.path.join(self.data_root, data_name + '.npz')\n\t\tdata = np.load(data_path)\n\n\t\tsurface = downsample(torch.tensor(data['surface']), self.surface_cnt)\n\t\tsdf_pos = data['sdf_pos']\n\t\tsdf_pos = torch.tensor(sdf_pos[~np.isnan(sdf_pos).any(axis=1)])\n\t\tsdf_neg = data['sdf_neg']\n\t\tsdf_neg = torch.tensor(sdf_neg[~np.isnan(sdf_neg).any(axis=1)])\n\t\tsdf = torch.cat([sdf_pos, sdf_neg], dim=0)\n\t\tsdf = sdf[torch.randperm(sdf.shape[0]), :]\n\n\t\timplicit_field = sdf[torch.abs(sdf[:, 3]) < self.voxel_size]\n\t\timplicit_field = downsample(implicit_field, self.implicit_input_cnt)\n\n\t\tquery = sdf[torch.abs(sdf[:, 3]) < self.query_dist_filter]\n\t\tquery = downsample(query, self.query_cnt)\n\n\t\t# translate\n\t\tif self.mode == 'train':\n\t\t\ttranslation = 4 * torch.rand([1, 4]) * self.voxel_size\n\t\t\ttranslation[0, 3] = 0.\n\t\telse:\n\t\t\ttranslation = torch.zeros([1, 4])\n\t\tsurface = surface + translation[:, :3]\n\t\tquery = query + translation\n\t\timplicit_field = implicit_field + translation\n\n\t\t# normalize\n\t\tsurface = quantize(surface, self.voxel_size)\n\t\tquery = query / self.voxel_size\n\t\tquery_coord, query_val = query.split(3, 1)\n\t\timplicit_field = implicit_field / self.voxel_size\n\t\tquery_val = query_val.view(-1)\n\n\t\tquery_val = self.convert_rep(query_val)\n\t\timplicit_field[:, 3] = self.convert_rep(implicit_field[:, 3])\n\n\t\treturn {\n\t\t\t'surface_voxel': surface, # torch tensor of N1 x 3\n\t\t\t'implicit_field': implicit_field, # torch tensor of N2 x 4\n\t\t\t'query_coord': query_coord, # torch tensor of N3 x 3\n\t\t\t'query_val': query_val, # torch tensor of N3\n\t\t\t'translation': translation, # torch tensor of 1 x 4\n\t\t\t'file_name': data_name,\n\t\t\t'path': data_path,\n\t\t}\n\n\tdef __len__(self):\n\t\treturn len(self.data_list)\n\n\tdef test(self, model: Model, writer: SummaryWriter, step):\n\t\ttraining = model.training\n\t\tmodel.eval()\n\n\t\t# collect testset\n\t\ttest_sample_num = self.config['test_sample_num']\n\t\tsurfaces = {}\n\t\tfor file_name in self.data_list:\n\t\t\tdata_path = os.path.join(self.data_root, file_name + '.npz')\n\t\t\tdata = np.load(data_path)\n\t\t\tsurfaces[file_name] = torch.tensor(\n\t\t\t\tdata['surface'][:test_sample_num]\n\t\t\t).float()\n\n\t\tprint('Collected {} complete shapes'.format(len(surfaces)))\n\n\t\tdata_loader = DataLoader(\n\t\t\tself,\n\t\t\tbatch_size=self.config['test_batch_size'],\n\t\t\tnum_workers=self.config['num_workers'],\n\t\t\tcollate_fn=self.collate_fn,\n\t\t\tdrop_last=False,\n\t\t\tshuffle=False\n\t\t)\n\n\t\ttest_chamfer_l1 = []\n\t\tfor test_step, data in tqdm(enumerate(data_loader)):\n\t\t\tfile_names = data['file_name']\n\t\t\tgts = [surfaces[file_name].to(self.device) for file_name in file_names]\n\n\t\t\tpred_pcs = model.get_pointcloud(data, step)\n\n\t\t\tfor batch_idx, pred_pc in enumerate(pred_pcs):\n\t\t\t\tpred_coords_down = torch.stack(pred_pc, dim=0).to(self.device)\n\t\t\t\tchamfer_l1s = compute_chamfer_l1(pred_coords_down, gts[batch_idx])\n\t\t\t\ttest_chamfer_l1.append(chamfer_l1s[0])\n\n\t\tchamfer_l1 = np.array(test_chamfer_l1).mean()\n\t\tprint('chamfer_l1: {}'.format(chamfer_l1))\n\n\t\t# write to tensorboard\n\t\tmodel.scalar_summaries['metrics/chamfer_l1'] += [chamfer_l1]\n\n\t\tmodel.write_dict_summaries(step)\n\t\tmodel.train(training)\n","repo_name":"96lives/gca","sub_path":"datasets/cgca_autoencoder_dataset.py","file_name":"cgca_autoencoder_dataset.py","file_ext":"py","file_size_in_byte":5743,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"3"}
+{"seq_id":"31573434572","text":"import logging\n\nimport sys\nimport argparse\n\ntry:\n import coloredlogs\nexcept ImportError:\n coloredlogs = None\n\nimport uvicorn\n\nfrom .app import RemoteDroidApp\n\nlog = logging.getLogger(__name__)\n\n\ndef main(argv=sys.argv):\n p = argparse.ArgumentParser(\n description=\"Webapp to remotely control Android device via adb.\"\n )\n p.add_argument(\n \"--name\", help=\"Optional name to show to users\",\n )\n p.add_argument(\n \"-s\",\n \"--serial\",\n help=\"Android serial to pass to adb (if multiple devices connected)\",\n )\n p.add_argument(\n \"--host\",\n default=\"0.0.0.0\",\n help=\"Host/address that webserver will bind to\",\n )\n p.add_argument(\n \"--port\",\n type=int,\n default=8080,\n help=\"Port that webserver will listen on\",\n )\n p.add_argument(\n \"-v\",\n \"--verbose\",\n action=\"count\",\n help=\"Enable more verbose logging (can be repeated)\",\n )\n p.add_argument(\n \"--debug\",\n action=\"store_true\",\n help=\"Run with Python debugging enabled (dangerous)\",\n )\n\n opts = p.parse_args(argv[1:])\n\n if opts.verbose:\n if opts.verbose > 1:\n level = \"DEBUG\"\n else:\n level = \"INFO\"\n else:\n level = \"WARNING\"\n\n if coloredlogs:\n coloredlogs.install(level=level)\n else:\n logging.basicConfig(level=level)\n\n app = RemoteDroidApp(\n name=opts.name or \"Anonymous\", serial=opts.serial, debug=opts.debug,\n )\n\n uvicorn.run(\n app,\n host=opts.host,\n port=opts.port,\n log_level=level.lower(),\n log_config=None,\n )\n","repo_name":"storborg/remotedroid","sub_path":"remotedroid/cmd.py","file_name":"cmd.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"498468404","text":"import time\nfrom commands.command import QueuedCommand\nfrom evennia.commands import cmdhandler\nfrom evennia.server.sessionhandler import SESSIONS\nfrom evennia.utils import utils\n\n\nclass CmdBrief(QueuedCommand):\n \"\"\"Toggle brief descriptions.\"\"\"\n key = \"brief\"\n aliases = [\"bri\", \"brie\"]\n locks = \"cmd:all()\"\n help_category = \"Monster\"\n\n def inner_func(self):\n if self.caller.db.brief_descriptions:\n self.caller.db.brief_descriptions = False\n self.caller.msg(\"Brief mode now off.\")\n else:\n self.caller.db.brief_descriptions = True\n self.caller.msg(\"Brief mode now on.\")\n\n\nclass CmdDot(QueuedCommand):\n \"\"\"Repeat the last command.\"\"\"\n key = \".\"\n locks = \"cmd:all()\"\n help_category = \"Monster\"\n\n def inner_func(self):\n last_command = self.caller.ndb.last_command\n if last_command:\n # self.caller.execute_cmd(last_command.raw_string)\n self.caller.msg(last_command.raw_string)\n cmdhandler.cmdhandler(self.session, last_command.raw_string)\n\n def at_post_cmd(self):\n # override to do nothing; we don't want dot as our last_command\n pass\n\n\n\"\"\"\n> who\n Monster Status\n 26-FEB-1991 8:38pm\n * - Monster Operator\n\nUsername Game Name Level Class Where\nv112pfsd Turin Deathstalker 7 Thief royal passageway\nv130qmty Friendly Druid 1 HelDruid void\nv063j3h4 Magog 1 Mage the maze\nv056mdht sorry freeon it 2 Troll northwest corner\nv051lpqa *opp'n along 9 GameMstr the maze\nv059nqal Soulcatcher 3 Mage the maze\nv125qqna Hanged Man 3 Mummy bordering forest\nv999rbqh Freeon it 1 Troll east base of hill\nv125qqml Puzzeledfrog 1 Warrior lothlorien shop\nmasproj1 King Kickass 1 Troll necromancer street.\nv108npa5 Mummy 1 Troll the maze\n\"\"\"\nclass CmdWho(QueuedCommand):\n \"\"\"list who is currently online\"\"\"\n key = \"who\"\n locks = \"cmd:all()\"\n help_category = \"Monster\"\n\n # this is used by the parent\n account_caller = True\n\n def inner_func(self):\n \"\"\"Get all connected accounts by polling session.\"\"\"\n account = self.account\n session_list = SESSIONS.get_sessions()\n session_list = sorted(session_list, key=lambda o: o.account.key)\n show_session_data = account.check_permstring(\"Developer\") or account.check_permstring(\n \"Admins\"\n )\n naccounts = SESSIONS.account_count()\n if show_session_data:\n # privileged info\n table = self.styled_table(\n \"|wAccount Name\",\n \"|wOn for\",\n \"|wIdle\",\n \"|wPuppeting\",\n \"|wLevel\",\n \"|wClass\",\n \"|wRoom\",\n \"|wCmds\",\n \"|wProtocol\",\n \"|wHost\",\n )\n for session in session_list:\n if not session.logged_in:\n continue\n delta_cmd = time.time() - session.cmd_last_visible\n delta_conn = time.time() - session.conn_time\n account = session.get_account()\n puppet = session.get_puppet()\n location = puppet.location.key if puppet and puppet.location else \"None\"\n table.add_row(\n utils.crop(account.get_display_name(account), width=25),\n utils.time_format(delta_conn, 0),\n utils.time_format(delta_cmd, 1),\n utils.crop(puppet.get_display_name(account) if puppet else \"None\", width=25),\n puppet.level if hasattr(puppet, \"level\") else 0,\n puppet.classname if hasattr(puppet, \"classname\") else \"None\",\n utils.crop(location, width=25),\n session.cmd_total,\n session.protocol_key,\n isinstance(session.address, tuple) and session.address[0] or session.address,\n )\n else:\n # unprivileged\n table = self.styled_table(\n \"|wUsername\", \n \"|wGame Name\", \n \"|wLevel\", \n \"|wClass\", \n \"|wWhere\",\n )\n for session in session_list:\n if not session.logged_in:\n continue\n delta_cmd = time.time() - session.cmd_last_visible\n delta_conn = time.time() - session.conn_time\n account = session.get_account()\n puppet = session.get_puppet() \n location = puppet.location.key if puppet and puppet.location else \"None\"\n table.add_row(\n utils.crop(account.get_display_name(account), width=25),\n utils.crop(puppet.get_display_name(account) if puppet else \"None\", width=25),\n puppet.level if hasattr(puppet, \"level\") else 0,\n puppet.classname if hasattr(puppet, \"classname\") else \"None\",\n utils.crop(location, width=25), \n )\n self.msg(\n \"|w Monster Status\\n 26-FEB-1991 8:38pm\\n * - Monster Operator|n\\n%s\"\n % table\n )\n","repo_name":"mcglincy/monster","sub_path":"commands/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":4892,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"34070456666","text":"import os\nimport re\nimport shutil\nimport subprocess\n\nfrom werkzeug.utils import secure_filename as werkzeug_secure_filename\n\nfrom licenseware.common.constants import envs\nfrom licenseware.utils.logger import log\n\naccepted_archives = (\n \".zip\",\n \".tar\",\n \".tar.bz2\",\n \".tar.gz\",\n \".tgz\",\n)\n\n\ndef is_archive(filepath: str):\n return filepath.endswith(accepted_archives)\n\n\ndef list_files_from_path(dir_path: str):\n filepaths = []\n for root, dirs, filenames in os.walk(dir_path):\n for filename in filenames:\n filepaths.append(os.path.join(root, filename))\n\n return filepaths\n\n\ndef unzip(file_path: str):\n \"\"\"\n Extracts: “zip”, “tar”, “gztar”, “bztar”, or “xztar”.\n Returns the path where the file was extracted\n \"\"\"\n\n if not is_archive(file_path):\n raise ValueError(\n f\"Invalid archive type, currently accepting: {accepted_archives}\"\n )\n\n file_name = os.path.basename(file_path)\n file_dir = os.path.dirname(file_path)\n extract_path = os.path.join(file_dir, file_name + \"_extracted\")\n\n try:\n shutil.unpack_archive(file_path, extract_path)\n except:\n # RUN apt install bzip2\n shutil.rmtree(extract_path)\n os.chmod(file_path, 432) # 432 is the int representation of the oct 660\n cmd1 = [\"mkdir\", \"-p\", extract_path]\n cmd = [\"/bin/tar\", \"-xvf\", file_path, \"-C\", extract_path]\n subprocess.Popen(cmd1)\n error = subprocess.Popen(cmd)\n log.warning(error)\n finally:\n if not os.path.exists(extract_path):\n os.makedirs(extract_path)\n\n return extract_path\n\n\ndef recursive_unzip(file_path: str):\n \"\"\"\n Iterate over an archive and recursively extract all archives using unzip function\n \"\"\"\n\n if is_archive(file_path):\n unziped_base = unzip(file_path)\n if unziped_base is None:\n return\n else:\n unziped_base = file_path\n\n archives_found = False\n for root, dirs, filenames in os.walk(unziped_base):\n for filename in filenames:\n\n fpath = os.path.join(root, filename)\n\n if not is_archive(fpath):\n continue\n if not os.path.exists(fpath):\n continue\n\n unzipped_path = unzip(fpath)\n if unzipped_path is None:\n continue\n\n os.remove(fpath)\n archives_found = True\n\n file_path = (\n file_path + \"_extracted\" if not file_path.endswith(\"_extracted\") else file_path\n )\n\n if archives_found:\n recursive_unzip(file_path)\n\n return file_path\n\n\ndef secure_filename(fname):\n \"\"\"\n Custom handling of secure filename\n Added an exception for review lite files `~` char\n \"\"\"\n\n review_lite_file = re.search(r\"feature|option|version\", fname, re.IGNORECASE)\n\n if review_lite_file:\n fname = re.sub(\"~\", \"_SECURE_TILDA_\", fname)\n\n fname = werkzeug_secure_filename(fname)\n\n if review_lite_file:\n fname = re.sub(\"_SECURE_TILDA_\", \"~\", fname)\n\n return fname\n\n\ndef save_file(file, tenant_id=None, path=None):\n \"\"\"\n Save to disk flask file stream\n \"\"\"\n\n if envs.DESKTOP_ENVIRONMENT and tenant_id is None:\n tenant_id = envs.DESKTOP_TENANT_ID\n\n filename = secure_filename(file.filename)\n\n # dir_id = generate_id() # doing this to avoid overwriting already uploaded files\n # dir_id works only for uploaders with one file sent for processing\n save_path = path or os.path.join(envs.FILE_UPLOAD_PATH, tenant_id)\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n\n try:\n file.seek(0) # move cursor to 0 (stream left it on last read)\n except ValueError:\n log.error(\"File is not a stream, can't seek\")\n\n file_path = os.path.join(save_path, filename)\n file.save(file_path)\n file.close()\n\n return file_path\n","repo_name":"licenseware/licenseware-sdk-v2","sub_path":"licenseware/utils/file_utils.py","file_name":"file_utils.py","file_ext":"py","file_size_in_byte":3878,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"35707682123","text":"# -*- coding: utf-8 -*-\r\n# @Time: 2022/3/8 17:53\r\n# @Author: Mr.Lee\r\n# @File: hogconv.py\r\n# @Software: PyCharm\r\n# -*- coding: utf-8 -*-\r\n# @Time: 2022/3/8 17:00\r\n# @Author: Mr.Lee\r\n# @File: res_hog+conv.py\r\n# @Software: PyCharm\r\nimport torch.nn as nn\r\nimport torch\r\nfrom hog import hog,gamma\r\nimport cv2\r\n\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\nimport numpy as np\r\n\r\nclass BasicBlock(nn.Module):\r\n expansion = 1\r\n\r\n def __init__(self, in_channel, out_channel, stride=1, downsample=None, **kwargs):\r\n super(BasicBlock, self).__init__()\r\n self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=out_channel,\r\n kernel_size=3, stride=stride, padding=1, bias=False)\r\n self.bn1 = nn.BatchNorm2d(out_channel)\r\n self.relu = nn.ReLU()\r\n self.conv2 = nn.Conv2d(in_channels=out_channel, out_channels=out_channel,\r\n kernel_size=3, stride=1, padding=1, bias=False)\r\n self.bn2 = nn.BatchNorm2d(out_channel)\r\n self.downsample = downsample\r\n\r\n def forward(self, x):\r\n identity = x\r\n\r\n if self.downsample is not None:\r\n identity = self.downsample(x)\r\n\r\n out = self.conv1(x)\r\n out = self.bn1(out)\r\n out = self.relu(out)\r\n\r\n out = self.conv2(out)\r\n out = self.bn2(out)\r\n\r\n out += identity\r\n out = self.relu(out)\r\n\r\n return out\r\n\r\n\r\nclass Bottleneck(nn.Module):\r\n \"\"\"\r\n 注意：原论文中，在虚线残差结构的主分支上，第一个1x1卷积层的步距是2，第二个3x3卷积层步距是1。\r\n 但在pytorch官方实现过程中是第一个1x1卷积层的步距是1，第二个3x3卷积层步距是2，\r\n 这么做的好处是能够在top1上提升大概0.5%的准确率。\r\n 可参考Resnet v1.5 https://ngc.nvidia.com/c` ````````````````````````````````````````````````````````````````````````````````````````````````````````````````````````atalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch\r\n \"\"\"\r\n expansion = 4\r\n\r\n def __init__(self, in_channel, out_channel, stride=1, downsample=None,\r\n groups=1, width_per_group=64):\r\n super(Bottleneck, self).__init__()\r\n\r\n width = int(out_channel * (width_per_group / 64.)) * groups\r\n\r\n self.conv1 = nn.Conv2d(in_channels=in_channel, out_channels=width,\r\n kernel_size=1, stride=1, bias=False) # squeeze channels\r\n self.bn1 = nn.BatchNorm2d(width)\r\n # -----------------------------------------\r\n self.conv2 = nn.Conv2d(in_channels=width, out_channels=width, groups=groups,\r\n kernel_size=3, stride=stride, bias=False, padding=1)\r\n self.bn2 = nn.BatchNorm2d(width)\r\n # -----------------------------------------\r\n self.conv3 = nn.Conv2d(in_channels=width, out_channels=out_channel*self.expansion,\r\n kernel_size=1, stride=1, bias=False) # unsqueeze channels\r\n self.bn3 = nn.BatchNorm2d(out_channel*self.expansion)\r\n self.relu = nn.ReLU(inplace=True)\r\n self.downsample = downsample\r\n\r\n def forward(self, x):\r\n identity = x\r\n if self.downsample is not None:\r\n identity = self.downsample(x)\r\n\r\n out = self.conv1(x)\r\n out = self.bn1(out)\r\n out = self.relu(out)\r\n\r\n out = self.conv2(out)\r\n out = self.bn2(out)\r\n out = self.relu(out)\r\n\r\n out = self.conv3(out)\r\n out = self.bn3(out)\r\n\r\n out += identity\r\n out = self.relu(out)\r\n\r\n return out\r\n\r\ndef noramlization(data):\r\n minVals = data.min(0)\r\n maxVals = data.max(0)\r\n ranges = maxVals - minVals\r\n # normData = np.zeros(np.shape(data))\r\n m = data.shape[0]\r\n normData = data - np.tile(minVals, (m, 1))\r\n normData = normData/np.tile(ranges, (m, 1))\r\n return normData, ranges, minVals\r\n\r\n\r\n\r\n\r\nclass ResNet(nn.Module):\r\n\r\n def __init__(self,\r\n block,\r\n blocks_num,\r\n num_classes=744,\r\n include_top=True,\r\n groups=1,\r\n width_per_group=64):\r\n super(ResNet, self).__init__()\r\n self.include_top = include_top\r\n self.in_channel = 64\r\n\r\n self.groups = groups\r\n self.width_per_group = width_per_group\r\n\r\n self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2,\r\n padding=3, bias=False)\r\n self.hogmaxpool1 = nn.MaxPool2d(kernel_size=3, stride=3, padding=1)\r\n\r\n self.hog_conv = nn.Conv2d(1,64,kernel_size=1,stride=3,padding=2)\r\n self.bn1 = nn.BatchNorm2d(self.in_channel)\r\n self.relu = nn.ReLU(inplace=True)\r\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\r\n self.layer1 = self._make_layer(block, 64, blocks_num[0])\r\n self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)\r\n self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)\r\n self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)\r\n if self.include_top:\r\n self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) # output size = (1, 1)\r\n self.fc = nn.Linear(512 * block.expansion, num_classes)\r\n\r\n for m in self.modules():\r\n if isinstance(m, nn.Conv2d):\r\n nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\r\n\r\n def _make_layer(self, block, channel, block_num, stride=1):\r\n downsample = None\r\n if stride != 1 or self.in_channel != channel * block.expansion:\r\n downsample = nn.Sequential(\r\n nn.Conv2d(self.in_channel, channel * block.expansion, kernel_size=1, stride=stride, bias=False),\r\n nn.BatchNorm2d(channel * block.expansion))\r\n\r\n layers = []\r\n layers.append(block(self.in_channel,\r\n channel,\r\n downsample=downsample,\r\n stride=stride,\r\n groups=self.groups,\r\n width_per_group=self.width_per_group))\r\n self.in_channel = channel * block.expansion\r\n\r\n for _ in range(1, block_num):\r\n layers.append(block(self.in_channel,\r\n channel,\r\n groups=self.groups,\r\n width_per_group=self.width_per_group))\r\n\r\n return nn.Sequential(*layers)\r\n\r\n def forward(self, x): # B,C,H,W\r\n j = int(len(x[:, 0, 0, 0]))\r\n Temp = []\r\n for i in range(0, j):\r\n y = x[i, 0, :, :]\r\n y = torch.Tensor.cpu(y)\r\n resizeimg = np.array(y)\r\n cell_w = 8\r\n cell_x = int(resizeimg.shape[0] / cell_w) # cell行数\r\n cell_y = int(resizeimg.shape[1] / cell_w) # cell列数\r\n # print('The size of cellmap is {}*{} '.format(cell_x, cell_y))\r\n gammaimg = gamma(resizeimg) * 255\r\n feature = hog(gammaimg, cell_x, cell_y, cell_w)\r\n # feature = exposure.rescale_intensity(feature, in_range=(0, 10))\r\n feature = feature.reshape(162,162)\r\n feature = cv2.resize(feature, (166, 166))\r\n newgroup, _, _ = noramlization(feature)\r\n Temp.append(newgroup)\r\n Temp = torch.tensor(Temp)\r\n Temp = torch.unsqueeze(Temp, 0).permute(1,0,2,3).type(torch.FloatTensor).to(device)\r\n Temp = self.hogmaxpool1(Temp)\r\n Temp1 = Temp.repeat(1, 64, 1, 1).type(torch.FloatTensor).to(device)\r\n\r\n x = self.conv1(x)\r\n x = self.bn1(x)\r\n x = self.relu(x)\r\n x = self.maxpool(x)\r\n\r\n x = 0.7*x + 0.3*Temp\r\n\r\n\r\n\r\n x = self.layer1(x) #16 64 56 56\r\n x = self.layer2(x) #16 128 28 28\r\n x = self.layer3(x) #16 256 14 14\r\n x = self.layer4(x) #16 512 7 7\r\n\r\n if self.include_top:\r\n x = self.avgpool(x)\r\n x = torch.flatten(x, 1)\r\n x = self.fc(x)\r\n\r\n return x\r\n\r\n\r\ndef resnet34(num_classes=744, include_top=True):\r\n # https://download.pytorch.org/models/resnet34-333f7ec4.pth\r\n return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)\r\n\r\n\r\ndef resnet50(num_classes=200, include_top=True):\r\n # https://download.pytorch.org/models/resnet50-19c8e357.pth\r\n return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, include_top=include_top)\r\n\r\n\r\ndef resnet101(num_classes=200, include_top=True):\r\n # https://download.pytorch.org/models/resnet101-5d3b4d8f.pth\r\n return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=num_classes, include_top=include_top)\r\n\r\n\r\ndef resnext50_32x4d(num_classes=200, include_top=True):\r\n # https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth\r\n groups = 32\r\n width_per_group = 4\r\n return ResNet(Bottleneck, [3, 4, 6, 3],\r\n num_classes=num_classes,\r\n include_top=include_top,\r\n groups=groups,\r\n width_per_group=width_per_group)\r\n\r\n\r\ndef resnext101_32x8d(num_classes=200, include_top=True):\r\n # https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth\r\n groups = 32\r\n width_per_group = 8\r\n return ResNet(Bottleneck, [3, 4, 23, 3],\r\n num_classes=num_classes,\r\n include_top=include_top,\r\n groups=groups,\r\n width_per_group=width_per_group)\r\n\r\n\r\n\r\n","repo_name":"Li2Silence/ResNet_HOG","sub_path":"hogconv.py","file_name":"hogconv.py","file_ext":"py","file_size_in_byte":9547,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"36047630705","text":"def abundant_number(num):\n n = 1\n L = []\n while n < num:\n if num % n == 0:\n L.append(n)\n n += 1\n res = sum(i for i in L)\n if res < num: return \"deficient \"\n elif res > num: return \"abundant \"\n else: return \"perfect \"\n\n\ndef amicable_numbers(n1, n2):\n def ab_num1(num):\n n = 1\n L = []\n while n < num:\n if num % n == 0:\n L.append(n)\n n += 1\n return sum(i for i in L)\n return \"amicable\" if ab_num1(n1) == n2 and ab_num1(n2) == n1 else \"not amicable\"\n\n\ndef deficiently_abundant_amicable_numbers(n1, n2):\n num1 = str(abundant_number(n1))\n num2 = str(abundant_number(n2))\n num3 = str(amicable_numbers(n1, n2))\n return num1 + num2 + num3","repo_name":"SawlStone/CodeWars","sub_path":"python/6kyu_Deficiently Abundant Perfect Amicable Numbers.py","file_name":"6kyu_Deficiently Abundant Perfect Amicable Numbers.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"13856596830","text":"import logging\nimport docker\nimport threading\nimport time\nimport platform\n\nfrom os import environ\nfrom watchdog.events import PatternMatchingEventHandler\nfrom watchdog.observers import Observer\nfrom watchdog.observers.polling import PollingObserver\n\n\nlogging.basicConfig(\n format=\"%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s\",\n datefmt=\"%H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(\"reloader\")\n\n\nclass Reloader(object):\n def __init__(self):\n self.init_docker()\n self.last_reload_thread = None\n self.reload_dirs = self.get_target_dirs()\n self.reload_delay = float(environ.get(\"RELOAD_DELAY\", 1.5))\n self.restart_timeout = int(environ.get(\"RESTART_TIMEOUT\", 10))\n self.reload_container = environ.get(\"RELOAD_CONTAINER\")\n self.observer_type = int(environ.get(\"OBSERVER_TYPE\",0))\n self.must_run = int(environ.get(\"MUST_RUN\", 1))\n\n @staticmethod\n def _get_own_container_id():\n \"\"\"\n Identifies reloader own container id, in cases we want to run multiple reloaders.\n \"\"\"\n return platform.node()\n\n def event_handler_factory(\n self, *args, patterns=[\"*\"], ignore_directories=True, **kwargs\n ):\n event_handler = PatternMatchingEventHandler(\n *args, patterns=patterns, ignore_directories=ignore_directories, **kwargs\n )\n\n def on_any_event_callback(event):\n \"\"\"\n Callback to react on any watchdog filesystem event.\n \"\"\"\n containers = self.get_target_containers()\n if containers:\n logger.info(event)\n logger.info(f\"Scheduling reloading of containers\")\n self.scheduled_reload(containers)\n\n event_handler.on_any_event = on_any_event_callback\n return event_handler\n\n def init_docker(self):\n \"\"\"\n Initializes docker client with binded docker socket.\n \"\"\"\n self.client = docker.DockerClient(base_url=\"unix://var/run/docker.sock\")\n\n def get_target_dirs(self):\n \"\"\"\n Returns a the target directory to be monitored in this order:\n 1. If the RELOAD_DIR environment variable is set, uses that.\n 2. Otherwise it attempts to derive the directory from mounted directories on the container\n \"\"\"\n dirs = environ.get(\"RELOAD_DIR\", None)\n if dirs is not None:\n dirs = [x.strip() for x in dirs.split(\",\")]\n return dirs\n\n container = self.client.containers.get(container_id=self._get_own_container_id())\n\n dirs_to_watch = []\n for mount in container.attrs[\"Mounts\"]:\n if (\n \"/var/run/docker.sock\" not in mount[\"Destination\"]\n and \"/reloader\" not in mount[\"Destination\"]\n ):\n logger.info(f'Watching directory: {mount[\"Destination\"]}')\n dirs_to_watch.append(mount[\"Destination\"])\n\n return dirs_to_watch if dirs_to_watch else None\n\n def get_target_containers(self):\n \"\"\"\n Returns a docker container instance if exists, based on the RELOAD_CONTAINER\n environment variable.\n Enforces exact match for name, by using regex ^/nRELOAD_CONTAINER$\n To target multiple reload targets, RELOAD_LABEL is used.\n \"\"\"\n container = self.client.containers.list(filters={\"name\": f\"^/{self.reload_container}$\"})\n\n by_name = [container[0]] if container else []\n\n by_labels = []\n label = environ.get(\"RELOAD_LABEL\", None)\n if label:\n if self.must_run == 1:\n by_labels = self.client.containers.list(\n filters={\"label\": label, \"status\": \"running\"}\n )\n else:\n by_labels = self.client.containers.list(\n filters={\"label\": label}\n )\n by_labels.extend(self.client.containers.list(\n filters={\"label\": label, \"status\": \"exited\"}\n ))\n\n return list(set(by_name + by_labels))\n\n def scheduled_reload(self, containers):\n \"\"\"\n Schedules a thread to reload the container based on the given RELOAD DELAY.\n It overwrites any previously scheduled reloading threads.\n \"\"\"\n\n def containers_reload():\n \"\"\"\n Restarts given containers and reports any errors.\n \"\"\"\n try:\n for container in containers:\n logger.info(\"Reloading container: {0}\".format(container.name))\n container.restart(timeout=self.restart_timeout)\n except Exception as e:\n logger.error(\"Something went wrong while reloading: \")\n logger.error(e)\n\n # if containers:\n if self.last_reload_thread:\n self.last_reload_thread.cancel()\n del self.last_reload_thread\n self.last_reload_thread = threading.Timer(self.reload_delay, containers_reload)\n self.last_reload_thread.start()\n\n def start(self):\n \"\"\"\n Runs watchdog process to monitor file changes and reload container\n \"\"\"\n\n if self.observer_type == 0:\n observer = Observer()\n elif self.observer_type == 1:\n observer = PollingObserver()\n\n if self.reload_dirs:\n for a_dir in self.reload_dirs:\n observer.schedule(self.event_handler_factory(), a_dir, recursive=True)\n observer.start()\n else:\n logger.error(\"Did not find any source code paths to monitor!!\")\n try:\n while True:\n time.sleep(1)\n except KeyboardInterrupt:\n observer.stop()\n observer.join()\n","repo_name":"apogiatzis/docker-compose-livereloader","sub_path":"src/reloader.py","file_name":"reloader.py","file_ext":"py","file_size_in_byte":5749,"program_lang":"python","lang":"en","doc_type":"code","stars":74,"dataset":"github-code","pt":"3"}
+{"seq_id":"13549232145","text":"import pandas as pd\r\nimport re\r\nimport random\r\nimport numpy as np\r\nfrom keras.layers import Input, Embedding, Dot, Reshape\r\nfrom keras.models import Model\r\nfrom unidecode import unidecode\r\n\r\n\r\n# constrains\r\npath = r\"dataset.csv\"\r\npositive_samples_per_batch = 512\r\n\r\n\r\ndef convert_tags_to_idx(tags):\r\n splitted = tags.split(' ')\r\n splitted = [tag_idx[x] for x in splitted if x in tag_idx]\r\n return splitted\r\n\r\n\r\ndef book_embedding_model(embedding_size=50):\r\n book = Input(name='book', shape=[1])\r\n tag = Input(name='tag', shape=[1])\r\n\r\n book_embedding = Embedding(name='book_embedding',\r\n input_dim=len(idx_title),\r\n output_dim=embedding_size)(book)\r\n\r\n tag_embedding = Embedding(name='tag_embedding',\r\n input_dim=len(idx_tag),\r\n output_dim=embedding_size)(tag)\r\n\r\n merged = Dot(name='dot_product', normalize=True, axes=2)([book_embedding, tag_embedding])\r\n merged = Reshape(target_shape=[1])(merged)\r\n\r\n model = Model(inputs=[book, tag], outputs=merged)\r\n model.compile(optimizer='Adam', loss='mse')\r\n return model\r\n\r\n\r\ndef generator(pairs, n_positive=50):\r\n \"\"\"Generate batches of samples for training\"\"\"\r\n batch_size = n_positive * 2\r\n batch = np.zeros((batch_size, 3))\r\n\r\n neg_label = -1\r\n\r\n while True:\r\n for idx, (book_id, tag_id) in enumerate(random.sample(pairs, n_positive)):\r\n batch[idx, :] = (book_id, tag_id, 1)\r\n\r\n idx += 1\r\n\r\n while idx < batch_size:\r\n random_book = random.randrange(len(book_idx))\r\n random_tag = random.randrange(len(tag_idx))\r\n\r\n if (random_book, random_tag) not in pairs:\r\n batch[idx, :] = (random_book, random_tag, neg_label)\r\n idx += 1\r\n\r\n np.random.shuffle(batch)\r\n yield {'book': batch[:, 0], 'tag': batch[:, 1]}, batch[:, 2]\r\n\r\n\r\ndef save_model(model):\r\n with open('model.json', 'w') as f:\r\n f.write(model.to_json())\r\n model.save('weights.h5')\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n # data preprocessing\r\n df = pd.read_csv(path, encoding='utf-8-sig', sep=';')\r\n df['tags'] = df['tags'] + ' ' + df['genre']\r\n df['tags'] = df.tags.astype(str)\r\n df['tags'] = df.tags.apply(lambda x: x.lower())\r\n df['tags'] = df.tags.apply(lambda x: re.sub(r\"[^a-z0-9]+\", ' ', unidecode(x.lower())))\r\n tags = list(set(df.tags.values))\r\n tags = [x for x in tags if not x.startswith('http')]\r\n tags = [x for x in tags if not x.startswith('www')]\r\n\r\n tags_final = []\r\n for tag in tags:\r\n splitted = tag.split(' ')\r\n for x in splitted:\r\n if len(x) > 2:\r\n tags_final.append(x)\r\n\r\n # get unique tags dict\r\n tags_final = list(set(tags_final))\r\n idx_tag = dict(enumerate(tags_final))\r\n tag_idx = {v: k for k, v in idx_tag.items()}\r\n\r\n # get books dict\r\n idx_book = dict(zip(df.idx, df.title))\r\n book_idx = {v: k for k, v in idx_book.items()}\r\n\r\n # get tain data\r\n df = df[['idx', 'title', 'tags']]\r\n df['tags'] = df['tags'].apply(convert_tags_to_idx)\r\n df = df.explode('tags')\r\n train_pairs = list(zip(df.idx, df.tags))\r\n\r\n # training\r\n model = book_embedding_model()\r\n\r\n gen = generator(train_pairs, positive_samples_per_batch)\r\n\r\n h = model.fit(gen, epochs=12,\r\n steps_per_epoch=len(train_pairs) // positive_samples_per_batch,\r\n verbose=2)\r\n\r\n save_model(model)\r\n","repo_name":"atosfazi/BookEmbeddings","sub_path":"train_custom_embeddings.py","file_name":"train_custom_embeddings.py","file_ext":"py","file_size_in_byte":3523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71609244562","text":"\"\"\"\nThis Python module represents a pipeline for performing graph pattern analysis using various utilities. \nIt processes a collection of OntoUML models in JSON format, converts them into NetworkX graphs,\nmines patterns using the gSpan algorithm, and generates PlantUML diagrams to visualize the patterns\n\"\"\"\n\n\nimport os\nimport utils.ontoumlimport\nimport utils.generateinput\nimport utils.gspanMiner\nimport utils.patterns \nimport utils.back2UML\nimport utils.graphClustering0\nimport utils.graphClustering1\nimport func_timeout\nimport utils.UMLviz\nimport utils.command\nimport func_timeout\nimport subprocess\nimport os\nimport warnings\nimport networkx as nx\n\ndirectory_path = './models' # replace with the path to your directory\nextension = '.json' # replace with the desired file extension\npatternspath = \"input/outputpatterns.txt\" # replace with patterns file name\numl_folder = \"./patterns\"\nplantuml_jar_path = \"utils/plantumlGenerator.jar\"\nnode_labels0 = [\"gen\", \"characterization\", \"comparative\", \"externalDependence\", \"material\", \"mediation\",\n \"componentOf\", \"memberOf\", \"subCollectionOf\", \"subQuantityOf\", \"bringsAbout\",\n \"creation\", \"historicalDependence\", \"manifestation\", \"participation\",\n \"participational\", \"termination\", \"triggers\", \"instantiation\", \"relation\", \"derivation\"] \nedge_labels0 = [\"target\", \"specific\", \"general\",\"source\", \"generalization\"]\n\nfile_names = []\nfor filename in os.listdir(directory_path):\n if filename.endswith(extension):\n file_names.append(filename)\n\n#subkind pattern \"A\"\nsAnodes = [('0', {'label': 'kind'}),('1', {'label': 'gen'}),('2', {'label': 'gen'}),('3', {'label': 'subkind'}),('4', {'label': 'subkind'})]\nsAedges = [('0', '1', {'label': 'general'}),('0', '2', {'label': 'general'}),('1', '3', {'label': 'specific'}),('2', '4', {'label': 'specific'})]\nsA = nx.Graph()\nsA.add_nodes_from(sAnodes)\nsA.add_edges_from(sAedges)\n\n#subkind pattern \"B\"\nsBnodes = [('0', {'label': 'kind'}),('1', {'label': 'gen'}),('2', {'label': 'gen'}),('3', {'label': 'gen'}),('4', {'label': 'subkind'}),('5', {'label': 'subkind'}),('6', {'label': 'subkind'})]\nsBedges = [('0', '1', {'label': 'general'}),('0', '2', {'label': 'general'}),('0', '3', {'label': 'general'}),('1', '4', {'label': 'specific'}),('2', '5', {'label': 'specific'}),('3', '6', {'label': 'specific'})]\nsB = nx.Graph()\nsB.add_nodes_from(sBnodes)\nsB.add_edges_from(sBedges)\n\n#category pattern \"A\"\ncAnodes = [('0', {'label': 'category'}),('1', {'label': 'gen'}),('2', {'label': 'gen'}),('3', {'label': 'kind'}),('4', {'label': 'kind'})]\ncAedges = [('0', '1', {'label': 'general'}),('0', '2', {'label': 'general'}),('1', '3', {'label': 'specific'}),('2', '4', {'label': 'specific'})]\ncA = nx.Graph()\ncA.add_nodes_from(cAnodes)\ncA.add_edges_from(cAedges)\n\n#category pattern \"B\"\ncBnodes = [('0', {'label': 'category'}),('1', {'label': 'gen'}),('2', {'label': 'gen'}),('3', {'label': 'gen'}),('4', {'label': 'subkind'}),('5', {'label': 'subkind'}),('6', {'label': 'subkind'})]\ncBedges = [('0', '1', {'label': 'general'}),('0', '2', {'label': 'general'}),('0', '3', {'label': 'general'}),('1', '4', {'label': 'specific'}),('2', '5', {'label': 'specific'}),('3', '6', {'label': 'specific'})]\ncB = nx.Graph()\ncB.add_nodes_from(cBnodes)\ncB.add_edges_from(cBedges)\n\n#phase pattern \"A\"\npAnodes = [('0', {'label': 'kind'}),('1', {'label': 'gen'}),('2', {'label': 'gen'}),('3', {'label': 'phase'}),('4', {'label': 'phase'})]\npAedges = [('0', '1', {'label': 'general'}),('0', '2', {'label': 'general'}),('1', '3', {'label': 'specific'}),('2', '4', {'label': 'specific'})]\npA = nx.Graph()\npA.add_nodes_from(pAnodes)\npA.add_edges_from(pAedges)\n\n#phase pattern \"B\"\npBnodes = [('0', {'label': 'kind'}),('1', {'label': 'gen'}),('2', {'label': 'gen'}),('3', {'label': 'gen'}),('4', {'label': 'phase'}),('5', {'label': 'phase'}),('6', {'label': 'phase'})]\npBedges = [('0', '1', {'label': 'general'}),('0', '2', {'label': 'general'}),('0', '3', {'label': 'general'}),('1', '4', {'label': 'specific'}),('2', '5', {'label': 'specific'}),('3', '6', {'label': 'specific'})]\npB = nx.Graph()\npB.add_nodes_from(pBnodes)\npB.add_edges_from(pBedges)\n\n#relator pattern \"A\"\nrAnodes = [('0', {'label': 'relator'}),('1', {'label': 'role'}),('2', {'label': 'role'}),('3', {'label': 'mediation'}),('4', {'label': 'mediation'})]\nrAedges = [('0', '3', {'label': 'source'}),('3', '1', {'label': 'target'}),('0', '4', {'label': 'source'}),('4', '2', {'label': 'target'})]\nrA = nx.Graph()\nrA.add_nodes_from(rAnodes)\nrA.add_edges_from(rAedges)\n\n#roleMixin pattern \"A\"\nrmAnodes = [('0', {'label': 'rolemixin'}),('1', {'label': 'gen'}),('2', {'label': 'gen'}),('3', {'label': 'role'}),('4', {'label': 'role'})]\nrmAedges = [('0', '1', {'label': 'general'}),('0', '2', {'label': 'general'}),('1', '3', {'label': 'specific'}),('2', '4', {'label': 'specific'})]\nrmA = nx.Graph()\nrmA.add_nodes_from(rmAnodes)\nrmA.add_edges_from(rmAedges)\n\n#roleMixin pattern \"B\"\nrmBnodes = [('0', {'label': 'rolemixin'}),('1', {'label': 'gen'}),('2', {'label': 'gen'}),\n ('3', {'label': 'role'}),('4', {'label': 'role'}),('5', {'label': 'gen'}),\n ('6', {'label': 'gen'}),('7', {'label': 'kind'}),('8', {'label': 'kind'})]\nrmBedges = [('0', '1', {'label': 'general'}),('0', '2', {'label': 'general'}),\n ('1', '3', {'label': 'specific'}),('2', '4', {'label': 'specific'}),('7', '5', {'label': 'general'}),('5', '3', {'label': 'specific'}),('8', '6', {'label': 'general'}),('6', '4', {'label': 'specific'})]\nrmB = nx.Graph()\nrmB.add_nodes_from(rmAnodes)\nrmB.add_edges_from(rmAedges)\n\n\nkpattenrs = [sA,sB,cA,cB,pA,pB,rA,rmA,rmB]\n\nif __name__ == \"__main__\":\n\n # Generate graphs from imported data\n print(\"Importing models...\")\n graphs = utils.ontoumlimport.generateFullUndirected(file_names)\n \n # Filter class and relation labels\n class_labels = utils.command.filterClasses()\n relation_labels = utils.command.filterRelations()\n node_labels = class_labels + relation_labels\n edge_labels = utils.command.filterEdges()\n \n\n # Process graphs\n newgraphs = utils.generateinput.process_graphs(node_labels, edge_labels, graphs)\n newgraphs_with_names = utils.generateinput.process_graphs_with_names(node_labels, edge_labels, graphs)\n \n # Save and process graphs\n downloadgraphs = utils.generateinput.save_graphs_to_pickle(newgraphs, './input/graphs.pickle')\n data = utils.generateinput.graphs_to_data_file(newgraphs_with_names, 'graphs')\n\n\n # Set parameters and run gSpan Miner\n gsParameters = utils.command.parameters()\n inputs = utils.gspanMiner.gsparameters(gsParameters)\n \n # Define timeout handler and run gSpan Miner\n patterns = \"\"\n try:\n patterns = func_timeout.func_timeout(900, utils.gspanMiner.run_gspan, args=(inputs,))\n except func_timeout.FunctionTimedOut:\n print(\"Function execution timed out\")\n if patterns is not None:\n utils.command.firststop()\n\n # Load and process pattern graphs\n uploadgraphs = utils.patterns.load_graphs_from_pickle('./input/graphs.pickle')\n known_patterns = utils.command.known_patterns()\n \n if known_patterns == \"yes\":\n pattern_graphs0 = utils.patterns.convertPatterns(patternspath)\n pattern_graphs = utils.patterns.remove_graphs_from_list(pattern_graphs0,kpattenrs)\n pro_pattern_graphs = utils.patterns.return_all_domain_info(pattern_graphs)\n else:\n pattern_graphs = utils.patterns.convertPatterns(patternspath)\n pro_pattern_graphs = utils.patterns.return_all_domain_info(pattern_graphs) \n #print(pattern_graphs0)\n #print(pattern_graphs)\n\n # Process pattern graphs for clustering\n patterns_features = utils.graphClustering1.graphs2dataframes2vectors(pattern_graphs)\n patterns_dataframe = utils.graphClustering1.transform2singledataframe(patterns_features)\n patterns_similarity_matrix = utils.graphClustering1.calculate_similarity(patterns_dataframe)\n similarity_threshold = utils.command.ask_similarity_threshold()\n patterns_cluster_labels = utils.graphClustering1.group_similar_items(patterns_similarity_matrix,similarity_threshold)\n pattern_graphs_clustered_ = utils.graphClustering1.merge_lists(patterns_cluster_labels,pattern_graphs)\n pattern_graphs_clustered = utils.back2UML.process_genset_cardinalities(pattern_graphs_clustered_)\n \n # Convert pattern graphs to UML diagrams\n converted_patterns = utils.back2UML.convert_graphs_new(pattern_graphs_clustered)\n converted_patterns_filtered = utils.generateinput.process_graphs__(node_labels0, edge_labels0, converted_patterns)\n utils.UMLviz.convert_to_plantuml_clusters(converted_patterns_filtered)\n warnings.filterwarnings(\"ignore\")\n\n # Generate PlantUML diagrams\n for root, dirs, files in os.walk(uml_folder):\n for dir in dirs:\n subfolder_path = os.path.join(root, dir)\n cmd = f\"java -jar {plantuml_jar_path} {subfolder_path}\"\n subprocess.run(cmd, shell=True, check=True)\n\n # Perform final processing and cleanup\n utils.command.secondstop()\n utils.command.process_pattern(pro_pattern_graphs, uploadgraphs, converted_patterns_filtered)\n utils.command.laststop()\n\n","repo_name":"unibz-core/CM-Mining","sub_path":"scripts/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9206,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71638164561","text":"import torch\nimport torch.nn as nn\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nfrom IPython.display import clear_output\nfrom LinGANcfg import device, NOISE_DIM\n\nclass Generator(nn.Module):\n def __init__(self):\n super(Generator, self).__init__()\n self.model = nn.Sequential( \n nn.Linear(NOISE_DIM, 200),\n nn.ReLU(inplace=True),\n nn.Linear(200, 28*28),\n nn.Sigmoid())\n \n def forward(self, x):\n return self.model(x)\n\n def plot_results(self):\n # plot 10x10 images\n plt.figure(figsize=(15,15))\n for i in range(100):\n noise = torch.randn(1, NOISE_DIM).to(device)\n fake = self(noise)\n plt.subplot(10,10,i+1)\n plt.imshow(fake.cpu().detach().numpy().squeeze().reshape(28, 28), cmap='gray')\n plt.axis('off')\n plt.show()\n\nclass Discriminator(nn.Module):\n def __init__(self):\n super(Discriminator, self).__init__()\n self.model = nn.Sequential(\n nn.Linear(28*28, 200),\n nn.ReLU(inplace=True),\n nn.Linear(200, 50),\n nn.ReLU(inplace=True),\n nn.Linear(50, 1), \n nn.Sigmoid())\n def forward(self, x):\n return self.model(x)\n\ndef train_GAN(generator, discriminator, criterion, optim_G, optim_D, dataloader, epochs=10, debug=100):\n history_G = []\n history_D = []\n for epoch in range(epochs):\n for i, (img, _) in enumerate(dataloader):\n # train discriminator\n optim_D.zero_grad()\n # real\n real = img.view(img.size(0), -1).to(device)\n real_label = torch.ones(real.size(0), 1).to(device)\n real_out = discriminator(real)\n loss_real = criterion(real_out, real_label)\n # fake\n noise = torch.randn(real.size(0), NOISE_DIM).to(device)\n fake = generator(noise)\n fake_label = torch.zeros(real.size(0), 1).to(device)\n fake_out = discriminator(fake.detach())\n loss_fake = criterion(fake_out, fake_label)\n # backward\n loss_D = loss_real + loss_fake\n loss_D.backward()\n optim_D.step()\n \n # train generator\n optim_G.zero_grad()\n fake_label = torch.ones(real.size(0), 1).to(device)\n fake_out = discriminator(fake)\n loss_G = criterion(fake_out, fake_label)\n loss_G.backward()\n optim_G.step()\n \n history_G.append(loss_G.item())\n history_D.append(loss_D.item())\n\n if i % debug == 0:\n clear_output(True)\n # plot losses and generated images using gridspec\n gs = gridspec.GridSpec(2, 4)\n plt.figure(figsize=(15,10))\n plt.subplot(gs[:, :2])\n plt.plot(history_G, label='Generator')\n plt.plot(history_D, label='Discriminator')\n plt.legend()\n for j in range(4):\n plt.subplot(gs[j // 2, 2 + j % 2])\n plt.imshow(fake[j].cpu().detach().numpy().squeeze().reshape(28, 28), cmap='gray')\n plt.axis('off')\n plt.suptitle(f'Epoch: {epoch+1}/{epochs}\\nBatch: {i}/{len(dataloader)}\\nLoss_G: {loss_G.item():.4f}\\nLoss_D: {loss_D.item():.4f}')\n plt.show()","repo_name":"V-Kisielius/GANs","sub_path":"LinGAN.py","file_name":"LinGAN.py","file_ext":"py","file_size_in_byte":3435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7491704368","text":"class Oplot:\n def __init__(self):\n self.origin = \"Ukraine\"\n self.smoothbore_gun = \"125 mm\"\n self.rate_of_fire = \"8 shots/min\"\n self.dynamic_protection = True\n self.engine_power = \"1200 hp\"\n self.frontal_armour = \"1100 mm\"\n self.power_reserve = \"400 km\"\n self.maximum_speed = \"70 km/h\"\n self.transmission = \"7 forward and 4 reverse gears\"\n self.weight = \"51 t\"\n\n def __str__(self):\n return f\"{self.__class__.__name__}: {self.__dict__}\"\n\n\nif __name__ == '__main__':\n tank = Oplot()\n print(tank)\n","repo_name":"Benedict3141592/Hillel_Homework_Groshev","sub_path":"HW_lesson_12/HW_L12_Task_1.py","file_name":"HW_L12_Task_1.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29027704567","text":"from typing import Iterable\n\nfrom guardian.shortcuts import assign_perm\n\nfrom django.conf import settings\nfrom django.contrib.auth.models import Permission\nfrom django.db.models import Max\n\nfrom rest_framework import serializers, status\nfrom rest_framework.test import APITestCase\n\nfrom lms.core.tests.mixins import GenericRequestMixin, UrlNameMixin\nfrom lms.enrollments.tests.factories import EnrolledUserFactory\nfrom lms.moduletypes.models import ModuleType\nfrom lms.staff.tests.factories import StaffCityFactory, StaffOfficeFactory, UserWithStaffProfileFactory\nfrom lms.tags.tests.factories import TagFactory\nfrom lms.users.tests.factories import GroupFactory, PermissionPresetFactory, UserFactory\nfrom lms.utils.tests.test_decorators import parameterized_expand_doc\n\nfrom ..models import Course, CourseBlock, CourseGroup, CourseModule, CourseStudent, LinkedCourse\nfrom .factories import (\n CourseBlockFactory, CourseCategoryFactory, CourseCityFactory, CourseFactory, CourseGroupFactory,\n CourseModuleFactory, CourseStudentFactory, CourseTeamFactory, CourseVisibilityFactory, LinkedCourseFactory,\n ProviderFactory, StudentModuleProgressFactory, StudyModeFactory, TutorInternalFactory,\n)\n\n\nclass StudyModeListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:studymode-list'\n\n def setUp(self) -> None:\n self.study_modes = StudyModeFactory.create_batch(2)\n self.inactive_mode = StudyModeFactory(is_active=False)\n\n def build_expected(self, studymodes):\n return [{\n 'id': sm.id,\n 'slug': sm.slug,\n 'name': sm.name,\n } for sm in studymodes]\n\n def test_url(self):\n self.assertURLNameEqual('studymodes/', base_url=settings.API_BASE_URL)\n\n def test_list(self):\n expected = self.build_expected(self.study_modes)\n self.list_request(self.get_url(), expected=expected, pagination=False, num_queries=1)\n\n\nclass ProviderListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:provider-list'\n\n def setUp(self) -> None:\n self.providers = ProviderFactory.create_batch(5)\n self.inactive = ProviderFactory.create_batch(2, is_active=False)\n\n def test_url(self):\n self.assertURLNameEqual('providers/', base_url=settings.API_BASE_URL)\n\n def build_expected(self, providers):\n return [\n {\n 'id': p.id,\n 'name': p.name\n } for p in sorted(providers, key=lambda x: x.name)\n ]\n\n def test_list(self):\n expected = self.build_expected(self.providers)\n self.list_request(self.get_url(), expected=expected, pagination=False, num_queries=1)\n\n\nclass CourseCityListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-city-list'\n\n def setUp(self) -> None:\n self.cities = CourseCityFactory.create_batch(5)\n self.inactive = CourseCityFactory.create_batch(2, is_active=False)\n\n def test_url(self):\n self.assertURLNameEqual('course_cities/', base_url=settings.API_BASE_URL)\n\n def build_expected(self, cities):\n return [\n {\n 'id': c.id,\n 'name': c.name,\n 'slug': c.slug,\n } for c in sorted(cities, key=lambda x: x.name)\n ]\n\n def test_list(self):\n expected = self.build_expected(self.cities)\n self.list_request(self.get_url(), expected=expected, pagination=False, num_queries=1)\n\n\nclass CourseCategoryListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-category-list'\n\n def setUp(self) -> None:\n self.categories = CourseCategoryFactory.create_batch(5)\n self.inactive_categories = CourseCategoryFactory.create_batch(2, is_active=False)\n\n def test_url(self):\n self.assertURLNameEqual('course_categories/', base_url=settings.API_BASE_URL)\n\n def build_expected(self, categories):\n return [\n {\n 'id': c.id,\n 'parent_id': c.parent_id,\n 'slug': c.slug,\n 'name': c.name,\n 'depth': c.node_depth,\n 'color_theme': {\n 'id': c.color_theme.id,\n 'name': c.color_theme.name,\n 'slug': c.color_theme.slug,\n 'is_active': c.color_theme.is_active,\n 'created': serializers.DateTimeField().to_representation(c.color_theme.created),\n 'modified': serializers.DateTimeField().to_representation(c.color_theme.modified),\n 'course_card_gradient_color': c.color_theme.course_card_gradient_color,\n },\n } for c in sorted(categories, key=lambda x: x.id) # реальная сортировка идет по node__path\n ]\n\n def test_list(self):\n expected = self.build_expected(self.categories)\n self.list_request(self.get_url(), expected=expected, num_queries=2)\n\n\nclass CourseCategoryDetailTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-category-detail'\n\n def setUp(self) -> None:\n self.category = CourseCategoryFactory()\n\n def test_url(self):\n self.assertURLNameEqual('course_categories/{}/', args=(self.category.id,), base_url=settings.API_BASE_URL)\n\n def build_expected(self, category):\n return {\n 'id': category.id,\n 'parent_id': category.parent_id,\n 'slug': category.slug,\n 'name': category.name,\n 'depth': category.node_depth,\n 'color_theme': {\n 'id': category.color_theme.id,\n 'slug': category.color_theme.slug,\n 'name': category.color_theme.name,\n 'course_card_gradient_color': category.color_theme.course_card_gradient_color,\n },\n }\n\n def test_get(self):\n expected = self.build_expected(self.category)\n self.detail_request(self.get_url(self.category.id), expected=expected, num_queries=1)\n\n\nclass CourseCategoriesListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-categories'\n\n def setUp(self) -> None:\n self.course = CourseFactory(is_active=True)\n self.categories = CourseCategoryFactory.create_batch(5)\n self.inactive_categories = CourseCategoryFactory.create_batch(2, is_active=False)\n self.course.categories.add(*(self.categories + self.inactive_categories))\n\n def test_url(self):\n self.assertURLNameEqual('courses/{}/categories/', args=(self.course.id,), base_url=settings.API_BASE_URL)\n\n def build_expected(self, categories):\n return [\n {\n 'id': c.id,\n 'parent_id': c.parent_id,\n 'slug': c.slug,\n 'name': c.name,\n 'depth': c.node_depth,\n 'color_theme': {\n 'id': c.color_theme.id,\n 'name': c.color_theme.name,\n 'slug': c.color_theme.slug,\n 'is_active': c.color_theme.is_active,\n 'created': serializers.DateTimeField().to_representation(c.color_theme.created),\n 'modified': serializers.DateTimeField().to_representation(c.color_theme.modified),\n 'course_card_gradient_color': c.color_theme.course_card_gradient_color,\n },\n } for c in sorted(categories, key=lambda x: x.name)\n ]\n\n def test_list(self):\n url = self.get_url(self.course.id)\n expected = self.build_expected(self.categories)\n self.list_request(url, expected, num_queries=3)\n\n\nclass CourseListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-list'\n\n def setUp(self) -> None:\n self.user = UserFactory()\n self.author = UserFactory(is_staff=True)\n self.courses = CourseFactory.create_batch(10, is_active=True)\n self.inactive_courses = CourseFactory.create_batch(2, is_active=False)\n self.tags = TagFactory.create_batch(10)\n for i, course in enumerate(self.courses[:5]):\n course.tags.set(self.tags[i: i + 2])\n\n def build_expected(self, courses):\n return [\n {\n 'id': c.id,\n 'slug': c.slug,\n 'name': c.name,\n 'shortname': c.shortname,\n 'summary': c.summary,\n 'city_id': c.city_id,\n 'study_mode_id': c.study_mode_id,\n 'image_url': c.image_url,\n 'available_for_enroll': c.available_for_enroll,\n 'is_full': c.is_full,\n 'structure': c.structure,\n 'format': c.format,\n 'author': {\n 'id': c.author.id,\n 'username': c.author.username,\n 'first_name': c.author.first_name,\n 'last_name': c.author.last_name,\n },\n 'categories': [cc.id for cc in c.categories.all()],\n 'begin_date': c.begin_date,\n 'end_date': c.end_date,\n 'enroll_begin': c.enroll_begin,\n 'enroll_end': c.enroll_end,\n 'payment_method': c.payment_method,\n 'paid_percent': c.paid_percent,\n 'payment_terms': c.payment_terms,\n 'price': c.price,\n 'num_hours': c.num_hours,\n 'status': c.status,\n 'type': c.course_type,\n 'tags': list(c.tags.values_list('name', flat=True)),\n 'enable_groups': c.enable_groups,\n 'created': serializers.DateTimeField().to_representation(c.created),\n 'modified': serializers.DateTimeField().to_representation(c.modified),\n } for c in sorted(self.courses, key=lambda x: x.name)\n ]\n\n def test_url(self):\n self.assertURLNameEqual('courses/', base_url=settings.API_BASE_URL)\n\n def test_non_auth(self):\n response = self.client.get(self.get_url(), format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_list(self):\n self.client.force_login(user=self.user)\n url = self.get_url()\n expected = self.build_expected(self.courses)\n self.list_request(url, expected, num_queries=10)\n\n def test_list_with_groups(self):\n for c in self.courses:\n CourseGroupFactory.create_batch(5, course=c)\n c.refresh_from_db()\n\n self.client.force_login(user=self.user)\n url = self.get_url()\n expected = self.build_expected(self.courses)\n self.list_request(url, expected, num_queries=10)\n\n\nclass CourseWithVisibilityListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-list'\n\n def setUp(self) -> None:\n self.author = UserFactory(is_staff=True)\n self.courses_with_no_visibility_rules = CourseFactory.create_batch(4, is_active=True)\n self.inactive_courses = CourseFactory.create_batch(2, is_active=False)\n self.city = StaffCityFactory()\n self.office = StaffOfficeFactory(city=self.city)\n self.user = UserWithStaffProfileFactory(staffprofile__office=self.office)\n self.available_courses = CourseFactory.create_batch(3, is_active=True)\n [\n CourseVisibilityFactory(course=course, rules={\"eq\": [\"staff_city\", self.city.id]})\n for course in self.available_courses\n ]\n self.unavailable_courses = CourseFactory.create_batch(3, is_active=True)\n [\n CourseVisibilityFactory(course=course, rules={\"ne\": [\"staff_city\", self.city.id]})\n for course in self.unavailable_courses\n ]\n\n def build_expected(self, courses):\n return [\n {\n 'id': c.id,\n 'slug': c.slug,\n 'name': c.name,\n 'shortname': c.shortname,\n 'summary': c.summary,\n 'city_id': c.city_id,\n 'study_mode_id': c.study_mode_id,\n 'image_url': c.image_url,\n 'available_for_enroll': c.available_for_enroll,\n 'is_full': c.is_full,\n 'structure': c.structure,\n 'format': c.format,\n 'author': {\n 'id': c.author.id,\n 'username': c.author.username,\n 'first_name': c.author.first_name,\n 'last_name': c.author.last_name,\n },\n 'categories': [cc.id for cc in c.categories.all()],\n 'begin_date': c.begin_date,\n 'end_date': c.end_date,\n 'enroll_begin': c.enroll_begin,\n 'enroll_end': c.enroll_end,\n 'payment_method': c.payment_method,\n 'paid_percent': c.paid_percent,\n 'payment_terms': c.payment_terms,\n 'price': c.price,\n 'num_hours': c.num_hours,\n 'status': c.status,\n 'type': c.course_type,\n 'tags': list(c.tags.values_list('name', flat=True)),\n 'enable_groups': c.enable_groups,\n 'created': serializers.DateTimeField().to_representation(c.created),\n 'modified': serializers.DateTimeField().to_representation(c.modified),\n } for c in sorted(courses, key=lambda x: x.name)\n ]\n\n def test_list_with_visibilities(self):\n self.client.force_login(user=self.user)\n url = self.get_url()\n expected_courses = self.courses_with_no_visibility_rules + self.available_courses\n expected = self.build_expected(expected_courses)\n self.list_request(url, expected, num_queries=11)\n\n def test_list_with_view_permission(self):\n assign_perm(\"courses.view_course\", self.user)\n self.client.force_login(user=self.user)\n url = self.get_url()\n expected_courses = self.courses_with_no_visibility_rules + self.available_courses + self.unavailable_courses\n expected = self.build_expected(expected_courses)\n self.list_request(url, expected, num_queries=9)\n\n\nclass CourseDetailTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-detail'\n\n def setUp(self) -> None:\n self.user = UserFactory()\n self.author = UserFactory()\n self.course = CourseFactory(author=self.author, is_active=True)\n self.inactive_courses = CourseFactory.create_batch(2, is_active=False)\n self.tags = TagFactory.create_batch(5)\n self.course.tags.set(self.tags[:3])\n\n def build_expected(self, course):\n return {\n 'id': course.id,\n 'slug': course.slug,\n 'name': course.name,\n 'shortname': course.shortname,\n 'summary': course.summary,\n 'description': course.description,\n 'city_id': course.city_id,\n 'study_mode_id': course.study_mode_id,\n 'provider_id': course.provider_id,\n 'author': {\n 'id': course.author.id,\n 'username': course.author.username,\n 'first_name': course.author.first_name,\n 'last_name': course.author.last_name,\n },\n 'image_url': course.image_url,\n 'structure': course.structure,\n 'format': course.format,\n 'categories': sorted(category.id for category in course.categories.all()),\n 'available_for_enroll': course.available_for_enroll,\n 'is_full': course.is_full,\n 'begin_date': serializers.DateTimeField().to_representation(course.begin_date),\n 'end_date': serializers.DateTimeField().to_representation(course.end_date),\n 'enroll_begin': serializers.DateTimeField().to_representation(course.enroll_begin),\n 'enroll_end': serializers.DateTimeField().to_representation(course.enroll_end),\n 'payment_method': course.payment_method,\n 'paid_percent': course.paid_percent,\n 'price': course.price,\n 'payment_terms': course.payment_terms,\n 'num_hours': course.num_hours,\n 'status': course.status,\n 'enable_groups': course.enable_groups,\n 'enable_followers': course.enable_followers,\n 'enrollments_only': course.enrollments_only,\n 'multi_enrollments': course.multi_enrollments,\n 'retries_allowed': course.retries_allowed,\n 'is_enroll_open': course.is_enroll_open,\n 'enroll_will_begin': course.enroll_will_begin,\n 'has_open_seats': course.has_open_seats,\n 'type': course.course_type,\n 'tags': list(course.tags.values_list('name', flat=True)),\n 'created': serializers.DateTimeField().to_representation(course.created),\n 'modified': serializers.DateTimeField().to_representation(course.modified),\n }\n\n def test_url(self):\n self.assertURLNameEqual('courses/{}/', args=(1,), base_url=settings.API_BASE_URL)\n\n def test_non_auth(self):\n response = self.client.get(self.get_url(self.course.id), format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_get(self):\n self.client.force_login(user=self.user)\n url = self.get_url(self.course.id)\n expected = self.build_expected(self.course)\n self.detail_request(url, expected=expected, num_queries=5)\n\n def test_get_with_groups(self):\n CourseGroupFactory.create_batch(10, course=self.course)\n self.course.refresh_from_db()\n\n self.client.force_login(user=self.user)\n url = self.get_url(self.course.id)\n expected = self.build_expected(self.course)\n self.detail_request(url, expected=expected, num_queries=10)\n\n\nclass CourseGroupListTestCase(UrlNameMixin, APITestCase):\n URL_NAME = 'api:course-groups'\n\n def setUp(self) -> None:\n self.user = UserFactory()\n self.course = CourseFactory(is_active=True)\n self.course_groups: Iterable[CourseGroup] = CourseGroupFactory.create_batch(4, course=self.course)\n self.inactive_groups = CourseGroupFactory.create_batch(2, course=self.course, is_active=False)\n\n def test_url(self):\n self.assertURLNameEqual('courses/{}/groups/', args=(1,), base_url=settings.API_BASE_URL)\n\n def test_non_auth(self):\n course = CourseFactory(author=self.user, is_active=True)\n response = self.client.get(self.get_url(course.id), format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_no_course(self):\n self.client.force_login(user=self.user)\n max_course_id = Course.objects.aggregate(max_id=Max('id')).get('max_id', 0) or 0\n with self.assertNumQueries(3):\n response = self.client.get(self.get_url(max_course_id + 1), format='json')\n\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n response_data = response.data\n self.assertEqual('not_found', response_data['detail'].code)\n\n @parameterized_expand_doc(\n [\n (False, False, False, False, False, False, 3, False),\n (False, False, True, False, False, False, 3, False),\n (False, True, False, False, False, False, 5, False),\n (False, True, True, False, False, False, 7, True),\n (True, False, False, False, False, False, 5, True),\n (True, False, True, False, False, False, 5, True),\n (True, True, False, False, False, False, 7, True),\n (True, True, True, False, False, False, 7, True),\n (False, False, False, True, False, False, 3, False),\n (False, False, False, False, True, False, 3, False),\n (False, False, False, False, False, True, 3, False),\n (False, True, False, True, False, False, 7, True),\n (False, True, False, False, True, False, 7, True),\n (False, True, False, False, False, True, 7, True),\n ]\n )\n def test_list(\n self,\n is_active,\n is_preview_mode,\n user_is_author,\n user_has_permisssion,\n user_group_has_permission,\n user_in_course_team,\n num_queries,\n has_result\n ):\n course = CourseFactory(author=self.user if user_is_author else UserFactory(), is_active=is_active)\n course_groups: Iterable[CourseGroup] = CourseGroupFactory.create_batch(4, course=course)\n CourseGroupFactory.create_batch(2, course=course, is_active=False)\n if user_has_permisssion:\n assign_perm('courses.view_course', self.user, course)\n if user_group_has_permission:\n group = GroupFactory()\n group.user_set.add(self.user)\n assign_perm('courses.view_course', group, course)\n if user_in_course_team:\n preset = PermissionPresetFactory()\n permission = Permission.objects.get(codename='view_course')\n preset.permissions.add(permission)\n course_team = CourseTeamFactory(permission_preset=preset)\n course_team.add_user(self.user.id)\n course.teams.add(course_team)\n self.assertTrue(self.user.has_perm('courses.view_course', course))\n url = self.get_url(course.id)\n if is_preview_mode:\n url += '?preview=true'\n self.client.force_login(user=self.user)\n with self.assertNumQueries(num_queries):\n response = self.client.get(url, format='json')\n\n expected_status = status.HTTP_200_OK if has_result else status.HTTP_404_NOT_FOUND\n self.assertEqual(response.status_code, expected_status)\n\n if has_result:\n self.assertEqual(response.data.keys(), {'count', 'next', 'previous', 'results'})\n self.assertEqual(response.data.get('count'), 4)\n results = response.data.get('results')\n\n expected = [{\n 'id': cg.id,\n 'name': cg.name,\n 'summary': cg.summary,\n 'can_join': cg.can_join,\n 'num_participants': cg.num_participants,\n 'max_participants': cg.max_participants,\n 'available_for_enroll': cg.available_for_enroll,\n 'is_enroll_open': cg.is_enroll_open,\n 'enroll_will_begin': cg.enroll_will_begin,\n 'has_open_seats': cg.has_open_seats,\n 'is_full': cg.is_full,\n 'begin_date': cg.begin_date,\n 'end_date': cg.end_date,\n 'enroll_begin': cg.enroll_begin,\n 'enroll_end': cg.enroll_end,\n 'tutor': None,\n } for cg in sorted(course_groups, key=lambda cg: cg.name)]\n\n self.assertEqual(results, expected, msg=results)\n\n def test_filter_is_full(self):\n group = CourseGroupFactory(course=self.course, max_participants=3)\n EnrolledUserFactory.create_batch(3, course=self.course, group=group)\n\n self.client.force_login(user=self.user)\n with self.assertNumQueries(5):\n response = self.client.get(self.get_url(self.course.id), data={'is_full': False}, format='json')\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data.get('count'), 4)\n\n\nclass CourseModuleListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-modules'\n\n def setUp(self) -> None:\n self.user = UserFactory()\n self.course = CourseFactory(is_active=True)\n self.course_modules: Iterable[CourseModule] = CourseModuleFactory.create_batch(5, course=self.course)\n\n def test_url(self):\n self.assertURLNameEqual('courses/{}/modules/', args=(1,), base_url=settings.API_BASE_URL)\n\n def build_expected(self, course_modules):\n return [{\n 'id': m.id,\n 'module_type': {\n 'name': m.module_type.name,\n 'app_label': m.module_type.app_label,\n 'model': m.module_type.model,\n },\n 'name': m.name,\n 'description': m.description,\n 'estimated_time': m.estimated_time,\n 'block_id': None,\n } for m in course_modules]\n\n def test_non_auth(self):\n course = CourseFactory(author=self.user, is_active=True)\n response = self.client.get(self.get_url(course.id), format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_list(self):\n self.client.force_login(user=self.user)\n url = self.get_url(self.course.id)\n expected = self.build_expected(self.course_modules)\n self.list_request(url, expected, num_queries=4)\n\n\nclass CourseModuleDetailTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-module-detail'\n\n def setUp(self) -> None:\n self.user = UserFactory()\n self.course = CourseFactory(is_active=True)\n self.course_module = CourseModuleFactory(course=self.course)\n\n def test_url(self):\n self.assertURLNameEqual('course_modules/{}/', args=(1,), base_url=settings.API_BASE_URL)\n\n def build_expected(self, module: CourseModule) -> dict:\n return {\n 'id': module.id,\n 'module_type': {\n 'name': module.module_type.name,\n 'app_label': module.module_type.app_label,\n 'model': module.module_type.model,\n },\n 'name': module.name,\n 'description': module.description,\n 'estimated_time': module.estimated_time,\n }\n\n def test_non_auth(self):\n course_module = CourseModuleFactory(course=self.course)\n response = self.client.get(self.get_url(course_module.id), format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_detail(self):\n self.client.force_login(user=self.user)\n url = self.get_url(self.course_module.id)\n expected = self.build_expected(self.course_module)\n self.detail_request(url, expected, num_queries=3)\n\n\nclass TutorDetailTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:tutor-detail'\n\n def setUp(self) -> None:\n self.user = UserFactory()\n self.tutor = TutorInternalFactory()\n\n def test_url(self):\n self.assertURLNameEqual('tutors/{}/', args=(self.tutor.id,), base_url=settings.API_BASE_URL)\n\n def test_detail(self):\n expected = {\n 'id': self.tutor.id,\n 'is_internal': True,\n 'user': {\n 'id': self.tutor.user.id,\n 'username': self.tutor.user.username,\n 'first_name': self.tutor.user.first_name,\n 'last_name': self.tutor.user.last_name,\n },\n 'name': f'{self.tutor.user.first_name} {self.tutor.user.last_name}',\n 'email': self.tutor.email,\n 'url': f'{settings.STAFF_BASE_URL}/{self.tutor.user.username}',\n 'position': self.tutor.position,\n }\n\n self.client.force_login(user=self.user)\n self.detail_request(url=self.get_url(self.tutor.id), expected=expected, num_queries=3)\n\n\nclass CourseBlockListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-blocks'\n\n def build_expected(self, course_blocks):\n return [{\n 'id': b.id,\n 'name': b.name,\n 'summary': b.summary,\n } for b in course_blocks]\n\n def test_url(self):\n self.assertURLNameEqual('courses/{}/blocks/', args=(1,), base_url=settings.API_BASE_URL)\n\n def test_non_auth(self):\n user = UserFactory()\n course = CourseFactory(author=user, is_active=True)\n response = self.client.get(self.get_url(course.id), format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_list(self):\n user = UserFactory()\n self.client.force_login(user=user)\n course = CourseFactory(author=user, is_active=True)\n course_blocks = CourseBlockFactory.create_batch(9, course=course)\n CourseBlockFactory.create_batch(9)\n expected = self.build_expected(course_blocks)\n url = self.get_url(course.id)\n self.list_request(url, expected, num_queries=4)\n\n\nclass CourseBlockDetailTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:course-block-detail'\n\n def build_expected(self, block: CourseBlock) -> dict:\n return {\n 'id': block.id,\n 'name': block.name,\n 'summary': block.summary,\n }\n\n def test_url(self):\n self.assertURLNameEqual('course_blocks/{}/', args=(1,), base_url=settings.API_BASE_URL)\n\n def test_non_auth(self):\n user = UserFactory()\n course = CourseFactory(author=user, is_active=True)\n course_block = CourseBlockFactory(course=course)\n response = self.client.get(self.get_url(course_block.id), format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_detail(self):\n user = UserFactory()\n course = CourseFactory(author=user, is_active=True)\n course_block = CourseBlockFactory(course=course)\n expected = self.build_expected(course_block)\n self.client.force_login(user=user)\n url = self.get_url(course_block.id)\n self.detail_request(url, expected, num_queries=3)\n\n\nclass CourseStudentCompleteTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:my-course-complete'\n\n def test_url(self):\n self.assertURLNameEqual('my/courses/{}/complete/', args=(1,), base_url=settings.API_BASE_URL)\n\n def test_non_auth(self):\n course = CourseFactory()\n response = self.client.put(self.get_url(course.id), format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_complete(self):\n course = CourseFactory(\n is_active=True,\n structure=Course.StructureChoices.NO_MODULES,\n )\n student: CourseStudent = CourseStudentFactory(\n course=course,\n status=CourseStudent.StatusChoices.ACTIVE,\n )\n\n self.client.force_login(user=student.user)\n with self.assertNumQueries(7):\n response = self.client.put(self.get_url(course.id), format='json')\n\n self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n student.refresh_from_db()\n self.assertEqual(student.status, CourseStudent.StatusChoices.COMPLETED)\n\n def test_course_with_modules(self):\n course = CourseFactory(\n is_active=True,\n structure=Course.StructureChoices.MULTI,\n )\n student: CourseStudent = CourseStudentFactory(\n course=course,\n status=CourseStudent.StatusChoices.ACTIVE,\n )\n self.client.force_login(user=student.user)\n with self.assertNumQueries(3):\n response = self.client.put(self.get_url(course.id), format='json')\n\n self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n data = response.data\n self.assertEqual(data[0].code, 'cannot_complete')\n\n def test_course_not_found(self):\n wrong_course = CourseFactory(is_active=True)\n student = CourseStudentFactory(status=CourseStudent.StatusChoices.ACTIVE)\n self.client.force_login(user=student.user)\n with self.assertNumQueries(3):\n response = self.client.put(self.get_url(wrong_course.id), format='json')\n\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n def test_student_already_complete(self):\n course = CourseFactory(\n is_active=True,\n structure=Course.StructureChoices.NO_MODULES,\n )\n student: CourseStudent = CourseStudentFactory(\n course=course,\n status=CourseStudent.StatusChoices.COMPLETED,\n )\n self.client.force_login(user=student.user)\n with self.assertNumQueries(3):\n response = self.client.put(self.get_url(course.id), format='json')\n\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n\nclass MyCourseModuleListTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:my-course-module-list'\n\n def setUp(self):\n self.course = CourseFactory(is_active=True)\n self.user = UserFactory()\n\n def test_url(self):\n self.assertURLNameEqual('my/courses/{}/modules/', args=(1,), base_url=settings.API_BASE_URL)\n\n def test_non_auth(self):\n response = self.client.get(self.get_url(self.course.id), format='json')\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n def test_empty_list(self):\n StudentModuleProgressFactory.create_batch(19, course=self.course)\n self.client.force_login(user=self.user)\n self.list_request(self.get_url(self.course.id), check_ids=False, expected=[], num_queries=4)\n\n def build_expected(self, progresses):\n return [\n {\n 'module_id': p.module_id,\n 'score': p.score,\n } for p in progresses]\n\n def test_list(self):\n StudentModuleProgressFactory.create_batch(19, course=self.course)\n student = CourseStudentFactory(course=self.course, user=self.user)\n another_course = CourseFactory(is_active=True)\n another_student = CourseStudentFactory(course=another_course, user=self.user)\n progresses = StudentModuleProgressFactory.create_batch(5, course=self.course, student=student)\n StudentModuleProgressFactory.create_batch(9, course=another_course, student=another_student)\n expected = self.build_expected(progresses)\n self.client.force_login(user=self.user)\n\n self.list_request(self.get_url(self.course.id), check_ids=False, expected=expected, num_queries=5)\n\n def test_course_not_found(self):\n not_active_course = CourseFactory()\n self.client.force_login(user=self.user)\n expected_error = {'detail': 'not_found'}\n\n with self.assertNumQueries(3):\n url = self.get_url(not_active_course.id)\n response = self.client.get(url, format='json')\n self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n self.assert_errors(response.data, expected_error)\n\n def test_list_expelled_student_with_progress(self):\n completed_student = CourseStudentFactory(course=self.course, user=self.user)\n StudentModuleProgressFactory.create_batch(5, course=self.course, student=completed_student)\n completed_student.expell()\n\n CourseStudentFactory(course=self.course, user=self.user)\n self.client.force_login(user=self.user)\n\n self.list_request(self.get_url(self.course.id), check_ids=False, expected=[], num_queries=4)\n\n\nclass LinkedCourseDetailTestCase(UrlNameMixin, GenericRequestMixin, APITestCase):\n URL_NAME = 'api:linked-course-detail'\n\n def setUp(self):\n ModuleType.objects.get_for_model(LinkedCourse)\n self.user = UserFactory()\n self.course = CourseFactory(course_type=Course.TypeChoices.TRACK.value)\n self.linked_course = CourseFactory(course_type=Course.TypeChoices.COURSE.value)\n self.student = CourseStudentFactory(course=self.course, user=self.user)\n self.linked_course_module = LinkedCourseFactory(course=self.course, linked_course=self.linked_course)\n\n def build_expected(self, linked_course_module: LinkedCourse):\n return {\n \"id\": linked_course_module.id,\n \"linked_course\": {\n \"id\": linked_course_module.linked_course.id,\n \"slug\": linked_course_module.linked_course.slug,\n \"name\": linked_course_module.linked_course.name,\n \"shortname\": linked_course_module.linked_course.shortname,\n },\n \"name\": linked_course_module.name,\n \"description\": linked_course_module.description,\n \"estimated_time\": linked_course_module.estimated_time,\n }\n\n def test_url(self):\n self.assertURLNameEqual(\n 'linkedcourse_modules/{}/',\n args=(self.linked_course_module.id,),\n base_url=settings.API_BASE_URL\n )\n\n def test_no_auth(self):\n self.detail_request(\n url=self.get_url(self.linked_course_module.id),\n status_code=status.HTTP_403_FORBIDDEN,\n num_queries=0\n )\n\n def test_another_course(self):\n another_course = CourseFactory()\n another_student = CourseStudentFactory(course=another_course)\n self.client.force_login(user=another_student.user)\n self.create_request(\n url=self.get_url(self.linked_course_module.id),\n status_code=status.HTTP_403_FORBIDDEN,\n num_queries=5\n )\n\n def test_not_exists(self):\n self.client.force_login(user=self.user)\n self.detail_request(\n url=self.get_url(self.linked_course_module.id + 1),\n status_code=status.HTTP_404_NOT_FOUND,\n num_queries=3\n )\n\n def test_get_result(self):\n self.client.force_login(user=self.user)\n self.detail_request(\n url=self.get_url(self.linked_course_module.id),\n expected=self.build_expected(self.linked_course_module),\n num_queries=6\n )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/test_api_views (3).py","file_name":"test_api_views (3).py","file_ext":"py","file_size_in_byte":37511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36440112635","text":"'\"Welcome to the main page of the this project. This file will contain the structure of complete prjoect.\"'\n# to play song using the gabbar , give command Gabbar play SongName\n# improting the necessary files required to run\n# import section start \nimport os\nimport pyttsx3\nimport datetime\nimport speech_recognition as sr \nimport wikipedia\nimport webbrowser as web\nimport Intro\nimport Features\nimport keyboard\nimport mouse\nimport webbrowser as web\nimport pywhatkit\nfrom AppPath import Paths\nfrom Intro import OpeningCallsResonse1Hindi , Check , IpAdd , Weather , striver , News , lovebabbar , AppraisalGabbar , IntroText , OpeningCalls , OpeningCallsResponse1Eng , OpeningCallsResponse2Eng , OpeningCallsResponse2Hindi , OpeningCallsResponse3Eng , OpeningCallsResponse3Hindi , CloseGabbar , Text , QuestionEng , QuestionHindi , GoodBye , IntroText , NiteeshHindi , NiteeshQuestion , SearchBox , OpeningCalls , AskingGabbarEng , AskingGabbarHindi\nfrom Features import YouTubeSearch , PlayOnYoutube , search_on_google , search_on_codeforces , search_contest, OpenLeetCode , OpenCmd , LeetCodeProfile , OpenLinkedIn , SearchLinkedIn , OpenProfile\nfrom Features import OpenCmd , send_whatsapp_message , open_camera , OpenNotePad, OpenEclipse , Openmsword , OpenSublime , devc , OpenPaint , Openxamp\nfrom Features import OpenGmail , find_my_ip , OpenSDESheet, OpenWhatsapp , CloseApp\nfrom Features import SwitchTabs , open_music , SearchWikipedia , get_random_joke, get_latest_news , Close , OpenTaskManager , get_weather_report\n\n\n# import section end\n\n\n# varible set for the voice rate and volume.\nx = 160\nv = 1.0\ntime = int(datetime.datetime.now().hour)\n'\"creating engine and using speech api to make the assistant to speak.\"'\nengine = pyttsx3.init('sapi5')\n\n# now getting the voices property from this engine\nvoices = engine.getProperty('voices')\n\n'\"The below line is used to find the number of voices in my system . There are currently 4 voices.\"'\n# print(voices[1].id)\n\n'\"now we will set the property of voices actually here we are choosing whether we want male voice or female\"'\nengine.setProperty('voice', voices[1].id)\nengine.setProperty('rate', x)\n\n\n\n'\" Below is the speak function which will make system to output the voice\"'\ndef speak(audio):\n engine.say(audio)\n engine.runAndWait()\n\n'\"Below function will wish the user\"'\ndef wish():\n hour = time\n if ( hour >= 0 and hour < 12):\n speak(\"Good Morning , Niteesh\")\n elif ( hour >= 12 and hour < 19):\n speak(\"Good Noon , Niteesh\")\n elif ( hour >= 19 and hour < 24):\n speak(\"Good Evening , Niteesh \")\n\n'\"Below function is used to take the input from the user through the help of mic as source \"'\ndef takeCommand():\n # taking the user input as speech and returning string as output\n r = sr.Recognizer() # will help in recognizing the voice\n with sr.Microphone() as source:\n print( \"Listening.... \")\n r.pause_threshold = 1 # doing so with this line we are trying to wait for 1 seconds atleast to have input\n # since this thing can give error so we will use try except block\n audio = r.listen(source)\n \n\n try:\n print(\"Recongnizing\")\n query = r.recognize_google(audio, language=\"en-In\")\n print(\"In here\")\n print(\"User Said : - \" , query)\n \n\n except Exception as e:\n return \"none\" # we are returning normal string if dont find any thing\n\n\n return query.lower()\n\nif __name__ == \"__main__\":\n while True:\n query = takeCommand()\n \n # gabbar section\n if query in QuestionEng:\n speak(Text[3]) \n \n elif query in QuestionHindi:\n speak(Text[4])\n \n elif query in NiteeshHindi:\n speak(Text[6])\n \n elif query in NiteeshQuestion:\n speak(Text[5])\n\n elif query in AppraisalGabbar:\n speak(\"Dhanywad!.Sir ji!! Yeh aapki he dain hai..\")\n\n # below section contains the features of the Gabbar\n # google section\n elif 'spotify' in query:\n speak(\"Ok sir ! Just a Question ? Do you want to search specific artist or song ! Then please let me know???\")\n res = takeCommand()\n if 'yes' in res:\n speak(\"Please tell the song name or the artist\")\n res = takeCommand()\n if res :\n speak(\"Ok sir ! playing the related song...\")\n path = \"https://open.spotify.com/search/\" + res\n web.open(path)\n\n else: \n speak(\"Opening the spotify for you\")\n web.open(\"https://open.spotify.com/\")\n \n\n elif 'on google' in query or 'on chrome' in query:\n Query = query.replace(\"gabbar search\", \"\")\n query = Query.replace(\"on google\", \"\")\n speak(f\"Searching {query} on google\")\n search_on_google(query)\n\n elif 'wikipedia' in query:\n Query = query.replace(\"gabbar search\" , \"\")\n query = Query.replace(\"on wikipedia\" , \"\")\n speak(f\"Searching {query} on wikipedia\")\n SearchWikipedia(query)\n \n # youtube section\n elif 'on youtube' in query:\n Query = query.replace(\"gabbar search\",\"\")\n query = Query.replace(\"on youtube\",\"\")\n speak(f\"searching {query} on youtube...\")\n YouTubeSearch(query)\n\n elif 'play' in query:\n Query = query.replace(\"gabbar play\", \"\")\n query = Query.replace(\"on youtube\", \"\")\n PlayOnYoutube(query)\n \n \n # codeforces section\n elif 'on codeforces' in query:\n Query = query.replace(\"gabbar search\", \"\")\n query = Query.replace(\"on codeforces\", \"\")\n speak(f\"Ok sir , Searching {query} on codeforces....\")\n search_on_codeforces(query)\n \n elif 'codeforces profile' in query:\n Query = query.replace(\"gabbar open my\", \"\")\n query = Query.replace(\"codeforces profile\", \"\")\n speak(f\"Ok sir, Opening yours codeforces profile page....\")\n search_on_codeforces(\"Sinchu.itachi\")\n\n elif 'open codeforces contest page' in query:\n Query = query.replace(\"gabbar open codeforces contest page\", \"\")\n speak(\"Ok sir , Opening the contest page\")\n search_contest()\n\n \n \n\n # leetcode section\n elif 'leetcode profile' in query:\n Query = query.replace(\"gabbar open\", \"\")\n query = Query.replace(\"leetcode profile\", \"\")\n speak(\"Ok sir, Opening yours leetcode profile page\")\n LeetCodeProfile()\n \n elif 'open leetcode' in query:\n Query = query.replace(\"gabbar open\", \"\")\n query = Query.replace(\"leetcode\", \"\")\n speak(\"Ok sir, Opening leetcode....\")\n OpenLeetCode()\n\n #linked in section\n elif 'linkedin profile' in query:\n Query = query.replace(\"gabbar open my linkedin account\" , \"\")\n speak(f\"Ok sir.. Opening yours linkedin account\")\n OpenProfile()\n \n elif 'open linkedin' in query:\n OpenLinkedIn()\n\n elif 'on linkedin' in query:\n Query = query.replace(\"gabbar search\" , \"\")\n query = Query.replace(\"on linkedin\" , \"\")\n SearchLinkedIn(query)\n\n\n # for closing chrome any app \n elif 'close the chrome' in query:\n query = query.replace(\"gabbar close the\",\"\")\n if 'youtube' in query:\n CloseApp(chrome)\n \n else: \n CloseApp(query)\n \n # closing all app\n elif 'close' in query:\n speak(\"Ok sir..\")\n Close()\n\n # doing system task section\n elif 'copy this' in query:\n Query = query.replace(\"gabbar\", \"\")\n query = Query.replace(\"copy this\", \"\")\n speak(\"Ok sir, You said :- \")\n speak(query)\n\n elif 'open cmd' in query:\n speak(\"Opening Command Prompt sir\")\n OpenCmd()\n\n elif 'open camera' in query:\n speak(\"Ok sir, Opening Camera...\")\n open_camera()\n\n elif 'open music' in query:\n speak(\"Ok sir, Opening Music\")\n open_music()\n \n elif 'open notepad' in query:\n speak(\"Ok sir , Opening Notepad for you\")\n OpenNotePad()\n\n elif 'open sublime' in query:\n speak(\"Ok sir , Opening the Sublime text editor for you\")\n print(Paths['sublime-text'])\n OpenSublime()\n \n elif 'open eclipse' in query:\n speak(\"Ok sir , Opening Eclipse ide for you sir\")\n OpenEclipse()\n\n \n elif 'open paint' in query:\n speak(\"Ok sir, opening paint \")\n OpenPaint()\n \n elif 'open ms word' in query:\n speak(\"Ok sir , opening ms word for you\")\n Openmsword()\n\n elif 'open dev' in query:\n speak(\"ok sir , opening dev c++ ide\")\n devc()\n \n \n elif 'open xamp' in query:\n speak(\"ok sir , opening xampp control pannel for you\")\n Openxamp()\n \n\n #social media section\n elif 'send message on whatsapp' in query:\n speak(\"Ok sir..!\")\n\n while True:\n speak(\"Ok sir !!! Tell me the number to whom you are sending the message\")\n number = takeCommand()\n speak(f\"Is the number {number} correct sir??\")\n res = takeCommand()\n if 'yes' in res:\n speak(\"Got the Reciever\")\n \n while True:\n speak(\"What message has to be sent sir!\")\n message = takeCommand()\n speak(f\"This is the message which i will send sir ! {message}\")\n speak(\"Is it correct ?\")\n res = takeCommand()\n if 'yes' in res:\n speak(\"ok sir\")\n message = message.replace (\"gabbar send\" , \"\")\n send_whatsapp_message(number , message)\n break\n \n else:\n speak(\"Lets do it again sir!!\")\n continue\n else:\n speak(\"Sorry sir !! You have to try again!!\")\n continue\n break\n \n elif 'open whatsapp' in query:\n speak(\"Ok sir..\")\n speak(Text[2])\n OpenWhatsapp()\n \n elif 'open gmail' in query:\n speak(\"Ok sir\")\n speak(Text[2])\n OpenGmail()\n \n elif 'sheet' in query :\n speak(\"Ok sir\")\n speak(\"Which s d e sheet you want to open!? Love Babbar's or Striver's!\")\n response = takeCommand()\n if response in lovebabbar:\n speak(\"Ok sir , Openin Love babbar's s d e sheet\")\n OpenSDESheet()\n elif response in striver: \n speak(\"Ok sir , opening striver's S D E SHEET\")\n web.open(\"https://takeuforward.org/interviews/strivers-sde-sheet-top-coding-interview-problems/\")\n\n\n #keyboard functionality \n elif 'switch' in query:\n speak(\"Ok sir..\")\n SwitchTabs()\n \n elif 'task manager' in query:\n speak(\"Ok sir..\")\n OpenTaskManager()\n \n elif IpAdd[0] in query or IpAdd[1] in query:\n i = 0\n flag = 0\n while i < 3:\n speak(\"Tell me one thing which only you I know about you sir??\")\n res = takeCommand()\n if Check[0] in res:\n flag = 1\n find_my_ip()\n break\n \n else:\n speak(\"Wrong information.. You got 2 more attempt after that the system will be shut\")\n i += 1\n\n if not flag:\n speak(\"You are not authorised to have this details!! And using me is only allowed to Niteesh sir !! So i m shutting down\")\n keyboard.press_and_release('alt+f4')\n\n \n\n # new and other stuffs\n elif News[0] in query:\n speak(\"Ok sir..\")\n get_latest_news()\n\n elif News[1] in query:\n speak(\"Ok sir..\")\n get_latest_news()\n\n elif Weather[0] in query:\n speak(\"Ok sir...\")\n speak(\"Please enter the city name whose whether you want \")\n name = input (\"Enter the city name :- \")\n speak(f\"finding the weather report of city {name}\")\n speak(Text[2])\n get_weather_report(name)\n\n elif Weather[1] in query:\n speak(\"Ok sir...\")\n speak(\"Please enter the city name whose whether report you want \")\n name = input (\"Enter the city name :- \")\n speak(f\"finding the weather report of city {name}\")\n speak(Text[2])\n get_weather_report(name)\n \n elif 'joke' in query:\n speak(\"Ok sir!! Ready for the laughter therapy..\")\n get_random_joke()\n\n # below section is for setting change\n elif 'speak slowly' in query:\n speak(\"..Oh , I see ..... Ok sir ! Tell me what is causing you the problem .. My Speech or Volume\")\n response = takeCommand()\n if 'speech' in response:\n speak(\"Ok..Sir.. Roger That..\")\n while True:\n volume = x - 10\n engine.setProperty('rate', volume)\n speak(\"Is it fine sir??\")\n res = takeCommand()\n if 'ok' in res:\n speak(\"Ok sir , my speech rate has been reset for you....\")\n break\n \n elif 'not fine' in res:\n x -= 10\n speak(\"Ok sir ... Reducing it...\") \n elif 'nothing' in respons:\n speak(\"Ok sir! Seems everything is fine.\")\n \n\n # below section is for the greeting and further responses\n elif query in AskingGabbarHindi:\n speak(IntroText[5])\n\n elif query in AskingGabbarEng or 'thankyou' in query:\n speak(IntroText[6])\n\n elif query in OpeningCalls:\n wish()\n speak(IntroText[0])\n \n elif query in OpeningCallsResponse1Eng:\n speak(IntroText[2])\n \n elif query in OpeningCallsResonse1Hindi:\n speak(IntroText[7])\n\n elif query in OpeningCallsResponse2Eng:\n speak(IntroText[3]) \n \n elif query in OpeningCallsResponse2Hindi:\n speak(IntroText[8])\n \n elif query in OpeningCallsResponse3Eng:\n speak(IntroText[4])\n \n elif query in OpeningCallsResponse3Hindi:\n speak(IntroText[9])\n\n #random statements\n elif 'apna time aaega' in query:\n speak(\"Jaroor sir !! Apna time aaega..\")\n\n\n\n\n # closing or signing off\n elif query in CloseGabbar:\n speak(\"Ok sir , hope to see you soon. Bye!!\")\n keyboard.press_and_release('alt + f4')\n exit(0)\n \n elif 'take rest' in query:\n speak(\"..Thankyou sir !! ..You to take rest ..... \")\n keyboard.press_and_release(\"alt + f4\")\n exit(0) \n \n \n\n # this is to handle only if gabbar is called \n elif 'Gabbar' in query or 'gabbar' in query: \n speak(IntroText[1]) \n\n \n \n\n # if nothing matches aur unbound values while capturing voice from the user\n else:\n SearchBox.append(query)\n print(SearchBox)\n speak(\"Sorry sir , but I could not understand\") \n \n\n \n","repo_name":"shinchu-itachi/Personal-Voice-Assistance-Gabbar-","sub_path":"MyPersonalVoiceAssistant/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":16239,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31800023865","text":"import cv2 as cv\r\nimport numpy as np\r\n\r\n\r\ndef pic_dir():\r\n return 'e:/image/'\r\n\r\n\r\nsrc = cv.imread(pic_dir() + 'pic.jpg')\r\n\r\n# 阈值\r\n# ret, thresh = cv.threshold(src, 127, 255, cv.THRESH_BINARY)\r\n\r\n'''\r\n图像平滑\r\n'''\r\n# 均值滤波\r\nblur = cv.blur(src, (3, 3))\r\n# 方��滤波, 基本和均值滤波一样，可以选择归一化(normalize)，容易越界\r\nbox = cv.boxFilter(src, -1, (3, 3), normalize=True)\r\n# 高斯滤波\r\naussian = cv.GaussianBlur(src, (5, 5), 1)\r\n# 中值滤波\r\nwedian = cv.medianBlur(src, 5)\r\n\r\nres = np.hstack((src, blur, wedian)) # hstack:横着拼接 vstack:竖着拼接\r\ncv.imshow('pic', res)\r\n\r\ncv.waitKey(0) # 按任意键关闭窗口，cv2.waitKey(1000) 延时一秒关闭窗口\r\ncv.destroyAllWindows()\r\n","repo_name":"YangXJie/clothingmatch","sub_path":"practice/cv2blurbasic.py","file_name":"cv2blurbasic.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73481691280","text":"#!/usr/bin/env python\nimport csv\n\nMAX_PROCS=12\n\ndef parse_bool(string):\n if string in ['true', 'True', 't']:\n return True\n elif string in ['false', 'False', 'f']:\n return False\n else:\n raise \"error\"\n\ndef get_filename(scaled, problem_size):\n if scaled:\n return \"scaling_{}.csv\".format(problem_size)\n else:\n return \"non_scaling_{}.csv\".format(problem_size)\n\ndef get_speedup(scaled, one_cpu_time, n_proc_time, n_proc):\n if scaled:\n return one_cpu_time/n_proc_time*n_proc\n else:\n return one_cpu_time/n_proc_time\n\ndef get_efficiency(speedup, scaled, one_cpu_time, n_proc_time, n_proc):\n return speedup/float(n_proc)\n\ndef get_serial_fraction(speedup, scaled, one_cpu_time, n_proc_time, n_proc):\n if n_proc != 1:\n a = 1/speedup-1/float(n_proc)\n b = 1-1/float(n_proc)\n return a/b\n else:\n return 1.0\n\ndef get_measurements(*args):\n speedup = get_speedup(*args)\n eff = get_efficiency(speedup, *args)\n serial_fraction = get_serial_fraction(speedup, *args)\n return (speedup, eff, serial_fraction)\n\nwith open(\"data/result_raw.csv\") as file:\n data_reader = csv.reader(file, delimiter=',')\n headers = data_reader.next()\n\n for row in data_reader:\n scaled = parse_bool(row[0])\n procs = int(row[1])\n time = float(row[3])\n if procs == 1:\n problem_size = int(row[2])\n one_proc_time = time\n filename = \"data/{}\".format(get_filename(scaled, problem_size))\n out_file = open(filename, 'wb')\n out_csv = csv.writer(out_file, delimiter=',')\n\n (speedup, eff, serial_fraction) = get_measurements(scaled, one_proc_time, time, procs)\n row = [procs, time, speedup, eff, serial_fraction]\n out_csv.writerow(row)\n if procs == MAX_PROCS:\n out_file.close()\n","repo_name":"bszaf/tpr","sub_path":"lab2/parse_results.py","file_name":"parse_results.py","file_ext":"py","file_size_in_byte":1869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8806737772","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\n\nfrom cloudshell.shell.flows.autoload.basic_flow import AbstractAutoloadFlow\n\nfrom cloudshell.huawei.autoload.huawei_generic_snmp_autoload import (\n HuaweiGenericSNMPAutoload,\n)\n\n\nclass HuaweiSnmpAutoloadFlow(AbstractAutoloadFlow):\n MIBS_FOLDER = os.path.join(os.path.dirname(__file__), os.pardir, \"mibs\")\n\n def __init__(self, logger, snmp_handler):\n super(HuaweiSnmpAutoloadFlow, self).__init__(logger)\n self._snmp_handler = snmp_handler\n\n def _autoload_flow(self, supported_os, resource_model):\n with self._snmp_handler.get_service() as snmp_service:\n snmp_service.add_mib_folder_path(self.MIBS_FOLDER)\n snmp_service.load_mib_tables(\n [\n \"HUAWEI-PORT-MIB\",\n \"HUAWEI-TC-MIB\",\n ]\n )\n snmp_autoload = HuaweiGenericSNMPAutoload(snmp_service, self._logger)\n\n return snmp_autoload.discover(\n supported_os, resource_model, validate_module_id_by_port_name=False\n )\n","repo_name":"QualiSystems/cloudshell-huawei","sub_path":"cloudshell/huawei/flows/huawei_autoload_flow.py","file_name":"huawei_autoload_flow.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"25115650936","text":"import urllib.request, urllib.parse, urllib.error\nimport ssl\nimport json\n\n# Ignore SSL certificate errors\nctx = ssl.create_default_context()\nctx.check_hostname = False\nctx.verify_mode = ssl.CERT_NONE\n\nurl = input(\"URL:\" )\nif len(url)<1:\n url = \"http://py4e-data.dr-chuck.net/comments_689722.json\"\ndata = urllib.request.urlopen(url).read().decode()\njs = json.loads(data)\nnum = 0\ni = 0\nwhile True:\n try:\n num = num + int( js['comments'][i]['count'] )\n i = i+1\n except:\n break\nprint(num)\n\n\n ","repo_name":"JasonString/learnpython","sub_path":"json1.py","file_name":"json1.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18922800191","text":"import csv\n\n\n\nbasic_csv = \"../basic17.csv\"\nmcas = \"../NextGenMCAS17.csv\"\nbasic_only_mcas = \"../basic_only_mcas.csv\"\nracegender17 = \"../racegender17.csv\"\nracegender_only_mcas = \"../racegender_only_mcas.csv\"\nselectedpop17 = \"../selectedpopulations17.csv\"\nselectedpop_only_mcas = \"../selectedpop_only_mcas.csv\"\nenrollment17 = \"../enrollment17.csv\"\nenrollment_only_mcas = \"../enrollment_only_mcas.csv\"\n\n\nwith open(mcas, 'r', encoding='utf-8-sig') as file:\n\n\tcsv_reader = csv.DictReader(file)\n\n\tids = {}\n\n\tfor row in csv_reader:\n\t\tmcas_id = row['id']\n\n\t\tif mcas_id not in ids:\n\t\t\tids[mcas_id] = row['school']\n\n\twith open(enrollment17, 'r', encoding='utf-8') as basic_file:\n\t\treader = csv.reader(basic_file)\n\t\tnext(reader)\n\n\t\twith open(enrollment_only_mcas, 'w') as new_file:\n\t\t\twriter = csv.writer(new_file)\n\t\t\t\n\t\t\t#writer.writerow(['School', 'id', 'Type', 'Function', 'Contact Name', 'Address', 'Town', 'State', 'Zip', 'Phone', 'Fax', 'Grade'])\n\t\t\t#writer.writerow(['school','id','african_american','asian','hispanic','white','native','Native Hawaiian, Pacific Islander','Multi-Race, Non-Hispanic','male','female'])\n\t\t\t#writer.writerow(['school_id', 'year', 'first_lang_not_eng_num', 'first_lang_not_eng_per', \\\n\t\t\t#\t\t\t\t'ELL_num', 'ELL_per', 'disabilities_num', 'disabilities_per', 'low_inc_num', 'low_inc_per'])\n\n\t\t\twriter.writerow(['school_id', 'year', 'total'])\n\n\t\t\ti = 0\n\t\t\tfor row in reader:\n\t\t\t\t\n\t\t\t\t\"\"\"sid = row[1].replace(u'\\xa0', '')\n\t\t\t\tsid = sid.lstrip('0')\n\t\t\t\tprint(sid)\"\"\"\n\t\t\t\t\n\t\t\t\tif row[0] in ids:\n\t\t\t\t\ti+=1\n\t\t\t\t\t\n\t\t\t\t\twriter.writerow(row)\n\n\t\t\tprint(i)","repo_name":"callumnelson95/path-to-school","sub_path":"Data/New_Data/cleaning_scripts/unique_ids.py","file_name":"unique_ids.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12605590743","text":"\"\"\"Tests for self-paced course due date overrides.\"\"\"\n\nimport datetime\n\nfrom unittest.mock import patch\nimport pytz\nfrom django.test.utils import override_settings\n\nfrom common.djangoapps.student.tests.factories import BetaTesterFactory\nfrom lms.djangoapps.courseware.access import has_access\nfrom lms.djangoapps.ccx.tests.test_overrides import inject_field_overrides\nfrom lms.djangoapps.courseware.field_overrides import OverrideFieldData, OverrideModulestoreFieldData\nfrom openedx.core.djangoapps.discussions.utils import get_accessible_discussion_xblocks\nfrom xmodule.modulestore.tests.django_utils import ModuleStoreTestCase # lint-amnesty, pylint: disable=wrong-import-order\nfrom xmodule.modulestore.tests.factories import CourseFactory, BlockFactory # lint-amnesty, pylint: disable=wrong-import-order\n\n\n@override_settings(\n XBLOCK_FIELD_DATA_WRAPPERS=['lms.djangoapps.courseware.field_overrides:OverrideModulestoreFieldData.wrap'],\n MODULESTORE_FIELD_OVERRIDE_PROVIDERS=[\n 'lms.djangoapps.courseware.self_paced_overrides.SelfPacedDateOverrideProvider'\n ],\n)\nclass SelfPacedDateOverrideTest(ModuleStoreTestCase):\n \"\"\"\n Tests for self-paced due date overrides.\n \"\"\"\n\n def setUp(self):\n self.reset_setting_cache_variables()\n super().setUp()\n\n self.non_staff_user, __ = self.create_non_staff_user()\n self.now = datetime.datetime.now(pytz.UTC).replace(microsecond=0)\n self.future = self.now + datetime.timedelta(days=30)\n\n def tearDown(self):\n self.reset_setting_cache_variables()\n super().tearDown()\n\n def reset_setting_cache_variables(self):\n \"\"\"\n The overridden settings for this class get cached on class variables.\n Reset those to None before and after running the test to ensure clean\n behavior.\n \"\"\"\n OverrideFieldData.provider_classes = None\n OverrideModulestoreFieldData.provider_classes = None\n\n def setup_course(self, **course_kwargs):\n \"\"\"Set up a course with provided course attributes.\n\n Creates a child block with a due date, and ensures that field\n overrides are correctly applied for both blocks.\n \"\"\"\n course = CourseFactory.create(**course_kwargs)\n section = BlockFactory.create(parent=course, due=self.now)\n inject_field_overrides((course, section), course, self.user)\n return (course, section)\n\n def create_discussion_xblocks(self, parent): # lint-amnesty, pylint: disable=missing-function-docstring\n # Create a released discussion xblock\n BlockFactory.create(\n parent=parent,\n category='discussion',\n display_name='released',\n start=self.now,\n )\n\n # Create a scheduled discussion xblock\n BlockFactory.create(\n parent=parent,\n category='discussion',\n display_name='scheduled',\n start=self.future,\n )\n\n def test_instructor_paced_due_date(self):\n __, ip_section = self.setup_course(display_name=\"Instructor Paced Course\", self_paced=False)\n assert ip_section.due == self.now\n\n def test_self_paced_due_date(self):\n __, sp_section = self.setup_course(display_name=\"Self-Paced Course\", self_paced=True)\n assert sp_section.due is None\n\n @patch.dict('lms.djangoapps.courseware.access.settings.FEATURES', {'DISABLE_START_DATES': False})\n def test_course_access_to_beta_users(self):\n \"\"\"\n Test that beta testers can access `self_paced` course prior to start date.\n \"\"\"\n now = datetime.datetime.now(pytz.UTC)\n one_month_from_now = now + datetime.timedelta(days=30)\n course_options = {\n 'days_early_for_beta': 100,\n 'self_paced': True,\n 'start': one_month_from_now,\n }\n # Create a `self_paced` course and add a beta tester in it\n self_paced_course, self_paced_section = self.setup_course(**course_options)\n beta_tester = BetaTesterFactory(course_key=self_paced_course.id)\n\n # Verify course is `self_paced` and course has start date but not section.\n assert self_paced_course.self_paced\n assert self_paced_course.start == one_month_from_now\n assert self_paced_section.start is None\n\n # Verify that non-staff user do not have access to the course\n assert not has_access(self.non_staff_user, 'load', self_paced_course)\n\n # Verify beta tester can access the course as well as the course sections\n assert has_access(beta_tester, 'load', self_paced_course)\n assert has_access(beta_tester, 'load', self_paced_section, self_paced_course.id)\n\n @patch.dict('lms.djangoapps.courseware.access.settings.FEATURES', {'DISABLE_START_DATES': False})\n def test_instructor_paced_discussion_xblock_visibility(self):\n \"\"\"\n Verify that discussion xblocks scheduled for release in the future are\n not visible to students in an instructor-paced course.\n \"\"\"\n course, section = self.setup_course(start=self.now, self_paced=False)\n self.create_discussion_xblocks(section)\n\n # Only the released xblocks should be visible when the course is instructor-paced.\n xblocks = get_accessible_discussion_xblocks(course, self.non_staff_user)\n assert all((xblock.display_name == 'released') for xblock in xblocks)\n\n @patch.dict('lms.djangoapps.courseware.access.settings.FEATURES', {'DISABLE_START_DATES': False})\n def test_self_paced_discussion_xblock_visibility(self):\n \"\"\"\n Regression test. Verify that discussion xblocks scheduled for release\n in the future are visible to students in a self-paced course.\n \"\"\"\n course, section = self.setup_course(start=self.now, self_paced=True)\n self.create_discussion_xblocks(section)\n\n # The scheduled xblocks should be visible when the course is self-paced.\n xblocks = get_accessible_discussion_xblocks(course, self.non_staff_user)\n assert len(xblocks) == 2\n assert any((xblock.display_name == 'scheduled') for xblock in xblocks)\n","repo_name":"openedx/edx-platform","sub_path":"lms/djangoapps/courseware/tests/test_self_paced_overrides.py","file_name":"test_self_paced_overrides.py","file_ext":"py","file_size_in_byte":6124,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"73346706960","text":"\nglobal isRunning\nglobal numdevices\nglobal devicenumber\nglobal inputdeviceindex\nglobal samplerateIDX\nglobal CHANNELS\n\nglobal Ch1DirPath\nglobal Ch2DirPath\nglobal Ch3DirPath\nglobal Ch4DirPath\nglobal Ch1fileName\nglobal Ch2fileName\nglobal Ch3fileName\nglobal Ch4fileName\n\nglobal threshold1\nglobal threshold2\nglobal threshold3\nglobal threshold4\n\n\n\ndef loadConfig(loadfilename,ui):\n from configparser import SafeConfigParser\n import os\n import GlobalVars\n import serial\n from numpy import arange,array\n \n parser = SafeConfigParser()\n loadfilename=loadfilename.replace('/','\\\\')\n \n if not parser.read(str(loadfilename)): #.replace('/','\\\\')):\n raise(IOError, 'cannot load')\n \n \n GlobalVars.buffertime=int(parser.get('main','GlobalVars.buffertime'))\n GlobalVars.InputSelection=parser.get('main','GlobalVars.InputSelectionText');\n \n GlobalVars.Ch1DirPath=(parser.get('main','GlobalVars.Ch1DirPath'))\n GlobalVars.Ch2DirPath=(parser.get('main','GlobalVars.Ch2DirPath'))\n GlobalVars.Ch3DirPath=(parser.get('main','GlobalVars.Ch3DirPath'))\n GlobalVars.Ch4DirPath=(parser.get('main','GlobalVars.Ch4DirPath'))\n \n GlobalVars.Ch1fileName=(parser.get('main','GlobalVars.Ch1fileName'))\n GlobalVars.Ch2fileName=(parser.get('main','GlobalVars.Ch2fileName'))\n GlobalVars.Ch3fileName=(parser.get('main','GlobalVars.Ch3fileName'))\n GlobalVars.Ch4fileName=(parser.get('main','GlobalVars.Ch4fileName'))\n \n GlobalVars.threshold1=float(parser.get('main','GlobalVars.threshold1'))\n GlobalVars.threshold2=float(parser.get('main','GlobalVars.threshold2'))\n GlobalVars.threshold3=float(parser.get('main','GlobalVars.threshold3'))\n GlobalVars.threshold4=float(parser.get('main','GlobalVars.threshold4'))\n GlobalVars.SampleRate=int(parser.get('main','GlobalVars.SampleRate'))\n\n ui.Ch1FileNameLabel.setText(GlobalVars.Ch1fileName);\n ui.Ch1FileDirectoryLabel.setText(GlobalVars.Ch1DirPath); \n ui.Ch2FileNameLabel.setText(GlobalVars.Ch2fileName)\n ui.Ch2FileDirectoryLabel.setText(GlobalVars.Ch2DirPath); \n ui.Ch3FileNameLabel.setText(GlobalVars.Ch3fileName)\n ui.Ch3FileDirectoryLabel.setText(GlobalVars.Ch3DirPath); \n ui.Ch4FileNameLabel.setText(GlobalVars.Ch4fileName)\n ui.Ch4FileDirectoryLabel.setText(GlobalVars.Ch4DirPath);\n ui.BufferTimeSpinBox.setValue(GlobalVars.buffertime)\n ui.ThresholdLineEdit1.setText(str(GlobalVars.threshold1))\n ui.ThresholdLineEdit2.setText(str(GlobalVars.threshold2))\n ui.ThresholdLineEdit3.setText(str(GlobalVars.threshold3))\n ui.ThresholdLineEdit4.setText(str(GlobalVars.threshold4))\n \n ui.SampleRatecomboBox.setCurrentText(str(GlobalVars.SampleRate)) \n ui.InputSelectioncomboBox.setCurrentText(GlobalVars.InputSelection)\n \n \n ui.Ch1SaveDirPushButton.setEnabled(False);\n ui.Ch2SaveDirPushButton.setEnabled(False);\n ui.Ch3SaveDirPushButton.setEnabled(False);\n ui.Ch4SaveDirPushButton.setEnabled(False);\n\n ui.Ch1checkBox.setEnabled(False);\n ui.Ch1checkBox.setEnabled(False);\n ui.Ch1checkBox.setEnabled(False);\n ui.Ch1checkBox.setEnabled(False); \n\n\n if (GlobalVars.CHANNELS >0):\n ui.Ch1SaveDirPushButton.setEnabled(True);\n if (GlobalVars.Ch1fileName!=''):\n ui.Ch1checkBox.setEnabled(True);\n\n if (GlobalVars.CHANNELS >1):\n ui.Ch2SaveDirPushButton.setEnabled(True);\n if (GlobalVars.Ch2fileName!=''):\n ui.Ch2checkBox.setEnabled(True);\n if (GlobalVars.CHANNELS >2):\n ui.Ch3SaveDirPushButton.setEnabled(True);\n if (GlobalVars.Ch3fileName!=''):\n ui.Ch3checkBox.setEnabled(True);\n \n if (GlobalVars.CHANNELS >3):\n ui.Ch4SaveDirPushButton.setEnabled(True);\n if (GlobalVars.Ch4fileName!=''):\n ui.Ch4checkBox.setEnabled(True); \n \n\ndef saveConfig(savefilename,ui):\n from configparser import SafeConfigParser\n import os\n import GlobalVars\n from numpy import array \n \n SaveFile= open((savefilenamegithub),'w')\n \n parser = SafeConfigParser()\n \n parser.add_section('main')\n\n \n parser.set('main','GlobalVars.buffertime',str(GlobalVars.buffertime))\n parser.set('main','GlobalVars.Ch1DirPath',str(GlobalVars.Ch1DirPath))\n parser.set('main','GlobalVars.Ch2DirPath',str(GlobalVars.Ch2DirPath))\n parser.set('main','GlobalVars.Ch3DirPath',str(GlobalVars.Ch3DirPath))\n parser.set('main','GlobalVars.Ch4DirPath',str(GlobalVars.Ch4DirPath))\n\n parser.set('main','GlobalVars.Ch1fileName',str(GlobalVars.Ch1fileName))\n parser.set('main','GlobalVars.Ch2fileName',str(GlobalVars.Ch2fileName))\n parser.set('main','GlobalVars.Ch3fileName',str(GlobalVars.Ch3fileName))\n parser.set('main','GlobalVars.Ch4fileName',str(GlobalVars.Ch4fileName))\n \n parser.set('main','GlobalVars.threshold1',str(GlobalVars.threshold1))\n parser.set('main','GlobalVars.threshold2',str(GlobalVars.threshold2))\n parser.set('main','GlobalVars.threshold3',str(GlobalVars.threshold3))\n parser.set('main','GlobalVars.threshold4',str(GlobalVars.threshold4))\n parser.set('main','GlobalVars.SampleRate',str(GlobalVars.SampleRate))\n parser.set('main','GlobalVars.InputSelectionText',ui.InputSelectioncomboBox.currentText()); \n \n parser.write(SaveFile) \n SaveFile.close()\n","repo_name":"whmehaffey/PyCBS-4Channel","sub_path":"GlobalVars.py","file_name":"GlobalVars.py","file_ext":"py","file_size_in_byte":5327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71986461843","text":"from django.urls import path\nfrom .views import *\n\nurlpatterns = [\n path('create/user/', CreateUser.as_view(), name = 'user_create'),\n path('create/profile/', CreateProfile.as_view(), name = 'profile_create'),\n path('follow/unfollow/user//', FollowUnfollow.as_view(), name = 'follows_create_delete'),\n path('profile/detail/user/', MyProfileRetrieve.as_view(), name = 'profile_detail'),\n path('profile/detail/user//', OtherProfileRetrieve.as_view(), name = 'other_profile_detail'),\n path('update/profile/', MyProfileUpdate.as_view(), name = 'my_profile_detail_update'),\n path('register/', RegisterView.as_view(), name='register_user'),\n path('login/', LoginView.as_view(), name='login_user'),\n path('logout/', LogoutView.as_view(), name='logout_user'),\n path('checkAuthenticated/', CheckAuthenticatedView.as_view(), name='checkAuthenticated_user'),\n path('feed/', HomeView.as_view(), name='feed_user'),\n]","repo_name":"Damurq/ShowYS-backend","sub_path":"users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29214495477","text":"import asyncio\nimport logging\n\nimport click\nimport pytest\nfrom click.testing import CliRunner\n\nfrom maps.infra.sedem.machine import cli_settings\nfrom maps.infra.sedem.machine.lib.cli_settings_api import CliSettingsApi, CliSettings\nfrom maps.infra.sedem.lib.oauth import SEDEM_OAUTH_ENV_VAR\nfrom maps.infra.sedem.machine.tests.typing import MongoFixture, CliSettingsFactory\nfrom maps.pylibs.infrastructure_api.arc.arc_client import ArcClient, Commit\n\n\nlogger = logging.getLogger(__name__)\n\n\n@pytest.fixture(autouse=True)\ndef mock_oauth_token(monkeypatch) -> None:\n monkeypatch.setenv(SEDEM_OAUTH_ENV_VAR, 'fake-token')\n\n\n@pytest.fixture(autouse=True)\ndef patch_db_uri(monkeypatch, mongo: MongoFixture) -> None:\n monkeypatch.setattr(cli_settings, 'deduce_mongo_uri', lambda **_: mongo.uri)\n\n\ndef invoke_tool_command(*args: str) -> str:\n runner = CliRunner()\n result = runner.invoke(cli_settings.tool, ('-e', 'testing') + args)\n if result.exception:\n raise result.exception\n logger.info(result.output)\n assert result.exit_code == 0\n return click.unstyle(result.output)\n\n\ndef test_show_settings(monkeypatch,\n cli_settings_factory: CliSettingsFactory) -> None:\n asyncio.run(cli_settings_factory(min_valid_version=42))\n\n monkeypatch.setattr(\n ArcClient, 'try_get_commit',\n lambda *args, **kwargs: Commit(Oid='oid', SvnRevision=42, Author='john-doe', Message='Commit message'),\n )\n assert invoke_tool_command('show') == (\n 'Current CLI settings\\n'\n 'Minimal valid version: r42 (@john-doe) Commit message\\n'\n )\n\n\ndef test_deprecate_version(monkeypatch,\n mongo: MongoFixture,\n cli_settings_factory: CliSettingsFactory) -> None:\n asyncio.run(cli_settings_factory(min_valid_version=1))\n\n monkeypatch.setattr(\n ArcClient, 'try_get_commit',\n lambda *args, **kwargs: Commit(Oid='oid', SvnRevision=42),\n )\n invoke_tool_command('deprecate', '42')\n\n async def load_settings() -> CliSettings:\n async with await mongo.async_client.start_session() as session:\n return await CliSettingsApi(session).load_settings()\n\n settings = asyncio.run(load_settings())\n assert settings.min_valid_version == 42\n\n\ndef test_deprecate_invalid_version(monkeypatch) -> None:\n monkeypatch.setattr(\n ArcClient, 'try_get_commit', lambda *args, **kwargs: None, # non-existing commit\n )\n with pytest.raises(cli_settings.ToolError, match='Commit r42 not found in trunk'):\n invoke_tool_command('deprecate', '42')\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/cli_settings_tool_tests/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8067606472","text":"from sys import argv\n\n\n'''\nargv[1] - file with reference genome in fasta format\nargc[2] - vcf file\n'''\n\n\ndef find_repeat(snp, genome):\n if snp[0] not in genome:\n return\n chr_seq = genome[snp[0]]\n pos, ref, alt = int(snp[1])-1, snp[3], snp[4]\n for len_repeat in range(4,16):\n i = 0\n slice_chr = chr_seq[pos - 2 * len_repeat + 1: pos + 2 * len_repeat]\n slice_alt = chr_seq[pos - 2 * len_repeat + 1: pos] + snp[4] + chr_seq[pos + len(ref): pos + 2 * len_repeat]\n while i < len_repeat:\n pat_before = slice_chr[i:i+len_repeat]\n pat = slice_chr[i+len_repeat:i+len_repeat*2]\n pat_after = slice_chr[i+len_repeat*2: i + len_repeat*3]\n pat_alt = slice_alt[i+len_repeat:i+len_repeat*2]\n if pat_after == pat or pat_before == pat:\n return snp\n if pat_after == pat_alt or pat_before == pat_alt:\n return snp\n i += 1\n return\n\n\ngenome = {}\nchr_names = {\"chr1\": \"chr01\", \"chr2\": \"chr02\", \"chr3\": \"chr03\", \"chr4\": \"chr04\", \"chr5\": \"chr05\", \"chr6\": \"chr06\"}\n\nwith open(argv[1]) as f:\n while True:\n line = f.readline().strip()\n if line:\n if line.startswith(\">\"):\n chr_name = line[1:]\n if chr_name in chr_names:\n chr_name = chr_names[chr_name]\n genome[chr_name] = \"\"\n else:\n genome[chr_name] = genome[chr_name] + line\n else:\n break\n\n\nwith open(argv[2]) as f:\n while True:\n line = f.readline().strip()\n if line:\n if line.startswith(\"#\"):\n continue\n else:\n snp = line.strip().split(\"\\t\")\n res = find_repeat(snp, genome)\n if res:\n print(\"\\t\".join(snp))\n else:\n break\n","repo_name":"Neonbird/spbu_py","sub_path":"hw7/tandem_repeats.py","file_name":"tandem_repeats.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39462768628","text":"import random\ndef noReplacementSimulation(numTrials):\n '''\n Runs numTrials trials of a Monte Carlo simulation\n of drawing 3 balls out of a bucket containing\n 3 red and 3 green balls. Balls are not replaced once\n drawn. Returns the a decimal - the fraction of times 3 \n balls of the same color were drawn.\n '''\n # Your code here\n success = 0\n for trials in range(numTrials):\n c = draw_1()\n if draw_2(c) == c:\n# print \"two\" + c\n if draw_3(c) == c:\n# print \"three\" + c\n success +=1\n return success/float(numTrials)\n\ndef draw_1():\n if random.random() > 0.5:\n color = 'red'\n else:\n color = 'green'\n return color\ndef draw_2(color):\n if color == 'red':\n c2 = random.choice(['red','red','green','green','green'])\n else:\n c2 = random.choice(['red','red','red','green', 'green'])\n return c2\ndef draw_3(color):\n if color == 'red':\n c3 = random.choice(['red','green','green','green'])\n else:\n c3 = random.choice(['red','red','red','green'])\n return c3\n\n\nfor x in range(50):\n noReplacementSimulation(10000)\n","repo_name":"prodg1974/6.00.2x","sub_path":"ProblemSet3/MonteCarlo3balls.py","file_name":"MonteCarlo3balls.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4210794756","text":"from math import ceil\nimport sys\n\ndp = [[0 for column in range(106)] for row in range(106)]\nt = 1\nwhile True:\t\n\ttry:\n\t\tnodes, edges = [int(i) for i in sys.stdin.readline().split()]\n\texcept:\n\t\tbreak\n\t\n\tif nodes == 0 and edges == 0: break\n\t\t\n\tfor i in range(1, nodes+1):\n\t\tfor j in range(1, nodes+1):\n\t\t\tdp[i][j] = -1e6\n\t\tdp[i][i] = 0\n\n\tfor _ in range(edges):\n\t\tsrc, dest, weight = [int(i) for i in sys.stdin.readline().split()]\n\t\tdp[src][dest] = dp[dest][src] = weight\n\n\tsrc, dest, weight = [int(i) for i in sys.stdin.readline().split()]\n\n\tfor k in range(1, nodes+1):\n\t\tfor i in range(1, nodes+1):\n\t\t\tfor j in range(1, nodes+1):\n\t\t\t\tdp[i][j] = dp[j][i] = max(dp[i][j], min(dp[i][k], dp[k][j]))\n\n\tresult = ceil(weight/(dp[src][dest]-1))\n\tprint('Scenario #%d' % t)\n\tprint('Minimum Number of Trips = %d\\n' % result)\n\tt += 1\n\"\"\"\n#include \n#include \n#include\nusing namespace std;\nint src, dest, weight, nodes, edges;\nint dp[105][105];\n\nint main(){\n int t=1;\n while(cin>>nodes >> edges){\n if(nodes == 0 and edges == 0) return 0;\n for(int i=1; i<=nodes; i++){\n for(int j=1; j<=nodes; j++){\n dp[i][j] = -1e6;\n }\n dp[i][i] = 0;\n }\n for(int i =0; i>src >>dest >>weight;\n dp[src][dest] = dp[dest][src] = weight;\n }\n cin >> src >> dest >> weight;\n for(int k=1; k<=nodes; k++){\n for(int i=1; i<=nodes; i++){\n for(int j=1; j<=nodes; j++){\n dp[i][j] = dp[j][i] = max(dp[i][j], min(dp[i][k], dp[k][j]));\n }\n }\n }\n int result = ceil(((double)weight*1.0) / (dp[src][dest]-1));\n printf(\"Scenario #%d\\n\", t++);\n printf(\"Minimum Number of Trips = %d\\n\\n\", result);\n\n\n }\n\n return 0;\n}\n\n\"\"\"","repo_name":"hnko/uva-judge","sub_path":"floyd-Warshall/10099.py","file_name":"10099.py","file_ext":"py","file_size_in_byte":1716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72724714000","text":"import os\nimport re\n\n\ndef part_one(input):\n def abba(input):\n for i in range(1, len(input)-2):\n if input[i] == input[i+1] and \\\n input[i-1] == input[i+2] and \\\n input[i-1] != input[i]:\n return True\n return False\n\n input = input.strip().splitlines()\n input = [i for i in input\n if not any([abba(j) for j in re.findall(r\"\\[.*?\\]\", i)])]\n input = [any([abba(j) for j in re.split(r\"\\[.*?\\]\", i)]) for i in input]\n\n return sum(input)\n\n\ndef part_two(input):\n def ssl(input):\n supernet = \" \".join(re.split(r\"\\[.*?\\]\", input))\n hypernet = \"\".join(re.findall(r\"\\[.*?\\]\", input))\n for s in range(1, len(supernet)-1):\n if supernet[s-1] == supernet[s+1] and \\\n supernet[s] != supernet[s-1] and \\\n supernet[s:s+2] + supernet[s] in hypernet:\n return True\n return False\n\n return sum(map(ssl, input.strip().splitlines()))\n\n\nif __name__ == \"__main__\":\n input = open(os.path.join(os.path.dirname(__file__), \"input.txt\")).read()\n print(part_one(input)) # 105\n print(part_two(input)) # 258\n","repo_name":"Andreal2000/advent-of-code","sub_path":"2016/07/internet_protocol_version_7.py","file_name":"internet_protocol_version_7.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27953001146","text":"# This file is part of For The New Lunar Republic.\n\n# For The New Lunar Republic 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# For The New Lunar Republic 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 For The New Lunar Republic. If not, see ')\ndef send_file(path):\n return send_from_directory('user_interface/app', path) \n\n@app.route('/', methods=['GET','POST'])\ndef hello_world():\n if request.method == \"GET\":\n return redirect(\"/app/index.html\")\n else:\n pprint.pprint(request.form)\n pprint.pprint(request.files)\n \n #Language check\n if request.form['language'] not in ['english', 'dutch']:\n return jsonify(status='error', message=\"Invalid language!\")\n \n #Input normalization\n if request.form['upload_option'] == 'text_field':\n input_text = request.form['upload_textarea']\n elif request.form['upload_option'] == 'url':\n page_text = requests.get(request.form['upload_url']).text\n soup = BeautifulSoup(page_text, \"html.parser\")\n input_text = soup.text\n elif request.form['upload_option'] == 'file':\n input_text = UnicodeDammit(request.files.get('upload_file').read()).unicode_markup\n \n #Stemmer selection\n if request.form['stemmer'] == 'no_stemmer':\n stemmer = None\n elif request.form['stemmer'] == 'porter':\n if request.form['language'] != 'english':\n return jsonify(status='error', message=\"Invalid language for stemmer porter!\")\n stemmer = PorterStemmer()\n elif request.form['stemmer'] == 'snowball':\n stemmer = SnowballStemmer(request.form['language'])\n else:\n return jsonify(status='error', message=\"Invalid stemmer!\")\n \n #Lemmatizer selection\n if request.form['lemmatizer'] == 'lemmatizer_off':\n lemmatizer = None\n elif request.form['language'] == 'english':\n lemmatizer = lemmatizer_en\n else:\n lemmatizer = lemmatizer_nl\n \n #Stopwords selection \n if request.form['stopwords'] == 'no_stopwords': \n stopwords = None\n elif request.form['stopwords'] == 'our_stopwords':\n stopwords = obo.stopwords\n elif request.form['stopwords'] == 'custom_stopwords':\n custom_stopword_text = UnicodeDammit(request.files.get('custom_stopword_file').read()).unicode_markup\n stopwords = obo.stripNonAlphaNum(custom_stopword_text)\n \n #Process the text \n input_text_word_count = 0\n resulting_text = \"\"\n final_wordlist = []\n for word_type, word in text_processor.parse_text(input_text):\n if word_type == \"non-word\":\n resulting_text += word\n else:\n input_text_word_count += 1\n processed_word = word\n if stemmer:\n processed_word = stemmer.stem(processed_word)\n if lemmatizer:\n processed_word = lemmatizer(processed_word)\n if not stopwords or processed_word not in stopwords:\n if request.form['exclude_vowels'] == 'exclude_vowels_yes':\n if request.form['language'] == 'english':\n regex = re_vowel_en\n else:\n regex = re_vowel_nl\n processed_word = regex.sub(\"\", processed_word)\n resulting_text += processed_word\n final_wordlist.append(processed_word)\n \n dictionary = obo.wordListToFreqDict(final_wordlist)\n sorteddict = obo.sortFreqDict(dictionary) \n \n ignore_results_amount = int(request.form['ignore_results_amount']) \n \n if ignore_results_amount > 0:\n initial_index = ignore_results_amount\n ignored_words = [word for rank, word in sorteddict[:initial_index]]\n sorteddict = sorteddict[initial_index:] \n new_text = \"\"\n new_wordlist = []\n for word_type, word in text_processor.parse_text(resulting_text):\n if word_type == \"non-word\":\n new_text += word\n elif word not in ignored_words:\n new_text += word\n new_wordlist.append(word)\n resulting_text = new_text\n final_wordlist = new_wordlist\n \n else:\n initial_index = 0 \n \n #Do the math! \n input_text_char_count = len(input_text)\n word_count = len(final_wordlist) \n distinct_words_count = len(sorteddict)\n words = []\n frequencies = []\n word_cloud = []\n for frequency, word in sorteddict:\n words.append(word)\n frequencies.append(frequency)\n word_cloud.append([word, frequency])\n\n acum_perc = Decimal(0)\n percentages = []\n acum_perc_list = []\n for freq in frequencies:\n perc = Decimal((freq*100.0)/word_count)\n percentages.append(round(perc, 2))\n acum_perc += perc\n acum_perc_list.append(round(acum_perc, 2))\n \n \n logarithms = [] \n for i in range(len(sorteddict)): \n logarithms.append((math.log(i+1), math.log(frequencies[i])))\n \n #Calculate Linear regression\n #http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html#numpy.linalg.lstsq\n x = numpy.array([math.log(f) for f in frequencies])\n y = numpy.array([math.log(rank) for rank in range(1, distinct_words_count + 1)])\n A = numpy.vstack([x, numpy.ones(len(x))]).T\n m, c = numpy.linalg.lstsq(A, y)[0]\n \n #Calculate the regression line start and end, \n # and sort making the start be the one with the lower X value\n # (highcharts requires this)\n regline_start = (0, c)\n regline_end = (math.log(distinct_words_count), math.log(distinct_words_count) * m + c)\n regression_line = {\n 'start': regline_start,\n 'end': regline_end\n }\n \n return jsonify(status='success', \n words=words,\n frequencies=frequencies,\n percentages=percentages,\n acum_perc_list=acum_perc_list,\n logarithms=logarithms,\n regression_line=regression_line,\n resulting_text=resulting_text,\n input_text_char_count=input_text_char_count,\n input_text_word_count=input_text_word_count,\n output_text_word_count=word_count,\n word_cloud=word_cloud,\n sorteddict=sorteddict)\n\nif __name__ == '__main__':\n app.run(debug=True)\n\n\n\n\n","repo_name":"TheResearchProject/textprocessing","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71391726483","text":"import os\n\nfrom linstor.linstorapi import MultiLinstor\n\ntry:\n from configparser import ConfigParser\nexcept ImportError:\n from ConfigParser import SafeConfigParser as ConfigParser\n\n\nclass Config(object):\n @staticmethod\n def read_config(config_file):\n cp = ConfigParser()\n cp.read(config_file)\n config = {}\n for section in cp.sections():\n config[section] = cp.items(section)\n return config\n\n @staticmethod\n def get_section(section, config_file_name=None):\n home_dir = os.path.expanduser(\"~\")\n config_file = \"linstor-client.conf\"\n user_conf = os.path.join(home_dir, \".config\", \"linstor\", config_file)\n sys_conf = os.path.join('/etc', 'linstor', config_file)\n\n config = None\n if config_file_name and os.path.exists(config_file_name):\n config = Config.read_config(config_file_name)\n elif os.path.exists(user_conf):\n config = Config.read_config(user_conf)\n elif os.path.exists(sys_conf):\n config = Config.read_config(sys_conf)\n\n entries = config.get(section, []) if config else []\n return {k: v for k, v in entries}\n\n @staticmethod\n def get_controllers(section='global', config_file_name=None, fallback='linstor://localhost'):\n \"\"\"\n :param str section: Section to parse, defaults to 'global'\n :param str config_file_name: Config file to parse, if not set, the default files in /etc and $HOME are\n parsed\n :param str fallback: Fallback controller if none was found in the environment or the config. Defaults\n to 'linstor://localhost'.\n :return: List of linstor uris. This list is intended to be used via the MultiLinstor class.\n :rtype: list[str]\n \"\"\"\n controllers = os.environ.get('LS_CONTROLLERS', None)\n if not controllers:\n cfg = Config.get_section(section, config_file_name)\n controllers = cfg.get('controllers', fallback)\n\n return MultiLinstor.controller_uri_list(controllers)\n","repo_name":"LINBIT/linstor-api-py","sub_path":"linstor/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2057,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"37179776462","text":"import pickle\nimport os\nimport torch\nimport numpy as np\nfrom collections import OrderedDict\n\n# import paddle.fluid as fluid\n\n\noptional_parameter = 'Model/UnifiedTransformer_0.predictor/FC_0.w_0'\n\n# def paddle2numpy(input_pd, output_np):\n# state_dict_numpy = {}\n# if hasattr(fluid, \"load_dygraph\"):\n# # >= 1.6.0 compatible\n# state_dict_paddle, optimizers = fluid.load_dygraph(input_pd)\n# else:\n# state_dict_paddle, optimizers = fluid.dygraph.load_persistables(input_pd)\n#\n# for name, param in state_dict_paddle.items():\n# if hasattr(param, \"numpy\"):\n# arr = param.numpy()\n# else:\n# value = param.value()\n# tensor = value.get_tensor()\n# arr = np.array(tensor)\n# state_dict_numpy[name] = arr\n#\n# with open(output_np, 'wb') as fo:\n# pickle.dump(state_dict_numpy, fo)\n\n\ndef get_match_value(name, state_dict_numpy):\n \"\"\"\n Need be overridden towards different models, here for UnifiedTransformer Model\n \"\"\"\n if name == 'mask_embed':\n return state_dict_numpy['Model/UnifiedTransformer_0.mask_embed']\n elif name == 'latent_embeddings':\n return state_dict_numpy['Model/UnifiedTransformer_0.latent_embeddings']\n elif name == 'embedder.token_embedding.weight':\n return state_dict_numpy['Model/UnifiedTransformer_0/Embedder_0/Embedding_0.w_0']\n elif name == 'embedder.pos_embedding.weight':\n return state_dict_numpy['Model/UnifiedTransformer_0/Embedder_0/Embedding_1.w_0']\n elif name == 'embedder.type_embedding.weight':\n return state_dict_numpy['Model/UnifiedTransformer_0/Embedder_0/Embedding_2.w_0']\n elif name == 'embedder.turn_embedding.weight':\n return state_dict_numpy['Model/UnifiedTransformer_0/Embedder_0/Embedding_3.w_0']\n elif name == 'embed_layer_norm.weight':\n return state_dict_numpy['Model/UnifiedTransformer_0/LayerNorm_0.w_0']\n elif name == 'embed_layer_norm.bias':\n return state_dict_numpy['Model/UnifiedTransformer_0/LayerNorm_0.b_0']\n elif name == 'post_network.weight':\n return state_dict_numpy['Model/UnifiedTransformer_0.post_network/FC_0.w_0'].T\n elif name == 'discriminator.0.weight':\n return state_dict_numpy['Model/UnifiedTransformer_0.discriminator/FC_0.w_0'].T\n elif name == 'discriminator.0.bias':\n return state_dict_numpy['Model/UnifiedTransformer_0.discriminator/FC_0.b_0']\n elif name == 'bow_predictor.weight':\n return state_dict_numpy['Model/UnifiedTransformer_0.bow_predictor/FC_0.w_0'].T\n else:\n num = name.split('.')[1]\n assert num in [str(i) for i in range(12)]\n if name == f'layers.{num}.attn.linear_qkv.weight':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/'\n f'MultiheadAttention_0/FC_0.w_0'].T\n elif name == f'layers.{num}.attn.linear_qkv.bias':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/'\n f'MultiheadAttention_0/FC_0.b_0']\n elif name == f'layers.{num}.attn.linear_out.weight':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/'\n f'MultiheadAttention_0/FC_1.w_0'].T\n elif name == f'layers.{num}.attn.linear_out.bias':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/'\n f'MultiheadAttention_0/FC_1.b_0']\n elif name == f'layers.{num}.attn_norm.weight':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/LayerNorm_0.w_0']\n elif name == f'layers.{num}.attn_norm.bias':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/LayerNorm_0.b_0']\n elif name == f'layers.{num}.ff.linear_hidden.0.weight':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/FeedForward_0/FC_0.w_0'].T\n elif name == f'layers.{num}.ff.linear_hidden.0.bias':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/FeedForward_0/FC_0.b_0']\n elif name == f'layers.{num}.ff.linear_out.weight':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/FeedForward_0/FC_1.w_0'].T\n elif name == f'layers.{num}.ff.linear_out.bias':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/FeedForward_0/FC_1.b_0']\n elif name == f'layers.{num}.ff_norm.weight':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/LayerNorm_1.w_0']\n elif name == f'layers.{num}.ff_norm.bias':\n return state_dict_numpy[f'Model/UnifiedTransformer_0/TransformerBlock_{num}/LayerNorm_1.b_0']\n else:\n raise ValueError('No matched name in state_dict_numpy!')\n\n\ndef numpy2pytorch(input_np, input_pt, output_pt):\n with open(input_np, 'rb') as fi:\n state_dict_numpy = pickle.load(fi)\n\n state_dict_pytorch = OrderedDict()\n state_dict_init_pytorch = torch.load(input_pt, map_location=lambda storage, loc: storage)\n for name, value in state_dict_init_pytorch.items():\n match_value = get_match_value(name, state_dict_numpy)\n assert match_value.shape == value.numpy().shape\n assert match_value.dtype == value.numpy().dtype\n dtype = value.dtype\n device = value.device\n state_dict_pytorch[name] = torch.tensor(match_value, dtype=dtype, device=device)\n\n torch.save(state_dict_pytorch, output_pt)\n\n\nif __name__ == '__main__':\n input_paddle = '../model/PLATO'\n output_numpy = '../model/PLATO.np'\n input_numpy = output_numpy\n input_pytorch = '../model/PLATO_INIT.pt'\n output_pytorch = '../model/PLATO.pt'\n\n # 1. convert paddle state_dict to numpy state_dict\n # if not os.path.isfile(input_numpy):\n # place = fluid.CPUPlace()\n # with fluid.dygraph.guard(place):\n # paddle2numpy(input_pd=input_paddle, output_np=output_numpy)\n\n # 2. convert numpy state_dict to pytorch state_dict\n numpy2pytorch(input_np=input_numpy, input_pt=input_pytorch, output_pt=output_pytorch)\n","repo_name":"HwwAncient/Pytorch-PLATO","sub_path":"tools/paddle2pytorch.py","file_name":"paddle2pytorch.py","file_ext":"py","file_size_in_byte":6265,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"3"}
+{"seq_id":"71847101520","text":"import base64\r\nfrom urllib.parse import urljoin\r\n\r\nfrom core.validators import ingredients_validator, tags_validator\r\nfrom django.core.files.base import ContentFile\r\nfrom django.db.models import F\r\nfrom djoser.serializers import UserSerializer\r\nfrom rest_framework import serializers\r\n\r\nfrom recipes.models import (Favorite, Ingredient, IngredientinRecipe, Recipe,\r\n Shopping, Tag)\r\nfrom users.models import Follow, User\r\n\r\n\r\nclass Base64ImageField(serializers.ImageField):\r\n\r\n def to_internal_value(self, data):\r\n if isinstance(data, str) and data.startswith('data:image'):\r\n file_format, img_code = data.split(';base64,')\r\n ext = file_format.split('/')[-1]\r\n data = ContentFile(base64.b64decode(img_code), name='temp.' + ext)\r\n return super().to_internal_value(data)\r\n\r\n\r\nclass RecipeMiniSerializer(serializers.ModelSerializer):\r\n\r\n class Meta:\r\n model = Recipe\r\n fields = ('id', 'name', 'image', 'cooking_time')\r\n read_only_fields = ('__all__',)\r\n\r\n\r\nclass TagSerializer(serializers.ModelSerializer):\r\n\r\n class Meta:\r\n model = Tag\r\n fields = '__all__'\r\n read_only_fields = ('__all__',)\r\n\r\n\r\nclass IngredientSerializer(serializers.ModelSerializer):\r\n\r\n class Meta:\r\n model = Ingredient\r\n fields = '__all__'\r\n read_only_fields = ('__all__',)\r\n\r\n\r\nclass CustomUserSerializer(UserSerializer):\r\n is_subscribed = serializers.SerializerMethodField(read_only=True)\r\n\r\n def get_is_subscribed(self, obj):\r\n user = self.context.get('request').user\r\n if user.is_authenticated:\r\n return Follow.objects.filter(user=user, following=obj).exists()\r\n return False\r\n\r\n def create(self, validated_data):\r\n user = User(\r\n email=validated_data[\"email\"],\r\n username=validated_data[\"username\"],\r\n first_name=validated_data[\"first_name\"],\r\n last_name=validated_data[\"last_name\"],\r\n )\r\n user.set_password(validated_data[\"password\"])\r\n user.save()\r\n return user\r\n\r\n class Meta:\r\n model = User\r\n fields = (\r\n 'email', 'id', 'username', 'first_name',\r\n 'last_name', 'is_subscribed')\r\n read_only_fields = ('__all__',)\r\n\r\n\r\nclass UserSubscription(CustomUserSerializer):\r\n recipes = serializers.SerializerMethodField()\r\n recipes_count = serializers.SerializerMethodField()\r\n\r\n def get_recipes(self, obj):\r\n request = self.context.get('request')\r\n recipes = obj.recipes.values(\r\n 'id', 'name', 'image', 'cooking_time'\r\n )\r\n for recipe in recipes:\r\n image_url = urljoin(\r\n request.build_absolute_uri('/media/'), recipe['image'])\r\n recipe['image'] = image_url\r\n return recipes\r\n\r\n def get_recipes_count(self, obj):\r\n return obj.recipes.count()\r\n\r\n class Meta:\r\n model = User\r\n fields = (\r\n 'email', 'id', 'username', 'first_name',\r\n 'last_name', 'is_subscribed', 'recipes', 'recipes_count')\r\n read_only_fields = ('__all__',)\r\n\r\n\r\nclass RecipeSerializer(serializers.ModelSerializer):\r\n is_favorited = serializers.SerializerMethodField()\r\n is_in_shopping_cart = serializers.SerializerMethodField()\r\n tags = TagSerializer(read_only=True, many=True)\r\n author = CustomUserSerializer(read_only=True)\r\n ingredients = serializers.SerializerMethodField()\r\n image = Base64ImageField()\r\n\r\n def get_is_favorited(self, obj):\r\n user = self.context.get('request').user\r\n if user.is_authenticated:\r\n return Favorite.objects.filter(\r\n user=user, favorite_recipe=obj).exists()\r\n return False\r\n\r\n def get_is_in_shopping_cart(self, obj):\r\n user = self.context.get('request').user\r\n if user.is_authenticated:\r\n return Shopping.objects.filter(\r\n user=user, recipe=obj).exists()\r\n return False\r\n\r\n def get_ingredients(self, recipe):\r\n ingredients = recipe.ingredients.values(\r\n \"id\", \"name\", \"measurement_unit\",\r\n amount=F(\"recipe_with_ingredient__amount\")\r\n )\r\n return ingredients\r\n\r\n def validate(self, data):\r\n method = self.context['request'].method\r\n tags = self.initial_data.get(\"tags\")\r\n ingredients = self.initial_data.get(\"ingredients\")\r\n\r\n if method == \"POST\":\r\n tags = tags_validator(tags, Tag)\r\n ingredients = ingredients_validator(ingredients, Ingredient)\r\n\r\n data.update(\r\n {\r\n \"tags\": tags,\r\n \"ingredients\": ingredients,\r\n \"author\": self.context.get(\"request\").user,\r\n }\r\n )\r\n return data\r\n\r\n def create_ingredients(self, ingredients, recipe):\r\n for ing in ingredients:\r\n ingredient = Ingredient.objects.get(id=ing['id'])\r\n IngredientinRecipe.objects.get_or_create(\r\n ingredient=ingredient, recipe=recipe, amount=ing['amount']\r\n )\r\n\r\n def create(self, validated_data):\r\n tags = validated_data.pop(\"tags\")\r\n ingredients = validated_data.pop(\"ingredients\")\r\n recipe = Recipe.objects.create(**validated_data)\r\n recipe.tags.set(tags)\r\n self.create_ingredients(ingredients, recipe)\r\n return recipe\r\n\r\n def update(self, instance, validated_data):\r\n instance.name = validated_data.get('name', instance.name)\r\n instance.cooking_time = validated_data.get(\r\n 'cooking_time', instance.cooking_time)\r\n instance.text = validated_data.get('text', instance.text)\r\n instance.image = validated_data.get('image', instance.image)\r\n if isinstance(validated_data['tags'], list):\r\n tags = validated_data.pop('tags')\r\n instance.tags.clear()\r\n instance.tags.set(tags)\r\n if isinstance(validated_data['ingredients'], list):\r\n ingredients = validated_data.pop('ingredients')\r\n instance.ingredients.clear()\r\n self.create_ingredients(ingredients, instance)\r\n instance.save()\r\n return instance\r\n\r\n class Meta:\r\n model = Recipe\r\n fields = '__all__'\r\n read_only_fields = (\r\n 'author', 'pub_date', 'tags', 'is_favorited',\r\n 'is_in_shopping_cart',)\r\n\r\n\r\nclass ShoppingSerializer(serializers.ModelSerializer):\r\n\r\n class Meta:\r\n model = Shopping\r\n fields = '__all__'\r\n read_only_fields = ('__all__',)\r\n\r\n\r\nclass FavoriteSerializer(serializers.ModelSerializer):\r\n\r\n class Meta:\r\n model = Favorite\r\n fields = '__all__'\r\n read_only_fields = ('__all__',)\r\n","repo_name":"Mariooooo37/foodgram-project-react","sub_path":"backend/api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":6777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71869629202","text":"import supervisely as sly\nimport os\nfrom collections import defaultdict\nfrom dataset_tools.convert import unpack_if_archive\nimport src.settings as s\nfrom urllib.parse import unquote, urlparse\nfrom supervisely.io.fs import get_file_name, get_file_size\nfrom supervisely.io.json import load_json_file\nimport shutil\n\nfrom tqdm import tqdm\n\n\ndef download_dataset(teamfiles_dir: str) -> str:\n \"\"\"Use it for large datasets to convert them on the instance\"\"\"\n api = sly.Api.from_env()\n team_id = sly.env.team_id()\n storage_dir = sly.app.get_data_dir()\n\n if isinstance(s.DOWNLOAD_ORIGINAL_URL, str):\n parsed_url = urlparse(s.DOWNLOAD_ORIGINAL_URL)\n file_name_with_ext = os.path.basename(parsed_url.path)\n file_name_with_ext = unquote(file_name_with_ext)\n\n sly.logger.info(f\"Start unpacking archive '{file_name_with_ext}'...\")\n local_path = os.path.join(storage_dir, file_name_with_ext)\n teamfiles_path = os.path.join(teamfiles_dir, file_name_with_ext)\n\n fsize = api.file.get_directory_size(team_id, teamfiles_dir)\n with tqdm(\n desc=f\"Downloading '{file_name_with_ext}' to buffer...\",\n total=fsize,\n unit=\"B\",\n unit_scale=True,\n ) as pbar:\n api.file.download(team_id, teamfiles_path, local_path, progress_cb=pbar)\n dataset_path = unpack_if_archive(local_path)\n\n if isinstance(s.DOWNLOAD_ORIGINAL_URL, dict):\n for file_name_with_ext, url in s.DOWNLOAD_ORIGINAL_URL.items():\n local_path = os.path.join(storage_dir, file_name_with_ext)\n teamfiles_path = os.path.join(teamfiles_dir, file_name_with_ext)\n\n if not os.path.exists(get_file_name(local_path)):\n fsize = api.file.get_directory_size(team_id, teamfiles_dir)\n with tqdm(\n desc=f\"Downloading '{file_name_with_ext}' to buffer...\",\n total=fsize,\n unit=\"B\",\n unit_scale=True,\n ) as pbar:\n api.file.download(\n team_id, teamfiles_path, local_path, progress_cb=pbar\n )\n\n sly.logger.info(f\"Start unpacking archive '{file_name_with_ext}'...\")\n unpack_if_archive(local_path)\n else:\n sly.logger.info(\n f\"Archive '{file_name_with_ext}' was already unpacked to '{os.path.join(storage_dir, get_file_name(file_name_with_ext))}'. Skipping...\"\n )\n\n dataset_path = storage_dir\n return dataset_path\n\n\ndef count_files(path, extension):\n count = 0\n for root, dirs, files in os.walk(path):\n for file in files:\n if file.endswith(extension):\n count += 1\n return count\n\n\ndef convert_and_upload_supervisely_project(\n api: sly.Api, workspace_id: int, project_name: str\n) -> sly.ProjectInfo:\n ### Function should read local dataset and upload it to Supervisely project, then return project info.###\n images_path = os.path.join(\"car-segmentation\", \"images\")\n anns_path = os.path.join(\"car-segmentation\", \"masks.json\")\n batch_size = 10\n ds_name = \"ds\"\n\n def create_ann(image_path):\n labels = []\n\n image_name = get_file_name(image_path)\n img_height = name_to_shape[image_name][\"height\"]\n img_wight = name_to_shape[image_name][\"width\"]\n\n curr_image_data = name_to_data[image_name]\n for info in curr_image_data:\n obj_class = meta.get_obj_class(info[0])\n exterior = []\n for coords in info[2]:\n exterior.append([coords[\"y\"], coords[\"x\"]])\n poligon = sly.Polygon(exterior)\n label_poly = sly.Label(poligon, obj_class)\n labels.append(label_poly)\n\n left = info[1][\"left\"]\n right = left + info[1][\"width\"]\n top = info[1][\"top\"]\n bottom = top + info[1][\"height\"]\n rectangle = sly.Rectangle(top=top, left=left, bottom=bottom, right=right)\n label = sly.Label(rectangle, obj_class)\n labels.append(label)\n\n return sly.Annotation(img_size=(img_height, img_wight), labels=labels)\n\n obj_class_car = sly.ObjClass(\"car\", sly.AnyGeometry, color=(0, 255, 255))\n obj_class_wheel = sly.ObjClass(\"wheel\", sly.AnyGeometry, color=(128, 0, 0))\n obj_class_lights = sly.ObjClass(\"lights\", sly.AnyGeometry, color=(255, 255, 0))\n obj_class_window = sly.ObjClass(\"window\", sly.AnyGeometry, color=(128, 128, 0))\n\n project = api.project.create(\n workspace_id, project_name, change_name_if_conflict=True\n )\n meta = sly.ProjectMeta(\n obj_classes=[obj_class_car, obj_class_wheel, obj_class_lights, obj_class_window]\n )\n api.project.update_meta(project.id, meta.to_json())\n\n ann_data = load_json_file(anns_path)[\"assets\"]\n\n name_to_data = defaultdict(list)\n name_to_shape = {}\n\n for _, image_data in ann_data.items():\n im_name = get_file_name(image_data[\"asset\"][\"name\"])\n im_shape = image_data[\"asset\"][\"size\"]\n name_to_shape[im_name] = im_shape\n regions = image_data[\"regions\"]\n for region in regions:\n name_to_data[im_name].append(\n [region[\"tags\"][0], region[\"boundingBox\"], region[\"points\"]]\n )\n\n images_names = os.listdir(images_path)\n\n dataset = api.dataset.create(project.id, ds_name, change_name_if_conflict=True)\n\n progress = sly.Progress(\"Create dataset {}\".format(ds_name), len(images_names))\n\n for img_names_batch in sly.batched(images_names, batch_size=batch_size):\n images_pathes_batch = [\n os.path.join(images_path, image_name) for image_name in img_names_batch\n ]\n\n img_infos = api.image.upload_paths(\n dataset.id, img_names_batch, images_pathes_batch\n )\n img_ids = [im_info.id for im_info in img_infos]\n\n anns_batch = [create_ann(image_path) for image_path in images_pathes_batch]\n api.annotation.upload_anns(img_ids, anns_batch)\n\n progress.iters_done_report(len(img_names_batch))\n\n return project\n","repo_name":"dataset-ninja/semantics-car-segmentation","sub_path":"src/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":6143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40075354334","text":"import math\n\ndef getNumberOfDivisors(n):\n divisorsCount = 0\n div = list(range(1, int(math.ceil(math.sqrt(n) + 1))))\n\n for i in div:\n if n%i == 0:\n result = n / i\n if i == result:\n divisorsCount = divisorsCount + 1\n else:\n divisorsCount = divisorsCount + 2\n\n\n return divisorsCount\n\n","repo_name":"ZofiaDudziuk/ProjectEuler","sub_path":"lib/divisors.py","file_name":"divisors.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33377359330","text":"from pessoa import Pessoa\n\np1 = Pessoa('Luccas', 18)\np2 = Pessoa('Roberval', 57)\n\np1.comer('carne')\np1.falar('emprego')\np2.falar('emprego')\np2.comer('mamão')\np2.falar('escola')\np2.parar_falar()\np1.get_ano_nascimento()\np2.get_ano_nascimento()\n\n# class method\np3 = Pessoa.por_ano_nascimento('Claudia', 1975)\nprint(p3.nome, p3.idade)\n\nprint(p1.gera_id())\nprint(p2.gera_id())\nprint(p3.gera_id())\n","repo_name":"luccashiroshi/Curso-Python","sub_path":"orientacao a objetos/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"17294505693","text":"# https://leetcode.com/problems/add-two-numbers/\n\n# Definition for singly-linked list.\n# class ListNode(object):\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution(object):\n def addTwoNumbers(self, l1, l2):\n ans = ListNode()\n node = ans\n surplus = 0\n while not(l1 == None and l2 == None and surplus == 0):\n node.next = ListNode()\n node = node.next\n a, b = 0, 0\n if l1 != None:\n a = l1.val\n l1 = l1.next\n if l2 != None:\n b = l2.val\n l2 = l2.next\n s = a + b + surplus\n if s >= 10:\n node.val = s - 10\n surplus = 1\n else:\n node.val = s\n surplus = 0\n \n return ans.next","repo_name":"eldor-galiev/LeetcodeTasks","sub_path":"Linked List/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35781681174","text":"import tailer\nimport requests\nfrom telegram.ext import Updater, CommandHandler, Filters\nimport csv\nfrom threading import Thread\nimport glob\n\nupdater = Updater(token='')\ndispatcher = updater.dispatcher\njob = updater.job_queue\n\ndef start(bot, update):\n bot.send_message(chat_id=update['message']['chat']['id'], text=\n \"\"\"*Commands*\n say - \n sayall - \n \"\"\", parse_mode='MARKDOWN')\n\ndef say(bot, update, args):\n message = ' '.join(str(x) for x in args[1:])\n if ';' in message:\n bot.send_message(chat_id=update['message']['chat']['id'], text='No command injection for u faggot')\n else:\n user_name = update['message']['chat']['username']\n requests.post('http://localhost:8081/api/direct/exec',\n json={\n 'target': args[0],\n 'cmd': 'say \"[RELAY]' + user_name + \": \" + message + '\"'\n })\n print(user_name + ' sent \"' + message + '\" to catbot ' + args[0])\n bot.send_message(chat_id=update['message']['chat']['id'], text='message sent!')\n bot.send_message(chat_id=-1001203927071, text='@' + user_name + ' sent: \"' + message.replace('\"', \"'\") + '\", to catbot' + args[0])\n\ndef sayall(bot, update, args):\n message = ' '.join(str(x) for x in args)\n if ';' in message:\n bot.send_message(chat_id=update['message']['chat']['id'], text='No command injection for u faggot')\n else:\n user_name = update['message']['chat']['username']\n requests.post('http://localhost:8081/api/direct/exec_all',\n json={\n 'cmd': 'say \"[RELAY]' + user_name + \": \" + message + '\"'\n })\n print(user_name + ' sent \"' + message + '\" to all catbots')\n bot.send_message(chat_id=update['message']['chat']['id'], text='message sent!')\n bot.send_message(chat_id=-1001203927071, text='@' + user_name + ' sent: \"' + message.replace('\"', \"'\") + '\", to all catbots')\n\nmessage_queue = []\ndef tail(bot, job, file):\n print('tailing ' + file)\n while True:\n try:\n for line in tailer.follow(open(file)):\n out = csv.reader(line, delimiter=',')\n dict_message = []\n for row in out:\n dict_message.append(','.join(row).strip(','))\n new_id = int(dict_message[2]) + 76561197960265728\n cleaned_name = dict_message[4].replace('[', '').replace(']', '')\n md = \"[ %n ](https://steamcommunity.com/profiles/%i): %m\"\n new = md.replace('%n', cleaned_name).replace('%m', dict_message[6]).replace('%i', str(new_id))\n print(new)\n message_obj = {\n 'ipc_id': dict_message[-1],\n 'text': new\n }\n message_queue.append(message_obj)\n except Exception as e:\n print(e)\n\ndef relay(bot ,job):\n logs = glob.glob('/opt/cathook/data/*.csv')\n for file in logs:\n if file:\n Thread(target=tail, args=(bot, job, file)).start()\n\ndef message_send(bot, job):\n if message_queue[:]:\n filter_list = []\n for message in message_queue:\n filter_list.append(message['text'])\n a, seen, result = filter_list, set(), []\n for idx, item in enumerate(a):\n if item not in seen:\n seen.add(item)\n else:\n result.append(idx)\n for indx in result:\n print('Removing duplicate at queue pos:' + str(indx))\n message_queue.pop(indx)\n del filter_list[:]\n print(\"Sending Messages!\")\n try:\n for message_obj in message_queue:\n message = \"*cat-bot \" + str(message_obj['ipc_id']) + \"* \\n\" + str(message_obj['text'])\n try:\n bot.send_message(chat_id='-1001203927071', text=message, parse_mode='MARKDOWN', disable_web_page_preview=True, disable_notification=True)\n except Exception as e:\n break\n del message_queue[:]\n except Exception as e:\n print(e)\n finally:\n print(\"Messages Sent!\")\n\ndispatcher.add_handler(CommandHandler(\"start\", start))\ndispatcher.add_handler(CommandHandler(\"say\", say, pass_args=True))\ndispatcher.add_handler(CommandHandler(\"sayall\", sayall, pass_args=True))\nupdater.start_polling()\njob.run_once(relay, 1)\njob.run_repeating(message_send, interval=10, first=0)\nupdater.idle()\n","repo_name":"cheddar-cheeze/catbot-chat-relay","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4542,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28398831076","text":"import sys\nfrom settings import SCALES\nfrom notes import degree_to_note\n\n\ndef tonic_to_scale(\n tonic: str,\n scale: str\n ) -> list:\n \n # find the list of degrees for the given scale\n degrees = SCALES[scale]\n\n notes = [degree_to_note(starting_note=tonic, degree=degree) for degree in degrees]\n \n return notes\n\n\nif __name__ == '__main__':\n print(\n tonic_to_scale(sys.argv[1], sys.argv[2])\n )\n","repo_name":"adrijan-parmakovski/music_python","sub_path":"theory/functions/scales.py","file_name":"scales.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15026190004","text":"#!/usr/bin/python\nusage = \"whiten.py data.pkl\"\ndescription = \"whitens data and writes into a new output file (name based on the old one)\"\nauthor = \"reed.essick@ligo.org\"\n\n#-------------------------------------------------\n\n### custom packages\nimport parseData\nimport whitenData\n\n### standard packages\nfrom optparse import OptionParser\n\n#-------------------------------------------------\n\nparser = OptionParser(usage=usage, description=description)\n\nparser.add_option('-v', '--verbose', default=False, action='store_true')\nparser.add_option('-t', '--time', default=False, action='store_true')\n\nparser.add_option('-m', '--method', default='zeroMean', type='string', help='\"zeroMean\" or \"minMax\"')\nparser.add_option('-e', '--skip', default=[], action='append', type='string', help='do NOT whiten these cols')\n\nopts, args = parser.parse_args()\n\nassert len(args)==1, 'please supply exactly 1 input argument\\n%s'%usage\npklNAME = args[0]\n\n#-------------------------------------------------\n\n### read data\nh, d, kwargs = parseData.load_data( pklNAME, verbose=opts.verbose, timing=opts.time )\n\n### whiten data\nif opts.method == \"zeroMean\":\n d = whitenData.zeroMean_whiten( h, d, skip=opts.skip )\nelif opts.method == \"minMax\":\n d = whitenData.minMax_whiten( h, d, skip=opts.skip )\nelse:\n raise ValueError('--method=%s not understood'%opts.method)\n\n### write data\npklOUT = pklNAME[:-4]+\"_%s.pkl\"%(opts.method)\nparseData.write_data( h, d, pklOUT, verbose=opts.verbose, timing=opts.time, whiten=True, **kwargs)\n","repo_name":"reedessick/NBA-Hackathon","sub_path":"day-of/bin/whiten.py","file_name":"whiten.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32749486347","text":"from django.shortcuts import render\nfrom rest_framework.response import Response\nfrom rest_framework import viewsets\nfrom .serializers import ProductSerializer\nfrom .models import Product, Category\nfrom rest_framework import status\n\n\n# Create your views here.\nclass ProductListViewSet(viewsets.ModelViewSet):\n queryset = Product.objects.all()\n serializer_class = ProductSerializer\n\n def list(self, request, *args, **kwargs):\n \"\"\"List product instances.\n\n Args:\n self: The instance of the view class.\n request: The HTTP request object containing request parameters.\n *args: Variable-length positional arguments.\n **kwargs: Variable-length keyword arguments.\n\n Returns:\n Response: An HTTP response containing a list of serialized product instances.\n\n This method is used to retrieve a list of product instances and serialize them using the\n ProductSerializer. The serialized data is then returned in an HTTP response.\n\n Note:\n - Ensure that the appropriate HTTP method (e.g., GET) is used for retrieving the list of products.\n - The 'request' parameter may contain query parameters or other information for filtering or\n customizing the list of products.\n - This function is typically used as part of a Django REST framework API view for listing products.\n \"\"\"\n serializer = ProductSerializer(self.queryset, many=True)\n return Response(serializer.data)\n\n def create(self, request, *args, **kwargs):\n \"\"\"Create a new Product.\n\n Args:\n request (Request): The HTTP request object containing the data for creating the Product.\n *args: Variable-length positional arguments.\n **kwargs: Variable-length keyword arguments.\n\n Returns:\n Response: A response containing the created Product data or error messages.\n\n This method is used to delete a product instance. First, it checks if the user is authenticated.\n If the user is not authenticated, a 401 Unauthorized response is returned, indicating that\n login is required.\n\n This function is responsible for creating a new Product based on the data provided in the request. It performs the following steps:\n\n 1. Deserialize the request data using the ProductSerializer.\n 2. Check if the 'category' field is provided in the request data.\n 3. If 'category' is provided, it attempts to find the corresponding Category object in the database.\n 4. If the Category object does not exist, a 400 Bad Request response is returned.\n 5. If the serializer is valid and the Category object exists, the Product is created with the current user as the 'by' field.\n 6. The function returns a response with the created Product data if successful, or validation errors if there are any issues.\n\n Raises:\n HTTP_401_UNAUTHORIZED: If the user is not AUTHORIZED.\n HTTP_400_BAD_REQUEST: If there are validation errors or the 'category' does not exist in the database.\n HTTP_201_CREATED: If the Product is successfully created.\n\n\n Example:\n ```\n POST /api/products/\n {\n \"name\": \"Product Name\",\n \"description\": \"Product description.\",\n \"category\": \"Category Name\",\n \"mrp\": 100,\n \"discount\": 10\n }\n\n Returns:\n\n HTTP 201 Created\n {\n \"id\": 1,\n \"name\": \"Product Name\",\n \"description\": \"Product description.\",\n \"category\": \"Category Name\",\n \"mrp\": 100,\n \"discount\": 10\n }\n \"\"\"\n if not request.user.is_authenticated:\n return Response(\n {\"Login\": \"Login required.\"},\n status=status.HTTP_401_UNAUTHORIZED,\n )\n\n serializer = ProductSerializer(data=request.data)\n\n category = request.data.get(\"category\")\n if category is not None:\n try:\n category = Category.objects.get(name=category)\n except Category.DoesNotExist:\n return Response(\n {\"category\": \"Category does not exist in the database.\"},\n status=status.HTTP_400_BAD_REQUEST,\n )\n\n if serializer.is_valid():\n serializer.save(by=request.user, category=category)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n def update(self, request, *args, **kwargs):\n \"\"\"Update an existing product instance.\n\n Args:\n self: The instance of the view class.\n request: The HTTP request object containing the updated data.\n *args: Variable-length positional arguments.\n **kwargs: Variable-length keyword arguments.\n\n Returns:\n Response: An HTTP response containing the updated product data or error messages.\n\n This method is used to delete a product instance. First, it checks if the user is authenticated.\n If the user is not authenticated, a 401 Unauthorized response is returned, indicating that\n login is required.\n\n This method is used to update an existing product instance with the data provided in the\n HTTP request. It first retrieves the instance to be updated, and if a 'category' field is\n included in the request data, it checks if the specified category exists in the database.\n If the 'category' is provided and doesn't exist, an error response is returned.\n\n Then, the request data is deserialized using the ProductSerializer, and if the data is valid,\n the product instance is updated with the new data, and the updated product data is returned\n in the response. If there are validation errors, an error response with the validation errors\n is returned.\n\n Note:\n - Make sure to use the appropriate HTTP method (e.g., PUT or PATCH) for updating the product.\n - The 'request' parameter should contain the data for the product update.\n - This function is typically used as part of a Django REST framework API view for product updates.\n\n\n Raises:\n HTTP_401_UNAUTHORIZED: If the user is not AUTHORIZED.\n HTTP_400_BAD_REQUEST: If there are validation errors or the 'category' does not exist in the database.\n HTTP_201_CREATED: If the Product is successfully created.\n\n\n Example:\n ```\n PUT /api/products/id\n {\n \"id\": 1\n \"name\": \"Product Name\",\n \"description\": \"Product description.\",\n \"category\": \"Category Name\",\n \"mrp\": 100,\n \"discount\": 10\n }\n\n Returns:\n\n HTTP 201 Created\n {\n \"id\": 1,\n \"by: \"user\"\n \"name\": \"Product Name\",\n \"description\": \"Product description.\",\n \"category\": \"Category Name\",\n \"mrp\": 100,\n \"discount\": 10\n }\n\n \"\"\"\n if not request.user.is_authenticated:\n print(\"\\n\\nYes\\n\\n\")\n return Response(\n {\"Login\": \"Login required.\"},\n status=status.HTTP_401_UNAUTHORIZED,\n )\n\n instance = self.get_object()\n category = request.data.get(\"category\")\n\n if category is not None:\n try:\n category = Category.objects.get(name=category)\n except Category.DoesNotExist:\n return Response(\n {\"category\": \"Category does not exist in the database.\"},\n status=status.HTTP_400_BAD_REQUEST,\n )\n\n serializer = ProductSerializer(instance, data=request.data)\n if serializer.is_valid():\n serializer.save(category=category)\n return Response(serializer.data)\n else:\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n def destroy(self, request, *args, **kwargs):\n \"\"\"Delete a product instance.\n\n Args:\n self: The instance of the view class.\n request: The HTTP request object containing the request information.\n *args: Variable-length positional arguments.\n **kwargs: Variable-length keyword arguments.\n\n Returns:\n Response: An HTTP response indicating the result of the delete operation.\n\n This method is used to delete a product instance. First, it checks if the user is authenticated.\n If the user is not authenticated, a 401 Unauthorized response is returned, indicating that\n login is required.\n\n If the user is authenticated, the product instance is retrieved using the 'get_object' method\n (presumably from a Django REST framework viewset), and then it is deleted. A 204 No Content\n response is returned to indicate a successful deletion with no content in the response body.\n\n Note:\n - Ensure that the appropriate HTTP method (e.g., DELETE) is used for deleting the product.\n - This function is typically used as part of a Django REST framework API view for deleting products.\n - Make sure to use proper authentication mechanisms before accessing this endpoint.\n \"\"\"\n if not request.user.is_authenticated:\n return Response(\n {\"Login\": \"Login required.\"},\n status=status.HTTP_401_UNAUTHORIZED,\n )\n # Custom logic to delete a Product\n instance = self.get_object()\n if not instance:\n pass\n\n instance.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n","repo_name":"Anshul-Dishoriya/byte-quest","sub_path":"base/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30319028227","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef WF_sim(start_al_freq, pop_size):\n # Wright-Fisher simulation of allele frequencies\n # Input: starting allele frequency, population size\n # end simulation once allele frequency reaches 0 or 1\n # Output: list of allele frequencies\n \n al_freq = start_al_freq\n sel_strength = 0\n al_freq_list = []\n \n al_freq_list.append(al_freq)\n \n while (al_freq < 1) and (al_freq > 0):\n num_al = np.random.binomial(2*pop_size, al_freq)\n al_freq = (num_al*(1+sel_strength))/((2*pop_size) - num_al + (num_al*(1+sel_strength)))\n al_freq_list.append(al_freq)\n \n return al_freq_list\n\n\n\n# plot single simulation\n# pop_size = 100\n# start_al_frq = 0.1\n# WF_sim = WF_sim(start_al_frq, pop_size)\n# fig, ax = plt.subplots()\n# ax.plot(WF_sim)\n# ax.set_xlabel(f\"Generations\\nPopulation Size: {pop_size}\\nStarting Allele Freq.: {start_al_frq}\")\n# ax.set_ylabel(\"Allele Frequency\")\n# ax.set_title(\"Allele Frequency over generations\")\n# plt.tight_layout()\n# plt.savefig(\"2.png\")\n# plt.close()\n\ndef run_sim(start_al_freq, pop_size, n_iters):\n # running the Wright-Fisher simulatio n_iters times\n # output: list of fixation times\n fix_times = []\n while n_iters > 0:\n freq_list = WF_sim(start_al_freq, pop_size)\n fix_time = len(freq_list)\n fix_times.append(fix_time)\n n_iters -= 1\n return fix_times\n \n# 3 - time to fixation\n# pop_size = 100\n# start_al_frq = 0.5\n# n_iters = 1000\n# fix_times = run_sim(start_al_frq, pop_size, n_iters)\n# fig, ax = plt.subplots()\n# ax.hist(fix_times, density=True)\n# ax.set_xlabel(f\"Time to Fixation (Generations)\\nPopulation Size: {pop_size}\\nStarting Allele Freq.: {start_al_frq}\\nRuns: {n_iters}\")\n# ax.set_title(\"Time to fixation\")\n# plt.tight_layout()\n# plt.savefig(\"3.png\")\n# plt.close()\n\n# 4 - plot fixation time vs. N (population)\n# pop_size = 200\n# start_al_frq = 0.5\n# pop_sizes = []\n# fix_times = []\n# for i in range(6):\n# # run the simulation using six different population sizes\n# pop_sizes.append(pop_size)\n# # use the median fix time from several iterations\n# fix_time = len(WF_sim(start_al_frq, pop_size))\n# fix_times.append(fix_time)\n# pop_size = pop_size*2\n#\n# fig, ax = plt.subplots()\n# ax.plot(pop_sizes, fix_times)\n# ax.set_xlabel(f\"Population Size (N)\\nStarting Allele Freq.: {start_al_frq}\")\n# ax.set_ylabel(\"Fixation Time\")\n# ax.set_title(\"Effect of population on fixation time\")\n# ax.set_xscale(\"log\")\n# ax.set_xticks(pop_sizes)\n# ax.set_xticklabels(pop_sizes)\n# plt.tight_layout()\n# plt.savefig(\"4.png\")\n# plt.close()\n\n# 5 - simulation under different allele frequencies with same population size\npop_size = 200\nal_freq = 0.025\nn_iters = 100\nfix_times = []\nal_freqs = []\nfor i in range(10):\n # run the simulation using ten different allele frequencies\n al_freqs.append(al_freq)\n # run_sim and save to fix_times list\n fix_time = run_sim(al_freq, pop_size, n_iters)\n fix_times.append(fix_time)\n al_freq = round(al_freq + 0.1, 3)\n \n\nfig, ax = plt.subplots()\nax.boxplot(fix_times)\nax.set_xlabel(f\"Starting Allele Frequency\\nPopulation Size: {pop_size}\\nRuns for each freq.: {n_iters}\")\nax.set_ylabel(\"Fixation Time\")\nax.set_title(\"Effect of allele frequency on fixation time\")\nax.set_xticklabels(al_freqs)\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.savefig(\"5.png\")\nplt.close()","repo_name":"pelizal1/qbb2022-answers","sub_path":"week13/week13.py","file_name":"week13.py","file_ext":"py","file_size_in_byte":3429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24537171898","text":"import unittest\n\nimport torch\n\nfrom autocare_dlt.core.model.detector import RetinaNet\n\n\nclass TestRetinaNet(unittest.TestCase):\n def setUp(self):\n self.dummy_model_cfg = {\n \"backbone\": {\"name\": \"resnet18\", \"out_indices\": [2, 3, 4]},\n \"neck\": {\n \"name\": \"FeaturePyramidNetwork\",\n \"in_channels\": [128, 256, 512],\n \"out_channels\": 256,\n },\n \"head\": {\n \"name\": \"RetinaNetHead\",\n \"in_channels\": 256,\n \"aspect_ratio\": [0.5, 1.0, 2.0],\n \"anchor_size\": [32, 64, 128, 256, 512],\n \"topk_candidates\": 1000,\n \"num_classes\": 10,\n },\n }\n self.dummy_img = torch.rand(4, 3, 512, 512)\n self.dummy_targets = [\n {\"boxes\": torch.rand(3, 4), \"labels\": torch.tensor([3, 6, 1])},\n {\"boxes\": torch.rand(1, 4), \"labels\": torch.tensor([0])},\n {\"boxes\": torch.rand(1, 4), \"labels\": torch.tensor([2])},\n {\"boxes\": torch.rand(1, 4), \"labels\": torch.tensor([0])},\n ]\n\n def tearDown(self):\n pass\n\n def test_build_RetinaNet(self):\n from autocare_dlt.core.model.head import RetinaNetHead\n from autocare_dlt.core.model.neck import FeaturePyramidNetwork\n\n detector = RetinaNet(model_cfg=self.dummy_model_cfg)\n self.assertIsInstance(detector.neck, FeaturePyramidNetwork)\n self.assertIsInstance(detector.head, RetinaNetHead)\n\n def test_run_RetinaNet(self):\n detector = RetinaNet(model_cfg=self.dummy_model_cfg)\n detector.train()\n pred = detector(self.dummy_img)\n self.assertIsInstance(pred, dict)\n self.assertEqual(len(pred), 3)\n\n def test_run_RetinaNet_infer(self):\n detector = RetinaNet(model_cfg=self.dummy_model_cfg)\n detector.eval()\n res_infer = detector(self.dummy_img)\n self.assertEqual(len(res_infer), 3)\n","repo_name":"snuailab/autocare_dlt","sub_path":"tests/core/model/detector/test_retinanet.py","file_name":"test_retinanet.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"6436909804","text":"# from mysqlrpi import *\nfrom datainterfacerpi import *\n# import os\nfrom PIL import Image\nimport pygame\n# import sys\nfrom pygame.locals import *\nimport pygame.camera\nfrom pyzbar.pyzbar import decode\n\n'''\nDesired string that is taken from QR code\n2 Bilaxten 20 1591419800 1 3 1591420000 1 2\n[0] - Indicates iterations required for string\n[1] - Name of medication\n[2] - Total quantity inserted into Meco\n[3] - Dispensing Time\n[4] - Indicates daily requirement\n[5] - Number of pills that is required to be dispensed at timing, given at [3]\n[6,7,8] - Required if there is a second, third time. Iterates [3,4,5]\n'''\n\ndef read_qr_loading(compartment): #input float(compartment number)\n '''read qr using camera at each compartment and save data to compartments'''\n width = 640\n height = 480\n\n done = False\n while not done:\n try:\n #initialise pygame \n pygame.init()\n pygame.camera.init()\n cam = pygame.camera.Camera(\"/dev/video1\",(width,height))\n cam.start()\n #take a picture\n init_image = cam.get_image()\n cam.stop()\n #save picture\n pygame.image.save(init_image,'/home/pi/Desktop/Capstone_rpi_integrate/local_image/qr_scan/pill{}.jpg'.format(str(compartment)))\n image = '/home/pi/Desktop/Capstone_rpi_integrate/local_image/qr_scan/pill{}.jpg'.format(str(compartment))\n \n #qr code decoding\n data = decode(Image.open(image)) \n data_str = data[0][0].decode('utf-8')\n #print(data_str)\n #compartments[compartment] = data_str #not needed unless multiple compartments\n #print(compartments)\n done = True\n return data_str\n \n except IndexError:\n print('read_qr_error_{}'.format(compartment))\n \n\n\ndef sendQRtoSQL(qr_string,compartment_number, user_id='c123',currentUnix='1590285600',table='routines (user_id, med_name, currentUnix, nextUnix, timeRange, quantity, compartment, total_quantity)'):\n med_list = qr_string.split()\n iterations = int(med_list[0])\n med_name = med_list[1]\n total_quantity = med_list[2]\n\n for i in range(iterations):\n #table_id = str(i+3)\n nextUnix_index= (i*3) + 3 #finds nextUnix value position in med_list\n nextUnix = med_list[nextUnix_index] #finds nextUnix value \n timeRange_index = (i*3) + 4 #finds timeRange value position in med_list\n timeRange = med_list[timeRange_index] #finds timeRange value\n quantity_index = (i*3) + 5 #finds quantity value position in med_list\n quantity = med_list[quantity_index] #finds quantity value\n values = user_id, med_name, currentUnix, nextUnix, timeRange, quantity, compartment_number, total_quantity\n print(table,values)\n mysqldb_insert(table,values)\n \n\n# qr_string = read_qr_loading(1)\n# sendQRtoSQL(qr_string)","repo_name":"NichLok/mecoqr","sub_path":"05072020Capstone_rpi_integrate/qrrpi.py","file_name":"qrrpi.py","file_ext":"py","file_size_in_byte":2930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32715974411","text":"from CustomAPI.Execution import Execution\nfrom futu import *\nimport CustomAPI.Helper as helper\n\nTIMERANGE = None\nSYMBOLS = [\"HK.MHImain\", \"HK.00700\"]\nSUBTYPES= [SubType.K_30M]\n\ntimeNow = datetime.now().strftime(\"%Y-%m-%d %H-%M-%S\")\nhelper.projectName = \"close, trade by cci\" + \"{}-{}-{}-{}\".format(SYMBOLS[0:2], SUBTYPES[0:2], TIMERANGE, timeNow)\n\nimport copy\nimport matplotlib.pyplot as plt\n\nsharpeList = []\nns = range(4,10)\nexecution2 = Execution(SYMBOLS, SUBTYPES, TIMERANGE)\n\nfor i in range(4,10):\n execution = copy.deepcopy(execution2)\n execution.runBatchTests(cciParameters=(26, 0.015, 100, i))\n summaries = execution.runSummary(feePerOrder= 10.6)\n ratio = summaries[0].analytics[\"SharpeRatio\"][0]\n print (summaries[0].analytics)\n sharpeList.append(ratio)\n\nplt.figure(figsize=(15, 8))\nplt.plot(ns,sharpeList)\nplt.title(\"{}-{}\".format(SYMBOLS[0:2], SUBTYPES[0:2]))\nplt.show()\n# plt.savefig(SYMBOLS + \"-\" + SUBTYPES + \".png\", dpi=100)\n # execution.plotTimeSeries()\n # execution.plotCumulativeReturn()\n # execution.outputAnalyticsXLSX()\n # execution.outputOrdersXLSX()\n # execution.outputModelDatasXLSX()","repo_name":"princetonwong/AlgoTrading","sub_path":"Archive/PJ-cciDelaySensitivityTest.py","file_name":"PJ-cciDelaySensitivityTest.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11682431233","text":"import os\nimport os.path\nimport time\n\nfrom glideinwms.frontend import glideinFrontendMonitoring\nfrom glideinwms.lib import logSupport, rrdSupport, timeConversion, xmlFormat, xmlParse\n\n############################################################\n#\n# Configuration\n#\n############################################################\n\n\nclass MonitorAggregatorConfig:\n def __init__(self):\n # The name of the attribute that identifies the glidein\n self.monitor_dir = \"monitor/\"\n\n # list of entries\n self.entries = []\n\n # name of the status files\n self.status_relname = \"frontend_status.xml\"\n\n def config_frontend(self, monitor_dir, groups):\n self.monitor_dir = monitor_dir\n self.groups = groups\n glideinFrontendMonitoring.monitoringConfig.monitor_dir = monitor_dir\n\n\n# global configuration of the module\nmonitorAggregatorConfig = MonitorAggregatorConfig()\n\n###########################################################\n#\n# Functions\n#\n###########################################################\n\n# PM: Nov 26, 2014\n# There is a limit on rrd field names. Max allowed is 20 chars long.\n# RRD enforces this limit while creating fields, but will not enforce the limits\n# when trying to read from a field with name longer than 20 chars.\n# Truncate the names for following to be in limits to avoid above issue.\nfrontend_status_attributes = {\n \"Jobs\": (\"Idle\", \"OldIdle\", \"Running\", \"Total\", \"Idle_3600\"),\n \"Glideins\": (\"Idle\", \"Running\", \"Total\"),\n \"MatchedJobs\": (\"Idle\", \"EffIdle\", \"OldIdle\", \"Running\", \"RunningHere\"),\n \"MatchedGlideins\": (\"Total\", \"Idle\", \"Running\", \"Failed\"),\n # 'MatchedGlideins':(\"Total\",\"Idle\",\"Running\",\"Failed\",\"TCores\",\"ICores\",\"RCores\"),\n \"MatchedCores\": (\"Total\", \"Idle\", \"Running\"),\n \"Requested\": (\"Idle\", \"MaxRun\"),\n}\n\nfrontend_total_type_strings = {\n \"Jobs\": \"Jobs\",\n \"Glideins\": \"Glidein\",\n \"MatchedJobs\": \"MatchJob\",\n \"MatchedGlideins\": \"MatchGlidein\",\n \"MatchedCores\": \"MatchCore\",\n \"Requested\": \"Req\",\n}\n\nfrontend_job_type_strings = {\n \"MatchedJobs\": \"MatchJob\",\n \"MatchedGlideins\": \"MatchGlidein\",\n \"MatchedCores\": \"MatchCore\",\n \"Requested\": \"Req\",\n}\n\n\n################################################\n# Function used by Frontend reconfig/upgrade\n# No logging available, output is to stdout/err\n\n\ndef verifyRRD(fix_rrd=False, backup=False):\n \"\"\"\n Go through all known monitoring rrds and verify that they\n match existing schema (could be different if an upgrade happened)\n If fix_rrd is true, then also attempt to add any missing attributes.\n\n Args:\n fix_rrd (bool): if True, will attempt to add missing attrs\n backup (bool): if True, backup the old RRD before fixing\n\n Returns:\n bool: True if all OK, False if there is a problem w/ RRD files\n\n \"\"\"\n rrd_problems_found = False\n mon_dir = monitorAggregatorConfig.monitor_dir\n # Frontend monitoring dictionaries\n status_dict = {}\n status_total_dict = {}\n # initialize the RRD dictionaries to match the current schema for verification\n for tp in list(frontend_status_attributes.keys()):\n if tp in list(frontend_total_type_strings.keys()):\n tp_str = frontend_total_type_strings[tp]\n attributes_tp = frontend_status_attributes[tp]\n for a in attributes_tp:\n status_total_dict[f\"{tp_str}{a}\"] = None\n if tp in list(frontend_job_type_strings.keys()):\n tp_str = frontend_job_type_strings[tp]\n attributes_tp = frontend_status_attributes[tp]\n for a in attributes_tp:\n status_dict[f\"{tp_str}{a}\"] = None\n # check all the existing files\n if not os.path.isdir(mon_dir):\n print(\"WARNING: monitor/ directory does not exist, skipping rrd verification.\")\n return True\n for dir_name, sdir_name, f_list in os.walk(mon_dir):\n for file_name in f_list:\n if file_name == \"Status_Attributes.rrd\":\n if os.path.basename(dir_name) == \"total\":\n if rrdSupport.verifyHelper(os.path.join(dir_name, file_name), status_total_dict, fix_rrd, backup):\n rrd_problems_found = True\n else:\n if rrdSupport.verifyHelper(os.path.join(dir_name, file_name), status_dict, fix_rrd, backup):\n rrd_problems_found = True\n return not rrd_problems_found\n\n\n####################################\n# PRIVATE - Used by aggregateStatus\n# Write one RRD\ndef write_one_rrd(name, updated, data, fact=0):\n if fact == 0:\n type_strings = frontend_total_type_strings\n else:\n type_strings = frontend_job_type_strings\n\n # initialize the RRD dictionary, so it gets created properly\n val_dict = {}\n for tp in list(frontend_status_attributes.keys()):\n if tp in list(type_strings.keys()):\n tp_str = type_strings[tp]\n attributes_tp = frontend_status_attributes[tp]\n for a in attributes_tp:\n val_dict[f\"{tp_str}{a}\"] = None\n\n for tp in list(data.keys()):\n # values (RRD type) - Status or Requested\n if not (tp in list(frontend_status_attributes.keys())):\n continue\n if not (tp in list(type_strings.keys())):\n continue\n\n tp_str = type_strings[tp]\n attributes_tp = frontend_status_attributes[tp]\n\n tp_el = data[tp]\n\n for a in list(tp_el.keys()):\n if a in attributes_tp:\n a_el = int(tp_el[a])\n if not isinstance(a_el, dict): # ignore subdictionaries\n val_dict[f\"{tp_str}{a}\"] = a_el\n\n glideinFrontendMonitoring.monitoringConfig.establish_dir(\"%s\" % name)\n glideinFrontendMonitoring.monitoringConfig.write_rrd_multi(\"%s\" % name, \"GAUGE\", updated, val_dict)\n\n\n##############################################################################\n# create an aggregate of status files, write it in an aggregate status file\n# end return the values\ndef aggregateStatus():\n global monitorAggregatorConfig\n\n type_strings = {\n \"Jobs\": \"Jobs\",\n \"Glideins\": \"Glidein\",\n \"MatchedJobs\": \"MatchJob\",\n \"MatchedGlideins\": \"MatchGlidein\",\n \"MatchedCores\": \"MatchCore\",\n \"Requested\": \"Req\",\n }\n global_total = {\n \"Jobs\": None,\n \"Glideins\": None,\n \"MatchedJobs\": None,\n \"Requested\": None,\n \"MatchedGlideins\": None,\n \"MatchedCores\": None,\n }\n status = {\"groups\": {}, \"total\": global_total}\n global_fact_totals = {}\n\n for fos in (\"factories\", \"states\"):\n global_fact_totals[fos] = {}\n\n nr_groups = 0\n for group in monitorAggregatorConfig.groups:\n # load group status file\n status_fname = os.path.join(\n monitorAggregatorConfig.monitor_dir, f\"group_{group}\", monitorAggregatorConfig.status_relname\n )\n try:\n group_data = xmlParse.xmlfile2dict(status_fname)\n except xmlParse.CorruptXML as e:\n logSupport.log.error(\"Corrupt XML in %s; deleting (it will be recreated).\" % (status_fname))\n os.unlink(status_fname)\n continue\n except OSError:\n continue # file not found, ignore\n\n # update group\n status[\"groups\"][group] = {}\n for fos in (\"factories\", \"states\"):\n try:\n status[\"groups\"][group][fos] = group_data[fos]\n except KeyError:\n # first time after upgrade factories may not be defined\n status[\"groups\"][group][fos] = {}\n\n this_group = status[\"groups\"][group]\n for fos in (\"factories\", \"states\"):\n for fact in list(this_group[fos].keys()):\n this_fact = this_group[fos][fact]\n if not fact in list(global_fact_totals[fos].keys()):\n # first iteration through, set fact totals equal to the first group's fact totals\n global_fact_totals[fos][fact] = {}\n for attribute in list(type_strings.keys()):\n global_fact_totals[fos][fact][attribute] = {}\n if attribute in list(this_fact.keys()):\n for type_attribute in list(this_fact[attribute].keys()):\n this_type_attribute = this_fact[attribute][type_attribute]\n try:\n global_fact_totals[fos][fact][attribute][type_attribute] = int(this_type_attribute)\n except:\n pass\n else:\n # next iterations, factory already present in global fact totals, add the new factory values to the previous ones\n for attribute in list(type_strings.keys()):\n if attribute in list(this_fact.keys()):\n for type_attribute in list(this_fact[attribute].keys()):\n this_type_attribute = this_fact[attribute][type_attribute]\n if isinstance(this_type_attribute, type(global_fact_totals[fos])):\n # dict, do nothing\n pass\n else:\n if attribute in list(\n global_fact_totals[fos][fact].keys()\n ) and type_attribute in list(global_fact_totals[fos][fact][attribute].keys()):\n global_fact_totals[fos][fact][attribute][type_attribute] += int(\n this_type_attribute\n )\n else:\n global_fact_totals[fos][fact][attribute][type_attribute] = int(\n this_type_attribute\n )\n # nr_groups+=1\n # status['groups'][group]={}\n\n if \"total\" in group_data:\n nr_groups += 1\n status[\"groups\"][group][\"total\"] = group_data[\"total\"]\n\n for w in list(global_total.keys()):\n tel = global_total[w]\n if w not in group_data[\"total\"]:\n continue\n # status['groups'][group][w]=group_data[w]\n el = group_data[\"total\"][w]\n if tel is None:\n # new one, just copy over\n global_total[w] = {}\n tel = global_total[w]\n for a in list(el.keys()): # coming from XML, everything is a string\n tel[a] = int(el[a]) # pylint: disable=unsupported-assignment-operation\n else:\n # successive, sum\n for a in list(el.keys()):\n if a in tel: # pylint: disable=unsupported-membership-test\n tel[a] += int(el[a]) # pylint: disable=unsupported-assignment-operation\n\n # if any attribute from prev. factories are not in the current one, remove from total\n for a in list(tel.keys()):\n if a not in el:\n del tel[a] # pylint: disable=unsupported-delete-operation\n\n for w in list(global_total.keys()):\n if global_total[w] is None:\n del global_total[w] # remove group if not defined\n\n # Write xml files\n\n updated = time.time()\n xml_str = (\n '\\n\\n'\n + \"\\n\"\n + xmlFormat.time2xml(updated, \"updated\", indent_tab=xmlFormat.DEFAULT_TAB, leading_tab=xmlFormat.DEFAULT_TAB)\n + \"\\n\"\n + xmlFormat.dict2string(\n status[\"groups\"],\n dict_name=\"groups\",\n el_name=\"group\",\n subtypes_params={\n \"class\": {\n \"dicts_params\": {\n \"factories\": {\n \"el_name\": \"factory\",\n \"subtypes_params\": {\n \"class\": {\n \"subclass_params\": {\n \"Requested\": {\n \"dicts_params\": {\n \"Parameters\": {\"el_name\": \"Parameter\", \"subtypes_params\": {\"class\": {}}}\n }\n }\n }\n }\n },\n },\n \"states\": {\n \"el_name\": \"state\",\n \"subtypes_params\": {\n \"class\": {\n \"subclass_params\": {\n \"Requested\": {\n \"dicts_params\": {\n \"Parameters\": {\"el_name\": \"Parameter\", \"subtypes_params\": {\"class\": {}}}\n }\n }\n }\n }\n },\n },\n }\n }\n },\n leading_tab=xmlFormat.DEFAULT_TAB,\n )\n + \"\\n\"\n + xmlFormat.class2string(status[\"total\"], inst_name=\"total\", leading_tab=xmlFormat.DEFAULT_TAB)\n + \"\\n\"\n + xmlFormat.dict2string(\n global_fact_totals[\"factories\"],\n dict_name=\"factories\",\n el_name=\"factory\",\n subtypes_params={\n \"class\": {\n \"subclass_params\": {\n \"Requested\": {\n \"dicts_params\": {\"Parameters\": {\"el_name\": \"Parameter\", \"subtypes_params\": {\"class\": {}}}}\n }\n }\n }\n },\n leading_tab=xmlFormat.DEFAULT_TAB,\n )\n + \"\\n\"\n + xmlFormat.dict2string(\n global_fact_totals[\"states\"],\n dict_name=\"states\",\n el_name=\"state\",\n subtypes_params={\n \"class\": {\n \"subclass_params\": {\n \"Requested\": {\n \"dicts_params\": {\"Parameters\": {\"el_name\": \"Parameter\", \"subtypes_params\": {\"class\": {}}}}\n }\n }\n }\n },\n leading_tab=xmlFormat.DEFAULT_TAB,\n )\n + \"\\n\"\n + \" \\n\"\n )\n\n glideinFrontendMonitoring.monitoringConfig.write_file(monitorAggregatorConfig.status_relname, xml_str)\n\n # Write rrds\n\n glideinFrontendMonitoring.monitoringConfig.establish_dir(\"total\")\n write_one_rrd(os.path.join(\"total\", \"Status_Attributes\"), updated, global_total, 0)\n\n for fact in list(global_fact_totals[\"factories\"].keys()):\n fe_dir = os.path.join(\"total\", f\"factory_{glideinFrontendMonitoring.sanitize(fact)}\")\n glideinFrontendMonitoring.monitoringConfig.establish_dir(fe_dir)\n write_one_rrd(os.path.join(fe_dir, \"Status_Attributes\"), updated, global_fact_totals[\"factories\"][fact], 1)\n for fact in list(global_fact_totals[\"states\"].keys()):\n fe_dir = os.path.join(\"total\", f\"state_{glideinFrontendMonitoring.sanitize(fact)}\")\n glideinFrontendMonitoring.monitoringConfig.establish_dir(fe_dir)\n write_one_rrd(os.path.join(fe_dir, \"Status_Attributes\"), updated, global_fact_totals[\"states\"][fact], 1)\n\n return status\n","repo_name":"glideinWMS/glideinwms","sub_path":"frontend/glideinFrontendMonitorAggregator.py","file_name":"glideinFrontendMonitorAggregator.py","file_ext":"py","file_size_in_byte":15791,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"11640867969","text":"\"\"\"\nLeetcode 124: Binary Tree Maximum Path Sum\nhttps://leetcode.com/problems/binary-tree-maximum-path-sum/\n\"\"\"\n\nfrom typing import Optional\nfrom tree_node import TreeNode\n\n\ndef maxPathSum(root: Optional[TreeNode]) -> int:\n \"\"\"\n Time complexity: O(n)\n Space complexity: O(n)\n Algorithm: DFS\n - At each node, we calculate the maximum path sum that goes through the node\n - We update the max_sum if the current path sum is greater than the max_sum\n - We return the maximum path sum that goes through the node\n \"\"\"\n max_sum = float('-inf')\n\n def dfs(root):\n nonlocal max_sum\n if not root:\n return 0\n left = dfs(root.left)\n right = dfs(root.right)\n path_sum = root.val + left + right\n child_sum = root.val + max(left, right)\n max_sum = max(max_sum, root.val, path_sum, child_sum)\n return max(root.val, child_sum)\n dfs(root)\n return max_sum\n","repo_name":"ngugimuchangi/coding_challenges","sub_path":"leetcode/trees/binary_tree_maximum_path_sum.py","file_name":"binary_tree_maximum_path_sum.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29045848614","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Aug 13 17:45:55 2017\n\n@author: hre070\n\"\"\"\nfrom collections import namedtuple\nfrom statistics import mean\nfrom histogram import Intersection\n\n\n\n#Weighting function: Cumulative difference over bins\ndef cumu_diff(graph, node1, node2, **kwargs):\n '''Old weighting function. Don't use!'''\n\n node1 = graph.get_node_data(node1, percentages=True)\n node2 = graph.get_node_data(node2, percentages=True)\n\n c_diff = 0\n for key, val in node1['tex'].items():\n c_diff += abs(val - node2['tex'][key])\n return c_diff\n\n\n\ndef _cumu_weight(graph, dst, n):\n '''Old weighting function. Don't use!'''\n return {'weight': cumu_diff(graph, dst, n)}\n\n\n\ndef config_weighting(attr_dict={'tex': (Intersection, 1), 'color': (Intersection, 1) },\n result_label='weight'):\n '''Closure that recieves a specification of attributes along with associated\n compare function and weight. It produces a weighting function\n with an argument signature compliant with 'merge_hierarchical()'\n that compares graph nodes according to the specification.'''\n\n weighting_obj = namedtuple('WeightFunc', ['graph', 'merge'])\n\n def weight_func(graph, node1, node2, neighbor_node, *args, **kwargs):\n '''Weighting function that wraps around attribute compare functions\n and returns a final weight. It gets dynamically defined by the surrounding closure.'''\n #Deep copies\n node1 = graph.deepcopy_node(node1)\n node2 = graph.deepcopy_node(node2)\n neighbor = graph.deepcopy_node(neighbor_node)\n\n #Iteration\n par_results = []\n\n for attr, info in attr_dict.items():\n func = info[0]\n factor = info[1]\n\n if isinstance(node1[attr], list):\n result = mean([func(v[0](), v[1]()) for v in zip(node2[attr], neighbor[attr])])\n else:\n result = func(node2[attr](), neighbor[attr]())\n\n par_results.append(result*factor)\n\n\n final_result = mean(par_results)\n return {result_label: final_result}\n\n def weight_func_minimal(graph, node1, node2, *args, **kwargs):\n '''Minimal version of 'weight_func()' with different argument signature.'''\n return weight_func(graph, node1=0, node2=node1, neighbor_node=node2, *args, **kwargs)\n\n return weighting_obj(weight_func_minimal, weight_func)","repo_name":"H-Reinarz/SBPA","sub_path":"SBPA/weighting.py","file_name":"weighting.py","file_ext":"py","file_size_in_byte":2420,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"72962064720","text":"import math\n#import data\n\n\nif False:\n def compareWithExperiment(protein, file=None, list=None, set=None, label=\"ALL\", **argv):\n \"\"\"\n Compares the calculated pKa values with experiment if it is found in 'experiment'\n \"\"\"\n str = \"# %s\\n\" % (protein.name)\n file.write(str)\n experimental = data.getExperiment(set=set, name=protein.name)\n for chain in protein.chains:\n for residue in chain.residues:\n key = residue.label\n if label == key or label == \"ALL\":\n if key in experimental:\n pka_exp = experimental[key]\n dpka_exp = pka_exp - residue.pKa_mod\n dpka_clc = residue.pKa_pro - residue.pKa_mod\n diff = residue.pKa_pro - pka_exp\n print( \"compare:%s%6.2lf%6.2lf %6.2lf\" % (key, residue.pKa_pro, pka_exp, diff) )\n # adding to 'compare' list\n list.append(diff)\n # writing to 'comp.dat' file\n str = \"%8.2lf%6.2lf\\n\" % (dpka_exp, dpka_clc)\n file.write(str)\n\n\ndef makeErrorPlot(points, filename=None):\n \"\"\"\n do patented error graph data\n \"\"\"\n # open file if needed\n if filename != None:\n file = open(filename, 'w')\n\n # setup error-list\n error_list = []\n for i in range(0, 301):\n error_list.append(0.00)\n\n # loop over points and error bins\n for point in points:\n abs_diff = abs(point)\n i = 0\n while abs_diff > float(i)/100.0 and i < 301:\n error_list[i] += 1.0\n i += 1\n\n number_of_points = float(len(points))\n\n # print/write out error fraction\n for i in range(len(error_list)):\n error = float(i)/100.0\n fraction = error_list[i]/number_of_points\n str = \"%6.2lf%6.3lf\" % (error, fraction)\n if filename == None:\n print(str)\n else:\n str += \"\\n\"; file.write(str)\n\n if filename != None:\n file = open(filename, 'w')\n\n\ndef calculateMUE(points):\n \"\"\"\n do mean unsigned error of all points\n \"\"\"\n number_of_points = len(points)\n abs_sum = 0.00\n for point in points:\n abs_sum += abs(point)\n mue = abs_sum/number_of_points\n\n print(\"mue = %6.4lf\" % (mue))\n\n\ndef calculateRMSD(points):\n \"\"\"\n do rmsd of all points\n \"\"\"\n number_of_points = len(points)\n sqr_sum = 0.00\n for point in points:\n sqr_sum += pow(point, 2)\n rmsd = pow(sqr_sum/number_of_points, 0.5)\n\n print(\"rmsd = %6.4lf (%d)\" % (rmsd, number_of_points))\n\n\ndef calculateShift(points):\n \"\"\"\n calculate an average shift dG_clc - dG_exp\n \"\"\"\n number_of_points = len(points)\n sum_diff = 0.00\n for pH, experiment, calculation in points:\n sum_diff += experiment - calculation\n\n return sum_diff/number_of_points\n\n\ndef printDesolvationDistances(protein):\n \"\"\"\n printing out wanted distances\n \"\"\"\n label = \"GLU 66 A\"\n sum = 0.00; enumerator = 130.0\n center_residue = protein.getResidue(label=label)\n for chain in protein.chains:\n for residue in chain.residues:\n distance = 999.; closest = None\n for atom in residue.atoms:\n dX = atom.x - center_residue.x\n dY = atom.y - center_residue.y\n dZ = atom.z - center_residue.z\n test_distance = math.sqrt(dX*dX + dY*dY + dZ*dZ)\n if residue.label in [\"THR 62 A\", \"VAL 23 A\", \"LEU 14 A\", \"VAL 99 A\", \"ILE 92 A\", \"LEU 36 A\"]:\n if atom.element == \"C\" and atom.name not in [\"C\", \"CA\"]:\n contribution = enumerator/pow(test_distance, 4)\n sum += contribution\n print(\"%s %-3s %6.2lf %6.2lf\" % (residue.label, atom.name, test_distance, contribution))\n if test_distance < distance and atom.element == \"C\":\n distance = test_distance\n closest = atom\n if distance < 8.0 and False:\n print(\"%s %6.2lf %s\" % (residue.label, distance, closest.name))\n print(\"sum:%8.4lf\" % (sum))\n\n\ndef regression(points):\n \"\"\"\n do linear regression of all points\n \"\"\"\n sum_x = 0.00\n sum_y = 0.00\n sum_xx = 0.00\n sum_xy = 0.00\n sum_yy = 0.00\n number_of_points = len(points)\n for x, y in points:\n print(\"%6.2lf %6.2lf\" % (x, y))\n sum_x += x\n sum_y += y\n sum_xx += x*x\n sum_xy += x*y\n sum_yy += y*y\n\n slope = (number_of_points*sum_xy - sum_x*sum_y) / (number_of_points*sum_xx - sum_x*sum_x)\n intercept = (sum_y*sum_xx - sum_x*sum_xy) / (number_of_points*sum_xx - sum_x*sum_x)\n correlation = (number_of_points*sum_xy - sum_x*sum_y) / math.sqrt((number_of_points*sum_xx - sum_x*sum_x)*(number_of_points*sum_yy - sum_y*sum_y))\n\n print(\"%6.2lf + %8.4lf*x, r=%6.3lf\" % (intercept, slope, correlation))\n\n\n","repo_name":"jensengroup/propka-3.0","sub_path":"compare.py","file_name":"compare.py","file_ext":"py","file_size_in_byte":4778,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"27604350335","text":"from time import time\nfrom pytube import YouTube\nfrom telegram import Update\nfrom telegram.ext import ApplicationBuilder, CommandHandler, ContextTypes\nimport datetime\nfrom spy import *\n\n\n# def add(name):\n# name - name.get()\n# DLlowpres_video()\n\nasync def DLlowpres_video(update: Update, context: ContextTypes.DEFAULT_TYPE):\n # log(Update, context)\n msg = update.message.text\n url = msg\n print(url)\n getVideo = YouTube(url)\n getVideo.streams.filter(progressive=True, file_extension='mp4').get_lowest_resolution().download('D:\\GeekBrains\\Python')\n print('Видео в низком качестве успешно скачалось')\n await update.message.reply_text('Видео в низком качестве успешно скачалось')\n\n\nasync def hi_command(update: Update, context: ContextTypes.DEFAULT_TYPE):\n print(update)\n log(Update,context)\n await update.message.reply_text(f'hi {update.effective_user.first_name}') \n\ndef time_command(update: Update, context: ContextTypes.DEFAULT_TYPE): \n log(Update,context)\n update.message.reply_text(f'time {datetime.datetime.now().time()}')\n\ndef help_command(update: Update, context: ContextTypes.DEFAULT_TYPE):\n log(Update,context)\n update.message.reply_text(f'/hi\\n/time\\n/help')\n\n\n","repo_name":"Martovsky910/sem9","sub_path":"bot_commands.py","file_name":"bot_commands.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11558059082","text":"import pytest\n\nimport numpy as np\n\nfrom stempy.image import com_dense, com_sparse, radial_sum_sparse\nfrom stempy.io.sparse_array import SparseArray\n\n\ndef test_com_sparse(sparse_array_small, full_array_small):\n # Do a basic test of com_sparse() with multiple frames per scan\n # position.\n data = np.empty((2, 2), dtype=object)\n data[0][0] = np.array([0])\n data[0][1] = np.array([3])\n data[1][0] = np.array([0])\n data[1][1] = np.array([0, 1, 2])\n kwargs = {\n 'data': data,\n 'scan_shape': (2, 1),\n 'frame_shape': (2, 2),\n }\n array = SparseArray(**kwargs)\n\n com = com_sparse(array)\n\n assert np.array_equal(com[:, 0], [[0.5], [0.5]])\n assert np.array_equal(com[:, 1], [[0.25], [0.25]])\n\n # Now, test and make sure the center of mass computed for some real\n # data matches the dense array version.\n\n com = com_sparse(sparse_array_small)\n\n full_com = np.empty((2, *full_array_small.shape[:2]), dtype=np.float32)\n for i in range(full_array_small.shape[0]):\n for j in range(full_array_small.shape[1]):\n full_com[:, i, j] = com_dense(full_array_small[i, j])[:, 0]\n\n assert np.array_equal(com, full_com)\n\n\ndef test_radial_sum_sparse(sparse_array_10x10):\n\n rr = radial_sum_sparse(sparse_array_10x10, center=(5, 5))\n\n assert np.array_equal(rr[0, 0, :], [0, 6, 0, 0, 3])\n\ndef test_com_sparse_parameters(simulate_sparse_array):\n \n sp = simulate_sparse_array #((100,100), (100,100), (30,70), (0.8), (10))\n \n # Test no inputs. This should be the full frame COM\n com0 = com_sparse(sp)\n assert round(com0[0,].mean()) == 30\n \n # Test crop_to input. Initial COM should be full frame COM\n com1 = com_sparse(sp, crop_to=(10,10))\n assert round(com1[0,].mean()) == 30\n \n # Test crop_to and init_center input.\n # No counts will be in the center so all positions will be np.nan\n com2 = com_sparse(sp, crop_to=(10,10), init_center=(1,1))\n assert np.isnan(com2[0,0,0])\n","repo_name":"OpenChemistry/stempy","sub_path":"tests/test_image.py","file_name":"test_image.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"12598432653","text":"\"\"\"\nUse the 'Dummy' auth provider for generic integration tests of third_party_auth.\n\"\"\"\nfrom common.djangoapps.third_party_auth.tests import testutil\nfrom common.djangoapps.third_party_auth.tests.utils import skip_unless_thirdpartyauth\nfrom .base import IntegrationTestMixin\n\n\n@skip_unless_thirdpartyauth()\nclass GenericIntegrationTest(IntegrationTestMixin, testutil.TestCase):\n \"\"\"\n Basic integration tests of third_party_auth using Dummy provider\n \"\"\"\n PROVIDER_ID = \"oa2-dummy\"\n PROVIDER_NAME = \"Dummy\"\n PROVIDER_BACKEND = \"dummy\"\n\n USER_EMAIL = \"adama@fleet.colonies.gov\"\n USER_NAME = \"William Adama\"\n USER_USERNAME = \"Galactica1\"\n\n def setUp(self):\n super().setUp()\n self.configure_dummy_provider(enabled=True, visible=True)\n\n def do_provider_login(self, provider_redirect_url):\n \"\"\"\n Mock logging in to the Dummy provider\n \"\"\"\n # For the Dummy provider, the provider redirect URL is self.complete_url\n assert provider_redirect_url == (self.url_prefix + self.complete_url)\n return self.client.get(provider_redirect_url)\n","repo_name":"openedx/edx-platform","sub_path":"common/djangoapps/third_party_auth/tests/specs/test_generic.py","file_name":"test_generic.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"4314421793","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n# for reproducibility\ntorch.manual_seed(100)\n\n# Dictionary\nsample_sentence_1 = \"if you want to build a ship, don't drum up people together to \"\nsample_sentence_2 = \"collect wood and don't assign them tasks and work, but rather \"\nsample_sentence_3 = \"teach them to long for the endless immensity of the sea.\"\nsample_sentence = sample_sentence_1 + sample_sentence_2 + sample_sentence_3\nchar_set = list(set(sample_sentence))\ndic = {c: i for i, c in enumerate(char_set)}\n\n# Parameters\ndic_size = len(dic)\ninput_size = dic_size\nhidden_size = dic_size * 2\noutput_size = dic_size\nunit_sequence_length = 20\n\n# Dataset setting\ninput_batch = []\ntarget_batch = []\n\nmake_batch = []\n\nfor i in range(len(sample_sentence)):\n if i < len(sample_sentence) - 18:\n make_batch.append(sample_sentence[i : i+19])\n else:\n break\n\nfor sentence in make_batch:\n x_data = [dic[c] for c in sentence[:-1]]\n x_one_hot = [np.eye(dic_size)[x] for x in x_data]\n y_data = [dic[c] for c in sentence[1:]]\n\n input_batch.append(x_one_hot)\n target_batch.append(y_data)\n\nX = torch.FloatTensor(input_batch)\nY = torch.LongTensor(target_batch)\n\nprint(X.shape)\nprint(Y.shape)\n\nclass Custom_model(nn.Module):\n def __init__(self, input_dim, hidden_dim, output_dim, layers):\n super(Custom_model, self).__init__()\n self.LSTM = nn.LSTM(input_dim, hidden_dim, num_layers=layers)\n self.fc = nn.Linear(hidden_dim, output_dim, bias = True)\n\n def forward(self, x):\n out, status_ = self.LSTM(x)\n out = self.fc(out)\n\n return out\n\n\nmodel = Custom_model(input_size, hidden_size, output_size, 2)\noptimizer = torch.optim.Adam(model.parameters(), lr = 0.005)\ncriterion = nn.CrossEntropyLoss()\n\nfor epoch in range(500):\n hypothesis = model(X)\n cost = criterion(hypothesis.reshape(-1, dic_size), Y.reshape(-1))\n\n optimizer.zero_grad()\n cost.backward()\n optimizer.step()\n\n if epoch % 10 == 0:\n print(f\" Cost : {cost.item()}\")\n\n\n# Check Result\n\nresult = hypothesis.data.numpy().argmax(axis = 2)\n\nresult_sentence1 = []\nfor i, character in enumerate(result):\n print(char_set[character[0]], end = '')\n result_sentence1.append(char_set[character[0]])\n if i == len(result) - 1:\n for sen in character:\n print( char_set[sen], end = '')\n result_sentence1.append(char_set[sen])\n\n\n\n\n\n\n","repo_name":"dldnxks12/A.I","sub_path":"Deep Learning/# AI Application/EXAM/Final/Sequence prediction.py","file_name":"Sequence prediction.py","file_ext":"py","file_size_in_byte":2422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26023582432","text":"#https://realpython.com/linear-regression-in-python/#beyond-linear-regression\r\n#Polynomial Regression With scikit-learn (two column input)\r\n#𝑓(𝑥₁, 𝑥₂) = 𝑏₀ + 𝑏₁𝑥₁ + 𝑏₂𝑥₂ + 𝑏₃𝑥₁² + 𝑏₄𝑥₁𝑥₂ + 𝑏₅𝑥₂². polynominal for two inputs\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom sklearn.preprocessing import PolynomialFeatures\r\n\r\n\r\nx = np.array([[0, 1], [5, 1], [15, 2], [25, 5], [35, 11], [45, 15], [55, 34], [60, 35]])\r\ny = np.array([4, 5, 20, 14, 32, 22, 38, 43])\r\nx, y = np.array(x), np.array(y)\r\n\r\n# Step 2b: Transform input data\r\nx_ = PolynomialFeatures(degree=2, include_bias=False).fit_transform(x)\r\n\r\n# Step 3: Create a model and fit it\r\nmodel = LinearRegression().fit(x_, y)\r\n\r\n# Step 4: Get results\r\nr_sq = model.score(x_, y)\r\nintercept, coefficients = model.intercept_, model.coef_\r\n\r\n# Step 5: Predict\r\ny_pred = model.predict(x_)\r\n\r\nprint(('coefficient of determination:', r_sq))\r\nprint(\"=\"*40)\r\n\r\nprint('intercept:', intercept)\r\nprint(\"=\"*40)\r\n\r\nprint('coefficients:', coefficients, sep='\\n')\r\nprint(\"=\"*40)\r\n\r\nprint('predicted response:', y_pred, sep='\\n')\r\nprint(\"=\"*40)\r\n\r\n#------------------------------------------------------------------------\r\n","repo_name":"soheil2017/Machine-Learning","sub_path":"Linear Regression/03 - Polynomial Regression/Regression Polyminal_two input.py","file_name":"Regression Polyminal_two input.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5143385552","text":"\"global mutable variables\"\nimport os\nfrom collections import defaultdict\nfrom . import build\n\n\ndef load_settings(path=None, trybuild=True):\n \"Load the settings file at SETTINGS_PATH; return settings dict\"\n if path is None:\n path = os.sep.join([os.path.dirname(__file__), \"env\", \"settings\"])\n try: # if the settings file already exists, read it\n with open(path) as settingsfile:\n lines = [line[:-1].split(\" : \") for line in settingsfile\n if len(line.split(\" : \")) == 2]\n settings_ = {name: value.split(\", \") for name, value in lines}\n for name, value in settings_.items():\n # flatten 1-element lists unless they're the solver list\n if len(value) == 1 and name != \"installed_solvers\":\n settings_[name], = value\n except IOError: # pragma: no cover\n settings_ = {\"installed_solvers\": [\"\"]}\n if settings_[\"installed_solvers\"] == [\"\"] and trybuild: # pragma: no cover\n print(\"Found no installed solvers, beginning a build.\")\n build()\n settings_ = load_settings(path, trybuild=False)\n if settings_[\"installed_solvers\"] != [\"\"]:\n settings_[\"just built!\"] = True\n else:\n print(\"\"\"\n=============\nBuild failed! :(\n=============\nYou may need to install a solver and then `import gpkit` again;\nsee https://gpkit.readthedocs.io/en/latest/installation.html\nfor troubleshooting details.\n\nBut before you go, please post the output above\n(starting from \"Found no installed solvers, beginning a build.\")\nto gpkit@mit.edu or https://github.com/convexengineering/gpkit/issues/new\nso we can prevent others from having to see this message.\n\n Thanks! :)\n\"\"\")\n settings_[\"default_solver\"] = settings_[\"installed_solvers\"][0]\n return settings_\n\n\nsettings = load_settings()\n\n\nclass SignomialsEnabledMeta(type):\n \"Metaclass to implement falsiness for SignomialsEnabled\"\n def __bool__(cls): return cls._true # pylint: disable=multiple-statements\n\nclass SignomialsEnabled(metaclass=SignomialsEnabledMeta): # pylint: disable=no-init\n \"\"\"Class to put up and tear down signomial support in an instance of GPkit.\n\n Example\n -------\n >>> import gpkit\n >>> x = gpkit.Variable(\"x\")\n >>> y = gpkit.Variable(\"y\", 0.1)\n >>> with SignomialsEnabled():\n >>> constraints = [x >= 1-y]\n >>> gpkit.Model(x, constraints).localsolve()\n \"\"\"\n _true = False # default signomial permissions\n # pylint: disable=multiple-statements\n def __enter__(self): SignomialsEnabled._true = True\n def __exit__(self, type_, val, traceback): SignomialsEnabled._true = False\n\n\nclass Vectorize:\n \"\"\"Creates an environment in which all variables are\n exended in an additional dimension.\n \"\"\"\n vectorization = () # the current vectorization shape\n\n def __init__(self, dimension_length):\n self.dimension_length = dimension_length\n\n def __enter__(self):\n \"Enters a vectorized environment.\"\n Vectorize.vectorization = (self.dimension_length,) + self.vectorization\n\n def __exit__(self, type_, val, traceback):\n \"Leaves a vectorized environment.\"\n Vectorize.vectorization = self.vectorization[1:]\n\n\nclass NamedVariables:\n \"\"\"Creates an environment in which all variables have\n a model name and num appended to their varkeys.\n \"\"\"\n lineage = () # the current model nesting\n modelnums = defaultdict(int) # the number of models of each lineage\n namedvars = defaultdict(list) # variables created in the current nesting\n\n @classmethod\n def reset_modelnumbers(cls):\n \"Clear all model number counters\"\n for key in list(cls.modelnums):\n del cls.modelnums[key]\n\n def __init__(self, name):\n self.name = name\n\n def __enter__(self):\n \"Enters a named environment.\"\n num = self.modelnums[(self.lineage, self.name)]\n self.modelnums[(self.lineage, self.name)] += 1\n NamedVariables.lineage += ((self.name, num),) # NOTE: Side effects\n return self.lineage, self.namedvars[self.lineage]\n\n def __exit__(self, type_, val, traceback):\n \"Leaves a named environment.\"\n del self.namedvars[self.lineage]\n NamedVariables.lineage = self.lineage[:-1] # NOTE: Side effects\n","repo_name":"convexengineering/gpkit","sub_path":"gpkit/globals.py","file_name":"globals.py","file_ext":"py","file_size_in_byte":4348,"program_lang":"python","lang":"en","doc_type":"code","stars":200,"dataset":"github-code","pt":"3"}
+{"seq_id":"21099817261","text":"import itertools\nimport math\n\nimport numpy as np\nimport pandas as pd\nimport scipy.stats\nfrom sklearn.linear_model import LinearRegression\n\nfrom figures.figure1 import find_leaves\nfrom figures.util import produce_figure, main_regions, get_subplots, plot_grid, \\\n plot_annotations, plot_scatter\n\nCORRELATION_THRESHOLD = 0.25\nMIN_SAMPLE_COUNT = 100\nSYMMETRY_SCORE_THRESHOLD = 0.155\nCELL_COUNT_THRESHOLD = 20000\n\n\ndef prepare_data(data, plot=False):\n valid_data = data\n valid_data = filter_dark_brains(valid_data)\n valid_data = filter_small_regions(valid_data)\n valid_data = filter_brightness_correlated_data(valid_data, plot)\n valid_data = filter_asymmetric_regions(valid_data)\n valid_data = filter_count_outliers(valid_data)\n\n print(f\"Brains discarded: {len(data.experiment_id.unique()) - len(valid_data.experiment_id.unique())} out \"\n f\"of {len(data.experiment_id.unique())}. {len(valid_data.experiment_id.unique())} good brains left.\")\n print(f\"Regions discarded: {len(data.region.unique()) - len(valid_data.region.unique())} out \"\n f\"of {len(data.region.unique())}. {len(valid_data.region.unique())} good regions left.\")\n\n return valid_data\n\n\ndef filter_count_outliers(data):\n regions = data.region.unique()\n control_regions = regions[find_leaves(regions)]\n\n table = pd.pivot_table(data[data.region.isin(control_regions)],\n columns=['experiment_id', 'region']).loc['count'].unstack()\n table = ((table - table.median()) / (table.mad() * 1.4826))\n\n left_tail = table < -3\n right_tail = table > 3\n\n left_per_experiment = left_tail.transpose().sum()\n right_per_experiment = right_tail.transpose().sum()\n left_outliers = left_per_experiment[left_per_experiment > 3]\n right_outliers = right_per_experiment[right_per_experiment > 3]\n\n return data[~data.experiment_id.isin(left_outliers.index.to_list() + right_outliers.index.to_list())]\n\n\ndef filter_asymmetric_regions(data, plot=False):\n if plot:\n res = plot_symmetry_score(data, annotate_filtered=True)\n else:\n res = calculate_symmetry_score(data)\n\n res = pd.DataFrame(res.items(), columns=['region', 'score'])\n return data[data.region.isin(res[res.score <= SYMMETRY_SCORE_THRESHOLD].region.unique())]\n\n\ndef filter_small_regions(data):\n means = data.groupby('region').median()\n regions = means[(means['count'] > 500) & (means.volume > 0.3)].index.tolist()\n return data[data.region.isin(regions)]\n\n\ndef filter_dark_brains(data):\n valid_experiments = data[(data.region == 'grey') & (data['brightness|median'] > 25)].experiment_id.unique()\n return data[data.experiment_id.isin(valid_experiments)]\n\n\ndef determine_correlation_thresholds(data, param1, param2, correlation_threshold=CORRELATION_THRESHOLD):\n res = {p: data.groupby('region')[p].apply(np.array).to_dict()\n for p in (param1, param2)}\n max_threshold = data[param2].max()\n\n results = dict()\n for region in res[param1].keys():\n x = np.copy(res[param2][region])\n y = np.copy(res[param1][region])\n for p2_threshold in range(5, 200, 5):\n valid_indices = np.where(x > p2_threshold)\n x = x[valid_indices]\n y = y[valid_indices]\n if min(len(x), len(y)) < MIN_SAMPLE_COUNT:\n break\n correlation, pvalue = scipy.stats.pearsonr(x, y)\n if abs(correlation) < correlation_threshold:\n results[region] = (p2_threshold, data[(data.region == region) &\n (data[param2] > p2_threshold)].experiment_id.unique().tolist())\n break\n if region not in results:\n results[region] = (max_threshold + 1, [])\n\n return results\n\n\ndef plot_symmetry_score(data, annotate_filtered=False):\n scores = calculate_symmetry_score(data)\n res = sorted(scores.items(), key=lambda e: e[1], reverse=True)\n x, y = np.array(list(zip(*res)))\n y = y.astype(np.double)\n fig, ax = get_subplots()\n\n for region, structs in main_regions.items():\n indices = np.where(np.isin(x.astype(str), structs))\n plot_scatter(ax, indices, y[indices], label=region)\n\n ax.set_xticks(list(range(len(x))[::50]))\n ax.set_xticklabels(x[::50], size=20)\n plot_grid(ax, [], [SYMMETRY_SCORE_THRESHOLD])\n\n if annotate_filtered:\n anns = np.where(y > SYMMETRY_SCORE_THRESHOLD)\n plot_annotations(ax, x[anns], np.arange(len(anns[0])), y[anns])\n\n produce_figure(ax, fig, \"symmetry_score_linear\", \"\", \"\", legend=True, format_xticks=False)\n ax.set_yscale('log')\n produce_figure(ax, fig, \"symmetry_score_log\", \"\", \"\", legend=False, format_xticks=False)\n return scores\n\n\ndef calculate_symmetry_score(data):\n mean_data = pd.DataFrame({'region': data.region,\n 'absdiff': (data.count_left - data.count_right).abs(),\n 'count': data['count']\n }).groupby('region').mean()\n return (mean_data.absdiff / mean_data['count']).to_dict()\n\n\ndef find_inflection_point(xs, ys):\n diffs = np.abs(ys[:-1] - ys[1:])\n inf_pts = np.where(diffs > 2)[0]\n return inf_pts[-1] if len(inf_pts) > 0 else -1\n\n\ndef determine_correlation_intersection(ax, data, plot=False, correlation_threshold=CORRELATION_THRESHOLD):\n thresholds = determine_correlation_thresholds(data, 'count', 'brightness|median', correlation_threshold)\n # exps = sorted([(s, set(es)) for s, (t, es) in thresholds.items()], key=lambda s: len(s[1]), reverse=True)\n exp_thresholds = {e: {s for s, es in thresholds.items() if e in es[1]} for e in data.experiment_id.unique()}\n exps = sorted([(e, s) for e, s in exp_thresholds.items()], key=lambda s: len(s[1]), reverse=True)\n intersections = [tuple(zip(*exps[:i])) for i in range(1, len(exps))]\n intersections = [(set(s), set.intersection(*e)) for s, e in intersections]\n exp_sets, struct_sets = tuple(zip(*sorted(intersections, key=lambda t: len(t[1]), reverse=True)))\n ys = np.array([len(s) for s in struct_sets])\n xs = np.array([len(s) for s in exp_sets])\n inf_pt = find_inflection_point(xs, ys)\n if plot:\n plot_scatter(ax, xs, ys, label=str(correlation_threshold))\n plot_scatter(ax, xs[inf_pt], ys[inf_pt], s=100)\n plot_grid(ax, [xs[inf_pt]], [ys[inf_pt]])\n return thresholds, inf_pt, intersections, xs, ys\n\n\ndef plot_brightness_threshold_curves(data):\n fig, ax = get_subplots()\n for i in range(10, 51, 2):\n thresholds, inf_pt, intersections, xs, ys = determine_correlation_intersection(ax, data, True, i / 100)\n plot_annotations(ax, f'{i / 100} ({ys[inf_pt]} regions, {xs[inf_pt]} brains)', [xs[inf_pt]], [ys[inf_pt]])\n produce_figure(ax, fig, \"valid_regions_vs_brains\", \"brains\", \"regions\", legend=True)\n\n\ndef filter_brightness_correlated_data(data, plot=False):\n if plot:\n fig, ax = get_subplots()\n else:\n fig, ax = (None, None)\n\n # thresholds, inf_pt, intersections, xs, ys = determine_correlation_intersection(ax, data, plot=plot)\n thresholds = determine_correlation_thresholds(data, 'count', 'brightness|median', CORRELATION_THRESHOLD)\n\n if plot:\n produce_figure(ax, fig, \"valid_regions_vs_brains\", \"brains\", \"regions\")\n counts = data[data.region.isin(thresholds.keys())].groupby('region')['count'].mean().to_dict()\n regions = sorted([(k, sorted(list(g), key=lambda x: counts[x], reverse=True))\n for k, g in itertools.groupby(sorted(thresholds.keys(),\n key=lambda x: thresholds[x][0]),\n lambda s: thresholds[s][0])], key=lambda x: x[0])\n\n fig, ax = get_subplots()\n x = data[data.region == regions[0][1][1]]['count'].dropna().to_numpy()\n y = data[data.region == regions[0][1][1]]['brightness|mean'].dropna().to_numpy()\n reg = LinearRegression().fit(x.reshape(-1, 1), y)\n plot_scatter(ax, x, y)\n ax.plot(x, reg.predict(x.reshape(-1, 1)), color='blue', linewidth=3)\n produce_figure(ax, fig, \"valid_count_vs_brightness\",\n xlabel=f'{regions[0][1][1]}. corr: {scipy.stats.pearsonr(x, y)[0]:.2f}')\n\n fig, ax = get_subplots()\n x = data[data.region == regions[-1][1][0]]['count'].dropna().to_numpy()\n y = data[data.region == regions[-1][1][0]]['brightness|mean'].dropna().to_numpy()\n reg = LinearRegression().fit(x.reshape(-1, 1), y)\n plot_scatter(ax, x, y)\n ax.plot(x, reg.predict(x.reshape(-1, 1)), color='blue', linewidth=3)\n produce_figure(ax, fig, \"invalid_count_vs_brightness\",\n xlabel=f'{regions[-1][1][0]}. corr: {scipy.stats.pearsonr(x, y)[0]:.2f}')\n\n nan_data = [[{'experiment_id': i, 'region': r, **{c: -12345 for c in data\n if c not in ['experiment_id', 'region']}}\n for i in set(data.experiment_id.unique()) - set(d[1])] for r, d in thresholds.items()]\n nan_data = [e for ee in nan_data for e in ee]\n nan_data = pd.DataFrame(nan_data).set_index(['experiment_id', 'region'])\n data.set_index(['experiment_id', 'region'], inplace=True)\n data.update(nan_data, overwrite=True)\n data[data < 0] = math.nan\n data.reset_index(inplace=True)\n\n # valid_structs = list(intersections[inf_pt][1])\n # valid_experiments = list(intersections[inf_pt][0])\n # valid_data = data[data.experiment_id.isin(valid_experiments) & data.region.isin(valid_structs)]\n\n return data\n","repo_name":"delkind/mouse-brain-cell-counting","sub_path":"figures/clean_data.py","file_name":"clean_data.py","file_ext":"py","file_size_in_byte":9623,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"29026695817","text":"from staff.reports.views.departments import yandex_structure_gen\n\nfrom staff.lib.testing import StaffFactory, DepartmentFactory, DepartmentStaffFactory\nfrom staff.departments.models import DepartmentRoles\n\nimport pytest\n\n\ndef generate_department_tree(root, height):\n if height == 0:\n return\n\n for _ in range(2):\n current_department = DepartmentFactory(parent=root)\n generate_department_tree(current_department, height-1)\n\n\n@pytest.fixture()\ndef generate_initial_data(settings, db):\n # create two head departments\n yandex_dep = DepartmentFactory(name=\"Yandex\")\n outstaff_dep = DepartmentFactory(name=\"Outstaff\")\n # redefine settings\n settings.YANDEX_DEPARTMENT_ID = yandex_dep.id\n settings.OUTSTAFF_DEPARTMENT_ID = outstaff_dep.id\n # creating departent tree for each head department\n generate_department_tree(yandex_dep, 2)\n generate_department_tree(outstaff_dep, 2)\n\n DepartmentFactory.create_batch(10, name=\"Other Department\")\n\n yandex_staff_chief = StaffFactory(department=yandex_dep)\n outstaff_chief = StaffFactory(department=outstaff_dep)\n\n DepartmentStaffFactory(staff=yandex_staff_chief, department=yandex_dep, role_id=DepartmentRoles.CHIEF.value)\n DepartmentStaffFactory(staff=outstaff_chief, department=outstaff_dep, role_id=DepartmentRoles.CHIEF.value)\n\n\ndef test_outstaff_excel(generate_initial_data):\n response = yandex_structure_gen()\n\n columns = True\n\n for data_list in response:\n # first line in response are column names, so skip it\n if columns:\n columns = False\n continue\n # 4th item contains department chain starting from root\n dep_chain = data_list[3].split(\" => \")[0]\n assert dep_chain in (\"Yandex\", \"Outstaff\")\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/outstaff_test (2).py","file_name":"outstaff_test (2).py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74859222801","text":"\"\"\"Tests of rook design optimizer\n\nScientific Machine Learning Benchmark:\nA benchmark of regression models in chem- and materials informatics.\n(c) 2019-20, Citrine Informatics.\n\"\"\"\nimport pytest\n\nimport numpy as np\n\nfrom smlb import ExpectedValue, TrackedTransformation, InvalidParameterError\n\nfrom smlb.datasets.synthetic.schwefel26_1981.schwefel26_1981 import Schwefel261981Data\nfrom smlb.optimizers.rook_design import RookDesignOptimizer\nfrom smlb.learners.identity_learner import IdentityLearner\n\n\n@pytest.fixture(scope='module')\ndef data():\n num_dimensions = 4\n return Schwefel261981Data(dimensions=num_dimensions)\n\n\n@pytest.fixture(scope='module')\ndef func(data) -> TrackedTransformation:\n learner = IdentityLearner(data)\n scorer = ExpectedValue()\n return TrackedTransformation(learner, scorer, maximize=False)\n\n\ndef test_single_iteration(data, func):\n \"\"\"Check that the expected number of candidates are explored in a single iteration.\"\"\"\n num_iters = 1\n num_seeds = 2\n resolution = 16\n fractional_dims = 0.5\n dimensions_varied = int(data.dimensions * fractional_dims)\n optimizer = RookDesignOptimizer(\n rng=0,\n max_iters=num_iters,\n num_seeds=num_seeds,\n resolution=resolution,\n dimensions_varied=fractional_dims\n )\n trajectory = optimizer.optimize(data, func)\n assert trajectory.num_evaluations == num_seeds * dimensions_varied * resolution * num_iters\n\n\ndef test_multiple_iterations(data, func):\n \"\"\"Test that the best scores from each iteration become seeds for the next iteration.\n Note that this is only strictly true when the jump size can span the entire domain.\n If the jumps are small then there is no fixed grid and the candidates in a given step\n might not _exactly_ include a seed.\n \"\"\"\n min_num_iters = 5\n num_seeds = 2\n resolution = 8\n expected_candidates_per_step = num_seeds * resolution * data.dimensions\n num_evals = expected_candidates_per_step * min_num_iters\n optimizer = RookDesignOptimizer(\n rng=0,\n num_seeds=num_seeds,\n resolution=resolution,\n max_evals=num_evals\n )\n trajectory = optimizer.optimize(data, func)\n\n for i in range(len(trajectory.steps) - 1):\n old_scores = trajectory.steps[i].scores\n best_scores = np.sort(old_scores)[:num_seeds]\n new_scores = trajectory.steps[i+1].scores\n for good_score in best_scores:\n assert good_score in new_scores\n\n # Subsequent iterations are likely to have duplicates resulting in additional\n # iterations that push the number of evaluations over the soft limit\n assert trajectory.num_evaluations > num_evals\n assert len(trajectory.steps) > min_num_iters\n\n\ndef test_short_moves(data, func):\n \"\"\"Test that the new seeds aren't very far away when using a small max_relative_jump.\"\"\"\n num_iters = 1\n num_seeds = 1\n resolution = 8\n max_relative_jump = 0.01\n lb, ub = data.domain[0]\n max_jump = max_relative_jump * (ub - lb) # domain is uniform for Schwefel function\n\n optimizer = RookDesignOptimizer(\n rng=0,\n max_iters=num_iters,\n num_seeds=num_seeds,\n resolution=resolution,\n max_relative_jump=max_relative_jump\n )\n trajectory = optimizer.optimize(data, func)\n candidates = trajectory.steps[0]._input.samples()\n\n # Calculate the Manhattan distances between the (arbitrary) first candidate and all other\n # candidates. The max should be no more than max_jump * 2 (if the two candidates are on\n # opposite sides of the seed).\n distances = np.sum(np.abs(candidates - candidates[0]), axis=1)\n assert np.max(distances) <= max_jump * 2\n\n\ndef test_rook_design_parameters():\n \"\"\"Test more complicated parameter validation.\"\"\"\n with pytest.raises(InvalidParameterError):\n RookDesignOptimizer() # no rng seed\n\n with pytest.raises(InvalidParameterError):\n RookDesignOptimizer(rng=0, max_relative_jump=0.0, max_iters=1) # jump size cannot be 0\n\n with pytest.raises(InvalidParameterError):\n RookDesignOptimizer(rng=0, dimensions_varied=0, max_iters=1) # dimensions varied cannot be 0\n\n with pytest.raises(InvalidParameterError):\n RookDesignOptimizer(rng=0, dimensions_varied=-3, max_iters=1) # dims varied cannot be negative\n\n with pytest.raises(InvalidParameterError):\n RookDesignOptimizer(rng=0, dimensions_varied=\"foo\", max_iters=1) # not a valid keyword\n\n with pytest.raises(InvalidParameterError):\n RookDesignOptimizer(rng=0, max_iters=None, max_evals=None) # no stopping condition\n","repo_name":"CitrineInformatics/smlb","sub_path":"tests/optimizers/test_rook_design.py","file_name":"test_rook_design.py","file_ext":"py","file_size_in_byte":4600,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"491988286","text":"from django.conf.urls import patterns, include, url\nfrom portfolio.api import PortfolioResource\nfrom django.contrib import admin\nadmin.autodiscover()\n\nportfolio_resource = PortfolioResource()\n\nurlpatterns = patterns('',\n url(r'^admin/', include(admin.site.urls)),\n url(r'^clientui/',include('clientui.urls')),\n url(r'^api/', include(portfolio_resource.urls)),\n)\n","repo_name":"supritashankar/assignment","sub_path":"project/project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10390822445","text":"# Сложение, вычитание, и перевод в exe с помощью модуля pyistaller\n\n# Чтобы скомпилировать в ехе: pyistaller calc.py\n\nfrom colorama import init\nfrom colorama import Fore, Back, Style\ninit()\n\n# Тест становится красный\nprint(Fore.RED)\n\nwhat = input('Что делаем? (+, -): ')\n\na = float(input('Введи первое число: '))\nb = float(input('Введи второе число: '))\n\nif what == '+':\n c = a + b\n print('Результат: ' + str(c))\nelif what == '-':\n c = a - b\n print('Результат: ' + str(c))\nelse:\n print('Что это такое?')\n\ninput()\n","repo_name":"AnPeDev/Training_Python","sub_path":"calc.py","file_name":"calc.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"24848197689","text":"from docx.shared import Cm\nfrom docx import Document\nimport sys\n\n\nclass FakeNetwork:\n \"\"\"\n Klasa ukrywająca prawie wszystkie atrybuty obiektu.\n Zwraca jedynie pustego stringa bądź atrybut hosts w wypadku gdy\n zostanie o niego poproszona.\n \"\"\"\n def __init__(self, hosts):\n self.hosts = hosts\n\n def __getattribute__(self, item):\n \"\"\"\n Przysłonięcie metody do pobierania atrybutów tak aby wszystkie prócz hosts\n zwracały pustego stringa.\n\n :param item: parametr\n :return: Pusty string bądź liczbę hostów.\n \"\"\"\n if item == 'hosts':\n # zwróć się do rodzica o zasłoniętą metodę\n return super(FakeNetwork, self).__getattribute__('hosts')\n else:\n # zwróć pustego stringa\n return ''\n\n\ndef generate_raport(network, subnets, path, answers=True, fullpage=False):\n \"\"\"\n Generuje raport na podstawie dostarczonych parametrów.\n :param network: BinNetwork służący jako adres ip w poleceniu\n :param subnets: Lista obiektów Binetwork, które zawierają już wszystkie parametry\n :param answers: pokaż odpowiedzi?\n :param fullpage: Wydrukować ilość sprawdzianów tak aby zajeły one całą stronę?\n \"\"\"\n subnets_list = ', '.join(str(subnet.hosts) for subnet in subnets)\n number_of_repeats = [0, 5, 5, 4, 4, 4, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2]\n rows = len(subnets)\n\n if answers is False:\n # jeżeli odpowiedzi nie mają być wyświetlane, to podmień podsieci\n # na specjalnych tajnych agentów\n for i in range(len(subnets)):\n subnets[i] = FakeNetwork(subnets[i].hosts)\n\n document = Document()\n sections = document.sections\n for section in sections:\n # marginesy\n section.top_margin = Cm(1)\n section.bottom_margin = Cm(1)\n section.left_margin = Cm(1)\n section.right_margin = Cm(1)\n columns = \"hosts allocated_size network_with_mask broadcast first_address last_address\".split()\n # w jakiej kolejności mają pojawiać się parametry sieci\n try:\n times = number_of_repeats[rows] if fullpage else 1\n except:\n times = 1\n for _ in range(times):\n p = document.add_paragraph('Imię i nazwisko: ')\n\n document.add_paragraph(\"Podziel sieć o adresie \" + str(network) + \"/\" + str(\n network.mask) + \" na podsieci po \" + subnets_list + \" hostów.\")\n\n # tworzenie tabeli i jej nagłówków\n table = document.add_table(rows=len(subnets) + 1, cols=6)\n header = table.rows[0].cells\n header[0].text = \"Hosty\"\n header[1].text = \"Zaalokowane hosty\"\n header[2].text = \"Adres podsieci\"\n header[3].text = \"Broadcast\"\n header[4].text = \"Pierwszy wolny adres\"\n header[5].text = \"Ostatni wolny adres\"\n table.style = 'TableGrid'\n\n # wypełnij komórki tabeli\n for network_id, row in enumerate(table.rows[1:]):\n for id, col in enumerate(row.cells):\n col.text = str(getattr(subnets[network_id], columns[id]))\n try:\n # zapisz dokument\n document.save(path)\n except:\n print('Wystąpił problem z zapisem', file=sys.stderr)\n if _ != times - 1:\n document.add_paragraph('')\n document.add_paragraph('')\n","repo_name":"Boberkraft/VLSM-sieci","sub_path":"saving.py","file_name":"saving.py","file_ext":"py","file_size_in_byte":3353,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41858146457","text":"from os import walk\n\nf = []\nlayer = 1\nw = walk(\"done\")\nfile = open(\"fileInDone.txt\", \"w\")\nfor (dirpath, dirnames, filenames) in w:\n for i in filenames:\n print(dirpath+\"\\\\\"+i)\n file.write(dirpath+\"\\\\\"+i+\"\\n\")\n f.append(dirpath+\"\\\\\"+i)\n # if layer == 2:\n # f.extend(dirnames)\n # break\n # layer += 1\n\n\n\nfile.close()\n","repo_name":"mohammadrezaeicode/open-screenshot-close","sub_path":"walk.py","file_name":"walk.py","file_ext":"py","file_size_in_byte":357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12577918711","text":"# https://www.interviewbit.com/courses/programming/topics/backtracking/problems/palindrome-partitioning/\n# \n\nclass Solution:\n # @param A : string\n # @return a list of list of strings\n def partition(self, A):\n string = A\n result = set([string]) if self.isPalindrome(string) else set()\n\n if len(string) == 1:\n return [string]\n\n for i in xrange(1, len(string)):\n part1, part2 = string[:i], string[i:]\n\n if self.isPalindrome(part1):\n if self.isPalindrome(part2):\n result.add((part1, part2))\n\n for partitions in self.partition(string[i:]):\n new = [part1]\n new.extend(partitions)\n result.add(tuple(new))\n\n return sorted([list(x) if isinstance(x, tuple) else [x] for x in result], cmp=self.cmp)\n \n def cmp(self, list1, list2):\n i = 0\n while i < len(list1) and i < len(list2):\n if len(list1[i]) < len(list2[i]):\n return -1\n elif len(list1[i]) > len(list2[i]):\n return 1\n else:\n i += 1\n \n return 1 if len(list1) > len(list2) else -1\n \n # @param A : string\n # @return an integer\n def isPalindrome(self, A):\n return list(A) == list(reversed(A))\n \n","repo_name":"diegomontoyas/Algorithms","sub_path":"InterviewBit/palindrome-partitioning.py","file_name":"palindrome-partitioning.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74115820242","text":"# 实现实例对象的迭代：\n# 方法一： for in __getitem__\n\nclass Son:\n def __init__(self):\n self.res = 0\n\n def __getitem__(self, item):\n self.res +=1\n if self.res >= 6:\n raise StopIteration('stop')\n return self.res\n\ns = Son()\n\nfor i in s:\n print(i)\n\n\n# 实现实例对象的迭代：\n# 方法二： __iter__ __next__\n\nclass Son:\n def __init__(self):\n self.res = 0\n\n # def __getitem__(self, item):\n # print('getitem')\n # self.res +=1\n # if self.res >= 6:\n # raise StopIteration('stop')\n # return self.res\n\n # 利用__iter__产生迭代器,优先级比__getitem__高\n def __iter__(self):\n # 此处赋值，以便可以对实例进行多次迭代，每次调用时对判定条件进行赋值\n self.res = 0\n print('iter')\n # 不是self.res\n return self\n\n def __next__(self):\n self.res += 1\n if self.res >= 6:\n raise StopIteration('stop')\n return self.res\n\ns = Son()\nprint('-----------------------------s')\nf = iter(s)\nfor i in f:\n print(i)\n\nprint(id(s) == id(f)) #True\n\nprint('-----------------------------s')\nfor i in s:\n print(i)\n\n# 由于在iter中定义了res 的初始值，所以可以多次for in 调用\nfor i in s:\n print(i)\n\n\nprint('----------next---------')\n# 在next()中无法赋值实现迭代器的复用\n# print(next(s))\n# print(next(s))\n# print(next(s))\n# print(next(s))\n\n# 迭代器：必须同时实现iter与next方法\n# 迭代对象：实现iter方法即可\nimport collections\n# 判定是否迭代器\nprint(isinstance(s,collections.Iterator))\n# 判定是否迭代对象\nprint(isinstance(s,collections.Iterable))\n\n\nprint('---------------iter--------------')\n# 会报错，需要定义__call__方法，方可被调用，遇到4终止，4不会被打印\n# print(iter(p,4))","repo_name":"CuteSmartTiger/mastering_python","sub_path":"OOP built method about instance/Traversal in instance.py","file_name":"Traversal in instance.py","file_ext":"py","file_size_in_byte":1889,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"17839596537","text":"from django.shortcuts import render\nimport datetime\nimport hashlib\nimport json\nfrom uuid import uuid4\nimport socket\nfrom urllib.parse import urlparse\nfrom django.http import JsonResponse, HttpResponse, HttpRequest\nfrom django.views.decorators.csrf import csrf_exempt #New\n\n\nclass Blockchain:\n\n def __init__(self):\n self.chain = []\n self.transactions = [] #New\n self.create_block(nonce = 1, previous_hash = '0')\n self.nodes = set() #New\n\n def create_block(self, nonce, previous_hash):\n block = {'index': len(self.chain) + 1,\n 'timestamp': str(datetime.datetime.now()),\n 'nonce': nonce,\n 'previous_hash': previous_hash,\n 'transactions': self.transactions #New\n }\n self.transactions = [] #New\n self.chain.append(block)\n return block\n\n def get_last_block(self):\n return self.chain[-1]\n\n def proof_of_work(self, previous_nonce):\n new_nonce = 1\n check_nonce = False\n while check_nonce is False:\n hash_operation = hashlib.sha256(str(new_nonce**2 - previous_nonce**2).encode()).hexdigest()\n if hash_operation[:4] == '0000':\n check_nonce = True\n else:\n new_nonce += 1\n return new_nonce\n\n def hash(self, block):\n encoded_block = json.dumps(block, sort_keys = True).encode()\n return hashlib.sha256(encoded_block).hexdigest()\n\n def is_chain_valid(self, chain):\n previous_block = chain[0]\n block_index = 1\n while block_index < len(chain):\n block = chain[block_index]\n if block['previous_hash'] != self.hash(previous_block):\n return False\n previous_nonce = previous_block['nonce']\n nonce = block['nonce']\n hash_operation = hashlib.sha256(str(nonce**2 - previous_nonce**2).encode()).hexdigest()\n if hash_operation[:4] != '0000':\n return False\n previous_block = block\n block_index += 1\n return True\n\n def add_transaction(self, sender, receiver, amount, time): #New\n self.transactions.append({'sender': sender,\n 'receiver': receiver,\n 'amount': amount,\n 'time': str(datetime.datetime.now())})\n previous_block = self.get_last_block()\n return previous_block['index'] + 1\n\n def add_node(self, address): #New\n parsed_url = urlparse(address)\n self.nodes.add(parsed_url.netloc)\n\n\n def replace_chain(self): #New\n network = self.nodes\n longest_chain = None\n max_length = len(self.chain)\n for node in network:\n response = requests.get(f'http://{node}/get_chain')\n if response.status_code == 200:\n length = response.json()['length']\n chain = response.json()['chain']\n if length > max_length and self.is_chain_valid(chain):\n max_length = length\n longest_chain = chain\n if longest_chain:\n self.chain = longest_chain\n return True\n return False\n\n\n# Creating our Blockchain\nblockchain = Blockchain()\n# Creating an address for the node running our server\nnode_address = str(uuid4()).replace('-', '') #New\nroot_node = 'e36f0158f0aed45b3bc755dc52ed4560d' #New\n\n# Mining a new block\ndef mine_block(request):\n if request.method == 'GET':\n previous_block = blockchain.get_last_block()\n previous_nonce = previous_block['nonce']\n nonce = blockchain.proof_of_work(previous_nonce)\n previous_hash = blockchain.hash(previous_block)\n blockchain.add_transaction(sender = root_node, receiver = node_address, amount = 1.15, time=str(datetime.datetime.now()))\n block = blockchain.create_block(nonce, previous_hash)\n response = {'message': 'Congratulations, you just mined a block!',\n 'index': block['index'],\n 'timestamp': block['timestamp'],\n 'nonce': block['nonce'],\n 'previous_hash': block['previous_hash'],\n 'transactions': block['transactions']}\n return JsonResponse(response)\n\n# Getting the full Blockchain\ndef get_chain(request):\n if request.method == 'GET':\n response = {'chain': blockchain.chain,\n 'length': len(blockchain.chain)}\n return JsonResponse(response)\n\n# Checking if the Blockchain is valid\ndef is_valid(request):\n if request.method == 'GET':\n is_valid = blockchain.is_chain_valid(blockchain.chain)\n if is_valid:\n response = {'message': 'All good. The Blockchain is valid.'}\n else:\n response = {'message': 'Houston, we have a problem. The Blockchain is not valid.'}\n return JsonResponse(response)\n\n# Adding a new transaction to the Blockchain\n@csrf_exempt\ndef add_transaction(request): #New\n if request.method == 'POST':\n received_json = json.loads(request.body)\n transaction_keys = ['sender', 'receiver', 'amount','time']\n if not all(key in received_json for key in transaction_keys):\n return 'Some elements of the transaction are missing', HttpResponse(status=400)\n index = blockchain.add_transaction(received_json['sender'], received_json['receiver'], received_json['amount'],received_json['time'])\n response = {'message': f'This transaction will be added to Block {index}'}\n return JsonResponse(response)\n\n# Connecting new nodes\n@csrf_exempt\ndef connect_node(request): #New\n if request.method == 'POST':\n received_json = json.loads(request.body)\n nodes = received_json.get('nodes')\n if nodes is None:\n return \"No node\", HttpResponse(status=400)\n for node in nodes:\n blockchain.add_node(node)\n response = {'message': 'All the nodes are now connected. The Sudocoin Blockchain now contains the following nodes:',\n 'total_nodes': list(blockchain.nodes)}\n return JsonResponse(response)\n\n# Replacing the chain by the longest chain if needed\ndef replace_chain(request): #New\n if request.method == 'GET':\n is_chain_replaced = blockchain.replace_chain()\n if is_chain_replaced:\n response = {'message': 'The nodes had different chains so the chain was replaced by the longest one.',\n 'new_chain': blockchain.chain}\n else:\n response = {'message': 'All good. The chain is the largest one.',\n 'actual_chain': blockchain.chain}\n return JsonResponse(response)","repo_name":"iamrraj/React_Django_Blockchain","sub_path":"blockchain/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6700,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"3952821232","text":"from collections import defaultdict\n\n\nf = open(\"5/input.txt\")\nlines = iter(f.readlines())\ncrates_height = 8\ncrates = defaultdict(list)\ncrates2 = defaultdict(list)\ninstructions = []\nfor i, l in enumerate(lines):\n if i < crates_height:\n for c in range(0, len(l), 4):\n if l[c + 1] != ' ':\n crates[int(c/4 + 1)].append(l[c + 1])\n crates2[int(c/4 + 1)].append(l[c + 1])\n if i >crates_height + 1:\n ammount, from_to = l.lstrip('move ').split(\" from \")\n origin, destiny = from_to.split(\" to \")\n origin = int(origin)\n destiny = int(destiny)\n ammount = int(ammount)\n # print(ammount)\n # print(origin)\n # print(destiny)\n # part 1\n for j in range(ammount):\n print(j)\n print(crates)\n item = crates[origin].pop(0)\n crates[destiny].insert(0, item)\n print(crates)\n # part 2\n chunk = crates2[origin][:ammount]\n crates2[origin] = crates2[origin][ammount:]\n chunk.extend(crates2[destiny])\n crates2[destiny] = chunk\n \n\n\n\n\nresult = [crates[i][0] for i in range(1, len(crates.keys()) + 1)]\nprint(\"\".join(result))\nprint(result)\n\nresult2 = [crates2[i][0] for i in range(1, len(crates2.keys()) + 1)]\nprint(\"\".join(result2))\nprint(result2)\n\nf.close()\n","repo_name":"marco-silva0000/aoc22","sub_path":"5/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70737322642","text":"import os\nimport json\nfrom datetime import datetime\nimport warnings\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nimport pandas as pd\nimport seaborn as sns\n\nBASIN_STATES = ['CA', 'CO', 'ID', 'MT', 'NM', 'NV', 'OR', 'UT', 'WA', 'WY']\n\nwarnings.filterwarnings(action='once')\n\nlarge = 22\nmed = 16\nsmall = 12\nparams = {'axes.titlesize': large,\n 'legend.fontsize': med,\n 'figure.figsize': (12, 12),\n 'axes.labelsize': med,\n 'xtick.labelsize': med,\n 'ytick.labelsize': med,\n 'figure.titlesize': large,\n 'xtick.color': 'black',\n 'ytick.color': 'black',\n 'xtick.direction': 'out',\n 'ytick.direction': 'out',\n 'xtick.bottom': True,\n 'xtick.top': True,\n 'ytick.left': True,\n 'ytick.right': True,\n }\nplt.rcParams.update(params)\nplt.style.use('seaborn-darkgrid')\nsns.set_style(\"dark\", {'axes.linewidth': 0.5})\n\n\ndef aggregated_heatmap(data_dir, fig_d, param='r2', basins=True, desc_str='basins', weighted=False):\n met_periods = list(range(1, 13)) + [18, 24, 30, 36]\n target_timescales = []\n for month in range(8, 9):\n use_periods = list(range(1, month - 2))\n\n data = os.path.join(data_dir, '{}_{}.json'.format(desc_str, month))\n\n with open(data, 'r') as f_obj:\n data = json.load(f_obj)\n\n if basins:\n combos = [('SPI', 'SFI'), ('SPEI', 'SFI'), ('SFI', 'SCUI'), ('SFI', 'SIMI')]\n else:\n combos = [('SPEI', 'SIMI'), ('SPI', 'SIMI'), ('SPEI', 'SCUI'), ('SPI', 'SCUI')]\n\n keys = [list(v.keys()) for k, v in data.items()]\n keys = list(set([item for sublist in keys for item in sublist]))\n\n param_vals = {k: [] for k in keys}\n area_vals = []\n\n for sid, v in data.items():\n for key in keys:\n if key == 'irr_area':\n area_vals.append(v[key])\n continue\n try:\n param_vals[key].append(v[key][param])\n except KeyError:\n param_vals[key].append(np.nan)\n\n for met, use in combos:\n grid = np.zeros((len(use_periods), len(met_periods)))\n for i, u in enumerate(use_periods):\n for j, m in enumerate(met_periods):\n slice_ = param_vals['{}_{}_{}_{}'.format(met, m, use, u)]\n if weighted:\n slice_ = np.array(slice_)\n nan = np.isnan(slice_)\n area_sum_, area_vals_ = np.sum(np.array(area_vals)[~nan]), np.array(area_vals)[~nan]\n weight = area_vals_ / area_sum_\n grid[i, j] = np.sum(slice_[~nan] * weight)\n else:\n grid[i, j] = np.mean(slice_)\n\n grid = pd.DataFrame(index=use_periods, columns=met_periods, data=grid).values\n\n ax = sns.heatmap(grid, square=True, annot=True, cmap='rocket', cbar=False,\n xticklabels=met_periods, yticklabels=use_periods, fmt='.2f')\n\n y, x = np.unravel_index(np.argmax(np.abs(grid), axis=None), grid.shape)\n ax.add_patch(Rectangle((x, y), 1, 1, fill=False, edgecolor='green', lw=4, clip_on=False))\n\n if met == 'SPEI':\n xaxis_str = 'Standardized Precipitation and Evaporation Index\\nMonths'\n else:\n xaxis_str = 'Standardized Precipitation Index'\n\n yaxis_str = 'Standardized Irrigation Managment Index\\nMonths'\n\n plt.xlabel(xaxis_str)\n plt.ylabel(None)\n plt.yticks(rotation=0)\n ax.tick_params(axis='y', which='major', pad=30)\n plt.tight_layout()\n fig_file = os.path.join(fig_d, param, '{}_{}_{}_heatmap.png'.format(met.lower(), use.lower(), month))\n plt.savefig(fig_file, bbox_inches='tight')\n plt.close()\n target_timescales.append((month, x + 1, y + 1))\n print('{:.3f} {}'.format(grid[y, x], os.path.basename(fig_file)))\n\n print('max correlation timescales: \\n', target_timescales)\n\n\ndef fields_heatmap(csv, attrs, fig_d):\n met_periods = list(range(1, 13)) + [18, 24, 30, 36]\n first = True\n bnames = [x for x in os.listdir(csv) if x.strip('.csv') in BASIN_STATES]\n bnames.sort()\n csv = [os.path.join(csv, x) for x in bnames]\n attrs = [os.path.join(attrs, x) for x in os.listdir(attrs) if x in bnames]\n attrs.sort()\n for m, f in zip(attrs, csv):\n c = pd.read_csv(f, index_col=0)\n meta = pd.read_csv(m, index_col='OPENET_ID')\n match = [i for i in c.index if i in meta.index]\n c.loc[match, 'usbrid'] = meta.loc[match, 'usbrid']\n\n if first:\n df = c.copy()\n first = False\n else:\n df = pd.concat([df, c])\n\n for i in range(3):\n if i == 0:\n part = 'all'\n tdf = df.copy()\n elif i == 1:\n part = 'usbr'\n tdf = df[df['usbrid'] > 0]\n else:\n part = 'nonusbr'\n tdf = df[df['usbrid'] == 0]\n\n for month in range(4, 11):\n use_periods = list(range(1, month - 2))\n\n title = 'Correlation'\n\n combos = [('SPEI', 'SIMI')]\n\n for met, use in combos:\n grid = np.zeros((len(use_periods), len(met_periods)))\n for i, u in enumerate(use_periods):\n for j, m in enumerate(met_periods):\n slice_ = tdf['met{}_ag{}_fr{}'.format(m, u, month)].values\n grid[i, j] = np.mean(slice_)\n\n grid = pd.DataFrame(index=use_periods, columns=met_periods, data=grid).values\n ax = sns.heatmap(grid, square=True, annot=True, cmap='rocket', fmt='.3g')\n y, x = np.unravel_index(np.argmax(np.abs(grid), axis=None), grid.shape)\n ax.add_patch(Rectangle((x, y), 1, 1, fill=False, edgecolor='green', lw=4, clip_on=False))\n plt.xlabel('{} - Months'.format(met))\n plt.ylabel('{}\\nMonths'.format(use))\n plt.yticks(rotation=0)\n ax.tick_params(axis='y', which='major', pad=30)\n mstr = datetime(1991, month, 1).strftime('%B')\n plt.title('Mean Field {}\\n{}'.format(title, mstr))\n plt.subplots_adjust(left=0.5)\n plt.tight_layout()\n fig_file = os.path.join(fig_d, part, '{}_{}_{}_heatmap.png'.format(met.lower(), use.lower(), month))\n plt.savefig(fig_file, bbox_inches='tight')\n plt.close()\n print('{:.3f} {}'.format(grid[y, x], os.path.basename(fig_file)))\n\n\nif __name__ == '__main__':\n root = '/media/research/IrrigationGIS/expansion'\n if not os.path.exists(root):\n root = '/home/dgketchum/data/IrrigationGIS/expansion'\n\n figs = os.path.join(root, 'figures', 'heatmaps', 'huc8')\n js_ = os.path.join(root, 'analysis', 'huc8_sensitivities')\n aggregated_heatmap(js_, figs, param='r2', basins=False, desc_str='huc8', weighted=True)\n\n p = 'scui'\n in_ = '/media/nvm/field_pts/indices/{}'.format(p)\n meta = '/media/nvm/field_pts/fields_data/fields_shp'\n figs = os.path.join(root, 'figures', 'heatmaps', 'fields', '{}'.format(p))\n # fields_heatmap(in_, meta, figs)\n\n# ========================= EOF ===================================================================\n","repo_name":"dgketchum/irr_expansion","sub_path":"figures/sensitivity_heatmap.py","file_name":"sensitivity_heatmap.py","file_ext":"py","file_size_in_byte":7505,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"70358484882","text":"from utils.print_seq import seqlog\nfrom utils.logger import log\nimport zmq\n\nLRU_READY = \"\\x01\"\nWORKER_ADDR = \"tcp://*:5556\"\nCLIENT_ADDR = \"tcp://*:5555\"\n\n\ndef queue():\n \"\"\"\n Load balancer\n - takes requests from the client and sends them into the workers' socket\n - takes messages from the workers and forwards them back to the clients\n \"\"\"\n context = zmq.Context(1)\n frontend = context.socket(zmq.ROUTER)\n backend = context.socket(zmq.ROUTER)\n frontend.bind(CLIENT_ADDR)\n backend.bind(WORKER_ADDR)\n poll_workers = zmq.Poller()\n poll_workers.register(backend, zmq.POLLIN)\n poll_both = zmq.Poller()\n poll_both.register(frontend, zmq.POLLIN)\n poll_both.register(backend, zmq.POLLIN)\n workers = []\n log.info(\"Queue has started\")\n while True:\n if len(workers) > 0:\n socks = dict(poll_both.poll())\n else:\n socks = dict(poll_workers.poll())\n if socks.get(backend) == zmq.POLLIN:\n # Handle worker activity on backend\n # Use worker address for LRU routing\n msg = backend.recv_multipart()\n if not msg:\n continue\n address = msg[0]\n workers.append(address)\n # Everything after the second (delimiter) frame is reply\n reply = msg[2:]\n # Forward message to client if it's not a READY\n if reply[0] != LRU_READY:\n seqlog(['Worker', 'Queue'], payload=reply[-1], rtl=False)\n frontend.send_multipart(reply)\n if socks.get(frontend) == zmq.POLLIN:\n # Get client request, route to first available worker\n msg = frontend.recv_multipart()\n request = [workers.pop(0), ''.encode()] + msg\n seqlog(['Client', 'Queue'], msg[-1])\n backend.send_multipart(request)\n\n\nif __name__ == '__main__':\n pass\n","repo_name":"jayrbolton/zmq-ch4","sub_path":"simple_pirate/queue.py","file_name":"queue.py","file_ext":"py","file_size_in_byte":1884,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"72718069200","text":"import os\nimport time\nf_names = open('venv/teste_cases/names_case4.txt', 'r')\nfs_names = f_names.readlines()\nfs_names = [x.replace('\\n', '') for x in fs_names]\nf_phones = open('venv/teste_cases/phones_case4.txt', 'r')\nfs_phones = f_phones.readlines()\nfs_phones = [x.replace('\\n', '') for x in fs_phones]\nf_emails = open('venv/teste_cases/emails_case4.txt', 'r')\nfs_emails = f_emails.readlines()\nfs_emails = [x.replace('\\n', '') for x in fs_emails]\ndef organizer(ff_names,ff_phones,ff_emails):\n x=[]\n y=[]\n if ff_names == [] or ff_phones == [] or ff_emails == []:\n return print('Error one or more files are empty')\n elif len(ff_names) != len(ff_phones) or len(fs_names) != len(ff_emails) or len(ff_phones) != len(ff_emails):\n return print('Error the number of inputs does not mach')\n else:\n for i in range(len(ff_names)):\n if len(ff_phones[i]) > 11 or len(ff_phones[i]) < 11:\n return print('Error the phone number', ff_phones[i], 'have a invalid number of digits')\n elif any(char.isdigit() for char in fs_names[i]):\n return print('Error the name',ff_names[i],'contain a number')\n else:\n x.append([ff_names[i]] + [ff_phones[i]] + [ff_emails[i]])\n# ------------------------------------------------------------------------------------------------------------------------------------\n# Code reference found on: https://www.geeksforgeeks.org/create-a-sorted-array-using-binary-search/ Accessed on: 27/02/2020 at 18:00\n if len(y)==0:\n y.append(x[i])\n else:\n start= 0\n end=len(y)-1\n pos= 0\n while start <= end:\n mid= (start + end)//2\n if y[mid] == x[i]:\n y.insert(max(0, mid + 1), x[i])\n break\n elif y[mid] > x[i]:\n pos=end=mid - 1\n else:\n pos=start=mid + 1\n if start > end:\n pos=start\n y.insert(max(0, pos), x[i])\n break\n# ------------------------------------------------------------------------------------------------------------------------------------\n return print(y)\nf_names.close()\nf_phones.close()\nf_emails.close()\nt1 = time.time()\norganizer(fs_names,fs_phones,fs_emails)\nt2 = time.time()\nprint('runing time is',t2-t1)","repo_name":"gustavo-varollo/algorithms_and_data_structures","sub_path":"Optimized Proposed Solution.py","file_name":"Optimized Proposed Solution.py","file_ext":"py","file_size_in_byte":2489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35832290989","text":"# Author: Xinyu Li\n# Email: xl1796@nyu.edu\n# Date: August 2019\n# Description: Some tools to determine function value based on linear approximation has been included here\nimport numpy as np\n\"\"\"not useful any more\"\"\"\n\ndef Linear_interpolation(t, I_list, t_list):\n \"\"\" Find out I at time t using a linear interpolation between two points in t_list\n @:param t: given time\n @:param I_list: a list of function values with respect to t\n @:param t_list: the corresponding time discretization \"\"\"\n\n # np.searchsorted - return i satisfies t_list[i-1] <= t < t_list[i]\n idx1 = np.searchsorted(t_list, t, side=\"right\") - 1\n idx2 = idx1+1\n\n if idx2 np.ndarray(), \"key2\" -> np.ndarray(), etc...}\n Using this dict we zip ndarrays rows and we serialize them as tfrecords\n to the output_filename. The schema (attributes) of the serialized tfrecords\n is based on the dictionary keys. The order of the keys in the input dictionary is\n preserved and is used to serialize to tfrecords.\n :param dataset_records_mappings:\n :param output_filename:\n :return:\n \"\"\"\n assert isinstance(dataset_records_mappings, dict)\n for val in dataset_records_mappings.values():\n assert isinstance(val, np.ndarray)\n\n # Attributes tf.data_type is returned in alphabetical order\n tfrecords_schema = collections.OrderedDict(\n {attr: tf.as_dtype(val.dtype.name) for attr, val in dataset_records_mappings.items()})\n\n # Open file writer\n tf_record_writer = tf.io.TFRecordWriter(output_filename)\n # Iterate over dataset's features generator\n for feature in cls._generate_tffeature(dataset_records_mappings):\n example = tf.train.Example(\n features=tf.train.Features(feature=feature))\n # Serialize the example to a string\n serialized = example.SerializeToString()\n # Write the serialized object to the file\n tf_record_writer.write(serialized)\n # Close file writer\n tf_record_writer.close()\n\n return tfrecords_schema\n\n\nclass MRIScanGen(object):\n\n def __init__(self, filepath, image_column, label_column,\n rows=91, cols=109, depth=91, channels=1):\n\n if not os.path.exists(filepath):\n print(\"Error: Filepath {} does not exist!\".format(filepath))\n exit(1)\n\n self.filepath = filepath\n\n print(self.filepath)\n self.tfrecord_output = self.filepath + \".tfrecord\"\n self.tfrecord_schema_output = self.filepath + \".tfrecord.schema\"\n\n self.image_column = image_column\n self.label_column = label_column\n self.rows = rows\n self.cols = cols\n self.depth = depth\n self.channels = channels\n\n def parse_csv_table(self, subj_table):\n\n if not set([self.image_column] + [self.label_column]).issubset(subj_table.columns):\n print(\"Error: Missing columns in table!\")\n exit(1)\n\n # Remove all irrelevant columns\n subj_table = subj_table[[self.image_column] + [self.label_column]]\n\n return subj_table\n\n def generate_tfrecord(self):\n \"\"\"\n If tfrecord already exists, then just return the tfrecord, else parse\n the .csv file and generate a new tfrecord.\n :return:\n \"\"\"\n if os.path.exists(self.tfrecord_output) \\\n and os.path.exists(self.tfrecord_schema_output):\n tfrecord_schema = cloudpickle.load(\n file=open(self.tfrecord_schema_output, \"rb\"))\n else:\n subj_table = pd.read_csv(self.filepath)\n subj_table = self.parse_csv_table(subj_table)\n\n images = subj_table.values[:, 0].tolist()\n labels = subj_table.values[:, 1].tolist()\n\n parsed_images = []\n parsed_labels = []\n for si, sl in zip(images, labels):\n image, label = self.load_v1(si, sl)\n parsed_images.append(image)\n parsed_labels.append(label)\n\n final_mappings = collections.OrderedDict()\n final_mappings[self.image_column] = np.array(parsed_images)\n final_mappings[self.label_column] = np.array(labels)\n\n tfrecord_schema = TFDatasetUtils.serialize_to_tfrecords(\n final_mappings, self.tfrecord_output)\n cloudpickle.dump(obj=tfrecord_schema, file=open(\n self.tfrecord_schema_output, \"wb+\"))\n\n return tfrecord_schema\n\n def load_v1(self, scan_path, label):\n img = nib.load(scan_path).get_fdata()\n # Normalize image.\n img_min = np.amax(img)\n img_max = np.amin(img)\n img = (img - img_min) / (img_max - img_min)\n img = img.astype(\"float32\")\n # Standardize image.\n # img = (img - img.mean()) / img.std()\n # scan = np.float32(img[:, :, :, np.newaxis]) \\\n # .reshape([self.rows, self.cols, self.depth, self.channels])\n label = float(label)\n return img, label\n\n def process_record(self, image, label):\n # The label is the assigned label to the MRI scan.\n # This could be the age of the scan (regression) or the AD value (classification/binary).\n image = tf.reshape(\n image, [self.rows, self.cols, self.depth, self.channels])\n label = tf.squeeze(label)\n return image, label\n\n def load_dataset(self, tfrecord_schema):\n # automatically interleaves reads from multiple files\n dataset = tf.data.TFRecordDataset(self.tfrecord_output)\n dataset = dataset.map(map_func=lambda x: TFDatasetUtils\n .deserialize_single_tfrecord_example(example_proto=x, example_schema=tfrecord_schema),\n num_parallel_calls=3)\n dataset = dataset.map(map_func=lambda x, y: self.process_record(x, y))\n return dataset\n\n def get_dataset(self):\n tfrecord_schema = self.generate_tfrecord()\n dataset = self.load_dataset(tfrecord_schema)\n return dataset\n\n\nclass ModelDatasetHelper(object):\n\n def __init__(self, image_column, label_column):\n self.image_column = image_column\n self.label_column = label_column\n\n def regression_task_dataset_recipe_fn(self, dataset_fp):\n dataset = MRIScanGen(filepath=dataset_fp,\n image_column=self.image_column,\n label_column=self.label_column).get_dataset()\n data_iter = iter(dataset)\n ages = []\n for d in data_iter:\n ages.append(float(d[1]))\n mode_values, mode_counts = scipy.stats.mode(ages)\n if np.all((mode_counts == 1)):\n mode_val = np.max(ages)\n else:\n mode_val = mode_values[0]\n model_dataset = ModelDatasetRegression(\n x=dataset, size=len(ages),\n min_val=np.min(ages), max_val=np.max(ages),\n mean_val=np.mean(ages), median_val=np.median(ages),\n mode_val=mode_val, stddev_val=np.std(ages))\n return model_dataset\n\n def classification_task_dataset_recipe_fn(self, dataset_fp):\n dataset = MRIScanGen(filepath=dataset_fp,\n image_column=self.image_column,\n label_column=self.label_column).get_dataset()\n data_iter = iter(dataset)\n classes = []\n for d in data_iter:\n classes.append(int(d[1]))\n records_num = len(classes)\n examples_per_class = {cid: 0 for cid in set(classes)}\n for cid in classes:\n examples_per_class[cid] += 1\n model_dataset = ModelDatasetClassification(\n x=dataset, size=records_num,\n examples_per_class=examples_per_class)\n return model_dataset\n\n\nif __name__ == \"__main__\":\n\n default_neuroimaging_task = \"brainage\"\n default_neuroimaging_task_model = \"3dcnn\"\n default_federation_environment_config_fp = os.path.join(\n SCRIPT_CWD, \"../config/brainage/brainage_test_localhost_synchronous.yaml\")\n default_mri_scans_csv_mapping = os.path.join(\n SCRIPT_CWD, \"datasets/ukbb/ukbb_datapaths_absolute.csv\")\n\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--neuroimaging_task\",\n default=default_neuroimaging_task,\n help=\"Either `brainage` OR `alzheimers`.\")\n parser.add_argument(\"--neuroimaging_task_model\",\n default=default_neuroimaging_task_model,\n help=\"Either `3dcnn` OR `2dcnn`.\")\n parser.add_argument(\"--federation_environment_config_fp\",\n default=default_federation_environment_config_fp)\n parser.add_argument(\"--dummy_scans_csv_mapping_fp\",\n default=default_mri_scans_csv_mapping)\n args = parser.parse_args()\n print(args, flush=True)\n\n nn_engine = \"keras\"\n metis_filepath_prefix = \"/tmp/metis/model\"\n if not os.path.exists(metis_filepath_prefix):\n os.makedirs(metis_filepath_prefix)\n\n model_definition_dir = \"{}/model_definition\".format(metis_filepath_prefix)\n train_dataset_recipe_fp_pkl = \"{}/model_train_dataset_ops.pkl\".format(\n metis_filepath_prefix)\n validation_dataset_recipe_fp_pkl = \"{}/model_validation_dataset_ops.pkl\".format(\n metis_filepath_prefix)\n test_dataset_recipe_fp_pkl = \"{}/model_test_dataset_ops.pkl\".format(\n metis_filepath_prefix)\n\n batch_size = FederationEnvironment(\n args.federation_environment_config_fp).local_model_config.batch_size\n if args.neuroimaging_task == \"brainage\":\n volume_attr, label_attr = \"9dof_2mm_vol\", \"age_at_scan\"\n dataset_recipe_fn = ModelDatasetHelper(volume_attr, label_attr) \\\n .regression_task_dataset_recipe_fn\n if args.neuroimaging_task_model == \"3dcnn\":\n nn_model = BrainAge3DCNN(batch_size=batch_size).get_model()\n elif args.neuroimaging_task_model == \"2dcnn\":\n nn_model = BrainAge2DCNN(batch_size=batch_size).get_model()\n else:\n raise RuntimeError(\"Unknown error.\")\n elif args.neuroimaging_task == \"alzheimers\":\n volume_attr, label_attr = \"volume\", \"label\"\n dataset_recipe_fn = ModelDatasetHelper(volume_attr, label_attr) \\\n .classification_task_dataset_recipe_fn\n if args.neuroimaging_task_model == \"3dcnn\":\n nn_model = AlzheimersDisease3DCNN().get_model()\n elif args.neuroimaging_task_model == \"2dcnn\":\n nn_model = AlzheimersDisease2DCNN().get_model()\n else:\n raise RuntimeError(\"Unknown error.\")\n else:\n raise RuntimeError(\"Unknown task.\")\n\n # Load dummy data for model initialization purposes.\n dummy_dataset = MRIScanGen(\n filepath=args.dummy_scans_csv_mapping_fp,\n image_column=volume_attr,\n label_column=label_attr) \\\n .get_dataset()\n\n nn_model.summary()\n nn_model.evaluate(x=dummy_dataset.batch(1))\n nn_model.save(model_definition_dir)\n\n cloudpickle.dump(obj=dataset_recipe_fn, file=open(\n train_dataset_recipe_fp_pkl, \"wb+\"))\n cloudpickle.dump(obj=dataset_recipe_fn, file=open(\n test_dataset_recipe_fp_pkl, \"wb+\"))\n cloudpickle.dump(obj=dataset_recipe_fn, file=open(\n validation_dataset_recipe_fp_pkl, \"wb+\"))\n\n driver_session = DriverSession(args.federation_environment_config_fp,\n model=nn_model,\n train_dataset_recipe_fn=dataset_recipe_fn,\n validation_dataset_recipe_fn=dataset_recipe_fn,\n test_dataset_recipe_fn=dataset_recipe_fn)\n driver_session.initialize_federation()\n driver_session.monitor_federation()\n driver_session.shutdown_federation()\n statistics = driver_session.get_federation_statistics()\n\n with open(os.path.join(SCRIPT_CWD, \"experiment.json\"), \"w+\") as fout:\n print(\"Execution File Output Path:\", fout.name, flush=True)\n json.dump(statistics, fout, indent=4)\n","repo_name":"NevronAI/metisfl","sub_path":"examples/legacy/keras_neuroimaging/neuroimaging.py","file_name":"neuroimaging.py","file_ext":"py","file_size_in_byte":14923,"program_lang":"python","lang":"en","doc_type":"code","stars":81,"dataset":"github-code","pt":"3"}
+{"seq_id":"33410787966","text":"#!/usr/bin/env python3.5\n# encoding: utf-8\nimport logging\nfrom json import dumps as jdumps\n\nfrom sanic.response import json\n\nfrom views.utils import get_user_name\nfrom views.utils import MCV\n\nfrom models import Question\nfrom models import Users\n\nfrom utils import safe_del_key\n\n\n# noinspection PyBroadException\nclass QuestionView(MCV):\n _cls = Question\n _urls = ['/api/question', '/api/question/']\n\n async def post(self):\n try:\n req = self.req.json\n if req['qtype'] == 'abcd':\n req['answers'] = jdumps(\n [req['ans_a'], req['ans_b'], req['ans_c'], req['ans_d']]\n )\n req = safe_del_key(req, ['ans_a', 'ans_b', 'ans_c', 'ans_d'])\n question = Question(**req)\n await question.create()\n return json({'success': True})\n except:\n logging.exception('err question.post')\n return json({}, status=500)\n\n async def put(self, qid=0):\n try:\n req = self.req.json\n user = await Users.get_first('email', req['reviewer'])\n question = await Question.get_by_id(qid)\n question.reviewer = user.id\n question.active = req['accept']\n await question.update()\n return json({'success': True})\n except:\n logging.exception('err question.update')\n return json({}, status=500)\n\n async def get(self, qid=0):\n if qid:\n question = await Question.get_by_id(qid)\n return json(await question.to_dict())\n questions = await Question.get_all()\n resp = []\n for q in questions:\n data = await q.to_dict()\n data['creator'] = await get_user_name(data['users'])\n resp.append(data)\n return json(resp, sort_keys=True)\n","repo_name":"pdyba/aioquiz","sub_path":"aioquiz/views/learning/question.py","file_name":"question.py","file_ext":"py","file_size_in_byte":1850,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"3"}
+{"seq_id":"30416672611","text":"def f_gold ( num ) :\n series = [ 1 , 3 , 2 , - 1 , - 3 , - 2 ] ;\n series_index = 0 ;\n result = 0 ;\n for i in range ( ( len ( num ) - 1 ) , - 1 , - 1 ) :\n digit = ord ( num [ i ] ) - 48 ;\n result += digit * series [ series_index ] ;\n series_index = ( series_index + 1 ) % 6 ;\n result %= 7 ;\n if ( result < 0 ) :\n result = ( result + 7 ) % 7 ;\n return result ;\n\n\n#TOFILL\n\nif __name__ == '__main__':\n param = [\n ('K',),\n ('850076',),\n ('00111',),\n ('X',),\n ('1',),\n ('10010001100',),\n (' pgPeLz',),\n ('53212456821275',),\n ('101',),\n ('V',)\n ]\n n_success = 0\n for i, parameters_set in enumerate(param):\n if f_filled(*parameters_set) == f_gold(*parameters_set):\n n_success+=1\n print(\"#Results: %i, %i\" % (n_success, len(param)))","repo_name":"facebookresearch/TransCoder","sub_path":"data/evaluation/geeks_for_geeks_successful_test_scripts/python/REMAINDER_7_LARGE_NUMBERS.py","file_name":"REMAINDER_7_LARGE_NUMBERS.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":1646,"dataset":"github-code","pt":"3"}
+{"seq_id":"72500078800","text":"import torch\r\nimport os\r\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\r\nflag = torch.cuda.is_available()\r\nprint(flag)\r\n\r\nngpu = 1\r\n# Decide which device we want to run on\r\ndevice = torch.device(\"cuda:0\" if (torch.cuda.is_available() and ngpu > 0) else \"cpu\")\r\nprint(device)\r\nprint(torch.cuda.get_device_name(0))\r\nprint(torch.rand(3, 3).cuda())\r\ngpu_id = os.environ['CUDA_VISIBLE_DEVICES']\r\ngpu_id = str(gpu_id)\r\nprint(gpu_id)","repo_name":"Roy2021098/-","sub_path":"code/Torch GPU test.py","file_name":"Torch GPU test.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32322195912","text":"class CPU:\n def __init__(self):\n self.PC = 0\n self.R7 = 0\n self.registers = [0] * 32\n self.zero_flag = False\n\n def execute_instruction(self, instruction):\n opcode = instruction[0:6]\n if opcode == '000000': \n funct = instruction[-6:]\n if funct == '100000':\n self.execute_add(instruction)\n elif opcode == '001000':\n self.execute_addi(instruction)\n\n def execute_add(self, instruction):\n rd = int(instruction[16:21], 2)\n rs = int(instruction[6:11], 2)\n rt = int(instruction[11:16], 2)\n \n \n result = self.registers[rs] + self.registers[rt]\n self.registers[rd] = result\n self.PC += 4 \n\n def execute_addi(self, instruction):\n \n rt = int(instruction[11:16], 2)\n rs = int(instruction[6:11], 2)\n immediate = int(instruction[16:], 2)\n \n \n result = self.registers[rs] + immediate\n self.registers[rt] = result\n self.PC += 4\n\n \n\n def halt_execution(self):\n print(\"Execution halted.\")\n \n\n def get_state(self):\n return {\n 'PC': self.PC,\n 'R7': self.R7,\n 'registers': self.registers,\n 'zero_flag': self.zero_flag\n }","repo_name":"Solon1te/Cpu_Simulator","sub_path":"CPU.py","file_name":"CPU.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18611895446","text":"import re\nimport sys\n\nstmt_tok_patterns = [\n (r'\\(', 'LPAREN'),\n (r'\\)', 'RPAREN'),\n (r'[A-Za-z_][A-Za-z0-9_]*', 'ATOM'),\n (r'\\\\bot', 'BOT'),\n (r'\\->', 'IMPL'),\n (r'\\!', 'NOT'),\n (r'/\\\\', 'AND'),\n (r'\\\\/', 'OR'),\n (r'\\|-', 'DERIVES'),\n (r'\\,', 'SEP'),\n (r'\\[[^\\]]+\\]', 'EXPL')\n]\n\ntoken_stmt_regex = '|'.join(f'({pattern})' for pattern, token_type in stmt_tok_patterns)\n\ndef lex_stmt(expression):\n tokens = []\n while expression:\n match = re.match(token_stmt_regex, expression)\n if match:\n for i in range(1, len(match.groups()) + 1):\n if match.group(i):\n token_value = match.group(i)\n token_type = stmt_tok_patterns[i - 1][1]\n tokens.append((token_type, token_value))\n expression = expression[len(token_value):].lstrip()\n break\n # If unable to match\n else:\n print(\"Unable to tokenize expression\")\n break\n return tokens\n\n# Test the lexer\n# expression = \"[or-el 4, 5-6, 7-9] p /\\ r\"\n# expression = \"q -> r, p, q |- p \\/ q -> p \\/ r\"\n# tokens = lex_stmt(expression)\n# print(tokens)\n# for token_type, token_value in tokens:\n# print(f\"Token Type: {token_type}, Token Value: {token_value}\")\n\n# EXPRESSION TREE DECLARATION\n\nclass TreeNode:\n def __init__(self, val, left, right):\n self.val = val\n self.left = left\n self.right = right\n\n# For testing\ndef inorder_traversal(root):\n if root is not None:\n inorder_traversal(root.left)\n print(root.val, end=' ')\n inorder_traversal(root.right)\n\ndef parse_stmt(s, i):\n if (len(s) == 0):\n return (None,1)\n c = s[i]\n i += 1\n if (c[0]=='ATOM'):\n return (TreeNode(c[1], None, None), i)\n\n if (c[0] == 'BOT'):\n return (TreeNode(c[0], None, None), i)\n\n if (c[0] == 'NOT'):\n # (exp, j) = parse_stmt(s, i)\n assert s[i][0] == 'LPAREN', 'syntax error'\n i += 1\n if (s[i+1][0] == 'RPAREN'):\n return (TreeNode('NOT', None, TreeNode(s[i][0], None, None)), i+2)\n (left, j) = parse_stmt(s, i)\n i = j\n op = s[i][0]\n i += 1\n (right, j) = parse_stmt(s, i)\n i = j\n c = s[i]\n assert c[0] == 'RPAREN', 'syntax error'\n i+= 1\n # return (, i)\n return (TreeNode('NOT', None, TreeNode(op, left, right)), j)\n\n if c[0] == 'LPAREN':\n if (s[i][0] == 'NOT'):\n i += 1\n (exp, j) = parse_stmt(s, i)\n i = j\n c = s[i]\n assert c[0] == 'RPAREN', 'syntax error'\n i += 1\n return (TreeNode('NOT', None, exp), i)\n else:\n (left, j) = parse_stmt(s, i)\n i = j\n op = s[i][0]\n i += 1\n (right, j) = parse_stmt(s, i)\n i = j\n c = s[i]\n assert c[0] == 'RPAREN', 'syntax error'\n i+= 1\n return (TreeNode(op, left, right), i)\n\ndef parse(s):\n return parse_stmt(s,0)[0]\n\n# expr = \"((!q) -> r)\"\n# toks = lex_stmt(expr)\n# print(toks)\n# # node = parse_stmt(toks,0)[0]\n# node = parse(toks)\n# inorder_traversal(node)\n\nexpl_tok_patterns = [\n (r'\\[', 'IGNORE'),\n (r'\\]', 'IGNORE'),\n (r'[a-zA-z]+[-A-Za-z0-9]+', 'RULE'),\n # (r'[a-zA-z]+\\-[a-zA-z0-9]+', 'RULE'),\n (r'[0-9]+', 'LINENUM'),\n (r'\\,', 'SEP'),\n (r'-', 'RANGETO'),\n]\n\ntoken_expl_regex = '|'.join(f'({pattern})' for pattern, token_type in expl_tok_patterns)\n\ndef lex_expl_stmt(expression):\n tokens = []\n while expression:\n match = re.match(token_expl_regex, expression)\n if match:\n for i in range(1, len(match.groups()) + 1):\n if match.group(i):\n token_value = match.group(i)\n token_type = expl_tok_patterns[i - 1][1]\n tokens.append((token_type, token_value))\n expression = expression[len(token_value):].lstrip()\n break\n # If unable to match\n else:\n print(\"Unable to tokenize expression\")\n break\n return tokens\n\n# Test the lexer\n# expression = \"[or-el 4, 5-6, 7-9] p /\\ r\"\n# expl = \"[or-el 4, 5-6, 7-9]\"\n# toks_expl = lex_expl_stmt(expl)\n# print(toks_expl)\n# for token_type, token_value in toks_expl:\n# print(f\"Token Type: {token_type}, Token Value: {token_value}\")\n\n## Decode each explanation\n\ndef decode_expl_toks(toks):\n opcode = None\n lines = []\n ranges = []\n i = 0\n assert toks[i][0] == 'IGNORE', 'syntax error'\n i += 1\n curr_range = []\n\n while (toks[i][0] != 'IGNORE'):\n c = toks[i]\n if (c[0] == 'RULE'):\n opcode = c[1]\n i += 1\n elif (c[0] == 'LINENUM'):\n if (len(curr_range) == 0):\n if (toks[i+1][0] == 'SEP' or toks[i+1][0] == 'IGNORE'):\n lines.append(int(c[1])-1)\n i+= 1\n else:\n curr_range.append(int(c[1])-1)\n i += 2\n else:\n curr_range.append(int(c[1])-1)\n ranges.append(tuple(curr_range))\n curr_range = []\n i += 1\n else:\n i += 1\n return (opcode, lines, ranges)\n\n# decode_expl_toks(toks_expl)\n\n## Check a line of the proof\n\ndef check_expr_trees(t1, t2):\n if (t1 == None and t2 == None):\n return True\n elif (t1 != None and t2 != None):\n if (t1.val != t2.val):\n return False\n else:\n return check_expr_trees(t1.left, t2.left) and check_expr_trees(t1.right, t2.right)\n else:\n return False\n\n# Function to check if line is in scope\ndef chk_line_in_scope(ct, idx, scope, scope_start_end_map):\n # ct = current statement idx\n # idx = statement to be checked idx\n # check if the closest scope of idx contains the scope of ct\n for s in scope[ct]:\n if (s == scope[idx][0]):\n return True\n return False\n\ndef chk_line_in_par_scope(ct, idx, scope, scope_start_end_map):\n # ct = current statement idx\n # idx = statement to be checked idx\n # check if the parent scope of idx is the scope of ct\n f = scope[idx]\n if (len(f) > 1):\n x = f[1]\n for s in scope[ct]:\n if (s == x):\n return True\n return False\n return False\n\ndef chk_proof_line(statement, exprs, premises, opcode, lines, ranges,\n ct, scope, scope_start_end_map):\n ## Check based on operand\n if (opcode == 'premise'):\n # inorder_traversal(statement)\n # print(\"\\n\")\n for pr in premises:\n # inorder_traversal(pr)\n # print(\"\\n\")\n if check_expr_trees(pr, statement):\n return True\n return False\n\n elif (opcode == 'assumption'):\n return True\n\n elif (opcode == 'copy'):\n # Allow copy from a parent\n chk0 = chk_line_in_scope(ct, lines[0], scope, scope_start_end_map)\n # check_line_in_scope_or_parent(lines[0], scope, scope_start_end_map)\n return chk0 and check_expr_trees(exprs[lines[0]], statement)\n\n elif (opcode == 'mp'):\n j = lines[0] # a\n i = lines[1] # a -> b\n chk1 = (exprs[i].val == 'IMPL')\n chk2 = check_expr_trees(exprs[i].left, exprs[j])\n chk3 = check_expr_trees(exprs[i].right, statement)\n chk4 = chk_line_in_scope(ct, j, scope, scope_start_end_map)\n chk5 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n # print(chk1, chk2, chk3)\n return chk1 and chk2 and chk3 and chk4 and chk5\n\n elif (opcode == 'mt'):\n i = lines[0] # a -> b\n j = lines[1] # neg b\n chk1 = (exprs[i].val == 'IMPL')\n chk2 = exprs[j].val == 'NOT' and check_expr_trees(exprs[i].right, exprs[j].right)\n chk3 = statement.val == 'NOT' and check_expr_trees(exprs[i].left, statement.right)\n chk4 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n chk5 = chk_line_in_scope(ct, j, scope, scope_start_end_map)\n # print(chk1, chk2, chk3, chk4, chk5)\n return chk1 and chk2 and chk3 and chk4 and chk5\n\n elif (opcode == 'and-in'):\n i = lines[0] # a\n j = lines[1] # b\n chk1 = (statement.val == 'AND')\n chk2 = check_expr_trees(statement.left, exprs[i])\n chk3 = check_expr_trees(statement.right, exprs[j])\n chk4 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n chk5 = chk_line_in_scope(ct, j, scope, scope_start_end_map)\n return chk1 and chk2 and chk3 and chk4 and chk5\n\n elif (opcode == 'and-e1'):\n i = lines[0] # a ^ b\n chk1 = (exprs[i].val == 'AND')\n chk2 = check_expr_trees(exprs[i].left, statement)\n # chk3 = check_expr_trees(exprs[i].left, statement)\n chk3 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n return chk1 and chk2 and chk3\n\n elif (opcode == 'and-e2'):\n i = lines[0] # a ^ b\n chk1 = (exprs[i].val == 'AND')\n chk2 = check_expr_trees(exprs[i].right, statement)\n # chk3 = check_expr_trees(exprs[i].left, statement)\n chk3 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n return chk1 and chk2\n\n elif (opcode == 'or-in1'):\n i = lines[0] # a\n chk1 = check_expr_trees(statement.left, exprs[i])\n chk2 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n return chk1 and chk2\n\n elif (opcode == 'or-in2'):\n i = lines[0] # a\n chk1 = check_expr_trees(statement.right, exprs[i])\n chk2 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n return chk1 and chk2\n\n elif (opcode == 'or-el'):\n i = lines[0] # a\n j,k = ranges[0]\n l,m = ranges[1]\n\n chk0 = (exprs[i].val == \"OR\")\n phi = exprs[i].left\n psi = exprs[i].right\n\n chk1 = check_expr_trees(phi, exprs[j])\n chk2 = check_expr_trees(psi, exprs[l])\n # inorder_traversal(exprs[k])\n # print(\"\\n\")\n # inorder_traversal(exprs[m])\n # print(\"\\n\")\n # inorder_traversal(statement)\n # print(\"\\n\")\n chk3 = check_expr_trees(exprs[k], exprs[m])\n chk4 = check_expr_trees(exprs[k], statement)\n # print(chk0, chk1, chk2, chk3, chk4)\n\n # Scope checking\n chk5 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n chk6 = chk_line_in_par_scope(ct, j, scope, scope_start_end_map)\n chk7 = chk_line_in_par_scope(ct, l, scope, scope_start_end_map)\n\n return chk0 and chk1 and chk2 and chk3 and chk4 and chk5 and chk6 and chk7\n\n elif (opcode == 'impl-in'):\n i, j = ranges[0]\n chk1 = (statement.val == 'IMPL')\n chk2 = check_expr_trees(statement.left, exprs[i])\n chk3 = check_expr_trees(statement.right, exprs[j])\n # print(chk1,chk2,chk3)\n chk4 = chk_line_in_par_scope(ct, i, scope, scope_start_end_map)\n chk5 = (i in scope_start_end_map) and (j == scope_start_end_map[i])\n # print(i,j, scope_start_end_map)\n # print(chk5)\n # inorder_traversal(exprs[i])\n # inorder_traversal(statement.left)\n # print(\"\\n\")\n # inorder_traversal(exprs[j])\n # inorder_traversal(statement.right)\n # print(\"\\n\")\n # print(chk1,chk2,chk3, chk4)\n return chk1 and chk2 and chk3 and chk4 and chk5\n\n elif (opcode == 'neg-in'):\n i, j = ranges[0]\n chk1 = (statement.val == 'NOT')\n chk2 = check_expr_trees(exprs[i], statement.right)\n chk3 = (exprs[j].val == 'BOT')\n chk4 = chk_line_in_par_scope(ct, i, scope, scope_start_end_map)\n return chk1 and chk2 and chk3 and chk4\n\n elif (opcode == 'neg-el'):\n i = lines[0]\n j = lines[1]\n chk1 = (exprs[j].val == 'NOT') and check_expr_trees(exprs[j].right, exprs[i])\n chk2 = (statement.val == 'BOT')\n chk3 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n chk4 = chk_line_in_scope(ct, j, scope, scope_start_end_map)\n return chk1 and chk2 and chk3 and chk4\n\n elif (opcode == 'bot-el'):\n i = lines[0]\n chk1 = (exprs[i].val == 'BOT')\n chk2 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n return chk1 and chk2\n\n elif (opcode == 'dneg-in'):\n i = lines[0]\n chk1 = (statement.val == 'NOT')\n chk2 = (statement.right != None and statement.right.val == 'NOT')\n chk3 = check_expr_trees(statement.right.right, exprs[i])\n chk4 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n return chk1 and chk2 and chk3 and chk4\n\n elif (opcode == 'dneg-el'):\n i = lines[0]\n chk1 = (exprs[i].val == 'NOT')\n chk2 = (exprs[i].right != None and exprs[i].right.val == 'NOT')\n chk3 = check_expr_trees(exprs[i].right.right, statement)\n chk4 = chk_line_in_scope(ct, i, scope, scope_start_end_map)\n return chk1 and chk2 and chk3 and chk4\n\n elif (opcode == 'pbc'):\n i, j = ranges[0]\n chk1 = (exprs[i].val == 'NOT')\n chk2 = check_expr_trees(exprs[i].right, statement)\n chk3 = (exprs[j].val == 'BOT')\n chk4 = chk_line_in_par_scope(ct, i, scope, scope_start_end_map)\n return chk1 and chk2 and chk3 and chk4\n\n elif (opcode == 'lem'):\n chk1 = (statement.val == 'OR')\n chk2 = (statement.right != None and statement.right.val == 'NOT')\n chk3 = check_expr_trees(statement.left, statement.right.right)\n # print(chk1,chk2,chk3)\n return chk1 and chk2 and chk3\n\n else:\n print(\"Unknown opcode!\", opcode)\n return False\n\n return True\n\n## SCOPE CHECKER\n\ndef get_lines_from_proof_file(pf_file):\n proof_file = open(pf_file, 'r')\n lines = proof_file.readlines()\n return lines\n\ndef get_scope_info(lines):\n # Stores the id of the closest binding scope for each line\n scope_map = {}\n scope_start_end_map = {}\n ct = 2\n\n m = ct\n while (m < len(lines)):\n scope_map[m] = list([])\n m += 1\n\n while(ct < len(lines)):\n # Parse the line\n toks = lex_stmt(lines[ct])\n\n expl_toks = lex_expl_stmt(toks[0][1])\n opcode, e_lines, e_ranges = decode_expl_toks(expl_toks)\n\n # Check to detect a scope\n close_scope_ops = ['neg-in', 'pbc', 'impl-in']\n if (opcode in close_scope_ops):\n i, j = e_ranges[0]\n # print(i,j)\n if (i in scope_start_end_map):\n scope_start_end_map[i] = max(j,scope_start_end_map[i])\n else:\n scope_start_end_map[i] = j\n for z in range(i, j+1):\n # print(z)\n scope_map[z].append(i)\n\n elif (opcode == 'or-el'):\n i, j = e_ranges[0]\n k, l = e_ranges[1]\n if (i in scope_start_end_map):\n scope_start_end_map[i] = max(j,scope_start_end_map[i])\n else:\n scope_start_end_map[i] = j\n if (k in scope_start_end_map):\n scope_start_end_map[k] = max(l,scope_start_end_map[k])\n else:\n scope_start_end_map[k] = l\n # scope_start_end_map[k] = l\n # print(i,j,k,l)\n for z in range(i, j+1):\n scope_map[z].append(i)\n for z in range(k, l+1):\n scope_map[z].append(k)\n ct += 1\n\n m = 2\n while(m < len(lines)):\n scope_map[m].append(2)\n m+=1\n\n # At the exit of the functions, each line now has the info\n # About the starting lines of ALL the scopes they are contained in\n return (scope_map, scope_start_end_map)\n\n## NATURAL DEDUCTION PROOF CHECKER\n\n## Main Function to Check Proof\ndef check_proof(proof_filename):\n ## Get lines from the input file\n lines = get_lines_from_proof_file(proof_filename)\n scope_map, scope_start_end_map = get_scope_info(lines)\n ct = 0\n\n # Premises and target\n premises = []\n target = None\n\n # Array to store the available expressions at each scope level\n # maps[0] stores the map at the topmost scope level\n scopes = []\n top_level_scope = {}\n scopes.append(top_level_scope)\n\n ## Lex the first line and get the premises and the proposition\n toks = lex_stmt(lines[ct])\n ct += 2\n\n i = 0\n premise = []\n # Get the premises\n while(True):\n if(toks[i][0] != 'SEP' and toks[i][0] != 'DERIVES'):\n premise.append(toks[i])\n i+=1\n else:\n if(toks[i][0] == 'SEP'):\n premises.append(parse(premise))\n premise = []\n i += 1\n else:\n # print(premise)\n premises.append(parse(premise))\n i+=1\n break\n # Set the target\n target = parse(toks[i:])\n\n while(ct < len(lines)):\n # For each line in the proof\n # First see the proof rule being applied and the line numbers\n # Then parse the rest of the line\n # Finally, conclude by seeing the line numbers and the proof rule\n # whether this line of the proof is correct or not\n # If correct, push it into the current scope\n toks = lex_stmt(lines[ct])\n # print(toks)\n\n # Process the explanation here\n expl_toks = lex_expl_stmt(toks[0][1])\n # print(expl_toks)\n opcode, e_lines, e_ranges = decode_expl_toks(expl_toks)\n\n # Check if we need to start a new scope\n if (opcode == 'assumption'):\n # Check if some start of scope has been detected here\n if ct not in scope_start_end_map:\n # print(scope_start_end_map)\n return False\n\n # Get scope of current line\n curr_line_scope = scope_map[ct]\n\n # if (opcode == 'assumption'):\n # new_scope = scopes[-1].copy()\n # scopes.append(new_scope)\n\n # Parse and convert the rest of the statement\n scopes[-1][ct] = parse(toks[1:])\n # inorder_traversal(scopes[-1][ct])\n\n # inorder_traversal(scopes[-1][ct])\n # print(\"\\n---\")\n\n # Check the proof line\n correct = chk_proof_line(scopes[-1][ct], scopes[-1], premises, \\\n opcode, e_lines, e_ranges, ct, \\\n scope_map, scope_start_end_map)\n\n if (not correct):\n return False\n else:\n print(\"Line \", ct+1, \" correct\")\n\n # Go to next line\n ct += 1\n\n # Cleanup:\n if check_expr_trees(scopes[-1][ct-1], target):\n return True\n return False\n\ndef checkProof(filename):\n res = check_proof(filename)\n if (res):\n return \"correct\"\n else:\n return \"incorrect\"\n\n# Main Function\nif __name__ == '__main__':\n if len(sys.argv) != 2:\n print(\"Usage: python script.py \")\n sys.exit(1)\n filename = sys.argv[1]\n res = checkProof(filename)\n print(res)","repo_name":"ChinmayMittal/IITD-CSE","sub_path":"4th-year/Courses/COL703/Assignments/Fall'23/Programs/A1.py","file_name":"A1.py","file_ext":"py","file_size_in_byte":17383,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"3"}
+{"seq_id":"36112005177","text":"#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport contextlib\nimport os\nimport re\nimport textwrap\nimport time\nfrom urllib import parse\n\nfrom oslo_serialization import jsonutils\nfrom oslo_utils import encodeutils\nfrom oslo_utils import uuidutils\nimport prettytable\n\nfrom novaclient import exceptions\nfrom novaclient.i18n import _\n\n\nVALID_KEY_REGEX = re.compile(r\"[\\w\\.\\- :]+$\", re.UNICODE)\n\n\ndef env(*args, **kwargs):\n \"\"\"Returns the first environment variable set.\n\n If all are empty, defaults to '' or keyword arg `default`.\n \"\"\"\n for arg in args:\n value = os.environ.get(arg)\n if value:\n return value\n return kwargs.get('default', '')\n\n\ndef get_service_type(f):\n \"\"\"Retrieves service type from function.\"\"\"\n return getattr(f, 'service_type', None)\n\n\ndef unauthenticated(func):\n \"\"\"Adds 'unauthenticated' attribute to decorated function.\n\n Usage:\n\n >>> @unauthenticated\n ... def mymethod(f):\n ... pass\n \"\"\"\n func.unauthenticated = True\n return func\n\n\ndef isunauthenticated(func):\n \"\"\"Checks if the function does not require authentication.\n\n Mark such functions with the `@unauthenticated` decorator.\n\n :returns: bool\n \"\"\"\n return getattr(func, 'unauthenticated', False)\n\n\ndef arg(*args, **kwargs):\n \"\"\"Decorator for CLI args.\n\n Example:\n\n >>> @arg(\"name\", help=\"Name of the new entity\")\n ... def entity_create(args):\n ... pass\n \"\"\"\n def _decorator(func):\n add_arg(func, *args, **kwargs)\n return func\n return _decorator\n\n\ndef add_arg(func, *args, **kwargs):\n \"\"\"Bind CLI arguments to a shell.py `do_foo` function.\"\"\"\n\n if not hasattr(func, 'arguments'):\n func.arguments = []\n\n # NOTE(sirp): avoid dups that can occur when the module is shared across\n # tests.\n if (args, kwargs) not in func.arguments:\n # Because of the semantics of decorator composition if we just append\n # to the options list positional options will appear to be backwards.\n func.arguments.insert(0, (args, kwargs))\n\n\ndef service_type(stype):\n \"\"\"Adds 'service_type' attribute to decorated function.\n\n Usage:\n\n .. code-block:: python\n\n @service_type('volume')\n def mymethod(f):\n ...\n \"\"\"\n def inner(f):\n f.service_type = stype\n return f\n return inner\n\n\ndef pretty_choice_list(values):\n return ', '.join(\"'%s'\" % x for x in values)\n\n\ndef pretty_choice_dict(values):\n \"\"\"Returns a formatted dict as 'key=value'.\"\"\"\n return pretty_choice_list(\n ['%s=%s' % (k, values[k]) for k in sorted(values)])\n\n\ndef print_list(objs, fields, formatters={}, sortby_index=None):\n if sortby_index is None:\n sortby = None\n else:\n sortby = fields[sortby_index]\n mixed_case_fields = ['serverId']\n pt = prettytable.PrettyTable([f for f in fields], caching=False)\n pt.align = 'l'\n\n for o in objs:\n row = []\n for field in fields:\n if field in formatters:\n row.append(formatters[field](o))\n else:\n if field in mixed_case_fields:\n field_name = field.replace(' ', '_')\n else:\n field_name = field.lower().replace(' ', '_')\n data = getattr(o, field_name, '')\n if data is None:\n data = '-'\n # '\\r' would break the table, so remove it.\n data = str(data).replace(\"\\r\", \"\")\n row.append(data)\n pt.add_row(row)\n\n if sortby is not None:\n result = encodeutils.safe_encode(pt.get_string(sortby=sortby))\n else:\n result = encodeutils.safe_encode(pt.get_string())\n\n result = result.decode()\n\n print(result)\n\n\ndef _flatten(data, prefix=None):\n \"\"\"Flatten a dict, using name as a prefix for the keys of dict.\n\n >>> _flatten('cpu_info', {'arch':'x86_64'})\n [('cpu_info_arch': 'x86_64')]\n\n \"\"\"\n if isinstance(data, dict):\n for key, value in data.items():\n new_key = '%s_%s' % (prefix, key) if prefix else key\n if isinstance(value, (dict, list)) and value:\n for item in _flatten(value, new_key):\n yield item\n else:\n yield new_key, value\n else:\n yield prefix, data\n\n\ndef flatten_dict(data):\n \"\"\"Return a new dict whose sub-dicts have been merged into the\n original. Each of the parents keys are prepended to the child's\n to prevent collisions. Any string elements will be JSON parsed\n before flattening.\n\n >>> flatten_dict({'service': {'host':'cloud9@compute-068', 'id': 143}})\n {'service_host': colud9@compute-068', 'service_id': 143}\n\n \"\"\"\n data = data.copy()\n # Try and decode any nested JSON structures.\n for key, value in data.items():\n if isinstance(value, str):\n try:\n data[key] = jsonutils.loads(value)\n except ValueError:\n pass\n\n return dict(_flatten(data))\n\n\ndef print_dict(d, dict_property=\"Property\", dict_value=\"Value\", wrap=0):\n pt = prettytable.PrettyTable([dict_property, dict_value], caching=False)\n pt.align = 'l'\n for k, v in sorted(d.items()):\n # convert dict to str to check length\n if isinstance(v, (dict, list)):\n v = jsonutils.dumps(v, ensure_ascii=False)\n if wrap > 0:\n v = textwrap.fill(str(v), wrap)\n # if value has a newline, add in multiple rows\n # e.g. fault with stacktrace\n if v and isinstance(v, str) and (r'\\n' in v or '\\r' in v):\n # '\\r' would break the table, so remove it.\n if '\\r' in v:\n v = v.replace('\\r', '')\n lines = v.strip().split(r'\\n')\n col1 = k\n for line in lines:\n pt.add_row([col1, line])\n col1 = ''\n else:\n if v is None:\n v = '-'\n pt.add_row([k, v])\n\n result = encodeutils.safe_encode(pt.get_string())\n\n result = result.decode()\n\n print(result)\n\n\ndef find_resource(manager, name_or_id, wrap_exception=True, **find_args):\n \"\"\"Helper for the _find_* methods.\"\"\"\n # for str id which is not uuid (for Flavor, Keypair and hypervsior in cells\n # environments search currently)\n if getattr(manager, 'is_alphanum_id_allowed', False):\n try:\n return manager.get(name_or_id)\n except exceptions.NotFound:\n pass\n\n # first try to get entity as uuid\n try:\n tmp_id = encodeutils.safe_encode(name_or_id)\n\n tmp_id = tmp_id.decode()\n\n if uuidutils.is_uuid_like(tmp_id):\n return manager.get(tmp_id)\n except (TypeError, exceptions.NotFound):\n pass\n\n # then try to get entity as name\n try:\n try:\n resource = getattr(manager, 'resource_class', None)\n name_attr = resource.NAME_ATTR if resource else 'name'\n kwargs = {name_attr: name_or_id}\n kwargs.update(find_args)\n return manager.find(**kwargs)\n except exceptions.NotFound:\n pass\n\n # then try to find entity by human_id\n try:\n return manager.find(human_id=name_or_id, **find_args)\n except exceptions.NotFound:\n pass\n except exceptions.NoUniqueMatch:\n msg = (_(\"Multiple %(class)s matches found for '%(name)s', use an ID \"\n \"to be more specific.\") %\n {'class': manager.resource_class.__name__.lower(),\n 'name': name_or_id})\n if wrap_exception:\n raise exceptions.CommandError(msg)\n raise exceptions.NoUniqueMatch(msg)\n\n # finally try to get entity as integer id\n try:\n return manager.get(int(name_or_id))\n except (TypeError, ValueError, exceptions.NotFound):\n msg = (_(\"No %(class)s with a name or ID of '%(name)s' exists.\") %\n {'class': manager.resource_class.__name__.lower(),\n 'name': name_or_id})\n if wrap_exception:\n raise exceptions.CommandError(msg)\n raise exceptions.NotFound(404, msg)\n\n\ndef format_servers_list_networks(server):\n output = []\n for (network, addresses) in server.networks.items():\n if len(addresses) == 0:\n continue\n addresses_csv = ', '.join(addresses)\n group = \"%s=%s\" % (network, addresses_csv)\n output.append(group)\n\n return '; '.join(output)\n\n\ndef format_security_groups(groups):\n return ', '.join(group['name'] for group in groups)\n\n\ndef _format_field_name(attr):\n \"\"\"Format an object attribute in a human-friendly way.\"\"\"\n # Split at ':' and leave the extension name as-is.\n parts = attr.rsplit(':', 1)\n name = parts[-1].replace('_', ' ')\n # Don't title() on mixed case\n if name.isupper() or name.islower():\n name = name.title()\n parts[-1] = name\n return ': '.join(parts)\n\n\ndef make_field_formatter(attr, filters=None):\n \"\"\"\n Given an object attribute, return a formatted field name and a\n formatter suitable for passing to print_list.\n\n Optionally pass a dict mapping attribute names to a function. The function\n will be passed the value of the attribute and should return the string to\n display.\n \"\"\"\n filter_ = None\n if filters:\n filter_ = filters.get(attr)\n\n def get_field(obj):\n field = getattr(obj, attr, '')\n if field and filter_:\n field = filter_(field)\n return field\n\n name = _format_field_name(attr)\n formatter = get_field\n return name, formatter\n\n\ndef safe_issubclass(*args):\n \"\"\"Like issubclass, but will just return False if not a class.\"\"\"\n\n try:\n if issubclass(*args):\n return True\n except TypeError:\n pass\n\n return False\n\n\ndef _get_resource_string(resource):\n if hasattr(resource, 'human_id') and resource.human_id:\n if hasattr(resource, 'id') and resource.id:\n return \"%s (%s)\" % (resource.human_id, resource.id)\n else:\n return resource.human_id\n elif hasattr(resource, 'id') and resource.id:\n return resource.id\n else:\n return resource\n\n\ndef do_action_on_many(action, resources, success_msg, error_msg):\n \"\"\"Helper to run an action on many resources.\"\"\"\n failure_flag = False\n\n for resource in resources:\n try:\n action(resource)\n print(success_msg % _get_resource_string(resource))\n except Exception as e:\n failure_flag = True\n print(encodeutils.safe_encode(str(e)))\n\n if failure_flag:\n raise exceptions.CommandError(error_msg)\n\n\ndef is_integer_like(val):\n \"\"\"Returns validation of a value as an integer.\"\"\"\n try:\n int(val)\n return True\n except (TypeError, ValueError, AttributeError):\n return False\n\n\ndef validate_flavor_metadata_keys(keys):\n for key in keys:\n valid_name = VALID_KEY_REGEX.match(key)\n if not valid_name:\n msg = _('Invalid key: \"%s\". Keys may only contain letters, '\n 'numbers, spaces, underscores, periods, colons and '\n 'hyphens.')\n raise exceptions.CommandError(msg % key)\n\n\n@contextlib.contextmanager\ndef record_time(times, enabled, *args):\n \"\"\"Record the time of a specific action.\n\n :param times: A list of tuples holds time data.\n :param enabled: Whether timing is enabled.\n :param args: Other data to be stored besides time data, these args\n will be joined to a string.\n \"\"\"\n if not enabled:\n yield\n else:\n start = time.time()\n yield\n end = time.time()\n times.append((' '.join(args), start, end))\n\n\ndef prepare_query_string(params):\n \"\"\"Convert dict params to query string\"\"\"\n # Transform the dict to a sequence of two-element tuples in fixed\n # order, then the encoded string will be consistent in Python 2&3.\n if not params:\n return ''\n params = sorted(params.items(), key=lambda x: x[0])\n return '?%s' % parse.urlencode(params) if params else ''\n\n\ndef get_url_with_filter(url, filters):\n query_string = prepare_query_string(filters)\n url = \"%s%s\" % (url, query_string)\n return url\n","repo_name":"openstack/python-novaclient","sub_path":"novaclient/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":12759,"program_lang":"python","lang":"en","doc_type":"code","stars":381,"dataset":"github-code","pt":"3"}
+{"seq_id":"17212603171","text":"from gtFrame.basic import Frame2d, origin2d\nfrom gtFrame.rotation import Rotation2d\nimport math\nimport numpy as np\n\nsun = origin2d\n\nplanet = Frame2d(np.array([0, 0], dtype=np.float64), Rotation2d(0))\nmoon = Frame2d(np.array([0, 0], dtype=np.float64), Rotation2d(0),\n parent_frame=planet)\n\n# initial poositions\nplanet.position = np.array([10, 0], dtype=np.float64)\nmoon.position = np.array([0, 1], dtype=np.float64)\n\n# Transform position of the moon to the origin frame of reference.\nprint(planet.transform_to(sun, moon.position))\n\n# Update the position of planet and moon\nplanet.position = np.array([10 * math.cos(math.pi / 4),\n 10 * math.sin(math.pi / 4)], dtype=np.float64)\nmoon.position = np.array([1 * math.cos(math.pi), 1 * math.sin(math.pi)],\n dtype=np.float64)\n\n# Transform the altered position of the moon to the origin frame.\nprint(planet.transform_to(sun, moon.position))\n","repo_name":"bluePlatinum/gtFrame","sub_path":"examples/gettingStarted.py","file_name":"gettingStarted.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43458268832","text":"from __future__ import division, generators, print_function\n\nimport macarico\nfrom macarico.data.vocabulary import NoVocabulary\n\nclass Sequences(macarico.Example):\n def __init__(self, tokens, labels, token_vocab, label_vocab):\n if isinstance(token_vocab, int): token_vocab = NoVocabulary(token_vocab)\n if isinstance(label_vocab, int): label_vocab = NoVocabulary(label_vocab)\n self.n_labels = len(label_vocab)\n self.token_vocab = token_vocab\n self.label_vocab = label_vocab\n self.tokens = tokens\n self.labels = labels\n self.N = len(tokens)\n \n X = [token_vocab(x) for x in tokens]\n Y = None if labels is None else \\\n [label_vocab(x) for x in labels]\n self.N = len(X)\n\t\n super(Sequences, self).__init__(X, Y)\n\n def __str__(self):\n words = []\n length = max(len(self.tokens),\n 0 if self.labels is None else len(self.labels),\n 0 if self.Yhat is None else len(self.Yhat))\n\n for n in range(length):\n w = '' if n >= len(self.tokens) else \\\n str(self.tokens[n])\n \n l = '' if self.labels is None else \\\n '_' if n >= len(self.labels) else \\\n ('_' + str(self.labels[n]))\n\n p = '' if self.Yhat is None else \\\n '_' if n >= len(self.Yhat) else \\\n ('_' + str(self.label_vocab(self.Yhat[n])))\n\n words.append(w + l + p)\n\n return ' '.join(words)\n\n def input_str(self):\n return ' '.join(map(str, self.tokens))\n\n def output_str(self):\n return '?' if self.labels is None else \\\n ' '.join(map(str, self.labels))\n\n def prediction_str(self):\n return '?' if self.Yhat is None else \\\n ' '.join([str(self.label_vocab(y)) for y in self.Yhat])\n \nclass Dependencies(Sequences):\n def __init__(self, tokens, heads, token_vocab, tags=None, tag_vocab=None, rels=None, rel_vocab=None):\n if isinstance(token_vocab, int): token_vocab = NoVocabulary(token_vocab)\n if tags is not None:\n if isinstance(tag_vocab, int): tag_vocab = NoVocabulary(tag_vocab)\n assert len(tags) == len(tokens)\n \n if rels is None:\n self.n_rels = 0\n else:\n if isinstance(rel_vocab, int): rel_vocab = NoVocabulary(rel_vocab)\n self.n_rels = len(rel_vocab)\n\n self.tokens = tokens\n self.raw_tags = tags\n self.tags = None if tags is None else [tag_vocab(t) for t in tags]\n self.N = len(tokens)\n \n self.token_vocab = token_vocab\n self.tag_vocab = tag_vocab\n self.rel_vocab = rel_vocab\n\n X = [token_vocab(x) for x in tokens]\n Y = None\n if heads is not None:\n Y = DependencyTree(self.N, rels is not None)\n Y.heads = heads\n if rels is not None:\n Y.rels = [rel_vocab(r) for r in rels]\n\t\n super(Sequences, self).__init__(X, Y)\n\n def is_projective(self):\n return self.Y.is_projective()\n\n def __str__(self):\n return ' ### '.join([self.input_str(), str(self.tags), str(self.raw_tags), self.output_str(), self.prediction_str()])\n\n def input_str(self):\n return ' '.join([w for w in self.tokens]) if self.tags is None else \\\n ' '.join(['%s_%s' % (w,t) for w,t in zip(self.tokens, self.tags)])\n\n def output_str(self):\n return '?' if self.Y is None else str(self.Y)\n \n def prediction_str(self):\n return '?' if self.Yhat is None else str(self.Yhat)\n\n \nclass DependencyTree(object):\n def __init__(self, n, labeled=False):\n self.n = n+1\n self.labeled = labeled\n self.heads = [None] * n\n self.rels = ([None] * n) if labeled else None\n\n def add(self, head, child, rel=None):\n self.heads[child] = head\n if self.labeled:\n self.rels[child] = rel\n\n def __getitem__(self, i):\n return self.heads[i], (None if self.rels is None else self.rels[i])\n \n def is_projective(self):\n return not self.is_non_projective()\n \n def is_non_projective(self):\n for dep1, head1 in enumerate(self.heads):\n for dep2, head2 in enumerate(self.heads):\n if head1 < 0 or head2 < 0:\n continue\n if (dep1 > head2 and dep1 < dep2 and head1 < head2) or \\\n (dep1 < head2 and dep1 > dep2 and head1 < dep2):\n return True\n if dep1 < head1 and head1 != head2 and \\\n ((head1 > head2 and head1 < dep2 and dep1 < head2) or \\\n (head1 < head2 and head1 > dep2 and dep1 < dep1)):\n return True\n return False\n \n def __repr__(self):\n s = 'heads = %s' % str(self.heads)\n if self.labeled:\n s += '\\nrels = %s' % str(self.rels)\n return s\n\n def __str__(self):\n S = []\n for i in range(self.n-1):\n x = '%d->%s' % (i, self.heads[i])\n if self.labeled:\n x = '%s[%s]' % (x, self.rels[i])\n S.append(x)\n return ' '.join(S)\n \n","repo_name":"hal3/macarico","sub_path":"macarico/data/types.py","file_name":"types.py","file_ext":"py","file_size_in_byte":5257,"program_lang":"python","lang":"en","doc_type":"code","stars":111,"dataset":"github-code","pt":"3"}
+{"seq_id":"35297459723","text":"import pickle\nimport util\nimport numpy as np\nimport tensorflow as tf\nfrom camera import Perspective_Camera\nfrom tensorflow.contrib.opt import ScipyOptimizerInterface as scipy_pt\nimport os\nimport cv2\nfrom smpl_batch_body_parsing import SMPL\nfrom opendr_render import render\nimport pickle as pkl\ndd = pickle.load(open(util.NORMAL_SMPL_PATH))\nweights = dd['weights']\nvert_sym_idxs = dd['vert_sym_idxs']\nv_template = dd['v_template']\nleg_index = [1, 4, 7, 10, 2, 5, 8, 11]\narm_index = [17, 19, 21, 23, 16, 18, 20, 22, 14, 13]\nbody_index = [0, 3, 6, 9]\nhead_index = [12, 15]\nbody_parsing_idx = [] ###body head\n_leg_idx = np.zeros(6890)\n_arm_idx = np.zeros(6890)\n_body_idx = np.zeros(6890)\n_head_idx = np.zeros(6890)\nplaceholder_idx = np.zeros(6890)\n_test_idx = np.zeros(6890)\nfor _, iii in enumerate(body_index):\n length = len(weights[:, iii])\n for ii in range(length):\n if weights[ii, iii] > 0.3 and placeholder_idx[ii] == 0:\n _body_idx[ii] = 1\n placeholder_idx[ii] = 1\n _test_idx[ii] = 1\nbody_idx = np.where(_body_idx == 1)\nbody_parsing_idx.append(body_idx)\n\nfor _, iii in enumerate(head_index):\n length = len(weights[:, iii])\n for ii in range(length):\n if weights[ii, iii] > 0.3 and placeholder_idx[ii] == 0:\n _head_idx[ii] = 1\n placeholder_idx[ii] = 1\n _test_idx[ii] = 1\nhead_idx = np.where(_head_idx == 1)\nbody_parsing_idx.append(head_idx)\n\nfor _, iii in enumerate(leg_index):\n length = len(weights[:, iii])\n for ii in range(length):\n if weights[ii, iii] > 0.3 and placeholder_idx[ii] == 0:\n _leg_idx[ii] = 1\n placeholder_idx[ii] = 1\n _test_idx[ii] = 1\nleg_idx = np.where(_leg_idx == 1)\nbody_parsing_idx.append(leg_idx)\n\nfor _, iii in enumerate(arm_index):\n length = len(weights[:, iii])\n for ii in range(length):\n if weights[ii, iii] > 0.3 and placeholder_idx[ii] == 0:\n _arm_idx[ii] = 1\n placeholder_idx[ii] = 1\n _test_idx[ii] = 1\narm_idx = np.where(_arm_idx == 1)\nbody_parsing_idx.append(arm_idx)\n\n##","repo_name":"abcdcba3210123/human_reconstruction_SR","sub_path":"demo_for_paper.py","file_name":"demo_for_paper.py","file_ext":"py","file_size_in_byte":2088,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"42000232255","text":"import pandas as pd\nimport os\nimport numpy as np\nimport editdistance\nimport time\nfrom itertools import combinations\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.decomposition import TruncatedSVD\n\nclass DataPreparation():\n ca = 'case:concept:name'\n ac = 'concept:name'\n ts = 'time:timestamp'\n pickles_folder = '{}/experiment/pickles/'.format(os.path.abspath(os.curdir))\n if not os.path.isdir(pickles_folder):\n os.mkdir(pickles_folder)\n\n def __init__(self, name, path, ca=ca, ac=ac, ts=ts, forceReload=True):\n '''\n Prepare the data (load csv, build distance matrices, build signature)\n and export pickles (so we don't have to load them again if forceReload is False)\n :param name: name of the dataset\n :param path: path of the csv\n :param ca: column of the csv corresponding to case identifier\n :param ac: column of the csv corresponding to activity\n :param ts: column of the csv corresponding to timestamp\n :param forceReload: If True, will create the pickles. If False it will load existing pickles\n '''\n self.name = name\n self.path = path\n self.ca = ca\n self.ac = ac\n self.ts = ts\n self.distanceMatrix, self.signature, self.variants, self.facts = [None]*4\n if forceReload:\n self.distanceMatrix, self.signature, self.variants, self.facts = self.load_from_csv()\n np.save('{}{}_dm.pickle.npy'.format(self.pickles_folder, self.name), self.distanceMatrix)\n np.save('{}{}_sig.pickle.npy'.format(self.pickles_folder, self.name), self.signature)\n self.variants.to_pickle('{}{}_var.pickle'.format(self.pickles_folder, self.name))\n self.facts.to_pickle('{}{}_facts.pickle'.format(self.pickles_folder, self.name))\n else:\n self.distanceMatrix = np.load('{}{}_dm.pickle.npy'.format(self.pickles_folder, self.name), allow_pickle=True)\n self.signature = np.load('{}{}_sig.pickle.npy'.format(self.pickles_folder, self.name), allow_pickle=True)\n self.variants = pd.read_pickle('{}{}_var.pickle'.format(self.pickles_folder, self.name))\n self.facts = pd.read_pickle('{}{}_facts.pickle'.format(self.pickles_folder, self.name))\n\n def load_from_csv(self):\n facts = {}\n\n # Load the CSV\n s = time.time()\n df = self.load_csv()\n facts['time_load_csv'] = time.time()-s\n\n # Extract variants\n s = time.time()\n variants = self.extract_variants(df)\n facts['time_extract_variants'] = time.time()-s\n\n # Build the distance matrix between variants\n s = time.time()\n distanceMatrix = self.buildDistanceMatrix(variants['seq'].tolist())\n facts['time_build_distance_matrix'] = time.time()-s\n\n s = time.time()\n signature = self.buildSignature(variants['seq'].tolist())\n facts['time_to_build_signature'] = time.time()-s\n\n # Extract more facts about the dataset\n # (for descriptive statistics purpose)\n facts['dataset'] = self.name\n facts['ds_n_variants'] = variants.shape[0]\n facts['ds_n_events'] = df.shape[0]\n facts['ds_n_unique_activity'] = df[self.ac].nunique()\n facts['ds_n_unique_trace'] = df[self.ca].nunique()\n facts['cov_top5_vars'] = variants.head(5)['count'].sum()/variants['count'].sum()\n facts['cov_top10_vars'] = variants.head(10)['count'].sum()/variants['count'].sum()\n facts['cov_top20_vars'] = variants.head(20)['count'].sum()/variants['count'].sum()\n facts['cov_top50_vars'] = variants.head(50)['count'].sum()/variants['count'].sum()\n facts['average_levenshtein'] = distanceMatrix.mean()\n facts = pd.Series(facts)\n\n return distanceMatrix, signature, variants, facts\n\n\n def load_csv(self):\n df = pd.read_csv(self.path, nrows=None, low_memory=False)\n df[self.ts] = pd.to_datetime(df[self.ts])\n df.sort_values([self.ca, self.ts], inplace=True)\n df = df[[self.ca,self.ac]]\n df = df.loc[df.notna().all(axis=1),:]\n return df\n\n def extract_variants(self, df):\n variants = df.groupby(self.ca)[self.ac].agg(list)\\\n .value_counts().reset_index().rename({'index':'seq', self.ac:'count'}, axis=1)\n variants['length'] = variants['seq'].str.len()\n return variants\n\n def distance_function(self, x1, x2):\n return editdistance.eval(x1, x2) / max([len(x1), len(x2)])\n\n def buildDistanceMatrix(self, seq):\n m = np.zeros([len(seq), len(seq)])\n for x, y in combinations(range(0,len(seq)), 2):\n d = self.distance_function(seq[x], seq[y])\n m[x,y] = d\n m[y,x] = d\n for x in range(len(seq)):\n m[x,x] = 0\n return m.astype(np.float64)\n\n def buildSignature(self, seq):\n cv = CountVectorizer(ngram_range=(1,2), tokenizer=lambda doc: doc, lowercase=False, max_features=1024)\n data = cv.fit_transform([['$$START$$']+x+['$$END$$'] for x in seq])\n data = TruncatedSVD(min(64, int(data.shape[1]/2)+1)).fit_transform(data).astype(np.float32)\n return data\n\n def randomlyReOrder(self, seed):\n\n # The order of the distance matrix\n # or signature will influence the results\n # For reproducibility purpose, we manage the order\n np.random.seed(seed)\n self.variants['random'] = np.random.random(self.variants.shape[0])\n new_order = self.variants.sort_values(['count', 'random'], ascending=False).index\n self.variants = self.variants.loc[new_order,:].reset_index()\n if self.distanceMatrix is not None:\n self.distanceMatrix = self.distanceMatrix[new_order,:][:,new_order]\n self.distanceMatrix = np.ascontiguousarray(self.distanceMatrix, dtype=np.float64)\n if self.signature is not None:\n self.signature = self.signature[new_order,:]\n self.signature = np.ascontiguousarray(self.signature[new_order,:])\n\n\n\n\n\n","repo_name":"gaelbernard/sampling","sub_path":"experiment/Class/DataPrep.py","file_name":"DataPrep.py","file_ext":"py","file_size_in_byte":6037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28867463876","text":"# ESTA ES UNA LISTA DE LA REGIONES CON SU CODIGO \r\nRegiónes2 = {\"01\":\"Tarapaca\",\"02\":\"Antofagasta\",\"03\":\"Atacama\",\"04\":\"Coquimbo\",\"05\":\"Valparaiso\",\"06\":\"OHiggins\",\"07\":\"Maule\",\"08\":\"Biobio\",\"09\":\"La Araucanía\",\r\n\"10\":\"Los Lagos\",\"11\":\"Aysen\",\"12\":\"Magallanes\",\"13\":\"Metropolitana\",\"14\":\"Los Rios\",\"15\":\"Arica y Parinacota\",\"16\":\"Ñuble\"}\r\n\r\n# ESTAS SON LAS COMUNAS DE CADA REGION Y SU CODIGO \r\nComunas = {\"15101\":\"Arica\",\"15102\":\"Camarones\",\"15202\":\"General Lagos\",\"15201\":\"Putre\",\"01107\":\"Alto Hospicio\",\r\n\"01402\":\"Camina\",\"01403\":\"Colchane\",\"01404\":\"Huara\",\"01101\":\"Iquique\",\"01405\":\"Pica\",\"01401\":\"PozoAlmonte\",\r\n\"02101\":\"Antofagasta\",\"02201\":\"Calama\",\"02302\":\"MariaElena\",\"02102\":\"Mejillones\",\"02202\":\"Ollague\",\r\n\"02203\":\"SanPedroDeAtacama\",\"02103\":\"SierraGorda\",\"02104\":\"Taltal\",\"02301\":\"Tocopilla\",\"03302\":\"Alto del Carmen\",\r\n\"03102\":\"Caldera\",\"03201\":\"Chanaral\",\"03101\":\"Copiapo\",\"03202\":\"Diego de Almagro\",\"03303\":\"Freirina\",\"03304\":\"Huasco\"\r\n,\"03103\":\"Tierra Amarilla\",\"03301\":\"Vallenar\",\"04103\":\"Andacollo\",\"04202\":\"Canela\",\"04302\":\"Combarbala\",\"04102\":\"Coquimbo\",\r\n\"04201\":\"Illapel\",\"04104\":\"La Higuera\",\"04101\":\"La Serena\",\"04203\":\"Los Vilos\",\"04303\":\"Monte Patria\",\"04301\":\"Ovalle\",\r\n\"04105\":\"Paiguano\",\"04304\":\"Punitaqui\",\"04305\":\"Rio Hurtado\",\"04204\":\"Salamanca\",\"04106\":\"Vicuna\",\"05602\":\"Algarrobo\",\r\n\"05402\":\"Cabildo\",\"05502\":\"Calera\",\"05302\":\"CalleLarga\",\"05603\":\"Cartagena\",\"05102\":\"Casablanca\",\"05702\":\"Catemu\",\r\n\"Concon\":\"05103\",\"ElQuisco\":\"05604\", \"ElTabo\":\"05605\", \"Hijuelas\":\"05503\",\"IslaDePascua\":\"05201\",\"JuanFernandez\":\"05104\",\r\n\"05504\":\"LaCruz\",\"05401\":\"LaLigua\",\"05802\":\"Limache\",\"05703\":\"Llayllay\",\"05301\":\"LosAndes\",\"05506\":\"Nogales\",\"05803\":\"Olmue\",\r\n\"Panquehue\":\"05704\",\"Papudo\":\"05403\",\"Petorca\":\"05403\",\"Punchuncavi\":\"05105\",\"Putaendo\":\"05705\",\"Quillota\":\"05501\",\"Quilpue\":\"05801\",\r\n\"05107\":\"Quintero\",\"05303\":\"Rinconada\",\"05601\":\"SanAntonio\",\"05304\":\"SanEsteban\",\"05701\":\"SanFelipe\",\"05706\":\"SantaMaria\",\r\n\"05606\":\"SantoDomingo\",\"05101\":\"Valpaiso\",\"05804\":\"VillaAlemana\",\"05804\":\"ViñaDelMar\",\"05405\":\"Zapallar\",\"13502\":\"Alhue\",\r\n\"13402\":\"Buin\",\"13403\":\"Calera de Tango\",\"13102\":\"Cerrillos\",\"13103\":\"Cerro Navia\",\"13301\":\"Colina\",\"13104\":\"Conchali\",\r\n\"13503\":\"Curacavi\",\"13105\":\"El Bosque\",\"13602\":\"El Monte\",\"13106\":\"Estacion Central\",\"13107\":\"Huechuraba\",\"13108\":\"Independencia\",\r\n\"13603\":\"Isla de Maipo\",\"13109\":\"La Cisterna\",\"13110\":\"La Florida\",\"13111\":\"La Granja\",\"13112\":\"La Pintana\",\"13113\":\"La Reina\",\r\n\"13302\":\"Lampa\",\"13114\":\"Las Condes\",\"13115\":\"Lo Barnechea\",\"13116\":\"Lo Espejo\",\"13117\":\"Lo Prado\",\"13118\":\"Macul\",\"13119\":\"Maipu\",\r\n\"13504\":\"Maria Pinto\",\"13501\":\"Melipilla\",\"13120\":\"Nunoa\",\"13604\":\"Padre Hurtado\",\"13404\":\"Paine\",\"13121\":\"Pedro Aguirre Cerda\",\r\n\"13605\":\"Penaflor\",\"13122\":\"Penalolen\",\"13202\":\"Pirque\",\"13123\":\"Providencia\",\"13124\":\"Pudahuel\",\"13201\":\"Puente Alto\",\r\n\"13125\":\"Quilicura\",\"13126\":\"Quinta Normal\",\"13127\":\"Recoleta\",\"13128\":\"Renca\",\"13401\":\"San Bernardo\",\r\n\"13129\":\"San Joaquin\",\"13203\":\"San Jose de Maipo\",\"13130\":\"San Miguel\",\"13505\":\"San Pedro\",\"13131\":\"San Ramon\",\r\n\"13101\":\"Santiago\",\"13601\":\"Talagante\",\"13303\":\"Tiltil\",\"13132\":\"Vitacura\",\"06302\":\"Chepica\",\"06303\":\"Chimbarongo\",\r\n\"06102\":\"Codegua\",\"06103\":\"Coinco\",\"06104\":\"Coltauco\",\"06105\":\"Donihue\",\"06106\":\"Graneros\",\r\n\"06202\":\"La Estrella\",\"06107\":\"Las Cabras\",\"06203\":\"Litueche\",\"06304\":\"Lolol\",\"06108\":\"Machali\",\"06109\":\"Malloa\",\r\n\"06204\":\"Marchihue\",\"06110\":\"Mostazal\",\"06305\":\"Nancagua\",\"06205\":\"Navidad\",\"06111\":\"Olivar\",\"06306\":\"Palmilla\",\r\n\"06206\":\"Paredones\",\"06307\":\"Peralillo\",\"06112\":\"Peumo\",\"06113\":\"Pichidegua\",\"06201\":\"Pichilemu\",\"06308\":\"Placilla\",\r\n\"06309\":\"Pumanque\",\"06114\":\"Quinta de Tilcoco\",\"06101\":\"Rancagua\",\"06115\":\"Rengo\",\"06116\":\"Requinoa\",\"06301\":\"San Fernando\",\r\n\"06117\":\"San Vicente\",\"06310\":\"Santa Cruz\",\"07201\":\"Cauquenes\",\"07202\":\"Chanco\",\"07402\":\"Colbun\",\"07102\":\"Constitucion\",\"07103\":\"Curepto\",\r\n\"07301\":\"Curico\",\"07104\":\"Empedrado\",\"07302\":\"Hualane\",\"07303\":\"Licanten\",\"07401\":\"Linares\",\"07403\":\"Longavi\",\"07105\":\"Maule\",\r\n\"07304\":\"Molina\",\"07404\":\"Parral\",\"07106\":\"Pelarco\",\"07203\":\"Pelluhue\",\"07107\":\"Pencahue\",\"07305\":\"Rauco\",\"07405\":\"Retiro\",\r\n\"07108\":\"Rio Claro\",\"07306\":\"Romeral\",\"07307\":\"Sagrada Familia\",\"07109\":\"San Clemente\",\"07406\":\"San Javier\",\"07110\":\"San Rafael\",\r\n\"07101\":\"Talca\",\"07308\":\"Teno\",\"07309\":\"Vichuquen\",\"07407\":\"Villa Alegre\",\"07408\":\"Yerbas Buenas\",\"16102\":\"Bulnes\",\"16101\":\"Chillan\",\r\n\"16103\":\"Chillan Viejo\",\"16202\":\"Cobquecura\",\"16203\":\"Coelemu\",\"16302\":\"Coihueco\",\"16104\":\"El Carmen\",\"16204\":\"Ninhue\",\"16303\":\"Niquen\",\r\n\"16105\":\"Pemuco\",\"16106\":\"Pinto\",\"16205\":\"Portezuelo\",\"16107\":\"Quillon\",\"16201\":\"Quirihue\",\"16206\":\"Ranquil\",\"16304\":\"San Fabian\",\"16108\":\"San Ignacio\",\r\n\"16305\":\"San Nicolas\",\"16207\":\"Treguaco\",\"16109\":\"Yungay\",\"08314\":\"Alto Biobio\",\"08302\":\"Antuco\",\"08202\":\"Arauco\",\"08303\":\"Cabrero\",\"08203\":\"Canete\",\r\n\"08103\":\"Chiguayante\",\"08101\":\"Concepcion\",\"08204\":\"Contulmo\",\"08102\":\"Coronel\",\"08205\":\"Curanilahue\",\"08104\":\"Florida\",\"08112\":\"Hualpen\",\"08105\":\"Hualqui\",\r\n\"08304\":\"Laja\",\"08201\":\"Lebu\",\"08206\":\"Los Alamos\",\"08301\":\"Los Angeles\",\"08106\":\"Lota\",\"08305\":\"Mulchen\",\"08306\":\"Nacimiento\",\"08307\":\"Negrete\",\"08107\":\"Penco\",\r\n\"08308\":\"Quilaco\",\"08309\":\"Quilleco\",\"08108\":\"San Pedro de la Paz\",\"08310\":\"San Rosendo\",\"08311\":\"Santa Barbara\",\"08109\":\"Santa Juana\",\"08110\":\"Talcahuano\",\"08207\":\"Tirua\",\r\n\"08111\":\"Tome\",\"08312\":\"Tucapel\",\"08313\":\"Yumbel\",\"09201\":\"Angol\",\"09102\":\"Carahue\",\"09121\":\"Cholchol\",\"09202\":\"Collipulli\",\"09103\":\"Cunco\",\"09203\":\"Curacautin\",\"09104\":\"Curarrehue\",\r\n\"09204\":\"Ercilla\",\"09105\":\"Freire\",\"09106\":\"Galvarino\",\"09107\":\"Gorbea\",\"09108\":\"Lautaro\",\"09109\":\"Loncoche\",\"09205\":\"Lonquimay\",\"09206\":\"Los Sauces\",\"09207\":\"Lumaco\",\"09110\":\"Melipeuco\",\r\n\"09111\":\"Nueva Imperial\",\"09112\":\"Padre Las Casas\",\"09113\":\"Perquenco\",\"09114\":\"Pitrufquen\",\"09115\":\"Pucon\",\"09208\":\"Puren\",\"09209\":\"Renaico\",\"09116\":\"Saavedra\",\"09101\":\"Temuco\",\"09117\":\"Teodoro Schmidt\",\r\n\"09118\":\"Tolten\",\"09210\":\"Traiguen\",\"09211\":\"Victoria\",\"09119\":\"Vilcun\",\"09120\":\"Villarrica\",\"14102\":\"Corral\",\"14202\":\"Futrono\",\"14201\":\"La Union\",\"14203\":\"Lago Ranco\",\"14103\":\"Lanco\",\"14104\":\"Los Lagos\",\r\n\"14105\":\"Mafil\",\"14106\":\"Mariquina\",\"14107\":\"Paillaco\",\"14108\":\"Panguipulli\",\"14204\":\"Rio Bueno\",\"14101\":\"Valdivia\",\"10202\":\"Ancud\",\"10102\":\"Calbuco\",\"10201\":\"Castro\",\"10401\":\"Chaiten\",\"10203\":\"Chonchi\",\r\n\"10103\":\"Cochamo\",\"10204\":\"Curaco de Velez\",\"10205\":\"Dalcahue\",\"10104\":\"Fresia\",\"10105\":\"Frutillar\",\"10402\":\"Futaleufu\",\"10403\":\"Hualaihue\",\"10107\":\"Llanquihue\",\"10106\":\"Los Muermos\",\"10108\":\"Maullin\",\r\n\"10301\":\"Osorno\",\"10404\":\"Palena\",\"10101\":\"Puerto Montt\",\"10302\":\"Puerto Octay\",\"10109\":\"Puerto Varas\",\"10206\":\"Puqueldon\",\"10303\":\"Purranque\",\"10304\":\"Puyehue\",\"10207\":\"Queilen\",\"10208\":\"Quellon\",\r\n\"10209\":\"Quemchi\",\"10210\":\"Quinchao\",\"10305\":\"Rio Negro\",\"10306\":\"San Juan de la Costa\",\"10307\":\"San Pablo\",\"11201\":\"Aysen\",\"11401\":\"Chile Chico\",\"11202\":\"Cisnes\",\"11301\":\"Cochrane\",\"11101\":\"Coyhaique\",\r\n\"11203\":\"Guaitecas\",\"11102\":\"Lago Verde\",\"11302\":\"OHiggins\",\"11402\":\"Rio Ibañez\",\"11303\":\"Tortel\",\"12202\":\"Antartica\",\"12201\":\"Cabo de Hornos\",\"12102\":\"Laguna Blanca\",\"12401\":\"Natales\",\"12301\":\"Porvenir\",\r\n\"12302\":\"Primavera\",\"12101\":\"Punta Arenas\",\"12103\":\"Rio Verde\",\"12104\":\"San Gregorio\",\"12303\":\"Timaukel\",\"12402\":\"Torres del Paine\"}\r\n\r\n# ESTA LISTAS SON DE LAS REGIONES CON SU COMUNA \r\nAricayParinacota = {\"Arica\":\"15101\",\"Camarones\": \"15102\", \"General Lagos\": \"15202\", \"Putre\": \"15201\"}\r\nTarapaca = { \"Alto Hospicio\":\"01107\", \"Camina\":\"01402\",\"Colchane\":\"01403\",\"Huara\":\"01404\",\r\n\"Iquique\":\"01101\", \"Pica\":\"01405\",\"PozoAlmonte\":\"01401\"}\r\nAntofagasta ={\"Antofagasta\":\"02101\",\"Calama\":\"02201\",\"MariaElena\":\"02302\",\"Mejillones\":\"02102\",\"Ollague\":\"02202\",\r\n\"SanPedroDeAtacama\":\"02203\",\"SierraGorda\":\"02103\",\"Taltal\":\"02104\",\"Tocopilla\":\"02301\"}\r\nAtacama = {\"Alto del Carmen\":\"03302\",\"Caldera\":\"03102\",\"Chanaral\":\"03201\",\"Copiapo\":\"03101\",\"Diego de Almagro\":\"03202\",\r\n\"Freirina\":\"03303\",\"Huasco\":\"03304\",\"Tierra Amarilla\":\"03103\",\"Vallenar\":\"03301\"}\r\nCoquimbo ={\"Andacollo\":\"04103\",\"Canela\":\"04202\",\"Combarbala\":\"04302\",\"Coquimbo\":\"04102\",\"Illapel\":\"04201\",\r\n\"La Higuera\":\"04104\",\"La Serena\":\"04101\",\"Los Vilos\":\"04203\",\"Monte Patria\":\"04303\",\"Ovalle\":\"04301\",\r\n\"Paiguano\":\"04105\",\"Punitaqui\":\"04304\",\"Rio Hurtado\":\"04305\",\"Salamanca\":\"04204\",\"Vicuna\":\"04106\"}\r\nValparaiso = {\"Algarrobo\":\"05602\",\"Cabildo\":\"05402\",\"Calera\":\"05502\",\"CalleLarga\":\"05302\",\"Cartagena\":\"05603\",\r\n\"Casablanca\":\"05102\",\"Catemu\":\"05702\",\"Concon\":\"05103\",\"ElQuisco\":\"05604\", \"ElTabo\":\"05605\", \"Hijuelas\":\"05503\",\r\n\"IslaDePascua\":\"05201\",\"JuanFernandez\":\"05104\",\"LaCruz\":\"05504\",\"LaLigua\":\"05401\",\"Limache\":\"05802\",\"Llayllay\":\"05703\",\r\n\"LosAndes\":\"05301\",\"Nogales\":\"05506\",\"Olmue\":\"05803\",\"Panquehue\":\"05704\",\"Papudo\":\"05403\",\"Petorca\":\"05403\",\r\n\"Punchuncavi\":\"05105\",\"Putaendo\":\"05705\",\"Quillota\":\"05501\",\"Quilpue\":\"05801\",\"Quintero\":\"05107\",\r\n\"Rinconada\":\"05303\",\"SanAntonio\":\"05601\",\"SanEsteban\":\"05304\",\"SanFelipe\":\"05701\",\"SantaMaria\":\"05706\",\r\n\"SantoDomingo\":\"05606\",\"Valpaiso\":\"05101\",\"VillaAlemana\":\"05804\",\"ViñaDelMar\":\"05804\",\"Zapallar\":\"05405\"}\r\nMetropolitana = { \"Alhue\":\"13502\",\"Buin\":\"13402\",\"Calera de Tango\":\"13403\",\"Cerrillos\":\"13102\",\"Cerro Navia\":\"13103\",\r\n\"Colina\":\"13301\",\"Conchali\":\"13104\",\"Curacavi\":\"13503\",\"El Bosque\":\"13105\",\"El Monte\":\"13602\",\"Estacion Central\":\"13106\",\r\n\"Huechuraba\":\"13107\",\"Independencia\":\"13108\",\"Isla de Maipo\":\"13603\",\"La Cisterna\":\"13109\",\"La Florida\":\"13110\",\"La Granja\":\"13111\",\r\n\"La Pintana\":\"13112\",\"La Reina\":\"13113\",\"Lampa\":\"13302\",\"Las Condes\":\"13114\",\"Lo Barnechea\":\"13115\",\"Lo Espejo\":\"13116\",\r\n\"Lo Prado\":\"13117\",\"Macul\":\"13118\",\"Maipu\":\"13119\",\"Maria Pinto\":\"13504\",\"Melipilla\":\"13501\",\"Nunoa\":\"13120\",\r\n\"Padre Hurtado\":\"13604\",\"Paine\":\"13404\",\"Pedro Aguirre Cerda\":\"13121\",\"Penaflor\":\"13605\",\"Penalolen\":\"13122\",\r\n\"Pirque\":\"13202\",\"Providencia\":\"13123\",\"Pudahuel\":\"13124\",\"Puente Alto\":\"13201\",\"Quilicura\":\"13125\",\r\n\"Quinta Normal\":\"13126\",\"Recoleta\":\"13127\",\"Renca\":\"13128\",\"San Bernardo\":\"13401\",\"San Joaquin\":\"13129\",\r\n\"San Jose de Maipo\":\"13203\",\"San Miguel\":\"13130\",\"San Pedro\":\"13505\",\"San Ramon\":\"13131\",\"Santiago\":\"13101\",\r\n\"Talagante\":\"13601\",\"Tiltil\":\"13303\",\"Vitacura\":\"13132\"}\r\nO_Higgins = {\"Chepica\":\"06302\",\"Chimbarongo\":\"06303\",\"Codegua\":\"06102\",\"Coinco\":\"06103\",\r\n\"Coltauco\":\"06104\",\"Donihue\":\"06105\",\"Graneros\":\"06106\",\" La Estrella\":\"06202\",\"Las Cabras\":\"06107\",\"Litueche\":\"06203\",\r\n\"Lolol\":\"06304\",\"Machali\":\"06108\",\"Malloa\":\"06109\",\"Marchihue\":\"06204\",\"Mostazal\":\"06110\",\"Nancagua\":\"06305\",\"Navidad\":\"06205\",\r\n\"Olivar\":\"06111\",\"Palmilla\":\"06306\",\"Paredones\":\"06206\",\"Peralillo\":\"06307\",\"Peumo\":\"06112\",\"Pichidegua\":\"06113\",\"Pichilemu\":\"06201\",\r\n\"Placilla\":\"06308\",\"Pumanque\":\"06309\",\"Quinta de Tilcoco\":\"06114\",\"Rancagua\":\"06101\",\"Rengo\":\"06115\",\"Requinoa\":\"06116\",\r\n\"San Fernando\":\"06301\",\"San Vicente\":\"06117\",\"Santa Cruz\":\"06310\"}\r\nMaule = { \"Cauquenes\":\"07201\",\"Chanco\":\"07202\",\"Colbun\":\"07402\",\"Constitucion\":\"07102\",\"Curepto\":\"07103\",\"Curico\":\"07301\",\r\n\"Empedrado\":\"07104\",\"Hualane\":\"07302\",\"Licanten\":\"07303\",\"Linares\":\"07401\",\"Longavi\":\"07403\",\"Maule\":\"07105\",\"Molina\":\"07304\",\r\n\"Parral\":\"07404\",\"Pelarco\":\"07106\",\"Pelluhue\":\"07203\",\"Pencahue\":\"07107\",\"Rauco\":\"07305\",\"Retiro\":\"07405\",\"Rio Claro\":\"07108\",\r\n\"Romeral\":\"07306\",\"Sagrada Familia\":\"07307\",\"San Clemente\":\"07109\",\"San Javier\":\"07406\",\"San Rafael\":\"07110\",\"Talca\":\"07101\",\r\n\"Teno\":\"07308\",\"Vichuquen\":\"07309\",\"Villa Alegre\":\"07407\",\"Yerbas Buenas\":\"07408\"}\r\nÑuble = { \"Bulnes\":\"16102\",\"Chillan\":\"16101\",\"Chillan Viejo\":\"16103\",\r\n\"Cobquecura\":\"16202\",\"Coelemu\":\"16203\",\"Coihueco\":\"16302\",\"El Carmen\":\"16104\",\"Ninhue\":\"16204\",\r\n\"Niquen\":\"16303\",\"Pemuco\":\"16105\",\"Pinto\":\"16106\",\"Portezuelo\":\"16205\",\"Quillon\":\"16107\",\"Quirihue\":\"16201\",\r\n\"Ranquil\":\"16206\",\"San Fabian\":\"16304\",\"San Ignacio\":\"16108\",\"San Nicolas\":\"16305\",\"Treguaco\":\"16207\",\"Yungay\":\"16109\"}\r\nBiobio = {\"Alto Biobio\":\"08314\",\"Antuco\":\"08302\",\"Arauco\":\"08202\",\"Cabrero\":\"08303\",\"Canete\":\"08203\",\"Chiguayante\":\"08103\",\r\n\"Concepcion\":\"08101\",\"Contulmo\":\"08204\",\"Coronel\":\"08102\",\"Curanilahue\":\"08205\",\"Florida\":\"08104\",\"Hualpen\":\"08112\",\"Hualqui\":\"08105\",\r\n\"Laja\":\"08304\",\"Lebu\":\"08201\",\"Los Alamos\":\"08206\",\"Los Angeles\":\"08301\",\"Lota\":\"08106\",\"Mulchen\":\"08305\",\"Nacimiento\":\"08306\",\r\n\"Negrete\":\"08307\",\"Penco\":\"08107\",\"Quilaco\":\"08308\",\"Quilleco\":\"08309\",\"San Pedro de la Paz\":\"08108\",\"San Rosendo\":\"08310\",\r\n\"Santa Barbara\":\"08311\",\"Santa Juana\":\"08109\",\"Talcahuano\":\"08110\",\"Tirua\":\"08207\",\"Tome\":\"08111\",\"Tucapel\":\"08312\",\"Yumbel\":\"08313\"}\r\nLa_Araucania = {\"Angol\":\"09201\",\"Carahue\":\"09102\",\"Cholchol\":\"09121\",\"Collipulli\":\"09202\",\"Cunco\":\"09103\",\"Curacautin\":\"09203\",\r\n\"Curarrehue\":\"09104\",\"Ercilla\":\"09204\",\"Freire\":\"09105\",\"Galvarino\":\"09106\",\"Gorbea\":\"09107\",\"Lautaro\":\"09108\",\"Loncoche\":\"09109\",\r\n\"Lonquimay\":\"09205\",\"Los Sauces\":\"09206\",\"Lumaco\":\"09207\",\"Melipeuco\":\"09110\",\"Nueva Imperial\":\"09111\",\"Padre Las Casas\":\"09112\",\r\n\"Perquenco\":\"09113\",\"Pitrufquen\":\"09114\",\"Pucon\":\"09115\",\"Puren\":\"09208\",\"Renaico\":\"09209\",\"Saavedra\":\"09116\",\"Temuco\":\"09101\",\r\n\"Teodoro Schmidt\":\"09117\",\"Tolten\":\"09118\",\"Traiguen\":\"09210\",\"Victoria\":\"09211\",\"Vilcun\":\"09119\",\"Villarrica\":\"09120\"}\r\nLos_Rios = { \"Corral\":\"14102\",\"Futrono\":\"14202\",\"La Union\":\"14201\",\"Lago Ranco\":\"14203\",\"Lanco\":\"14103\",\"Los Lagos\":\"14104\",\r\n\"Mafil\":\"14105\",\"Mariquina\":\"14106\",\"Paillaco\":\"14107\",\"Panguipulli\":\"14108\",\"Rio Bueno\":\"14204\",\"Valdivia\":\"14101\"}\r\nLos_Lagos = { \"Ancud\":\"10202\",\"Calbuco\":\"10102\",\"Castro\":\"10201\",\"Chaiten\":\"10401\",\"Chonchi\":\"10203\",\"Cochamo\":\"10103\",\r\n\"Curaco de Velez\":\"10204\",\"Dalcahue\":\"10205\",\"Fresia\":\"10104\",\"Frutillar\":\"10105\",\"Futaleufu\":\"10402\",\"Hualaihue\":\"10403\",\r\n\"Llanquihue\":\"10107\",\"Los Muermos\":\"10106\",\"Maullin\":\"10108\",\"Osorno\":\"10301\",\"Palena\":\"10404\",\"Puerto Montt\":\"10101\",\r\n\"Puerto Octay\":\"10302\",\"Puerto Varas\":\"10109\",\"Puqueldon\":\"10206\",\"Purranque\":\"10303\",\"Puyehue\":\"10304\",\"Queilen\":\"10207\",\r\n\"Quellon\":\"10208\",\"Quemchi\":\"10209\",\"Quinchao\":\"10210\",\"Rio Negro\":\"10305\",\"San Juan de la Costa\":\"10306\",\"San Pablo\":\"10307\"}\r\nAysen = { \"Aysen\":\"11201\",\"Chile Chico\":\"11401\",\"Cisnes\":\"11202\",\"Cochrane\":\"11301\",\"Coyhaique\":\"11101\",\"Guaitecas\":\"11203\",\r\n\"Lago Verde\":\"11102\",\"OHiggins\":\"11302\",\"Rio Ibañez\":\"11402\",\"Tortel\":\"11303\"}\r\nMagallanes_y_la_Antartica = { \"Antartica\":\"12202\",\"Cabo de Hornos\":\"12201\",\"Laguna Blanca\":\"12102\",\"Natales\":\"12401\",\"Porvenir\":\"12301\",\r\n\"Primavera\":\"12302\",\"Punta Arenas\":\"12101\",\"Rio Verde\":\"12103\",\"San Gregorio\":\"12104\",\"Timaukel\":\"12303\",\"Torres del Paine\":\"12402\"}\r\n\r\n# ESTE ES EL MENU \r\nprint(\"1-Ver una región\")\r\nprint(\"2-Ver una comuna\")\r\nprint(\"3-Ver región con mayor y menor tasa de incidencia\")\r\nprint(\"0-Salir\") # \r\nopción = int(input(\"Ingrese una opción\\n\"))\r\n\r\nwhile opción != 0: # ESTAS SON LAS OPCIONES\r\n\r\n\r\n if opción == 1: # EN ESTA OPCIÓN SE MUESTRAN LAS REGIONES \r\n print(Regiónes2,\"\\n\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n r = input(\"Ingrese una región\\n\")\r\n if r == \"Tarapaca\" or r == \"01\":\r\n print(\"1-ver grafico\") # LAS OPCIONES PARA VER SU GRAFICO O COMUNA \r\n print(\"2-ver comunas de esta región\") \r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n tara() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Tarapaca,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Antofagasta\" or r == \"02\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n anto() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Antofagasta,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Atacama\" or r == \"03\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n atac() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Atacama,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Coquimbo\" or r == \"04\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n coqu() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Coquimbo,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Valparaiso\" or r == \"05\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n valpa() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Valparaiso,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Ohiggins\" or r == \"06\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n ohig() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(O_Higgins,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0 \r\n elif r == \"Maule\" or r == \"07\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n ma() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Maule,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n \r\n elif r == \"Biobio\" or r == \"08\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n bi() # MUESTRA SU GRAFICO \r\n elif ver == \"2\": \r\n print(Biobio,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n \r\n elif r == \"La Araucanía\" or r == \"09\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n laara() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(La_Araucania,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Los Lagos\" or r == \"10\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n los() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Los_Lagos,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Aysen\" or r == \"11\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n ay() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Aysen,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Magallanes\" or r == \"12\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n maga() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Magallanes_y_la_Antartica,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Metropolitana\" or r == \"13\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n metro() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Metropolitana,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Los Rios\" or r == \"14\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n rios() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Los_Rios,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Arica y Parinacota\" or r == \"15\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n ari() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(AricayParinacota,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"Ñuble\" or r == \"16\":\r\n print(\"1-ver grafico\")\r\n print(\"2-ver comunas de esta región\")\r\n print(\"0-TERMINAR PROGRAMA\")\r\n ver = input(\"Ingrese una opción\\n\")\r\n if ver == \"1\":\r\n ñu() # MUESTRA SU GRAFICO \r\n elif ver == \"2\":\r\n print(Ñuble,\"\\n\",\"----------°----------\")# AQUI MUESTRA TODAS LAS COMUNAS DE ESA REGIÓN \r\n opción = 0\r\n elif r == \"0\":\r\n opción = 0\r\n else:\r\n print(\"Error, Ingrese región valida\\n\") # AQUI ES POR SI SE EQUIVOCAN DE OPCIÓN\r\n print(Regiónes2) \r\n\r\n elif opción == 2: # EN ESTA OPCIÓN ESTAN TODAS LAS COMUNAS CON SU GRAFICO \r\n print(Comunas)\r\n print(\"0-TERMINAR PROGRAMA\") # CON ESTA OPCIÓN SE CIERRA EL PROGRAMA \r\n c = input(\"Mostrar grafico de\\n\") \r\n if c == \"Arica\" or c == \"15101\":\r\n arica() # MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Camarones\" or c == \"15102\":\r\n camarones()\r\n opción = 0 \r\n elif c == \"General Lagos\" or c == \"15202\":\r\n generallagos()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Putre\" or c == \"15201\":\r\n putre()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Alto Hospicio\" or c == \"01107\":\r\n altohospicio()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Camina\" or c == \"01402\":\r\n camina()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Colchane\" or c == \"01403\":\r\n colchane()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Huara\" or c == \"01404\":\r\n huara()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Iquique\" or c == \"01101\":\r\n iquique()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Pica\" or c == \"01405\":\r\n pica()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Pozo Almonte\" or c == \"01401\":\r\n pozoalmonte()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Antofagasta\" or c == \"02101\":\r\n antofagasta()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Calama\" or c == \"02201\":\r\n calama()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Maria Elena\" or c == \"02302\":\r\n mariaelena()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Mejillones\" or c == \"02102\":\r\n mejillones()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Ollague\" or c == \"02202\":\r\n ollague()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"San Pedro de Atacama\" or c == \"02203\":\r\n sanpedrodeatacama()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Sierra Gorda\" or c == \"02103\":\r\n sierragorda()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Taltal\" or c == \"02104\":\r\n taltal()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Tocopilla\" or c == \"02301\":\r\n tocopilla()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Alto del Carmen\" or c == \"03302\":\r\n altodelcarmen()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Caldera\" or c == \"03102\":\r\n caldera()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Chanaral\" or c == \"03201\":\r\n chanaral()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Copiapo\" or c == \"03101\":\r\n copiapo()\r\n opción = 0 \r\n elif c == \"Diego de Almagro\" or c == \"03202\":\r\n diegodealmagro()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Freirina\" or c == \"03303\":\r\n freirina()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Huasco\" or c == \"03304\":\r\n huasco()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Tierra Amarilla\" or c == \"03103\":\r\n tierraamarilla()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Vallenar\" or c == \"03301\":\r\n vallenar()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Andacollo\" or c == \"04103\":\r\n andacollo()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Canela\" or c == \"04202\":\r\n canela()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Combarbala\" or c == \"04302\":\r\n combarbala()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Coquimbo\" or c == \"04102\":\r\n coquimbo()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Illapel\" or c == \"04201\":\r\n illapel()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"La Higuera\" or c == \"04104\":\r\n lahiguera()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"La Serena\" or c == \"04101\":\r\n laserena()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Los Vilos\" or c == \"04203\":\r\n losvilos()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Monte Patria\" or c == \"04303\":\r\n montepatria()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Ovalle\" or c == \"04301\":\r\n ovalle()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Paiguano\" or c == \"04105\":\r\n paiguano()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Punitaqui\" or c == \"04304\":\r\n punitaqui()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Rio Hurtado\" or c == \"04305\":\r\n riohurtado()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Salamanca\" or c == \"04204\":\r\n salamanca()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Vicuña\" or c == \"04106\":\r\n vicuña()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Algarrobo\" or c == \"05602\":\r\n algarrobo()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Cabildo\" or c == \"05402\":\r\n cabildo()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Calera\" or c == \"05502\":\r\n calera()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Calle Larga\" or c == \"05302\":\r\n callelarga()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Cartagena\" or c == \"05603\":\r\n cartagena()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Casablanca\" or c == \"05102\":\r\n casablanca()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Catemu\" or c == \"05702\":\r\n catemu()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Concon\" or c == \"05103\":\r\n concon()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"El Quisco\" or c == \"05604\":\r\n elquisco()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"El Tabo\" or c == \"05605\":\r\n eltabo()# MUESTRA SU GRAFICO \r\n elif c == \"Hijuelas\" or c == \"05503\":\r\n hijuelas()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Isla de Pascua\" or c == \"05201\":\r\n isladepascua()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Juan Fernandez\" or c == \"05104\":\r\n juanfernandez()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"La Cruz\" or c == \"05504\":\r\n lacruz()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"La Ligua\" or c == \"05401\":\r\n laligua()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Limache\" or c == \"05802\":\r\n limache() # MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Llaillay\" or c == \"05703\":\r\n llaillay()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Los Andes\" or c == \"05301\":\r\n losandes()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Nogales\" or c == \"05506\":\r\n nogales()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Olmue\" or c == \"05803\":\r\n olmue()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Panquehue\" or c == \"05704\":\r\n panquehue()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Papudo\" or c == \"05403\":\r\n papudo()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Petorca\" or c == \"05404\":\r\n petorca()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Puchuncavi\" or c == \"05105\":\r\n puchuncavi()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Putaendo\" or c == \"05705\":\r\n putaendo()# MUESTRA SU GRAFICO \r\n opción = 0 \r\n elif c == \"Quillota\" or c == \"05501\":\r\n quillota()# MUESTRA SU GRAFICO\r\n opción = 0 \r\n elif c == \"Quilpue\" or c == \"05801\":\r\n quilpue()# MUESTRA SU GRAFICO\r\n opción = 0 \r\n elif c == \"Quintero\" or c == \"05107\":\r\n quintero()# MUESTRA SU GRAFICO\r\n opción = 0 \r\n elif c == \"Rinconada\" or c == \"05303\":\r\n rinconada()# MUESTRA SU GRAFICO\r\n opción = 0 \r\n elif c == \"San Antonio\" or c == \"05601\":\r\n sanantonio()# MUESTRA SU GRAFICO\r\n opción = 0 \r\n elif c == \"San Esteban\" or c == \"05304\":\r\n sanesteban()# MUESTRA SU GRAFICO\r\n opción = 0 \r\n elif c == \"San Felipe\" or c == \"05701\":\r\n sanfelipe()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Santa Maria\" or c == \"05706\":\r\n santamaria()\r\n opción = 0\r\n elif c == \"Santo Domingo\" or c == \"05606\":\r\n santodomingo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Valparaiso\" or c == \"05101\":\r\n valparaiso()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Villa Alemana\" or c == \"05804\":\r\n villaalemana()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Viña del Mar\" or c == \"05109\":\r\n viñadelmar()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Zapallar\" or c == \"05405\":\r\n zapallar()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Alhue\" or c == \"13502\":\r\n alhue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Buin\" or c == \"13402\":\r\n buin()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Calera de Tango\" or c == \"13403\":\r\n caleradetango()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cerrillos\" or c == \"13102\":\r\n Cerrillos()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cerro Navia\" or c == \"13103\":\r\n cerronavia()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Colina\" or c == \"13301\":\r\n colina()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Conchali\" or c == \"13104\":\r\n conchali()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Curacavi\" or c == \"13503\":\r\n curacavi()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"El Bosque\" or c == \"13105\":\r\n elbosque()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"El Monte\" or c == \"13602\":\r\n elmonte()\r\n opción = 0\r\n elif c == \"Estacion Central\" or c == \"13106\":\r\n estacioncentral()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Huechuraba\" or c == \"13107\":\r\n huechuraba()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Independencia\" or c == \"13108\":\r\n independencia()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Isla de Maipo\" or c == \"13603\":\r\n islademaipo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"La Cisterna\" or c == \"13109\":\r\n lacisterna()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"La Florida\" or c == \"13110\":\r\n laflorida()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"La Granja\" or c == \"13111\":\r\n lagranja()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"La Pintana\" or c == \"13112\":\r\n lapintana()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"La Reina\" or c == \"13113\":\r\n lareina()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lampa\" or c == \"13302\":\r\n lampa()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Las Condes\" or c == \"13114\":\r\n lascondes()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lo Barnechea\" or c == \"13115\":\r\n lobarnechea()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lo Espejo\" or c == \"13116\":\r\n loespejo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lo Prado\" or c == \"13117\":\r\n loprado()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Macul\" or c == \"13118\":\r\n macul()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Maipu\" or c == \"13119\":\r\n maipu()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Maria Pinto\" or c == \"13504\":\r\n mariapinto()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Melipilla\" or c == \"13501\":\r\n melipilla()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Ñuñoa\" or c == \"13120\":\r\n nuñoa()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Padre Hurtado\" or c == \"13604\":\r\n padrehurtado()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Paine\" or c == \"13404\":\r\n paine()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pedro Aguirre Cerda\" or c == \"13121\":\r\n pedroaguirrecerda()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Peñaflor\" or c == \"13605\":\r\n penaflor()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Peñalolen\" or c == \"13122\":\r\n penalolen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pirque\" or c == \"13202\":\r\n pirque()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Providencia\" or c == \"13123\":\r\n providencia()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pudahuel\" or c == \"13124\":\r\n pudahuel()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Puente Alto\" or c == \"13201\":\r\n puentealto()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quilicura\" or c == \"13125\":\r\n quilicura()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quinta Normal\" or c == \"13126\":\r\n quintanormal()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Recoleta\" or c == \"13127\":\r\n recoleta()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Renca\" or c == \"13128\":\r\n renca()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Bernardo\" or c == \"13401\":\r\n sanbernardo()# MUESTRA SU GRAFICO\r\n opción= 0\r\n elif c == \"San Joaquin\" or c == \"13129\":\r\n sanjoaquin()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Jose de Maipo\" or c == \"13203\":\r\n sanjosedemaipo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Miguel\" or c == \"13130\":\r\n sanmiguel()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Pedro\" or c == \"13505\":\r\n sanpedro()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Ramon\" or c == \"13131\":\r\n sanramon()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Santiago\" or c == \"13101\":\r\n santiago()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Talagante\" or c == \"13601\":\r\n talagante()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Tiltil\" or c == \"13303\":\r\n tiltil()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Vitacura\" or c == \"13132\":\r\n vitacura()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Chepica\" or c == \"06302\":\r\n chepica()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Chimbarongo\" or c == \"06303\":\r\n chimbarongo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Codegua\" or c == \"06102\":\r\n codegua()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Coinco\" or c == \"06103\":\r\n Coinco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Coltauco\" or c == \"06104\":\r\n coltauco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Donihue\" or c == \"06105\":\r\n donihue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Graneros\" or c == \"06106\":\r\n graneros()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"La Estrella\" or c == \"06202\":\r\n laestrella()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Las Cabras\" or c == \"06107\":\r\n lascabras()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Litueche\" or c == \"06203\":\r\n litueche()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lolol\" or c == \"06304\":\r\n lolol()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Machali\" or c == \"06108\":\r\n machali()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Malloa\" or c == \"06109\":\r\n malloa()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Marchihue\" or c == \"06204\":\r\n marchihue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Mostazal\" or c == \"06110\":\r\n mostazal()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Nancagua\" or c == \"06305\":\r\n nancagua()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Navidad\" or c == \"06205\":\r\n navidad()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Olivar\" or c == \"06111\":\r\n olivar()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Palmilla\" or c == \"06306\":\r\n palmilla()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Paredones\" or c == \"06206\":\r\n paredones()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Peralillo\" or c == \"06307\":\r\n peralillo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Peumo\" or c == \"06112\":\r\n peumo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pichidegua\" or c == \"06113\":\r\n pichidegua()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pichilemu\" or c == \"06201\":\r\n pichilemu()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Placilla\" or c == \"06308\":\r\n placilla()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pumanque\" or c == \"06309\":\r\n pumanque()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quinta de Tilcoco\" or c == \"06114\":\r\n quintadetilcoco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Rancagua\" or c == \"06101\":\r\n rancagua()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Rengo\" or c == \"06115\":\r\n rengo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Requinoa\" or c == \"06116\":\r\n requinoa()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Fernando\" or c == \"06301\":\r\n sanfernando()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Vicente\" or c == \"06117\":\r\n sanvicente()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Santa Cruz\" or c == \"06310\":\r\n santacruz()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cauquenes\" or c == \"07201\":\r\n cauquenes()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Chanco\" or c == \"07202\":\r\n chanco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Colbun\" or c == \"07402\":\r\n colbun()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Constitucion\" or c == \"07102\":\r\n constitucion()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Curepto\" or c == \"07103\":\r\n curepto()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Curico\" or c == \"07301\":\r\n curico()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Empedrado\" or c == \"07104\":\r\n empedrado()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Hualane\" or c == \"07302\":\r\n hualane()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Licanten\" or c == \"07303\":\r\n licanten()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Linares\" or c == \"07401\":\r\n linares()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Longavi\" or c == \"07403\":\r\n longavi()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Maule\" or c == \"07105\":\r\n maule()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Molina\" or c == \"07304\":\r\n molina()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Parral\" or c == \"07404\":\r\n parral()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pelarco\" or c == \"07106\":\r\n pelarco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pelluhue\" or c == \"07203\":\r\n pelluhue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pencahue\" or c == \"07107\":\r\n pencahue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Rauco\" or c == \"07305\":\r\n rauco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Retiro\" or c == \"07405\":\r\n retiro()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Rio Claro\" or c == \"07108\":\r\n rioclaro()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Romeral\" or c == \"07306\":\r\n romeral()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Sagrada Familia\" or c == \"07307\":\r\n sagradafamilia()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Clemente\" or c == \"07109\":\r\n sanclemente()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Javier\" or c == \"07406\":\r\n sanjavier()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Rafael\" or c == \"07110\":\r\n sanrafael()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Talca\" or c == \"07101\":\r\n talca()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Teno\" or c == \"07308\":\r\n teno()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Vichuquen\" or c == \"07309\":\r\n vichuquen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Villa Alegre\" or c == \"07407\":\r\n villaalegre()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Yerbas Buenas\" or c == \"07408\":\r\n yerbasbuenas()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Bulnes\" or c == \"16102\":\r\n bulnes()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Chillan\" or c == \"16101\":\r\n chillan()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Chillan Viejo\" or c == \"16103\":\r\n chillanviejo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cobquecura\" or c == \"16202\":\r\n cobquecura()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Coelemu\" or c == \"16203\":\r\n coelemu()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Coihueco\" or c == \"16302\":\r\n coihueco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"El Carmen\" or c == \"16104\":\r\n elcarmen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Ninhue\" or c == \"16204\":\r\n ninhue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Niquen\" or c == \"16303\":\r\n niquen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pemuco\" or c == \"16105\":\r\n pemuco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pinto\" or c == \"16106\":\r\n pinto()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Portezuelo\" or c == \"16205\":\r\n portezuelo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quillon\" or c == \"16107\":\r\n quillon()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quirihue\" or c == \"16201\":\r\n quirihue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Ranquil\" or c == \"16206\":\r\n ranquil()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Carlos\" or c == \"16301\":\r\n sancarlos()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Fabian\" or c == \"16304\":\r\n sanfabian()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Ignacio\" or c == \"16108\":\r\n sanignacio()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Nicolas\" or c == \"16305\":\r\n sannicolas()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Treguaco\" or c == \"16207\":\r\n treguaco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Yungay\" or c == \"16109\":\r\n yungay()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Alto Biobio\" or c == \"08314\":\r\n altobiobio()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Antuco\" or c == \"08302\":\r\n antuco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Arauco\" or c == \"08202\":\r\n arauco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cabrero\" or c == \"08303\":\r\n cabrero()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Canete\" or c == \"08203\":\r\n canete()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Chiguayante\" or c == \"08103\":\r\n chiguayante()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Concepcion\" or c == \"08101\":\r\n concepcion()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Contulmo\" or c == \"08204\":\r\n contulmo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Coronel\" or c == \"08102\":\r\n coronel()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Curanilahue\" or c == \"08205\":\r\n curanilahue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Florida\" or c == \"08104\":\r\n florida()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Hualpen\" or c == \"08112\":\r\n hualpen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Hualqui\" or c == \"08105\":\r\n hualqui()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Laja\" or c == \"08304\":\r\n laja()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lebu\" or c == \"08201\":\r\n lebu()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Los Alamos\" or c == \"08206\":\r\n losalamos()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Los Angeles\" or c == \"08301\":\r\n losangeles()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lota\" or c == \"08106\":\r\n lota()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Mulchen\" or c == \"08305\":\r\n mulchen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Nacimiento\" or c == \"08306\":\r\n nacimiento()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Negrete\" or c == \"08307\":\r\n negrete()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Penco\" or c == \"08107\":\r\n penco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quilaco\" or c == \"08308\":\r\n quilaco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quilleco\" or c == \"08309\":\r\n quilleco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Pedro de la Paz\" or c == \"08108\":\r\n sanpedrodelapaz()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Rosendo\" or c == \"08310\":\r\n sanrosendo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Santa Barbara\" or c == \"08311\":\r\n santabarbara()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Santa Juana\" or c == \"08109\":\r\n santajuana()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Talcahuano\" or c == \"08110\":\r\n talcahuano()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Tirua\" or c == \"08207\":\r\n tirua()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Tome\" or c == \"08111\":\r\n tome()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Tucapel\" or c == \"Tucapel\":\r\n tucapel()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Yumbel\" or c == \"08313\":\r\n yumbel()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Angol\" or c == \"09201\":\r\n angol()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Carahue\" or c == \"09102\":\r\n carahue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cholchol\" or c == \"09121\":\r\n cholchol()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Collipulli\" or c == \"09202\":\r\n collipulli()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cunco\" or c == \"09103\":\r\n cunco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Curacautin\" or c == \"09203\":\r\n curacautin()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Curarrehue\" or c == \"09104\":\r\n curarrehue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Ercilla\" or c == \"09204\":\r\n ercilla()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Freire\" or c == \"09105\":\r\n freire()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Galvarino\" or c == \"09106\":\r\n galvarino()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Gorbea\" or c == \"09107\":\r\n gorbea()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lautaro\" or c == \"09108\":\r\n lautaro()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Loncoche\" or c == \"09109\":\r\n loncoche()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lonquimay\" or c == \"09205\":\r\n lonquimay()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Los Sauces\" or c == \"09206\":\r\n lossauces()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lumaco\" or c == \"09207\":\r\n lumaco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Melipeuco\" or c == \"09110\":\r\n melipeuco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Nueva Imperial\" or c == \"09111\":\r\n nuevaimperial()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Padre Las Casas\" or c == \"09112\":\r\n padrelascasas()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Perquenco\" or c == \"09113\":\r\n perquenco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pitrufquen\" or c == \"09114\":\r\n pitrufquen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Pucon\" or c == \"09115\":\r\n pucon()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Renaico\" or c == \"09209\":\r\n renaico()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Saavedra\" or c == \"09116\":\r\n saavedra()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Temuco\" or c == \"09101\":\r\n temuco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Teodoro Schmidt\" or c == \"09117\":\r\n teodoroschmidt()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Tolten\" or c == \"09118\":\r\n tolten()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Traiguen\" or c == \"09210\":\r\n traiguen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Victoria\" or c == \"09211\":\r\n victoria()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Vilcun\" or c == \"09119\":\r\n vilcun()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Villarrica\" or c == \"09120\":\r\n villarrica()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Corral\" or c == \"Corral\":\r\n corral()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Futrono\" or c == \"14202\":\r\n futrono()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"La Union\" or c == \"14201\":\r\n launion()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lago Ranco\" or c == \"14203\":\r\n lagoranco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lanco\" or c == \"14103\":\r\n lanco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Los Lagos\" or c == \"14104\":\r\n loslagos()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Mafil\" or c == \"14105\":\r\n mafil()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Mariquina\" or c == \"14106\":\r\n mariquina()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Paillaco\" or c == \"14107\":\r\n paillaco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Panguipulli\" or c == \"14108\":\r\n panguipulli()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Rio Bueno\" or c == \"14204\":\r\n riobueno()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Valdivia\" or c == \"14101\":\r\n valdivia()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Ancud\" or c == \"10202\":\r\n ancud()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Calbuco\" or c == \"10102\":\r\n calbuco()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Castro\" or c == \"10201\":\r\n castro()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Chaiten\" or c == \"10401\":\r\n chaiten()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Chonchi\" or c == \"10203\":\r\n chonchi()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cochamo\" or c == \"10103\":\r\n cochamo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Curaco de Velez\" or c == \"10204\":\r\n curacodevelez()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Dalcahue\" or c == \"10205\":\r\n dalcahue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Fresia\" or c == \"10104\":\r\n fresia()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Frutillar\" or c == \"Frutillar\":\r\n frutillar()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Futaleufu\" or c == \"10402\":\r\n futaleufu()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Hualaihue\" or c == \"10403\":\r\n hualaihue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Llanquihue\" or c == \"10107\":\r\n llanquihue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Los Muermos\" or c == \"10106\":\r\n losmuermos()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Maullin\" or c == \"Maullin\":\r\n maullin()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Osorno\" or c == \"10301\":\r\n osorno()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Palena\" or c == \"10404\":\r\n palena()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Puerto Montt\" or c == \"10101\":\r\n puertomontt()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Puerto Octay\" or c == \"10302\":\r\n puertooctay()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Puerto Varas\" or c == \"10109\":\r\n puertovaras()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Puqueldon\" or c == \"10206\":\r\n puqueldon()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Purranque\" or c == \"10303\":\r\n purranque()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Puyehue\" or c == \"10304\":\r\n puyehue()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Queilen\" or c == \"10207\":\r\n queilen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quellon\" or c == \"10208\":\r\n quellon()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quemchi\" or c == \"10209\":\r\n quemchi()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Quinchao\" or c == \"10210\":\r\n quinchao()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Rio Negro\" or c == \"10305\":\r\n rionegro()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Juan de la Costa\" or c == \"10306\":\r\n sanjuandelacosta()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Pablo\" or c == \"10307\":\r\n sanpablo()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Aysen\" or c == \"11201\":\r\n aysen()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Chile Chico\" or c == \"11401\":\r\n chilechico()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cisnes\" or c == \"11202\":\r\n cisnes()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cochrane\" or c == \"11301\":\r\n cochrane()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Coyhaique\" or c == \"11101\":\r\n coyhaique()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Guaitecas\" or c == \"11203\":\r\n guaitecas()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Lago Verde\" or c == \"11102\":\r\n lagoverde()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"OHiggins\" or c == \"11302\":\r\n OHiggins()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Rio Ibañez\" or c == \"11402\":\r\n rioibañez()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Tortel\" or c == \"11303\":\r\n tortel()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Antartica\" or c == \"12202\":\r\n antartica()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Cabo de Hornos\" or c == \"12201\":\r\n cabodehornos()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Laguna Blanca\" or c == \"12102\":\r\n lagunablanca()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Natales\" or c == \"12401\":\r\n natales()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Porvenir\" or c == \"12301\":\r\n porvenir()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Primavera\" or c == \"12302\":\r\n primavera()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Punta Arenas\" or c == \"12101\":\r\n puntaarenas()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Rio Verde\" or c == \"12103\":\r\n rioverde()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"San Gregorio\" or c == \"12104\":\r\n sangregorio()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Timaukel\" or c == \"12303\":\r\n timaukel()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"Torres del Paine\" or c == \"12402\":\r\n torresdelpaine()# MUESTRA SU GRAFICO\r\n opción = 0\r\n elif c == \"0\": # CON ESTA OPCIÓN SE CIERRA EL PROGRAMA \r\n opción = 0\r\n else :\r\n print(\"Ingrese una comuna valida\") # AQUI ES POR SI SE EQUIVOCAN DE OPCIÓN\r\n\r\n \r\n elif opción == 3: # AQUI MUESTRA LA REGION CON MAYOR Y MENOR TASA DE INCIDENCIA \r\n graficoregiones()\r\n opción = 0\r\n \r\n else:\r\n opción = int(input(\"Error, Ingrese una opción valida\\n\")) # AQUI ES POR SI SE EQUIVOCAN DE OPCIÓN","repo_name":"NachoxGG/NachoxGG","sub_path":"proyecto5(casi final).py","file_name":"proyecto5(casi final).py","file_ext":"py","file_size_in_byte":67328,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11344663996","text":"import re\n\nfrom flask import abort, request as frequest, Blueprint\n\n\nclass AuthedBlueprint(Blueprint):\n @classmethod\n def blocked_user_agent_decorator(cls, method, user_agents):\n\n def real_method(*args, **kwargs):\n user_agent = frequest.headers.get('User-Agent') or ''\n for regex in user_agents:\n if regex.match(user_agent):\n abort(404, description='No data available for user')\n\n return method(*args, **kwargs)\n\n real_method.__name__ = method.__name__\n return real_method\n\n def __init__(self, name, import_name, auth_decorator=None, blocked_user_agents=None, **kwargs):\n super(AuthedBlueprint, self).__init__(name, import_name, **kwargs)\n self.auth_decorator = auth_decorator\n self.blocked_user_agents = [\n re.compile(value) if isinstance(value, str) else value\n for value in blocked_user_agents or []\n ]\n\n def add_url_rule(self, rule, endpoint=None, view_func=None, authed=True, **options):\n if self.blocked_user_agents:\n view_func = self.blocked_user_agent_decorator(view_func, self.blocked_user_agents)\n if not self.auth_decorator or not authed:\n return super(AuthedBlueprint, self).add_url_rule(rule, endpoint=endpoint, view_func=view_func, **options)\n return super(AuthedBlueprint, self).add_url_rule(rule, endpoint=endpoint, view_func=self.auth_decorator(view_func), **options)\n","repo_name":"WebKit/WebKit","sub_path":"Tools/Scripts/libraries/webkitflaskpy/webkitflaskpy/authed_blueprint.py","file_name":"authed_blueprint.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"19281560113","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('webclient', '0004_remove_game_created_by'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='game',\n name='is_game_started',\n field=models.BooleanField(default=False),\n ),\n ]\n","repo_name":"connectthefuture/deckr2","sub_path":"clients/webserver/deckr/webclient/migrations/0005_game_is_game_started.py","file_name":"0005_game_is_game_started.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42848052929","text":"\nimport pytest\nfrom freezegun import freeze_time\nfrom user_app.storages.user_storage_implementation\\\n import UserStorageImplementation\n\n\n@pytest.mark.django_db\n@freeze_time(\"2012-01-13\")\ndef test_get_valid_user_ids_given_user_ids_returns_valids_user_ids(\n create_users):\n\n # Arrange\n storage = UserStorageImplementation()\n user_ids = [1, 2]\n\n # Act\n user_ids_response = storage.get_valid_user_ids(user_ids=user_ids)\n\n # Assert\n assert user_ids_response == user_ids\n\n","repo_name":"raviteja1766/ib_mini_projects","sub_path":"user_app/tests/storages/test_get_valid_user_ids.py","file_name":"test_get_valid_user_ids.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"3535013036","text":"from utils import run, ParsingConfig, get_input_data\n\nexample_answer = 13\n\nexample_data = \"\"\"\nR 4\nU 4\nL 3\nD 1\nR 4\nD 1\nL 5\nR 2\n\"\"\"\n\n\nparsing_config = ParsingConfig(\n value_converter=[None, int],\n)\n\n\ndef _follow(tx, ty, hx, hy):\n if tx == hx:\n if ty < hy-1:\n ty += 1\n elif ty > hy+1:\n ty -= 1\n elif ty == hy:\n if tx < hx-1:\n tx += 1\n elif tx > hx+1:\n tx -= 1\n elif abs(hy-ty) + abs(hx-tx) > 2:\n tx += 1 if tx < hx else -1\n ty += 1 if ty < hy else -1\n return tx, ty\n\n\ndef solve(data):\n visited = {(0, 0)}\n hx, hy = 0, 0\n tx, ty = 0, 0\n\n for record in data:\n [direction, distance] = record\n for _ in range(distance):\n if direction == \"U\":\n hy += 1\n elif direction == \"D\":\n hy -= 1\n elif direction == \"L\":\n hx -= 1\n else:\n hx += 1\n tx, ty = _follow(tx, ty, hx, hy)\n visited.add((tx, ty))\n return len(visited)\n\n\nreal_answer = 6284\n\n\nif __name__ == \"__main__\":\n run(example_data, example_answer, parsing_config, solve, real_answer)\n","repo_name":"csymeonides/advent-of-code","sub_path":"src/solutions/2022/d9p1.py","file_name":"d9p1.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37683495025","text":"import socket\nimport string\nimport threading\nfrom queue import Queue\n\nimport sys\n\nimport time\n\nimport os\n\n\nclass SlaveHandler:\n \"\"\"\n recieves : 1 fail #no result\n 2 pass:[job]:[decrypted str] #result\n 3 probe #probes when free [prober]\n\n responds : 1 q #kill\n 2 rest #wait for instruction kill current task\n 3 job:[to_break]:[task] #new job for\n \"\"\"\n\n def __init__(self, slave_socket, break_room, job_master, slave_id):\n \"\"\"\n\n :type job_master: JobMaster\n :type break_room: BreakRoom\n \"\"\"\n self.slave_id = slave_id\n self.job_master = job_master\n self.break_room = break_room\n self.socket = slave_socket\n self.socket.setblocking(0)\n self.socket.settimeout(1)\n self.dead = False\n threading.Thread(target=self.connection_manager).start()\n\n def connection_manager(self):\n waiter_thread = threading.Thread(target=self.waiter)\n waiter_thread.start()\n waiter_thread.join()\n self.socket.close()\n self.break_room.remove_slave_handler(self)\n\n def waiter(self):\n \"\"\"\n receiver + responder[jobs]\n \"\"\"\n while True:\n try:\n if self.dead:\n break\n reply = self.socket.recv(1024).decode()\n if reply == '':\n self.dead = True\n reply = reply.split(\":\")\n # print(\"{} received {}\".format(self.slave_id, reply))\n if reply[0] == \"probe\":\n self.send_new_job()\n elif reply[0] == \"fail\":\n pass\n # print(\"One fail\")\n elif reply[0] == \"pass\":\n self.break_room.got_result(reply[1] + \":\" + reply[2])\n except socket.timeout:\n pass\n print(\"Slave{} down \".format(self.slave_id))\n\n def dispatcher(self):\n \"\"\"\n responder[kill(q)]\n \"\"\"\n while True:\n if self.dead:\n self.socket.send(\"q\".encode())\n break\n time.sleep(3)\n\n def send_new_job(self):\n self.socket.send(self.job_master.get_task().encode())\n\n def close_connection(self):\n self.dead = True\n\n def send_rest(self):\n self.socket.send(\"rest\".encode())\n\n\nclass JobMaster:\n def __init__(self, sha1code, break_room):\n \"\"\"\n\n :type break_room: BreakRoom\n \"\"\"\n self.break_room = break_room\n self.the_queue = Queue()\n self.currentJob = None\n self.from_file = False\n self.jobs = []\n if os.path.isfile(sha1code):\n self.from_file = True\n self.result_file = open(\"decrypted\" + sha1code + \".txt\", 'w')\n print(\"Decrypting from file. Results in Decrypted+{}\".format(sha1code))\n for x in open(sha1code, 'r').read().split():\n self.jobs.append(x)\n else:\n self.jobs.append(sha1code)\n\n # print(\"JOB MASTER'S JOBS : {}\".format(self.jobs))\n self._new_job()\n\n def _generate_new_queue(self):\n nums = '1234567890'\n special = '`~!@#$%^&*()_+-='\n space = ' '\n self.the_queue = Queue()\n self.the_queue.put(str(string.ascii_lowercase))\n self.the_queue.put(str(string.ascii_letters))\n self.the_queue.put(str(string.ascii_lowercase + nums))\n self.the_queue.put(str(string.ascii_letters + nums))\n self.the_queue.put(str(string.ascii_uppercase))\n self.the_queue.put(str(string.ascii_uppercase + nums))\n self.the_queue.put(str(string.ascii_lowercase + nums + special))\n self.the_queue.put(str(string.ascii_lowercase + special))\n self.the_queue.put(str(string.ascii_letters + special))\n self.the_queue.put(str(string.ascii_letters + nums + special))\n self.the_queue.put(str(string.ascii_uppercase + nums + special))\n self.the_queue.put(str(string.ascii_uppercase + special))\n self.the_queue.put(str(string.ascii_lowercase + nums + space))\n self.the_queue.put(str(string.ascii_uppercase + nums + space))\n self.the_queue.put(str(string.ascii_letters + nums + space))\n self.the_queue.put(str(string.ascii_lowercase + nums + space))\n self.the_queue.put(str(string.ascii_uppercase + nums + space))\n self.the_queue.put(str(string.ascii_letters + nums + space))\n\n def _new_job(self):\n if len(self.jobs) > 0:\n self.currentJob = self.jobs.pop()\n self._generate_new_queue()\n print(\"Decrypting : {}\".format(self.currentJob))\n elif len(self.jobs) == 0:\n self.currentJob = None\n self.break_room.we_are_done()\n\n def get_task(self):\n if not self.currentJob:\n temp_str = \"q\"\n elif self.the_queue.empty():\n self._new_job()\n temp_str = \"rest\"\n else:\n temp_str = \"job:\" + self.currentJob + ':' + self.the_queue.get()\n # print(\"JOb master : {}\".format(temp_str))\n return temp_str\n\n def take_result(self, result):\n result = result.split(\":\")\n print(\"########### {} -> {}\".format(result[0], result[1]))\n self.break_room.send_rest()\n self._new_job()\n if self.from_file:\n self.result_file.write(result[0] + \" -> \" + result[1] + '\\n')\n\n\nclass BreakRoom:\n def __init__(self, host, port, sha1code):\n self.failure = 0\n self.we_are_done_switch = False\n self.jobMaster = JobMaster(sha1code, self)\n\n self.slave_handlers = []\n\n self.master_socket = socket.socket()\n self.master_socket.bind((host, port))\n\n threading.Thread(name=\"receiptionist\", target=self.receive_incoming_request).start()\n\n def receive_incoming_request(self):\n self.master_socket.listen(20)\n self.master_socket.setblocking(0)\n self.master_socket.settimeout(2)\n slave_id = 0\n while True:\n try:\n slave_socket, addr = self.master_socket.accept()\n print('Got connection from {}'.format(addr))\n self.slave_handlers.append(SlaveHandler(slave_socket, self, self.jobMaster, slave_id))\n slave_id += 1\n except socket.timeout:\n if self.we_are_done_switch and not len(self.slave_handlers):\n self._close_all_connections()\n break\n print(\"Receiptionist down.\")\n\n def _close_all_connections(self):\n for slave in self.slave_handlers:\n slave.close_connection()\n # print(\"Slaves closed\")\n\n def got_result(self, result):\n self.jobMaster.take_result(result)\n\n def we_are_done(self):\n self.we_are_done_switch = True\n # print(\"WE ARE DONE!!\")\n\n def remove_slave_handler(self, slave_handler):\n self.slave_handlers.pop(self.slave_handlers.index(slave_handler))\n\n def send_rest(self):\n for x in self.slave_handlers:\n x.send_rest()\n\n\nif __name__ == '__main__':\n pass\n # print(sys.argv)\n if len(sys.argv) == 4:\n BreakRoom(sys.argv[1], int(sys.argv[2]), sys.argv[3])\n else:\n print(\"syntax : python3 master.py [local ip addr] [port] [sha1 code /sha1 file] optional[no of localslaves]\\n \"\n \"eg : python3 master.py 192.168.1.105 9987 e5acb1a96e34cd7f263aada0b69aa58bdd722a03 3 \")\n","repo_name":"sylveryte/mini-projects","sub_path":"npl/master.py","file_name":"master.py","file_ext":"py","file_size_in_byte":7548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19023637534","text":"\n# coding: utf-8\n\n# In[546]:\n\n\nfrom __future__ import print_function\nimport sys\n\nif __name__ == '__main__':\n\n train_input = sys. argv [1]\n test_input= sys. argv [2]\n train_out= sys. argv [3]\n test_out = sys. argv [4]\n metrics_out = sys. argv [5]\n num_epoch = int(sys. argv [6])\n hidden_units = int(sys. argv [7])\n init_flag = int(sys. argv [8])\n learning_rate = float(sys. argv [9])\n\n\n import numpy as np\n import math\n\n\n# train_input = 'handout/smallTrain.csv'\n# test_input = 'handout/smallTest.csv'\n# train_out = 'handout/to1.labels'\n# test_out = 'handout/to2.labels'\n# metrics_out = 'handout/to3.txt'\n# num_epoch = 2\n# hidden_units = 4\n# init_flag = 2\n# learning_rate = 0.1\n\n \n \n train_input = open(train_input,\"r\")\n test_input = open(test_input,\"r\") \n train_out= open(train_out,\"w\") \n test_out= open(test_out,\"w\") \n metrics_out = open(metrics_out,\"w\") \n\n\n x=[]\n y=[]\n x_test=[]\n y_test=[]\n train_out_raw = ''\n test_out_raw = ''\n metrics_out_raw = ''\n\n\n\n\n with train_input as f:\n for n in f:\n nn = (np.array((n.replace('\\n','').split(',')),dtype = float))\n y.append(nn[0])\n nn[0] = 1\n x.append(nn)\n\n with test_input as f2:\n for n in f2:\n nn = (np.array((n.replace('\\n','').split(',')),dtype = float))\n y_test.append(nn[0])\n nn[0] = 1\n x_test.append(nn)\n\n y_vec = []\n for i in y:\n a = np.zeros(10)\n a[int(i)] = 1\n y_vec.append(a)\n\n y_vec_test = [] \n for i in y_test:\n a = np.zeros(10)\n a[int(i)] = 1\n y_vec_test.append(a)\n\n\n def innerprod(x,y):\n inner = 0\n for i in range(len(x)):\n inner += x[i]*y[i]\n return inner\n\n\n if init_flag == 1:\n al = np.random.random((hidden_units,129))\n be = np.random.random((10,hidden_units+1))\n alpha = (al-0.5)/5\n beta = (be-0.5)/5\n alpha[:,0] = 0\n beta[:,0] = 0\n \n# print(alpha)\n# print(beta)\n elif init_flag == 2:\n alpha = np.zeros(129*hidden_units).reshape(hidden_units,129) \n beta = np.zeros((hidden_units+1)*10).reshape(10,hidden_units+1) \n \n\n\n\n\n for m in range((num_epoch)):\n for n in range(len(x)):\n a = []\n for i in range(hidden_units):\n a.append(innerprod(x[n],alpha[i]))\n# print('a',a)\n # print(alpha[:,i])\n\n z = [1/(1+np.exp(-i)) for i in a]\n z = [1.] + z\n# print('z',z)\n\n # b = []\n # for i in range(10):\n # b.append(innerprod(z,beta[i]))\n\n b = np.matmul(z,beta.T)\n# print('b',b)\n\n bb = 0\n for i in range(len(b)):\n bb += np.exp(b[i])\n\n y_hat = [np.exp(i)/bb for i in b]\n\n# print('y_hat',y_hat)\n\n\n gJ = 1\n gy = [-y_vec[n][i]/y_hat[i] for i in range(10)]\n# print('gy',gy)\n\n # gb = [-y_vec[n][i]*(1-y_hat[j]) for i in range(10)]\n\n dy_db = np.ndarray((10,10))\n for i in range(10):\n for j in range(10):\n if i == j:\n dy_db[i][i] = (1-y_hat[i])*y_hat[i] \n else: \n dy_db[i][j] = (-y_hat[i])*y_hat[j]\n\n gb = np.matmul(np.array(gy),dy_db)\n# print('gb',gb)\n\n gbeta = np.ndarray(len(gb)*len(z)).reshape(len(z),len(gb))\n for i in range(len(z)):\n gbeta[i] = gb*z[i]\n gbeta = gbeta.T\n# print('gbeta',gbeta)\n\n gz = np.matmul(beta.T,gb)\n# print('gz',gz)\n\n\n# a_back = np.array(a)\n# a_back = np.exp(-a_back)/(np.exp(-a_back)+1)**2\n\n# dz_da = []\n# for i in range(hidden_units):\n# dz_da.append(a_back)\n# dz_da = np.array(dz_da)\n dz_da = np.zeros((len(a),len(a)))\n for i in range(len(a)):\n for j in range(len(a)):\n if i==j:\n dz_da[i][j] = np.exp(-a[i])/(np.exp(-a[i])+1)**2\n\n\n\n\n\n \n# print('dz_da',dz_da)\n ga = np.matmul(gz[1:],dz_da.T) ##fixed\n# print('ga',ga)\n\n galpha = np.ndarray(hidden_units*len(x[n])).reshape(hidden_units,len(x[n]))\n for i in range(hidden_units):\n galpha[i] = ga[i]*x[n]\n# print('galpha',galpha)\n\n\n\n alpha = alpha - learning_rate * galpha\n# print(alpha)\n beta = beta - learning_rate * gbeta\n# print(beta)\n\n\n ##train prediction\n cross_entropy = 0\n error = 0\n\n\n for n in range(len(x)):\n a = []\n for i in range(hidden_units):\n a.append(innerprod(x[n],alpha[i]))\n\n\n z = [1/(1+np.exp(-i)) for i in a]\n z = [1.] + z\n\n\n b = np.matmul(z,beta.T)\n\n\n bb = 0\n for i in range(len(b)):\n bb += np.exp(b[i])\n\n y_hat = [np.exp(i)/bb for i in b]\n\n # print('y_hat',y_hat)\n\n max_predict = 0\n predict = 0\n for i in range(10):\n if y_hat[i] >max_predict:\n max_predict = y_hat[i]\n predict = i\n\n if m == num_epoch-1:\n train_out_raw += str(predict) + '\\n'\n\n cross_entropy -= np.log(y_hat[int(y[n])])\n if predict != int(y[n]):\n error += 1\n\n\n cross_entropy /= len(x)\n error /= len(x)\n\n\n metrics_out_raw += 'epoch='+ str(m+1) + ' crossentropy(train): ' + str(cross_entropy) + '\\n'\n # print('error',error)\n\n ##test prediction\n cross_entropy_test = 0\n error_test = 0\n\n\n for n in range(len(x_test)):\n a = []\n # error = 0\n for i in range(hidden_units):\n a.append(innerprod(x_test[n],alpha[i]))\n\n\n z = [1/(1+np.exp(-i)) for i in a]\n z = [1.] + z\n\n\n b = np.matmul(z,beta.T)\n\n\n bb = 0\n for i in range(len(b)):\n bb += np.exp(b[i])\n\n y_hat = [np.exp(i)/bb for i in b]\n\n # print('y_hat',y_hat)\n\n max_predict = 0\n predict = 0\n for i in range(10):\n if y_hat[i] >max_predict:\n max_predict = y_hat[i]\n predict = i\n\n if m == num_epoch-1:\n test_out_raw += str(predict) + '\\n'\n\n cross_entropy_test -= np.log(y_hat[int(y_test[n])])\n if predict != int(y_test[n]):\n error_test += 1\n\n\n cross_entropy_test /= len(x_test)\n error_test /= len(x_test)\n\n\n metrics_out_raw += 'epoch='+ str(m+1) + ' crossentropy(test): ' + str(cross_entropy_test) + '\\n'\n # print('error',error)\n\n metrics_out_raw += 'error(train): ' + str(error) +'\\n'\n metrics_out_raw += 'error(test): ' + str(error_test) +'\\n'\n\n\n\n train_out.writelines(train_out_raw)\n test_out.writelines(test_out_raw)\n metrics_out.writelines(metrics_out_raw)\n\n\n# In[545]:\n\n\n# print(metrics_out_raw)\n\n\n# In[542]:\n\n\n# print(test_out_raw)\n\n","repo_name":"winasvin/win-asvin","sub_path":"Neural Network/neuralnet.py","file_name":"neuralnet.py","file_ext":"py","file_size_in_byte":7371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30705513980","text":"import os.path\n\nimport sys\nimport yaml\nimport datetime # For current Date/Time\nimport re # For regex\nimport paramiko # For remote ssh\n\n\ndef setup_config():\n\n #\n # System wide defaults\n #\n config_data = dict(\n\n # Your logfile\n LOG=os.path.expanduser(\"~\") + \"/esxi-vm.log\",\n writeLog=False, # By default, do NOT write logs\n\n # Enable/Disable dryrun by default\n isDryRun=False,\n\n # Enable/Disable Verbose output by default\n isVerbose=False,\n\n # Enable/Disable exit summary by default\n isSummary=False,\n\n # ESXi host/IP, port, root login & password\n HOST=\"esxi\",\n PORT=22,\n USER=\"root\",\n PASSWORD=\"\",\n KEY=\"\",\n\n # Default number of vCPU's, GB Mem, & GB boot disk\n CPU=2,\n MEM=4,\n HDISK=20,\n\n # Default Disk format thin, zeroedthick, eagerzeroedthick\n DISKFORMAT=\"thin\",\n\n # Virtual Disk device type\n VIRTDEV=\"pvscsi\",\n\n # Specify default Disk store to \"LeastUsed\"\n STORE=\"LeastUsed\",\n\n # Default Network Interface (vswitch)\n NET=\"None\",\n\n # Default ISO\n ISO=\"None\",\n\n # Default GuestOS type. (See VMware documentation for all available options)\n GUESTOS=\"centos-64\",\n\n # Extra VMX options\n VMXOPTS=\"\"\n )\n\n config_data_file_location = os.path.expanduser(\"~\") + \"/.esxi-vm.yml\"\n\n #\n # Get ConfigData from ConfigDataFile, then merge.\n #\n if os.path.exists(config_data_file_location):\n from_file_config_data = yaml.safe_load(open(config_data_file_location))\n config_data.update(from_file_config_data)\n\n try:\n with open(config_data_file_location, 'w') as FD:\n yaml.dump(config_data, FD, default_flow_style=False)\n FD.close()\n except Exception as e:\n print(\"Unable to create/update config file {}\".format(config_data_file_location))\n print(\"The Error is {}\".format(e))\n sys.exit(1)\n return config_data\n\n\ndef save_config(config_data):\n config_data_file_location = os.path.expanduser(\"~\") + \"/.esxi-vm.yml\"\n try:\n with open(config_data_file_location, 'w') as FD:\n yaml.dump(config_data, FD, default_flow_style=False)\n FD.close()\n except Exception as e:\n print(\"Unable to create/update config file {}\".format(config_data_file_location))\n print(\"The Error is {}\".format(e))\n return 1\n return 0\n\n\ndef the_current_date_time():\n i = datetime.datetime.now()\n return str(i.isoformat())\n\n\ndef exec_ssh_command(message, command, ssh, verbose):\n if verbose:\n if message:\n print(message)\n print(\"SSH: \" + command)\n return ssh.exec_command(command)\n\n\ndef get_esxi_version(ssh, verbose):\n try:\n (stdin, stdout, stderr) = exec_ssh_command(\"Get ESXi version\", \"esxcli system version get |grep Version\",\n ssh, verbose)\n if re.match(\"Version\", str(stdout.readlines())) is not None:\n print(\"Unable to determine if this is a ESXi Host\")\n sys.exit(1)\n\n except Exception as e:\n print(\"Unable to get ESXi version\")\n print(\"The Error is {}\".format(e))\n sys.exit(1)\n\n\ndef connect_to_esxi(host, port, user, password, key, verbose):\n try:\n ssh = paramiko.SSHClient()\n ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n if key:\n ssh.connect(host, port=port, username=user, key_filename=key)\n else:\n ssh.connect(host, port=port, username=user, password=password)\n get_esxi_version(ssh, verbose)\n return ssh\n except Exception as e:\n print(\"Unable to access ESXi Host: {}, port: {}, username: {}, key: {}\".format(host, port, user, key))\n print(\"The Error is {}\".format(e))\n sys.exit(1)\n","repo_name":"andrivet/esxi-vm","sub_path":"esxi_vm_functions.py","file_name":"esxi_vm_functions.py","file_ext":"py","file_size_in_byte":3958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"11203320636","text":"import re\n\ndef cssminify(css):\n rules = (\n (r\"\\/\\*.*?\\*\\/\", \"\"), # delete comments\n (r\"\\n\", \"\"), # delete new lines\n (r\"\\s+\", \" \"), # change multiple spaces to one space\n (r\"\\s?([;{},~>!])\\s?\", r\"\\1\"), # delete space where it is not needed\n (r\":\\s\", \":\"), # delete spaces after colons, but not before. E.g. do not break selectors \"a :focus\", \"b :matches(...)\", \"c :not(...)\" where the leading space is significant\n (r\"\\s?([-+])(?:\\s(?![0-9(])(?!var))\", r\"\\1\"), # delete whitespace around + and - when not followed by a number, paren, or var(). E.g. strip for selector \"a + b\" but not \"calc(a + b)\" which requires spaces.\n (r\";}\", \"}\") # change ';}' to '}' because the semicolon is not needed\n )\n\n css = css.replace(\"\\r\\n\", \"\\n\")\n for rule in rules:\n css = re.compile(rule[0], re.MULTILINE | re.UNICODE | re.DOTALL).sub(rule[1], css)\n return css\n\nif __name__ == \"__main__\":\n import sys\n if sys.version_info[0] >= 3:\n import io\n if sys.stdin.encoding != 'UTF-8':\n sys.stdin = io.TextIOWrapper(sys.stdin.buffer, encoding='UTF-8')\n if sys.stdout.encoding != 'UTF-8':\n sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='UTF-8')\n sys.stdout.write(cssminify(sys.stdin.read()))\n","repo_name":"WebKit/WebKit","sub_path":"Source/JavaScriptCore/Scripts/cssmin.py","file_name":"cssmin.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"25185448534","text":"import matplotlib.pyplot as plt\r\nfrom sklearn.preprocessing import LabelBinarizer\r\nfrom keras import backend as K\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\r\nfrom keras.layers import Dropout\r\nfrom keras.utils import np_utils\r\nfrom utils import load_dataset\r\n\r\nX_train, y_train, X_test, y_test, classes = load_dataset()\r\n\r\nlabel_binrizer = LabelBinarizer()\r\ny_train = label_binrizer.fit_transform(y_train)\r\ny_test = label_binrizer.fit_transform(y_test)\r\n\r\nX_train = X_train / 255\r\nX_test = X_test / 255\r\n\r\nX_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\r\nX_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\r\n\r\nK.clear_session()\r\n\r\n# creating model\r\nmodel = Sequential()\r\n\r\nmodel.add(Conv2D(128, kernel_size=(3,3), activation = 'relu', input_shape=(28, 28 ,1) ))\r\nmodel.add(MaxPooling2D(pool_size = (2, 2)))\r\n\r\nmodel.add(Conv2D(128, kernel_size = (3, 3), activation = 'relu'))\r\nmodel.add(MaxPooling2D(pool_size = (2, 2)))\r\n\r\nmodel.add(Flatten())\r\n\r\nmodel.add(Dense(256, activation = 'relu'))\r\nmodel.add(Dropout(0.10))\r\n\r\nmodel.add(Dense(24, activation = 'softmax'))\r\nmodel.add(Dropout(0.10))\r\n\r\nmodel.compile(loss = 'categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\r\n\r\nmodel.summary()","repo_name":"deveshshakya/MNIST-Sign-Kaggle","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25782456281","text":"import builtins\nfrom itertools import chain\nfrom pathlib import PurePath\nimport re\nfrom shutil import get_terminal_size\nimport sys\nfrom types import MappingProxyType, MemberDescriptorType\nfrom typing import (\n Any,\n Callable,\n List,\n Iterable,\n Tuple,\n TypeVar,\n Type,\n IO,\n Iterator,\n Dict,\n Mapping,\n Union,\n Optional,\n)\n\n\nclass _HAS_DEFAULT_FACTORY_CLASS:\n def __repr__(self) -> str:\n return \"\"\n\n\nclass _MISSING_TYPE:\n pass\n\n\nMISSING = _MISSING_TYPE()\n_HAS_DEFAULT_FACTORY = _HAS_DEFAULT_FACTORY_CLASS()\n_EMPTY_METADATA: Mapping = MappingProxyType({})\n\n# list will be treated by the parser as List[str] (items are parsed as strings)\n_SUPPORTED_TYPES = (\n str,\n int,\n float,\n bool,\n List[str],\n List[int],\n List[float],\n list,\n List,\n)\n_SUPPORTED_LIST_TYPES = (list, List[str], List[int], List[float], List)\n_LIST_CASTS: Dict[Any, type] = {\n list: str,\n List: str,\n List[str]: str,\n List[int]: int,\n List[float]: float,\n}\n_TYPE_STR: Dict[Any, str] = {\n str: \"string\",\n int: \"integer\",\n float: \"decimal\",\n bool: \"flag\",\n list: \"string(s)\",\n List: \"string(s)\",\n List[str]: \"string(s)\",\n List[int]: \"integer(s)\",\n List[float]: \"decimal(s)\",\n}\n_HELP_OPT = (\"help\", \"h\")\n\n# default prefixes used by the parser to match\n# against option strings\n_DEFAULT_PREFIXES = [\"-\"]\n\n# raw strings for building regex\n# to match against option strings\n_ALLOWED_PREFIXES = {\n \"-\": r\"\\-\",\n \"+\": r\"\\+\",\n \"/\": r\"\\/\",\n}\n\nCookedBirria = TypeVar(\"CookedBirria\")\nUserClass = TypeVar(\"UserClass\")\nOptionalType = Union[_MISSING_TYPE, Type, None]\nCookedIngredient = Union[str, int, float, bool, List[int], List[float], List[str]]\n\n\n# The name of an attribute on the class where we store the Field\n# objects. Also used to check if a class is a cooked class.\n_FIELDS = \"__arg_fields__\"\n\n# The name of an attribute on the class that stores the parameters to\n# @dataclass.\n_PARAMS = \"__arg_params__\"\n\n\nclass Ingredient:\n \"\"\"Class that represents an ingredient.\n\n This class is used as a more elaborate declaration of an ingredient/argument.\n You should probably use ingredient() instead of calling this directly.\n \"\"\"\n\n __slots__ = (\"name\", \"type\", \"default\", \"default_factory\", \"help\", \"alias\")\n\n def __init__(\n self,\n default: Any,\n default_factory: Callable[[], Any],\n help: Optional[str],\n alias: Optional[str],\n ):\n self.name: str = None # type: ignore[assignment]\n self.type: type = None # type: ignore[assignment]\n self.default = default\n self.default_factory = default_factory\n self.help = help\n self.alias = alias\n\n def __repr__(self) -> str:\n return (\n \"Ingredient(\"\n f\"name={self.name!r},\"\n f\"type={self.type!r},\"\n f\"default={self.default!r},\"\n f\"default_factory={self.default_factory!r},\"\n f\"help={self.help!r}\"\n f\"alias={self.alias!r}\"\n \")\"\n )\n\n # This is used to support the PEP 487 __set_name__ protocol in the\n # case where we're using a field that contains a descriptor as a\n # default value. For details on __set_name__, see\n # https://www.python.org/dev/peps/pep-0487/#implementation-details.\n #\n # Note that in _process_class, this Field object is overwritten\n # with the default value, so the end result is a descriptor that\n # had __set_name__ called on it at the right time.\n def __set_name__(self, owner, name: str):\n func = getattr(type(self.default), \"__set_name__\", None)\n if func:\n # __set_name__ present, call it\n func(self.default, owner, name)\n\n\ndef ingredient(\n *, default=MISSING, default_factory=MISSING, help: str = None, alias: str = None\n) -> Ingredient:\n \"\"\"Instantiate and returns an Ingredient.\n\n This is a factory function to create Ingredient class instance.\n You probably should use this instead of using Ingredient directly.\n\n Args:\n default: Default value for the ingredient\n default_factory: Callable that takes no parameters and returns\n a value to be used as the default value.\n help: Short description of the ingredient that will be printed\n as part of the help string.\n alias: Alias for option strings. This is only applicable for\n optional ingredient.\n\n Returns:\n An Ingredient class instance.\n \"\"\"\n if default is not MISSING and default_factory is not MISSING:\n raise ValueError(\"can't specify both default and default factory\")\n return Ingredient(default, default_factory, help, alias)\n\n\ndef _create_fn(\n name: str,\n args: Iterable[str],\n body: Iterable[str],\n *,\n globals: dict = None,\n locals: dict = None,\n return_type: OptionalType = MISSING,\n):\n if locals is None:\n locals = {}\n if \"BUILTINS\" not in locals:\n locals[\"BUILTINS\"] = builtins\n\n return_annotation = \"\"\n if return_type is not MISSING:\n locals[\"_return_type\"] = return_type\n return_annotation = \"->_return_type\"\n args_str = \",\".join(args)\n body_str = \"\\n\".join(f\" {b}\" for b in body)\n\n # compute text for the entire function\n txt = f\" def {name}({args_str}){return_annotation}:\\n{body_str}\"\n\n local_vars = \", \".join(locals.keys())\n txt = f\"def __create_fn__({local_vars}):\\n{txt}\\n return {name}\"\n ns: dict = {}\n exec(txt, globals, ns)\n return ns[\"__create_fn__\"](**locals)\n\n\ndef _field_init(f: Ingredient, globals: dict, self_name: str):\n default_name = f\"_dflt_{f.name}\"\n if f.default_factory is not MISSING:\n globals[default_name] = f.default_factory\n value: Optional[str] = (\n f\"{default_name}() \"\n f\"if {f.name} is _HAS_DEFAULT_FACTORY \"\n f\"else {f.name}\"\n )\n else:\n if f.default is MISSING:\n # no default, simple assignment\n value = f.name\n elif f.default is not MISSING:\n globals[default_name] = f.default\n value = f.name\n\n return _field_assign(f.name, value, self_name)\n\n\ndef _field_assign(name: Optional[str], value: Optional[str], self_name: str) -> str:\n return f\"{self_name}.{name}={value}\"\n\n\ndef _init_param(f: Ingredient) -> str:\n # Return __init__ parameter string for this field\n # Ex: equivalent of \"x:int=3\"\n\n if f.default is MISSING and f.default_factory is MISSING:\n default = \"\"\n elif f.default is not MISSING:\n # There's a default, this will be the name that's used to look\n # it up.\n default = f\"=_dflt_{f.name}\"\n elif f.default_factory is not MISSING:\n # There's a factory function. Set a marker.\n default = \"=_HAS_DEFAULT_FACTORY\"\n return f\"{f.name}:_type_{f.name}{default}\"\n\n\ndef _init_fn(fields: Iterable[Ingredient], self_name: str, globals: dict):\n locals: Dict[str, Any] = {f\"_type_{f.name}\": f.type for f in fields}\n locals.update({\"MISSING\": MISSING, \"_HAS_DEFAULT_FACTORY\": _HAS_DEFAULT_FACTORY})\n\n body_lines = [_field_init(f, locals, self_name) for f in fields]\n if not body_lines:\n body_lines = [\"pass\"]\n\n return _create_fn(\n \"__init__\",\n [self_name] + [_init_param(f) for f in fields],\n body_lines,\n locals=locals,\n globals=globals,\n return_type=None,\n )\n\n\ndef _repr_fn(fields: Iterable[Ingredient], globals: dict):\n return _create_fn(\n \"__repr__\",\n (\"self\",),\n [\n 'return self.__class__.__qualname__ + f\"('\n + \", \".join([f\"{f.name}={{self.{f.name}!r}}\" for f in fields])\n + ')\"'\n ],\n globals=globals,\n )\n\n\ndef _eq_fn(self_tuple: str, other_tuple: str, globals: dict):\n return _create_fn(\n \"__eq__\",\n (\"self\", \"other\"),\n [\n \"if other.__class__ is self.__class__:\",\n f\" return {self_tuple}=={other_tuple}\",\n \"return NotImplemented\",\n ],\n globals=globals,\n )\n\n\ndef _set_new_attributes(cls, a_name: str, value: Any) -> bool:\n # Don't overwrite existing attribute\n # Return True if attribute already exists\n if a_name in cls.__dict__:\n return True\n setattr(cls, a_name, value)\n return False\n\n\ndef _get_ingredient(cls, a_name: str, a_type: type) -> Ingredient:\n default = getattr(cls, a_name, MISSING)\n f: Ingredient\n if isinstance(default, Ingredient):\n f = default\n else:\n # field in __slots__, so no default value\n if isinstance(default, MemberDescriptorType):\n default = MISSING\n f = ingredient(default=default)\n\n f.name = a_name\n f.type = a_type\n\n # disallow mutable defaults for known container types\n if isinstance(f.default, list):\n raise ValueError(\n f\"mutable default {type(f.default)} for field \"\n f\"{f.name} is not allowed: use default_factory() for list\"\n )\n if isinstance(f.default, (set, dict)):\n raise ValueError(\n f\"mutable default {type(f.default)} for field \"\n \"dict and set are not supported as default values: use default_factory() for list\"\n )\n return f\n\n\ndef _tuple_str(name: str, fields: List[Ingredient]) -> str:\n if not fields:\n return \"()\"\n # trailing commas for tuple with 1 item\n return f\"({','.join([f'{name}.{f.name}' for f in fields])},)\"\n\n\ndef _proc_class(cls: Type[UserClass]) -> Type[UserClass]:\n # print(f\"Processing class {cls.__name__}\")\n fields = {}\n\n if cls.__module__ in sys.modules:\n globals = sys.modules[cls.__module__].__dict__\n else:\n # Theoretically this can happen if someone writes\n # a custom string to cls.__module__. In which case\n # such dataclass won't be fully introspectable\n # (w.r.t. typing.get_type_hints) but will still function\n # correctly.\n globals = {}\n\n base_non_default_fields = []\n base_default_fields = []\n for b in cls.__mro__[-1:0:-1]:\n # only include classes that use our decorator\n base_fields = getattr(b, _FIELDS, None)\n if base_fields:\n # print(f\"Base field for {b.__name__}\")\n # pprint(base_fields)\n for f in base_fields.values():\n if f.default is MISSING and f.default_factory is MISSING:\n base_non_default_fields.append(f)\n else:\n base_default_fields.append(f)\n\n # Annotations defined in this class and not the base classses.\n # if __annotations__ is not present, then this class adds no\n # new annotations. This is for fields added by this class.\n\n # Fields are from __annotations__, which is ordered. Defaults\n # are from class attributes.\n\n cls_annotations = cls.__dict__.get(\"__annotations__\", {})\n cls_fields = []\n for fname, ftype in cls_annotations.items():\n if ftype not in _SUPPORTED_TYPES:\n raise TypeError(f\"type {ftype} not supported\")\n cls_fields.append(_get_ingredient(cls, fname, ftype))\n # pprint(cls_fields)\n\n # sort the fields into default and non-default\n non_default_fields = []\n default_fields = []\n\n for f in cls_fields:\n # if the class attribute exists and is a \"Field\"\n # replace it with the real default.\n attr = getattr(cls, f.name, MISSING)\n if isinstance(attr, Ingredient):\n if f.default is MISSING:\n # if no default set, delete the class attribute\n delattr(cls, f.name)\n if f.default_factory is MISSING:\n non_default_fields.append(f)\n else:\n default_fields.append(f)\n else:\n setattr(cls, f.name, f.default)\n default_fields.append(f)\n elif attr is MISSING:\n non_default_fields.append(f)\n else:\n default_fields.append(f)\n\n # Dict insertion is ordered, so default fields follow\n # non-default fields\n for f in chain(\n base_non_default_fields, non_default_fields, base_default_fields, default_fields\n ):\n # print(f\"Inserting {f.name} in {cls.__name__}\")\n fields[f.name] = f\n\n # raise TypeError for Field members with no annotations\n for name, val in cls.__dict__.items():\n if isinstance(val, Ingredient) and name not in cls_annotations:\n raise TypeError(f\"{name!r} is a field but has no type annotations\")\n\n if not fields:\n raise TypeError(\"No ingredients found in class or any base classes\")\n\n # set all the fields to __arg_fields__\n # and mark this class as a cooked class\n # pprint(fields)\n setattr(cls, _FIELDS, fields)\n flds = list(fields.values())\n\n # attach __init__\n _set_new_attributes(\n cls,\n \"__init__\",\n _init_fn(flds, \"__cooked_self__\" if \"self\" in fields else \"self\", globals),\n )\n\n # attach __repr__\n _set_new_attributes(cls, \"__repr__\", _repr_fn(flds, globals))\n\n # attach __eq___\n # we want this for easy debugging and testing\n self_tuple = _tuple_str(\"self\", flds)\n other_tuple = _tuple_str(\"other\", flds)\n _set_new_attributes(cls, \"__eq__\", _eq_fn(self_tuple, other_tuple, globals=globals))\n\n return cls\n\n\n# _cls should not be specified by keyword, hence the underscore,\n# it's only used to detect if the decorator is called with params\n# or not\ndef cook(_cls=None):\n \"\"\"Decorator that takes a class and returns a CookedBirria class.\n\n Takes a class with typed variable annotations and a class based\n on the input class with __init__, __repr__, __eq__ dunder methods.\n This constructed class can then be used as a declaration of\n arguments for serve().\n \"\"\"\n\n def wrap(cls):\n return _proc_class(cls)\n\n # called as @dataclass()\n if _cls is None:\n return wrap\n\n # called as @dataclass\n return wrap(_cls)\n\n\ndef ingredients(class_or_instance: object) -> Tuple[Ingredient, ...]:\n try:\n fields = getattr(class_or_instance, _FIELDS)\n except AttributeError:\n raise TypeError(\"must be called with a birria class type or instance\")\n return tuple(fields.values())\n\n\ndef is_cooked_instance(obj: object) -> bool:\n return hasattr(type(obj), _FIELDS)\n\n\ndef is_cooked_class(cls: object) -> bool:\n if not isinstance(cls, type):\n return False\n\n return hasattr(cls, _FIELDS)\n\n\ndef is_cooked(obj: object) -> bool:\n cls = obj if isinstance(obj, type) else type(obj)\n return hasattr(cls, _FIELDS)\n\n\ndef _print_err_and_exit(msg: str, out: IO = sys.stderr, exit_code=1, cr: str = \"\\n\"):\n print(f\"{msg}{cr}\", file=out)\n sys.exit(exit_code)\n\n\ndef _str_to_lines(s: str, width: int) -> List[str]:\n # splits the string into a list\n # of lines, separated at whitespaces\n # so that each line's length is as close\n # to the width as possible\n split_idx = []\n last_space_idx = 0\n for i, c in enumerate(s):\n if c == \" \":\n last_space_idx = i\n if i and (i + 1) % width == 0:\n split_idx.append(last_space_idx)\n\n if not split_idx:\n return [s]\n\n lines = []\n start_from = 0\n for i in split_idx:\n lines.append(s[start_from:i])\n start_from = i + 1\n lines.append(s[split_idx[-1] + 1 :])\n return lines\n\n\ndef _help_str(\n name: str,\n type_str: str,\n width: int,\n help_padding: int,\n help_str: str,\n) -> str:\n\n name_and_type = f\"{name}: {type_str}\"\n help_str_width = width - len(name_and_type) - help_padding\n\n help_lines = _str_to_lines(help_str, help_str_width)\n padding = \" \" * help_padding\n help_lines[0] = f\"{name_and_type}{padding}{help_lines[0]}\"\n padding = \" \" * (len(name_and_type) + help_padding)\n for i in range(1, len(help_lines)):\n help_lines[i] = f\"{padding}{help_lines[i]}\"\n\n return \"\\n\".join(help_lines)\n\n\ndef _print_help(\n file: IO,\n prog: str,\n description: Optional[str],\n required: Iterable[Ingredient],\n optional: Iterable[Ingredient],\n width: Optional[int],\n max_width: Optional[int],\n prefixes: Optional[List[str]],\n):\n\n term_width = get_terminal_size().columns\n if not width:\n width = term_width - 2 if term_width <= 90 else 60\n if max_width:\n width = min(max_width, width)\n\n if width >= term_width:\n width = term_width - 2\n\n space_rgx = re.compile(r\"\\s+\")\n max_padding = width // 2\n\n req_help_strs = []\n for ingredient in required:\n type_str = _TYPE_STR[ingredient.type]\n help_msg = \"\" if not ingredient.help else space_rgx.sub(\" \", ingredient.help)\n\n if not help_msg:\n req_help_strs.append(f\"{ingredient.name}: {type_str}\")\n else:\n req_help_strs.append(\n _help_str(\n ingredient.name,\n type_str,\n width,\n max_padding - len(ingredient.name) - len(type_str),\n help_msg,\n )\n )\n\n opt_help_strs = []\n if prefixes:\n for ingredient in optional:\n name = \", \".join(f\"{p}{ingredient.name}\" for p in prefixes)\n if ingredient.alias:\n alias = \", \".join(f\"{p}{ingredient.alias}\" for p in prefixes)\n name += f\", {alias}\"\n\n type_str = _TYPE_STR[ingredient.type]\n help_msg = (\n \"\" if not ingredient.help else space_rgx.sub(\" \", ingredient.help)\n )\n\n if not help_msg:\n opt_help_strs.append(f\"{name}: {type_str}\")\n else:\n opt_help_strs.append(\n _help_str(\n name,\n type_str,\n width,\n max_padding - len(name) - len(type_str),\n help_msg,\n )\n )\n else:\n for ingredient in optional:\n type_str = _TYPE_STR[ingredient.type]\n help_msg = (\n \"\" if not ingredient.help else space_rgx.sub(\" \", ingredient.help)\n )\n\n if not help_msg:\n opt_help_strs.append(f\"{ingredient.name}: {type_str}\")\n else:\n opt_help_strs.append(\n _help_str(\n ingredient.name,\n type_str,\n width,\n max_padding - len(ingredient.name) - len(type_str),\n help_msg,\n )\n )\n\n sep = \"-\" * width\n if description:\n prog = f\"{prog}: {description}\"\n\n prog = \"\\n\".join(_str_to_lines(prog, width))\n\n file.write(f\"\\n{prog}\\n\")\n\n if req_help_strs:\n file.write(f\"\\nRequired\\n{sep}\\n\")\n file.write(\"\\n\".join(req_help_strs) + \"\\n\")\n\n if opt_help_strs:\n file.write(f\"\\nOptional\\n{sep}\\n\")\n file.write(\"\\n\".join(opt_help_strs) + \"\\n\")\n\n\ndef _match_opt_strings(\n names: List[str], prefixes: List[str], args_in: List[str]\n) -> Iterator[Tuple[int, str]]:\n # iterates through a list of arguments\n # and yields the index and the option string (excluding the prefix)\n # whenever there's a match\n\n name_group = r\"|\".join(names) if len(names) > 1 else names[0]\n prefix_group = (\n r\"|\".join(_ALLOWED_PREFIXES[p] for p in prefixes)\n if len(prefixes) > 1\n else _ALLOWED_PREFIXES[prefixes[0]]\n )\n opt_rgx = re.compile(rf\"^({prefix_group})({name_group})$\")\n\n for i, arg in enumerate(args_in):\n match = opt_rgx.match(arg)\n if match is not None:\n yield (i, match.groups()[1])\n\n\ndef serve(\n recipe: Type[CookedBirria],\n raw_ingredients: List[str] = None,\n prefixes: List[str] = None,\n extra_prefixes: List[str] = None,\n description: str = None,\n width: int = None,\n max_width: int = None,\n) -> CookedBirria:\n \"\"\"Parses argument from the command line.\n\n Takes a class decorated with the cook decorator as\n a list of declarations of arguments. Returns the parsed\n values from command line or argument list in an instance\n of the \"cooked\" class.\n\n Args:\n recipe: Class decorated with cook().\n raw_ingredients: Optional list of arguments. Defaults to sys.argv.\n prefixes: List of prefix characters for option strings. Defaults to ['-'].\n extra_prefixes: Extra prefixes characters. Useful for adding extra\n characters to the default list.\n description: Optional description of the program. This is printed out\n along with the help strings for individual arguments.\n width: Optional integer. This is the width of the help message. If not\n provided, the width will be set to a value based on the current terminal size.\n max_width: Optional integer. This is the maximum width (in columns)\n of the program's help message, useful if you don't want to specify a width\n but want to set a boundary for the message. This has no effect\n if width is set.\n\n Returns:\n Instance of a \"cooked\" class with parsed values.\n \"\"\"\n if not is_cooked(recipe):\n raise ValueError(\n \"Argument arg_cls must be either a PyArg class or an instance of an PyArg class\"\n )\n\n # use slice to avoid modifying the original list\n if raw_ingredients is None:\n # exclude the first argument (the program invocation)\n preprepped_ingredients = sys.argv[1:]\n prog = PurePath(sys.argv[0]).name\n else:\n preprepped_ingredients = raw_ingredients[:]\n prog = \"prog\"\n\n opt_ingredients: Dict[str, Ingredient] = {}\n # map aliasses to real ingredient names\n # when applicable\n opt_ingredients_aliases: Dict[str, str] = {}\n req_ingredients: List[Ingredient] = []\n cooking: Dict[str, CookedIngredient] = {}\n\n # check for type annotation as well as real values (best effort)\n # if class attr exists or field type is boolean (flag),\n # it's a named argument\n\n # boolean is always a named argument (aka a flag)\n # if the default for a boolean is set to a boolean,\n # the behaviour is to reverse that value if the\n # corresponding flag is present in the argument list.\n # In all other cases (no default set, default not set to\n # a boolean), set the value to True if the flag is present.\n\n # list types are a bit more tricky. First of all\n # multiple list fields are basically impossible to\n # handle if they are not optional (and therefore preceded\n # by an option string). So if there are list arguments,\n # the restrictions are:\n # 1. If a non-default field specifies a list type, only one positional\n # argument is allowed (all items parsed not preceded by an option string\n # will be gathered into a list)\n # in addition to other default fields\n # 2. Following 1), the ordering of argument is now \"strict\", meaning\n # that positional arguments come first in the argument list, followed\n # by named arguments\n # 3. If a default field specifies a list type, and provides a default\n # list through default_factory, any corresponding parsed items will\n # be added to the default list.\n # 4. Following 3), if the field specifies a default value that's not\n # a list, the default value will be overridden by a list of the parsed\n # items.\n\n seen_req_list = False\n for f in ingredients(recipe):\n if f.name in _HELP_OPT:\n raise TypeError(f\"{f.name} is reserved as help option\")\n\n default = getattr(recipe, f.name, MISSING)\n if default is MISSING:\n if f.type == bool:\n # if no default, set the \"parsed\" value\n # to False here so we can reverse it later\n opt_ingredients[f.name] = f\n cooking[f.name] = False\n elif f.type in _SUPPORTED_LIST_TYPES:\n # list type and default_factory exists -> default field\n if f.default_factory is not MISSING:\n opt_ingredients[f.name] = f\n else:\n if seen_req_list:\n raise TypeError(\n f\"{f.name} cannot be a list,\"\n \" only one non-default ingredient can be list\"\n )\n seen_req_list = True\n req_ingredients.append(f)\n else:\n if seen_req_list:\n raise TypeError(\n \"Only one non-default field allowed if using list type,\"\n \" use a default field if you want to specify more list fields\"\n )\n req_ingredients.append(f)\n else:\n if f.type == bool:\n # if default is a bool, add it to the parsed\n # dictionary, else set it to False\n if type(default) == bool:\n cooking[f.name] = default\n else:\n cooking[f.name] = False\n opt_ingredients[f.name] = f\n\n num_req = len(req_ingredients)\n if not prefixes:\n prefixes = _DEFAULT_PREFIXES\n\n if extra_prefixes:\n prefixes += extra_prefixes\n\n for p in prefixes:\n if p not in _ALLOWED_PREFIXES:\n raise ValueError(f\"'{p}' not supported as a prefix\")\n\n prefix_group = r\"|\".join(_ALLOWED_PREFIXES[p] for p in prefixes)\n help_opt_group = r\"|\".join(_HELP_OPT)\n help_opt_rgx = re.compile(rf\"^({prefix_group})({help_opt_group})$\")\n\n num_raw_ingredients = len(preprepped_ingredients)\n\n # print help if the help option string is the only argument\n if num_raw_ingredients == 1 and help_opt_rgx.match(preprepped_ingredients[0]):\n _print_help(\n sys.stderr,\n prog,\n description,\n req_ingredients,\n opt_ingredients.values(),\n width,\n max_width,\n prefixes,\n )\n sys.exit(0)\n\n # just some sanity checks\n if num_req and not num_raw_ingredients:\n _print_err_and_exit(f\"No arguments! Need at least {num_req}\")\n\n if not seen_req_list and num_raw_ingredients < num_req:\n _print_err_and_exit(f\"Too few arguments! Need at least {num_req}\")\n\n if num_req > 1 and seen_req_list:\n raise TypeError(\n \"Only one non-default field allowed if using list type,\"\n \"Use a default field if you want to specify more list fields\"\n )\n\n if not num_raw_ingredients:\n return recipe(**cooking) # type: ignore[call-arg]\n\n # if the first argument is a named argument,\n # all the postional arguments will follow the last\n # named argument. Likewise, if the first argument\n # is a positional argument, all the named arguments\n # follow the last positional argument.\n # Force this convention to make our life easier\n name_list = []\n for n in opt_ingredients.keys():\n name_list.append(n)\n if \"_\" in n:\n dash_name = \"-\".join(n.split(\"_\"))\n name_list.append(dash_name)\n opt_ingredients_aliases[dash_name] = n\n alias = opt_ingredients[n].alias\n if alias:\n if alias == n:\n raise ValueError(f\"Alias {alias} is the same as ingredient name\")\n opt_ingredients_aliases[alias] = n\n name_list.append(alias)\n\n name_group = \"|\".join(name_list)\n opt_rgx = re.compile(rf\"^({prefix_group})({name_group})$\")\n parsed_opts = set()\n first_opt_idx = -1\n last_opt_idx = num_raw_ingredients\n\n opt_first = bool(opt_rgx.match(preprepped_ingredients[0]))\n if opt_first:\n # non-default field is a list, all named arguments\n # must be at the end of the list\n if seen_req_list:\n _print_err_and_exit(\n \"When using list type for non-default field\"\n \"All default arguments must be specified after the non-default argument\"\n )\n\n first_opt_idx = 0\n last_opt_idx -= num_req\n\n if opt_ingredients:\n cur = 0\n while cur < num_raw_ingredients:\n arg = preprepped_ingredients[cur]\n match = opt_rgx.match(arg)\n if not match:\n cur += 1\n continue\n\n name = match.groups()[1]\n if first_opt_idx == -1:\n first_opt_idx = cur\n if name not in opt_ingredients:\n name = opt_ingredients_aliases[name]\n if name in parsed_opts:\n _print_err_and_exit(f\"Duplicate instance of {name}\")\n parsed_opts.add(name)\n\n ingredient = opt_ingredients[name]\n itype = ingredient.type\n if itype not in _SUPPORTED_LIST_TYPES:\n if itype == bool:\n cooking[name] = not cooking[name]\n cur += 1\n continue\n else:\n try:\n cooking[name] = itype(preprepped_ingredients[cur + 1])\n except ValueError:\n _print_err_and_exit(f\"Wrong type for {name}, needs {itype}\")\n cur += 2\n continue\n else:\n vals = []\n cast = _LIST_CASTS[itype]\n for item in preprepped_ingredients[cur + 1 : last_opt_idx]:\n if opt_rgx.match(item):\n break\n try:\n vals.append(cast(item))\n except ValueError:\n _print_err_and_exit(\n f\"value {item} for {name} has the wrong type, needs {cast}\"\n )\n if vals:\n if ingredient.default_factory is not MISSING:\n def_list = ingredient.default_factory()\n if isinstance(def_list, list):\n cooking[name] = def_list + vals\n else:\n cooking[name] = vals\n else:\n cooking[name] = vals\n else:\n _print_err_and_exit(f\"Needs at least one value for {name}\")\n cur += len(vals) + 1\n\n if not opt_ingredients or not parsed_opts:\n # no named arguments (either from the class definition or from the list),\n # just parse the list for positional argument\n if not seen_req_list:\n for i, arg in enumerate(preprepped_ingredients):\n try:\n ingredient = req_ingredients[i]\n cooking[ingredient.name] = ingredient.type(arg)\n except IndexError:\n _print_err_and_exit(f\"Too many arguments, only need {num_req}\")\n except ValueError:\n _print_err_and_exit(\n f\"Value {arg} of the wrong type, needs {ingredient.type}\"\n )\n return recipe(**cooking) # type: ignore[call-arg]\n else:\n vals = []\n ingredient = req_ingredients[0]\n cast = _LIST_CASTS[ingredient.type]\n try:\n for arg in preprepped_ingredients:\n vals.append(cast(arg))\n if not vals:\n _print_err_and_exit(\n f\"Need at least one value for {ingredient.name}\"\n )\n except ValueError:\n _print_err_and_exit(\n f\"Value {arg} for {ingredient.name} of the wrong type, needs {cast}\"\n )\n\n return recipe(vals) # type: ignore[call-arg]\n\n # if the first named arg appears first in the argument list,\n # all the positional arguments are at the end of the list,\n # otherwise they are at the start of the list.\n\n if num_req:\n pos_args_slice: List[str]\n if not opt_first:\n # if there are named arguments and the first one\n # doesn't start at at the beginning, start from the\n # beginning.\n pos_args_slice = preprepped_ingredients[:num_req]\n # named arguments start from the last positional argument\n else:\n # named arguments start at the beginning, go to\n # back of the list\n pos_args_slice = preprepped_ingredients[-num_req:]\n # named arguments go until the first positional argument\n\n if not seen_req_list:\n for i, arg in enumerate(pos_args_slice):\n try:\n ingredient = req_ingredients[i]\n cooking[ingredient.name] = ingredient.type(arg)\n except IndexError:\n _print_err_and_exit(f\"Need positinal argument: {ingredient.name}\")\n except ValueError:\n _print_err_and_exit(\n f\"Value {arg} for {ingredient.name} of the wrong type, needs {ingredient.type}\"\n )\n else:\n vals = []\n ingredient = req_ingredients[0]\n cast = _LIST_CASTS[ingredient.type]\n try:\n for arg in preprepped_ingredients[:first_opt_idx]:\n # gather items into a list until we find an option string\n vals.append(cast(arg))\n if not vals:\n _print_err_and_exit(\n f\"Need at least one value for {ingredient.name}\"\n )\n except ValueError:\n _print_err_and_exit(\n f\"Value {arg} for {ingredient.name} of the wrong type, needs {cast}\"\n )\n cooking[ingredient.name] = vals\n\n return recipe(**cooking) # type: ignore[call-arg]\n","repo_name":"ntn9995/birria","sub_path":"src/birria/_birria.py","file_name":"_birria.py","file_ext":"py","file_size_in_byte":33897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35934083285","text":"class Employee:\n \n raise_amt = 1.04\n\n def __init__(self,first,last,pay):\n self.first = first\n self.last = last\n self.pay = pay\n self.email = first + '.' + last + '@' + 'gmail.com'\n\n def fullname(self):\n return f'My fullname is {self.first} {self.last}'\n\n def apply_raise(self):\n self.pay = int(self.pay * self.raise_amt)\n return self.pay\n\nemp_1 = Employee('Akhil', 'Tripathi', 50000)\n\nprint(emp_1.fullname())\nemp_1.apply_raise()\nprint(emp_1.pay)","repo_name":"choubeyji-git/Python-Coding-Interview-questions","sub_path":"code_practice/oops/classVariable/new.py","file_name":"new.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1779244741","text":"''' After the new database structure has been created and the reference tables populated, \nthe real data from the Access database is imported into the new \"Main\" table. \n\nTo do this, all reference tables should be looked up in order to exchange the values from\nthe original database for the new corresponding IDs.\n\nAfter this is done, the tables Audio_in_file, Genre_in_file and Subs_in_file\nare populated using the information from the original database and the reference tables.\n\nBefore running this script, copy the newly created '01_ImportDatabase.db' to keep a copy\nand rename it /Test.db.'''\n\nfrom configparser import ConfigParser\nimport common.dbConnector as dbConnector\nimport pandas as pd\nimport sqlite3 as sql\n\n\ndef import_df_to_db(dataFrame, connector, cursor):\n ''' After finding the new ID values for the fields 'Calidad', 'Disco' and 'Pais'\n the whole database from the Excel file is imported into the new SQL database.'''\n\n for i in range(dataFrame.shape[0]):\n # ' are replace by '' so SQL is able to process them properly\n tit = dataFrame.loc[i, 'Titulo'].replace(\"\\'\",\"\\'\\'\")\n origTit = dataFrame.loc[i, 'TituloOriginal'].replace(\"\\'\",\"\\'\\'\")\n disc = dataFrame.loc[i, 'Disco']\n qt = dataFrame.loc[i, 'Calidad']\n year = dataFrame.loc[i, 'Año']\n country = dataFrame.loc[i, 'Pais']\n dur = dataFrame.loc[i, 'Duracion']\n dir = dataFrame.loc[i, 'Director'].replace(\"\\'\",\"\\'\\'\")\n script = dataFrame.loc[i, 'Guion'].replace(\"\\'\",\"\\'\\'\")\n sql_query = f\"\"\"INSERT INTO Main (Titulo, TituloOriginal, DiscoID, CalidadID, Year,\n PaisID, Duracion, Director, Guion) \n VALUES \n (\\'{tit}\\', \\'{origTit}\\', {disc}, {qt}, {year}, \n {country}, {dur}, \\'{dir}\\', \\'{script}\\')\"\"\"\n\n try:\n cursor.execute(sql_query)\n connector.commit()\n \n except sql.Error as error:\n print(f'Error during the execution of {sql_query}', error)\n\n\ndef import_genres(movie_id, value, connector, cursor):\n ''' This function will read all values from 'Genero' column\n in the Excel database, split them using the ',' character in the movies\n with more than one genre and then populate the table Genero_in_file with the genres found.'''\n \n # Obtain corresponding GeneroID from the new 'Genero' table for each genre in 'value'\n if value != '-':\n genres = value.split(',')[0:-1] # value always ends with ',' so the last element is not needed\n\n for i in genres:\n # All genres have a category defined between brackets that is not needed for now\n pos1 = i.find('[')\n pos2 = i.find(']')\n category = i[(pos1 + 1):pos2]\n genre = i[(pos2 + 2):]\n\n # Some genres have changed in the new database, so to find them:\n if genre == 'Palma de Oro': genre = 'Cannes - Palma de Oro'\n if genre == 'Mejor película' and category == 'Premios - Goya': \n genre = 'Goya - Mejor película'\n if genre == 'Mejor director': genre = 'Oscars - Mejor director'\n if genre == 'Mejor extranjera': genre = 'Oscars - Mejor extranjera'\n if genre == 'Mejor fotografía': genre = 'Oscars - Mejor fotografía'\n if genre == 'Mejor película' and category == 'Premios - Óscars':\n genre = 'Oscars - Mejor película'\n\n sql_query = f'SELECT id FROM Genero WHERE Nombre = \\'{genre}\\''\n res = db.execute(sql_query).fetchone()\n \n if res != None:\n sql_query = f'''INSERT INTO Genero_in_file (pelicula_id, genero_id) \n VALUES ({movie_id}, {res[0]})'''\n try:\n cursor.execute(sql_query)\n connector.commit()\n \n except sql.Error as error:\n print(f'Error during the execution of {sql_query}', error)\n\n else:\n print(f'{sql_query} found nothing')\n\n\ndef import_languages(movie_id, category, value, connector, cursor):\n ''' This function will read all values from 'IdiomaAudio' and 'IdiomSubtitulos' columns\n in the Excel database, split them using the '-' character in the movies\n with two languages in 'Audio' or 'Subs' and then populate the tables \n Audio_in_file and Subs_in_file with the languages found.'''\n\n id = []\n # To match the new values in the database these two have to be changed,\n # because in the original database they were 'May' and 'Var', instead of 'Maya' and 'Varios'\n # (see function 'get_individual_values' in /01_ImportDatabase/databaseImporter.py for reference)\n if value == 'Maya': \n value = 'May'\n \n if value == 'Var':\n value = 'Varios'\n\n # Obtain corresponding LanguageID from the new 'Idioma' table for each language in 'value'\n if value != '-':\n if value.find('-') != -1:\n [lang1, lang2] = value.split('-')\n sql_query = f'SELECT id FROM Idioma WHERE IdiomaAbreviado = \\'{lang1}\\''\n id.append(db.execute(sql_query).fetchone()[0])\n\n sql_query = f'SELECT id FROM Idioma WHERE IdiomaAbreviado = \\'{lang2}\\''\n id.append(db.execute(sql_query).fetchone()[0])\n\n elif value.find('-') == -1:\n sql_query = f'SELECT id FROM Idioma WHERE IdiomaAbreviado = \\'{value}\\''\n id.append(db.execute(sql_query).fetchone()[0])\n\n # Populate Audio_in_file and Subs_in_file with the obtained values\n for i in id:\n if category == 'Audio':\n sql_query = f'INSERT INTO Audio_in_file (pelicula_id, idioma_id) VALUES ({movie_id}, {i})'\n\n elif category == 'Subs':\n sql_query = f'INSERT INTO Subs_in_file (pelicula_id, idioma_id) VALUES ({movie_id}, {i})'\n\n try:\n cursor.execute(sql_query)\n connector.commit()\n \n except sql.Error as error:\n print(f'Error during the execution of {sql_query}', error)\n\n\ndef obtainID(field, value):\n ''' Function to obtain the ID for a given 'value' in a given table.\n\n - field: Name of the table in which to look into (which is the same as the column's name within that table)\n - value: Value to look for in the database and obtain its ID.'''\n\n table = field\n sql_query = f'SELECT id FROM {table} WHERE {field} = \\'{value}\\''\n id = db.execute(sql_query).fetchone()\n\n return id[0]\n\n\nif __name__ == '__main__':\n # Load the configuration.ini file\n config = ConfigParser()\n config.read('./config/configuration.ini')\n\n # Import data from Excel file (obtained from the original Access database)\n excel_path = config.get('Aux_files', 'excel_database')\n movie_database = pd.read_excel(excel_path)\n\n # # Connect with 'test_database' which is in [Paths] section from .ini file\n [conn, db] = dbConnector.connect_to_db('test_database')\n\n # Obtain IDs from database for old 'Pais', 'Disco' and 'Calidad' values\n # and update dataframe values\n for i in range(movie_database.shape[0]):\n movie_database.loc[i, 'Pais'] = obtainID('Pais', movie_database.loc[i, 'Pais'])\n movie_database.loc[i, 'Disco'] = obtainID('Disco', movie_database.loc[i, 'Disco'])\n movie_database.loc[i, 'Calidad'] = obtainID('Calidad', movie_database.loc[i, 'Calidad'])\n\n # Import dataFrame with updated 'Pais', 'Disco' and 'Calidad' values into SQL\n import_df_to_db(movie_database, conn, db)\n\n # After the old database is imported into the new one, the following can be done without breaking\n # any constraints related with FOREIGN KEYS.\n for i in range(movie_database.shape[0]):\n # Obtain Audios and Subs from 'movie_database' for each film \n # and generate 'Audio_in_file' and 'Subs_in_file' tables\n import_languages(movie_database.loc[i, 'Id'], 'Audio', \n movie_database.loc[i, 'IdiomaAudio'], conn, db)\n\n import_languages(movie_database.loc[i, 'Id'], 'Subs', \n movie_database.loc[i, 'IdiomaSubtitulos'], conn, db)\n\n # Do the same with genres\n import_genres(movie_database.loc[i, 'Id'], movie_database.loc[i, 'Genero'], conn, db)\n\n print('All data imported into the new database')\n\n # Export values to Excel to be able to compare with the original ones to verify the process\n # See macro inside DataChecker.xlsm to verify that the imported data is the same as the original\n excel_newPath = config.get('Aux_files', 'excel_newDatabase')\n movie_database.to_excel(excel_newPath, index = False)\n\n db.close()\n conn.close()","repo_name":"Monzon3/MovieDatabase","sub_path":"02_dataImporter.py","file_name":"02_dataImporter.py","file_ext":"py","file_size_in_byte":8689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43724877333","text":"import random\n\ndef lotto():\n numbers = set()\n while 6 > len(numbers):\n numbers.add(random.randint(1, 49))\n return numbers\n\ndef checkNumbers(lottoNumbers, winningNumbers):\n matches = 0\n for number in winningNumbers:\n for guess in lottoNumbers:\n if guess == number:\n matches += 1\n if 2 == matches:\n return \"Free Play\"\n if 3 == matches:\n return \"10\"\n if 4 == matches:\n return \"90.50\"\n if 5 == matches:\n return \"5000\"\n if 6 == matches:\n return \"13000000\"\n\ndef main():\n winningNumbers = {9, 20, 27, 35, 37, 43}\n lottoNumbers = lotto()\n prize = checkNumbers(winningNumbers, lottoNumbers)\n print(\"9, 20, 27, 35, 37, 43\")\n lottoString = \"\"\n for number in lottoNumbers:\n lottoString += f\"{number}, \"\n print(lottoString.removesuffix(\", \"))\n print(prize)\n\n\nif __name__ == '__main__':\n main()\n\n","repo_name":"Chrisharnett/Spring-Repo","sub_path":"Assignment 4/lottery.py","file_name":"lottery.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40775905580","text":"import tkinter as tk\nclass find:\n def __init__(self,text):\n self.window=tk.Tk()\n self.window.title('find')\n self.window.geometry('150x75')\n self.window.resizable(False,False)\n self.text=text\n self.search_label=tk.Label(self.window,text='text to find',font=(\"comic sans ms\",8,\"normal\"))\n self.search_entry=tk.Entry(self.window,font=(\"comic sans ms\",8,\"normal\"))\n self.search_button=tk.Button(self.window, text='FIND', font=(\"comic sans ms\",12,\"normal\"), command=self.on_find)\n self.search_label.pack()\n self.search_entry.pack()\n self.search_button.pack()\n self.find_t=('1.0','1.0')\n def on_find(self,*args):\n self.find_t=self.text.find(self.search_entry.get())\n","repo_name":"HelpMy2/notepad","sub_path":"GUI/edit.pyw","file_name":"edit.pyw","file_ext":"pyw","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"19594662661","text":"from aiogram.types import Message\n\nfrom bin.cash_var import users_cash as uc\n\nfrom bot.content import LOC_TYPES\n\n\nasync def loc_miss(mes: Message):\n if not uc.select_id(mes.from_user.id):\n await mes.answer('Доступ запрещён.')\n return\n\n res = mes.db.checkall('SELECT name, lvl FROM loc WHERE code LIKE \"NoneCode%\"', [])\n answer = '🔎Разыскиваемые локации:\\n\\n{}'\n txt = ''\n if not res:\n answer = answer.format('Пусто')\n await mes.answer(answer)\n return\n for i, l in enumerate(res):\n txt += '{}){} {} lvl.{}\\n\\n'.format(i+1, LOC_TYPES.get(l[0].split(\" \")[-1]), l[0], l[1])\n await mes.answer(answer.format(txt))\n\n\n","repo_name":"Irrenriel/SovenokFesya2","sub_path":"bot/functions/location_section/loc_miss_func.py","file_name":"loc_miss_func.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3511131141","text":"\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function\n\n\"\"\"\n\"\"\"\n__credits__ = [\"Upi Lab\"]\n__author__ = \"Aviral Goel\"\n__license__ = \"GPL3\"\n__version__ = \"1.0.0\"\n__maintainer__ = \"Aviral Goel\", \"HarshaRani\"\n__email__ = \"goel.aviral@gmail.com\"\n__status__ = \"Development\"\n__updated__ = \"Jul 26 2017\"\n\n\nimport sys\nimport os\nimport PyQt4\nfrom PyQt4 import QtGui, Qt\nfrom PyQt4.QtGui import QWidget\nfrom PyQt4.QtGui import QSizeGrip\nfrom PyQt4.QtGui import QDockWidget\nfrom PyQt4.QtGui import QLayout\nfrom PyQt4.QtGui import QVBoxLayout\nfrom PyQt4.QtGui import QGridLayout\nfrom PyQt4.QtGui import QScrollArea\nfrom PyQt4.QtGui import QToolBar\nfrom PyQt4.QtGui import QSizeGrip\nfrom PyQt4.QtGui import QSplitter\n\nimport moose\nimport default\nimport sidebar\n# from default import PlotWidget\n\nELECTRICAL = 0\nCHEMICAL = 1\nclass PlotWidgetContainer(QWidget):\n\n def __init__(self, modelRoot, *args, **kwargs):\n\n super(PlotWidgetContainer, self).__init__(*args)\n self.modelRoot = modelRoot\n if len(moose.wildcardFind(modelRoot + \"/##[ISA=ChemCompt]\")) == 0:\n self.modelType = ELECTRICAL\n else:\n self.modelType = CHEMICAL\n\n self.model = moose.element(self.modelRoot)\n if self.modelRoot != \"/\":\n self.modelRoot = self.findModelPath(self.modelRoot)\n if moose.exists(modelRoot + \"/data\"):\n self.data = moose.element(self.modelRoot + \"/data\")\n else:\n self.data = moose.Neutral(self.modelRoot + \"/data\")\n\n else:\n self.data = moose.element(\"/data\")\n\n self._layout = QVBoxLayout()\n self.graphs = QSplitter()\n self.graphs.setOrientation(PyQt4.QtCore.Qt.Vertical)\n self.graphsArea = QScrollArea()\n # self.graphsLayout = QGridLayout()\n # self.menubar = self.createMenuBar()\n self.rowIndex = 0\n # self.setSizePolicy( QtGui.QSizePolicy.Expanding\n # , QtGui.QSizePolicy.Expanding\n # )\n\n self.graphs.setSizePolicy( QtGui.QSizePolicy.Expanding\n , QtGui.QSizePolicy.Expanding\n )\n self.setAcceptDrops(True)\n # self._layout.setSizeConstraint( QLayout.SetNoConstraint )\n # self.graphs.setLayout(self.graphsLayout)\n self.graphsArea.setWidget(self.graphs)\n self.graphsArea.setWidgetResizable(True)\n self.graphWidgets = []\n # self._layout.addWidget(self.menubar)\n self._layout.addWidget(self.graphsArea)\n self.setLayout(self._layout)\n\n for graph in self.data.children:\n self.addPlotWidget(graph = graph)\n\n if len(self.data.children) == 0:\n self.addPlotWidget()\n\n def mooseIsInstance(self,element, classNames):\n return moose.element(element).__class__.__name__ in classNames\n\n\n def findModelPath(self,element):\n child = element\n while not self.mooseIsInstance(element, \"Shell\"):\n child = moose.element(element).path\n element = moose.element(element).parent\n return child\n \n def deleteWidget(self, graphWidget):\n # print(\"Deleted => \", graphWidget)\n self.graphWidgets.remove(graphWidget)\n graphWidget.setParent(None)\n graphWidget.close()\n\n def createMenuBar(self):\n bar = sidebar.sidebar()\n bar.addAction(sidebar.add_graph_action(bar, lambda event: self.addPlotWidget() ))\n # bar.addAction(sidebar.delete_graph_action(bar, lambda event: self.addPlotWidget() ))\n # bar.addAction(sidebar.list_action(bar, self.showPlotView))\n return bar\n\n def addPlotWidget(self, row = None, col = 0, graph = None):\n if graph == None:\n graph = moose.Neutral(self.data.path + \"/graph_\" + str(self.rowIndex))\n widget = default.PlotWidget(self.model, graph, self.rowIndex, self)\n\n if self.modelType == ELECTRICAL:\n for axes in widget.canvas.axes.values():\n # axes.autoscale(False, axis='x', tight=True)\n axes.set_ylim(bottom = -0.07, top= 0.03)\n\n if row == None:\n row = self.rowIndex\n self.graphs.addWidget(widget)\n self.rowIndex += 1\n self.graphWidgets.append(widget)\n widget.widgetClosedSignal.connect(self.deleteWidget)\n widget.addGraph.connect(lambda event : self.addPlotWidget())\n # widget.resize(1, 1);\n return widget\n\n def showPlotView(self):\n pass\n\n def setModelRoot(self, *args):\n pass\n\n def getMenus(self, *args):\n return []\n\n def setDataRoot(self, *args):\n pass\n\n def updatePlots(self):\n for graphWidget in self.graphWidgets:\n graphWidget.updatePlots()\n\n def rescalePlots(self):\n for graphWidget in self.graphWidgets:\n graphWidget.rescalePlots()\n\n def extendXAxes(self, xlim):\n for graphWidget in self.graphWidgets:\n graphWidget.extendXAxes(xlim)\n\n def plotAllData(self):\n for graphWidget in self.graphWidgets:\n graphWidget.plotAllData()\n\n #print(graphWidget)\n # def plotAll(self):\n # self.apply(lambda obj: obj.plotAll())\n\n # def plotAllData(self):\n # selt.plotWidgetContainer.plotAllData()\n\n # def genColorMap(self,tableObject):\n # pass\n\n # def onclick(self,event1):\n # pass\n\n # def addTimeSeries(self, table, *args, **kwargs):\n # pass\n\n # def addRasterPlot(self, eventtable, yoffset=0, *args, **kwargs):\n # pass\n\n # def extendXAxes(self, xlim):\n # pass\n\n # def saveCsv(self, line,directory):\n # pass\n\n # def saveAllCsv(self):\n # pass\n","repo_name":"BhallaLab/moose","sub_path":"moose-gui/PlotWidgetContainer.py","file_name":"PlotWidgetContainer.py","file_ext":"py","file_size_in_byte":5811,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"3"}
+{"seq_id":"19759817903","text":"# Importing Modules\n\nfrom tkinter import *\nfrom tkinter.messagebox import showinfo\n\n# Reteriving file data into list\ndef listmake():\n with open(\"record.txt\",\"r+\") as f:\n d = []\n c= f.readlines()\n for i in c:\n if \"\\n\" in i:\n d.append(i[:-1])\n else:\n d.append(i)\n e = []\n for i in range(3):\n e.append(d[i::3])\n g = []\n for i in e:\n for j in i:\n g.append(j)\n h = []\n for i in g:\n t = i.split(\"\\t\")\n h.append(t)\n return h\n\n# Creating a file\ndef filemake():\n with open(\"record.txt\",\"w\") as f:\n f.write(\"Student Name\"+\"\\t\"+\"Roll No\"+\"\\t\"+\"Marks\")\n\n# Function to add data in the file\ndef entry():\n try:\n with open(\"record.txt\",\"r+\") as f:\n pass\n with open(\"record.txt\",\"a+\") as f:\n f.write(\"\\n\"+f\"{name.get()}\"+\"\\t\"+f\"{roll.get()}\"+\"\\t\"+f\"{marks.get()}\")\n except:\n showinfo(message=\"Please create a file first\")\n\n# Function to create average class marks\ndef avg():\n try:\n with open(\"record.txt\",\"r+\") as f:\n d = []\n c= f.readlines()\n for i in c:\n if \"\\n\" in i:\n d.append(i[:-1])\n else:\n d.append(i)\n e = []\n for i in range(3):\n e.append(d[i::3])\n g = []\n for i in e:\n for j in i:\n g.append(j)\n h = []\n for i in g:\n t = i.split(\"\\t\")\n h.append(t)\n count = 0\n sum = 0\n for i in range(len(h)):\n if i>=1:\n sum+=float(h[i][2])\n count+=1\n avg = sum/count\n showinfo(message=f\"The average marks of the class is {avg}\")\n except:\n showinfo(message=\"No file found\")\n\n# Function to calculate Standard deviation of the class\ndef sd():\n try:\n with open(\"record.txt\",\"r+\") as f:\n d = []\n c= f.readlines()\n for i in c:\n if \"\\n\" in i:\n d.append(i[:-1])\n else:\n d.append(i)\n e = []\n for i in range(3):\n e.append(d[i::3])\n g = []\n for i in e:\n for j in i:\n g.append(j)\n h = []\n for i in g:\n t = i.split(\"\\t\")\n h.append(t)\n count = 0\n sum = 0\n for i in range(len(h)):\n if i>=1:\n sum+=float(h[i][2])\n count+=1\n avg = sum/count\n counter = 0\n for i in range(len(h)):\n if i>=1:\n counter+=(float(h[i][2])-avg)**2\n sd = round((counter/(len(h)-2))**0.5,2)\n showinfo(message=f\"Student deviation is {sd}\") \n except:\n showinfo(message=\"No file found\")\n\n# Main Program\n\na = Tk()\na.title(\"Database\")\na.geometry(\"800x550+300+60\")\na.maxsize(\"800\",\"550\")\na.minsize(\"800\",\"550\")\na.configure(bg=\"#9BCFD0\")\nname = StringVar()\nroll = StringVar()\nmarks = StringVar()\nl = Label(a,text= \"Students Record\",font=(\"Times New Roman\",\"35\",\"bold\"),background=\"#9BCFD0\")\nl.pack()\nf = Frame(a,background=\"#9BCFD0\")\nl = Label(f,text=\"Want to make a new file?\",font=\"lucids 11 bold\",background=\"#9BCFD0\")\nl.pack(side=LEFT,anchor=\"w\",padx=30,pady=10)\nb = Button(a,text=\"Create File First\",font=\"lucida 11 bold\",borderwidth=5,relief=\"groove\",command=filemake) \nb.pack(side=RIGHT,anchor=\"n\",pady=20,padx=50)\nf.pack(pady= 20,anchor=\"w\",padx=20)\nf = Frame(a,background=\"#9BCFD0\")\nl = Label(f,text=\"Enter name of the student:\",font=\"lucids 11 bold\",background=\"#9BCFD0\")\nl.pack(side=LEFT,anchor=\"w\",padx=30)\ne = Entry(f,font=\"lucida 11 bold\",textvar=name)\ne.pack(ipadx=20,fill=X,padx=20)\nf.pack(pady= 20,anchor=\"w\",padx=20)\nf = Frame(a,background=\"#9BCFD0\")\nl = Label(f,text=\"Enter Roll no of the student:\",font=\"lucids 11 bold\",background=\"#9BCFD0\")\nl.pack(side=LEFT,anchor=\"w\",padx=30)\ne = Entry(f,font=\"lucida 11 bold\",textvar=roll)\ne.pack(ipadx=20,fill=X,padx=6)\nf.pack(pady= 20,anchor=\"w\",padx=20)\nf = Frame(a,background=\"#9BCFD0\")\nl = Label(f,text=\"Enter marks of the student:\",font=\"lucids 11 bold\",background=\"#9BCFD0\")\nl.pack(side=LEFT,anchor=\"w\",padx=30)\ne = Entry(f,font=\"lucida 11 bold\",textvar=marks)\ne.pack(ipadx=20,fill=X,padx=12)\nf.pack(pady= 20,anchor=\"w\",padx=20)\nb = Button(a,text=\"Submit Entry\",font=\"lucida 11 bold\",borderwidth=5,relief=\"groove\",command=entry) \nb.pack(pady=10)\nl = Label(a,text=\"Other options\",font=(\"Times New Roman\",\"25\",\"bold\"),background=\"#9BCFD0\")\nl.pack(anchor=\"w\",padx=40,pady=10)\nf = Frame(a)\nb = Button(a,text=\"Average\",font=\"lucida 11 bold\",borderwidth=5,relief=\"groove\",command=avg) \nb.pack(anchor='nw',side=LEFT,padx=30,pady=10)\nb = Button(a,text=\"Standard Deviation\",font=\"lucida 11 bold\",borderwidth=5,relief=\"groove\",command=sd) \nb.pack(anchor='n',pady=10)\nf.pack(pady= 20,padx=20)\na.mainloop()\n","repo_name":"syedmuhammad12/-StudentRecordAnd-StandardDeviation-Calculator","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":5301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9980414356","text":"\n# Importaciones flask\n\nfrom flask import Flask, render_template\nfrom flask_session import Session\nfrom os import name, path, environ\nfrom flask_cors import CORS\nfrom config import config\nfrom app import db\n\n# Importaciones resources\n\nfrom app.resources import configuracion, iniciar_sesion_google, registrarse_google, usuario_autenticacion, usuario_crud, usuario_google, punto_encuentro, recorrido, zonas, denuncia\nfrom app.resources.api import denuncias, puntodeencuentro, recorridoevacuacion\nfrom app.resources.api import zonasinundables\n\n# Otras importaciones\n\nfrom app.db import db, init_app\nfrom app.models.configuracion import Configuracion\nfrom app.helpers import permisos\nfrom app.helpers import auth\nfrom app.helpers import configuracion_colores\n\n\n# Importaciones OAuth\n\nfrom oauthlib.oauth2 import WebApplicationClient\nfrom flask_login import LoginManager\n\n# CLAVES PRIVADAS DE GOOGLE\nGOOGLE_CLIENT_ID = '853530354298-3hcp4cuv55hkjsphdsquknunb0tid1q1.apps.googleusercontent.com'\nGOOGLE_CLIENT_SECRET = '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'\nGOOGLE_DISCOVERY_URL = (\n \"https://accounts.google.com/.well-known/openid-configuration\"\n)\n\n\ndef create_app(environment=\"development\"):\n # Configuración inicial de la app\n app = Flask(__name__)\n \n # Política de seguridad en los navegadores\n CORS(app)\n\n\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True\n\n\n #init_app(app)\n\n #OAuth\n app.secret_key = 'randomsecretkey'\n \n # OAuth 2 client setup\n client = WebApplicationClient(GOOGLE_CLIENT_ID)\n \n login_manager = LoginManager()\n login_manager.init_app(app)\n \n \n @login_manager.user_loader\n def load_user(user_id):\n return Usuario.get(user_id)\n \n #FIN OAuth\n\n # Carga de la configuración\n env = environ.get(\"FLASK_ENV\", environment)\n app.config.from_object(config[env])\n\n app.config['SESSION_TYPE'] = 'filesystem'\n app.config['UPLOAD_FOLDER'] = 'app/static/uploads'\n Session(app)\n # Configuracion de la BD\n app.secret_key = 'hola'\n app.config['SQLALCHEMY_DATABASE_URI'] = \"mysql+pymysql://\" + \\\n app.config[\"DB_USER\"]+\":\"+app.config[\"DB_PASS\"]+\"@\"+app.config[\"DB_HOST\"]+\"/\"+app.config[\"DB_NAME\"]\n db.init_app(app)\n \n\n\n\n app.config['SESSION_TYPE'] = 'filesystem'\n Session(app)\n app.jinja_env.globals.update(permisos=permisos)\n app.jinja_env.globals.update(auth=auth)\n app.jinja_env.globals.update(configuracion_colores=configuracion_colores)\n \n\n # Autenticacion\n app.add_url_rule('/login', 'login', usuario_autenticacion.login, methods=[\"GET\", \"POST\"])\n app.add_url_rule('/logout', 'logout', usuario_autenticacion.logout)\n\n # Endpoints para Configuracion del Sitio\n app.add_url_rule('/configuracion/vista_configuracion', 'vista_configuracion',\n configuracion.vista_configuracion, methods=[\"POST\", \"GET\"])\n\n # Endpoints para Listado de usuarios\n\n app.add_url_rule('/user/listado', 'listado_usuarios', usuario_crud.listado, methods=[\"GET\", \"POST\"])\n\n app.add_url_rule('/user/listado/', 'listado',\n usuario_crud.listado, methods=[\"POST\", \"GET\"])\n \n # Endpoints para Listado de usuarios de google\n\n app.add_url_rule('/google/listado', 'listado_usuarios_google', usuario_google.listado, methods=[\"GET\", \"POST\"])\n\n # Endpoints para aprobar usuarios de google\n app.add_url_rule('/user/aprobar_usuario/', 'aprobar_usuario',\n usuario_google.aprobar_usuario, methods=['POST', 'GET'])\n\n\n # Endpoints para agregar un nuevo usuario\n app.add_url_rule('/user/agregar_usuario', 'agregar_usuario',\n usuario_crud.agregar_usuario, methods=[\"POST\", \"GET\"])\n \n\n # Endpoints para Editar usuario\n app.add_url_rule('/user/editar_usuario/', 'editar_usuario',\n usuario_crud.editar_usuario, methods=['POST', 'GET'])\n\n # Endpoints para Editar perfil\n app.add_url_rule('/user/ver_perfil', 'ver_perfil',\n usuario_crud.ver_perfil, methods=['POST', 'GET']) \n \n # Endpoints para Activar un usuario\n app.add_url_rule('/user/activar/', 'activar',\n usuario_crud.activar, methods=['POST', 'GET'])\n # Endpoints para Desactivar un usuario\n app.add_url_rule('/user/desactivar/', 'desactivar',\n usuario_crud.desactivar, methods=['POST', 'GET'])\n \n # Endpoints para cambiar password\n app.add_url_rule('/user/cambiar_password/', 'cambiar_password',\n usuario_crud.cambiar_password, methods=['POST', 'GET']) \n \n # PUNTOS DE ENCUENTRO \n app.add_url_rule('/puntos/listado', 'administrar_puntos',\n punto_encuentro.listado, methods=[\"GET\", \"POST\"])\n \n app.add_url_rule('/puntos/listado/', 'administrar_puntos',\n punto_encuentro.listado, methods=[\"POST\", \"GET\"])\n\n\n \n # Endpoints para agregar un nuevo punto de encuentro\n app.add_url_rule('/puntos/agregar_punto', 'agregar_punto',\n punto_encuentro.agregar_punto, methods=[\"POST\", \"GET\"])\n \n # Endpoints para Editar un punto de encuentro\n app.add_url_rule('/punto/editar_punto/', 'editar_punto',\n punto_encuentro.editar_punto, methods=['POST', 'GET'])\n # Endpoints para publicar punto\n app.add_url_rule('/punto/publicar/', 'publicar',\n punto_encuentro.publicar, methods=['POST', 'GET'])\n # Endpoints para Despublicar punto\n app.add_url_rule('/punto/despublicar/', 'despublicar',\n punto_encuentro.despublicar, methods=['POST', 'GET'])\n \n # Endpoints para Eliminar un punto de encuentro\n app.add_url_rule('/punto/borrar_punto/', 'borrar_punto',\n punto_encuentro.borrar_punto, methods=['POST', 'GET'])\n \n # Endpoints para agregar una nueva denuncia\n app.add_url_rule('/denuncia/crear_denuncia', 'agregarDenuncia',\n denuncia.crear_denuncia, methods=[\"POST\", \"GET\"]) \n \n # Endpoints para validar una denuncia\n app.add_url_rule('/denuncia/validar_denuncia/', 'validar_denuncia',\n denuncia.validar_denuncia, methods=[\"POST\", \"GET\"])\n\n # Endpoints para asignarme una denuncia\n app.add_url_rule('/denuncia/asignarme_denuncia/', 'asignarme_denuncia',\n denuncia.asignarme_denuncia, methods=[\"POST\", \"GET\"])\n\n # Endpoints para asignar una denuncia a un usuario\n app.add_url_rule('/denuncia/asignar_denuncia_usuario/', 'asignar_denuncia_usuario',\n denuncia.asignar_denuncia_usuario, methods=[\"POST\", \"GET\"])\n \n # Endpoints para validar las denuncias \n app.add_url_rule('/denuncia/listado_seguimiento', 'listado_seguimiento',\n denuncia.listado_seguimiento, methods=[\"POST\", \"GET\"])\n \n app.add_url_rule('/denuncia/listado_seguimiento/', 'listado_seguimiento',\n denuncia.listado_seguimiento, methods=[\"POST\", \"GET\"])\n \n # Endpoints listado de denuncias \n app.add_url_rule('/denuncia/listado', 'denuncias',\n denuncia.listado, methods=[\"GET\", \"POST\"])\n \n app.add_url_rule('/denuncia/listado/', 'denuncias',\n denuncia.listado, methods=[\"POST\", \"GET\"])\n \n # Endpoints para Editar denuncia\n app.add_url_rule('/denuncia/editar_denuncia/', 'editar_denuncia',\n denuncia.editar_denuncia, methods=['POST', 'GET'])\n\n # Endpoints para Eliminar una denuncia\n app.add_url_rule('/punto/borrar_denuncia/', 'borrar_denuncia',\n denuncia.borrar_denuncia, methods=['POST', 'GET'])\n \n # RECORRIDOS DE EVACUACION \n app.add_url_rule('/recorrido/listado', 'listado_recorrido',\n recorrido.listado, methods=[\"GET\", \"POST\"])\n \n app.add_url_rule('/recorrido/listado/', 'listado_recorrido',\n recorrido.listado, methods=[\"POST\", \"GET\"])\n\n # Endpoints para agregar una nueva recorrido de evacuación\n app.add_url_rule('/recorrido/agregar_recorrido', 'agregar_recorrido',\n recorrido.agregar_recorrido, methods=[\"POST\", \"GET\"])\n \n # Endpoints para Eliminar un recorrido de evacuación\n app.add_url_rule('/recorrido/borrar_recorrido/', 'borrar_recorrido',\n recorrido.borrar_recorrido, methods=['POST', 'GET'])\n \n # Endpoints para publicar recorrido\n app.add_url_rule('/recorrido/publicar/', 'publicar_recorrido',\n recorrido.publicar_recorrido, methods=['POST', 'GET'])\n\n # Endpoints para Despublicar recorrido\n app.add_url_rule('/recorrido/despublicar/', 'despublicar_recorrido',\n recorrido.despublicar_recorrido, methods=['POST', 'GET'])\n \n # Endpoints para Editar recorrido\n app.add_url_rule('/recorrido/editar_recorrido/', 'editar_recorrido',\n recorrido.editar_recorrido, methods=['POST', 'GET'])\n \n # AUTHLIB\n\n app.add_url_rule('/auth/login', 'registrarse_google',\n registrarse_google.login_with_google_registro, methods=[\"GET\", \"POST\"])\n app.add_url_rule('/auth/login/callback', \"auth_callback_registro\", registrarse_google.callback, methods=[\"GET\"]\n )\n\n app.add_url_rule('/auth/login/iniciar_sesion', 'iniciar_sesion_google',\n iniciar_sesion_google.login_with_google_iniciar, methods=[\"GET\", \"POST\"])\n app.add_url_rule('/auth/login/iniciar_sesion/callback_iniciar', \"auth_callback_iniciar\", iniciar_sesion_google.callback_iniciar, methods=[\"GET\"]\n )\n \n\n \n\n # Endpoints para la api\n app.add_url_rule('/api/denuncia/' , 'mostrar_denuncia',denuncias.mostrar_denuncia, methods=['GET'])\n app.add_url_rule('/api/denuncias/page' , 'mostrar_denuncias',denuncias.mostrar_denuncias, methods=['GET'])\n app.add_url_rule('/api/zona//' , 'mostrar_zona',zonasinundables.mostrar_zona, methods=['GET'])\n app.add_url_rule('/api/zonas' , 'mostrar_zonas',zonasinundables.mostrar_zonas, methods=['GET'])\n app.add_url_rule('/api/crear_denuncia' , 'crear_denuncia',denuncias.crear_denuncia, methods=['POST'])\n app.add_url_rule('/api/punto/' , 'mostrar_punto',puntodeencuentro.mostrar_punto, methods=['GET'])\n app.add_url_rule('/api/puntos' , 'mostrar_puntos',puntodeencuentro.mostrar_puntos, methods=['GET'])\n app.add_url_rule('/api/recorrido/' , 'mostrar_recorrido',recorridoevacuacion.mostrar_recorrido, methods=['GET'])\n app.add_url_rule('/api/recorridos' , 'mostrar_recorridos',recorridoevacuacion.mostrar_recorridos , methods=['GET'])\n \n\n\n # Zonas inundables\n app.add_url_rule('/zonas', 'zonas_index',\n zonas.index, methods=['POST', 'GET'])\n\n app.add_url_rule('/zonas/import', 'zonas_upload',\n zonas.upload, methods=['POST', 'GET'])\n\n # Endpoints para Editar usuario\n app.add_url_rule('/zonas/editar_zona/', 'editar_zona',\n zonas.editar_zona, methods=['POST', 'GET'])\n\n # Endpoints para Editar zona\n app.add_url_rule('/zonas/ver_zona', 'ver_zona',\n zonas.ver_zona, methods=['POST', 'GET']) \n \n # Endpoints para Activar un zona\n app.add_url_rule('/zonas/activar/', 'activar_zona',\n zonas.activar, methods=['POST', 'GET'])\n # Endpoints para Desactivar un zona\n app.add_url_rule('/zonas/desactivar/', 'desactivar_zona',\n zonas.desactivar, methods=['POST', 'GET'])\n # Endpoints para Desactivar un zona\n app.add_url_rule('/zonas/eliminar/', 'delete_zona',\n zonas.eliminar, methods=['POST', 'GET'])\n\n\n\n # Manejo de errores\n @app.errorhandler(404)\n def page_not_found(e):\n return render_template('errores/404.html'), 404\n\n @app.errorhandler(503)\n def page_not_found(e):\n return render_template('errores/503.html'), 503\n\n @app.errorhandler(401)\n def page_not_found(e):\n return render_template('errores/401.html'), 401\n\n @app.errorhandler(403)\n def page_not_found(e):\n return render_template('errores/403.html'), 403\n\n\n\n @app.route('/')\n def index():\n init_app(app)\n configuracion = Configuracion.obtener_configuracion()\n return render_template('index.html', configuracion=configuracion)\n return app","repo_name":"agustinsalum/flooding","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":12516,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1026986156","text":"import argparse\nimport pathlib\nfrom pypdf import PdfReader, PdfWriter\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"form\", type=pathlib.Path)\n args = parser.parse_args()\n\n reader = PdfReader(args.form)\n fields = reader.get_form_text_fields()\n data_dict = {a: a for a in fields.keys()}\n writer = PdfWriter()\n writer.append(reader)\n for page in writer.pages:\n writer.update_page_form_field_values(page, data_dict)\n with open(f\"form-labels/{args.form.name}\", \"wb\") as output_stream:\n writer.write(output_stream)\n","repo_name":"bitcastza/pdfff","sub_path":"pdfff/create_label_pdf.py","file_name":"create_label_pdf.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41025427288","text":"from io import BytesIO\n\nfrom flask import current_app\n\nfrom inspirehep.files.api import current_s3_instance\nfrom inspirehep.utils import get_inspirehep_url\n\nSITEMAP_MIME_TYPE = \"application/xml\"\n\n\ndef get_sitemap_page_filename(page):\n return f\"sitemap{page}.xml\"\n\n\ndef get_sitemap_page_absolute_url(page):\n base_url = current_app.config.get(\"SITEMAP_BASE_PAGE_ABSOLUTE_URL\")\n if not base_url:\n base_url = get_inspirehep_url()\n return f\"{base_url}/{get_sitemap_page_filename(page)}\"\n\n\ndef write_sitemap_page_content(page, page_content):\n bucket = current_app.config[\"S3_SITEMAP_BUCKET\"]\n file_data = page_content.encode(\"utf8\")\n file_data = BytesIO(file_data)\n filename = get_sitemap_page_filename(page)\n current_s3_instance.upload_file(\n file_data,\n filename,\n filename,\n SITEMAP_MIME_TYPE,\n current_app.config[\"S3_FILE_ACL\"],\n bucket,\n )\n","repo_name":"inspirehep/inspirehep","sub_path":"backend/inspirehep/sitemap/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"}
+{"seq_id":"74884011600","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom io import StringIO\n\n# Download the Iris dataset\nurl = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\nnames = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']\ndf = pd.read_csv(url, names=names)\n\n# Basic Data Exploration\nprint(df.head(), df.describe(), df.info(), df.isnull().sum())\n\n# Indexing, selecting and computational tools\nprint(df['class'], df.loc[:10, 'sepal_width'], df.corr(), df['class'].str.upper())\n\n# Groupby, filtering and visualization\ngrouped = df.groupby('class').mean()\nprint(grouped)\nfiltered = df[df['sepal_length'] > 5.0]\nprint(filtered)\nsns.scatterplot(x=\"sepal_length\", y=\"sepal_width\", hue=\"class\", data=df)\nplt.show()\n\n# Saving and loading a DataFrame\nfiltered.to_csv('filtered_iris.csv', index=False)\ndf_loaded = pd.read_csv('filtered_iris.csv')\nprint(df_loaded.head())\n\n# Changing pandas options\npd.set_option('display.max_rows', 10)\nprint(df)\n\n# Enhancing performance and handling duplicates\ndf['class'] = df['class'].astype('category')\nprint(df.info())\nprint(df[df.duplicated()])\n\n# Scaling to large datasets, working with categorical data and dates\ndf_sample = df.sample(frac=0.1)\nprint(df_sample.head())\ndf['class'] = df['class'].astype('category')\ndates = pd.date_range(start='1/1/2022', periods=len(df))\ndf['date'] = dates\nprint(df.head())\n\n# Advanced topics\ndf['interval'] = pd.cut(df['sepal_length'], 5)\nprint(df.head())\n\n# Download other datasets\ndatasets = {\n 'titanic.csv': 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv',\n 'iris.csv': 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/iris.csv',\n}\n\nfor filename, url in datasets.items():\n df = pd.read_csv(url)\n print(f\"\\n{filename} dataset head:\\n\", df.head())\n\n# Reshaping, sorting, and creating pandas objects\ndf = pd.DataFrame(np.random.rand(5, 5), columns=['A', 'B', 'C', 'D', 'E'])\ndf_melt = pd.melt(df, id_vars=['A'], value_vars=['B', 'C', 'D', 'E'])\nprint(\"\\nMelted DataFrame:\\n\", df_melt)\ndf_sorted = df.sort_values(by='A')\nprint(\"\\nSorted DataFrame:\\n\", df_sorted.head())\ns = pd.Series([1, 2, 3, 4, 5])\nprint(\"\\nSeries:\\n\", s)\n","repo_name":"drukpa1455/geo-min","sub_path":"dojo/basics/en/libs/pandas.py","file_name":"pandas.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33740600630","text":"# 判断式(if)\nx=input(\"请输入数字: \") #取得使用者输入字串\nx=int(x) #转换成数字\nif x>100:\n print(\">100\")\nelif x>50:\n print(\"x>50\")\nelse:\n print(\"<=50\")\n\n#====================函式=======================================\n#函式宣告\ndef divide(n1,n2=1): #预设值\n print(n1/n2)\n\n#不定参数\n# def 函式名称(*无限参数):\ndef avg(*ns):\n sum = 0\n for n in ns:\n sum += n\n print(sum/len(ns))\n\n#====================循环=======================================\n# while\nn=0\nwhile n<5:\n if n==3:\n break #终止循环\n print(n)\n n+=1\nprint(\"最后的n:\",n)\n\n# for\nn=0\nfor x in [0,1,2,3]: #逐一取得List内容\n if x%2==0:\n continue #进入下一次循环\n print(x)\n n+=1\nprint(\"最后的n\",n)\n\n# else \nsum=0\nfor n in range(3): #产生一个List [0,1,2] range(3, 6)=>[3,4,5]\n sum+=n\n break #break 不会触发else\nelse: #循环结束时执行\n print(sum)\n","repo_name":"abulaga/python","sub_path":"demo/b1_基本語法.py","file_name":"b1_基本語法.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37924731704","text":"from collections import namedtuple\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom res_utils import _conv as _utils_conv, _fc as _utils_fc, _bn as _utils_bn, _relu as _utils_relu\n\n\nHParams = namedtuple('HParams',\n 'batch_size, num_gpus, num_classes, weight_decay, '\n 'momentum, finetune')\n\nclass ResNet(object):\n def __init__(self,global_step, is_train, num_classes):\n self._global_step = global_step\n self.is_train = is_train\n self.num_classes = num_classes\n\n\n def build_tower(self, images):\n print('Building model')\n # filters = [128, 128, 256, 512, 1024]\n filters = [64, 64, 128, 256, 512]\n kernels = [7, 3, 3, 3, 3]\n strides = [2, 0, 2, 2, 2]\n\n # conv1\n print('\\tBuilding unit: conv1')\n with tf.variable_scope('conv1'):\n x = self._conv(images, kernels[0], filters[0], strides[0])\n x = self._bn(x)\n x = self._relu(x)\n x = tf.nn.max_pool(x, [1, 3, 3, 1], [1, 2, 2, 1], 'SAME')\n\n # conv2_x\n x = self._residual_block(x, name='conv2_1')\n x = self._residual_block(x, name='conv2_2')\n\n # conv3_x\n x = self._residual_block_first(x, filters[2], strides[2], name='conv3_1')\n x = self._residual_block(x, name='conv3_2')\n\n # conv4_x\n x = self._residual_block_first(x, filters[3], strides[3], name='conv4_1')\n x = self._residual_block(x, name='conv4_2')\n\n # conv5_x\n x = self._residual_block_first(x, filters[4], strides[4], name='conv5_1')\n x = self._residual_block(x, name='conv5_2')\n\n # Logit\n with tf.variable_scope('FullyConnected_1') as scope:\n print('\\tBuilding unit: %s' % scope.name)\n x = tf.reduce_mean(x, [1, 2])\n #add dropout\n\n x = self._fc(x, 256)\n x = tf.layers.dropout(\n inputs=x, rate=0.5,\n training=self.is_train)\n with tf.variable_scope('FullyConnected_2') as scope:\n x = self._fc(x, self.num_classes)\n\n logits = x\n #\n # # Probs & preds & acc\n # probs = tf.nn.softmax(x)\n # preds = tf.to_int32(tf.argmax(logits, 1))\n # ones = tf.constant(np.ones([self._hp.batch_size]), dtype=tf.float32)\n # zeros = tf.constant(np.zeros([self._hp.batch_size]), dtype=tf.float32)\n # correct = tf.where(tf.equal(preds, labels), ones, zeros)\n # acc = tf.reduce_mean(correct)\n #\n # # Loss & acc\n # losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=x, labels=labels)\n # loss = tf.reduce_mean(losses)\n\n return logits\n\n\n\n\n\n def _residual_block_first(self, x, out_channel, strides, name=\"unit\"):\n in_channel = x.get_shape().as_list()[-1]\n with tf.variable_scope(name) as scope:\n print('\\tBuilding residual unit: %s' % scope.name)\n\n # Shortcut connection\n if in_channel == out_channel:\n if strides == 1:\n shortcut = tf.identity(x)\n else:\n shortcut = tf.nn.max_pool(x, [1, strides, strides, 1], [1, strides, strides, 1], 'VALID')\n else:\n shortcut = self._conv(x, 1, out_channel, strides, name='shortcut')\n # Residual\n x = self._conv(x, 3, out_channel, strides, name='conv_1')\n x = self._bn(x, name='bn_1')\n x = self._relu(x, name='relu_1')\n x = self._conv(x, 3, out_channel, 1, name='conv_2')\n x = self._bn(x, name='bn_2')\n # Merge\n x = x + shortcut\n x = self._relu(x, name='relu_2')\n return x\n\n\n def _residual_block(self, x, input_q=None, output_q=None, name=\"unit\"):\n num_channel = x.get_shape().as_list()[-1]\n with tf.variable_scope(name) as scope:\n print('\\tBuilding residual unit: %s' % scope.name)\n # Shortcut connection\n shortcut = x\n # Residual\n x = self._conv(x, 3, num_channel, 1, input_q=input_q, output_q=output_q, name='conv_1')\n x = self._bn(x, name='bn_1')\n x = self._relu(x, name='relu_1')\n x = self._conv(x, 3, num_channel, 1, input_q=output_q, output_q=output_q, name='conv_2')\n x = self._bn(x, name='bn_2')\n\n x = x + shortcut\n x = self._relu(x, name='relu_2')\n return x\n\n\n\n\n # Helper functions(counts FLOPs and number of weights)\n def _conv(self, x, filter_size, out_channel, stride, pad=\"SAME\", input_q=None, output_q=None, name=\"conv\"):\n b, h, w, in_channel = x.get_shape().as_list()\n x = _utils_conv(x, filter_size, out_channel, stride, pad, input_q, output_q, name)\n f = 2 * (h/stride) * (w/stride) * in_channel * out_channel * filter_size * filter_size\n w = in_channel * out_channel * filter_size * filter_size\n scope_name = tf.get_variable_scope().name + \"/\" + name\n\n return x\n\n def _fc(self, x, out_dim, input_q=None, output_q=None, name=\"fc\"):\n b, in_dim = x.get_shape().as_list()\n x = _utils_fc(x, out_dim, input_q, output_q, name)\n f = 2 * (in_dim + 1) * out_dim\n w = (in_dim + 1) * out_dim\n scope_name = tf.get_variable_scope().name + \"/\" + name\n\n return x\n\n def _bn(self, x, name=\"bn\"):\n # TODO BN is allways training?\n x = _utils_bn(x, tf.constant(True, name=\"bnvar\"), self._global_step, name)\n # f = 8 * self._get_data_size(x)\n # w = 4 * x.get_shape().as_list()[-1]\n # scope_name = tf.get_variable_scope().name + \"/\" + name\n # self._add_flops_weights(scope_name, f, w)\n return x\n\n def _relu(self, x, name=\"relu\"):\n x = _utils_relu(x, 0.0, name)\n # f = self._get_data_size(x)\n # scope_name = tf.get_variable_scope().name + \"/\" + name\n # self._add_flops_weights(scope_name, f, 0)\n return x\n\n def _get_data_size(self, x):\n return np.prod(x.get_shape().as_list()[1:])\n\n\n","repo_name":"tuggeluk/EffiNet_AutoDL","sub_path":"res18/resnet.py","file_name":"resnet.py","file_ext":"py","file_size_in_byte":6049,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"70158715603","text":"\"\"\"empty message\n\nRevision ID: d8e19ad4183d\nRevises: \nCreate Date: 2022-06-10 17:36:52.539523\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'd8e19ad4183d'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('UsersModel',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('name', sa.Unicode(), nullable=True),\n sa.Column('email', sa.Unicode(), nullable=True),\n sa.Column('password', sa.Unicode(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('post',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('sku', sa.Unicode(), nullable=True),\n sa.Column('name', sa.Unicode(), nullable=True),\n sa.Column('price', sa.BigInteger(), nullable=True),\n sa.Column('description', sa.Unicode(), nullable=True),\n sa.Column('image', sa.String(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('GroupsModel',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('admin', sa.Integer(), nullable=True),\n sa.Column('name', sa.Unicode(), nullable=True),\n sa.ForeignKeyConstraint(['admin'], ['UsersModel.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n op.create_table('MembersModel',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('id_group', sa.Integer(), nullable=True),\n sa.Column('id_member', sa.Integer(), nullable=True),\n sa.Column('date', sa.Integer(), nullable=True),\n sa.Column('users_id', sa.Integer(), nullable=True),\n sa.Column('groups_id', sa.Integer(), nullable=True),\n sa.ForeignKeyConstraint(['id_member'], ['post.id'], ),\n sa.ForeignKeyConstraint(['users_id'], ['UsersModel.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('MembersModel')\n op.drop_table('GroupsModel')\n op.drop_table('post')\n op.drop_table('UsersModel')\n # ### end Alembic commands ###\n","repo_name":"arv1983/kanban","sub_path":"migrations/versions/d8e19ad4183d_.py","file_name":"d8e19ad4183d_.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31323966504","text":"#!/usr/bin/env python\n\nimport unittest\nimport logging\nimport os\nfrom multiprocessing import Process, Queue\nimport random\nimport sys\nimport time\n\nsys.path.insert(0, os.path.abspath('..'))\n\nfrom context import Context\nfrom worker import Worker\n\n\nclass WorkerTests(unittest.TestCase):\n\n def test_stop(self):\n\n logging.debug('*** Stop test ***')\n\n inbox = Queue()\n outbox = Queue()\n\n context = Context()\n worker = Worker(inbox, outbox)\n\n worker_process = Process(target=worker.work, args=(context,))\n worker_process.daemon = True\n worker_process.start()\n\n worker_process.join(1.0)\n if worker_process.is_alive():\n logging.debug('Stopping worker')\n context.set('general.switch', 'off')\n worker_process.join()\n\n self.assertFalse(worker_process.is_alive())\n self.assertEqual(context.get('worker.counter', 0), 0)\n\n def test_processing(self):\n\n logging.debug('*** Processing test ***')\n\n inbox = Queue()\n inbox.put(('deploy', ''))\n inbox.put(('dispose', ''))\n inbox.put(('unknownCommand', ''))\n inbox.put(Exception('EOQ'))\n\n outbox = Queue()\n\n context = Context()\n context.set('general.fittings',\n os.path.abspath(os.path.dirname(__file__)))\n\n worker = Worker(inbox, outbox)\n\n worker.work(context)\n\n self.assertEqual(context.get('worker.counter'), 3)\n with self.assertRaises(Exception):\n inbox.get_nowait()\n\nif __name__ == '__main__':\n logging.getLogger('').setLevel(logging.DEBUG)\n sys.exit(unittest.main())\n","repo_name":"bernard357/spark-plumbery","sub_path":"test/test_worker.py","file_name":"test_worker.py","file_ext":"py","file_size_in_byte":1654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29480547160","text":"import pandas as pd\nimport numpy as np\nimport os\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport torch\nimport util.metrics\nfrom examples.ParWH.models import ParallelWHNet\n\n\nif __name__ == '__main__':\n\n matplotlib.rc('font', **{'family': 'sans-serif', 'sans-serif': ['Helvetica'], 'size': 11})\n\n model_name = \"PWH_quant\"\n\n # Dataset constants\n amplitudes = 5 # number of different amplitudes\n realizations = 20 # number of random phase multisine realizations\n samp_per_period = 16384 # number of samples per period\n n_skip = 1000\n periods = 1 # number of periods\n seq_len = samp_per_period * periods # data points per realization\n\n # Column names in the dataset\n TAG_U = 'u'\n TAG_Y = 'y'\n\n# test_signal = \"100mV\"\n# test_signal = \"325mV\"\n# test_signal = \"550mV\"\n# test_signal = \"775mV\"\n# test_signal = \"1000mV\"\n test_signal = \"ramp\"\n\n #test_signal = \"1000mV\" #\"ramp\" #ramp\"#\"320mV\" #\"1000mV\"#\"ramp\"\n plot_input = False\n\n # In[Load dataset]\n\n dict_test = {\"100mV\": 0, \"325mV\": 1, \"550mV\": 2, \"775mV\": 3, \"1000mV\": 4, \"ramp\": 5}\n dataset_list_level = ['ParWHData_Validation_Level' + str(i) for i in range(1, amplitudes + 1)]\n dataset_list = dataset_list_level + ['ParWHData_ValidationArrow']\n\n df_X_lst = []\n for dataset_name in dataset_list:\n dataset_filename = dataset_name + '.csv'\n df_Xi = pd.read_csv(os.path.join(\"data\", dataset_filename))\n df_X_lst.append(df_Xi)\n\n\n df_X = df_X_lst[dict_test[test_signal]] # first\n\n # Extract data\n y_meas = np.array(df_X['y'], dtype=np.float32)\n u = np.array(df_X['u'], dtype=np.float32)\n fs = np.array(df_X['fs'].iloc[0], dtype=np.float32)\n N = y_meas.size\n ts = 1/fs\n t = np.arange(N)*ts\n\n # In[Set-up model]\n\n net = ParallelWHNet()\n model_folder = os.path.join(\"models\", model_name)\n net.load_state_dict(torch.load(os.path.join(model_folder, f\"{model_name}.pt\")))\n #log_sigma_hat = torch.load(os.path.join(model_folder, \"log_sigma_hat.pt\"))\n #sigma_hat = torch.exp(log_sigma_hat) + 1e-3\n # In[Predict]\n u_torch = torch.tensor(u[None, :, None], dtype=torch.float, requires_grad=False)\n\n with torch.no_grad():\n y_hat = net(u_torch)\n\n # In[Detach]\n\n y_hat = y_hat.detach().numpy()[0, :, 0]\n\n # In[Plot]\n if plot_input:\n fig, ax = plt.subplots(2, 1, sharex=True)\n ax[0].plot(t, y_meas, 'k', label=\"$\\mathbf{y}$\")\n ax[0].plot(t, y_hat, 'b', label=r\"$\\mathbf{y}^{\\rm sim}$\")\n ax[0].plot(t, y_meas - y_hat, 'r', label=\"$\\mathbf{e}$\")\n ax[0].legend(loc=\"upper right\")\n ax[0].set_ylabel(\"Voltage (V)\")\n ax[0].grid()\n\n ax[1].plot(t, u, 'k', label=\"$u$\")\n ax[1].legend(loc=\"upper right\")\n ax[1].set_ylabel(\"Voltage (V)\")\n ax[1].set_xlabel(\"Time (s)\")\n ax[1].grid()\n else:\n fig, ax = plt.subplots(1, 1, figsize=(6, 3))\n ax.plot(t, y_meas, 'k', label=\"$\\mathbf{y}$\")\n ax.plot(t, y_hat, 'b', label=r\"$\\mathbf{y}^{\\rm sim}$\")\n ax.plot(t, y_meas - y_hat, 'r', label=\"$\\mathbf{e}$\")\n if test_signal == \"ramp\":\n ax.legend(loc=\"upper left\")\n else:\n ax.legend(loc=\"upper right\")\n ax.set_ylabel(\"Voltage (V)\")\n ax.set_xlabel(\"Time (s)\")\n ax.grid()\n\n if test_signal == \"ramp\":\n ax.set_xlim([0.0, 0.21])\n\n fig.tight_layout()\n fig_folder = \"fig\"\n if not os.path.exists(fig_folder):\n os.makedirs(fig_folder)\n fig.savefig(os.path.join(fig_folder, f\"{model_name}_timetrace.pdf\"))\n\n\n # In[Metrics]\n\n idx_test = range(n_skip, N)\n\n e_rms = 1000*util.metrics.error_rmse(y_meas[idx_test], y_hat[idx_test])\n mae = 1000 * util.metrics.error_mae(y_meas[idx_test], y_hat[idx_test])\n fit_idx = util.metrics.fit_index(y_meas[idx_test], y_hat[idx_test])\n r_sq = util.metrics.r_squared(y_meas[idx_test], y_hat[idx_test])\n u_rms = 1000*util.metrics.error_rmse(u, 0)\n\n print(f\"RMSE: {e_rms:.2f}mV\\nMAE: {mae:.2f}mV\\nFIT: {fit_idx:.1f}%\\nR_sq: {r_sq:.1f}\\nRMSU: {u_rms:.2f}mV\")","repo_name":"forgi86/sysid-transfer-functions-pytorch","sub_path":"examples/ParWH/parWH_test.py","file_name":"parWH_test.py","file_ext":"py","file_size_in_byte":4093,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"7803160644","text":"\"\"\" Demonstration of a non-linear fit \"\"\"\r\n\r\n# Import the standard packages\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy import optimize\r\n\r\n\r\ndef find_start(t, x):\r\n \"\"\" Find where the exponential decay approximately starts \"\"\"\r\n max_val = np.max(x)\r\n level = 0.6 # 40% below maximum\r\n threshold = max_val * level\r\n first_below = np.min(np.where(x t_start\r\n x0 = [0, 1, 1] # initial values for the fit\r\n par = optimize.least_squares(err_fun, x0, args = (time[decay],\r\n values[decay]))\r\n print(f'Fitted parameters: {par.x}')\r\n\r\n plt.plot(time, values, '.', label='data')\r\n plt.plot(time[decay], model(par.x, time[decay]), label='fit')\r\n plt.legend()\r\n plt.xlabel('Time [s]')\r\n plt.show()\r\n","repo_name":"thomas-haslwanter/sapy","sub_path":"src/listings/L8_3_nonlinear_fit.py","file_name":"L8_3_nonlinear_fit.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"3"}
+{"seq_id":"30135368550","text":"'''\n计算文件夹中的代码行数\n'''\nimport sys,os \n \n#计算一个文件的行数 \ndef afileline(f_path): \n res = 0 \n f = open(f_path,\"r\",1,\"utf-8\",'ignore') \n for lines in f: \n #空行的判断借助一个拆分字符串的函数split\n if(lines.split()): \n res += 1 \n return res \n \ndef countFilesLines(f_path): \n allline = 0 \n allfiles = 0 \n for root,dirs,files in os.walk(f_path):\n for afile in files: \n ext = afile.split('.') \n ext = ext[-1] \n if(ext in ['java','xml','js','html','jsp','vm']): \n itpath = root + os.sep + afile \n allfiles += 1 \n allline += afileline(itpath) \n print (f_path,'文件数量:', allfiles) \n print (f_path,'代码行数:', allline) \n\nif __name__ == '__main__':\n countFilesLines('E:\\\\TCMP4.0\\\\trunk\\\\Sources\\\\trust\\\\webapp-hstdc\\\\WebContent')\n countFilesLines('E:\\\\TCMP4.0\\\\trunk\\\\Sources\\\\trust\\\\webapp-hstdc\\\\src')","repo_name":"Jueee/PythonApply","sub_path":"02_IO/I01_countFilesLines/countFilesLines.py","file_name":"countFilesLines.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"14375427985","text":"from functools import reduce\nfrom typing import Optional\n\nfrom exceptions import OutOfRangeError\nfrom vessel import Vessel\n\n\nclass Battlefield:\n def __init__(self, min_x: int, max_x: int, min_y: int, max_y: int,\n min_z: int, max_z: int, max_power: int = 22):\n self.vessels: list[Vessel] = []\n self.min_x = min_x\n self.min_y = min_y\n self.min_z = min_z\n self.max_x = max_x\n self.max_y = max_y\n self.max_z = max_z\n self.max_power = max_power\n\n def add_vessel(self, vessel: Vessel):\n x, y, z = vessel.get_coordinates()\n if x not in range(self.min_x, self.max_x) \\\n or y not in range(self.min_y, self.max_y) \\\n or z not in range(self.min_z, self.max_z):\n raise OutOfRangeError(\"Les coordonnées du vaisseau sont \"\n \"en dehors de l'espace réservé !\")\n if self.get_vessel_by_coordinates(x, y, z) is not None:\n raise ValueError(\"Il y a déjà un vaisseau positionné ici !\")\n if self.get_power() + vessel.get_hits() > self.max_power:\n raise ValueError(f\"La puissance dépasse la maximum autorisé \"\n f\"{self.max_power} !\")\n\n self.vessels.append(vessel)\n\n def fired_at(self, x, y, z) -> bool:\n vessel = self.get_vessel_by_coordinates(x, y, z)\n if vessel is None:\n return False\n vessel.touched()\n return True\n\n def get_vessels(self) -> list[Vessel]:\n return self.vessels\n\n def get_vessel_by_coordinates(self, x, y, z) -> Optional[Vessel]:\n vessels_found = list(\n filter(lambda vessel: vessel.get_coordinates() == (x, y, z),\n self.vessels))\n if len(vessels_found) != 0:\n return vessels_found[0]\n else:\n return None\n\n def get_power(self) -> int:\n return reduce(\n lambda vessel_n, vessel_n_plus_1:\n vessel_n + vessel_n_plus_1.get_hits(),\n self.vessels, 0)\n","repo_name":"ImadZaoug/TDLOG","sub_path":"model/battlefield.py","file_name":"battlefield.py","file_ext":"py","file_size_in_byte":2053,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"8167128562","text":"import os\r\n\r\ndir = \"data\"\r\n\r\nfor filename in os.listdir(dir):\r\n if filename.endswith(\".mp3\"):\r\n try:\r\n preparedName = filename.replace(\".\", \" \")\r\n splitted = preparedName.split(\" \")\r\n filtered = splitted[3:-1]\r\n splitIndex = filtered.index(\"\")\r\n\r\n artist = \" \".join(filtered[0:splitIndex])\r\n songname = \" \".join(filtered[splitIndex+1:])\r\n\r\n newFileName = artist + \" - \" + songname+\".mp3\"\r\n\r\n print(filename, \"---->\", newFileName)\r\n\r\n os.rename(f\"{dir}\\{filename}\",\r\n f\"{dir}\\{newFileName}\")\r\n except:\r\n print(filename, \"failed\")\r\n","repo_name":"ManuelLerchner/Random-Coding-Projects","sub_path":"Python/Rename Files/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31023006775","text":"\ndef readFromFile():\n file = pickAFile() # Asks user to select a file to read from\n \n if not file.endswith(\".txt\") and not file.endswith(\".csv\"):\n print(\"Error: Only .txt and .csv files are supported.\")\n return\n \n openedFile = open(file, \"rt\") # Opens file. \"rt\" means we will open the file for reading purposes.\n \n allContent = openedFile.read() # read() stores all content from the file into a string\n \n openedFile.close() # Close file after reading its contents\n \n # For lists\n if file.endswith(\".txt\"):\n rows = allContent.split(\"\\n\") # Since allContents is one string, this will separate the string based on the break line \"\\n\". \n print(rows)\n \n # For cells\n if file.endswith(\".csv\"): \n rows = allContent.split(\"\\n\") # Split each row in the CSV file\n csvData = []\n for row in rows:\n if(row != ''):\n cells = row.split(\",\") # Split each cell and store it separately\n csvData.append([float(cell) for cell in cells]) # Append to the double array so it looks like this: [[1,2,3],[4,5,6],[7,8,9]].\n print(csvData)\n \n \n\ndef writeToFile():\n file = pickAFile() \n \n openedFile = open(file, \"at\") # Open the file for appending \n #openedFile = open(file, \"w\") # Open the file for writing\n \n if file.endswith(\".txt\"):\n openedFile.write(\"\\nTesting\") # - this line appends \"testing\" to the end of the file.\n openedFile.write(\"\\nTesting1\\nTesting2\") # Will append multiple lines\n openedFile.write(\"\\nTesting3\")\n \n if file.endswith(\".csv\"): \n openedFile.write(\"\\n12,34,56\") # - this line appends a new row with cell data to the end of the file.\n \n openedFile.close()\n print(\"Wrote to file successfully\")\n \n","repo_name":"makeuseofcode/Read-and-write-files-using-JES","sub_path":"Example/files.py","file_name":"files.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19987276303","text":"#This function finds out if an integer is a prime number\n\ndef PrimeTime(num):\n if type(num) != int or num <= 0:\n return 'number must be positive integer'\n elif num == 1:\n return 'false'\n elif num == 2 or num == 3:\n return 'true'\n else:\n for x in range(2, num - 1):\n if num%x == 0:\n isPrimeNumber = 'false'\n break\n else:\n isPrimeNumber = 'true'\n return isPrimeNumber\n","repo_name":"jeremy1111/coding_challenges","sub_path":"prime_number.py","file_name":"prime_number.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73847152402","text":"import pygame as pg\nimport sys\nfrom pygame.locals import *\nfrom pathlib import Path\nimport random\nimport gameMenu as gm\n\nSCALE = 2\nFPS = 200 * SCALE\n\nbasePath = Path(sys.argv[0]).parent\nfontLoc = basePath / \"whackman\" / \"data\" / \"fonts\" / \"minotaur.ttf\"\n\nWINDOWWIDTH = 420 * SCALE\nWINDOWHEIGHT = 350 * SCALE\nWINMIDX = WINDOWWIDTH/2\nWINMIDY = WINDOWHEIGHT/2\nOBJECTWIDTH = 7 * SCALE\nPADDLESIZE = 50 * SCALE\nPADDLEHEIGHT = (WINDOWHEIGHT - PADDLESIZE)/2\n\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\n\ndef text_objects(text, font):\n textSurface = font.render(text, True, (255,255,255))\n return textSurface, textSurface.get_rect() \n\ndef drawArena():\n SCREEN.fill((0,0,0))\n pg.draw.line(SCREEN, WHITE, ((WINDOWWIDTH/2),0), ((WINDOWWIDTH/2), WINDOWHEIGHT), 1*SCALE)\n\ndef drawScore(score1, score2):\n if score1 < 10:\n score1 = '0' + str(score1)\n else:\n score1 = str(score1)\n if score2 < 10:\n score2 = '0' + str(score2)\n else:\n score2 = str(score2)\n\n p1ScoreSurface = FONT.render(score1, True, WHITE)\n p1ScoreRect = p1ScoreSurface.get_rect()\n p1ScoreRect.topleft = (WINDOWWIDTH/2 + (5 * SCALE), 15*SCALE)\n SCREEN.blit(p1ScoreSurface, p1ScoreRect)\n p2ScoreSurface = FONT.render(score2, True, WHITE)\n p2ScoreRect = p2ScoreSurface.get_rect()\n p2ScoreRect.topleft = (WINDOWWIDTH/2 - (28 * SCALE), 15*SCALE)\n SCREEN.blit(p2ScoreSurface, p2ScoreRect)\n\ndef drawPaddle(paddle):\n if paddle.bottom > WINDOWHEIGHT:\n paddle.bottom = WINDOWHEIGHT\n elif paddle.top < 1:\n paddle.top = 1\n pg.draw.rect(SCREEN, WHITE, paddle)\n\ndef drawBall(ball):\n pg.draw.rect(SCREEN, WHITE, ball)\n\ndef moveBall(ball, ballDirX, ballDirY):\n ball.x += ballDirX\n ball.y += ballDirY\n return ball\n\ndef checkEdgeTB(ball, ballDirY):\n if ball.top == 0 or ball.bottom == WINDOWHEIGHT:\n HITSOUND.play()\n return -1 \n return 1\n\ndef checkEdgeLR(ball, ballDirX, score1, score2):\n global SCORESTATUS\n if ball.left == 0:\n score1 += 1\n ballDirX *= -1\n SCORESTATUS = True\n elif ball.right == WINDOWWIDTH:\n score2 += 1\n ballDirX *= -1\n SCORESTATUS = True\n return ball, ballDirX, score1, score2\n\ndef resetObjects(ball, paddle1, paddle2):\n ball.x = WINMIDX - (OBJECTWIDTH/2)\n ball.y = WINMIDY - (OBJECTWIDTH/2)\n paddle1.y = PADDLEHEIGHT\n paddle2.y = PADDLEHEIGHT\n return ball, paddle1, paddle2\n\ndef checkHitBall(ball, paddle1, paddle2, ballDirX):\n # right paddle\n if ballDirX == 1 and paddle1.left == ball.right and paddle1.top < ball.bottom and paddle1.bottom > ball.top:\n HITSOUND.play() \n return (-1, [-1, 1][random.randint(0,1)])\n # REMOVE?\n # check top third\n if paddle1.top <= ball.centery < paddle1.top + (paddle1.bottom-paddle1.top)*(1/4):\n return (-1, 1)\n # check bottom third\n elif paddle1.bottom - (paddle1.bottom-paddle1.top)*(1/4) <= ball.centery < paddle1.bottom:\n return (-1 , -1)\n # else middle\n else:\n return (-1, 0)\n \n # left paddle\n elif ballDirX == -1 and paddle2.right == ball.left and paddle2.top < ball.bottom and paddle2.bottom > ball.top:\n HITSOUND.play()\n return (-1, [-1, 1][random.randint(0,1)])\n # REMOVE?\n # check top third\n if paddle2.top <= ball.centery < paddle2.top + (paddle2.bottom-paddle2.top)*(1/4):\n return (-1, 1)\n # check bottom third\n elif paddle2.bottom - (paddle2.bottom-paddle2.top)*(1/4) <= ball.centery < paddle2.bottom:\n return (-1 , -1)\n # else middle\n else:\n return (-1, 0)\n return (1, 1)\n\n# Count after space has been pressed\ndef countDownGameStart(SCREEN, score1, score2, ball, paddle1, paddle2): \n largeText = pg.font.Font(str(fontLoc), 200)\n smallText = pg.font.Font(str(fontLoc), 60)\n\n pressSpaceSurf, pressSpaceText = text_objects('PRESS SPACE TO START', smallText)\n pressSpaceText.center = (WINDOWWIDTH /2, WINDOWHEIGHT / 2)\n \n countSTarr = []\n for x in ['3', '2', '1', 'GO!']:\n countSurf, countText = text_objects(x, largeText)\n countText.center = (WINDOWWIDTH /2, WINDOWHEIGHT / 2)\n countSTarr.append((countSurf, countText))\n \n FPSCLOCK = pg.time.Clock()\n spacePressed = False\n counter = 0\n blinkSpeed = [int(FPS*(1/2)), FPS]\n while True:\n FPSCLOCK.tick(FPS)\n pg.event.pump()\n\n for ev in pg.event.get():\n if ev.type == pg.locals.QUIT:\n pg.quit()\n sys.exit()\n\n keyinput = pg.key.get_pressed()\n if keyinput[pg.K_SPACE]:\n spacePressed = True\n\n drawArena()\n drawScore(score1, score2)\n drawBall(ball)\n drawPaddle(paddle1)\n drawPaddle(paddle2)\n \n # countdown sequence\n if spacePressed:\n SCREEN.blit(countSTarr[counter][0], countSTarr[counter][1])\n pg.display.flip()\n for i in range(int(FPS * (2 / 3))):\n pg.event.pump()\n FPSCLOCK.tick(FPS)\n continue\n counter += 1\n if counter == 4:\n return\n else:\n for i, speed in enumerate(blinkSpeed):\n for x in range(speed):\n if i == 1:\n SCREEN.blit(pressSpaceSurf, pressSpaceText)\n pg.display.flip()\n pg.event.pump()\n FPSCLOCK.tick(FPS)\n keyinput = pg.key.get_pressed()\n if keyinput[pg.K_SPACE]:\n break\n\n\ndef main():\n pg.init()\n global SCREEN, FONTSIZE, FONT, HITSOUND, SCORESTATUS\n\n #Load Audio\n pg.mixer.pre_init(44100, 16, 2, 4096)\n audioPath = Path(sys.argv[0]).parent / \"randomPong\" / '4359__noisecollector__pongblipf4.wav'\n HITSOUND = pg.mixer.Sound(str(audioPath))\n\n #Setting up fonts for the score\n FONTSIZE = 20 * SCALE\n FONT = pg.font.Font('freesansbold.ttf', FONTSIZE)\n\n FPSCLOCK = pg.time.Clock()\n SCREEN = pg.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT))\n\n ballDirX , ballDirY= -1, -1\n score1, score2 = 0, 0\n SCORESTATUS = True \n\n ball = pg.Rect(WINMIDX - (OBJECTWIDTH/2), WINMIDY - (OBJECTWIDTH/2), OBJECTWIDTH, OBJECTWIDTH)\n paddle1 = pg.Rect(WINDOWWIDTH - (OBJECTWIDTH*2), PADDLEHEIGHT, OBJECTWIDTH, PADDLESIZE)\n paddle2 = pg.Rect(OBJECTWIDTH, PADDLEHEIGHT, OBJECTWIDTH, PADDLESIZE)\n\n pg.mouse.set_visible(0)\n\n countDownGameStart(SCREEN, score1, score2, ball, paddle1, paddle2)\n\n playGame = True\n while playGame:\n drawArena()\n drawScore(score1, score2)\n drawBall(ball)\n drawPaddle(paddle1)\n drawPaddle(paddle2)\n\n pg.display.update()\n\n # wait if either player scored\n if SCORESTATUS:\n pg.time.wait(500)\n SCORESTATUS = False\n\n keys = pg.key.get_pressed()\n\n # press the X to quit\n for event in pg.event.get():\n if event.type == QUIT:\n quit()\n sys.exit()\\\n \n if keys[K_UP]:\n paddle1.y -= 1\n elif keys[K_DOWN]:\n paddle1.y += 1\n if keys[K_w]:\n paddle2.y -= 1\n elif keys[K_s]:\n paddle2.y += 1\n elif keys[K_ESCAPE]:\n ret = gm.menu(SCREEN, WINDOWHEIGHT, WINDOWWIDTH, FPS)\n if ret == 2:\n resetObjects(ball, paddle1, paddle2)\n score1 = 0\n score2 = 0\n countDownGameStart(SCREEN, score1, score2, ball, paddle1, paddle2)\n elif ret == 0:\n return False\n \n ball = moveBall(ball, ballDirX, ballDirY)\n ballDirY *= checkEdgeTB(ball, ballDirY)\n tmpX, tmpY = checkHitBall(ball, paddle1, paddle2, ballDirX) \n ballDirX *= tmpX\n ballDirY *= tmpY\n ball, ballDirX, score1, score2 = checkEdgeLR(ball, ballDirX, score1, score2)\n \n if SCORESTATUS:\n ball, paddle1, paddle2 = resetObjects(ball, paddle1, paddle2)\n\n pg.display.update()\n FPSCLOCK.tick(FPS)\n\n\ndef play():\n main()","repo_name":"brynjarorng/Pythonic_Group_Project","sub_path":"randomPong/randomPong.py","file_name":"randomPong.py","file_ext":"py","file_size_in_byte":8212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27261993873","text":"\nimport math\nimport glob\n\nimport pandas as pd\nimport numpy as np\nimport json\nimport time\nimport re\nfrom fuzzywuzzy import fuzz\nfrom fuzzywuzzy import process\n\nfrom flask import Flask\nfrom flask import request\n\n\n\n# In[]: flask + expense module\n\n\napp = Flask(__name__)\n\n@app.route('/', methods = ['POST'])\n\ndef expenseModel_CC(cntLimIntRate = 3.00\n ,cntBalMonths = 36\n ,obsRecentRepayTxnBar = 31\n ,syph = 2\n ,infRecentTxnBar = 31\n ,infCountTxnBar = 3\n ,fuzzThreshold = 85 \n ):\n\n jSonData = request.json\n\n #grab app context\n appRef = jSonData['reference']\n appDate = jSonData['submissionTime'][:10] \n \n #stab commitment tables - observed & inferred\n obsCcVar = 'cntType accType accName accNum accBsb balCurrOut balAvail limit intRate minAmtDue minAmtDueDate cntLim cntBal cntObs cntRepayRatioObs'.split()\n ccCntObs = pd.DataFrame([[0]*len(obsCcVar)], columns=obsCcVar) \n \n infCcVar = 'cntType text infNarration txtHit amount cntInf accType accName accNum accBsb'.split()\n ccCntInf = pd.DataFrame([[0]*len(infCcVar)],columns=infCcVar) \n\n #cycle through accounts on bankstatment;\n for accIdx in np.arange(len(jSonData['bankData']['bankAccounts'])):\n \n #observed CC - get account characteristics - take conservative positions where no data \n if jSonData['bankData']['bankAccounts'][accIdx]['accountType'].upper().replace(\" \", \"\") == 'CREDITCARD':\n cntType = 'obs'\n accName = jSonData['bankData']['bankAccounts'][accIdx]['accountName']\n accType = jSonData['bankData']['bankAccounts'][accIdx]['accountType']\n accNum = jSonData['bankData']['bankAccounts'][accIdx]['accountNumber']\n accBsb = jSonData['bankData']['bankAccounts'][accIdx]['bsb']\n balCurrOut = abs(float(jSonData['bankData']['bankAccounts'][accIdx]['currentBalance']))\n balAvail = abs(float(jSonData['bankData']['bankAccounts'][accIdx]['availableBalance']))\n \n try:\n intRate = float(jSonData['bankData']['bankAccounts'][accIdx]['additionalDetails']['interestRate']) \n except (ValueError, KeyError): \n intRate = 21.00\n try:\n limit = float(jSonData['bankData']['bankAccounts'][accIdx]['additionalDetails']['creditLimit'])\n except (ValueError, KeyError): \n limit = (balCurrOut) + (balAvail)\n try:\n minAmtDue = float(jSonData['bankData']['bankAccounts'][accIdx]['additionalDetails']['minimumAmountDue'])\n except (ValueError, KeyError): \n minAmtDue = np.nan\n try:\n minAmtDueDate = jSonData['bankData']['bankAccounts'][accIdx]['additionalDetails']['minimumAmountDueDate']\n except (ValueError, KeyError): \n minAmtDueDate = np.nan\n \n #calc obs commitment\n #limit position\n cntLim = round(limit * cntLimIntRate/100,2)\n #balance position\n cntBal = abs(round(np.pmt(intRate/100/12,cntBalMonths,balCurrOut,0),2))\n #sum\n cntObs = round(cntLim + cntBal,2)\n\n #calc obs average monthly repayment\n #grab txns\n txnObs = pd.DataFrame(jSonData['bankData']['bankAccounts'][accIdx]['transactions'])\n\n #if no repayment txns, then ratio is null\n cntRepayRatioObs = np.nan\n if len(txnObs) > 0:\n #get repayment txns: credits, recent, repayment narration detection\n #credits\n txnRepay = txnObs[txnObs['amount']>0].copy()\n txnRepay['text'] = txnRepay['text'].str.upper()\n #repayment narration detection\n txnRepay = txnRepay[txnRepay.text.str.contains(\"CREDIT CARD REPAYMENT|CREDIT CARD PAYMENT|CREDIT CARD PAYMEN|CC REPAYMENT|CC PAYMENT|CC AUTO PAYMENT|PAYMENT THANKYOU\")]\n #some exist and recent\n if (len(txnRepay) > 0) and (np.datetime64(txnRepay.date.max()) >= (np.datetime64(appDate)-obsRecentRepayTxnBar)):\n #wrangle commitment at point of repayment\n txnRepay['monYY'] = pd.to_datetime(txnRepay['date'], format='%Y-%m-%d').dt.to_period(\"M\")\n txnRepay['balBeforeTxn'] = txnRepay.balance.astype(float) + txnRepay.amount.astype(float)\n txnRepay['cnt'] = abs(np.pmt(intRate/100/12,cntBalMonths,txnRepay.balBeforeTxn,0)) + cntLim\n txnRepay = txnRepay.groupby('monYY').agg({'amount':'sum','cnt':'sum'}) \\\n .merge( \\\n txnRepay.groupby('monYY').agg({'amount':'sum','cnt':'sum'}).groupby('amount').agg({'amount':'count'}).rename(columns={'amount':'amountCount'}).reset_index() \\\n ,how='left',on='amount')\n \n #exclude repayment anomalies i.e. principal payments - +- 2 standard deviations of repayment\n #max procedure\n if txnRepay[txnRepay.amount == txnRepay.amount.max()]['amountCount'].item() == 1:\n if txnRepay.amount.max() > (np.std(txnRepay[txnRepay.amount != txnRepay.amount.max()].amount) * syph) + (txnRepay[txnRepay.amount != txnRepay.amount.max()].amount.mean()):\n txnRepay = txnRepay[txnRepay.amount != txnRepay.amount.max()]\n \n #min procedure - may not need.\n if txnRepay[txnRepay.amount == txnRepay.amount.min()]['amountCount'].item() == 1:\n if txnRepay.amount.min() < (np.std(txnRepay[txnRepay.amount != txnRepay.amount.min()].amount) * syph) - (txnRepay[txnRepay.amount != txnRepay.amount.max()].amount.mean()):\n txnRepay = txnRepay[txnRepay.amount != txnRepay.amount.min()]\n \n #calculate ratio: repayment amount/commitment \n cntRepayRatioObs = (txnRepay.amount/txnRepay.cnt).mean()\n \n #write observed CC + commitment deets to dataframe \n tmpAppend = pd.DataFrame([[cntType,accType,accName,accNum,accBsb,balCurrOut,balAvail,limit,intRate,minAmtDue,minAmtDueDate,cntLim,cntBal,cntObs,cntRepayRatioObs]],columns=obsCcVar) \n ccCntObs = ccCntObs.append(tmpAppend,ignore_index=True)\n\n #inferred CC from transaction and savings accounts\n #cycle through transaction/savings accounts\n if jSonData['bankData']['bankAccounts'][accIdx]['accountType'].upper().replace(\" \", \"\") in 'TRANSACTION SAVINGS'.split():\n #grab transcations\n txnInf = pd.DataFrame(jSonData['bankData']['bankAccounts'][accIdx]['transactions'])\n \n #wrangle outbound CC repayment txns\n if len(txnInf) > 0:\n #debits\n txnInf = txnInf[txnInf['amount']<0]\n txnInf['text'] = txnInf['text'].str.upper()\n \n #get repayment types\n txnInfCcRepay = txnInf[txnInf.type.str.upper()==\"CREDIT CARD REPAYMENTS\"].copy()\n \n if len(txnInfCcRepay) > 0:\n \n #clean narrations\n txnInfCcRepay['infNarration'] = txnInfCcRepay.text.apply(lambda x: re.sub(\"\\d+\", \" \", x))\n \n #aggregate by narration cashflow\n infAgg = txnInfCcRepay.groupby('infNarration text'.split()).agg({'amount':'count sum'.split(),'date':'min max'.split()}).reset_index()\n infAgg.columns = [\"_\".join(x) for x in infAgg.columns.ravel()]\n \n #Recent txn threshold\n infAgg = infAgg[infAgg.date_max.apply(lambda x: np.datetime64(x) >= (np.datetime64(appDate)-infRecentTxnBar))==True] \n \n #at least 3 instances of repayment to be considered a commitment\n infAgg = infAgg[(infAgg['amount_count']>infCountTxnBar)]\n \n #exlcude failing txns\n txnInfCcRepay = txnInfCcRepay[txnInfCcRepay.infNarration.isin(infAgg.infNarration_.tolist())]\n \n #fuzzy deduplication of narrations;\n infAggList = infAgg.infNarration_.tolist()\n \n #set up inferred fuzzy match table\n infFuzz = pd.DataFrame([[0]*5], columns='idInf txt fuzzScore txtHit idInfHit'.split())\n \n tmpList = infAggList.copy()\n\n #fuzzy de-duping\n for idx, txt in enumerate(infAggList):\n tmpList = [t for t in tmpList if t is not txt]\n \n if len(tmpList) == 0:\n break\n \n appendFuzz = pd.DataFrame(process.extract(txt,tmpList))\n appendFuzz.rename(columns={0:'txtHit',1:'fuzzScore'},inplace=True)\n appendFuzz['idxHit'] = appendFuzz.index\n \n appendFuzz = appendFuzz[appendFuzz.fuzzScore >= fuzzThreshold]\n \n if len(appendFuzz) == 0:\n next\n else: \n appendFuzz.loc[appendFuzz.fuzzScore >= fuzzThreshold,'idInf'] = idx\n appendFuzz.loc[appendFuzz.fuzzScore >= fuzzThreshold,'txt'] = txt\n infFuzz = infFuzz.append(appendFuzz,ignore_index=True)\n tmpList = [t for t in tmpList if t not in appendFuzz.txtHit.tolist()]\n \n infFuzz = infFuzz[1:]\n\n if len(infFuzz) > 0:\n tmp0 = txnInfCcRepay.merge(infFuzz['idInf txt'.split()].drop_duplicates(['idInf','txt'],keep = 'first',inplace=False),how='left',left_on='infNarration',right_on='txt',suffixes=[\"\",\"\"]) \n tmp1 = tmp0[tmp0.idInf.isnull()].drop('idInf',axis=1).merge(infFuzz['idInf txtHit'.split()].drop_duplicates(['idInf','txtHit'],keep = 'first',inplace=False),how='left',left_on='infNarration',right_on='txtHit',suffixes=[\"\",\"\"])\n txnInfCcRepay = tmp0[tmp0.idInf.notnull()].append(tmp1,ignore_index=True).drop('txt',axis=1)\n \n else:\n txnInfCcRepay['txtHit'] = np.nan\n tmp0 = txnInfCcRepay.groupby('infNarration',as_index=False).agg({'amount':'mean'})\n tmp0['idInf'] = tmp0.index\n txnInfCcRepay = txnInfCcRepay.merge(tmp0['idInf infNarration'.split()],how='left',on='infNarration') \n\n #inferred CC deets \n infCc = txnInfCcRepay.drop_duplicates('idInf')['idInf text infNarration txtHit'.split()].copy()\n \n #calculate average monthly repayment of inferred faciltiees\n txnInfCcRepay['monYY'] = pd.to_datetime(txnInfCcRepay['date'], format='%Y-%m-%d').dt.to_period(\"M\")\n \n txnInfCcRepayMon = txnInfCcRepay.groupby('idInf monYY'.split()).agg({'amount':'sum'}).reset_index() \\\n .merge(txnInfCcRepay.groupby('monYY').agg({'amount':'count'}).rename(columns={'amount':'amountCount'}).reset_index() \\\n ,how='left',on='monYY')\n \n #apply anomaly repayment exclusion\n tmpSyph = pd.DataFrame([[0]*len(txnInfCcRepayMon.columns.values.tolist())],columns=txnInfCcRepayMon.columns.values.tolist())\n\n for idx in txnInfCcRepayMon.idInf.unique().tolist():\n tmp0 = txnInfCcRepayMon[txnInfCcRepayMon.idInf==idx]\n \n if tmp0[tmp0.amount == tmp0.amount.max()]['amountCount'].item() == 1:\n if tmp0.amount.max() > (np.std(tmp0[tmp0.amount != tmp0.amount.max()].amount) * syph) + (tmp0[tmp0.amount != tmp0.amount.max()].amount.mean()):\n tmp0 = tmp0[tmp0.amount != tmp0.amount.max()]\n \n #may not need min procedure - or specific syph threshold.\n if tmp0[tmp0.amount == tmp0.amount.min()]['amountCount'].item() == 1:\n if tmp0.amount.min() < (np.std(tmp0[tmp0.amount != tmp0.amount.min()].amount) * syph) - (tmp0[tmp0.amount != tmp0.amount.max()].amount.mean()):\n tmp0 = tmp0[tmp0.amount != tmp0.amount.min()]\n \n tmpSyph = tmpSyph.append(tmp0,ignore_index=True) \n \n txnInfCcRepayMon = tmpSyph[1:].copy()\n \n infCc = infCc.merge(txnInfCcRepayMon.groupby('idInf',as_index=False).agg({'amount':'mean'}),how='left',on='idInf') \n infCc['amount'] = abs(infCc['amount'])\n \n #get tran/savings account deets\n infCc['cntType'] = 'inf'\n infCc['accName'] = jSonData['bankData']['bankAccounts'][accIdx]['accountName']\n infCc['accType'] = jSonData['bankData']['bankAccounts'][accIdx]['accountType']\n infCc['accNum'] = jSonData['bankData']['bankAccounts'][accIdx]['accountNumber']\n infCc['accBsb'] = jSonData['bankData']['bankAccounts'][accIdx]['bsb']\n \n #write inferred CC + commitment deets to dataframe\n ccCntInf = ccCntInf.append(infCc,ignore_index=True)\n\n #aggregate observed and inferred\n ccCntObs = ccCntObs[1:]\n ccCntInf = ccCntInf[1:]\n \n #average repayment across inferred CCs\n# cntRepayRatioObsMu = ccCntObs.cntRepayRatioObs.mean() \n\n \n #apply repayRatio to inferred repayments to get commitment;\n #nan not resolving...\n# if cntRepayRatioObsMu != np.nan:\n# ccCntInf['cntInf'] = ccCntInf.apply(lambda x: x.amount/cntRepayRatioObsMu , axis=1)\n \n \n ccCntObs_jS = ccCntObs.to_json()\n# ccCntInf_jS = ccCntInf.to_json()\n \n# return(ccCntObs_jS,ccCntInf_jS)\n return(ccCntObs_jS)\n \n\nif __name__ == '__main__':\n app.run()\n \n#app.run(host='0.0.0.0', port= 8092)\n","repo_name":"tflexi/testccpoc","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":14685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27142851151","text":"import tkinter as tk\nimport matplotlib\nmatplotlib.use(\"TkAgg\")\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\nfrom matplotlib.figure import Figure\nfrom math import *\nimport numpy as np\n\nfrom control import *\n\nclass ClutterDisplay(tk.Frame):\n '''\n В этом приложении будут четыре графика и четыре кнопки. И одно окно\n с выпадающим меню.\n '''\n def __init__(self, master=None):\n tk.Frame.__init__(self, master)\n self._placeFrames()\n self._placeButtons()\n self._placeEmptyPlots()\n\n def _placeFrames(self):\n self._plotsArea = tk.Frame(self.master, bd=1, relief=tk.FLAT)\n self._plotsArea.grid(row=1, rowspan=2, columnspan=2)\n\n self._toolbar = tk.Frame(self.master, bd=1, relief=tk.FLAT)\n self._toolbar.grid(row=0, columnspan=4)\n\n self._topleft = tk.Frame(self._plotsArea, bd=1, relief=tk.FLAT)\n self._topright = tk.Frame(self._plotsArea, bd=1, relief=tk.FLAT)\n self._bottomleft = tk.Frame(self._plotsArea, bd=1, relief=tk.FLAT)\n self._bottomright = tk.Frame(self._plotsArea, bd=1, relief=tk.FLAT)\n self._topleft.grid(row=0, column=0)\n self._topright.grid(row=0, column=1)\n self._bottomleft.grid(row=1, column=0)\n self._bottomright.grid(row=1, column=1)\n\n return self\n\n def _placePlot(self, placer):\n fig = Figure(figsize=(6,4), dpi=100)\n ax = fig.add_subplot(111)\n canvas = FigureCanvasTkAgg(fig, placer)\n canvas.get_tk_widget().pack(side=\"top\")\n canvas.show()\n return self\n\n def _placeEmptyPlots(self):\n self._placePlot(self._topleft)\n self._placePlot(self._topright)\n self._placePlot(self._bottomleft)\n self._placePlot(self._bottomright)\n return self\n\n def _placeButtons(self):\n self._config = tk.Button(self._toolbar, text=\"Настройка\", command=self.setConfig)\n self._show = tk.Button(self._toolbar, text=\"Вывести на графики\", command=self.setPlots)\n self._save = tk.Button(self._toolbar, text=\"Сохранить графики\", command=self.savePlots)\n self._quit = tk.Button(self._toolbar, text=\"Выход\", fg=\"red\", command=root.destroy)\n\n self._config.grid(row=0, column=0)\n self._show.grid(row=0, column=1)\n self._save.grid(row=0, column=2)\n self._quit.grid(row=0, column=3)\n return self\n\n def setConfig(self):\n self._config[\"bg\"] = \"green\"\n self._config[\"fg\"] = \"white\"\n return self\n\n def setPlots(self):\n self._show[\"bg\"] = \"orange\"\n return self\n\n def savePlots(self):\n self._save[\"bg\"] = \"blue\"\n self._save[\"fg\"] = \"white\"\n return self\n\n def __del__(self):\n del self._bottomleft\n del self._bottomright\n del self._topleft\n del self._topright\n del self._save\n del self._show\n del self._config\n del self._quit\n del self._plotsArea\n del self._toolbar\n\nroot = tk.Tk()\napp = ClutterDisplay(master=root)\napp.mainloop()\n","repo_name":"vetakim/clutter","sub_path":"view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":3180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20647983431","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib import messages\nfrom django.db import transaction\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import UserForm, UserSettingsForm\n\n\ndef signup(request):\n if request.method == 'POST':\n user_form = UserForm(request.POST, request.FILES)\n if user_form.is_valid():\n with transaction.atomic():\n user = user_form.save()\n login(request, user)\n return redirect('settings')\n else:\n user_form = UserForm()\n return render(request, 'account/signup.html',\n {'user_form': user_form,})\n\n\ndef user_login(request):\n if request.method == 'POST':\n username = request.POST.get('username')\n password = request.POST.get('password')\n user = authenticate(username=username, password=password)\n if user:\n if user.is_active:\n login(request, user)\n return redirect('index')\n else:\n messages.add_message(request, messages.ERROR, 'Пользователь заблокирован!')\n return redirect('login')\n else:\n messages.add_message(request, messages.ERROR, 'Неверно введен логин или пароль!')\n return redirect('login')\n else:\n return render(request, 'account/login.html', {})\n\n\n@login_required\ndef settings(request):\n user = request.user\n if request.method == 'POST':\n user_form = UserSettingsForm(request.POST, request.FILES, instance=user)\n\n if user_form.is_valid():\n user_form.save()\n messages.add_message(request, messages.INFO, 'Настройки успешно сохранены!')\n return redirect('settings')\n\n return render(request,\n 'account/settings.html',\n {})\n\n\n","repo_name":"kochnev/hasker","sub_path":"hasker/account/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17488529924","text":"\"\"\"\nDjango settings for csua_backend project.\n\nFor more information on this file, see\nhttps://docs.djangoproject.com/en/2.0/topics/settings/\nFor the full list of settings and their values, see\nhttps://docs.djangoproject.com/en/2.0/ref/settings/\n\"\"\"\nimport os\nfrom pathlib import Path\n\nimport ldap3\nfrom decouple import config\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = config(\"DJANGO_DEBUG\", cast=bool, default=False)\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = str(Path(__file__).parent.parent.parent)\n\nPROJECT_HOME = BASE_DIR\n\nFIXTURE_DIRS = [os.path.join(BASE_DIR, \"fixtures\")]\n\n# Database\n# https://docs.djangoproject.com/en/2.0/ref/settings/#databases\n\nif config(\"CSUA_BACKEND_USE_MYSQL\", cast=bool, default=False):\n DB_PASS = config(\"DJANGO_DB_PASS\")\n\n DATABASES = {\n \"default\": {\n \"ENGINE\": \"django.db.backends.mysql\",\n \"NAME\": \"csua_backend\",\n # The following settings are not used with sqlite3:\n \"USER\": \"pnunez\",\n \"PASSWORD\": DB_PASS,\n \"HOST\": \"\", # Empty for localhost through domain sockets or '127.0.0.1' for localhost through TCP.\n \"PORT\": \"\", # Set to empty string for default.\n \"TEST\": {\"NAME\": \"test_csua_backend\", \"CHARSET\": \"utf8\"},\n }\n }\nelse:\n DATABASES = {\n \"default\": {\n \"ENGINE\": \"django.db.backends.sqlite3\",\n \"NAME\": os.path.join(PROJECT_HOME, \"csua.sqlite3\"),\n }\n }\n\n\n# Hosts/domain names that are valid for this site; required if DEBUG is False\n# See https://docs.djangoproject.com/en/1.5/ref/settings/#allowed-hosts\nALLOWED_HOSTS = [\n \"www.csua.berkeley.edu\",\n \"csua.berkeley.edu\",\n \"legacy.csua.berkeley.edu\",\n \"dev.csua.berkeley.edu\",\n]\nif DEBUG:\n ALLOWED_HOSTS.extend([\"*\"])\n\nSITE_ID = 1\n\n# Local time zone for this installation. Choices can be found here:\n# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name\n# although not all choices may be available on all operating systems.\n# In a Windows environment this must be set to your system time zone.\nTIME_ZONE = \"America/Los_Angeles\"\n\n# Language code for this installation. All choices can be found here:\n# http://www.i18nguy.com/unicode/language-identifiers.html\nLANGUAGE_CODE = \"en-us\"\n\n# Internationalization\n# https://docs.djangoproject.com/en/2.0/topics/i18n/\n\n# If you set this to False, Django will make some optimizations so as not\n# to load the internationalization machinery.\nUSE_I18N = True\n\n# If you set this to False, Django will not format dates, numbers and\n# calendars according to the current locale.\nUSE_L10N = True\n\n# If you set this to False, Django will not use timezone-aware datetimes.\nUSE_TZ = True\n\n# Absolute filesystem path to the directory that will hold user-uploaded files.\n# Example: \"/var/www/example.com/media/\"\nMEDIA_ROOT = os.path.join(PROJECT_HOME, \"media_root/\")\n\n# URL that handles the media served from MEDIA_ROOT. Make sure to use a\n# trailing slash.\n# Examples: \"http://example.com/media/\", \"http://media.example.com/\"\nMEDIA_URL = \"/media/\"\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/2.0/howto/static-files/\n\n# Absolute path to the directory static files should be collected to.\n# Don't put anything in this directory yourself; store your static files\n# in apps' \"static/\" subdirectories and in STATICFILES_DIRS.\n# Example: \"/var/www/example.com/static/\"\nSTATIC_ROOT = os.path.join(PROJECT_HOME, \"static_root/\")\n\n# URL prefix for static files.\n# Example: \"http://example.com/static/\", \"http://static.example.com/\"\nSTATIC_URL = \"/static/\"\n\n# Additional locations of static files\nSTATICFILES_DIRS = [\n # Put strings here, like \"/home/html/static\" or \"C:/www/django/static\".\n # Always use forward slashes, even on Windows.\n # Don't forget to use absolute paths, not relative paths.\n os.path.join(PROJECT_HOME, \"static\")\n]\n\n# List of finder classes that know how to find static files in\n# various locations.\nSTATICFILES_FINDERS = [\n \"django.contrib.staticfiles.finders.FileSystemFinder\",\n \"django.contrib.staticfiles.finders.AppDirectoriesFinder\",\n # 'django.contrib.staticfiles.finders.DefaultStorageFinder',\n]\n\n# Security\nif not DEBUG:\n CSRF_COOKIE_SECURE = True\n CSRF_COOKIE_HTTPONLY = True\n SESSION_COOKIE_SECURE = True\n SESSION_COOKIE_HTTPONLY = True\n SECURE_HSTS_SECONDS = 60\n SECURE_HSTS_PRELOAD = True\n SECURE_HSTS_INCLUDE_SUBDOMAINS = True\n SECURE_CONTENT_TYPE_NOSNIFF = True\n SECURE_BROWSER_XSS_FILTER = True\n SECURE_PROXY_SSL_HEADER = (\"HTTP_X_Forwarded_Proto\", \"https\")\nX_FRAME_OPTIONS = \"SAMEORIGIN\"\n\n# SECURITY WARNING: keep the secret key used in production secret!\n# Make this unique, and don't share it with anybody.\nif DEBUG:\n SECRET_KEY = \"CSUA)@3zekni&mwis6s031xsru2v&h(y=l89oa4@^&i#lxfoa9p9\"\nelse:\n SECRET_KEY = config(\"DJANGO_SECRET_KEY\")\n\n\n# Password validation\n# https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators\n\nAUTH_PASSWORD_VALIDATORS = [\n {\n \"NAME\": \"django.contrib.auth.password_validation.UserAttributeSimilarityValidator\"\n },\n {\n \"NAME\": \"django.contrib.auth.password_validation.MinimumLengthValidator\",\n \"OPTIONS\": {\"min_length\": 9},\n },\n {\"NAME\": \"django.contrib.auth.password_validation.CommonPasswordValidator\"},\n {\"NAME\": \"django.contrib.auth.password_validation.NumericPasswordValidator\"},\n]\nTEMPLATES = [\n {\n \"BACKEND\": \"django.template.backends.django.DjangoTemplates\",\n \"DIRS\": [\n # insert your TEMPLATE_DIRS here\n os.path.join(PROJECT_HOME, \"templates\")\n ],\n \"APP_DIRS\": True,\n \"OPTIONS\": {\n \"context_processors\": [\n # Insert your TEMPLATE_CONTEXT_PROCESSORS here or use this\n # list if you haven't customized them:\n \"django.template.context_processors.debug\",\n \"django.template.context_processors.request\",\n \"django.contrib.auth.context_processors.auth\",\n \"django.template.context_processors.i18n\",\n \"django.template.context_processors.media\",\n \"django.template.context_processors.static\",\n \"django.template.context_processors.tz\",\n \"django.contrib.messages.context_processors.messages\",\n ]\n },\n }\n]\n\n\nMIDDLEWARE = [\n \"django.middleware.security.SecurityMiddleware\",\n \"django.contrib.sessions.middleware.SessionMiddleware\",\n \"django.middleware.common.CommonMiddleware\",\n \"django.middleware.csrf.CsrfViewMiddleware\",\n \"django.contrib.auth.middleware.AuthenticationMiddleware\",\n \"django.contrib.messages.middleware.MessageMiddleware\",\n # 'django.contrib.auth.middleware.SessionAuthenticationMiddleware',\n # Uncomment the next line for simple clickjacking protection:\n \"django.middleware.clickjacking.XFrameOptionsMiddleware\",\n \"apps.csua_backend.middleware.TemporaryRedirectFallbackMiddleware\",\n \"django.contrib.flatpages.middleware.FlatpageFallbackMiddleware\",\n]\n\n\nROOT_URLCONF = \"apps.csua_backend.urls\"\n\n# Python dotted path to the WSGI application used by Django's runserver.\nWSGI_APPLICATION = \"apps.csua_backend.wsgi.application\"\n\nINSTALLED_APPS = [\n \"django.contrib.auth\",\n \"django.contrib.contenttypes\",\n \"django.contrib.sessions\",\n \"django.contrib.sites\",\n \"django.contrib.flatpages\",\n \"django.contrib.messages\",\n \"django.contrib.staticfiles\",\n # Uncomment the next line to enable the admin:\n \"django.contrib.admin\",\n # Uncomment the next line to enable admin documentation:\n # 'django.contrib.admindocs',\n \"django.contrib.redirects\",\n ## Our apps\n \"apps.db_data\",\n \"apps.discordbot\",\n \"apps.ldap\",\n \"apps.main_page\",\n \"apps.newuser\",\n \"apps.outreach\",\n \"apps.password_reset\",\n \"apps.slackbot\",\n \"apps.tracker\",\n ## Third-party\n \"markdown_deux\",\n \"sorl.thumbnail\",\n \"django_python3_ldap\",\n]\n\nSESSION_SERIALIZER = \"django.contrib.sessions.serializers.JSONSerializer\"\n\nADMINS = [(\"Root Staff\", \"root@csua.berkeley.edu\")]\n\nMANAGERS = ADMINS\n\nDEFAULT_FROM_EMAIL = \"django@csua.berkeley.edu\"\n\nSERVER_EMAIL = \"django-errors@csua.berkeley.edu\"\n\nEMAIL_HOST = \"localhost\"\n\nEMAIL_PORT = 10025\n\nEMAIL_USE_TLS = True\n\nif config(\"DJANGO_FILEBASED_EMAIL_BACKEND\", cast=bool, default=False):\n EMAIL_BACKEND = \"django.core.mail.backends.filebased.EmailBackend\"\n EMAIL_FILE_PATH = os.path.join(BASE_DIR, \"emails\")\n\nLOGGING = {\n \"version\": 1,\n \"disable_existing_loggers\": False,\n \"handlers\": {\n \"admin_mail_error\": {\n \"level\": \"ERROR\",\n \"class\": \"django.utils.log.AdminEmailHandler\",\n \"include_html\": True,\n },\n \"admin_mail_info\": {\n \"level\": \"INFO\",\n \"class\": \"django.utils.log.AdminEmailHandler\",\n \"include_html\": True,\n },\n \"slack_message\": {\n \"level\": \"DEBUG\",\n \"class\": \"apps.slackbot.log.SlackMessageHandler\",\n \"filters\": [\"slack_enabled\"],\n \"formatter\": \"slack\",\n },\n },\n \"filters\": {\"slack_enabled\": {\"()\": \"apps.slackbot.log.enabled\"}},\n \"formatters\": {\"slack\": {\"()\": \"apps.slackbot.log.formatter\"}},\n \"loggers\": {\n \"django.request\": {\n \"handlers\": [\"admin_mail_error\"],\n \"level\": \"ERROR\",\n \"propagate\": True,\n },\n \"sorl.thumbnail\": {\"handlers\": [\"admin_mail_error\"], \"level\": \"ERROR\"},\n \"apps.newuser.views\": {\n \"handlers\": [\"admin_mail_info\", \"slack_message\"],\n \"level\": \"INFO\",\n },\n },\n}\n\nDEFAULT_EXCEPTION_REPORTER_FILTER = \"apps.csua_backend.settings.ExceptionReporterFilter\"\n\nfrom django.views.debug import SafeExceptionReporterFilter\n\n\nclass ExceptionReporterFilter(SafeExceptionReporterFilter):\n \"\"\"By default, the filter is on when DEBUG=False, but I want to be able to test it when DEBUG=True\"\"\"\n\n def is_active(self, request):\n return True\n\n\nLOGIN_REDIRECT_URL = \"/\"\nLOGOUT_REDIRECT_URL = \"/\"\n\n## SORL-THUMBNAIL CONFIG ##\nTHUMBNAIL_DEBUG = DEBUG\nTHUMBNAIL_BACKEND = \"apps.csua_backend.thumbnail_backends.SEOThumbnailBackend\"\nTHUMBNAIL_PREFIX = \"thumbnails\"\nTHUMBNAIL_COLORSPACE = None\nTHUMBNAIL_PRESERVE_FORMAT = True\n\n\n## LDAP_AUTH CONFIG ##\nLDAP_AUTH_URL = \"ldaps://ldap.csua.berkeley.edu\"\nLDAP_AUTH_USE_TLS = True\nLDAP_AUTH_SEARCH_BASE = \"ou=People,dc=csua,dc=berkeley,dc=edu\"\nLDAP_AUTH_USER_FIELDS = {\"username\": \"uid\", \"gecos\": \"gecos\"}\nLDAP_AUTH_USER_LOOKUP_FIELDS = (\"username\",)\nLDAP_AUTH_OBJECT_CLASS = \"posixAccount\"\nLDAP_AUTH_CLEAN_USER_DATA = \"apps.csua_backend.settings.clean_ldap_user_data\"\nLDAP_AUTH_CONNECT_TIMEOUT = 2\n\nSTAFF_GROUPS = (\"excomm\", \"root\")\n\n\ndef clean_ldap_user_data(fields):\n \"\"\"\n Path to a callable that takes a dict of {model_field_name: value},\n returning a dict of clean model data. Use this to customize how data\n loaded from LDAP is saved to the User model. See django-python3-ldap docs\n for more info.\n\n Note: gecos may be missing, or missing an email, particularly for older\n users.\n \"\"\"\n if \"gecos\" in fields:\n gecos = fields[\"gecos\"].split(\",\")\n name = gecos[0].split(\" \", 1)\n if len(name) == 1:\n first_name, last_name = name[0], \"\"\n else:\n first_name, last_name = name\n if len(gecos) > 1:\n email = gecos[1]\n else:\n email = \"\"\n else:\n first_name, last_name, email = \"\", \"\", \"\"\n\n with ldap3.Connection(\"ldaps://ldap.csua.berkeley.edu\") as c:\n c.search(\n \"ou=Group,dc=csua,dc=berkeley,dc=edu\",\n \"(memberUid={})\".format(fields[\"username\"]),\n attributes=\"cn\",\n )\n groups = [str(group.cn) for group in c.entries]\n\n is_staff = any(staff_group in groups for staff_group in STAFF_GROUPS)\n\n return {\n \"username\": fields[\"username\"],\n \"first_name\": first_name,\n \"last_name\": last_name,\n \"email\": email,\n \"is_staff\": is_staff,\n \"is_superuser\": is_staff,\n }\n\n\nAUTHENTICATION_BACKENDS = [\n \"django.contrib.auth.backends.ModelBackend\",\n \"django_python3_ldap.auth.LDAPBackend\",\n]\n","repo_name":"CSUA/csua-backend","sub_path":"apps/csua_backend/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":12258,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"}
+{"seq_id":"72111040720","text":"from __future__ import with_statement\n\nimport unittest\nimport os\n\nimport nuke\nimport imath\n\nimport IECore\nimport IECoreScene\nimport IECoreNuke\n\nclass ParameterisedHolderTest( IECoreNuke.TestCase ) :\n\n\tdef __checkParameterKnobs( self, parameter, node, knobName=None, parameterPath=None, ignore=set() ) :\n\n\t\tif knobName is None :\n\t\t\tknobName = \"parm\"\n\n\t\tif parameterPath in ignore :\n\t\t\treturn\n\n\t\tif isinstance( parameter, IECore.CompoundParameter ) :\n\t\t\tfor k in parameter.keys() :\n\t\t\t\tchildKnobName = knobName + \"_\" + parameter[k].name\n\t\t\t\tif not parameterPath :\n\t\t\t\t\tchildParameterPath = k\n\t\t\t\telse :\n\t\t\t\t\tchildParameterPath = parameterPath + \".\" + k\n\t\t\t\tself.__checkParameterKnobs( parameter[k], node, childKnobName, childParameterPath, ignore )\n\t\telse :\n\n\t\t\tknob = node.knob( knobName )\n\t\t\tself.assertTrue( knob is not None )\n\n\t\t\tif isinstance( knob, nuke.Enumeration_Knob ) :\n\t\t\t\tself.assertEqual( knob.value(), parameter.getCurrentPresetName() )\n\t\t\telse :\n\n\t\t\t\tknobValue = None\n\t\t\t\ttry :\n\t\t\t\t\tknobValue = IECoreNuke.getKnobValue( knob )\n\t\t\t\t\tif isinstance( parameter, IECore.V2dParameter ) :\n\t\t\t\t\t\t# getKnobValue defaults to V2f\n\t\t\t\t\t\tknobValue = imath.V2d( knobValue[0], knobValue[1] )\n\t\t\t\t\telif isinstance( parameter, IECore.V3dParameter ) :\n\t\t\t\t\t\tknobValue = imath.V3d( knobValue[0], knobValue[1], knobValue[2] )\n\t\t\t\texcept :\n\t\t\t\t\t# not all knob types have accessors yet. some of the numeric\n\t\t\t\t\t# knobs don't have them because nuke has bugs and returns those\n\t\t\t\t\t# knobs as the wrong type. try to get the value another way.\n\t\t\t\t\tknobValue = knob.getValue()\n\n\t\t\t\tself.assertEqual( parameter.getValue().value, knobValue )\n\n\tdef testModifyParametersAndTransferToKnobs( self ) :\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"mult\", \"maths/multiply\", 2 )\n\n\t\tself.assertEqual( fnOH.node().knob( \"parm_a\" ).getValue(), 1 )\n\t\tself.assertEqual( fnOH.node().knob( \"parm_b\" ).getValue(), 2 )\n\n\t\twith fnOH.parameterModificationContext() as parameters :\n\n\t\t\tparameters[\"a\"].setNumericValue( 10 )\n\t\t\tparameters[\"b\"].setNumericValue( 20 )\n\n\t\tself.assertEqual( fnOH.node().knob( \"parm_a\" ).getValue(), 10 )\n\t\tself.assertEqual( fnOH.node().knob( \"parm_b\" ).getValue(), 20 )\n\n\tdef testModifyParametersAndUndo( self ) :\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"mult\", \"maths/multiply\", 2 )\n\n\t\tself.assertEqual( fnOH.node().knob( \"parm_a\" ).getValue(), 1 )\n\t\tself.assertEqual( fnOH.node().knob( \"parm_b\" ).getValue(), 2 )\n\n\t\twith IECoreNuke.UndoEnabled() :\n\n\t\t\twith fnOH.parameterModificationContext() as parameters :\n\n\t\t\t\tparameters[\"a\"].setNumericValue( 10 )\n\t\t\t\tparameters[\"b\"].setNumericValue( 20 )\n\n\t\t\tself.assertEqual( fnOH.node().knob( \"parm_a\" ).getValue(), 10 )\n\t\t\tself.assertEqual( fnOH.node().knob( \"parm_b\" ).getValue(), 20 )\n\n\t\tnuke.undo()\n\n\t\tself.assertEqual( fnOH.node().knob( \"parm_a\" ).getValue(), 1 )\n\t\tself.assertEqual( fnOH.node().knob( \"parm_b\" ).getValue(), 2 )\n\n\tdef testClassParameterSetClass( self ) :\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"test\", \"classParameterTest\", 1 )\n\n\t\twith fnOH.parameterModificationContext() as parameterised :\n\n\t\t\tparameterised[\"cp\"].setClass( \"maths/multiply\", 2 )\n\n\t\tself.__checkParameterKnobs( parameterised.parameters(), fnOH.node() )\n\n\t\tself.assertEqual( parameterised.parameters().getValue(), fnOH.getParameterised()[0].parameters().getValue() )\n\n\tdef testClassParameterSetClassAndValues( self ) :\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"test\", \"classParameterTest\", 1 )\n\n\t\twith fnOH.parameterModificationContext() as parameterised :\n\n\t\t\tparameterised[\"cp\"].setClass( \"maths/multiply\", 2 )\n\t\t\tparameterised[\"cp\"][\"a\"].setNumericValue( 10 )\n\t\t\tparameterised[\"cp\"][\"a\"].setNumericValue( 20 )\n\n\t\tself.__checkParameterKnobs( parameterised.parameters(), fnOH.node() )\n\n\t\tself.assertEqual( parameterised.parameters().getValue(), fnOH.getParameterised()[0].parameters().getValue() )\n\n\tdef testClassParameterSetClassAndValues( self ) :\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"test\", \"classParameterTest\", 1 )\n\n\t\twith fnOH.parameterModificationContext() as parameterised :\n\n\t\t\tparameterised[\"cp\"].setClass( \"maths/multiply\", 2 )\n\t\t\tparameterised[\"cp\"][\"a\"].setNumericValue( 10 )\n\t\t\tparameterised[\"cp\"][\"a\"].setNumericValue( 20 )\n\n\t\tself.__checkParameterKnobs( parameterised.parameters(), fnOH.node() )\n\n\t\tnuke.nodeCopy( \"test/IECoreNuke/parameterisedHolder.nk\" )\n\n\t\tnuke.scriptClear()\n\n\t\tn = nuke.nodePaste( \"test/IECoreNuke/parameterisedHolder.nk\" )\n\n\t\tfnOH = IECoreNuke.FnOpHolder( n )\n\n\t\tparameterised2 = fnOH.getParameterised()[0]\n\n\t\tself.assertEqual( parameterised.parameters().getValue(), parameterised2.parameters().getValue() )\n\n\tdef testNestedClassParameterSetClass( self ) :\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"test\", \"classParameterTest\", 1 )\n\n\t\twith fnOH.parameterModificationContext() as parameterised :\n\n\t\t\tparameterised[\"cp\"].setClass( \"classParameterTest\", 1 )\n\t\t\tparameterised[\"cp\"][\"cp\"].setClass( \"maths/multiply\", 2 )\n\n\t\tself.__checkParameterKnobs( parameterised.parameters(), fnOH.node() )\n\n\t\tself.assertEqual( parameterised.parameters().getValue(), fnOH.getParameterised()[0].parameters().getValue() )\n\n\tdef testClassVectorParameter( self ) :\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"test\", \"classVectorParameterTest\", 1 )\n\n\t\twith fnOH.parameterModificationContext() as parameterised :\n\n\t\t\tcv = parameterised[\"cv\"]\n\t\t\tcv.setClasses( [\n\t\t\t\t( \"p0\", \"maths/multiply\", 1 ),\n\t\t\t\t( \"p1\", \"floatParameter\", 1 ),\n\n\t\t\t] )\n\n\t\tself.__checkParameterKnobs( parameterised.parameters(), fnOH.node() )\n\n\t\tself.assertEqual( parameterised.parameters().getValue(), fnOH.getParameterised()[0].parameters().getValue() )\n\n\tdef testNestedClassVectorParameter( self ) :\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"test\", \"classVectorParameterTest\", 1 )\n\n\t\twith fnOH.parameterModificationContext() as parameterised :\n\n\t\t\tcv = parameterised[\"cv\"]\n\t\t\tcv.setClasses( [\n\t\t\t\t( \"p0\", \"classParameterTest\", 1 ),\n\t\t\t] )\n\n\t\t\tcp = parameterised[\"cv\"][\"p0\"][\"cp\"]\n\t\t\tcp.setClass( \"maths/multiply\", 2 )\n\n\t\tself.__checkParameterKnobs( parameterised.parameters(), fnOH.node() )\n\n\t\tself.assertEqual( parameterised.parameters().getValue(), fnOH.getParameterised()[0].parameters().getValue() )\n\n\tdef testMoreNestedClassVectorParameter( self ) :\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"test\", \"classVectorParameterTest\", 1 )\n\n\t\twith fnOH.parameterModificationContext() as parameterised :\n\n\t\t\tcv = parameterised[\"cv\"]\n\t\t\tcv.setClasses( [\n\t\t\t\t( \"p0\", \"classVectorParameterTest\", 1 ),\n\n\t\t\t] )\n\n\t\t\tcv2 = cv[\"p0\"][\"cv\"]\n\n\t\t\tcv2.setClasses( [\n\t\t\t\t( \"p0\", \"classParameterTest\", 1 ),\n\t\t\t] )\n\n\t\t\tcp = cv2[\"p0\"][\"cp\"]\n\t\t\tcp.setClass( \"maths/multiply\", 2 )\n\n\t\tself.__checkParameterKnobs( parameterised.parameters(), fnOH.node() )\n\n\t\tself.assertEqual( parameterised.parameters().getValue(), fnOH.getParameterised()[0].parameters().getValue() )\n\n\tdef testParameterTypes( self ) :\n\n\t\t# the parameters for which we know we have no handler\n\t\tunsupported = { \"c\", \"e\", \"f\", \"compound.k\", \"m\", \"s\", \"u\", \"v\", \"x\", \"y\", \"p1\", \"p4\" }\n\t\t# the parameters for which we'll do our own testing because they are not straightforward to deal with in __checkParameterKnobs\n\t\tnotEasy = set ( ( \"p2\", \"p3\" ) )\n\n\t\tmh = IECore.CapturingMessageHandler()\n\t\twith mh :\n\t\t\tfnOH = IECoreNuke.FnOpHolder.create( \"test\", \"parameterTypes\", 1 )\n\n\t\t# make sure there's an error reported for each unsupported parameter\n\t\tself.assertEqual( len( mh.messages ), len( unsupported ) )\n\t\tfor n in unsupported :\n\t\t\tfound = False\n\t\t\tt = \"for parameter \\\"%s\\\"\" % n.split( \".\" )[-1]\n\t\t\tfor m in mh.messages :\n\t\t\t\tif t in m.message :\n\t\t\t\t\tfound = True\n\t\t\t\t\tbreak\n\t\t\tself.assertEqual( found, True )\n\n\t\tself.__checkParameterKnobs( fnOH.getParameterised()[0].parameters(), fnOH.node(), ignore=unsupported | notEasy )\n\n\t\tself.assertEqual( fnOH.node().knob( \"parm_p2Start\" ).getValue(), [ 1, 1, 1 ] )\n\t\tself.assertEqual( fnOH.node().knob( \"parm_p2End\" ).getValue(), [ 1, 1, 1 ] )\n\n\t\twith fnOH.parameterModificationContext() as parameterised :\n\n\t\t\tparameterised.parameters()[\"d\"].setTypedValue( \"lalal\" )\n\t\t\tparameterised.parameters()[\"p2\"].setTypedValue( IECore.LineSegment3f( imath.V3f( 10, 11, 12 ), imath.V3f( 12, 10, 9 ) ) )\n\n\t\tself.__checkParameterKnobs( parameterised.parameters(), fnOH.node(), ignore=unsupported | notEasy )\n\t\tself.__checkParameterKnobs( fnOH.getParameterised()[0].parameters(), fnOH.node(), ignore=unsupported | notEasy )\n\n\t\tself.assertEqual( fnOH.node().knob( \"parm_p2Start\" ).getValue(), [ 10, 11, 12 ] )\n\t\tself.assertEqual( fnOH.node().knob( \"parm_p2End\" ).getValue(), [ 2, -1, -3 ] )\n\n\tdef testDefaultExpression( self ) :\n\n\t\t# create opholder and check the default expression we asked for works\n\n\t\tfnOH = IECoreNuke.FnOpHolder.create( \"op\", \"add\", 1 )\n\t\tself.assertEqual( fnOH.node().knob( \"parm_a\" ).toScript(), '{\"frame * 2\"}' )\n\t\tself.assertTrue( fnOH.node().knob( \"parm_a\" ).isAnimated() )\n\n\t\tself.assertEqual( nuke.frame(), 1 )\n\t\tself.assertEqual( fnOH.node().knob( \"parm_a\" ).getValue(), 2 )\n\n\t\t# remove the expression, cut and paste the node, and make sure\n\t\t# it doesn't reappear\n\n\t\tfnOH.node().knob( \"parm_a\" ).clearAnimated()\n\t\tself.assertFalse( fnOH.node().knob( \"parm_a\" ).isAnimated() )\n\n\t\tnuke.nodeCopy( \"test/IECoreNuke/parameterisedHolder.nk\" )\n\n\t\tnuke.scriptClear()\n\n\t\tn = nuke.nodePaste( \"test/IECoreNuke/parameterisedHolder.nk\" )\n\n\t\tfnOH = IECoreNuke.FnOpHolder( n )\n\t\tself.assertEqual( fnOH.node().knob( \"parm_a\" ).toScript(), \"2\" )\n\n\tdef tearDown( self ) :\n\n\t\tfor f in [\n\t\t\t\t\"test/IECoreNuke/parameterisedHolder.nk\",\n\t\t\t] :\n\n\t\t\tif os.path.exists( f ) :\n\t\t\t\tos.remove( f )\n\nif __name__ == \"__main__\":\n\tunittest.main()\n\n","repo_name":"ImageEngine/cortex","sub_path":"test/IECoreNuke/ParameterisedHolderTest.py","file_name":"ParameterisedHolderTest.py","file_ext":"py","file_size_in_byte":9526,"program_lang":"python","lang":"en","doc_type":"code","stars":510,"dataset":"github-code","pt":"3"}
+{"seq_id":"42747237184","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 23 16:16:29 2020\n\n@author: thomas.topilko\n\"\"\"\n\nfrom functools import reduce\n\nimport numpy as np\nimport pandas as pd\n\nfrom fiber_photometry_analysis import utilities as utils\n\n\ndef extract_behavior_data(file, **kwargs):\n \"\"\"Extracts the raw behavior data from an excel file and cleans it.\n \n Args : file (str) = the path to the excel file\n kwargs (dict) = dictionnary with additional parameters\n\n Returns : start (arr) = the starting timepoints for the behavioral bouts\n end (arr) = the ending timepoints for the behavioral bouts\n \"\"\"\n \n f = pd.read_excel(file, header=None)\n \n start_pos = np.where(f[0] == \"tStart{0}\".format(kwargs[\"behavior_to_segment\"]))[0]\n end_pos = np.where(f[0] == \"tEnd{0}\".format(kwargs[\"behavior_to_segment\"]))[0]\n \n start = f.iloc[start_pos[0]][1:][f.iloc[start_pos[0]][1:] > 0]\n end = f.iloc[end_pos[0]][1:][f.iloc[end_pos[0]][1:] > 0]\n \n return start, end\n\n\ndef estimate_minimal_resolution(start, end):\n \"\"\"Estimates the minimal time increment needed to fully capture the data.\n \n Args : start (arr) = the starting timepoints for the behavioral bouts\n end (arr) = the ending timepoints for the behavioral bouts\n\n Returns : minimal_resolution (float) = the starting timepoints for the behavioral bouts\n \"\"\"\n \n minimal_resolution = 1/min(end-start)\n \n utils.print_in_color(\"The smallest behavioral bout is {0}s long\".format(1 / minimal_resolution), \"GREEN\")\n \n return minimal_resolution\n\n\ndef create_bool_map(start, end, **kwargs):\n \"\"\"Creates a boolean map of the time during which the animal performs a given behavior.\n \n Args : start (arr) = the starting timepoints for the behavioral bouts\n end (arr) = the ending timepoints for the behavioral bouts\n\n Returns : bool_map (arr) = the boolean map of the time during which the animal performs the behavior\n \"\"\"\n \n bool_map = []\n\n for s, e in zip(start, end):\n for i in np.arange(len(bool_map)/kwargs[\"recording_sampling_rate\"], s, 1/kwargs[\"recording_sampling_rate\"]):\n bool_map.append(False) # FIXME: extend not append\n for i in np.arange(s, e, 1/kwargs[\"recording_sampling_rate\"]):\n bool_map.append(True)\n\n for i in np.arange(i+(1/kwargs[\"recording_sampling_rate\"]), round(kwargs[\"video_end\"]), 1/kwargs[\"recording_sampling_rate\"]):\n bool_map.append(False)\n \n if i != round(kwargs[\"video_end\"]):\n if not bool_map[-1]:\n bool_map.append(False)\n else:\n bool_map.append(True)\n \n print(\"\\n\")\n utils.print_in_color(\"Behavioral data extracted. Behavior = {0}\".format(kwargs[\"behavior_to_segment\"]), \"GREEN\")\n \n return bool_map\n\n\ndef trim_behavioral_data(start, end, **kwargs):\n \"\"\"Trims the behavioral data to fit the pre-processed photometry data.\n \n Args : start (arr) = the starting timepoints for the behavioral bouts\n end (arr) = the ending timepoints for the behavioral bouts\n kwargs (dict) = dictionnary with additional parameters\n\n Returns : start (arr) = trimmed list of start and end of each behavioral bout\n end (arr) = trimmed list of the length of each behavioral bout\n \"\"\"\n \n half_time_lost = int(kwargs[\"photometry_data\"][\"time_lost\"]/2)\n \n masks = [start > half_time_lost, end > half_time_lost,\n start < kwargs[\"video_end\"]-half_time_lost, end < kwargs[\"video_end\"]-half_time_lost]\n total_mask = reduce(np.logical_and, masks)\n \n start = start[total_mask] - half_time_lost\n end = end[total_mask] - half_time_lost\n \n return start, end\n\n\ndef extract_manual_bouts(start, end, **kwargs):\n \"\"\"Extracts the time and length of each behavioral bout.\n \n Args : start (arr) = the starting timepoints for the behavioral bouts\n end (arr) = the ending timepoints for the behavioral bouts\n kwargs (dict) = dictionnary with additional parameters\n\n Returns : position_bouts (list) = list of start and end of each behavioral bout\n length_bouts (list) = list of the length of each behavioral bout\n \"\"\"\n position_bouts = []\n length_bouts = []\n for s, e in zip(start, end):\n position_bouts.append((s, e))\n length_bouts.append(e-s)\n \n print(\"\\n\")\n utils.print_in_color(\"Behavioral data extracted. Behavior = {0}\".format(kwargs[\"behavior_to_segment\"]), \"GREEN\")\n \n return position_bouts, length_bouts\n\n\ndef merge_neighboring_bouts(position_bouts, **kwargs):\n \"\"\"Algorithm that merges behavioral bouts that are close together.\n \n Args : position_bouts (arr) = list of start and end of each behavioral bout\n kwargs (dict) = dictionnary with additional parameters\n\n Returns : position_bouts_merged (list) = list of merged start and end of each behavioral bout\n length_bouts_merged (list) = list of the length of each merged behavioral bout\n \"\"\"\n \n position_bouts_merged = []\n length_bouts_merged = []\n\n merged = False\n for i in np.arange(0, len(position_bouts) - 1, 1):\n if not merged:\n cur_bout = position_bouts[i]\n \n next_bout = position_bouts[i+1]\n if next_bout[0] - cur_bout[1] <= kwargs[\"peak_merging_distance\"]:\n merged = True\n cur_bout = (cur_bout[0], next_bout[1])\n if i == len(position_bouts)-2:\n position_bouts_merged.append(cur_bout)\n length_bouts_merged.append(cur_bout[1] - cur_bout[0])\n elif next_bout[0] - cur_bout[1] > kwargs[\"peak_merging_distance\"]:\n merged = False\n position_bouts_merged.append(cur_bout)\n length_bouts_merged.append(cur_bout[1] - cur_bout[0])\n \n if i == len(position_bouts)-2:\n position_bouts_merged.append(next_bout)\n length_bouts_merged.append(next_bout[1] - next_bout[0])\n \n print(\"\\n\")\n utils.print_in_color(\"Merged neighboring peaks that were closer than {0}s away\"\n .format(kwargs[\"peak_merging_distance\"]), \"GREEN\")\n \n return position_bouts_merged, length_bouts_merged\n\n\ndef detect_major_bouts(position_bouts_merged, **kwargs):\n \"\"\"Algorithm that detects major behavioral bouts based on size. (Seeds are\n behavioral bouts that are too short to be considered a \"major\" event)\n \n Args : position_bouts_merged (arr) = list of merged start and end of each behavioral bout\n kwargs (dict) = dictionnary with additional parameters\n\n Returns : position_major_bouts (list) = list of start and end of each major behavioral bout\n length_major_bouts (list) = list of the length of each major behavioral bout\n position_seed_bouts (list) = list of start and end of each seed behavioral bout\n length_seed_bouts (list) = list of the length of each seed behavioral bout\n \"\"\"\n position_major_bouts = []\n length_major_bouts = []\n \n position_seed_bouts = []\n length_seed_bouts = []\n \n for i in position_bouts_merged:\n bout_duration = i[1] - i[0]\n \n if bout_duration >= kwargs[\"minimal_bout_length\"]:\n if kwargs[\"peri_event\"][\"graph_distance_pre\"] <= i[0] <= kwargs[\"video_end\"]-kwargs[\"peri_event\"][\"graph_distance_post\"]-kwargs[\"photometry_data\"][\"time_lost\"]:\n position_major_bouts.append((i[0], i[1]))\n length_major_bouts.append(bout_duration)\n else:\n position_seed_bouts.append((i[0], i[1]))\n length_seed_bouts.append(bout_duration)\n elif bout_duration < kwargs[\"minimal_bout_length\"]:\n position_seed_bouts.append((i[0], i[1]))\n length_seed_bouts.append(bout_duration)\n \n return position_major_bouts, length_major_bouts, position_seed_bouts, length_seed_bouts\n\n\ndef extract_peri_event_photmetry_data(position_bouts, **kwargs):\n \"\"\"Algorithm that extracts the photometry data at the moment when the animal behaves.\n \n Args : position_bouts (arr) = list of start and end of each major behavioral bout\n kwargs (dict) = dictionnary with additional parameters\n\n Returns : df_around_peaks (list) = list of photometry data at the moment when the animal behaves\n \"\"\"\n \n df_around_peaks = []\n \n for bout in position_bouts:\n segment = kwargs[\"photometry_data\"][\"dFF\"][\"dFF\"][int((bout[0]*kwargs[\"recording_sampling_rate\"]) -\n (kwargs[\"peri_event\"][\"graph_distance_pre\"] * kwargs[\"recording_sampling_rate\"])):\n int((bout[0]*kwargs[\"recording_sampling_rate\"]) + ((kwargs[\"peri_event\"][\"graph_distance_post\"]+1) * kwargs[\"recording_sampling_rate\"]))]\n \n df_around_peaks.append(segment)\n \n return df_around_peaks\n\n\ndef reorder_by_bout_size(dF_around_peaks, length_bouts):\n \"\"\"Simple algorithm that re-orders the peri-event photometry data based on the size of the behavioral event.\n \n Args : dF_around_peaks (arr) = list of photometry data at the moment when the animal behaves\n length_bouts (list) = list of the length of each behavioral bout\n\n Returns : dd_around_peaks_ordered (list) = list of ordered photometry data at the moment when the animal behaves\n length_bouts_ordered (list) = list of the length of each behavioral bout\n \"\"\"\n order = np.argsort(-np.array(length_bouts))\n \n dd_around_peaks_ordered = np.array(dF_around_peaks)[order]\n length_bouts_ordered = np.array(length_bouts)[order]\n \n return dd_around_peaks_ordered, length_bouts_ordered\n\n\n\n\n\n\n\n","repo_name":"Tom-top/fiber_photometry_analysis","sub_path":"fiber_photometry_analysis/behavior_preprocessing.py","file_name":"behavior_preprocessing.py","file_ext":"py","file_size_in_byte":9847,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"36807157667","text":"from PySide6.QtCore import QObject, QStringListModel, Signal\nfrom PySide6.QtWidgets import QListView, QPushButton\n\nfrom gui.models.sort_model import SearchableListModel\nfrom gui.models.type_filter_model import TypeFilterModel\n\nfrom options import OPTIONS\n\n\nclass ListPair(QObject):\n listPairChanged = Signal(bool)\n\n def __init__(\n self,\n option_list: QListView,\n non_option_list: QListView,\n option_string: str,\n add_button: QPushButton,\n remove_button: QPushButton,\n type_meta: dict = {},\n ):\n super().__init__()\n self.option_list = option_list\n self.non_option_list = non_option_list\n self.option_string = str(option_string)\n self.has_type_filter = len(type_meta) > 0\n self._stored_filter_option_list = \"\"\n self._stored_filter_non_option_list = \"\"\n self._stored_type_filter_option_list = \"\"\n self._stored_type_filter_non_option_list = \"\"\n\n self.option_list_model = QStringListModel()\n if self.has_type_filter:\n self.option_list_proxy = TypeFilterModel(\n self.option_list, OPTIONS[self.option_string][\"choices\"], type_meta\n )\n else:\n self.option_list_proxy = SearchableListModel(\n self.option_list, OPTIONS[self.option_string][\"choices\"]\n )\n self.option_list_proxy.setSourceModel(self.option_list_model)\n self.option_list_model.setStringList(OPTIONS[self.option_string][\"default\"])\n self.option_list.setModel(self.option_list_proxy)\n\n self.non_option_list_model = QStringListModel()\n if self.has_type_filter:\n self.non_option_list_proxy = TypeFilterModel(\n self.non_option_list, OPTIONS[self.option_string][\"choices\"], type_meta\n )\n else:\n self.non_option_list_proxy = SearchableListModel(\n self.non_option_list, OPTIONS[self.option_string][\"choices\"]\n )\n self.non_option_list_proxy.setSourceModel(self.non_option_list_model)\n self.non_option_list_model.setStringList(OPTIONS[self.option_string][\"choices\"])\n self.non_option_list.setModel(self.non_option_list_proxy)\n\n add_button.clicked.connect(self.add)\n remove_button.clicked.connect(self.remove)\n\n def update_option_list_filter(self, new_text: str | None):\n self.option_list_proxy.filterRows(new_text)\n\n def update_option_list_type_filter(self, type_filter: str | None):\n self.option_list_proxy.filterType(type_filter)\n\n def update_non_option_list_filter(self, new_text: str | None):\n self.non_option_list_proxy.filterRows(new_text)\n\n def update_non_option_list_type_filter(self, type_filter: str | None):\n self.non_option_list_proxy.filterType(type_filter)\n\n def add(self):\n self.move_selected_rows(self.non_option_list, self.option_list)\n self.listPairChanged.emit(self.option_list_model.stringList())\n\n def remove(self):\n self.move_selected_rows(self.option_list, self.non_option_list)\n self.listPairChanged.emit(self.option_list_model.stringList())\n\n @staticmethod\n def append_row(model, value):\n model.insertRow(model.rowCount())\n new_row = model.index(model.rowCount() - 1, 0)\n model.setData(new_row, value)\n model.sort(0)\n\n def move_selected_rows(self, source: QListView, dest: QListView):\n self._store_and_remove_filters()\n\n selection = source.selectionModel().selectedIndexes()\n # Remove starting from the last so the previous indices remain valid\n selection.sort(reverse=True, key=lambda x: x.row())\n for item in selection:\n value = item.data()\n source.model().removeRow(item.row())\n self.append_row(dest.model(), value)\n\n self._restore_filters()\n\n def update(self, current_setting):\n self._store_and_remove_filters()\n\n not_chosen = OPTIONS[self.option_string][\"choices\"].copy()\n for choice in current_setting:\n not_chosen.remove(choice)\n\n self.option_list_model.setStringList(current_setting)\n self.non_option_list_model.setStringList(not_chosen)\n self.option_list.setModel(self.option_list_proxy)\n self.non_option_list.setModel(self.non_option_list_proxy)\n\n self._restore_filters()\n\n def get_added(self) -> list:\n return self.option_list_model.stringList()\n\n def _store_and_remove_filters(self):\n self._stored_filter_option_list = self.option_list_proxy.free_text_filter\n self._stored_filter_non_option_list = (\n self.non_option_list_proxy.free_text_filter\n )\n self.option_list_proxy.filterRows(\"\")\n self.non_option_list_proxy.filterRows(\"\")\n\n if self.has_type_filter:\n self._stored_type_filter_option_list = self.option_list_proxy.type_filter\n self._stored_type_filter_non_option_list = (\n self.non_option_list_proxy.type_filter\n )\n self.option_list_proxy.filterType(\"\")\n self.non_option_list_proxy.filterType(\"\")\n\n def _restore_filters(self):\n self.option_list_proxy.filterRows(self._stored_filter_option_list)\n self.non_option_list_proxy.filterRows(self._stored_filter_non_option_list)\n\n if self.has_type_filter:\n self.option_list_proxy.filterType(self._stored_type_filter_option_list)\n self.non_option_list_proxy.filterType(\n self._stored_type_filter_non_option_list\n )\n","repo_name":"ssrando/ssrando","sub_path":"gui/components/list_pair.py","file_name":"list_pair.py","file_ext":"py","file_size_in_byte":5572,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"3"}
+{"seq_id":"19236360112","text":"from copy import deepcopy\nfrom itertools import count,repeat\nimport json\nimport networkx as nx\n__author__ = \"\"\"Aric Hagberg (hagberg@lanl.gov))\"\"\"\n__all__ = ['adjacency_data', 'adjacency_graph']\n\ndef adjacency_data(G):\n \"\"\"Return data in adjacency format that is suitable for JSON serialization\n and use in Javascript documents.\n\n Parameters\n ----------\n G : NetworkX graph\n \n Returns\n -------\n data : dict\n A dictionary with node-link formatted data.\n\n Examples\n --------\n >>> from networkx.readwrite import json_graph\n >>> G = nx.Graph([(1,2)])\n >>> data = json_graph.adjacency_data(G)\n\n To serialize with json\n\n >>> import json\n >>> s = json.dumps(data)\n \n Notes\n -----\n Graph, node, and link attributes are stored in this format but keys \n for attributes must be strings if you want to serialize with JSON.\n\n See Also\n --------\n adjacency_graph, node_link_data, tree_data\n \"\"\"\n data = {}\n data['directed'] = G.is_directed()\n data['multigraph'] = G.is_multigraph()\n data['graph'] = list(G.graph.items())\n data['nodes'] = []\n data['adjacency'] = []\n for n,nbrdict in G.adjacency_iter():\n data['nodes'].append(dict(id=n, **G.node[n]))\n adj = []\n for nbr,d in nbrdict.items():\n adj.append(dict(id=nbr, **d))\n data['adjacency'].append(adj)\n return data\n\ndef adjacency_graph(data, directed=False, multigraph=True):\n \"\"\"Return graph from adjacency data format. \n\n Parameters\n ----------\n data : dict\n Adjacency list formatted graph data\n \n Returns\n -------\n G : NetworkX graph\n A NetworkX graph object\n\n directed : bool \n If True, and direction not specified in data, return a directed graph.\n\n multigraph : bool \n If True, and multigraph not specified in data, return a multigraph.\n\n Examples\n --------\n >>> from networkx.readwrite import json_graph\n >>> G = nx.Graph([(1,2)])\n >>> data = json_graph.adjacency_data(G)\n >>> H = json_graph.adjacency_graph(data)\n\n See Also\n --------\n adjacency_graph, node_link_data, tree_data\n \"\"\"\n multigraph = data.get('multigraph',multigraph)\n directed = data.get('directed',directed)\n if multigraph:\n graph = nx.MultiGraph()\n else:\n graph = nx.Graph()\n if directed:\n graph = graph.to_directed()\n graph.graph = dict(data.get('graph',[]))\n mapping=[]\n for d in data['nodes']:\n node = d.pop('id')\n mapping.append(node)\n graph.add_node(node, attr_dict=d)\n for i,d in enumerate(data['adjacency']):\n source = mapping[i]\n for tdata in d:\n target=tdata.pop('id')\n graph.add_edge(source,target,attr_dict=tdata)\n return graph\n\n","repo_name":"miniBloq/v0.83","sub_path":"source/Bin/Minibloq/lang/PPythonWin/v2.7.5.1/App/Lib/site-packages/networkx/readwrite/json_graph/adjacency.py","file_name":"adjacency.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"3"}
+{"seq_id":"36455779029","text":"import logging\nimport sys\n\nfrom django.test import override_settings, runner\n\nDUMMY_DOMAIN = 'http://localhost'\n\n\nclass TestRunner(runner.DiscoverRunner):\n '''\n A Django test runner that enables output buffering.\n '''\n\n def get_test_runner_kwargs(self):\n kwargs = super().get_test_runner_kwargs()\n\n kwargs.update(\n buffer=True,\n )\n\n return kwargs\n\n def setup_test_environment(self, **kwargs):\n super().setup_test_environment(**kwargs)\n self.log_stream_handler = logging.StreamHandler(sys.stdout)\n self.log_stream_handler.setLevel(logging.DEBUG)\n\n logging.getLogger('django').addHandler(self.log_stream_handler)\n\n def teardown_test_environment(self, **kwargs):\n super().teardown_test_environment(**kwargs)\n logging.getLogger('django').removeHandler(self.log_stream_handler)\n\n @override_settings(TESTING=True)\n def run_tests(self, *args, **kwargs):\n return super().run_tests(*args, **kwargs)\n","repo_name":"magenta-aps/gladdrreg","sub_path":"addrreg/tests/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1309070317","text":"\nimport torch\nimport torch.nn as nn\n\n\nclass VGG_Face(nn.Module):\n\n def __init__(self):\n super(VGG_Face, self).__init__()\n self.meta = {'mean': [129.186279296875, 104.76238250732422, 93.59396362304688],\n 'std': [1, 1, 1],\n 'imageSize': [224, 224, 3]}\n self.conv1_1 = nn.Conv2d(3, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu1_1 = nn.ReLU(inplace=True)\n self.conv1_2 = nn.Conv2d(64, 64, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu1_2 = nn.ReLU(inplace=True)\n self.pool1 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)\n self.conv2_1 = nn.Conv2d(64, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu2_1 = nn.ReLU(inplace=True)\n self.conv2_2 = nn.Conv2d(128, 128, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu2_2 = nn.ReLU(inplace=True)\n self.pool2 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)\n self.conv3_1 = nn.Conv2d(128, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu3_1 = nn.ReLU(inplace=True)\n self.conv3_2 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu3_2 = nn.ReLU(inplace=True)\n self.conv3_3 = nn.Conv2d(256, 256, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu3_3 = nn.ReLU(inplace=True)\n self.pool3 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)\n self.conv4_1 = nn.Conv2d(256, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu4_1 = nn.ReLU(inplace=True)\n self.conv4_2 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu4_2 = nn.ReLU(inplace=True)\n self.conv4_3 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu4_3 = nn.ReLU(inplace=True)\n self.pool4 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)\n self.conv5_1 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu5_1 = nn.ReLU(inplace=True)\n self.conv5_2 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu5_2 = nn.ReLU(inplace=True)\n self.conv5_3 = nn.Conv2d(512, 512, kernel_size=[3, 3], stride=(1, 1), padding=(1, 1))\n self.relu5_3 = nn.ReLU(inplace=True)\n self.pool5 = nn.MaxPool2d(kernel_size=[2, 2], stride=[2, 2], padding=0, dilation=1, ceil_mode=False)\n self.fc6 = nn.Linear(in_features=25088, out_features=4096, bias=True)\n self.relu6 = nn.ReLU(inplace=True)\n self.dropout6 = nn.Dropout(p=0.5)\n self.fc7 = nn.Linear(in_features=4096, out_features=4096, bias=True)\n self.relu7 = nn.ReLU(inplace=True)\n self.dropout7 = nn.Dropout(p=0.5)\n self.fc8 = nn.Linear(in_features=4096, out_features=2622, bias=True)\n\n def forward(self, x0):\n x1 = self.conv1_1(x0)\n x2 = self.relu1_1(x1)\n x3 = self.conv1_2(x2)\n x4 = self.relu1_2(x3)\n x5 = self.pool1(x4)\n x6 = self.conv2_1(x5)\n x7 = self.relu2_1(x6)\n x8 = self.conv2_2(x7)\n x9 = self.relu2_2(x8)\n x10 = self.pool2(x9)\n x11 = self.conv3_1(x10)\n x12 = self.relu3_1(x11)\n x13 = self.conv3_2(x12)\n x14 = self.relu3_2(x13)\n x15 = self.conv3_3(x14)\n x16 = self.relu3_3(x15)\n x17 = self.pool3(x16)\n x18 = self.conv4_1(x17)\n x19 = self.relu4_1(x18)\n x20 = self.conv4_2(x19)\n x21 = self.relu4_2(x20)\n x22 = self.conv4_3(x21)\n x23 = self.relu4_3(x22)\n x24 = self.pool4(x23)\n x25 = self.conv5_1(x24)\n x26 = self.relu5_1(x25)\n x27 = self.conv5_2(x26)\n x28 = self.relu5_2(x27)\n x29 = self.conv5_3(x28)\n x30 = self.relu5_3(x29)\n x31_preflatten = self.pool5(x30)\n x31 = x31_preflatten.view(x31_preflatten.size(0), -1)\n x32 = self.fc6(x31)\n x33 = self.relu6(x32)\n x34 = self.dropout6(x33)\n x35 = self.fc7(x34)\n x36 = self.relu7(x35)\n x37 = self.dropout7(x36)\n x38 = self.fc8(x37)\n return x38, x31_preflatten\n\ndef load_vgg_model(weights_path='./attacks/base_models/vgg_face_dag.pth'):\n \"\"\"\n load imported model instance\n\n Args:\n weights_path (str): If set, loads model weights from the given path\n \"\"\"\n model = VGG_Face()\n if weights_path:\n # model from\n # https://www.robots.ox.ac.uk/~albanie/pytorch-models.html\n state_dict = torch.load(weights_path)\n model.load_state_dict(state_dict)\n return model\n","repo_name":"will-jac/adverse-face","sub_path":"attacks/base_models/vgg_face_16_torch.py","file_name":"vgg_face_16_torch.py","file_ext":"py","file_size_in_byte":4813,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18869877296","text":"from twilio.rest import Client\nimport time\nfrom get import get_data\nfrom update_last_accessed import update_last_accessed\nimport requests\nimport re\nimport os\n\nclient = Client(os.environ['TWILIO_ACCOUNT_SID'], os.environ['TWILIO_AUTH_TOKEN'])\n\nmessage = client.messages.create(\n to=\"+15408096998\",\n from_=\"+12534005782\",\n body=\"STARTED\")\n\nwebsite = \"https://sisuva.admin.virginia.edu/psc/ihprd/UVSS/SA/s/WEBLIB_HCX_CM.H_CLASS_DETAILS.FieldFormula.IScript_Main?institution=UVA01&term=1238&class_nbr=\"\n\nprint(\"started\")\n\ntry:\n while True:\n data = get_data()\n print(data)\n for course_num in data.keys():\n try:\n html_content = requests.get(website+course_num).text\n\n reg = re.compile(r',\"status\":\"Open\",')\n\n is_open = reg.search(html_content)\n except:\n continue\n\n\n if is_open:\n last = data[course_num]['LastAccessed']\n\n if (time.time() - last) > 300:\n message = client.messages.create(\n to=\"+15408096998\",\n from_=\"+12534005782\",\n body=course_num + \" is open. Enroll now!!\")\n for phone_num in data[course_num]['PhoneNumbers']:\n message = client.messages.create(\n to=phone_num,\n from_=\"+12534005782\",\n body=course_num + \" is open. Enroll now!\")\n update_last_accessed(course_num)\n else:\n print(course_num + \" closed\")\n time.sleep(0)\nexcept Exception as e:\n print(e)\n message = client.messages.create(\n to=\"+15408096998\",\n from_=\"+12534005782\",\n body=\"Error: \"+str(e))","repo_name":"AlexFetea/ClassNotifier","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32687105242","text":"# -*- coding:utf-8 -*-\n# author: yushan\n# date: 2017-03-14\n\n\ndef dirReduc(arr):\n lst = []\n for i in arr:\n if len(lst) <= 0:\n lst.append(i)\n continue\n if i == 'NORTH':\n if lst[-1:][0] == 'SOUTH':\n lst.pop()\n else:\n lst.append(i)\n continue\n if i == 'SOUTH':\n if lst[-1:][0] == 'NORTH':\n lst.pop()\n else:\n lst.append(i)\n continue\n if i == 'EAST':\n if lst[-1:][0] == 'WEST':\n lst.pop()\n else:\n lst.append(i)\n continue\n if i == 'WEST':\n if lst[-1:][0] == 'EAST':\n lst.pop()\n else:\n lst.append(i)\n continue\n return lst\n\nopposite = {'NORTH': 'SOUTH', 'EAST': 'WEST', 'SOUTH': 'NORTH', 'WEST': 'EAST'}\n\ndef dirReduc(plan):\n new_plan = []\n for d in plan:\n if new_plan and new_plan[-1] == opposite[d]:\n new_plan.pop()\n else:\n new_plan.append(d)\n return new_plan\n\n\ndef dirReduc(arr):\n dir = \" \".join(arr)\n dir2 = dir.replace(\"NORTH SOUTH\", '').replace(\"SOUTH NORTH\", '').replace(\"EAST WEST\", '').replace(\"WEST EAST\",\n '')\n dir3 = dir2.split()\n return dirReduc(dir3) if len(dir3) < len(arr) else dir3\n\n# a = [\"NORTH\", \"SOUTH\", \"SOUTH\", \"EAST\", \"WEST\", \"NORTH\", \"WEST\"]\n# test.assert_equals(dirReduc(a), ['WEST'])\n# u=[\"NORTH\", \"WEST\", \"SOUTH\", \"EAST\"]\n# print dirReduc(u)\n# test.assert_equals(dirReduc(u), [\"NORTH\", \"WEST\", \"SOUTH\", \"EAST\"])","repo_name":"q13245632/CodeWars","sub_path":"Directions Reduction.py","file_name":"Directions Reduction.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16364760990","text":"import cv2\nimport numpy as np\n\ndef findContours():\n \n img_gray = cv2.cvtColor(modelo, cv2.COLOR_RGB2GRAY)\n contours, hierarchy = cv2.findContours(~img_gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n return contours\n\n\ndef cutImage (polygonExternalPoints):\n # Define the corners of the target rectangle\n width, height = 680, 966\n cutOutPoints = np.zeros(polygonExternalPoints.shape, dtype = np.float32)\n cutOutPoints[0] = (0, 0)\n cutOutPoints[1] = (0, height)\n cutOutPoints[2] = (width, height)\n cutOutPoints[3] = (width, 0)\n\n transformationMatrix = cv2.getPerspectiveTransform(polygonExternalPoints.astype(np.float32), cutOutPoints)\n\n # Apply perspective transformation\n bigRect = cv2.warpPerspective(modelo, transformationMatrix, (width, height))\n return bigRect\n \n\ndef extractBigRect():\n # Find contour of the biggest rectangle\n contours = findContours()\n templateRectangle = max(contours, key = cv2.contourArea)\n\n ## Stract the polygon points\n epsilonCurve = 0.01 * cv2.arcLength(templateRectangle, True)\n polygonExternalPoints = cv2.approxPolyDP(templateRectangle, epsilonCurve, True)\n\n bigRect = cutImage(polygonExternalPoints)\n return bigRect\n\n\ndef cutRect(bigRect, xCoord, yCoord, height, width):\n \n rect = bigRect[yCoord:yCoord + height, xCoord:xCoord + width]\n return rect\n\n\ndef getRects(bigRect):\n \n widthRect = 34\n heightRect = 35\n List_Vertex_X = [57, 95, 133, 171, 208, 246, 284, 322, 360, 397, 435, 473, 511, 549, 587]\n List_Coord_Y = [38, 83, 129, 174, 219, 264, 310, 355, 400, 445, 491, 536, 581, 627, 672, 717, 763, 808, 853, 899]\n \n listRects = []\n \n for Y in List_Coord_Y:\n for X in List_Vertex_X:\n rect = cutRect(bigRect, X, Y, heightRect, widthRect)\n listRects.append(rect)\n \n return listRects\n \nmodelo = cv2.imread('NumbersTest.png')\nbigRect = extractBigRect()\nlistRects = []\n\nlistRects = getRects(bigRect)\n\n#for rect in listRects:\n# \n# imS = cv2.resize(rect, (500, 500))\n# cv2.imshow('Rect', imS)\n# cv2.waitKey(0)\n# cv2.destroyAllWindows()","repo_name":"AlfredoFilho/AutoCT-Backend","sub_path":"OldFiles/DataNumbersStudents/GetNumbers.py","file_name":"GetNumbers.py","file_ext":"py","file_size_in_byte":2132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71014238161","text":"from apscheduler.executors.pool import ThreadPoolExecutor, ProcessPoolExecutor\nfrom apscheduler.schedulers.background import BackgroundScheduler\nfrom flask import Flask\nfrom flask import request\n\nfrom io import BytesIO\n\nfrom flask_cors import CORS\n\nfrom entity import mongo_setup\nfrom entity.queue_item import QueueItem\nfrom repository.queue_repository import QueueRepository\nfrom test import DeepDreamWS\n\napp = Flask(__name__)\nCORS(app)\n\n\njob_defaults = {\n 'coalesce': False,\n 'max_instances': 1\n}\n\nscheduler = BackgroundScheduler(job_defaults=job_defaults)\n\n\n@app.route('/')\ndef hello_world():\n return 'Hello World!'\n\n\n@app.route('/image', methods=['POST'])\ndef upload_image():\n if 'image' not in request.files:\n return 'Image missing', 404, {'Content-Type': 'text/plain'}\n\n file = request.files['image']\n file_content = BytesIO()\n\n file.save(file_content)\n\n job = QueueItem()\n\n job.base_image = file_content.getvalue()\n\n file_content.close()\n\n job = job.save()\n\n return str(job.id), 200, {'Content-Type': 'text/plain'}\n\n\n@app.route('/image/', methods=['GET'])\ndef get_image(id):\n job = QueueRepository.get_job_by_id(id)\n\n if job.canceled:\n return 'An error occurred while processing the image! Please make sure your image is at least 512x512 pixel in size.', 500, {'Content-Type': 'text/plain'}\n\n if not job.finished:\n return str(job.id), 202, {'Content-Type': 'text/plain'}\n\n image = job.computed_image\n\n return image, 200, {'Content-Type': 'image/jpeg'}\n\n\ndef job_runner():\n\n jobs = QueueRepository.get_open_jobs()\n\n for i in range(len(jobs)):\n id = str(jobs[i].id)\n\n dd = DeepDreamWS()\n\n dd.run(id)\n\n\nif __name__ == '__main__':\n mongo_setup.global_init(False)\n scheduler.add_job(job_runner, 'interval', minutes=1, id='deep-dream-job')\n scheduler.start()\n app.run(host='0.0.0.0', port=5000)\n scheduler.shutdown()\n","repo_name":"danielmpla/deep-dream-ws","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20275092944","text":"'''\r\nGiven an integer array nums, return true if any value appears at least twice in the array,\r\nand return false if every element is distinct.\r\n'''\r\n\r\nfrom typing import DefaultDict, List\r\n\r\n# solution 1:\r\n\r\n# class Solution:\r\n# def containsDuplicate(self, nums: List[int]) -> bool:\r\n# arr_s = len(nums)\r\n# set_s = len(set(nums))\r\n# or we can also check for sum of arr and sum of set of arr\r\n # arr_s = sum(nums)\r\n # set_s = sum(set(nums)) \r\n\r\n # if arr_s != set_s:\r\n # return True\r\n\r\n# Solution 2\r\nclass Solution:\r\n def containsDuplicate(self, nums: List[int]) -> bool:\r\n # m = {}\r\n m = DefaultDict(int)\r\n for num in nums:\r\n if (m[num]):\r\n # if num in m:\r\n return True\r\n # if not then add that elem as key in map with its count\r\n m[num] += 1\r\n return False\r\n \r\n\r\ns = Solution()\r\nans = s.containsDuplicate([1,2,1,3])\r\nprint(ans)","repo_name":"ada0110/LeetCode-Practice","sub_path":"Hash Tables/#217_contains_duplicates.py","file_name":"#217_contains_duplicates.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"33524986371","text":"import math\nimport pyaudio\ndef playbeep(RATE=44100,WAVE=1000, LEN=0.1):\n LEN=round(LEN*RATE)\n PyAudio = pyaudio.PyAudio\n data = ''.join([chr(int(math.sin(x/((RATE/WAVE)/math.pi))*127+128)) for x in range(1,LEN)])\n p = PyAudio()\n stream = p.open(format =\n p.get_format_from_width(1),\n channels = 1,\n rate = RATE,\n output = True)\n for DISCARD in range(1,2):\n stream.write(data)\n stream.stop_stream()\n stream.close()\n p.terminate()\n","repo_name":"chaithyagr/beat-track-rt","sub_path":"playbeep.py","file_name":"playbeep.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21819924884","text":"# This file is part of taxtastic.\n#\n# taxtastic 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# taxtastic 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 taxtastic. If not, see [0-9A-F]+)\\s*;\\s*[CS]\\s*;\\s*(?P[0-9A-F]+)\", re.IGNORECASE)\naliases = None\n\n\ndef openOrExit(path, mode):\n try:\n if sys.version_info.major >= 3:\n return open(path, mode, encoding=\"UTF-8\")\n else:\n return open(path, mode)\n except IOError as e:\n print(\"I/O error opening {0}, ({1}): {2}\".format(path, e.errno, e.strerror))\n exit(1)\n\n\ndef openUCDFileOrExit(path):\n if not UCDDirectoryPath:\n exit(1)\n\n return openOrExit(os.path.join(UCDDirectoryPath, path), 'r')\n\n\ndef verifyUCDFilesExist():\n if not UCDDirectoryPath:\n exit(1)\n\n missingFileCount = 0\n for file in RequiredUCDFiles:\n fullPath = os.path.join(UCDDirectoryPath, file)\n if not os.path.exists(fullPath):\n print(\"Couldn't find UCD file {0} at {1}\".format(file, fullPath))\n missingFileCount = missingFileCount + 1\n if missingFileCount:\n exit(1)\n\n\ndef ceilingToPowerOf2(size):\n powerOf2 = 1\n while size > powerOf2:\n powerOf2 = powerOf2 << 1\n\n return powerOf2\n\n\nclass Aliases:\n def __init__(self):\n self.globalNameToAliases = {}\n self.generalCategoryToAliases = {}\n self.aliasToGeneralCategory = {}\n self.scriptToAliases = {}\n self.aliasToScript = {}\n\n def parsePropertyAliasesFile(self, file):\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n aliases = [fields[0].strip()]\n fullName = fields[1].strip()\n for otherAlias in fields[2:]:\n aliases.append(otherAlias.strip())\n\n if fullName in self.globalNameToAliases:\n print(\"Error, already an alias for {}\".format(fullName))\n else:\n self.globalNameToAliases[fullName] = aliases\n\n def parsePropertyValueAliasesFile(self, file):\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n propertyType = fields[0].strip()\n\n if propertyType == \"gc\":\n mapToModify = self.generalCategoryToAliases\n reverseMapToModify = self.aliasToGeneralCategory\n elif propertyType == \"sc\":\n mapToModify = self.scriptToAliases\n reverseMapToModify = self.aliasToScript\n else:\n continue\n\n primaryAlias = fields[1].strip()\n fullName = fields[2].strip()\n aliases = [primaryAlias]\n for otherAlias in fields[3:]:\n aliases.append(otherAlias.strip())\n\n if fullName in mapToModify:\n print(\"Error, already an {} alias for {}\".format(propertyType, fullName))\n else:\n mapToModify[fullName] = aliases\n if reverseMapToModify != None:\n reverseMapToModify[primaryAlias] = fullName\n\n def globalAliasesFor(self, name):\n if name not in self.globalNameToAliases:\n return []\n return self.globalNameToAliases[name]\n\n def generalCategoryAliasesFor(self, name):\n if name not in self.generalCategoryToAliases:\n return \"\"\n return self.generalCategoryToAliases[name]\n\n def generalCategoryForAlias(self, name):\n if name not in self.aliasToGeneralCategory:\n return \"\"\n return self.aliasToGeneralCategory[name]\n\n def scriptAliasesFor(self, name):\n if name not in self.scriptToAliases:\n return \"\"\n return self.scriptToAliases[name]\n\n def scriptNameForAlias(self, name):\n if name not in self.aliasToScript:\n return \"\"\n return self.aliasToScript[name]\n\n\nclass PropertyData:\n allPropertyData = []\n\n def __init__(self, name):\n self.name = name\n self.aliases = []\n self.index = len(PropertyData.allPropertyData)\n self.hasBMPCharacters = False\n self.hasNonBMPCharacters = False\n self.hasStrings = False\n self.matchStrings = []\n self.matches = []\n self.ranges = []\n self.unicodeMatches = []\n self.unicodeRanges = []\n self.codePointCount = 0\n PropertyData.allPropertyData.append(self)\n\n def setAliases(self, aliases):\n self.aliases = aliases\n\n def makeCopy(self):\n result = copy.deepcopy(self)\n result.index = len(PropertyData.allPropertyData)\n PropertyData.allPropertyData.append(result)\n return result\n\n def getIndex(self):\n return self.index\n\n def getCreateFuncName(self):\n return \"createCharacterClass{}\".format(self.index)\n\n def addMatch(self, codePoint):\n if codePoint <= MaxBMP:\n self.hasBMPCharacters = True\n else:\n self.hasNonBMPCharacters = True\n if codePoint <= lastASCIICodePoint:\n if (len(self.matches) and self.matches[-1] > codePoint) or (len(self.ranges) and self.ranges[-1][1] > codePoint):\n self.addMatchUnordered(codePoint)\n return\n\n self.codePointCount = self.codePointCount + 1\n if len(self.matches) and self.matches[-1] == (codePoint - 1):\n lowCodePoint = self.matches.pop()\n self.ranges.append((lowCodePoint, codePoint))\n elif len(self.ranges) and self.ranges[-1][1] == (codePoint - 1):\n priorRange = self.ranges.pop()\n self.ranges.append((priorRange[0], codePoint))\n else:\n self.matches.append(codePoint)\n else:\n if (len(self.unicodeMatches) and self.unicodeMatches[-1] > codePoint) or (len(self.unicodeRanges) and self.unicodeRanges[-1][1] > codePoint):\n self.addMatchUnordered(codePoint)\n return\n\n self.codePointCount = self.codePointCount + 1\n if len(self.unicodeMatches) and self.unicodeMatches[-1] == (codePoint - 1):\n lowCodePoint = self.unicodeMatches.pop()\n self.unicodeRanges.append((lowCodePoint, codePoint))\n elif len(self.unicodeRanges) and self.unicodeRanges[-1][1] == (codePoint - 1):\n priorRange = self.unicodeRanges.pop()\n self.unicodeRanges.append((priorRange[0], codePoint))\n else:\n self.unicodeMatches.append(codePoint)\n\n def addMatchString(self, string):\n self.matchStrings.append(string)\n self.hasStrings = True\n\n def addRange(self, lowCodePoint, highCodePoint):\n if lowCodePoint <= MaxBMP:\n self.hasBMPCharacters = True\n if highCodePoint > MaxBMP:\n self.hasNonBMPCharacters = True\n if highCodePoint <= lastASCIICodePoint:\n if (len(self.matches) and self.matches[-1] > lowCodePoint) or (len(self.ranges) and self.ranges[-1][1] > lowCodePoint):\n self.addRangeUnordered(lowCodePoint, highCodePoint)\n return\n\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n if len(self.matches) and self.matches[-1] == (lowCodePoint - 1):\n lowCodePoint = self.matches.pop()\n elif len(self.ranges) and self.ranges[-1][1] == (lowCodePoint - 1):\n priorRange = self.ranges.pop()\n lowCodePoint = priorRange[0]\n self.ranges.append((lowCodePoint, highCodePoint))\n elif lowCodePoint <= lastASCIICodePoint:\n if lowCodePoint == lastASCIICodePoint:\n self.addMatch(lowCodePoint)\n else:\n self.addRange(lowCodePoint, lastASCIICodePoint)\n if highCodePoint == firstUnicodeCodePoint:\n self.addMatch(highCodePoint)\n else:\n self.addRange(firstUnicodeCodePoint, highCodePoint)\n else:\n if (len(self.unicodeMatches) and self.unicodeMatches[-1] > lowCodePoint) or (len(self.unicodeRanges) and self.unicodeRanges[-1][1] > lowCodePoint):\n self.addRangeUnordered(lowCodePoint, highCodePoint)\n return\n\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n if len(self.unicodeMatches) and self.unicodeMatches[-1] == (lowCodePoint - 1):\n lowCodePoint = self.unicodeMatches.pop()\n self.unicodeRanges.append((lowCodePoint, highCodePoint))\n elif len(self.unicodeRanges) and self.unicodeRanges[-1][1] == (lowCodePoint - 1):\n priorRange = self.unicodeRanges.pop()\n self.unicodeRanges.append((priorRange[0], highCodePoint))\n else:\n self.unicodeRanges.append((lowCodePoint, highCodePoint))\n\n def addMatchUnorderedForMatchesAndRanges(self, codePoint, matches, ranges):\n if codePoint in matches:\n return\n insertLocation = None\n lowCodePoint = None\n highCodePoint = None\n for idx in range(len(matches)):\n match = matches[idx]\n if codePoint == match + 1:\n lowCodePoint = match\n if idx < (len(matches) - 1) and codePoint == matches[idx + 1] - 1:\n highCodePoint = matches[idx + 1]\n del matches[idx + 1]\n self.codePointCount = self.codePointCount - 1\n else:\n highCodePoint = codePoint\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n break\n elif codePoint == match - 1:\n lowCodePoint = codePoint\n highCodePoint = match\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n break\n elif codePoint < match:\n insertLocation = idx\n break\n\n if insertLocation is None:\n insertLocation = len(matches)\n if lowCodePoint is None:\n lowCodePoint = codePoint\n highCodePoint = codePoint\n\n for idx in range(len(ranges)):\n cur_range = ranges[idx]\n if lowCodePoint >= cur_range[0] and highCodePoint <= cur_range[1]:\n return\n if lowCodePoint <= (cur_range[1] + 1) and highCodePoint >= (cur_range[0] - 1):\n while idx < len(ranges) and highCodePoint >= (ranges[idx][0] - 1):\n cur_range = ranges[idx]\n lowCodePoint = min(lowCodePoint, cur_range[0])\n highCodePoint = max(highCodePoint, cur_range[1])\n del ranges[idx]\n self.codePointCount = self.codePointCount - (cur_range[1] - cur_range[0]) - 1\n\n ranges.insert(idx, (lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n elif highCodePoint < cur_range[0]:\n if lowCodePoint != highCodePoint:\n ranges.insert(idx, (lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n break\n\n if lowCodePoint != highCodePoint:\n ranges.append((lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n\n if insertLocation is not None:\n matches.insert(insertLocation, codePoint)\n self.codePointCount = self.codePointCount + 1\n\n def addRangeUnorderedForMatchesAndRanges(self, lowCodePoint, highCodePoint, matches, ranges):\n if len(matches) and highCodePoint >= matches[0] and lowCodePoint <= matches[-1]:\n for idx in range(len(matches)):\n match = matches[idx]\n if lowCodePoint <= match and highCodePoint >= match:\n while idx < len(matches) and highCodePoint >= matches[idx]:\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n if idx < (len(matches) - 1) and highCodePoint == matches[idx + 1] - 1:\n highCodePoint = matches[idx + 1]\n del matches[idx + 1]\n self.codePointCount = self.codePointCount - 1\n break\n elif lowCodePoint == match + 1:\n lowCodePoint = match\n while idx < len(matches) and highCodePoint >= matches[idx]:\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n\n if idx < (len(matches) - 1) and highCodePoint == matches[idx + 1] - 1:\n highCodePoint = matches[idx + 1]\n del matches[idx + 1]\n self.codePointCount = self.codePointCount - 1\n break\n elif highCodePoint == match - 1:\n highCodePoint = match\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n break\n elif highCodePoint < match:\n break\n\n for idx in range(len(ranges)):\n cur_range = ranges[idx]\n if lowCodePoint >= cur_range[0] and highCodePoint <= cur_range[1]:\n return\n if lowCodePoint <= (cur_range[1] + 1) and highCodePoint >= (cur_range[0] - 1):\n while idx < len(ranges) and highCodePoint >= (ranges[idx][0] - 1):\n cur_range = ranges[idx]\n lowCodePoint = min(lowCodePoint, cur_range[0])\n highCodePoint = max(highCodePoint, cur_range[1])\n del ranges[idx]\n self.codePointCount = self.codePointCount - (cur_range[1] - cur_range[0]) - 1\n\n ranges.insert(idx, (lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n elif highCodePoint < cur_range[0]:\n ranges.insert(idx, (lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n\n ranges.append((lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n\n def addMatchUnordered(self, codePoint):\n if codePoint <= lastASCIICodePoint:\n self.addMatchUnorderedForMatchesAndRanges(codePoint, self.matches, self.ranges)\n else:\n self.addMatchUnorderedForMatchesAndRanges(codePoint, self.unicodeMatches, self.unicodeRanges)\n\n def addRangeUnordered(self, lowCodePoint, highCodePoint):\n if highCodePoint <= lastASCIICodePoint:\n self.addRangeUnorderedForMatchesAndRanges(lowCodePoint, highCodePoint, self.matches, self.ranges)\n elif lowCodePoint >= firstUnicodeCodePoint:\n self.addRangeUnorderedForMatchesAndRanges(lowCodePoint, highCodePoint, self.unicodeMatches, self.unicodeRanges)\n else:\n if lowCodePoint == lastASCIICodePoint:\n self.addMatchUnorderedForMatchesAndRanges(lowCodePoint, self.matches, self.ranges)\n else:\n self.addRangeUnorderedForMatchesAndRanges(lowCodePoint, lastASCIICodePoint, self.unicodeMatches, self.ranges)\n if highCodePoint == firstUnicodeCodePoint:\n self.addMatchUnorderedForMatchesAndRanges(highCodePoint, self.unicodeMatches, self.unicodeRanges)\n else:\n self.addRangeUnorderedForMatchesAndRanges(firstUnicodeCodePoint, highCodePoint, self.unicodeMatches, self.unicodeRanges)\n\n def removeMatchFromRanges(self, codePoint, ranges):\n for idx in range(len(ranges)):\n cur_range = ranges[idx]\n if cur_range[0] <= codePoint and codePoint <= cur_range[1]:\n ranges.pop(idx)\n if cur_range[0] < codePoint and codePoint < cur_range[1]:\n lowRange = (cur_range[0], codePoint - 1)\n highRange = (codePoint + 1, cur_range[1])\n # Since list.insert inserts before the index given, handle the high range first\n if highRange[0] == highRange[1]:\n self.addMatchUnordered(highRange[0])\n else:\n ranges.insert(idx, highRange)\n if lowRange[0] == lowRange[1]:\n self.addMatchUnordered(lowRange[0])\n else:\n ranges.insert(idx, lowRange)\n else:\n if cur_range[0] == codePoint:\n cur_range = (codePoint + 1, cur_range[1])\n else:\n cur_range = (cur_range[0], codePoint - 1)\n if cur_range[0] == cur_range[1]:\n self.addMatchUnordered(cur_range[0])\n else:\n ranges.insert(idx, cur_range)\n self.codePointCount = self.codePointCount - 1\n return\n\n def removeMatch(self, codePoint):\n if codePoint <= lastASCIICodePoint:\n if codePoint in self.matches:\n self.matches.remove(codePoint)\n self.codePointCount = self.codePointCount - 1\n else:\n self.removeMatchFromRanges(codePoint, self.ranges)\n else:\n if codePoint in self.unicodeMatches:\n self.unicodeMatches.remove(codePoint)\n self.codePointCount = self.codePointCount - 1\n else:\n self.removeMatchFromRanges(codePoint, self.unicodeRanges)\n\n def stringsCompare(self, bstr1, bstr2):\n # Longest strings first, then sort lexical.\n b1Len = len(bstr1)\n b2Len = len(bstr2)\n\n diff = b2Len - b1Len\n\n if diff != 0:\n return diff\n\n if bstr1 < bstr2:\n return -1\n\n if bstr1 > bstr2:\n return 1\n\n return 0\n\n def sortStrings(self):\n self.matchStrings.sort(key=cmp_to_key(self.stringsCompare))\n\n def dumpMatchData(self, file, valuesPerLine, dataList, formatter):\n valuesThisLine = 0\n firstValue = True\n\n file.write(\"{\")\n for elem in dataList:\n if firstValue:\n firstValue = False\n else:\n file.write(\", \")\n valuesThisLine = valuesThisLine + 1\n if valuesThisLine > valuesPerLine:\n file.write(\"\\n \")\n valuesThisLine = 1\n formatter(file, elem)\n file.write(\"}\")\n\n def convertStringToCppFormat(self, file, utf32String):\n output = \"\"\n for codePoint in utf32String:\n if codePoint <= '\\xff':\n output += codePoint\n else:\n output += \"\\\\x{:x}\".format(ord(codePoint))\n file.write(\"{{ reinterpret_cast(const_cast(U\\\"{}\\\")), {}}}\".format(output, len(utf32String)))\n\n def dump(self, file, commaAfter):\n file.write(\"static std::unique_ptr {}()\\n{{\\n\".format(self.getCreateFuncName()))\n file.write(\" // Name = {},\".format(self.name))\n if self.hasStrings:\n file.write(\" number of strings: {}\".format(len(self.matchStrings)))\n if self.codePointCount:\n file.write(\" number of codePoints: {}\".format(self.codePointCount))\n file.write(\"\\n\")\n file.write(\" auto characterClass = makeUnique(\\n\")\n if self.hasStrings:\n file.write(\" std::initializer_list>(\")\n self.dumpMatchData(file, 1, self.matchStrings, self.convertStringToCppFormat)\n file.write(\"),\\n\")\n file.write(\" std::initializer_list(\")\n self.dumpMatchData(file, 8, self.matches, lambda file, match: (file.write(\"{0:0=#4x}\".format(match))))\n file.write(\"),\\n\")\n file.write(\" std::initializer_list(\")\n self.dumpMatchData(file, 4, self.ranges, lambda file, range: (file.write(\"{{{0:0=#4x}, {1:0=#4x}}}\".format(range[0], range[1]))))\n file.write(\"),\\n\")\n file.write(\" std::initializer_list(\")\n self.dumpMatchData(file, 8, self.unicodeMatches, lambda file, match: (file.write(\"{0:0=#6x}\".format(match))))\n file.write(\"),\\n\")\n file.write(\" std::initializer_list(\")\n self.dumpMatchData(file, 4, self.unicodeRanges, lambda file, range: (file.write(\"{{{0:0=#6x}, {1:0=#6x}}}\".format(range[0], range[1]))))\n file.write(\"),\\n\")\n\n file.write(\" CharacterClassWidths::{}\".format((\"Unknown\", \"HasBMPChars\", \"HasNonBMPChars\", \"HasBothBMPAndNonBMP\")[int(self.hasNonBMPCharacters) * 2 + int(self.hasBMPCharacters)]))\n if self.hasStrings:\n file.write(\",\\n true\") # inCanonicalForm\n file.write(\");\\n\")\n file.write(\" return characterClass;\\n}\\n\\n\")\n\n @classmethod\n def dumpAll(cls, file):\n for propertyData in cls.allPropertyData:\n propertyData.dump(file, propertyData != cls.allPropertyData[-1])\n\n file.write(\"using CreateCharacterClass = std::unique_ptr (*)();\\n\")\n file.write(\"static CreateCharacterClass createCharacterClassFunctions[{}] = {{\\n \".format(len(cls.allPropertyData)))\n functionsOnThisLine = 0\n for propertyData in cls.allPropertyData:\n file.write(\" {},\".format(propertyData.getCreateFuncName()))\n functionsOnThisLine = functionsOnThisLine + 1\n if functionsOnThisLine == 4:\n file.write(\"\\n \")\n functionsOnThisLine = 0\n\n file.write(\"};\\n\\n\")\n\n @classmethod\n def createAndDumpHashTable(self, file, propertyDict, tablePrefix):\n def createAndDumpHashTableHelper(propertyDict, tablePrefix, isMac):\n propertyKeys = propertyDict.keys()\n numberOfKeys = len(propertyKeys)\n hashSize = ceilingToPowerOf2(numberOfKeys * 2)\n hashMask = hashSize - 1\n hashTable = [None] * hashSize\n valueTable = []\n tableSize = hashSize\n hashTableString = \"\"\n\n keyValuesToHash = []\n for propertyName in propertyKeys:\n propertyData = propertyDict[propertyName]\n keyValuesToHash.append((propertyName, propertyData.getIndex()))\n for alias in propertyData.aliases:\n keyValuesToHash.append((alias, propertyData.getIndex()))\n\n for keyValue in keyValuesToHash:\n key = keyValue[0]\n hash = stringHash(key, isMac) % hashSize\n while hashTable[hash] is not None:\n if hashTable[hash][1] is not None:\n hash = hashTable[hash][1]\n else:\n hashTable[hash] = (hashTable[hash][0], tableSize)\n hashTable.append(None)\n hash = tableSize\n tableSize = tableSize + 1\n\n hashTable[hash] = (len(valueTable), None)\n valueTable.append((key, keyValue[1]))\n\n hashTableString += \"static const struct HashIndex {}TableIndex[{}] = {{\\n\".format(tablePrefix, len(hashTable))\n\n for tableIndex in hashTable:\n value = -1\n next = -1\n if tableIndex is not None:\n value = tableIndex[0]\n if tableIndex[1] is not None:\n next = tableIndex[1]\n\n hashTableString += \" {{ {}, {} }},\\n\".format(value, next)\n\n hashTableString += \"};\\n\\n\"\n\n hashTableString += \"static const struct HashValue {}TableValue[{}] = {{\\n\".format(tablePrefix, len(valueTable))\n for value in valueTable:\n hashTableString += \" {{ \\\"{}\\\", {} }},\\n\".format(value[0], value[1])\n hashTableString += \"};\\n\\n\"\n\n hashTableString += \"static const struct HashTable {}HashTable = \\n\".format(tablePrefix)\n hashTableString += \" {{ {}, {}, {}TableValue, {}TableIndex }};\\n\\n\".format(len(valueTable), hashMask, tablePrefix, tablePrefix)\n return hashTableString\n\n hashTableForMacOS = createAndDumpHashTableHelper(propertyDict, tablePrefix, True)\n hashTableForIOS = createAndDumpHashTableHelper(propertyDict, tablePrefix, False)\n hashTableToWrite = hashTableForMacOS\n if hashTableForMacOS != hashTableForIOS:\n hashTableToWrite = \"#if PLATFORM(MAC)\\n{}#else\\n{}#endif\\n\".format(hashTableForMacOS, hashTableForIOS)\n file.write(hashTableToWrite)\n\n @classmethod\n def dumpMayContainStringFunc(cls, file):\n hasStringRanges = []\n currentRange = None\n for propertyData in cls.allPropertyData:\n if propertyData.hasStrings:\n propertyIndex = propertyData.index\n if not currentRange:\n currentRange = propertyIndex, propertyIndex\n elif currentRange[-1] == propertyIndex - 1:\n currentRange = currentRange[0], propertyIndex\n else:\n hasStringRanges.append(currentRange)\n currentRange = propertyIndex, propertyIndex\n\n if currentRange:\n hasStringRanges.append(currentRange)\n\n file.write(\"static bool unicodeCharacterClassMayContainStrings(unsigned unicodeClassId)\\n\")\n file.write(\"{\\n\")\n file.write(\" if (unicodeClassId >= {})\\n\".format(len(cls.allPropertyData)))\n file.write(\" return false;\\n\")\n file.write(\"\\n\")\n for range in hasStringRanges:\n if range[0] == range[1]:\n file.write(\" if (unicodeClassId == {})\\n\".format(range[0]))\n else:\n file.write(\" if (unicodeClassId >= {} && unicodeClassId <= {})\\n\".format(range[0], range[1]))\n file.write(\" return true;\\n\")\n file.write(\"\\n\")\n file.write(\" return false;\\n\")\n file.write(\"}\\n\\n\")\n\n\nclass Scripts:\n def __init__(self):\n self.allPropertyData = []\n self.scriptsByName = {}\n self.scriptExtensionsByName = {}\n self.unknownScript = PropertyData(\"Unknown\")\n self.unknownScript.setAliases(aliases.scriptAliasesFor(\"Unknown\"))\n self.allPropertyData.append(self.unknownScript)\n self.scriptsParsed = False\n\n def parseScriptsFile(self, file):\n currentScriptName = None\n currentPropertyData = None\n # To calculate the Unknown, we gather all the codePoints asigned to a scripts as ranges,\n # sort them, and then go the list to create the inverse of the assigned ranges.\n assignedCodePointRanges = []\n\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoints = fields[0].strip()\n scriptName = fields[1].strip()\n\n if scriptName != currentScriptName:\n currentScriptName = scriptName\n currentPropertyData = PropertyData(scriptName)\n currentPropertyData.setAliases(aliases.scriptAliasesFor(scriptName))\n self.allPropertyData.append(currentPropertyData)\n self.scriptsByName[scriptName] = currentPropertyData\n\n dotDot = codePoints.find(\"..\")\n if dotDot == -1:\n codePoint = int(codePoints, 16)\n currentPropertyData.addMatch(codePoint)\n assignedCodePointRanges.append((codePoint, codePoint))\n else:\n lowCodePoint = int(codePoints[:dotDot], 16)\n highCodePoint = int(codePoints[dotDot + 2:], 16)\n currentPropertyData.addRange(lowCodePoint, highCodePoint)\n assignedCodePointRanges.append((lowCodePoint, highCodePoint))\n\n assignedCodePointRanges.sort(key=lambda range: range[0])\n lastAssignedCodePoint = 0\n\n for range in assignedCodePointRanges:\n if range[0] - lastAssignedCodePoint > 1:\n if range[0] - lastAssignedCodePoint == 2:\n self.unknownScript.addMatch(lastAssignedCodePoint + 1)\n else:\n self.unknownScript.addRange(lastAssignedCodePoint + 1, range[0] - 1)\n lastAssignedCodePoint = range[1]\n\n if lastAssignedCodePoint < MaxUnicode:\n if MaxUnicode - lastAssignedCodePoint == 1:\n self.unknownScript.addMatch(MaxUnicode)\n else:\n self.unknownScript.addRange(lastAssignedCodePoint + 1, MaxUnicode)\n\n self.scriptsParsed = True\n\n def parseScriptExtensionsFile(self, file):\n currentPropertyData = None\n # To calculate the Unknown, we gather all the codePoints asigned to a scripts as ranges,\n # sort them, and then go the list to create the inverse of the assigned ranges.\n assignedCodePointRanges = []\n\n if not self.scriptsParsed:\n print(\"Error: parsing ScriptExtensions.txt before Scripts.txt\")\n exit(1)\n\n commonScriptExtenstionPropertyData = None\n inheritedScriptExtensionPropertyData = None\n\n scriptName = \"Common\"\n if scriptName in self.scriptsByName:\n commonScriptExtenstionPropertyData = self.scriptsByName[scriptName].makeCopy()\n else:\n commonScriptExtenstionPropertyData = PropertyData(scriptName)\n commonScriptExtenstionPropertyData.setAliases(aliases.scriptAliasesFor(scriptName))\n self.allPropertyData.append(commonScriptExtenstionPropertyData)\n self.scriptExtensionsByName[scriptName] = commonScriptExtenstionPropertyData\n\n scriptName = \"Inherited\"\n if scriptName in self.scriptsByName:\n inheritedScriptExtensionPropertyData = self.scriptsByName[scriptName].makeCopy()\n else:\n inheritedScriptExtensionPropertyData = PropertyData(scriptName)\n inheritedScriptExtensionPropertyData.setAliases(aliases.scriptAliasesFor(scriptName))\n self.allPropertyData.append(inheritedScriptExtensionPropertyData)\n self.scriptExtensionsByName[scriptName] = inheritedScriptExtensionPropertyData\n\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoints = fields[0].strip()\n scriptAliasList = fields[1].strip()\n\n for scriptAlias in scriptAliasList.split(' '):\n scriptName = aliases.scriptNameForAlias(scriptAlias)\n currentPropertyData = None\n\n if scriptName not in self.scriptExtensionsByName:\n currentPropertyData = self.scriptsByName[scriptName].makeCopy()\n self.allPropertyData.append(currentPropertyData)\n self.scriptExtensionsByName[scriptName] = currentPropertyData\n else:\n currentPropertyData = self.scriptExtensionsByName[scriptName]\n\n dotDot = codePoints.find(\"..\")\n if dotDot == -1:\n codePoint = int(codePoints, 16)\n currentPropertyData.addMatch(codePoint)\n commonScriptExtenstionPropertyData.removeMatch(codePoint)\n inheritedScriptExtensionPropertyData.removeMatch(codePoint)\n else:\n lowCodePoint = int(codePoints[:dotDot], 16)\n highCodePoint = int(codePoints[dotDot + 2:], 16)\n currentPropertyData.addRange(lowCodePoint, highCodePoint)\n for codePoint in range(lowCodePoint, highCodePoint + 1):\n commonScriptExtenstionPropertyData.removeMatch(codePoint)\n inheritedScriptExtensionPropertyData.removeMatch(codePoint)\n\n # For the scripts that don't have any additional extension codePoints, copy the script\n # data to the script extension with the same name\n for scriptName, propertyData in self.scriptsByName.items():\n if scriptName not in self.scriptExtensionsByName:\n self.scriptExtensionsByName[scriptName] = propertyData\n\n def dump(self, file):\n file.write(\"// Scripts:\\n\")\n PropertyData.createAndDumpHashTable(file, self.scriptsByName, \"script\")\n\n file.write(\"// Script_Extensions:\\n\")\n PropertyData.createAndDumpHashTable(file, self.scriptExtensionsByName, \"scriptExtension\")\n\n\nclass GeneralCategory:\n def __init__(self, file):\n self.file = file\n self.allPropertyData = []\n self.propertyDataByCategory = {}\n self.createSpecialPropertyData(\"Any\", (0, MaxUnicode))\n self.createSpecialPropertyData(\"ASCII\", (0, lastASCIICodePoint))\n self.assignedPropertyData = self.createSpecialPropertyData(\"Assigned\")\n self.unassignedProperyData = self.findPropertyGroupFor(\"Cn\")[1]\n self.casedLetterPropertyData = self.findPropertyGroupFor(\"LC\")[1]\n self.lastAddedCodePoint = 0\n\n def createSpecialPropertyData(self, name, range=None):\n propertyData = PropertyData(name)\n self.allPropertyData.append(propertyData)\n self.propertyDataByCategory[name] = propertyData\n if range:\n propertyData.addRange(range[0], range[1])\n\n return propertyData\n\n def findPropertyGroupFor(self, categoryAlias):\n category = aliases.generalCategoryForAlias(categoryAlias)\n allCategoryAliases = aliases.generalCategoryAliasesFor(category)\n categoryGroupAlias = categoryAlias[0]\n categoryGroup = aliases.generalCategoryForAlias(categoryGroupAlias)\n allCategoryGroupAlias = aliases.generalCategoryAliasesFor(categoryGroup)\n groupPropertyData = None\n propertyData = None\n\n if categoryGroup not in self.propertyDataByCategory:\n groupPropertyData = PropertyData(categoryGroup)\n groupPropertyData.setAliases(allCategoryGroupAlias)\n self.allPropertyData.append(groupPropertyData)\n self.propertyDataByCategory[categoryGroup] = groupPropertyData\n else:\n groupPropertyData = self.propertyDataByCategory[categoryGroup]\n\n if category not in self.propertyDataByCategory:\n propertyData = PropertyData(category)\n propertyData.setAliases(allCategoryAliases)\n self.allPropertyData.append(propertyData)\n self.propertyDataByCategory[category] = propertyData\n else:\n propertyData = self.propertyDataByCategory[category]\n\n return (groupPropertyData, propertyData)\n\n def addNextCodePoints(self, categoryAlias, codePoint, highCodePoint=None):\n if codePoint - self.lastAddedCodePoint > 1:\n propertyDatas = self.findPropertyGroupFor(\"Cn\")\n if codePoint - self.lastAddedCodePoint == 2:\n propertyDatas[0].addMatch(self.lastAddedCodePoint + 1)\n propertyDatas[1].addMatch(self.lastAddedCodePoint + 1)\n else:\n propertyDatas[0].addRange(self.lastAddedCodePoint + 1, codePoint - 1)\n propertyDatas[1].addRange(self.lastAddedCodePoint + 1, codePoint - 1)\n\n propertyDatas = self.findPropertyGroupFor(categoryAlias)\n if highCodePoint:\n propertyDatas[0].addRange(codePoint, highCodePoint)\n propertyDatas[1].addRange(codePoint, highCodePoint)\n if categoryAlias == \"Ll\" or categoryAlias == \"Lt\" or categoryAlias == \"Lu\":\n self.casedLetterPropertyData.addRange(codePoint, highCodePoint)\n self.assignedPropertyData.addRange(codePoint, highCodePoint)\n\n self.lastAddedCodePoint = highCodePoint\n else:\n propertyDatas[0].addMatch(codePoint)\n propertyDatas[1].addMatch(codePoint)\n if categoryAlias == \"Ll\" or categoryAlias == \"Lt\" or categoryAlias == \"Lu\":\n self.casedLetterPropertyData.addMatch(codePoint)\n self.assignedPropertyData.addMatch(codePoint)\n\n self.lastAddedCodePoint = codePoint\n\n def parse(self):\n lastLineFirstOfRange = None\n lastLineCodePoint = 0\n for line in self.file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoint = int(fields[0].strip(), 16)\n description = fields[1].strip()\n categoryAlias = fields[2].strip()\n\n if lastLineFirstOfRange:\n if description[-5:-1] == \"Last\":\n self.addNextCodePoints(categoryAlias, lastLineFirstOfRange, codePoint)\n lastLineFirstOfRange = None\n continue\n else:\n print(\"Malformed First..Last pair of lines\")\n\n if description[-6:-1] == \"First\":\n lastLineFirstOfRange = codePoint\n continue\n\n self.addNextCodePoints(categoryAlias, codePoint)\n\n if self.lastAddedCodePoint < MaxUnicode:\n propertyDatas = self.findPropertyGroupFor(\"Cn\")\n if MaxUnicode - self.lastAddedCodePoint == 1:\n propertyDatas[0].addMatch(MaxUnicode)\n propertyDatas[1].addMatch(MaxUnicode)\n else:\n propertyDatas[0].addRange(self.lastAddedCodePoint + 1, MaxUnicode)\n propertyDatas[1].addRange(self.lastAddedCodePoint + 1, MaxUnicode)\n\n def dump(self, file):\n file.write(\"// General_Category:\\n\")\n PropertyData.createAndDumpHashTable(file, self.propertyDataByCategory, \"generalCategory\")\n\n\nclass BinaryProperty:\n def __init__(self):\n self.allPropertyData = []\n self.propertyDataByProperty = {}\n\n def parsePropertyFile(self, file):\n currentPropertyName = None\n currentPropertyData = None\n\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoints = fields[0].strip()\n propertyName = fields[1].strip()\n\n if propertyName != currentPropertyName:\n if propertyName not in SupportedBinaryProperties:\n continue\n\n currentPropertyName = propertyName\n currentPropertyData = PropertyData(propertyName)\n currentPropertyData.setAliases(aliases.globalAliasesFor(propertyName))\n self.allPropertyData.append(currentPropertyData)\n self.propertyDataByProperty[propertyName] = currentPropertyData\n\n dotDot = codePoints.find(\"..\")\n if dotDot == -1:\n currentPropertyData.addMatch(int(codePoints, 16))\n else:\n currentPropertyData.addRange(int(codePoints[:dotDot], 16), int(codePoints[dotDot + 2:], 16))\n\n def dump(self, file):\n file.write(\"// binary properties:\\n\")\n PropertyData.createAndDumpHashTable(file, self.propertyDataByProperty, \"binaryProperty\")\n\n\nclass SequenceProperty:\n def __init__(self):\n self.allPropertyData = []\n self.propertyDataByProperty = {}\n self.RGIEmojiPropertyData = PropertyData(\"RGI_Emoji\")\n self.allPropertyData.append(self.RGIEmojiPropertyData)\n self.propertyDataByProperty[\"RGI_Emoji\"] = self.RGIEmojiPropertyData\n\n def parsePropertyFile(self, file):\n currentPropertyName = None\n currentPropertyData = None\n\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoints = fields[0].strip()\n propertyName = fields[1].strip()\n\n if propertyName != currentPropertyName:\n if propertyName not in SupportedSequenceProperties:\n continue\n\n if currentPropertyData is not None:\n currentPropertyData.sortStrings()\n\n currentPropertyName = propertyName\n currentPropertyData = PropertyData(propertyName)\n self.allPropertyData.append(currentPropertyData)\n self.propertyDataByProperty[propertyName] = currentPropertyData\n\n dotDot = codePoints.find(\"..\")\n if dotDot == -1:\n space = codePoints.find(\" \")\n if space == -1:\n currentPropertyData.addMatch(int(codePoints, 16))\n self.RGIEmojiPropertyData.addMatch(int(codePoints, 16))\n else:\n # String, convert into UTF16 format with surrogate pairs as needed.\n utf32String = \"\"\n for hex in codePoints.split(' '):\n c = int(hex, 16)\n utf32String += \"\".join(chr(c))\n\n currentPropertyData.addMatchString(utf32String)\n self.RGIEmojiPropertyData.addMatchString(utf32String)\n\n else:\n currentPropertyData.addRange(int(codePoints[:dotDot], 16), int(codePoints[dotDot + 2:], 16))\n self.RGIEmojiPropertyData.addRange(int(codePoints[:dotDot], 16), int(codePoints[dotDot + 2:], 16))\n\n currentPropertyData.sortStrings()\n self.RGIEmojiPropertyData.sortStrings()\n\n def dump(self, file):\n file.write(\"// sequences properties:\\n\")\n PropertyData.createAndDumpHashTable(file, self.propertyDataByProperty, \"sequenceProperty\")\n\n\nif __name__ == \"__main__\":\n parser = optparse.OptionParser(usage=\"usage: %prog \")\n (options, args) = parser.parse_args()\n\n if len(args) != 2:\n parser.error(\" \")\n\n UCDDirectoryPath = args[0]\n unicodeProertyDataHPath = args[1]\n\n verifyUCDFilesExist()\n\n propertyAliasesFile = openUCDFileOrExit(\"PropertyAliases.txt\")\n propertyValueAliasesFile = openUCDFileOrExit(\"PropertyValueAliases.txt\")\n scriptsFile = openUCDFileOrExit(\"Scripts.txt\")\n scriptExtensionsFile = openUCDFileOrExit(\"ScriptExtensions.txt\")\n unicodeDataFile = openUCDFileOrExit(\"UnicodeData.txt\")\n derivedBinaryPropertiesFile = openUCDFileOrExit(\"DerivedBinaryProperties.txt\")\n derivedCorePropertiesFile = openUCDFileOrExit(\"DerivedCoreProperties.txt\")\n derivedNormalizationPropertiesFile = openUCDFileOrExit(\"DerivedNormalizationProps.txt\")\n propListFile = openUCDFileOrExit(\"PropList.txt\")\n emojiDataFile = openUCDFileOrExit(\"emoji-data.txt\")\n emojiSequencesFile = openUCDFileOrExit(\"emoji-sequences.txt\")\n emojiZWJSequencesFile = openUCDFileOrExit(\"emoji-zwj-sequences.txt\")\n\n aliases = Aliases()\n\n propertyDataHFile = openOrExit(unicodeProertyDataHPath, \"w\")\n\n propertyDataHFile.write(header)\n\n aliases.parsePropertyAliasesFile(propertyAliasesFile)\n aliases.parsePropertyValueAliasesFile(propertyValueAliasesFile)\n\n generalCategory = GeneralCategory(unicodeDataFile)\n generalCategory.parse()\n\n binaryProperty = BinaryProperty()\n binaryProperty.parsePropertyFile(derivedBinaryPropertiesFile)\n binaryProperty.parsePropertyFile(derivedCorePropertiesFile)\n binaryProperty.parsePropertyFile(derivedNormalizationPropertiesFile)\n binaryProperty.parsePropertyFile(propListFile)\n binaryProperty.parsePropertyFile(emojiDataFile)\n\n scripts = Scripts()\n scripts.parseScriptsFile(scriptsFile)\n scripts.parseScriptExtensionsFile(scriptExtensionsFile)\n\n sequenceProperty = SequenceProperty()\n sequenceProperty.parsePropertyFile(emojiSequencesFile)\n sequenceProperty.parsePropertyFile(emojiZWJSequencesFile)\n\n PropertyData.dumpAll(propertyDataHFile)\n generalCategory.dump(propertyDataHFile)\n binaryProperty.dump(propertyDataHFile)\n scripts.dump(propertyDataHFile)\n sequenceProperty.dump(propertyDataHFile)\n\n PropertyData.dumpMayContainStringFunc(propertyDataHFile)\n\n propertyDataHFile.write(footer)\n\n exit(0)\n","repo_name":"WebKit/WebKit","sub_path":"Source/JavaScriptCore/yarr/generateYarrUnicodePropertyTables.py","file_name":"generateYarrUnicodePropertyTables.py","file_ext":"py","file_size_in_byte":48324,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"16823917001","text":"s = 'full_string'\n\nif 'string' in s:\n print('Substring found')\n\nindex = s.find('string')\nif index != -1:\n print(f'Substring found at index {index}')\n\n #assert \"substring \" in full_string, \"expected {}, got {}\".format(full_string, substring)","repo_name":"alina-zakharenko/stepik_lessons","sub_path":"module_3/substring.py","file_name":"substring.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11786472455","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport operator\nfrom scipy import stats\nfrom math import log\n\ndef get_top_stems(model, num):\n\t\"\"\"Gets the most frequently appeared stems in a text model and returns them as a list of tuples (stem, frequency).\n\n\tKeyword arguments:\n\tmodel -- The model to be used.\n\tnum -- Number of results to be returned.\n\t\"\"\"\n\tsorted_stems = sorted(model.stems.iteritems(), key=operator.itemgetter(1), reverse=True)\n\ttop_ten = list(sorted_stems)[:num]\n\ts = \"\"\n\ti = 1\n\tfor line in top_ten:\n\t\ts += str(i) + \"\\t\" + str(line[1]) + \"\\t\\t\" + line[0] + \"\\n\"\n\t\ti += 1\n\treturn s\n\n\ndef t_test_wlen(model1, model2):\n\t\"\"\"Performs Student's t-test on word lengths between model1 and model2.\n\n\tThe test used is unpaired two-sample Student's t-test with Welch's correction.\n\n\tReturns a tuple (t_value, p_value).\"\"\"\n\n\tdef _populate_sample_list(model):\n\t\tsample = []\n\t\tfor (length, incidence) in model.wordlengths.iteritems():\n\t\t\tlength = float(length)\n\t\t\tfor i in range(incidence):\n\t\t\t\tsample.append(length)\n\t\treturn sample\n\n\tsample1 = _populate_sample_list(model1)\n\tsample2 = _populate_sample_list(model2)\n\tresult = stats.ttest_ind(sample1, sample2, equal_var=False)\n\treturn result\n\ndef t_test_slen(model1, model2):\n\t\"\"\"Performs Student's t-test on sentence lengths between model1 and model2.\n\n\tThe test used is unpaired two-sample Student's t-test with Welch's correction.\n\n\tReturns a tuple (t_value, p_value).\"\"\"\n\n\tdef _populate_sent_sample_list(model):\n\t\tsample = []\n\t\tfor (length, incidence) in model.sentencelengths.iteritems():\n\t\t\tlength = float(length)\n\t\t\tfor i in range(incidence):\n\t\t\t\tsample.append(length)\n\t\treturn sample\n\n\tsample1 = _populate_sent_sample_list(model1)\n\tsample2 = _populate_sent_sample_list(model2)\n\tresult = stats.ttest_ind(sample1, sample2, equal_var=False)\n\treturn result\n\ndef bayes_score(model1, model2, ignore_common=False):\n\t\"\"\"Calculates the Naive Bayes score between model1 and model2.\"\"\"\n\tCOMMON_WORDS = ['sum', 'possum', 'eo', 'ad', 'non', 'ne', 'cum', 'res', 'omnis']\n\tmodel2_stems = sum(model2.stems.values())\n\tbayes_score = 0\n\tfor stem in model1.stems.keys():\n\t\tif ignore_common and stem in COMMON_WORDS:\n\t\t\tcontinue\n\t\tif stem not in model2.stems:\n\t\t\tmodel2_prob = 1/float(model2_stems)\n\t\t\tbayes_score += log(model2_prob, 2) * model1.stems[stem]\n\t\telse:\n\t\t\tmodel2_prob = float(model2.stems[stem])/model2_stems\n\t\t\tbayes_score += log(model2_prob, 2) * model1.stems[stem]\n\treturn bayes_score\n\n","repo_name":"marczuo/LatinTextID","sub_path":"stats_tests.py","file_name":"stats_tests.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23677173601","text":"import asyncio\nfrom datetime import datetime as dt\nfrom unittest.mock import MagicMock\nimport tempfile\nimport os\nfrom types import SimpleNamespace\n\n\nfrom django.core import signing\n\nfrom channels.auth import get_user\n\nimport pytest\nimport skpy\n\nfrom core.tests.fixtures import *\nfrom core.models import Chat, Contact\n\n\nclass MessageEvent(skpy.event.SkypeNewMessageEvent):\n def __init__(self):\n self.__dict__['msg'] = SimpleNamespace(\n content='Priviet maniunia',\n userId='123',\n time=dt(2019, 5, 4, 10),\n )\n\n def __getattr__(self, name):\n return self.__dict__['msg']\n\n def __str__(self):\n return ''\n\n def __repr__(self):\n return ''\n\n\nclass SkypeContactsMock:\n def getMsgs(self):\n return [\n SimpleNamespace(\n content='whats up',\n userId='123',\n timestamp='1556834306289',\n ),\n SimpleNamespace(\n content='I am fine',\n userId='me',\n timestamp='1556834206289',\n ),\n ]\n\n contacts = [\n SimpleNamespace(\n id='123',\n name=SimpleNamespace(\n first='Tomasz',\n last='jD',\n ),\n )\n ]\n\n def __iter__(self):\n self.it = iter(self.contacts)\n return self.it\n\n def __next__(self):\n return next(self.it)\n\n def __getitem__(self, idx):\n for c in self.contacts:\n if c.id == idx:\n c.getMsgs = self.getMsgs\n return c\n\n\n@pytest.fixture(autouse=True)\n@pytest.mark.asyncio\ndef my_skype_chat(monkeypatch):\n monkeypatch.setattr(skpy, 'Skype', MagicMock())\n monkeypatch.setattr(skpy, 'SkypeEventLoop', MagicMock())\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_log_to(comm):\n await comm.send_json_to({\n 'action': 'provider_skype_am_i_logged',\n })\n\n resp = await comm.receive_json_from()\n\n assert resp == {'status': 'ok', 'action': 'provider_skype_am_i_logged', 'is_logged': False}\n\n await comm.send_json_to({\n 'action': 'provider_skype_login',\n 'username': '579631148',\n 'password': '12qwertyU',\n })\n f = asyncio.Future()\n f.set_result(None)\n\n resp = await comm.receive_json_from()\n\n tmpdir = tempfile.gettempdir()\n dirname = (await get_user(comm.instance.scope)).temp_dir\n\n skpy.Skype.return_value.conn.setTokenFile.assert_called_once_with(\n os.path.join(tmpdir, dirname, 'token-skype-app')\n )\n skpy.Skype.return_value.conn.readToken.assert_called_once_with()\n skpy.Skype.return_value.conn.setUserPwd.assert_not_called()\n skpy.Skype.return_value.conn.getSkypeToken.assert_not_called()\n skpy.Skype.return_value.conn.connected = True\n\n assert resp == {\n 'action': 'provider_skype_login',\n 'status': 'ok',\n 'msg': 'Successfully logged into Skype',\n }\n await comm.send_json_to({\n 'action': 'provider_skype_am_i_logged',\n })\n\n resp = await comm.receive_json_from()\n\n assert resp == {'status': 'ok', 'action': 'provider_skype_am_i_logged', 'is_logged': True}\n await comm.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_required_cred(comm):\n await comm.send_json_to({\n 'action': 'provider_skype_get_required_credentials',\n })\n\n resp = await comm.receive_json_from()\n\n assert resp == {\n 'status': 'ok',\n 'action': 'provider_skype_get_required_credentials',\n 'username': {'type': 'text', 'help': 'Nickname or phone number'},\n 'password': {'type': 'password', 'help': 'Password'}\n }\n await comm.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_list_chats(logged_skype):\n await logged_skype.send_json_to({\n 'action': 'get_chats',\n })\n f = [\n SimpleNamespace(\n name=SimpleNamespace(\n first='Andrzej',\n last='Pączek',\n ),\n id=1,\n ),\n SimpleNamespace(\n name=SimpleNamespace(\n first='Piotr',\n last='Czaja',\n ),\n id=3,\n ),\n ]\n\n skpy.Skype.return_value.contacts = f\n resp = await logged_skype.receive_json_from()\n chats = resp['chats']\n\n assert Chat.objects.all().count() == len(chats)\n assert 'Andrzej Pączek' in [c['name'] for c in resp['chats']]\n await logged_skype.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_store_creds(comm):\n await comm.send_json_to({\n 'action': 'provider_skype_login',\n 'username': '579631148',\n 'password': '12qwertyU',\n })\n\n await comm.receive_json_from()\n\n user = await get_user(comm.instance.scope)\n\n creds = signing.loads(user.credentials)\n\n assert creds['skype'] == {'username': '579631148', 'password': '12qwertyU'}\n\n await comm.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_send_messages(logged_skype):\n\n andrzej_uid = 'andriy5852'\n andrzej = await database_sync_to_async(Contact.objects.create)(\n provider='skype',\n uid=andrzej_uid,\n chat=await database_sync_to_async(Chat.objects.create)(name='Andrzej'),\n owner=await get_user(logged_skype.instance.scope),\n )\n await logged_skype.send_json_to({\n 'action': 'send_message',\n 'provider': 'skype',\n 'chat_id': andrzej.chat.id,\n 'content': 'Priviet maniunia'\n })\n f = asyncio.Future()\n f.set_result(None)\n contact_mock = MagicMock()\n skpy.Skype.return_value.contacts.__getitem__.return_value = contact_mock\n send_mock = contact_mock.chat.sendMsg\n send_mock.return_value = f\n\n resp = await logged_skype.receive_json_from()\n skpy.Skype.return_value.contacts.__getitem__.assert_called_once_with(andrzej_uid)\n send_mock.assert_called_once_with('Priviet maniunia')\n assert send_mock.call_args[0][0] == 'Priviet maniunia'\n\n assert resp == {\n 'action': 'send_message',\n 'chat_id': andrzej.chat.id,\n 'provider': 'skype',\n 'status': 'ok',\n 'content': 'Priviet maniunia'\n }\n await logged_skype.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_receive_messages(logged_skype):\n skpy.Skype.return_value.contacts = SkypeContactsMock()\n\n skpy.SkypeEventLoop.return_value.onEvent(\n MessageEvent(),\n )\n\n resp = await logged_skype.receive_json_from()\n\n chat = Contact.objects.get(chat__name='Tomasz jD')\n resp == {\n 'action': 'new_message',\n 'content': 'Priviet maniunia',\n 'userId': chat.id,\n 'provider': 'skype',\n }\n\n assert resp['content'] == 'Priviet maniunia'\n assert resp['chat_id'] == chat.id\n\n await logged_skype.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_get_chat_messages(logged_skype):\n skpy.Skype.return_value.chats = SkypeContactsMock()\n chat = Chat.objects.create(name='Tomasz Dul')\n Contact.objects.create(\n provider='skype',\n uid='123',\n chat=chat,\n owner=await get_user(logged_skype.instance.scope),\n\n )\n\n f = asyncio.Future()\n f.set_result([])\n\n skpy.Skype.return_value.getMsgs.return_value = f\n skpy.Skype.return_value.user.id = 'me'\n\n await logged_skype.send_json_to({\n 'action': 'get_messages',\n 'chat_ids': [chat.id],\n 'count': 50,\n })\n\n resp = await logged_skype.receive_json_from()\n\n assert resp == {\n 'action': 'get_messages',\n 'chats': [{\n 'id': chat.id,\n 'messages': [\n {\n 'provider': 'skype',\n 'content': 'whats up',\n 'me': False,\n 'time': '2019-05-02 21:58:26+00:00',\n },\n {\n 'provider': 'skype',\n 'content': 'I am fine',\n 'me': True,\n 'time': '2019-05-02 21:56:46+00:00',\n },\n ],\n }],\n 'status': 'ok',\n }\n\n await logged_skype.disconnect()\n","repo_name":"llusaq/kukuchat","sub_path":"backend/kukuchat/core/tests/test_skype.py","file_name":"test_skype.py","file_ext":"py","file_size_in_byte":8304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2847804118","text":"import pandas as pd\nimport math\nimport numpy as np\nfrom sklearn import preprocessing, model_selection, svm\nfrom sklearn.linear_model import LinearRegression\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nimport datetime\nimport pickle\n\nstyle.use('ggplot')\npd.options.mode.chained_assignment = None\n\ndef process_data(df):\n df = df[['CPU Cores','Memory','Bandwidth','Hour1','Hour2','Day Start','Day End','Month Start','Month End','Year Start','Year End','Instances']]\n df['HR_PCT'] = (df['Hour2'] - df['Hour1'])/df['Hour1']*100\n df['DY_PCT'] = (df['Day Start'] - df['Day End'])/df['Day Start']*100\n df['MN_PCT'] = (df['Month Start'] - df['Month End'])/df['Month Start']*100\n df['YR_PCT'] = (df['Year Start'] - df['Year End'])/df['Year Start']*100\n df = df[['CPU Cores','Memory','Bandwidth','Instances','HR_PCT','DY_PCT','MN_PCT','Month End']]\n return df\n\ndef train_test_splitting(X,y):\n return model_selection.train_test_split(X, y, test_size = 0.2)\n\ndef linear_training(X_train,X_test,y_train,y_test,X_lately):\n clf = LinearRegression(n_jobs=-1)\n clf.fit(X_train, y_train)\n accuracy = clf.score(X_test, y_test)\n forecast_set = clf.predict([[2,8,128,2,236.0,212.0,54.1,52]])\n df['Forecast'] = np.nan\n last_date = df.iloc[-1].name\n # print(last_date)\n last_unix = last_date\n one_day = 86400\n next_unix = last_unix + one_day\n\n for i in forecast_set:\n next_date = datetime.datetime.fromtimestamp(next_unix)\n next_unix += one_day\n df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)] + [i]\n\n df['Month End'].plot()\n df['Forecast'].plot()\n plt.legend(loc=4)\n plt.xlabel('Date')\n plt.ylabel('Price')\n plt.show()\n with open('azure_model.pkl','wb') as File:\n pickle.dump(clf,File)\n File.close()\n print(forecast_set, accuracy, forecast_out)\n\ndef svm_training(X_train,X_test,y_train,y_test,X_lately):\n clf = svm.SVR()\n clf.fit(X_train, y_train)\n accuracy = clf.score(X_test, y_test)\n forecast_set = clf.predict(X_lately)\n df['Forecast'] = np.nan\n last_date = df.iloc[-1].name\n # print(last_date)\n last_unix = last_date\n one_day = 86400\n next_unix = last_unix + one_day\n\n for i in forecast_set:\n next_date = datetime.datetime.fromtimestamp(next_unix)\n next_unix += one_day\n df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)] + [i]\n\n df['Month End'].plot()\n df['Forecast'].plot()\n plt.legend(loc=4)\n plt.xlabel('Date')\n plt.ylabel('Price')\n plt.show()\n print(forecast_set, accuracy, forecast_out)\n \n\nif __name__ == '__main__':\n df = pd.read_csv(\"./data/AZURE_CSV.csv\")\n df = process_data(df) \n forecast_col = 'Month End' \n df.fillna(-99999, inplace = True)\n forecast_out = int(math.ceil(0.001*len(df)))\n # forecast_out = math.ceil(0)\n # forecast_out = math.ceil(-1)\n # forecast_out = math.ceil(-31.756)\n df['label'] = df[forecast_col].shift(-forecast_out)\n X = np.array(df.drop(['label'], 1))\n X = preprocessing.scale(X)\n X = X[:-forecast_out]\n X_lately = X[-forecast_out:]\n\n df.dropna(inplace = True)\n y = np.array(df['label'])\n\n X_train, X_test, y_train, y_test = train_test_splitting(X,y)\n print(X_lately)\n linear_training(X_train,X_test,y_train,y_test,X_lately)\n # svm_training(X_train,X_test,y_train,y_test,X_lately)\n\n\n","repo_name":"Lunaticsatoshi/BE-Final-Year-Project","sub_path":"Linearreg/linear.py","file_name":"linear.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"2544380276","text":"class Solution:\n def multiply(self, num1: str, num2: str) -> str:\n if num1 == '0' or num2 == '0':\n return '0'\n\n res = [0] * (len(num1) + len(num2))\n\n for i in range(len(num1) - 1, -1, -1):\n for j in range(len(num2) - 1, -1, -1):\n product = self.to_int(num1[i]) * self.to_int(num2[j])\n p1, p2 = i + j, i + j + 1\n total = product + res[p2]\n\n res[p1] += total // 10\n res[p2] = total % 10\n return ''.join(map(lambda x: str(x), res)).lstrip('0')\n\n def to_int(self, digit_str: str) -> int:\n return ord(digit_str) - ord('0')\n","repo_name":"xiaoyao2102/leetcode","sub_path":"0043_multiply_strings.py","file_name":"0043_multiply_strings.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39503524820","text":"# Exercise 1: Calculate the multiplication and sum of two numbers\n# Given two integer numbers return their product only if the product is equal to or lower than 1000, else return their sum.\n\na = int(input(\"Enter the first number! \"))\nb = int(input(\"Enter the second number! \"))\n\ndef product_or_sum(a, b):\n product = a * b\n sum = a + b\n\n if product <= 1000:\n return(product)\n else:\n return(sum)\n\nresult = product_or_sum(a, b)\nprint(result)","repo_name":"Naddalyy/Homework","sub_path":"FirstTask.py","file_name":"FirstTask.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31410251103","text":"from functools import wraps\nimport pyrogram\nfrom thebot import logging\nimport traceback\nimport sys\nfrom thebot import ALLOWED_USERS, LOG_CHANNEL\n\n\ndef split_limits(text):\n if len(text) < 2048:\n return [text]\n\n lines = text.splitlines(True)\n small_msg = \"\"\n result = []\n for line in lines:\n if len(small_msg) + len(line) < 2048:\n small_msg += line\n else:\n result.append(small_msg)\n small_msg = line\n else:\n result.append(small_msg)\n\n return result\n\n\ndef capture_err(func):\n @wraps(func)\n async def capture(client, message, *args, **kwargs):\n try:\n return await func(client, message, *args, **kwargs)\n except pyrogram.errors.exceptions.forbidden_403.ChatWriteForbidden as err:\n logging.info(\n \"Bot was muted in {} {}\".format(message.chat.title, message.chat.id)\n )\n await client.leave_chat(message.chat.id)\n except Exception as err:\n await message.reply(\"**Error:**\\n\" + str(err))\n exc_type, exc_obj, exc_tb = sys.exc_info()\n errors = traceback.format_exception(\n etype=exc_type, value=exc_obj, tb=exc_tb\n )\n error_feedback = split_limits(\n \"**EsseX ERROR** | `{}` | `{}`\\n\\n```{}```\\n\\n```{}```\\n\".format(\n 0 if not message.from_user else message.from_user.id,\n 0 if not message.chat else message.chat.id,\n message.text or message.caption,\n \"\".join(errors),\n )\n )\n for x in error_feedback:\n if LOG_CHANNEL:\n await client.send_message(LOG_CHANNEL, x)\n else:\n for m in ALLOWED_USERS:\n await client.send_message(m, x)\n raise err\n return capture","repo_name":"Dank-del/EsseX","sub_path":"thebot/utils/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"3"}
+{"seq_id":"17578190237","text":"from V1.imports import *\nfrom V1.model import create_model1, create_model2, create_features_vgg, create_features_resnet, create_features_xception, create_model3\nfrom keras.layers import concatenate\n\nphysical_devices = tf.config.experimental.list_physical_devices('GPU')\nif len(physical_devices) > 0:\n tf.config.experimental.set_memory_growth(physical_devices[0], True)\n\n# Taking images from dataset\npath = \"/home/prathamesh/Desktop/ML_Projects/Auto_Colorization/mirflickr25k\"\ntrain_datagen = ImageDataGenerator(rescale=1. / 255)\ntrain = train_datagen.flow_from_directory(path, target_size=(224, 224), batch_size=128, class_mode=None)\n\nX = []\nY = []\nfor img in train[0]:\n try:\n # converting rgb color space to lab\n lab = rgb2lab(img)\n # Appending L (Lightness in X)\n X.append(lab[:, :, 0])\n # Appending color in Y\n Y.append(lab[:, :, 1:] / 128)\n except:\n print('error')\nX = np.array(X)\nY = np.array(Y)\nX = X.reshape(X.shape + (1,))\nprint(X.shape)\nprint(Y.shape)\n\nmodel1 = create_model1()\nvgg_features = create_features_vgg(X, model1)\n\n#Encoder\nencoder_input = Input(shape=(7, 7, 512,))\n#Decoder\ndecoder_output = Conv2D(256, (3,3), activation='relu', padding='same')(encoder_input)\ndecoder_output = Conv2D(128, (3,3), activation='relu', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\ndecoder_output = Conv2D(64, (3,3), activation='relu', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\ndecoder_output = Conv2D(32, (3,3), activation='relu', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\ndecoder_output = Conv2D(16, (3,3), activation='relu', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\ndecoder_output = Conv2D(2, (3, 3), activation='tanh', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\nmodel = Model(inputs=encoder_input, outputs=decoder_output)\n\nmodel.compile(optimizer='Adam', loss='mse' , metrics=['accuracy'])\nadam = keras.optimizers.Adam(lr=1e-4)\nmodel.compile(optimizer=adam, loss='mse', metrics=['accuracy'])\nfilepath = \"/home/prathamesh/Desktop/ML_Projects/Auto_Colorization/V2/weights/weights-improvement-{epoch:02d}.hdf5\"\ncheckpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')\ncallbacks_list = [checkpoint]\nmodel.fit(vgg_features, Y, verbose=1, validation_split=0.1,epochs=100, batch_size=128, callbacks=callbacks_list)\n\ntestpath = '/home/prathamesh/Desktop/ML_Projects/Auto_Colorization/mirflickr25k/mirflickr/im22.jpg'\n\ntest = img_to_array(load_img(testpath))\ntest = resize(test, (224,224), anti_aliasing=True)\ntest*= 1.0/255\nlab = rgb2lab(test)\nl = lab[:,:,0]\nL = gray2rgb(l)\nL = L.reshape((1,224,224,3))\n#print(L.shape)\nvggpred = model1.predict(L)\nab = model.predict(vggpred)\n#print(ab.shape)\nab = ab*128\ncur = np.zeros((224, 224, 3))\ncur[:,:,0] = l\ncur[:,:,1:] = ab\nimsave(\"result2.jpg\", lab2rgb(cur))","repo_name":"PrathameshDeshpande/Auto_Colorization","sub_path":"V2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3021,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37202372389","text":"# -*- coding:utf-8 -*-\n\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n# 打开911文件\nfilepath = './PM2.5/BeijingPM20100101_20151231.csv'\ndf = pd.read_csv(filepath)\n\n# 第一步: 设置时间序列索引\n# PeriodIndex用于格式化时间序列,按小时生成时间序列\n# 把分开的时间字符串转换为Pandas转换为Pandas的时间序列类型\nperiod = pd.PeriodIndex(year=df['year'], month=df['month'], day=df['day'], hour=df['hour'], freq='H')\n\n# 首先：将上面的时间序列加入到df数组中，设置为一个列索引\ndf['datetime'] = period\n\n# 然后：将上面的datetime列索引设置为index索引(行索引）\ndf.set_index('datetime', inplace=True)\nprint(df.head(3))\nprint(df.columns)\nprint('*' * 100)\n\n# 由于数据量太多，绘图数据可以只取部分数据，上面添加时间序列的数据进行降采样\n# 求每天的平均值，如果值为NaN,会自动排除掉\ndf = df.resample('7D').mean()\n\n\n# 第二步:处理缺失数据\n# 处理缺失数据，查看原始数据，有些监测点的PM是NaN\nprint(df['PM_US Post'])\nprint('*' * 100)\n# 删除缺失的数据,由于是只有一列，不需要指定轴向axis\ndata = df['PM_US Post'].dropna()\nprint(data)\nprint(type(data))\nprint('*' * 100)\n\n\n# 第三步：绘图\n_x = data.index\n# 格式化X轴坐标格式\n_x = [i.strftime('%Y-%m-%d') for i in _x]\n_y = data.values\n\nplt.figure(figsize=(20, 8), dpi=128)\nplt.plot(range(len(_x)), _y)\n\n# 原始画图数据太多，X轴每隔10个填入坐标数据\nplt.xticks(range(0, len(_x), 10), list(_x)[::10], rotation=45)\n\nplt.show()\n","repo_name":"FelixZFB/Python_data_analysis","sub_path":"002_Python_data_analysis_from_entry_to_master/ch11_Pandas/05_Pandas_时间序列_综合案例/009_时间序列_PeriodIndex_格式化时间序列_PM2.5_US统计的数据.py","file_name":"009_时间序列_PeriodIndex_格式化时间序列_PM2.5_US统计的数据.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40065594943","text":"# author: David Gessner \n\"\"\"\nThis module provides classes to define a simulated ethernet network.\n\n\"\"\"\n\nimport collections\n\nimport simpy\n\nimport ft4fttsim.ethernet as ethernet\nfrom ft4fttsim.exceptions import FT4FTTSimException\nfrom ft4fttsim.simlogging import log\n\n\nclass Port(object):\n \"\"\"\n Models physical Ethernet ports.\n\n Instances of class NetworkDevice can have several such ports, and to each\n one of them an instance of Link can be attached.\n\n Each port has an input queue and an output queue. The input queue is where\n messages that are received through the port are queued. The output queue is\n where messages that are to be transmitted through the port are queued.\n\n Input queues are modeled as simpy stores with infinite capacity. The\n waiting time in an input queue is therefore always zero for any message.\n That is, a message received in an input queue does not suffer any queuing\n delay. Output queues, on the other hand, are modeled by a simpy store with\n capacity 1. This means that messages transmitted through the output queue\n may suffer a queuing delay.\n\n \"\"\"\n\n def __init__(self, env, name):\n \"\"\"\n Constructor for Port instances.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the Port instance.\n\n \"\"\"\n self.in_queue = simpy.Store(env)\n self.out_queue = simpy.Store(env, capacity=1)\n # indicates whether the port is already connected to a link\n self.is_free = True\n self.name = name\n\n def __repr__(self):\n return self.name\n\n\nclass Link(object):\n \"\"\"\n Models links used in an Ethernet network.\n\n A Link object models a bidirectional physical link between exactly 2\n network device ports (objects of class Port). A Link object has a certain\n transmission speed (expressed in megabits per second) and propagation delay\n (expressed in microseconds).\n\n \"\"\"\n def __init__(\n self, env,\n port1, port2,\n megabits_per_second, propagation_delay_us):\n \"\"\"\n Create a new instance of class Link.\n\n Arguments:\n env: A simpy.Environment instance.\n port1: An instance of Port to which the link will be connected. It\n models the port at one extreme of the modeled link.\n port2: Another instance of Port to which the link will be\n connected. It models the port at the other extreme of the\n modeled link.\n megabits_per_second: Speed of the link in megabits per second.\n propagation_delay_us: Propagation delay of the link in\n microseconds.\n\n Raises:\n FT4FTTSimException: error if the arguments have invalid values,\n e.g., a negative value for the propagation delay.\n\n \"\"\"\n if megabits_per_second <= 0:\n raise FT4FTTSimException(\"Mbps must be a positive number.\")\n if propagation_delay_us < 0:\n raise FT4FTTSimException(\"Propagation delay cannot be negative.\")\n if not isinstance(port1, Port):\n raise FT4FTTSimException(\n \"port1 argument must be a port and not {}.\".format(\n type(port1)))\n if not isinstance(port2, Port):\n raise FT4FTTSimException(\n \"port2 argument must be a port and not {}.\".format(\n type(port2)))\n assert port1.is_free\n assert port2.is_free\n self.sublink = (\n _Sublink(env, self, port1, port2),\n _Sublink(env, self, port2, port1)\n )\n port1.is_free = False\n port2.is_free = False\n self.megabits_per_second = megabits_per_second\n self.propagation_delay_us = propagation_delay_us\n\n def transmission_time_us(self, num_bytes):\n \"\"\"\n Gives the time in microseconds to transmit num_bytes on the link.\n\n Arguments:\n num_bytes: The number of bytes we want to know the transmission\n time for.\n\n Returns:\n A floating-point number representing the number of microseconds\n that it would take a Port attached to the link to transmit\n num_bytes, counting from when the first bit until the last bit has\n left the Port.\n\n Example:\n\n Assuming a link of 100 Mbps, and 1526 bytes to transmit, results in a\n transmission time of 1526 * 8 / 10**8 = 122.08 microseconds:\n\n >>> env = simpy.Environment()\n >>> d = NetworkDevice(env, \"some device\", 1)\n >>> d2 = NetworkDevice(env, \"another device\", 1)\n >>> link = Link(env, d.ports[0], d2.ports[0], 100, 0)\n >>> link.transmission_time_us(1526)\n 122.08\n\n \"\"\"\n bits_to_transmit = num_bytes * 8\n transmission_time_us = (bits_to_transmit / self.megabits_per_second)\n return transmission_time_us\n\n\nclass _Sublink(object):\n \"\"\"\n Models a directional sublink of a Link.\n\n Link instances are comprised of 2 _Sublinks each, one for each direction.\n This means that _Sublinks are directional, i.e., they have a single\n transmitter and a single receiver port. Messages that are being modeled as\n being transmitted can only be transmitted from the transmitter to the\n receiver port, but not in the opposite direction. At any one time at most\n one message can be transmitted through a _Sublink. If the transmission of\n additional messages is ordered through the _Sublink when it already\n contains a message, then the additional messages will be queued in the\n output queue of the transmitter port of that _Sublink.\n\n \"\"\"\n def __init__(\n self, env, link,\n transmitter_port, receiver_port):\n \"\"\"\n Create a new instance of class _Sublink.\n\n Arguments:\n env: A simpy.Environment instance.\n link: The link that the _Sublink instance is a part of.\n transmitter_port: An instance of Port that will be attached to\n the link instance as the transmitter.\n receiver_port: An instance of Port that will be attached to\n the link instance as the receiver.\n\n \"\"\"\n self.env = env\n self.link = link\n self._transmitter_port = transmitter_port\n self._receiver_port = receiver_port\n env.process(self.run())\n\n @property\n def transmitter_port(self):\n \"\"\"\n The port that is at the transmitting end of the _Sublink.\n\n \"\"\"\n return self._transmitter_port\n\n @property\n def receiver_port(self):\n \"\"\"\n The port that is at the receiving end of the _Sublink.\n\n \"\"\"\n return self._receiver_port\n\n def run(self):\n \"\"\"\n Get a message from the transmitter port and simulate its transmission.\n\n \"\"\"\n while True:\n get_request = self.transmitter_port.out_queue.get()\n message = yield get_request\n log.debug(\"{} transmission of {} started\".format(self, message))\n bytes_to_transmit = (ethernet.PREAMBLE_SIZE_BYTES +\n ethernet.SFD_SIZE_BYTES +\n message.size_bytes)\n # wait for the transmission + propagation time to elapse\n yield self.env.timeout(\n self.link.transmission_time_us(bytes_to_transmit) +\n self.link.propagation_delay_us)\n log.debug(\"{} transmission of {} finished\".format(self, message))\n self.receiver_port.in_queue.put(message)\n # wait for the duration of the ethernet interframe gap to elapse\n yield self.env.timeout(\n self.link.transmission_time_us(ethernet.IFG_SIZE_BYTES))\n log.debug(\"{} inter frame gap finished\".format(self))\n\n def __repr__(self):\n return \"{}->{}\".format(self.transmitter_port, self.receiver_port)\n\n def __str__(self):\n return \"{}->{}\".format(self.transmitter_port, self.receiver_port)\n\n\nclass NetworkDevice(object):\n \"\"\"\n Models generic network devices.\n\n A NetworkDevice instance models a generic network device with a certain\n number of ethernet ports. Network devices can be interconnected by means of\n links attached to their ports:\n\n >>> env = simpy.Environment()\n >>> d = NetworkDevice(env, \"some device\", 1)\n >>> d2 = NetworkDevice(env, \"another device\", 1)\n >>> L = Link(env, d.ports[0], d2.ports[0], 100, 3)\n\n The main purpose of this class is to serve as the superclass for specific\n network devices such as Switch, MessagePlaybackDevice,\n MessageRecordingDevice, etc.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports):\n \"\"\"\n Constructor for NetworkDevice instances.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the NetworkDevice instance.\n num_ports: The number of ports that the NetworkDevice instance\n should have.\n\n \"\"\"\n self.env = env\n self.ports = [Port(self.env, \"{}-port{}\".format(name, i))\n for i in range(num_ports)]\n self.name = name\n\n def listen_for_messages(self, callback):\n \"\"\"\n Simpy process that calls callback when messages are received.\n\n Arguments:\n callback: The function to be called when one or more messages are\n received. The function should accept a single parameter that is\n the list of received messages.\n\n Note that a simpy process is a generator function (a.k.a., co-routine).\n It should therefore not be called directly. Instead, it should be\n registered with a simpy environment using simpy.Environment().process\n\n Example:\n\n >>> class MyNetworkDevice(NetworkDevice):\n ... def __init__(self, env, name, num_ports):\n ... NetworkDevice.__init__(self, env, name, num_ports)\n ... env.process(self.listen_for_messages(self.hello))\n ... def hello(self, messages):\n ... for msg in messages:\n ... print(\"Hello message {}\".format(msg))\n ...\n >>> env = simpy.Environment()\n >>> d = MyNetworkDevice(env, \"some device\", 1)\n\n \"\"\"\n # generate get requests for all input queues\n requests = [port.in_queue.get() for port in self.ports]\n while requests:\n # helper variable for the asserts\n queues_with_pending_requests = [req.resource for req in requests]\n # There is a request for each input queue.\n assert set(self.input_queues) == set(queues_with_pending_requests)\n # For each input queue there's exactly one request.\n assert (\n len(queues_with_pending_requests) ==\n len(set(queues_with_pending_requests)))\n\n log.debug(\"{} waiting for next reception\".format(self))\n completed_requests = (yield self.env.any_of(requests))\n received_messages = list(completed_requests.values())\n log.debug(\"{} received {}\".format(\n self, received_messages))\n\n callback(received_messages)\n\n # Only leave the requests which have not been completed yet\n remaining_requests = [\n req for req in requests if req not in completed_requests]\n # Input queues that have been emptied since the last wake up.\n emptied_queues = [req.resource for req in completed_requests]\n # Add new get requests for the input queues that have been emptied.\n new_requests = []\n for input_queue in emptied_queues:\n new_requests.append(input_queue.get())\n requests = remaining_requests + new_requests\n\n def instruct_transmission(self, message, port):\n \"\"\"\n Simpy process that transmits a given message through a given port.\n\n Arguments:\n message: Message instance to be transmitted.\n port: Port of self through which to transmit the message.\n\n Raises:\n FT4FTTSimException if port is not an element of self.ports.\n\n Note that instruct_transmission() is a generator function. It should\n not be called directly, but only passed as a parameter to the process()\n method of a simpy Environment instance.\n\n Example:\n\n >>> env = simpy.Environment()\n >>> d = NetworkDevice(env, \"some device\", 1)\n >>> d2 = NetworkDevice(env, \"another device\", 1)\n >>> L = Link(env, d.ports[0], d2.ports[0], 100, 3)\n >>> m = Message(env, d, d2, 1234, \"some message\")\n >>> env.process(d.instruct_transmission(m, d.ports[0]))\n \n\n \"\"\"\n log.debug(\"{} instructing transmission of {} on {}\".format(\n self, message, port))\n if port not in self.ports:\n raise FT4FTTSimException(\"{} is not a port of {}\".format(\n port, self))\n log.debug(\"{} queued for transmission\".format(message))\n yield port.out_queue.put(message)\n\n @property\n def input_queues(self):\n \"\"\"\n Returns a list of all the input queues of the NetworkDevice instance.\n\n \"\"\"\n return [port.in_queue for port in self.ports]\n\n def __str__(self):\n return self.name\n\n def __repr__(self):\n return self.name\n\n\nclass EchoDevice(NetworkDevice):\n \"\"\"\n Models a device that retransmits any message it receives.\n\n Instances of this class are mainly used for testing purposes.\n\n \"\"\"\n\n def __init__(self, env, name):\n \"\"\"\n Creates a new EchoDevice instance.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the EchoDevice instance.\n\n \"\"\"\n NetworkDevice.__init__(self, env, name, 1)\n self.env.process(self.listen_for_messages(self.echo))\n\n def echo(self, messages):\n \"\"\"\n Transmits messages through port number 0 of self.\n\n Arguments:\n messages: An iterable of the messages to transmit.\n\n \"\"\"\n for msg in messages:\n self.env.process(self.instruct_transmission(msg, self.ports[0]))\n\n\nclass MessageRecordingDevice(NetworkDevice):\n \"\"\"\n Models receivers that record and timestamp each received message.\n\n The main purpose of instances of this class is to make testing easier.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports):\n \"\"\"\n Create a new MessageRecordingDevice instance.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the MessageRecordingDevice\n instance.\n num_ports: The number of ports that the MessageRecordingDevice\n instance should have.\n\n \"\"\"\n NetworkDevice.__init__(self, env, name, num_ports)\n self.reception_records = {}\n self.env.process(self.listen_for_messages(self.do_timestamp_messages))\n\n def do_timestamp_messages(self, messages):\n \"\"\"\n Timestamp each message in messages with the current time.\n\n \"\"\"\n timestamp = self.env.now\n self.reception_records[timestamp] = messages\n log.debug(\"{} recorded {}\".format(self, self.reception_records))\n\n @property\n def recorded_messages(self):\n \"\"\"\n Return the messages so far received by self.\n\n \"\"\"\n messages = []\n for time in sorted(self.reception_records):\n messages.extend(self.reception_records[time])\n return messages\n\n @property\n def recorded_timestamps(self):\n \"\"\"\n Return list of instants of time when messages have been received.\n\n The list is sorted from earlier time instants to later time instants.\n\n \"\"\"\n return sorted(self.reception_records.keys())\n\n\nclass MessagePlaybackDevice(NetworkDevice):\n \"\"\"\n Instances of this class model devices that transmit prespecified messages.\n\n Instances of this class accept a series of transmission commands and then\n execute them. Each transmission command specifies what message to transmit,\n at what time, and through which port.\n\n The main purpose of instances of this class is to make testing easier.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports):\n NetworkDevice.__init__(self, env, name, num_ports)\n env.process(self.run())\n self.transmission_commands = {}\n\n def load_transmission_commands(self, transmission_commands):\n \"\"\"\n Load the transmission commands to execute once the run method is\n activated by simpy.\n\n Arguments:\n transmission_commands: The transmission commands to execute once\n the run method is activated by the simulator. This argument\n should be a dictionary whose keys are instants of time when\n message transmissions should be instructed. Each of the values\n of the dictionary should be another dictionary whose keys are\n the ports on which transmissions should be ordered and\n whose values are lists of messages to be transmitted through\n the corresponding port. Example:\n\n {\n 0.0: {port1: [message1, message2]},\n 12.0: {port1: [message3]},\n 13.5: {port3: [message4, message5, message6]}\n }\n\n \"\"\"\n self.transmission_commands = transmission_commands\n log.debug(\"{} loaded transmissions: {}\".format(\n self, self.transmission_commands))\n\n def run(self):\n \"\"\"\n Simpy process that executes previously loaded transmission commands.\n\n \"\"\"\n for time in sorted(self.transmission_commands):\n delay_before_next_tx_order = time - self.env.now\n log.debug(\"{} waiting for next transmission time\".format(self))\n # wait until next transmission time\n yield self.env.timeout(delay_before_next_tx_order)\n for port, messages_to_tx in \\\n self.transmission_commands[time].items():\n for message in messages_to_tx:\n self.env.process(\n self.instruct_transmission(message, port))\n\n @property\n def transmission_start_times(self):\n \"\"\"\n Returns a list of time instants when transmissions will be instructed.\n\n \"\"\"\n return sorted(self.transmission_commands.keys())\n\n\nclass MessagePlaybackAndRecordingDevice(\n MessagePlaybackDevice, MessageRecordingDevice):\n \"\"\"\n Models devices that transmit pre-specified messages and record messages.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports):\n MessagePlaybackDevice.__init__(self, env, name, num_ports)\n self.reception_records = {}\n self.env.process(self.listen_for_messages(self.do_timestamp_messages))\n\n\nclass Switch(NetworkDevice):\n \"\"\"\n Models standard Ethernet switches.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports, forwarding_table=None):\n \"\"\"\n Creates a new Switch instance.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the new switch instance.\n num_ports: The number of ports that the new switch instance should\n have.\n forwarding_table: Dictionary whose keys are network devices and\n whose values are ports of the Switch instance.\n\n \"\"\"\n NetworkDevice.__init__(self, env, name, num_ports)\n env.process(self.listen_for_messages(self.forward_messages))\n if forwarding_table is None:\n self.forwarding_table = {}\n else:\n self.forwarding_table = forwarding_table\n\n def forward_messages(self, message_list):\n \"\"\"\n Forward each message in 'message_list' through the appropriate port.\n\n Note that forwarding a message from one port to another port is\n implemented as creating a new message instance based on the message\n in the first port, and transmitting the new message instance on the\n second port.\n\n \"\"\"\n\n def find_ports(destination):\n \"\"\"\n Return a list of the ports that according to the forwarding table\n lead to 'destination'.\n\n Arguments:\n destination: an instance of class NetworkDevice or an iterable\n of NetworkDevice instances.\n\n Returns:\n A set of the ports that lead to the devices in 'destination'.\n\n \"\"\"\n output_ports = set()\n if isinstance(destination, collections.Iterable):\n for device in destination:\n # ports leading to device\n ports_towards_device = self.forwarding_table.get(\n device, self.ports)\n output_ports.update(ports_towards_device)\n else:\n output_ports.update(\n self.forwarding_table.get(destination, self.ports))\n return output_ports\n\n for message in message_list:\n destinations = message.destination\n output_ports = find_ports(destinations)\n for port in output_ports:\n new_message = Message.from_message(message)\n self.env.process(\n self.instruct_transmission(new_message, port))\n\n\nclass Message(object):\n \"\"\"\n Class for messages that model Ethernet frames.\n\n \"\"\"\n # next available identifier for message objects\n next_identifier = 0\n\n def __init__(\n self, env, source, destination, size_bytes, message_type,\n data=None):\n \"\"\"\n Create an instance of Message.\n\n Arguments:\n source: usually an instance of NetworkDevice. It models the\n MAC source address field of an Ethernet frame.\n destination: usually an instance of NetworkDevice or a list of\n instances of NetworkDevice. It models the MAC destination\n address field of an Ethernet frame. If it is iterable, then it\n models a multicast address, otherwise it models a unicast\n address.\n size_bytes: indicates the size in bytes of the Ethernet frame\n modeled by the Message instance created. The size does not\n include the Ethernet preamble, the start of frame delimiter, or\n an IEEE 802.1Q tag.\n message_type: models the Ethertype field.\n data: The data to be carried within the message. It models the\n Ethernet data field.\n\n \"\"\"\n if not isinstance(size_bytes, int):\n raise FT4FTTSimException(\"Message size must be integer\")\n if not (ethernet.MIN_FRAME_SIZE_BYTES <= size_bytes <=\n ethernet.MAX_FRAME_SIZE_BYTES):\n raise FT4FTTSimException(\n \"Message size must be between {} and {}, but is {}\".format(\n ethernet.MIN_FRAME_SIZE_BYTES,\n ethernet.MAX_FRAME_SIZE_BYTES,\n size_bytes))\n self.env = env\n self._identifier = Message.next_identifier\n Message.next_identifier += 1\n # source of the message. Models the source MAC address.\n self.source = source\n # destination of the message. It models the destination MAC address. It\n # is a list to allow multicast addressing.\n self.destination = destination\n self.size_bytes = size_bytes\n self.message_type = message_type\n self.data = data\n self.name = \"({:03d}, {}, {}, {:d}, {}, {})\".format(\n self.identifier, self.source, self.destination, self.size_bytes,\n self.message_type, self.data)\n log.debug(\"{} created\".format(self))\n\n @property\n def identifier(self):\n \"\"\"\n Integer that uniquely identifies the Message instance.\n\n \"\"\"\n return self._identifier\n\n @classmethod\n def from_message(cls, template_message):\n \"\"\"\n Creates a new message instance using template_message as a template.\n\n \"\"\"\n new_equivalent_message = cls(\n template_message.env,\n template_message.source,\n template_message.destination,\n template_message.size_bytes,\n template_message.message_type,\n template_message.data)\n return new_equivalent_message\n\n def __eq__(self, message):\n \"\"\"\n Returns true if self and message are identical except for the message\n identifier.\n\n \"\"\"\n return (self.source == message.source and\n self.destination == message.destination and\n self.size_bytes == message.size_bytes and\n self.message_type == message.message_type and\n self.data == message.data)\n\n def __str__(self):\n return self.name\n\n def __repr__(self):\n return self.name\n","repo_name":"davitenio/ft4fttsim","sub_path":"ft4fttsim/networking.py","file_name":"networking.py","file_ext":"py","file_size_in_byte":25265,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18105284977","text":"import asyncio\r\nimport schedule\r\nfrom db_connection import StudentDB\r\nfrom datetime import datetime\r\nfrom handlers import send_schedule\r\n\r\n# Время, когда нужно отправлять расписание\r\nschedule_times = [\"02:00-02:23\", \"14:30-15:30\"]\r\ndb = StudentDB\r\n\r\nasync def send_schedule_at_specific_times():\r\n # while True:\r\n # current_time = datetime.now().strftime(\"%H:%M\")\r\n\r\n # if current_time in schedule_times:\r\n list_of_chat_ids = db.get_all_user_ids()\r\n for chat_id in list_of_chat_ids:\r\n await send_schedule(chat_id)\r\n\r\n # Заснуть на 1 минуту перед проверкой времени снова\r\n # await asyncio.sleep(60)\r\n\r\nasync def task_send_schedule_at_specific_times():\r\n # Здесь вставьте инициализацию бота и другие настройки\r\n\r\n # Задача для отправки в 9:10\r\n schedule.every().day.at(\"09:10\").do(asyncio.run, send_schedule_at_specific_times)\r\n\r\n # Задача для отправки в 14:30\r\n schedule.every().day.at(\"14:30\").do(asyncio.run, send_schedule_at_specific_times)\r\n\r\n # Пример задачи для отправки за 10 минут до времени из переменной\r\n # Здесь переменная next_time должна содержать желаемое время в формате HH:MM\r\n next_time = \"02:46\"\r\n schedule.every().day.at(next_time).do(asyncio.run, send_schedule_at_specific_times)\r\n\r\n while True:\r\n schedule.run_pending()\r\n await asyncio.sleep(1)\r\n","repo_name":"Ch1kez/Ranh_bot","sub_path":"Ranh_bot_1_2/task_send.py","file_name":"task_send.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5234479806","text":"from __future__ import print_function\nfrom abc import abstractmethod\nimport smtplib\nimport sqlite3\nfrom twilio.rest import Client as TWClient\nfrom log import Log\nfrom utilities import DB_CORE\n\nclass Notification(object):\n \"\"\"Hold information about nofication that can be sent via SMS or email.\"\"\"\n\n def __init__(self):\n self.subject = \"Alert - {site}: {asset}.\"\n self.message = \"{time}: an alert was triggered at {site}, {asset}, value: {value}.\"\n self.sender = \"\"\n self.receiver = \"\"\n\n @abstractmethod\n def send(self):\n \"\"\"Send notification.\"\"\"\n\n @abstractmethod\n def load_settings(self):\n \"\"\"Load mail settings.\"\"\"\n\nclass Email(Notification):\n \"\"\"Email notification.\"\"\"\n\n def __init__(self):\n Notification.__init__(self)\n self.serveraddr = \"\"\n self.serverport = \"\"\n self.username = \"\"\n self.password = \"\"\n self.tls = False\n\n def load_settings(self):\n \"\"\"Load mail settings from the core database.\"\"\"\n try:\n field_names = '''\n serveraddr\n serverport\n username\n password\n sender\n receiver\n tls\n '''.split()\n sql = 'SELECT {fields} FROM mail_settings LIMIT 1;'.format(\n fields=', '.join(field_names))\n database = sqlite3.connect(DB_CORE)\n db_elements = database.cursor().execute(sql).fetchone()\n for field, value in zip(field_names, db_elements):\n setattr(self, field, value)\n Log.info(\"Mail settings loaded.\")\n except Exception as excpt:\n Log.exception(\"Trying to load mail settings: %s.\", excpt)\n finally:\n database.close()\n\n\n def build_message(self, subject, message):\n \"\"\"Build message body for e-mail.\"\"\"\n message = \"\\r\\n\".join([\n \"From: \" + self.sender,\n \"To: \" + self.receiver,\n \"Subject: \" + subject,\n \"\",\n message])\n\n return message\n\n def send(self, subject, message):\n \"\"\"Send email notification using paremeters from the database.\"\"\"\n try:\n Log.info(\"Sending email notification.\")\n self.load_settings()\n server = smtplib.SMTP(str(self.serveraddr), str(self.serverport))\n server.ehlo()\n if self.tls:\n server.starttls()\n server.login(self.username, self.password)\n server.sendmail(self.sender, self.receiver, self.build_message(subject, message))\n Log.info(\"Email notification sent.\")\n except Exception as excpt:\n Log.exception(\"Trying to send notificaton via e-mail: %s.\", excpt)\n finally:\n server.quit()\n\nclass SMS(Notification):\n \"\"\"SMS notification.\"\"\"\n\n def __init__(self):\n Notification.__init__(self)\n self.twilio_acct_sid = \"\"\n self.twilio_auth_token = \"\"\n\n def send(self, sender, receiver, message):\n \"\"\"Send SMS Notification via Twilio.\"\"\"\n try:\n Log.info(\"Sending SMS notification.\")\n self.load_settings()\n client = TWClient(self.twilio_acct_sid, self.twilio_auth_token)\n message = client.messages.create(to=receiver, from_=sender, body=message)\n Log.info(\"SMS notification sent: %s\", message.sid)\n except Exception as excpt:\n Log.exception(\"Trying to send notificaton via SMS: %s.\", excpt)\n\n def load_settings(self):\n \"\"\"Load SMS settings from the core database.\"\"\"\n try:\n field_names = '''\n sender\n receiver\n twilio_acct_sid\n twilio_auth_token\n '''.split()\n sql = 'SELECT {fields} FROM site LIMIT 1;'.format(\n fields=', '.join(field_names))\n database = sqlite3.connect(DB_CORE)\n db_elements = database.cursor().execute(sql).fetchone()\n for field, value in zip(field_names, db_elements):\n setattr(self, field, value)\n Log.info(\"SMS settings loaded.\")\n except Exception as excpt:\n Log.exception(\"Trying to load SMS settings: %s.\", excpt)\n finally:\n database.close()\n","repo_name":"mayaculpa/hapi","sub_path":"src/smart_module/notification.py","file_name":"notification.py","file_ext":"py","file_size_in_byte":4330,"program_lang":"python","lang":"en","doc_type":"code","stars":68,"dataset":"github-code","pt":"3"}
+{"seq_id":"29047336187","text":"from balance.actions.dcs.utils import nirvana as nirvana_utils\n\n\ndef test_get_nirvana_io_by_type():\n input_example = {\n 'key': {\n 'name': 'key',\n 'type': 'INPUT',\n 'items': [{'dataType': 'json'}],\n }\n }\n\n assert nirvana_utils.get_io_by_type({}, 'text') is None\n assert nirvana_utils.get_io_by_type(input_example, 'text') is None\n assert nirvana_utils.get_io_by_type(input_example, 'json') == 'key'\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/dcs/utils/test_nirvana.py","file_name":"test_nirvana.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13734239423","text":"#!/usr/bin/python3\n'''\nThe last exercise in this chapter adds a small feature to the other continuous exercise project, the notebook. In this exercise, add a feature which includes the date and time of the written note to the program. The program works as earlier, but saves data in the form \"[note]:::[date and time]\" meaning that there is a three-colon separator between the note and timestamp. The timestamp can be generated as follows:\n\n\n\n\n\t\n>>> import time\t\t\n\n>>> time.strftime(\"%X %x\")\n'19:01:34 01/03/09'\n>>> \n\nThis returns the date and time in a nice, compact string. When working correctly, the program prints the following:\n\n\n\n\n\t\t\t\n>>> \n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 2\nWrite a new note: Call mom.\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 1\nCall mom.:::11:44:41 04/25/11\n\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 4\nNotebook shutting down, thank you.\n>>> \n\n\nExample output:\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 2\nWrite a new note: Play football\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 1\nPlay football:::21:11:24 11/04/11\n\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 4\nNotebook shutting down, thank you.\nThe verification of program output does not account for whitespace and is not case-sensitive (the least strict comparison level)\n'''\n\nimport time\n\ndef createNotebook(name):\n try:\n readfiles = open(name, 'r') \n readfiles.close()\n print(\"Now using file\", name)\n except IOError:\n if name == \"notebook.txt\":\n print(\"No default notebook was found, created one.\")\n else:\n print(\"No notebook with that name detected, created one.\")\n createFile = open(name, \"a\")\n print(\"Now using file \", name)\n createFile.close()\n \n\ndef readFile(name):\n try:\n readfile = open(name, 'r')\n content = readfile.read()\n readfile.close()\n print(content)\n except IOError:\n return False\n\n\n\ndef writeFile(name):\n stringuser = input(\"Write a new note: \")\n notebook = open(name, \"a\") \n check = time.strftime(\"%X %x\")\n all = stringuser + \":::\"+ check + \"\\n\"\n notebook.write(all)\n notebook.close()\ndef emptyFile():\n newFile = open(\"notebook.txt\", \"w+\")\n newFile.truncate(0)\n newFile.close()\n print(\"Notes deleted\")\n\nname = \"notebook.txt\"\noption = True\nwhile option == True:\n createNotebook(name)\n print(\"(1) Read the notebook \\n(2) Add note \\n(3) Empty the notebook\\n(4) Change the notebook \\n(5) Quit\")\n select = int(input(\"Please select one:\"))\n if (select == 1):\n readFile(name)\n elif (select == 2):\n writeFile(name)\n elif (select == 3):\n\t emptyFile()\n elif (select == 4):\n name = input(\"Give the name of the new file:\")\n elif (select == 5):\n option = False\n print(\"Notebook shutting down, thank you.\")\n\n else:\n print(\"Incorrect Selection.\")\n","repo_name":"sarakfouri/first_steps_python","sub_path":"task7_6.py","file_name":"task7_6.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6004266252","text":"from tkinter import *\n\njanela = Tk()\njanela.title('calculadora simples')\n\nentrd = Entry(janela, width=35, borderwidth=5)\nentrd.grid(row=0, column=0, columnspan=3, padx=10, pady=10)\n\n\n\n#entrd.insert(0, 'seu nome')\ndef Botao_clicado(numero):\n #entrd.delete(0, END)\n atual = entrd.get()\n\n entrd.delete(0, END)\n\n entrd.insert(0, str(atual) + str(numero))\n\ndef Botao_limpar():\n entrd.delete(0, END)\n \ndef Botao_mais():\n primeiro_numero = entrd.get()\n global p_num\n global matematica\n matematica = 'mais'\n p_num = int(primeiro_numero)\n entrd.delete(0, END)\n \ndef Botao_igual():\n segundo_numero = entrd.get()\n entrd.delete(0, END)\n \n if matematica == 'mais':\n entrd.insert(0, p_num + int(segundo_numero))\n \n if matematica == 'menos':\n entrd.insert(0, p_num - int(segundo_numero))\n \n if matematica == 'vezes':\n entrd.insert(0, p_num * int(segundo_numero))\n \n if matematica == 'divisao':\n entrd.insert(0, p_num / int(segundo_numero))\n \n \ndef Botao_menos():\n primeiro_numero = entrd.get()\n global p_num\n global matematica\n matematica = 'menos'\n p_num = int(primeiro_numero)\n entrd.delete(0, END)\n\ndef Botao_vezes():\n primeiro_numero = entrd.get()\n global p_num\n global matematica\n matematica = 'vezes'\n p_num = int(primeiro_numero)\n entrd.delete(0, END)\n \ndef Botao_dividir():\n primeiro_numero = entrd.get()\n global p_num\n global matematica\n matematica = 'divisao'\n p_num = int(primeiro_numero)\n entrd.delete(0, END)\n\nbt1 = Button(janela, text='1', padx=40, pady=20, command=lambda: Botao_clicado(1))\nbt2 = Button(janela, text='2', padx=40, pady=20, command=lambda: Botao_clicado(2))\nbt3 = Button(janela, text='3', padx=40, pady=20, command=lambda: Botao_clicado(3))\nbt4 = Button(janela, text='4', padx=40, pady=20, command=lambda: Botao_clicado(4))\nbt5 = Button(janela, text='5', padx=40, pady=20, command=lambda: Botao_clicado(5))\nbt6 = Button(janela, text='6', padx=40, pady=20, command=lambda: Botao_clicado(6))\nbt7 = Button(janela, text='7', padx=40, pady=20, command=lambda: Botao_clicado(7))\nbt8 = Button(janela, text='8', padx=40, pady=20, command=lambda: Botao_clicado(8))\nbt9 = Button(janela, text='9', padx=40, pady=20, command=lambda: Botao_clicado(9))\nbt0 = Button(janela, text='0', padx=40, pady=20, command=lambda: Botao_clicado(0))\n\nbt_mais = Button(janela, text='+', padx=39, pady=20, command=Botao_mais)\nbt_igual = Button(janela, text='=', padx=91, pady=20, command=Botao_igual)\nbt_menos = Button(janela, text='-', padx=40, pady=20, command=Botao_menos)\nbt_vezes = Button(janela, text='*', padx=40, pady=20, command=Botao_vezes)\nbt_dividir = Button(janela, text='/', padx=40, pady=20, command=Botao_dividir)\n\nbt_limpar = Button(janela, text='limpar', padx=79, pady=20, command= Botao_limpar)\n\n\n# colocando botoes em grid\n\nbt1.grid(row=3, column=0)\nbt2.grid(row=3, column=1)\nbt3.grid(row=3, column=2)\n\nbt4.grid(row=2, column=0)\nbt5.grid(row=2, column=1)\nbt6.grid(row=2, column=2)\n\nbt7.grid(row=1, column=0)\nbt8.grid(row=1, column=1)\nbt9.grid(row=1, column=2)\n\nbt0.grid(row=4, column=0)\n\nbt_limpar.grid(row=4, column=1, columnspan=2)\nbt_mais.grid(row=5, column=0)\nbt_igual.grid(row=5, column=1, columnspan=2)\n\nbt_menos.grid(row=6, column=0)\nbt_vezes.grid(row=6, column=1)\nbt_dividir.grid(row=6, column=2)\n\njanela.mainloop()","repo_name":"jonasht/tkinter-FreeCodeCamp.org","sub_path":"02-calculadoraSimples0.py","file_name":"02-calculadoraSimples0.py","file_ext":"py","file_size_in_byte":3409,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36388138976","text":"# Michael Rogers\nimport matplotlib.pyplot as plt\nimport random\n\nclear_board = [[0,0,0],\n [0,0,0],\n [0,0,0]]\n\nboard = clear_board\nX_wins = 0\nO_wins = 0\n\ndef play_games(games):\n X = 0\n O = 0\n for i in range(games+1):\n X1, O1 = play_game()\n X += X1\n O += O1\n print(\"X: \" + X + \" O: \" + O + \" Ties: \" + (100-X-O))\n\ndef play_game():\n for i in range(10):\n if i % 2 is 0:\n place_piece(1)\n else:\n place_piece(4)\n board = clear_board\n if check_winner() % 2 is 0:\n return 1, 0 \n else:\n return 0, 1\n\ndef check_winner():\n #Check one way\n if board[0][0] + board[0][1] + board[0][2] is 3:\n return 1\n elif board[0][0] + board[0][1] + board[0][2] is 12:\n return 2\n\n elif board[1][0] + board[1][1] + board[1][2] is 3:\n return 1\n elif board[1][0] + board[1][1] + board[1][2] is 12:\n return 2\n\n elif board[2][0] + board[2][1] + board[2][2] is 3:\n return 1\n elif board[2][0] + board[2][1] + board[2][2] is 12:\n return 2\n #Check other way\n elif board[0][0] + board[1][0] + board[2][0] is 3:\n return 1\n elif board[0][0] + board[1][0] + board[2][0] is 12:\n return 2\n\n elif board[0][1] + board[1][1] + board[2][1] is 3:\n return 1\n elif board[0][1] + board[1][1] + board[2][1] is 12:\n return 2\n \n elif board[0][2] + board[1][2] + board[2][2] is 3:\n return 1\n elif board[0][2] + board[1][2] + board[2][2] is 12:\n return 2\n #Check diagonals\n elif board[0][0] + board[1][1] + board[2][2] is 3:\n return 1\n elif board[0][0] + board[1][1] + board[2][2] is 12:\n return 2\n elif board[2][0] + board[1][1] + board[0][2] is 3:\n return 1\n elif board[2][0] + board[1][1] + board[0][2] is 12:\n return 2\n else:\n return 0\n\ndef place_piece(num):\n x = random.randint(0,2)\n y = random.randint(0,2)\n\n while board[x][y] is not 0:\n x = random.randint(0,2)\n y = random.randint(0,2)\n\n board[x][y] = num\n\nplay_games(100)","repo_name":"MRog40/Python","sub_path":"PyOther/TicTacToeOdds.py","file_name":"TicTacToeOdds.py","file_ext":"py","file_size_in_byte":2101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1414518242","text":"from __future__ import print_function # for calendar, must be at beginning of file\n\nprint(\"\\nLoading assistant...\")\n#################################\nimport datetime as DT\nimport random\nrandom.seed(DT.time.microsecond)\n# Google Calendar components\nimport httplib2\nimport os\nfrom apiclient import discovery\nfrom oauth2client import client\nfrom oauth2client import tools\nfrom oauth2client.file import Storage\ntry:\n\timport argparse\n\tflags = argparse.ArgumentParser(parents=[tools.argparser]).parse_args()\nexcept ImportError:\n\tflags = None\n# If modifying these scopes, delete your previously saved credentials\n# at ~/.credentials/calendar-python-quickstart.json\nSCOPES = 'https://www.googleapis.com/auth/calendar.readonly'\nCLIENT_SECRET_FILE = 'client_id.json'\nAPPLICATION_NAME = 'assistant'\n\n# Variables\nmyName = \"user\"\nNXTHOURS = DT.timedelta(hours=3) # in calendar, definition of an impending event in 3 hours\ntimeZone = DT.timedelta(hours=5) # shouldn't use this if possible\nstartUpTime = DT.datetime.now() # used for refreshing\n#################################\nprint(\"assistant has been loaded.\\n\")\n\ndef timeOfDay():\n\tprint(DT.time().hour,\"P\")\n\taroundTime = DT.datetime.now().hour + random.randint(-1,1)\n\tif(aroundTime <= 10):\n\t\treturn \"morning\"\n\telif(aroundTime <= 16):\n\t\treturn \"afternoon\"\n\telse:\n\t\treturn \"evening\"\n\n# for Google calendar\ndef get_credentials():\n\t\"\"\"Gets valid user credentials from storage.\n\tIf nothing has been stored, or if the stored credentials are invalid,\n\tthe OAuth2 flow is completed to obtain the new credentials.\n\tReturns:\n\t\tCredentials, the obtained credential.\n\t\"\"\"\n\thome_dir = os.path.expanduser('~')\n\tcredential_dir = os.path.join(home_dir, '.credentials')\n\tif not os.path.exists(credential_dir):\n\t\tos.makedirs(credential_dir)\n\tcredential_path = os.path.join(credential_dir,\n\t\t\t\t\t\t\t\t 'calendar-python-quickstart.json')\n\n\tstore = Storage(credential_path)\n\tcredentials = store.get()\n\tif not credentials or credentials.invalid:\n\t\tflow = client.flow_from_clientsecrets(CLIENT_SECRET_FILE, SCOPES)\n\t\tflow.user_agent = APPLICATION_NAME\n\t\tif flags:\n\t\t\tcredentials = tools.run_flow(flow, store, flags)\n\t\telse: # Needed only for compatibility with Python 2.6\n\t\t\tcredentials = tools.run(flow, store)\n\t\tprint('Storing credentials to ' + credential_path)\n\treturn credentials\ncredentials = get_credentials()\nhttp = credentials.authorize(httplib2.Http())\nservice = discovery.build('calendar', 'v3', http=http)\n\ndef formatGT(eventTime): # stands for format google thingy\n\teventTime = eventTime.split('T')\n\teventTime = ' '.join(eventTime)\n\teventTime = eventTime.split('-')\n\teventTime.pop(3)\n\teventTime = '-'.join(eventTime)\n\teventTime = DT.datetime.strptime(eventTime, \"%Y-%m-%d %H:%M:%S\")\n\treturn eventTime\n\ndef formatMinHr(numMinutes, fuzzy=0):\n\tif numMinutes == 1:\n\t\treturn \"1 minute\"\n\telif numMinutes < 60:\n\t\tif fuzzy == 1:\n\t\t\tnumMinutes = 10*round(numMinutes/10)\n\t\t\treturn \"about \"+str(numMinutes)+\" minutes\"\n\t\telse:\n\t\t\treturn str(numMinutes)+\" minutes\"\n\telse:\n\t\thrs = int(numMinutes/60) # floors it\n\t\tmins = numMinutes%60\n\t\tif hrs == 1:\n\t\t\thrs = \"1 hour\"\n\t\telse:\n\t\t\thrs = str(hrs)+\" hours\"\n\t\tif mins == 0:\n\t\t\treturn hrs # 0 minutes\n\t\telif mins == 1:\n\t\t\tif fuzzy == 1:\n\t\t\t\treturn \"about \"+hrs # 0 minutes\n\t\t\telse:\n\t\t\t\tmins = \"1 minute\"\n\t\telse:\n\t\t\tif fuzzy == 1:\n\t\t\t\tmins = 10*round(mins/10)\n\t\t\t\treturn \"about \"+hrs+\", \"+str(mins)+\" minutes\"\n\t\t\tmins = str(mins)+\" minutes\"\n\t\treturn hrs+\", \"+mins\n\t\ndef eventLen(myEvent):\n\ttimeT = formatGT(myEvent[\"end\"][\"dateTime\"])-formatGT(myEvent[\"start\"][\"dateTime\"])\n\treturn round(timeT.seconds/60)\n\t\ndef nextEvent(nowEvent = 0):\n\t# returns the next event to occur after the nowEvent, or if no next event, then 0; also returns time till\n\tif nowEvent == 0:\n\t\tmaxNow = DT.datetime.utcnow()\n\telse:\n\t\tmaxNow = formatGT(nowEvent[\"end\"][\"dateTime\"])\n\t# Getting the next 1 event\n\t# 'Z' indicates UTC time\n\teventsResult = service.events().list(calendarId='primary', timeMin=maxNow.isoformat()+\"-05:00\", maxResults=1, singleEvents=True,orderBy='startTime').execute()\n\tevents = eventsResult.get('items', [])\n\tif not events:\n\t\treturn 0\n\t# get time till\n\ttimeTill = formatGT(events[0][\"start\"][\"dateTime\"])-DT.datetime.now()\n\treturn (events[0], round(timeTill.seconds/60))\n\t\ndef imminEvent(nowEvent = 0, maxEvents = 20, maxLater = DT.datetime.now() + NXTHOURS):\n\t# returns all events within the next few hours, or a number representing the minutes of free time until the next event (-1 if no next)\n\tif nowEvent == 0:\n\t\tmaxNow = DT.datetime.now()\n\telse:\n\t\tmaxNow = formatGT(nowEvent[\"end\"][\"dateTime\"])\n\t# Getting the next 1 event\n\t# 'Z' indicates UTC time (taken off)\n\teventsResult = service.events().list(calendarId='primary', timeMin=maxNow.isoformat()+\"-05:00\", timeMax=maxLater.isoformat()+\"-05:00\", maxResults=maxEvents, singleEvents=True,orderBy='startTime').execute()\n\tevents = eventsResult.get('items', [])\n\tif not events:\n\t\t# return time till next event\n\t\t\tnxt, nxtTill = nextEvent(nowEvent)\n\t\t\tif nxt != 0:\n\t\t\t\treturn nxtTill\n\t\t\telse:\n\t\t\t\treturn -1\n\treturn events\n\t\ndef nowEvent():\n\t# returns the current event and number of other events, or (0,0) if none\n\tnow = DT.datetime.now()\n\t# Getting the \"current\" event\n\t# 'Z' indicates UTC time\n\teventsResult = service.events().list(calendarId='primary', timeMin=(now-2*NXTHOURS).isoformat()+\"-05:00\", timeMax=(now+2*NXTHOURS).isoformat()+\"-05:00\", maxResults=10, singleEvents=True,orderBy='startTime').execute()\n\tevents = eventsResult.get('items', [])\n\tevents = list(reversed(events))\n\tif not events:\n\t\treturn (0, now)\n\tfinalAns = []\n\tfor event in events:\n\t\tif formatGT(event[\"end\"][\"dateTime\"]) > now and formatGT(event[\"start\"][\"dateTime\"]) < now: # make sure is still running\n\t\t\tfinalAns.append(event)\n\tif len(finalAns) == 0:\n\t\treturn (0, 0)\n\treturn (finalAns[0], len(finalAns)-1)\n\nprint(\"Good %s, %s. It's %s.\" % (timeOfDay(), myName, DT.datetime.now().strftime('%A, %I:%M%p')))\nmyNowEvent, myNowEventEnd = nowEvent()\n\nuser = input()\nwhile user != \"thanks\":\n\tif user == \"refresh please\":\n\t\tmyNowEvent, myNowEventEnd = nowEvent()\n\t\tprint(\"Got it,\", myName)\n\t# check for conflicts XXXX\n\tif user == \"when's my next event?\":\n\t\tmyNextEvent, tillNextEvent = nextEvent(myNowEvent)\n\t\tprint(\"You've got\",myNextEvent[\"summary\"],\"scheduled for\",formatGT(myNextEvent[\"start\"][\"dateTime\"]).strftime(\"%I:%M\"))\n\tif user == \"how long until my next event?\":\n\t\tmyNextEvent, tillNextEvent = nextEvent(myNowEvent)\n\t\tprint(\"You've got\",formatMinHr(tillNextEvent,1))\n\tif user == \"how long is my next event?\":\n\t\tmyNextEvent, tillNextEvent = nextEvent(myNowEvent)\n\t\tprint(myNextEvent[\"summary\"],\"is for\",formatMinHr(eventLen(myNextEvent)))\n\tif user == \"anything coming up?\":\n\t\timEvents = imminEvent(myNowEvent)\n\t\tif isinstance(imEvents, int): # no events, just number\n\t\t\tif imEvents == -1:\n\t\t\t\tprint(\"You have no events coming up!\")\n\t\t\telse:\n\t\t\t\tprint(\"You've got\",imEvents,\"till your next event.\")\n\t\telse:\n\t\t\tprint(\"Yep. Next you have:\")\n\t\t\tfor imEv in imEvents:\n\t\t\t\tprint(imEv[\"summary\"])\n\tif user == \"can i move my next event up?\":\n\t\tmyNextEvent, tillNextEvent = nextEvent(myNowEvent)\n\t\tnxtEvLength = eventLen(myNextEvent)\n\t\tif myNextEvent == 0:\n\t\t\tprint(\"You don't have any events next.\")\n\t\telse:\n\t\t\t# get length of next event\n\t\t\tnxtImEvs = imminEvent(myNowEvent, maxLater=formatGT(myNextEvent[\"start\"][\"dateTime\"]))\n\t\t\tif isinstance(nxtImEvs,int) and nxtImEvs != -1:\n\t\t\t\tif nxtImEvs>nxtEvLength:\n\t\t\t\t\tprint(\"Sure, I can do that\")\n\t\t\t\telse:\n\t\t\t\t\tprint(\"You have\",formatMinHr(nxtImEvs),\"of time at X but it won't be enough\")\n\t\t\telse:\n\t\t\t\tprint(\"There's an event immediately after this so I can't do anything yet\")\n\tif user == \"add an event\":\n\t\tpass\n\tuser = input()\n\tuser = user.lower()\n\nprint(\"Closing assistant...\")\n","repo_name":"miya7090/calendar-assistant","sub_path":"sandbox.py","file_name":"sandbox.py","file_ext":"py","file_size_in_byte":7706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1002577016","text":"# -*- coding: utf-8 -*-\nimport sys\nimport click\nimport netaddr\nfrom ipamcli.cli import pass_context\nimport ipamcli.libs.phpipam.client as phpipam\n\n\n@click.command('search', short_help='search entry in phpIPAM')\n@click.option('--ip', help='ip address of entry to search')\n@click.option('--hostname', help='hostname of entry to search')\n@click.option('--first-empty', is_flag=True, help='search first empty IP address')\n@click.option('--last-empty', is_flag=True, help='search last empty IP address')\n@click.option('--network', help='network address for first-empty search')\n@click.option('--vlan-id', metavar='', help='vlan id for first-empty search')\n@click.option('--vlan-name', help='vlan name for first-empty search')\n@pass_context\ndef cli(ctx, ip, hostname, first_empty, last_empty, network, vlan_id, vlan_name):\n \"\"\"Search entry information in phpIPAM.\"\"\"\n if first_empty or last_empty:\n if not network and not vlan_id and not vlan_name:\n ctx.logerr('At least one of the --network / --vlan-id / --vlan-name option must be set when use --first-empty option.')\n sys.exit(1)\n\n if vlan_id:\n if vlan_id not in ctx.vlan_list:\n ctx.logerr('No such vlan id in list.')\n sys.exit(1)\n\n else:\n network = ctx.vlan_list[vlan_id]['prefix']\n\n elif vlan_name:\n for item in ctx.vlan_list:\n if ctx.vlan_list[item]['name'] == vlan_name:\n network = ctx.vlan_list[item]['prefix']\n\n if not network:\n ctx.logerr('No such vlan name in list.')\n sys.exit(1)\n\n try:\n network = netaddr.IPNetwork(network)\n\n except netaddr.core.AddrFormatError:\n ctx.logerr('Network address %s is invalid.', network)\n sys.exit(1)\n\n if first_empty:\n ip = phpipam.get_first_empty(ctx, network)\n elif last_empty:\n ip = phpipam.get_last_empty(ctx, network)\n\n if ip is None:\n sys.exit(1)\n else:\n ctx.log('Empty IP address in subnet %s(%s):\\n%s',\n str(network), phpipam.get_network_mask_by_subnet(str(network)), ip)\n\n else:\n if ip:\n if not phpipam.checkIP(ip):\n ctx.logerr('IP address %s is invalid.', ip)\n sys.exit(1)\n\n try:\n resp = phpipam.search_by_ip(ctx, ip)\n except Exception:\n ctx.logerr('Oops. HTTP API error occured.')\n sys.exit(1)\n\n elif hostname:\n try:\n resp = phpipam.search_by_hostname(ctx, hostname)\n except Exception:\n ctx.logerr('Oops. HTTP API error occured.')\n sys.exit(1)\n\n else:\n ctx.logerr('At least one of the search option must be set.')\n sys.exit(1)\n\n if resp:\n resp = sorted(resp, key=lambda k: netaddr.IPAddress(k['ip']))\n for item in resp:\n subnet = phpipam.get_subnet_by_id(ctx, item['subnetId'])\n ctx.log(u'ID: %s\\nSubnet prefix: %s\\nSubnet netmask: %s\\nSubnet description: %s\\nIP: %s\\nMAC: %s\\nFQDN: %s\\nDescription: %s\\n',\n item['id'],\n '{}/{}'.format(subnet['subnet'], subnet['mask']),\n phpipam.get_network_mask_by_subnet('{}/{}'.format(subnet['subnet'], subnet['mask'])),\n subnet['description'],\n item['ip'],\n item['mac'],\n item['hostname'],\n item['description'])\n\n else:\n if ip:\n ctx.log('There is no record for ip address %s.', ip)\n sys.exit(1)\n\n elif hostname:\n ctx.log('There is no record for hostname %s.', hostname)\n sys.exit(1)\n","repo_name":"verdel/ipamcli","sub_path":"ipamcli/commands/cmd_search.py","file_name":"cmd_search.py","file_ext":"py","file_size_in_byte":3918,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"16439771481","text":"import struct\nimport sys\nimport glob\nimport argparse\nimport datetime\n\n\nclass packet(object):\n \"\"\" Holds data for one 256-byte packet from the raw (numerical\n extension) RESmart data files. Also methods for parsing the data \"\"\"\n\n def __init__(self,start, pbuf):\n \"\"\" given pbuf list of bytes, parse out the data\"\"\"\n\n assert len(pbuf) >= 256\n\n # length of data fields (uint16_t), not counting zero pads and timestamp\n self.dlen = 106\n\n # extract timestamp\n self.parse_timestamp(pbuf)\n\n # extract data fields\n self.parse_data(pbuf)\n\n # generate human-readable labels\n self.setup_labels()\n\n if self.data[self.known_fields[\"spO2_pct\"]] > 0:\n self.has_pulse = True\n else:\n self.has_pulse = False\n \n\n def setup_labels(self):\n \"\"\" These are human-readable labels for the headers of .csv files\"\"\"\n # set up data structure for field labels\n self.timestamp_fields = [\"year\",\"month\",\"day\",\"hour\",\"minute\",\"second\",\"?\"]\n # default label is \"?\" for unknown data field\n self.data_fields = [\"?\" for i in range(self.dlen)]\n \n # first 85 fields are 25 Hz and 10 Hz measurements of pressure/flow\n for i in range(25):\n self.data_fields[4 + i ] = \"resA\".format(i)\n self.data_fields[4 + i + 25] = \"resB\".format(i)\n self.data_fields[4 + i + 50] = \"resC\".format(i)\n if i < 10:\n self.data_fields[4 + i + 75] = \"pulse\".format(i)\n \n # some data fields are known, label them\n self.known_fields = {\n \"Reslex\":1,\n \"IPAP\":2,\n \"EPAP\":3,\n \"tidal_vol\":99,\n \"spO2_pct\":102,\n \"HR_BPM\":103,\n \"rep_rate\":104}\n \n for key, val in self.known_fields.items():\n self.data_fields[val] = key\n \n def parse_timestamp(self,pbuf):\n \"\"\" the timestamp is the last 8 bytes of every packet\"\"\"\n self.timestamp = struct.unpack(\"HBBBBBB\",pbuf[256-8:256])\n\n # for readability and convenience, parse out individual fields.\n self.year = self.timestamp[0]\n self.month = self.timestamp[1]\n self.day = self.timestamp[2]\n self.hour = int(self.timestamp[3])\n self.minute = self.timestamp[4]\n self.second = self.timestamp[5]\n \n self.date = datetime.date(self.year,self.month,self.day)\n self.ordinal = self.date.toordinal()\n self.datestr = self.date.isoformat()\n\n def parse_data(self, pbuf):\n \"\"\" extract all 16-bit uint16_t data (dlen words) from the packet\"\"\"\n self.data = []\n assert 2*self.dlen < len(pbuf)\n for i in range(self.dlen):\n ptr = 2*i # uint16_t, 2-byte unsigned integers\n val = struct.unpack(\"H\",pbuf[ptr:ptr+2])\n self.data.append(val[0])\n\n def get_known_header_csv(self):\n # text descriptions of known values\n outstr = \"\"\n for key, val in self.known_fields.items():\n outstr += \"{}, \".format(key)\n return outstr\n\n def get_known_values_csv(self):\n # print only understood values\n outstr = \"\"\n for key, val in self.known_fields.items():\n outstr += \"{}, \".format(self.data[val])\n return outstr\n\n def get_all_values_csv(self):\n # print all data values whether we know what they are or not\n outstr = \"\"\n for i in range(1,3):\n outstr += \"{}, \".format(self.data[i])\n for i in range(89,self.dlen):\n outstr += \"{}, \".format(self.data[i])\n return outstr\n\n def get_all_values_header_csv(self):\n # print all data values whether we know what they are or not\n outstr = \"\"\n for i in range(1,3):\n outstr += \"{}, \".format(self.data_fields[i])\n for i in range(89,self.dlen):\n outstr += \"{}, \".format(self.data_fields[i])\n return outstr\n\n def fix_csv(self, csv_str):\n # remove trailing spaces & comma, add newline \n csv_str = csv_str.strip()\n if csv_str[-1] == ',':\n csv_str = csv_str[0:-1]\n return csv_str \n\n def get_time_ymd_header_csv(self):\n # return time string in year, month, day format\n return \",\".join(self.timestamp_fields)\n\n def get_time_ymd_csv(self):\n # return time string in year, month, day format\n outstr = \"\"\n for i in self.timestamp:\n outstr += \"{:d}, \".format(i)\n \n return outstr\n \n def get_time_seconds(self):\n return self.second + 60*self.minute + 3600*(self.hour + 24*(self.ordinal))\n def get_10hz_csv(self, i):\n return \"{}, \".format(self.data[4 + 75 + i])\n\n def get_25hz_csv(self, i):\n #These fields are arrays of 25 values/second, something to do with\n # respiration. Last one is cleanest -- filtered?\n outstr = \"{:d}, {:d}, {:d}, \".format(self.data[4 + i],\n self.data[4 + 25 + i],\n self.data[4 + 50 + i])\n return outstr\n\n \n\n#### methods for a collection of packets\ndef s2HMS(seconds):\n # should refactor this with datetime\n #return a string giving hours minutes seconds from seconds\n hours = int(seconds/3600.)\n minutes = int((seconds%3600)/60.)\n return\"{:02d}:{:02d}\".format(hours,minutes)\n\ndef get_day_info(packets):\n # given a list of packets, return a symbolic string of contents\n # one char per hour, '.' if no data, '+' if flow data, '0' if heartrate\n # should refactor this by date\n hstr = \"\"\n pptr = 0\n\n datestr = packets[0].datestr\n hour = -1\n # 24 chars, one for each hour, for graphical representation of data\n hstr = [\".\" for i in range(24)]\n infostr = \"\"\n daysecs = 0\n has_pulse = False\n for p in packets:\n\n daysecs += 1\n if p.has_pulse:\n has_pulse = True\n\n if p.hour > hour:\n hour = p.hour\n if has_pulse:\n hstr[hour] = 'O'\n has_pulse = False\n else: \n hstr[hour] = '+'\n\n if p.datestr != datestr:\n infostr += (datestr + \" \" + \"\".join(hstr) + \\\n \" {}\\n\".format(s2HMS(daysecs)))\n hstr = [\".\" for i in range(24)]\n daysecs = 0\n hour = -1\n datestr = p.datestr\n\n infostr += (datestr + \" \" + \"\".join(hstr) + \\\n \" {}\\n\".format(s2HMS(daysecs)))\n return infostr\n\n######################## main program starts here\n\nif sys.version_info.major < 3:\n print(\"sorry, requires Python 3.\")\n exit(1)\n\n\nparser = argparse.ArgumentParser(\n description='Extract data from BMC RESmart raw data files')\n\nparser.add_argument('--info','-i',\n action='store_true',\n help='Prints readable summary of data and dates to stdout.' )\n\nparser.add_argument('--f25_hz','-2',\n action='store_true',\n help='Print out all 25 Hz (flow) data. this will make output files 25x as big.' )\n\nparser.add_argument('--f10_hz','-1',\n action='store_true',\n help='Print out all 10 Hz (pulse) data. this will make output files 10x as big.' )\n\nparser.add_argument('--all_data','-a',\n action='store_true',\n help='Print out all 1Hz data fields known or unknown' )\n\nparser.add_argument('--time_ymd','-y',\n action='store_true',\n help='Print timestamp in Y, M, D, H, M, S format')\n\nparser.add_argument('--time_seconds','-s',\n action='store_true',\n help='Print timestamp in seconds since beginning of year')\n\nparser.add_argument('--quiet','-q',\n action='store_true',\n help='Do not print progress and info to stderr')\n\nparser.add_argument('output_file', nargs = '?', \n help='Output data CSV file, careful will overwrite existing data.',\n default=\"RESmart_data.csv\")\n\nparser.add_argument('--dates', '-d', nargs = '+', \n help='select date range in YYYY-MM-DD format. Single date is one day, two dates are start and end of time range.',\n default=[])\n\nargs = parser.parse_args()\n\n\n\nstart_date = None\nend_date = None\nif len(args.dates) > 0:\n try:\n start_date = datetime.datetime.strptime(args.dates[0], '%Y-%m-%d').date()\n except ValueError:\n raise ValueError(\"Incorrect -d date format, should be YYYY-MM-DD\")\n exit()\n\nif len(args.dates) > 1:\n try:\n end_date = datetime.datetime.strptime(args.dates[1], '%Y-%m-%d').date()\n except ValueError:\n raise ValueError(\"Incorrect -d date format, should be YYYY-MM-DD\")\n exit()\n\n\n# Should probably ensure these files all have the same root...\nfilesNNN = glob.glob('*.[0-9][0-9][0-9]')\nfilesNNN.sort()\n\npackets = []\nthispacket = None\npacketsize = 256\n\n\nfor datafile in filesNNN:\n with open(datafile, \"rb\") as f:\n databuff = f.read()\n\n oldday = -1\n p = 0 # pointer into byte array\n while p < (len(databuff) - packetsize):\n #print(i)\n\n val = int(struct.unpack(\"H\",databuff[p:p+2])[0])\n thispacket = packet(p, databuff[p:p+packetsize])\n p += packetsize\n packets.append(thispacket)\n\n if thispacket.day != oldday and not args.quiet:\n if thispacket.date == start_date:\n print(\"Found start date {}.\".format(start_date.isoformat()))\n oldday = thispacket.day\n print(\"reading data from {}\".format(thispacket.datestr))\n\nif not args.quiet:\n print(\"{:d} packets found in {} files\".format(len(packets), len(filesNNN)))\n \n\nif args.info:\n print(get_day_info(packets))\n\n\nif start_date is None:\n #default to beginning of data\n start_date = packets[0].date\nelse:\n if end_date is None:\n # if only start date is specified, use only that date\n end_date = start_date\n\nif end_date is None:\n #default to end of data\n end_date = packets[-1].date \n\n#print(start_date)\n#print(end_date)\n\nwith open(args.output_file, 'w') as outf:\n # read data \n \n day = -1\n for i, p in enumerate(packets):\n\n if p.date >= start_date and p.date <= end_date:\n\n\n if p.ordinal != day and not args.quiet:\n day = p.ordinal\n print(\"Writing {} data to {}\".format(p.datestr,\n args.output_file))\n\n outstr = \"\"\n if args.time_seconds:\n outstr += \"{}, \".format(p.get_time_seconds())\n\n if args.time_ymd:\n outstr += p.get_time_ymd_csv()\n\n if args.all_data:\n outstr += p.get_all_values_csv() \n else: \n outstr += p.get_known_values_csv()\n\n if args.f10_hz:\n for i in range(10):\n frac_sec = float(p.get_time_seconds()) + float(i)/10.\n tstr = \"{:.2f}, \".format(frac_sec)\n tstr += p.get_10hz_csv(i)\n outf.write(p.fix_csv(outstr + tstr) + \"\\n\") \n\n elif args.f25_hz:\n for i in range(25):\n frac_sec = float(p.get_time_seconds()) + float(i)/25.\n tstr = \"{:.2f}, \".format(frac_sec)\n tstr += p.get_25hz_csv(i)\n outf.write(p.fix_csv(outstr + tstr) + \"\\n\") \n else:\n outf.write(p.fix_csv(outstr) + \"\\n\") \n\n\n\n\n\n\n","repo_name":"headrotor/BMC_RESmart","sub_path":"resmart_parse.py","file_name":"resmart_parse.py","file_ext":"py","file_size_in_byte":11673,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"4708267385","text":"\"\"\"\n JOB SCRAPER for ARMANINO\n\n Created: 2020-12-03\n Modified: 2021-08-16\n Author: Israel Dryer\n\"\"\"\nfrom random import random\nimport wsl.webscraper as ws\nfrom wsl.datatools import DataTools, ConnectionString\n\nCONN_STRING = ConnectionString.build()\nINSERT_QUERY = \"INSERT INTO jobs.RawJobData VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?)\"\n\n\nclass JobScraper(ws.WebScraper):\n \"\"\"webscraper for Armanino jobs\"\"\"\n\n def __init__(self):\n super().__init__(name='Armanino')\n\n def extract_card_data(self, card):\n pass\n\n def extract_page_urls(self, _):\n url = \"https://careers-armaninollp.icims.com/jobs/search?ss=1&hashed=-435620445&in_iframe=1\"\n last_count = 0\n while True:\n soup = self.get_request(url, out_format='soup')\n for tag in soup.find_all('a', 'iCIMS_Anchor'):\n self.urls_to_scrape.add(tag['href'])\n url = soup.find('span', text='Next page of results').parent['href']\n if len(self.urls_to_scrape) == last_count:\n break\n last_count = len(self.urls_to_scrape)\n\n def extract_page_data(self, url):\n soup = self.get_request(url, out_format='soup', use_session=True)\n try:\n json_data = ws.json.loads(soup.find('script', {'type': 'application/ld+json'}).contents[0])\n except AttributeError:\n return # The job url has expired\n\n job_id = req_id = soup.find(\"span\", text=\"Job ID\").find_next_sibling().text.strip()\n title = json_data['title']\n city = json_data['jobLocation'][0]['address']['addressLocality']\n state = json_data['jobLocation'][0]['address']['addressRegion']\n country = json_data['jobLocation'][0]['address']['addressCountry']\n category = json_data['occupationalCategory']\n description = ws.BeautifulSoup(json_data['description'], 'lxml').text.replace('\\xa0', ' ')\n record_id = '170-' + self.today + str(job_id) + str(req_id)\n\n self.data_scraped.append([\n record_id, self.today, job_id, req_id, self.name, title,\n category, \"\", city, state, country, description, url])\n\n def run(self):\n self.create_session()\n self.extract_page_urls(None)\n\n for url in self.urls_to_scrape:\n self.extract_page_data(url)\n ws.sleep(random())\n\n if self.data_scraped:\n DataTools.save_to_database(self.data_scraped, CONN_STRING, INSERT_QUERY)\n print(f\"{self.name} >> {len(self.data_scraped)} records\")\n\n\nif __name__ == '__main__':\n print(\"Starting...\")\n scraper = JobScraper()\n scraper.run()\n print(\"Finished.\")\n","repo_name":"israel-dryer/PyJobScraper","sub_path":"acct_job_scrape/armanino.py","file_name":"armanino.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28950781320","text":"import re\nimport typing\n\n\ndef parse_input(data: str) -> typing.Tuple[typing.Dict[int, str], typing.List[str]]:\n rules, messages = data.strip().split(\"\\n\\n\")\n rules_dict = _parse_rules(rules)\n return rules_dict, messages.splitlines()\n\n\ndef _parse_rules(rules: str) -> typing.Dict[int, str]:\n rules_dict = {}\n\n pattern = r\"^(\\d+): ([0-9 |]+|\\\"[a-z]\\\")$\"\n for raw_rule in rules.splitlines():\n id_, rule_content = re.match(pattern, raw_rule.strip()).groups()\n rules_dict[int(id_)] = rule_content\n\n return rules_dict\n\n\ndef get_matching_patterns(\n key: int,\n raw_rules_dict: typing.Dict[int, str],\n current_matching_patterns: typing.Set[str] = None,\n) -> typing.Set[str]:\n value = raw_rules_dict[key]\n current_matching_patterns = current_matching_patterns or set()\n new_matching_patterns = set()\n\n if re.match(r\"^\\\"[a-z]\\\"$\", value):\n return set(value.strip('\"'))\n\n for or_section in value.split(\"|\"):\n for key in or_section.split():\n new_matching_patterns.update(get_matching_patterns(int(key), raw_rules_dict, current_matching_patterns))\n\n return new_matching_patterns\n","repo_name":"matthewelwell/adventofcode2020","sub_path":"utils/day19/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13425481386","text":"import random\nimport sys\nimport traceback\n\nimport cosim\nimport vfbdb\n\nWRITE_FIFO_PATH = sys.argv[1]\nREAD_FIFO_PATH = sys.argv[2]\n\ntry:\n iface = cosim.Iface(WRITE_FIFO_PATH, READ_FIFO_PATH)\n\n Main = vfbdb.Main(iface)\n\n subblocks = [Main.Blk0, Main.Blk1, Main.Blk1.Blk2]\n\n for i, sb in enumerate(subblocks):\n print(f\"Testing access to blk{i}\")\n r = random.randrange(0, 2 ** 32 - 1)\n print(f\"Writing value {r} to cfg register\")\n sb.Cfg.write(r)\n\n print(f\"Reading Cfg register\")\n read = sb.Cfg.read()\n assert read == r, f\"Read wrong value from Cfg register {read}\"\n\n print(f\"Reading St register\")\n read = sb.St.read()\n assert read == r, f\"Read wrong value from St register {read}\"\n\n iface.end(0)\n\nexcept Exception as E:\n iface.end(1)\n print(traceback.format_exc())\n","repo_name":"Functional-Bus-Description-Language/go-vfbdb","sub_path":"tests/co-simulations/subblock/0/python/tb.py","file_name":"tb.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21772521795","text":"import re\nimport unicodedata\nfrom urllib.parse import urlsplit, urlunsplit\nfrom urllib.parse import quote, unquote\n\n\ndef normalize_url(url, preferred_scheme='http', charset='utf-8'):\n def _clean(string):\n string = unquote(string)\n return unicodedata.normalize('NFC', string).encode('utf-8')\n\n default_port = {\n 'http': 80,\n 'https': 443,\n }\n\n if 'twitter' in url:\n url = url.replace('www.', '')\n\n if url[:4] == 'http' and preferred_scheme == 'https':\n url = url[7:]\n\n # if there is no scheme use http as default scheme\n if url[0] not in ['/', '-'] and ':' not in url[:7]:\n url = preferred_scheme + '://' + url\n\n # splitting url to useful parts\n scheme, auth, path, query, fragment = urlsplit(url.strip())\n (userinfo, host, port) = re.search('([^@]*@)?([^:]*):?(.*)', auth).groups()\n\n # Always provide the URI scheme in lowercase characters.\n scheme = scheme.lower()\n\n # Always provide the host, if any, in lowercase characters.\n host = host.lower()\n if host and host[-1] == '.':\n host = host[:-1]\n # take care about IDN domains\n host = host.encode(\"idna\").decode(\"utf-8\")\n\n # Only perform percent-encoding where it is essential.\n # Always use uppercase A-through-F characters when percent-encoding.\n # All portions of the URI must be utf-8 encoded NFC from Unicode strings\n path = quote(_clean(path), \"~:/?#[]@!$&'()*+,;=\")\n fragment = quote(_clean(fragment), \"~\")\n\n # note care must be taken to only encode & and = characters as values\n query = \"&\".join([\n \"=\".join([quote(_clean(t), \"~:/?#[]@!$'()*+,;=\") for t in q.split(\"=\", 1)]) for q in query.split(\"&\")\n ])\n\n # Prevent dot-segments appearing in non-relative URI paths.\n if scheme in [\"\", \"http\", \"https\", \"ftp\", \"file\"]:\n output = []\n for part in path.split('/'):\n if part == \"\":\n if not output:\n output.append(part)\n elif part == \".\":\n pass\n elif part == \"..\":\n if len(output) > 1:\n output.pop()\n else:\n output.append(part)\n if part in [\"\", \".\", \"..\"]:\n output.append(\"\")\n path = '/'.join(output)\n\n # For schemes that define a default authority, use an empty authority if\n # the default is desired.\n if userinfo in [\"@\", \":@\"]:\n userinfo = \"\"\n\n # For schemes that define an empty path to be equivalent to a path of \"/\",\n # use \"/\".\n if path == \"\" and scheme in [\"http\", \"https\", \"ftp\", \"file\"]:\n path = \"/\"\n\n # For schemes that define a port, use an empty port if the default is\n # desired\n if port and scheme in default_port.keys():\n if port.isdigit():\n port = str(int(port))\n if int(port) == default_port[scheme]:\n port = ''\n\n # Put it all back together again\n auth = (userinfo or \"\") + host\n if port:\n auth += \":\" + port\n if url.endswith(\"#\") and query == \"\" and fragment == \"\":\n path += \"#\"\n return urlunsplit((scheme, auth, path, query, fragment))\n","repo_name":"0x216/Hoop-Libaries","sub_path":"hoop/util/normalization.py","file_name":"normalization.py","file_ext":"py","file_size_in_byte":3150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74637300882","text":"from django.urls import path\n\nfrom . import views\n\napp_name = 'main'\n\nurlpatterns = [\n path('', views.allProdcat, name='allProdcat'),\n path('/', views.allProdcat, name='hackathonCategory'),\n path('//', views.hackathonList, name='hackathonList'),\n path('addcomment/', views.addcomment, name='addcomment'),\n]\n","repo_name":"it5prasoon/OrganiseHackathonsDjango","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"35398017304","text":"from typing import Any, List, Tuple\nimport numpy as np\nimport h5py\nimport tensorflow.keras as keras\nfrom ..encoder import get_encoder_by_name\nfrom .. import goboard\nfrom .. import utils\nfrom .base import Agent\nfrom .helpers import is_point_an_eye\nfrom ..rl.experience import ExperienceBuffer, ExperienceCollector\n\n__all__ = [\n 'ValueAgent',\n 'load_value_agent',\n]\n\n\nclass ValueAgent(Agent):\n\n def __init__(self,\n model: keras.Model,\n encoder: Any,\n policy: str = 'eps-greedy') -> None:\n Agent.__init__(self)\n \"\"\"\n 参数:\n model: 模型\n encoder: 编码器\n policy: eps-greedy | weighted\n eps-greedy epsilon-greedy的方式选择动作\n weighted 将values转换成概率, 然后抽样得到动作\n \"\"\"\n self.model = model\n self.encoder = encoder\n self.collector = None # ExperienceCollector\n self.temperature = 0.0 # Epsilon - Greedy的参数\n self.policy = policy\n self.last_move_value = 0\n\n def predict(self, game_state: goboard.GameState) -> np.ndarray:\n \"\"\"\n 预测一个状态的价值\n\n 返回:\n output: [1, ]\n \"\"\"\n encoded_state = self.encoder.encode(game_state)\n input_tensor = np.array([encoded_state])\n return self.model.predict(input_tensor)[0]\n\n def set_temperature(self, temperature: float) -> None:\n self.temperature = temperature\n\n def set_collector(self, collector: ExperienceCollector) -> None:\n \"\"\"\n 设置经验收集器\n \"\"\"\n self.collector = collector\n\n def set_policy(self, policy: str) -> None:\n if policy not in ('eps-greedy', 'weighted'):\n raise ValueError(policy)\n self.policy = policy\n\n def select_move(self, game_state: goboard.GameState) -> goboard.Move:\n \"\"\"\n 选择一个Move\n \n 参数:\n game_state: 游戏状态\n\n 返回:\n move: 动作\n \"\"\"\n\n moves = [] # list of int\n board_tensors = [\n ] # list of [num_planes, board_height, board_width], 每个元素是不同的\n # 遍历所有合法的动作\n for move in game_state.legal_moves():\n if not move.is_play:\n continue\n next_state = game_state.apply_move(move)\n # board_tensor.shape [num_planes, board_height, board_width]\n board_tensor = self.encoder.encode(next_state)\n moves.append(move)\n board_tensors.append(board_tensor)\n if not moves:\n return goboard.Move.pass_turn()\n\n num_moves = len(moves)\n # board_tensors.shape [num_moves, num_planes, board_height, board_width]\n board_tensors = np.array(board_tensors)\n\n # 要注意:\n # 这是对方视角的value, 并不是自己的value\n # opp_values.shape [num_moves, 1]\n opp_values = self.model.predict(board_tensors)\n # opp_values.shape [num_moves, ]\n opp_values = opp_values.reshape(num_moves)\n\n # 转换为自己的values\n # values.shape [num_moves, ]\n values = 1 - opp_values\n\n if self.policy == 'eps-greedy':\n ranked_moves = self.rank_moves_eps_greedy(values)\n elif self.policy == 'weighted':\n ranked_moves = self.rank_moves_weighted(values)\n else:\n ranked_moves = None\n\n for move_idx in ranked_moves:\n move = moves[move_idx]\n if not is_point_an_eye(game_state.board, move.point,\n game_state.next_player):\n # 选出来的动作不能是自己���眼\n if self.collector is not None:\n self.collector.record_decision(\n state=board_tensor,\n action=self.encoder.encode_point(move.point),\n )\n self.last_move_value = float(values[move_idx])\n return move\n return goboard.Move.pass_turn()\n\n def rank_moves_eps_greedy(self, values: List[float]) -> List[int]:\n \"\"\"\n epsilon-greedy的方式\n\n 参数:\n values: [num_moves, ]\n\n 返回:\n ranked_moves: [num_moves, ] 从大到小排序后的索引\n \"\"\"\n if np.random.random() < self.temperature:\n # 随机动作\n values = np.random.random(values.shape)\n # 挑选最优的动作\n ranked_moves = np.argsort(values)\n return ranked_moves[::-1]\n\n def rank_moves_weighted(self, values: List[float]) -> List[int]:\n \"\"\"\n 将values转换成概率, 然后根据概率大小采样\n\n 参数:\n values: [num_moves, ]\n\n 返回:\n ranked_moves: [num_moves, ] 从大到小排序后的索引\n \"\"\"\n # 将values转换成概率: p\n # p.shape [num_moves, ]\n p = values / np.sum(values)\n p = np.power(p, 1.0 / self.temperature)\n p = p / np.sum(p)\n return np.random.choice(np.arange(0, len(values)),\n size=len(values),\n p=p,\n replace=False)\n\n def train(self,\n experience: ExperienceBuffer,\n lr: float = 0.1,\n batch_size: int = 128) -> None:\n \"\"\"\n 用experience的数据来训练模型, 训练一个epoch\n\n 对experience的数据只迭代一个epoch, 不会重复利用experience的数据\n \"\"\"\n opt = keras.optimizers.SGD(lr=lr)\n # 直接使用MSE误差, 因为我们要学习的是一个连续值\n self.model.compile(loss='mse', optimizer=opt)\n\n n = experience.states.shape[0]\n # y.shape [n, ]\n y = np.zeros((n, ))\n for i in range(n):\n reward = experience.rewards[i]\n y[i] = 1 if reward > 0 else 0\n\n self.model.fit(experience.states, y, batch_size=batch_size, epochs=1)\n\n def serialize(self, h5file: h5py.File) -> None:\n \"\"\"\n 保存Agent(也就是保存Encoder信息 & model)到h5file\n\n h5file\n / \\\n encoder(group) model(group)\n |\n kerasmodel(group)\n\n >>> with h5py.File(output_file, 'w') as out_f:\n >>> agent.serialize(out_f)\n \"\"\"\n # 保存encoder信息(根据这些信息可以重构出encoder)\n h5file.create_group('encoder')\n h5file['encoder'].attrs['name'] = self.encoder.name()\n h5file['encoder'].attrs['board_width'] = self.encoder.board_width\n h5file['encoder'].attrs['board_height'] = self.encoder.board_height\n # 保存model\n h5file.create_group('model')\n utils.save_model_to_hdf5_group(self.model, h5file['model'])\n\n def diagnostics(self) -> Tuple[str, float]:\n return {'value': self.last_move_value}\n\n\ndef load_value_agent(h5file: h5py.File) -> ValueAgent:\n \"\"\"\n 从h5file中加载Agent\n\n h5file\n / \\\n encoder(group) model(group)\n |\n kerasmodel(group)\n \"\"\"\n # 加载model\n model = utils.load_model_from_hdf5_group(h5file['model'])\n encoder_name = h5file['encoder'].attrs['name']\n if not isinstance(encoder_name, str):\n encoder_name = encoder_name.decode('ascii')\n board_width = h5file['encoder'].attrs['board_width']\n board_height = h5file['encoder'].attrs['board_height']\n\n # 加载encoder\n encoder = get_encoder_by_name(encoder_name, (board_width, board_height))\n return ValueAgent(model, encoder)","repo_name":"relaxdl/relaxml","sub_path":"relaxml/relaxgo/agent/value.py","file_name":"value.py","file_ext":"py","file_size_in_byte":7659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15702399439","text":"## file: derivative_test.py\n##\n## Program to compare derivative approximation methods\n##\n## Programmer: Chrissy Schumm schummch@msu.edu\n##\n## Revision history:\n## 04/27/21 Original Code Created by Chrissy\n##\n## Notes:\n## * Based on the discussion of differentiation in Chap. 8\n## of \"Computational Physics\" by Landau and Paez.\n#*****************************************************************\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# define 3 derivative methods at the top, so h can be easily found and changed\ndef forward_diff(f,a,method='forward',h=10**-7.92):\n if method == 'forward':\n return (f(a + h) - f(a))/(h)\n\ndef central_diff(f,a,method='central',h=10**-4.91):\n if method == 'central':\n return (f(a + h/2) - f(a - h/2))/(h)\n\ndef extrap_diff(f,a,method='extrapolated',h= 10**-2.51):\n if method == 'extrapolated':\n return ( 8*(f(a + h/4) - f(a - h/4)) - (f(a + h/2) - f(a - h/2)) ) / (3*h)\n\n#************************** main program ***************************\n\nx = np.linspace(0,20,50) # use 50 evenly distributed x-values between 0 and 20\nf = lambda x: np.exp(-x) # This is our test function : e^-x (same one we used in class)\ny = f(x)\n\nfdydx = forward_diff(f,x) #find derivative of e^-x using forward difference method\ncdydx = central_diff(f, x) #find derivative of e^-x using central difference method\nedydx = extrap_diff(f, x) #find derivative of e^-x using extrapolated difference method\n\nexact = -np.exp(-x) #This is our exact value of the derivative we will use for comparison\n\n#************************** Plot the graph ***************************\nplt.figure(figsize=(12,5))\nplt.plot(x,y,label='y=f(x)')\nplt.plot(x,fdydx,'r.', label=\"Forward Difference y=f'(x)\")\nplt.plot(x,cdydx,'s', label=\"Central Difference y=f'(x)\")\nplt.plot(x,edydx,'p', label=\"Extrapolated Difference y=f'(x)\")\nplt.plot(x, exact, 'b', label= 'Exact')\nplt.title('Derivative Methods of y= e^-x')\nplt.xlabel('x value to take derivative')\nplt.ylabel('Derivative Value')\nplt.legend()\nplt.grid(True)\nplt.show()\n\n\n","repo_name":"schummch/PHY_905","sub_path":"Final Project/derivative_test.py","file_name":"derivative_test.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27985036280","text":"# 平方根取整问题\n# 输入一个整数, 返回其平方根的整数部分\n# 复杂度log(n)\ndef calculateSqrt(num):\n low = 0\n high = num\n # 0, 1, 2, 3, 4, 5, 6, 7, 8, 9\n while low < high:\n mid = (high - low) // 2 + low\n # [0, 1, 2, 3, 4] | 5, 6, 7, 8, 9\n if mid * mid > num:\n high = mid\n # 0, 1, 2, 3, 4 | [5, 6, 7, 8, 9]\n elif mid * mid < num:\n low = mid + 1\n else:\n return mid\n return high - 1\n\nprint(calculateSqrt(25))\n","repo_name":"Canadasunyan/codes","sub_path":"028-平方根取整.py","file_name":"028-平方根取整.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"24530745414","text":"#!/usr/bin/env python\nimport re\nimport os\nimport sys\nimport json\nimport time\nimport codecs\nimport signal\nimport requests\nfrom bs4 import BeautifulSoup\nfrom collections import deque\n\nqueue = deque([], 3)\nscript_name = \"Fast cli\"\n\n\nclass bcolors:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n FAIL = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\n\ndef display_title(title):\n print(title + \" by \" + bcolors.OKBLUE + \"@Kensai\" + bcolors.ENDC)\n\n\nclass ArgumentParser:\n def __init__(self, cmd_name, options):\n self.name = cmd_name\n self.options = options\n self.optional = {}\n self.args = []\n self.count = 0\n\n def parse(self):\n if len(sys.argv) == 1:\n return self.help(self.options)\n nextArg = \"\"\n for i in range(len(sys.argv)):\n elt = sys.argv[i]\n if elt[:2] == \"--\":\n self.optional[elt[2:]] = []\n nextArg = elt[2:]\n elif elt[0] == \"-\":\n self.optional[elt[1:]] = []\n nextArg = elt[1:]\n elif elt != sys.argv[0]:\n if nextArg == \"\":\n self.args.append(elt)\n else:\n self.optional[nextArg].append(elt)\n current = self.args[self.count]\n if current in self.options.keys():\n options = self.options[current]\n else:\n return self.help(self.options)\n while True:\n self.count += 1\n if type(options) is dict:\n if current in options.keys():\n current = self.args[self.count]\n options = options[current]\n else:\n if len(self.args) <= self.count:\n return self.help(options)\n else:\n current = self.args[self.count]\n if current in options.keys():\n options = options[current]\n else:\n return self.help(options)\n else:\n args = self.args[self.count:]\n if len(args) != 0 and args[0] == \"help\":\n return self.help(options)\n cmd = [script_name, self.name] + self.args[:self.count]\n if type(options[-1]) is str:\n return self.help(options)\n cmd_return = options[-1](*args, **self.optional)\n if cmd_return is None:\n return\n elif type(cmd_return) is str:\n print(\n bcolors.WARNING + \"Command {0} for {1} failed...\".format(\" \".join(cmd), \" \".join(args)))\n print(cmd_return)\n print(bcolors.ENDC)\n print(\"To get help: {0} help\".format(\" \".join(cmd)))\n return\n else:\n return self.help(options)\n\n def help(self, options):\n self.subs = self.args[:self.count]\n display_title(\"Fast cli\")\n if type(options) is dict:\n if not \"help\" in options.keys():\n options[\"help\"] = \"This message\"\n print(\"Usage: {0} {2}{{{1}}}\".format(\n self.name, \"|\".join(options.keys()), \" \".join(self.subs)))\n else:\n args = []\n for elt in options[1:]:\n if type(elt) != str:\n break\n if \"<\" == elt[0]:\n args.append(elt)\n if \"[--\" == elt[:3]:\n break\n print(\"Usage: {0} {1} {3} {2}\".format(script_name,\n self.name, \" \".join(args), \" \".join(self.subs)))\n print(\" {0}\".format(options[0]))\n if \" [--\" in \" \".join(options[:-1]):\n print()\n print(\"Options:\")\n length = 0\n for elt in options:\n if type(elt) != str:\n break\n if \"[--\" in elt:\n to_print = \" {0}\".format(\n elt.replace(\"[\", \"\").replace(\"]\", \"\"))\n length = len(to_print)+1\n print(to_print)\n elif length != 0:\n if elt[0] == \"<\" and elt[-1] == \">\":\n print(f\"\\033[F\\033[{length}G \"+elt)\n length += len(elt)+1\n else:\n print(\" {0}\".format(elt))\n return\n else:\n return\n print()\n print(\" Options\")\n maxsize = max([len(cmd) for cmd in options.keys()])\n if maxsize < 4:\n maxsize = 4\n for cmd, option in options.items():\n if type(option) is dict:\n print(\" {0}:{1}{2}\".format(\n cmd, (maxsize-len(cmd)+1) * \" \", option[\"help\"][0]))\n elif type(option) is list and cmd != \"help\":\n print(\" {0}:{1}{2}\".format(\n cmd, (maxsize-len(cmd)+1) * \" \", option[0]))\n print(\" help:{0}This message\".format((maxsize-3) * \" \"))\n print()\n print(\"For more usage info type: {0}{1} subcommand help\".format(\n self.name, \" \".join(self.subs)))\n\n\nclass Killer:\n now = False\n\n def __init__(self):\n signal.signal(signal.SIGINT, self.exit)\n\n def exit(self, signum, frame):\n self.now = True\n quit()\n\n\ndef quit():\n sys.stdout.write(\"\\x1b[2K\")\n sys.stdout.write(\"\\n\")\n sys.stdout.write(\"Program exits on user demand\\n\")\n sys.exit()\n\n\nkiller = Killer()\n\n\ndef get_path(path):\n return os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), \"../data\", path))\n\n\ndef printing(a, b, c):\n if killer.now:\n quit()\n\n for _ in range(len(queue)):\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n\n queue.append(a)\n queue.append(b)\n queue.append(c)\n\n for i in range(len(queue)):\n sys.stdout.write(queue[i] + \"\\n\")\n\n\ndef report_progress(job, current, total, name):\n\n progress_percent = current / total * 100\n total_percent = int((current / total) * 100)\n\n progress = \"Total progress: [{1:33}] {0:.2f}%\".format(progress_percent, \"#\" * int(progress_percent / 3),\n total_percent)\n job_spacing = len(progress) - len(str(total)) - \\\n len(str(current)) - len(job) - 1\n job_running = \"{0}{1}{2}/{3}\".format(job,\n \" \" * job_spacing, current, total)\n\n process = \"Processing:{0}{1}\"\n name_spacing = len(progress) - len(process) - len(name) + 6\n process_name = process.format(\" \" * name_spacing, name)\n printing(job_running, progress, process_name)\n\n\ndef get_text(url):\n return requests.get(url).text\n\n\ndef get_json(url):\n return requests.get(url).json()\n\n\ndef get_soup(url):\n return BeautifulSoup(get_text(url), 'html.parser')\n\n\ndef load_unicode(filename):\n f = codecs.open(filename, encoding='ISO-8859-1')\n return repr(f.readline())\n\n\ndef load_soup(filename):\n with open(get_path(filename), \"r\") as f:\n cnt = f.read()\n return BeautifulSoup(cnt, 'html.parser')\n\n\ndef load_json(filename):\n with open(get_path(filename), \"r\") as f:\n cnt = f.read()\n return json.loads(cnt)\n\n\ndef dump_json(filename, obj):\n json_string = json.dumps(obj, sort_keys=True, ensure_ascii=False)\n dump_file(filename, json_string)\n\n\ndef load_file(filename, strip=True):\n with open(get_path(filename), \"r\", encoding='utf-8') as f:\n cnt = f.readlines()\n if not strip:\n return [x.replace(\"\\n\", \"\") for x in cnt]\n return [x.strip() for x in cnt]\n\n\ndef dump_file(filename, string):\n with open(get_path(filename), \"w\") as f:\n f.write(string)\n\n\ndef arg_parse(cmd_name, options):\n ArgumentParser(cmd_name, options).parse()\n","repo_name":"Andrflor/fast_cli","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9377832844","text":"from turtle import Turtle\nimport random\n\nCOLORS = [\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"purple\"]\nSTARTING_MOVE_DISTANCE = 5\nMOVE_INCREMENT = 10\nLEVEL_UP_SPEED = 1\nCAR_SPAWN_RATE_CHANCE = 10\nCAR_START_POSITIONS = [-210, -200, -160, -120, -80, -40, 0, 40, 80, 120, 160, 200, 210]\n\n\nclass CarManager(Turtle):\n def __init__(self):\n super().__init__()\n self.hideturtle()\n self.all_cars = []\n self.car_speed = STARTING_MOVE_DISTANCE\n self.car_spawn_rate = CAR_SPAWN_RATE_CHANCE\n\n def create_car(self):\n random_chance = random.randint(1, self.car_spawn_rate)\n if random_chance == 1:\n new_car = Turtle(\"square\")\n new_car.penup()\n random_y_pos = random.choice(CAR_START_POSITIONS)\n new_car.color(random.choice(COLORS))\n new_car.shapesize(stretch_wid=1.25, stretch_len=3)\n new_car.goto(300, random_y_pos)\n self.all_cars.append(new_car)\n\n def car_movement(self):\n for car in self.all_cars:\n car.backward(self.car_speed)\n\n def level_up(self):\n self.car_speed += LEVEL_UP_SPEED\n if CAR_SPAWN_RATE_CHANCE > 3:\n self.car_spawn_rate -= 1\n","repo_name":"Reynbra/Portfolio","sub_path":"python/TurtleFrogger/car_manager.py","file_name":"car_manager.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6310205337","text":"dx = [0,1,0,-1]\ndy = [1,0,-1,0]\n\ndef check(i,j,cnt, s):\n global maxv, start\n for k in range(4):\n x, y = i + dx[k], j + dy[k]\n if 0 <= x < N and 0 <= y < N and room[x][y] == room[i][j] + 1:\n cnt += 1\n check(x,y, cnt, s)\n return\n if cnt > maxv:\n maxv = cnt\n start = s\n elif cnt == maxv and s < start:\n start = s\n\nT = int(input())\nfor tc in range(1, T+1):\n N = int(input())\n room = [list(map(int, input().split())) for _ in range(N)]\n maxv = 1\n start = (N * N) + 1\n for i in range(N):\n for j in range(N):\n if room[i][j] < start or room[i][j] > start + maxv - 1 :\n check(i,j,1, room[i][j])\n print(f'#{tc} {start} {maxv}')","repo_name":"judong93/TIL","sub_path":"algorithm/0824~/0826/정사각형 방.py","file_name":"정사각형 방.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43152996780","text":"import rospy\nimport rosbag\nimport argparse\nimport shutil\nimport os\n\ndef reorder_bag(bagfile):\n orig = os.path.splitext(bagfile)[0] + \".orig.bag\"\n shutil.move(bagfile, orig)\n with rosbag.Bag(bagfile, 'w') as outbag:\n for topic, msg, t in rosbag.Bag(orig).read_messages():\n if msg._has_header:\n outbag.write(topic, msg, msg.header.stamp)\n else:\n outbag.write(topic, msg, t)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Reorder a bagfile based on header timestamps.')\n parser.add_argument('bagfile', help='input bag file')\n args = parser.parse_args()\n reorder_bag(args.bagfile)\n\n","repo_name":"raphaelchang/omni_slam_eval","sub_path":"scripts/misc/reorder_bag.py","file_name":"reorder_bag.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"38772347925","text":"# -*- coding: utf-8 -*-\r\n\r\nclass Graaf:\r\n\r\n def __init__(self, maatriks):\r\n self.maatriks = maatriks\r\n \r\n def on_seos(self, i, j):\r\n return bool(self.maatriks[i][j])\r\n\r\n\r\n def kaal(self, i, j):\r\n if self.maatriks[i][j] != 0:\r\n return self.maatriks[i][j]\r\n else:\r\n return None\r\n\r\n def astak(self, tipp):\r\n \"\"\"\r\n Tagastab tipu astaku:\r\n sealt väjuvate seoste arvu\r\n \"\"\"\r\n return sum(self.maatriks[tipp])\r\n\r\n def tippude_arv(self):\r\n return len(self.maatriks)\r\n\r\n def teepikkus(self, marsruut):\r\n \"\"\"\r\n Tagastab tippude loendi 'marsruut' teepikkuse.\r\n Kui marsruut pole korrektne, siis None.\r\n \"\"\"\r\n if len(marsruut) < 2:\r\n return 0\r\n tulem = 0\r\n for alg in range(len(marsruut)-1):\r\n lopp = alg + 1\r\n k = self.kaal(marsruut[alg], marsruut[lopp])\r\n print(alg, lopp, marsruut[alg], marsruut[lopp], k)\r\n if k == None:\r\n return None\r\n else:\r\n tulem = tulem + k\r\n return tulem\r\n\r\n\r\n def seotud_tipud(self, tipp):\r\n \"\"\"\r\n Tagastab tipuga seotud tippude loendi\r\n \"\"\"\r\n tulem = []\r\n tippude_arv = len(self.maatriks)\r\n for i in range(tippude_arv):\r\n if self.on_seos(tipp, i):\r\n tulem.append(i)\r\n return tulem\r\n \r\n \r\n def tee(self, alg, lopp, senine_tee=[]):\r\n \"\"\"\r\n Tagastab mingi suvalise tee tipust alg tippu lopp\r\n \"\"\"\r\n seotud = self.seotud_tipud(alg)\r\n uus_tee = senine_tee + [alg]\r\n if lopp in seotud:\r\n return uus_tee + [lopp]\r\n else:\r\n tee_jargmisest = None\r\n for jargmine in seotud:\r\n if not jargmine in senine_tee:\r\n tee_jargmisest = self.tee(jargmine, lopp, uus_tee)\r\n if tee_jargmisest != None:\r\n return tee_jargmisest\r\n \r\n \r\n \r\n def uuenda_kaugused(self, valitu, kylastatud, kaugused):\r\n \"\"\"\r\n Uuenda sõnastikku kaugused nende teedega, mis lisavad valitud tipuni\r\n viivale teele ühe kaare(seose) külastamata tipuni, juhul kui leitud\r\n tee on lühem.\r\n \"\"\"\r\n valitu_kaugus, valitu_tee = kaugused[valitu]\r\n for tipp in self.seotud_tipud(valitu):\r\n if not tipp in kylastatud:\r\n kaugus_labi_valitu = valitu_kaugus + self.kaal(valitu, tipp)\r\n senine_kaugus, senine_tee = kaugused.get(tipp, (10**100, []))\r\n if kaugus_labi_valitu < senine_kaugus:\r\n kaugused[tipp] = (kaugus_labi_valitu, valitu_tee + [tipp])\r\n\r\n\r\n def lyhima_teega_tipp(self, kylastatud, kaugused):\r\n \"\"\"\r\n Lühima teega külastamata tipp sõnastikust kaugused\r\n None, kui ei ole külastamata tippu, milleni viib tee\r\n \"\"\"\r\n senine_lyhim = 10**100 # Lõpmatus\r\n tulem = None\r\n for tipp in kaugused:\r\n if not tipp in kylastatud:\r\n teepikkus, tee = kaugused[tipp]\r\n if teepikkus < senine_lyhim:\r\n senine_lyhim = teepikkus\r\n tulem = tipp\r\n return tulem\r\n\r\n \r\n def lyhim_tee(self, alg):\r\n \"\"\"\r\n Dijkstra algoritm\r\n Tagastab sõnastiku, mis sisaldab lühimaid teid graafi algtipust\r\n kõikidesse teistesse tippudesse, kuhu algtipust saab jõuda.\r\n Sõnastiku formaat: {tipu_indeks: (kaugus, [tee]),...}\r\n \"\"\"\r\n kaugused = {alg: (0, [alg])} # Tulem\r\n kylastatud = []\r\n valitu = alg\r\n # Kuni on veel teid, mis viivad külastamata tippudesse\r\n while valitu != None:\r\n self.uuenda_kaugused(valitu, kylastatud, kaugused)\r\n kylastatud.append(valitu)\r\n valitu = self.lyhima_teega_tipp(kylastatud, kaugused)\r\n return kaugused\r\n\r\n def __repr__(self):\r\n \"\"\"\r\n Objekti esitus stringina\r\n \"\"\"\r\n return \"\\n\".join([str(x) for x in self.maatriks])\r\n\r\n\r\ndef graaf_failist(fn):\r\n \"\"\"\r\n Loeb failist nimega fn graafi seosemaatriksi.\r\n Seosemaatriksi read peavad olema failis eraldatud reavahetustega.\r\n Elemendid peavad olema eraldatud komadega.\r\n \"\"\"\r\n f = open(fn)\r\n tulem = []\r\n for failirida in f.readlines():\r\n tulemirida = []\r\n for seoseStr in failirida.split(\",\"):\r\n tulemirida.append(int(seoseStr))\r\n tulem.append(tulemirida)\r\n return Graaf(tulem)\r\n\r\ndef suurim_astak(fn):\r\n \"\"\"\r\n Tagastab suurima astakuga tipu indeksi graafile, \r\n mille seosemaatriks on kirjeldatud failis nimega fn.\r\n \"\"\"\r\n graaf = graaf_failist(fn)\r\n seni_suurim_astak = -1\r\n for tipu_indeks in range(tippude_arv(graaf)):\r\n if astak(graaf, tipu_indeks) > seni_suurim_astak:\r\n seni_suurim_astak = astak(graaf, tipu_indeks)\r\n tulem = tipu_indeks\r\n return tulem\r\n \r\n\r\n\r\n\r\n \r\n \r\nif __name__ == \"__main__\":\r\n # Positiivne täisarv näitab seose olemasolu ja kaalu\r\n \r\n suhete_graaf = [[0, 10, 20, 0],\r\n [10, 0, 2, 0],\r\n [20, 2, 0, 0],\r\n [0, 0, 0, 0]]\r\n linnade_graaf = [[0, 200, 0, 200, 270],\r\n [200, 0, 100, 0, 0],\r\n [0, 100, 0, 0, 250],\r\n [200, 0, 0, 0 ,0],\r\n [270, 0, 250, 0, 0]]\r\n\r\n #print(tee(linnade_graaf, 3, 2))\r\n g = Graaf(suhete_graaf)\r\n print(g.lyhim_tee(1))\r\n print(g.seotud_tipud(1))\r\n linnaindeksid = {\"Tallinn\": 0,\r\n \"Kohtla-Jarve\": 1,\r\n \"Narva\": 2,\r\n \"Parnu\": 3,\r\n \"Tartu\": 4}\r\n\r\n def on_tee(linn1, linn2):\r\n return on_seos(linnade_graaf, linnaindeksid[linn1], linnaindeksid[linn2])\r\n\r\n #print(on_tee(\"Tallinn\", \"Kohtla-Jarve\"))\r\n #print(suurim_astak(\"linnadegraaf.txt\"))\r\n graaf2 = graaf_failist(\"linnadegraaf.txt\")\r\n print(graaf2)\r\n","repo_name":"NickOvt/Python-Projects","sub_path":"graaf_klassina.py","file_name":"graaf_klassina.py","file_ext":"py","file_size_in_byte":6128,"program_lang":"python","lang":"et","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73683379282","text":"import yaml\r\n\r\nfrom qgis.core import Qgis, QgsCoordinateReferenceSystem, QgsCoordinateTransform, QgsMessageLog, QgsPointXY, QgsProject\r\nfrom qgis.utils import iface\r\n\r\n\r\n# noinspection PyBroadException\r\ndef point2wgs84(point: QgsPointXY):\r\n \"\"\"\r\n Converts the coordinates of a point to WGS84\r\n\r\n :rtype: QgsPointXY\r\n :param point: A qgis.core.QgsPointXY object representing the point to convert\r\n :return: A qgis.core.QgsPointXY object representing the point in WGS84\r\n \"\"\"\r\n try:\r\n crs = iface.mapCanvas().mapRenderer().destinationCrs()\r\n except:\r\n crs = iface.mapCanvas().mapSettings().destinationCrs()\r\n\r\n t = QgsCoordinateReferenceSystem()\r\n t.createFromSrid(4326)\r\n f = crs\r\n\r\n try:\r\n transformer = QgsCoordinateTransform(f, t)\r\n except:\r\n transformer = QgsCoordinateTransform(f, t, QgsProject.instance())\r\n try:\r\n pt: QgsPointXY = transformer.transform(point)\r\n except:\r\n pt: QgsPointXY = transformer.transform(QgsPointXY(point))\r\n return pt\r\n\r\n\r\ndef dict2object(d: dict):\r\n \"\"\"\r\n Converts a dictionary to an object\r\n\r\n :rtype: new\r\n :param d: Dictionary to convert\r\n :return: The converted object\r\n \"\"\"\r\n top = type('new', (object,), d)\r\n seqs = tuple, list, set, frozenset\r\n for i, j in d.items():\r\n if isinstance(j, dict):\r\n setattr(top, i, dict2object(j))\r\n elif isinstance(j, seqs):\r\n setattr(top, i,\r\n type(j)(dict2object(sj) if isinstance(sj, dict) else sj for sj in j))\r\n else:\r\n setattr(top, i, j)\r\n return top\r\n\r\n\r\ndef load_config(config_file: str):\r\n \"\"\"\r\n Loads the yaml file as object\r\n\r\n :rtype: new\r\n :param config_file: The path to the yaml file\r\n :return: The object\r\n \"\"\"\r\n with open(config_file, 'r') as f:\r\n data_map = yaml.safe_load(f)\r\n return dict2object(data_map)\r\n\r\n\r\ndef save_config(config: dict, config_file: str):\r\n \"\"\"\r\n Saves the dict as a yaml file\r\n\r\n :rtype: None\r\n :param config: The dict to save\r\n :param config_file: The path to the yaml file\r\n \"\"\"\r\n with open(config_file, 'w') as f:\r\n yaml.dump(config, f)\r\n\r\n\r\ndef get_log_level(level):\r\n if level == 'info':\r\n lev = Qgis.Info\r\n elif level == 'warning':\r\n lev = Qgis.Warning\r\n elif level == 'critical':\r\n lev = Qgis.Critical\r\n elif level == 'success':\r\n lev = Qgis.Success\r\n else:\r\n lev = Qgis.Info\r\n return lev\r\n\r\n\r\ndef log_message(*msg, level: str = 'info'):\r\n \"\"\"\r\n Writes a message to the QGIS message log\r\n\r\n :rtype: None\r\n :param msg: The message / parts of the message to write\r\n :param level: The level that the message should have.
Can be 'info', 'warning', 'critical', 'success' or None\r\n \"\"\"\r\n message = ''\r\n for text in msg:\r\n message += '{0!s} '.format(text)\r\n QgsMessageLog.logMessage(message=message, level=get_log_level(level))\r\n\r\n\r\ndef message_bar(*msg, level: str = 'info'):\r\n \"\"\"\r\n Creates a new qgis message bar\r\n\r\n :rtype: None\r\n :param msg: The message / parts of the message to write\r\n :param level: The level that the message should have.
Can be 'info', 'warning', 'critical', 'success' or None\r\n \"\"\"\r\n message = ''\r\n for text in msg:\r\n message += '{0!s} '.format(text)\r\n iface.messageBar().pushMessage(text=message, level=get_log_level(level))\r\n\r\n\r\ndef rgb2abgr(rgb: str):\r\n \"\"\"\r\n Converts a rgb color (#rrggbb) to a google kml abgr (aabbggrr) color\r\n\r\n :rtype: str\r\n :param rgb: The rgb color code (with #)\r\n :return: The abgr color code (without #)\r\n \"\"\"\r\n abgr = 'ff'\r\n abgr += rgb[5:7]\r\n abgr += rgb[3:5]\r\n abgr += rgb[1:3]\r\n return abgr\r\n\r\n\r\nclass LayerNotFoundError(BaseException):\r\n \"\"\"An exception for a not-found layer\"\"\"\r\n pass\r\n\r\n\r\nclass FileNotFound(BaseException):\r\n \"\"\"An exception for a not-found file\"\"\"\r\n pass\r\n","repo_name":"bennetrr/graveyard","sub_path":"loeschwasser/loeschwasser-plugin/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4725217076","text":"import json\nimport FrontEnd.structure_alpha as Q\nimport time\nimport simple_functions_for_fit as sm\nimport copy\nimport numpy as np\nfrom simple_functions_for_fit import notice_positions\n\nscaleLimit = 5\nInf = 0.05\nwildInf = 0.015\n\n#rg = __import__(\"reel generator\")\n\ndef freq(obj, out):#генерируем начальную ленту. 1 аргумент - структура игры, 2 аргумент - структура с нужными параметрами hit rate\n average = out.total_length\n for i in obj.base.scatterlist:\n average -= out.scatter_index_with_frequency[i]\n average = average // (len(obj.base.symbol) - len(obj.base.scatterlist))\n tmp = [out.scatter_index_with_frequency[i] if i in obj.base.scatterlist else average for i in range(len(obj.base.symbol))]\n j = 0\n while sum(tmp) < out.total_length:\n if j not in obj.base.scatterlist:\n tmp[j] += 1\n j += 1\n frequency = []\n\n for i in range(obj.window[0]):\n frequency.append(copy.deepcopy(tmp))\n\n if sum(tmp) != out.total_length:\n print('ERROR')\n return -1\n\n return frequency\n\nclass point(object):\n def __init__(self, frequency, gametype):\n self.frequency = notice_positions(frequency, gametype)\n self.baseReel = '-1'\n self.freeReel = '-1'\n self.base_rtp = '-1'\n self.rtp = '-1'\n self.sdnew = '-1'\n self.hitrate = '-1'\n self.value = '-1'\n def fillVal(self, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew):\n self.value = F(base_rtp, rtp, sdnew, self.base_rtp, self.rtp, self.sdnew, err_base_rtp, err_rtp, err_sdnew)\n def scaling(self, scale=2):\n for i in range(len(self.frequency)):\n for j in range(len(self.frequency[i])):\n self.frequency[i][j] = scale*self.frequency[i][j]\n return self\n def fillPoint(self, obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew):\n\n n_symbols = len(obj.base.symbol)\n for symbol_id in range(n_symbols):\n for reel_id in range(obj.window[0]):\n #print(obj.base.symbol[symbol_id].position)\n if reel_id in obj.base.symbol[symbol_id].position:\n continue\n else:\n self.frequency[reel_id][symbol_id] = 0\n\n #print('frequency: ')\n #for i in range(obj.window[0]):\n # print(self.frequency[i])\n #print('\\n\\n\\n')\n\n obj.base.reel_generator(self.frequency, obj.window[0], obj.window[1])\n obj.free.reel_generator(self.frequency, obj.window[0], obj.window[1])\n obj.base.fill_frequency(self.frequency)\n obj.free.fill_frequency(self.frequency)\n\n obj.base.fill_count_killed(obj.window[0])\n #obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.base.fill_simple_num_comb(obj.window, obj.line)\n obj.base.fill_scatter_num_comb(obj.window)\n\n obj.free.fill_count_killed(obj.window[0])\n #obj.free.create_simple_num_comb(obj.window, obj.line)\n obj.free.fill_simple_num_comb(obj.window, obj.line)\n obj.free.fill_scatter_num_comb(obj.window)\n\n self.baseReel = obj.base.reels\n self.freeReel = obj.free.reels\n\n params = obj.count_parameters()\n\n self.base_rtp, self.rtp, self.sdnew, self.hitrate = params['base_rtp'], params['rtp'], params['sdnew'], params['hitrate']\n\n self.fillVal(base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew)\n\n def printBaseReel(self, file):\n max_length = 0\n file = file[:file.find('\\\\') + 1] + \"Base Reels\\\\\" + file[file.find('\\\\') + 1:]\n f = open(file, 'w')\n for l in range(len(self.baseReel)):\n if len(self.baseReel[l]) > max_length:\n max_length = len(self.baseReel[l])\n for i in range(max_length):\n s = ''\n for l in range(len(self.baseReel)):\n if i < len(self.baseReel[l]):\n t = (15 - len(self.baseReel[l][i].name))*' '\n s = s + self.baseReel[l][i].name + t\n else:\n s = 15*' '\n f.write(s + '\\n')\n f.write('\\n')\n #print('\\n')\n #print(s)\n f.close()\n\n\n\n#Класс окрестность. Аргументы: root - центр окрестности, obj - Юрина структура, base_rtp - параметр введенный пользователем, нужен для того, что бы\n#не обсчитывать всю окрестность, previous - окрестность на предыдущем шаге итерации, по дефолту предполагается фиктивной (для начальной точки).\n#Аргумент previous нужне для того, что бы на новой итерации не учитывать точки, отброшенные на предыдущей.\n\n\nclass neighbourhood(object):\n def __init__(self, root, obj, base_rtp, previous = 0):\n prev = []\n if previous != 0:\n prev = [previous.neighbour[i].frequency for i in range(len(previous.neighbour))]\n self.neighbour = []\n self.root = root\n if base_rtp > root.base_rtp:\n for i in range(len(obj.base.symbol) - 1):\n for j in range(i + 1, len(obj.base.symbol)):\n if not obj.base.symbol[i].scatter and not obj.base.symbol[j].scatter:\n if obj.base.symbol[i].payment[obj.window[0]] > obj.base.symbol[j].payment[obj.window[0]]:\n tmp = copy.deepcopy(root.frequency[0])\n tmp[i] += 1\n tmp[j] -= 1\n if tmp[i] > (1/obj.window[1])*sum(tmp) or tmp[j] < 0.05*sum(tmp):\n print('ABSOLUTELY DEGENERATE')\n break\n frequency = [copy.deepcopy(tmp) for i in range(obj.window[0])]\n if frequency not in prev:\n self.neighbour.append(point(frequency, obj.base))\n elif obj.base.symbol[i].payment[obj.window[0]] < obj.base.symbol[j].payment[obj.window[0]]:\n tmp = copy.deepcopy(root.frequency[0])\n tmp[j] += 1\n tmp[i] -= 1\n if tmp[j] > (1/obj.window[1])*sum(tmp) or tmp[i] < 0.05*sum(tmp):\n print('ABSOLUTELY DEGENERATE')\n break\n frequency = [copy.deepcopy(tmp) for i in range(obj.window[0])]\n if frequency not in prev:\n self.neighbour.append(point(frequency, obj.base))\n elif base_rtp < root.base_rtp:\n for i in range(len(obj.base.symbol) - 1):\n for j in range(i + 1, len(obj.base.symbol)):\n if not obj.base.symbol[i].scatter and not obj.base.symbol[j].scatter:\n if obj.base.symbol[i].payment[obj.window[0]] < obj.base.symbol[j].payment[obj.window[0]]:\n #print('C')\n tmp = copy.deepcopy(root.frequency[0])\n tmp[i] += 1\n tmp[j] -= 1\n if tmp[i] > (1/obj.window[1])*sum(tmp) or tmp[j] < 0.05*sum(tmp):\n print('ABSOLUTELY DEGENERATE')\n break\n frequency = [copy.deepcopy(tmp) for i in range(obj.window[0])]\n if frequency not in prev:\n self.neighbour.append(point(frequency, obj.base))\n #print('1st +1, sec -1', tmp, i, j)\n\n elif obj.base.symbol[i].payment[obj.window[0]] > obj.base.symbol[j].payment[obj.window[0]]:\n #print('D')\n tmp = copy.deepcopy(root.frequency[0])\n tmp[j] += 1\n tmp[i] -= 1\n if tmp[j] > (1/obj.window[1])*sum(tmp) or tmp[i] < 0.05*sum(tmp):\n print('ABSOLUTELY DEGENERATE')\n break\n frequency = [copy.deepcopy(tmp) for i in range(obj.window[0])]\n if frequency not in prev:\n self.neighbour.append(point(frequency, obj.base))\n #print('1st -1, sec +1', tmp, i, j)\n\n def fillParametrs(self, obj, base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew):\n for i in range(len(self.neighbour)):\n obj.base.reel_generator(self.neighbour[i].frequency, obj.window[0], obj.window[1])\n obj.free.reel_generator(self.neighbour[i].frequency, obj.window[0], obj.window[1])\n obj.base.fill_frequency(self.neighbour[i].frequency)\n obj.free.fill_frequency(self.neighbour[i].frequency)\n\n obj.base.fill_count_killed(obj.window[0])\n #obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.base.fill_simple_num_comb(obj.window, obj.line)\n obj.base.fill_scatter_num_comb(obj.window)\n\n obj.free.fill_count_killed(obj.window[0])\n #obj.free.create_simple_num_comb(obj.window, obj.line)\n obj.free.fill_simple_num_comb(obj.window, obj.line)\n obj.free.fill_scatter_num_comb(obj.window)\n\n self.neighbour[i].baseReel = obj.base.reels\n self.neighbour[i].freeReel = obj.free.reels\n\n\n params = obj.count_parameters()\n\n self.neighbour[i].base_rtp, self.neighbour[i].freemean, self.neighbour[i].rtp, self.neighbour[i].sdnew, self.neighbour[i].hitrate = params['base_rtp'], params['freemean'], params['rtp'], params['sdnew'], params['hitrate']\n\n self.neighbour[i].fillVal(base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n def neighbourVal(self):\n for i in range(len(self.neighbour)):\n print(self.neighbour[i].value, self.neighbour[i].frequency)\n def findMin(self):\n tmp_val = 100000\n tmp_ind = -1\n for i in range(len(self.neighbour)):\n if self.neighbour[i].value < tmp_val:\n tmp_val = self.neighbour[i].value\n tmp_ind = i\n print(tmp_val)\n return tmp_ind\n\n\ndef parametrs(file_name, out):\n file = open(file_name, 'r')\n j = file.read()\n\n interim = json.loads(j)\n\n obj = Q.Game(interim)\n\n distr = freq(obj, out)\n\n start_time = time.time()\n\n obj.base.reel_generator(distr, obj.window[0], obj.window[1])\n obj.free.reel_generator(distr, obj.window[0], obj.window[1])\n obj.base.fill_frequency(distr)\n obj.free.fill_frequency(distr)\n\n obj.base.fill_count_killed(obj.window[0])\n obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.base.fill_simple_num_comb(obj.window, obj.line)\n obj.base.fill_scatter_num_comb(obj.window)\n\n obj.free.fill_count_killed(obj.window[0])\n obj.free.create_simple_num_comb(obj.window, obj.line)\n obj.free.fill_simple_num_comb(obj.window, obj.line)\n obj.free.fill_scatter_num_comb(obj.window)\n\n root = point(distr, obj.base)\n root.baseReel = obj.base.reels\n root.freeReel = obj.free.reels\n\n params = obj.count_parameters()\n\n root.base_rtp, root.rtp, root.sdnew, root.hitrate = params['base_rtp'], params['rtp'], params['sdnew'], params['hitrate']\n\n roots = [root]\n\n roots = roots + initialDistributions(obj, out)\n print(time.time() - start_time)\n\n return distr, params['base_rtp'], params['rtp'], params['sdnew'], params['hitrate'], obj, roots\n\n\ndef initialDistributions(obj, out):\n t = [0 for j in range(len(obj.base.symbol))]\n numb_of_scatters = 0\n for j in range(len(obj.base.scatterlist)):\n t[obj.base.scatterlist[j]] = out.scatter_index_with_frequency[obj.base.scatterlist[j]]\n numb_of_scatters += t[obj.base.scatterlist[j]]\n num = (len(obj.base.symbol) - len(obj.base.scatterlist) - 1)#число доступных символов\n\n Sup = 1/obj.window[1]\n wildSup = 1/obj.window[1]\n\n\n initial = []\n for i in range(len(obj.base.symbol)):\n temp = copy.deepcopy(t)\n temp_ = copy.deepcopy(t)\n if not obj.base.symbol[i].scatter:\n if not obj.base.symbol[i].wild:\n temp[i] = int(Inf*out.total_length) + 1\n temp_[i] = int(Sup*out.total_length) - 1\n else:\n temp[i] = int(wildInf*out.total_length) + 1\n temp_[i] = int(wildSup*out.total_length) - 1\n total = out.total_length - sum(temp)\n total_ = out.total_length - sum(temp_)\n avg = total // num\n avg_ = total_ // num\n for j in range(len(obj.base.symbol)):\n if j not in obj.base.scatterlist and j != i:\n temp[j] = avg\n temp_[j] = avg_\n counter = 0\n for j in range(len(obj.base.symbol)):\n if j not in obj.base.scatterlist and j != i:\n temp[j] += 1\n counter += 1\n if counter >= total % num:\n break\n counter = 0\n for j in range(len(obj.base.symbol)):\n if j not in obj.base.scatterlist and j != i:\n temp_[j] += 1\n counter += 1\n if counter >= total_ % num:\n break\n temp2 = [temp for j in range(obj.window[0])]\n temp_2 = [temp_ for j in range(obj.window[0])]\n initial = initial + [point(temp2, obj.base),point(temp_2, obj.base)]\n\n for i in initial :\n i.baseReel = obj.base.reels\n i.freeReel = obj.free.reels\n\n params = obj.count_parameters()\n\n i.base_rtp, i.rtp, i.sdnew, i.hitrate = params['base_rtp'], params['rtp'], params['sdnew'], params['hitrate']\n #print(i.frequency[0], sum(i.frequency[0]))\n return initial\n\ndef F(base_rtp, rtp, sdnew, r_base_rtp, r_rtp, r_sdnew, err_base_rtp, err_rtp, err_sdnew):\n t1 = np.fabs(base_rtp - r_base_rtp)/err_base_rtp\n t2 = np.fabs(rtp - r_rtp)/err_rtp\n# t3 = np.fabs(sdnew - r_sdnew)/err_sdnew\n return max([t1])\n\n\n\n#1-ый метод. Работает в терминах окрестностей. Стартовой точкой является центральная - это та, в которой всех символов ~ поровну.\n#Минус метода в том, что при добавлении sd функционал F имеет множество локальных минимумов\n\ndef FirstMethod(hitrate, err_hitrate, file_name):\n\n #file_name = 'Games\\HappyBrauer.txt'\n #hitrate = 100\n\n out = sm.get_scatter_frequency(file_name, hitrate, err_hitrate)\n\n t_distr, r_base_rtp, r_freemean, r_rtp, r_sdnew, r_hitrate, obj, root = parametrs(file_name, out)\n\n print(root.frequency)\n\n\n\n #s = input()\n\n #s = s.split(', ')\n\n base_rtp, freemean, rtp, sdnew = 1.4, 1.5, 1.5, 6#float(s[0]), float(s[1]), float(s[2])\n err_base_rtp, err_freemean, err_rtp, err_sdnew = 0.1, 0.1, 0.1, 0.1\n\n print('assuming errors for base rtp, freemean, sd ', base_rtp, freemean, rtp, sdnew)\n print('assuming base rtp, freemean, sd ', err_base_rtp, err_freemean, err_rtp, err_sdnew)\n\n #s = input()\n\n #s = s.split(', ')\n\n #float(s[0]), float(s[1]), float(s[2])\n\n root.fill_point_metric(base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n print('root value ', root.value)\n\n print('assuming base_rtp, freemean, rtp, sdnew ', base_rtp, freemean, rtp, sdnew)\n\n print('assuming errors for base_rtp, freemean, rtp, sdnew ', err_base_rtp, err_freemean, err_rtp, err_sdnew)\n\n print(F(base_rtp, freemean, rtp, sdnew, r_base_rtp, r_freemean, r_rtp, r_sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew))\n\n #print(t_distr)\n\n\n\n neighbours = neighbourhood(root, obj, base_rtp)\n\n print('neigh', neighbours.neighbour[0].frequency)\n print(len(neighbours.neighbour))\n print(root.frequency)\n\n obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.free.create_simple_num_comb(obj.window, obj.line)\n\n neighbours.fillParametrs(obj, base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n neighbours.neighbourVal()\n\n counter = 0\n counter2 = 0\n\n while 1:\n print('AAA')\n tmp_neighbours = neighbours\n if tmp_neighbours.root.value < 1:\n print('000')\n print('minimum ', tmp_neighbours.root.value, tmp_neighbours.root.frequency)\n break\n tmp_min = neighbours.neighbour[neighbours.findMin()]\n\n while tmp_min.value > tmp_neighbours.root.value:\n print('Уперлись')\n tmp_root = tmp_neighbours.root.scaling()\n print('ы')\n tmp_neighbours = neighbourhood(tmp_root, obj, base_rtp)\n print('ыы')\n tmp_neighbours.fillParametrs(obj, base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n print('ыыы')\n tmp_min = tmp_neighbours.neighbour[neighbours.findMin()]\n print('scaling № ', counter2 , ' value ', tmp_min.value)\n counter2 += 1\n\n\n\n print('CHECKING ', tmp_min.frequency, tmp_min.value)\n if tmp_min.value < 1:\n print('111')\n print('minimum ', tmp_min.value, tmp_min.frequency)\n break\n neighbours = neighbourhood(tmp_min, obj, base_rtp, previous=tmp_neighbours)\n neighbours.fillParametrs(obj, base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n neighbours.neighbourVal()\n print('iteration ', counter, ' value ', tmp_min.value, ' distribution ', tmp_min.frequency)\n if counter > 500:\n print('222')\n break\n\n counter += 1\n\n#FirstMethod(100, 1,'Games\\HappyBrauer.txt')\n\n\nclass group:\n def __init__(self, root, num, sortedSymbols, obj, blocked_scatters=[]):\n self.root = root\n self.total = [sum(i) for i in root.frequency]\n\n self.groups = []\n for i in range(len(obj.base.symbol)):\n if obj.base.symbol[i].wild != False:\n self.groups.append([i])\n if obj.base.symbol[i].scatter and i not in blocked_scatters:\n self.groups.append([i])\n c = 0\n t_num = num - len(self.groups)\n k = len(sortedSymbols)//t_num\n for i in range(t_num):\n g = []\n if c < len(sortedSymbols)%t_num:\n for j in range(c, k + c + 1):\n g.append(sortedSymbols[i*k + j])\n c += 1\n else:\n for j in range(c, k + c):\n g.append(sortedSymbols[i*k + j])\n\n self.groups.append(g)\n\n\n\n distributions = []\n for i in range(num - 1):\n for j in range(i + 1, num):\n tmp1 = [copy.deepcopy(f) for f in root.frequency]\n tmp2 = [copy.deepcopy(f) for f in root.frequency]\n for l in range(obj.window[0]):\n for k in range(min(len(self.groups[i]), len(self.groups[j]))):\n tmp1[l][self.groups[i][k]] += 1\n tmp2[l][self.groups[j][k]] += 1\n tmp1[l][self.groups[j][k]] -= 1\n tmp2[l][self.groups[i][k]] -= 1\n if tmp1[l][self.groups[j][k]] < 0:\n tmp1[l][self.groups[j][k]] += 1\n tmp1[l][self.groups[i][k]] -= 1\n if tmp2[l][self.groups[i][k]] < 0:\n tmp2[l][self.groups[i][k]] += 1\n tmp2[l][self.groups[j][k]] -= 1\n\n for l in range(obj.window[0]):\n total_i_1 = 0\n total_i_2 = 0\n total_j_1 = 0\n total_j_2 = 0\n for k in self.groups[i]:\n total_i_1 += tmp1[l][k]\n total_i_2 += tmp2[l][k]\n for k in self.groups[j]:\n total_j_1 += tmp1[l][k]\n total_j_2 += tmp2[l][k]\n\n\n for k in self.groups[i]:\n tmp1[l][k] = total_i_1 // len(self.groups[i])\n tmp2[l][k] = total_i_2 // len(self.groups[i])\n for k in self.groups[j]:\n tmp1[l][k] = total_j_1 // len(self.groups[j])\n tmp2[l][k] = total_j_2 // len(self.groups[j])\n\n\n for k in range(total_i_1 % len(self.groups[i])):\n tmp1[l][self.groups[i][k]] += 1\n for k in range(total_i_2 % len(self.groups[i])):\n tmp2[l][self.groups[i][k]] += 1\n\n for k in range(total_j_1 % len(self.groups[j])):\n tmp1[l][self.groups[j][k]] += 1\n for k in range(total_j_2 % len(self.groups[j])):\n tmp2[l][self.groups[j][k]] += 1\n tmp1 = notice_positions(tmp1, obj.base)\n tmp2 = notice_positions(tmp2, obj.base)\n #print('tmp1: ', tmp1)\n #print('tmp2: ', tmp2)\n tmp1_ok = True\n tmp2_ok = True\n '''if flag:\n print('tmp1: ', tmp1)\n print('tmp2: ', tmp2)'''\n for l in range(obj.window[0]):\n for s in range(len(tmp1[l])):\n if s not in obj.base.scatterlist:\n if s not in obj.base.wildlist and s not in obj.base.ewildlist:\n if tmp1[l][s] == 0 and l not in obj.base.symbol[s].position:\n continue\n if tmp1[l][s] > (1/obj.window[1])*self.total[l] or tmp1[l][s] < Inf*self.total[l]:\n tmp1_ok = False\n break\n else:\n if tmp1[l][s] == 0 and l not in obj.base.symbol[s].position:\n continue\n if tmp1[l][s] > (1/obj.window[1])*self.total[l] or tmp1[l][s] < wildInf*self.total[l]:\n tmp1_ok = False\n break\n else:\n if tmp1[l][s] == 0 and l in obj.base.symbol[s].position:\n tmp1_ok = False\n break\n for s in range(len(tmp2[l])):\n if s not in obj.base.scatterlist:\n if s not in obj.base.wildlist and s not in obj.base.ewildlist:\n if tmp2[l][s] == 0 and l not in obj.base.symbol[s].position:\n continue\n if tmp2[l][s] > (1/obj.window[0])*self.total[l] or tmp2[l][s] < Inf*self.total[l]:\n tmp2_ok = False\n break\n else:\n if tmp2[l][s] == 0 and l not in obj.base.symbol[s].position:\n continue\n if tmp2[l][s] > (1/obj.window[1])*self.total[l] or tmp2[l][s] < wildInf*self.total[l]:\n tmp2_ok = False\n break\n else:\n if tmp2[l][s] == 0 and l in obj.base.symbol[s].position:\n tmp2_ok = False\n break\n #print(tmp1_ok, 'tmp1: ', tmp1)\n #print(tmp2_ok, 'tmp1: ', tmp2)\n if tmp1_ok:\n distributions = distributions + [tmp1]\n if tmp2_ok:\n distributions = distributions + [tmp2]\n\n\n #print('AAAA')\n self.points = [point(d, obj.base) for d in distributions]\n\n def fillGroup(self, obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew):\n for p in self.points:\n p.fillPoint(obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew)\n def printGroup(self):\n print('root ', self.root.frequency, ' root value ', self.root.value, ' root base rtp = ', self.root.base_rtp, ' rtp = ', self.root.rtp , ' sdnew = ', self.root.sdnew, ' hitrate = ', self.root.hitrate)\n for i in range(len(self.points)):\n print(self.points[i].frequency, 'total = ', sum(self.points[i].frequency[0]),' value ', self.points[i].value)\n def findMin(self):\n index = -1\n current = copy.deepcopy(self.root.value)\n for i in range(len(self.points)):\n if self.points[i].value < current:\n current = self.points[i].value\n index = i\n if index == -1:\n print('no minimum found')\n return -1\n else:\n return copy.deepcopy(self.points[index])\n\ndef sortSymbols(obj):\n sortedSymbols = []\n for i in range(len(obj.base.symbol)):\n if not obj.base.symbol[i].scatter and not obj.base.symbol[i].wild:\n sortedSymbols.append(i)\n #пузырьковая сортировка, не думаю, что стоит мудрить с другой\n for i in range(len(sortedSymbols) - 1):\n for j in range(i + 1, len(sortedSymbols)):\n if obj.base.symbol[sortedSymbols[i]].payment[obj.window[0]] < obj.base.symbol[sortedSymbols[j]].payment[obj.window[0]]:\n sortedSymbols[i], sortedSymbols[j] = sortedSymbols[j], sortedSymbols[i]\n return sortedSymbols\n\n\ndef double_bouble(a, b):\n for i in range(len(a) - 1):\n for j in range(i + 1, len(a)):\n if a[i] > a[j]:\n a[i], a[j] = a[j], a[i]\n b[i], b[j] = b[j], b[i]\n\n#Здесь происходит некоторая херня с hitrate.\n\ndef SecondMethod(hitrate, err_hitrate, file_name, base_rtp, err_base_rtp):\n file = open(file_name, 'r')\n j = file.read()\n interim = json.loads(j)\n game = Q.Game(interim)\n file.close()\n\n out = sm.get_scatter_frequency(file_name, hitrate, err_hitrate)\n if out == -1 and hitrate != -1:\n exit('no free spins')\n elif out == -1:\n\n out = sm.OutResult(game.base.scatterlist)\n out.add_symbols(game.base.symbol)\n blocked_scatters = []\n for scatter_id in game.base.scatterlist:\n if max(game.base.symbol[scatter_id].scatter) > 0:\n blocked_scatters.append(scatter_id)\n\n\n t_distr, r_base_rtp, r_rtp, r_sdnew, r_hitrate, obj, roots = parametrs(file_name, out)\n\n\n\n rtp, sdnew, err_rtp, err_sdnew = 0.9, 14, 0.01, 0.01\n\n print('INITIAL POINTS, THEIR DISTRIBUTIONS, VALUES AND PARAMETRES:')\n a = []\n # TODO когда-нибудь убрать slice из roots\n for root in roots[0:1]:\n root.fillPoint(obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew)\n root.fillVal(base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew)\n print(root.frequency, ' value is ', root.value,'base_rtp, rtp, sdnew, hitrate :', root.base_rtp, root.rtp, root.sdnew, root.hitrate)\n a = a + [root.value]\n b = [i for i in range(len(a))]\n double_bouble(a, b) # здесь массив b - массив индексов начальных точек, упорядоченных по значению F\n\n\n print('assuming base rtp, rtp, sd ', base_rtp, rtp, sdnew)\n print('assuming errors for base rtp, rtp, sd ', err_base_rtp, err_rtp, err_sdnew)\n\n b = [0] + b\n\n for i in b:\n print('TRYING POINT', roots[i].frequency, ' value is ', roots[i].value,'base_rtp, rtp, sdnew, hitrate :', roots[i].base_rtp, roots[i].rtp, roots[i].sdnew, roots[i].hitrate)\n\n root = roots[i]\n\n obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.free.create_simple_num_comb(obj.window, obj.line)\n sortedSymbols = sortSymbols(obj)\n check = False\n min_is_found = False\n currentScale = 0\n while not min_is_found and currentScale < scaleLimit:\n number_of_groups = len(obj.base.wildlist) + len(obj.base.ewildlist) + 1 + \\\n len(obj.base.scatterlist) - len(blocked_scatters)\n while number_of_groups < len(sortedSymbols) + len(obj.base.wildlist) + len(obj.base.ewildlist) + 1 + \\\n len(obj.base.scatterlist) - len(blocked_scatters):\n #print('is zis e los?')\n temp_group = group(root, number_of_groups, sortedSymbols, obj, blocked_scatters)\n print('группы ', temp_group.groups)\n temp_group.fillGroup(obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew )\n print('точки групп и значения в них')\n temp_group.printGroup()\n if temp_group.findMin() != -1:\n if temp_group.findMin().value < 1:\n min_is_found = True\n print('ending with ', temp_group.findMin().value)\n print('base ', temp_group.findMin().base_rtp)\n print('rtp ', temp_group.findMin().rtp)\n print('sdnew ', temp_group.findMin().sdnew)\n print('hitrate ', temp_group.findMin().hitrate)\n root = copy.deepcopy(temp_group.findMin())\n print(root.frequency)\n check = True\n temp_group.findMin().printBaseReel(file_name)\n break\n else:\n print('path ', temp_group.findMin().value)\n root = copy.deepcopy(temp_group.findMin())\n else:\n print(number_of_groups)\n number_of_groups += 1\n if number_of_groups > (len(obj.base.wildlist) + len(obj.base.ewildlist) + len(sortedSymbols) + 1\n + len(obj.base.scatterlist) - len(blocked_scatters))/2: # and number_of_groups < len(sortedSymbols):\n number_of_groups = len(sortedSymbols) + len(obj.base.wildlist) + len(obj.base.ewildlist) + 1 \\\n + len(obj.base.scatterlist) - len(blocked_scatters)\n #print(min_is_found)\n if not min_is_found:\n\n root = root.scaling()\n currentScale += 1\n print('SCALING ', currentScale)\n else:\n break\n if check:\n print('Блеск')\n break\n\n\n\n#SecondMethod(100, 1,'Games\\HappyBrauer.txt')\n\n\n\n","repo_name":"DmitryBol/reel","sub_path":"neighborhood.py","file_name":"neighborhood.py","file_ext":"py","file_size_in_byte":31142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14428041603","text":"from Utils.FileUtils import resource_path\nfrom Utils.CachingUtils import read_from_config, add_to_config\nfrom configparser import NoSectionError, NoOptionError\nfrom datetime import datetime\nimport pygame\npygame.mixer.init(48000)\n\n\nclass NotificationManager:\n def __init__(self, parent):\n self.parent = parent\n self.time_since_last_warning_tone = None\n self.time_since_last_alarm_tone = None\n self.time_since_last_reminder_tone = None\n\n def reset(self):\n self.time_since_last_warning_tone = None\n self.time_since_last_alarm_tone = None\n self.time_since_last_reminder_tone = None\n pygame.mixer.music.stop()\n\n @staticmethod\n def get_time_between_alarms():\n try:\n return int(read_from_config(\"SETTINGS\", \"time_between_alarms\"))\n except (NoOptionError, NoSectionError):\n add_to_config(\"SETTINGS\", \"time_between_alarms\", 60)\n return 60\n\n def interacted(self, user):\n if not self.check_warning(user) and not self.check_alarm(user):\n if user.user_clock.state:\n play_started_tone()\n return True\n if not user.user_clock.state:\n play_stopped_tone()\n\n def remind(self, user):\n if not user.user_clock.state:\n if self.time_since_last_reminder_tone is None:\n play_reminder_tone()\n self.time_since_last_reminder_tone = datetime.now()\n elif (datetime.now() - self.time_since_last_reminder_tone).total_seconds() > 240:\n play_reminder_tone()\n self.time_since_last_reminder_tone = datetime.now()\n return True\n\n def user_conditions(self, user):\n return user == self.parent.current_user and user.user_clock.state\n\n def check_warning(self, user):\n if self.user_conditions(user) and user.is_warning_time():\n if self.time_since_last_warning_tone is None:\n play_warning_tone()\n self.time_since_last_warning_tone = datetime.now()\n return True\n\n def check_alarm(self, user):\n if self.user_conditions(user) and user.is_time_up():\n if self.time_since_last_alarm_tone is None:\n play_alarm_tone()\n self.time_since_last_alarm_tone = datetime.now()\n elif (datetime.now() - self.time_since_last_alarm_tone).total_seconds() > self.get_time_between_alarms():\n play_alarm_tone(1)\n self.time_since_last_alarm_tone = datetime.now()\n return True\n\n\ndef play_warning_tone(loops=2):\n pygame.mixer.music.load(resource_path(\"Sounds/warning.mp3\"))\n pygame.mixer.music.play(loops)\n\n\ndef play_alarm_tone(loops=2):\n pygame.mixer.music.load(resource_path(\"Sounds/alarm.mp3\"))\n pygame.mixer.music.play(loops)\n\n\ndef play_reminder_tone(loops=2):\n pygame.mixer.music.load(resource_path(\"Sounds/reminder.mp3\"))\n pygame.mixer.music.play(loops)\n\n\ndef play_started_tone(loops=1):\n pygame.mixer.music.load(resource_path(\"Sounds/started.mp3\"))\n pygame.mixer.music.play(loops)\n\n\ndef play_stopped_tone(loops=1):\n pygame.mixer.music.load(resource_path(\"Sounds/stopped.mp3\"))\n pygame.mixer.music.play(loops)\n","repo_name":"smithbro2115/GameTimer","sub_path":"Notifications.py","file_name":"Notifications.py","file_ext":"py","file_size_in_byte":3247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23437655840","text":"#!/usr/bin/python3\n#***********************************************************************\n# FILES:\n# 09_PyGObject_GTK_Glade_Clock.py\n#\t\t\t\t glade/09_PyGObject_GTK_Glade_Clock.glade\n# \n# USAGE: \n#\t\t\t\t ./09_PyGObject_GTK_Glade_Clock.py\n#\t\t\t\tor\n#\t\t\t\t python3 09_PyGObject_GTK_Glade_Clock.py\n# \n# DESCRIPTION: This code consist of the following:\n# 1- Designed GTK + UI with Glade\n# 2- creates a modular code using a class to creating window widgets\n# 3- Practical use of signals, events, callbacks, \n# and functions concepts.\n#\t\t\t\t 4- Using GLib.timeout_add function\n#\n#\n# OPTIONS: ---\n# REQUIREMENTS: Python, GTK+, Glade\n# BUGS: ---\n# NOTES: PyGObject tutorial\n# AUTHOR: Mahdi Bahmani (www.itstorage.co)\n# ORGANIZATION: merdasco\n# CREATED: 06.06.2020\n# LAST EDITED: \n# REVISION: 1.1\n#**********************************************************************/\nimport gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk, GLib, GObject\nfrom datetime import datetime\n\n\n# class is used to manipulate window widgets\nclass MyWindow(Gtk.Window):\n\t\n\ttimeout_id = None\n\tcounter = 0\n\t# Initializer function\n\tdef __init__(self):\n\t\t\n\t\t# A Gtk.Builder is an auxiliary object that reads textual \n\t\t# descriptions of a user interface and instantiates the \n\t\t# described objects. \n\t\tbuilder = Gtk.Builder()\n\t\t\n\t\t# Import UI designed via Glade and connect signals\n\t\tbuilder.add_from_file('glade/09_PyGObject_GTK_Glade_Clock.glade')\n\t\tbuilder.connect_signals(self)\n\t\t\n\t\t# The functions builder.get_object() can be used to access \n\t\t# the widgets in the interface by the names assigned to them \n\t\t# inside the UI description. \n\t\twindow = builder.get_object('window1')\n\t\tself.label1 = builder.get_object('label1')\n\t\tself.buttonStart = builder.get_object('button1')\n\n\t\t\n\t\t# Set attributes\n\t\twindow.set_border_width(10)\n\t\twindow.set_size_request(320, 100)\n\t\twindow.set_title(\"PyGObject - Show Date/Time\")\n\t\tself.label1.set_text(\"Date/Time: \")\n\t\t\n\t\t# Register callbacks\n\t\tself.buttonStart.connect(\"clicked\", self.on_buttonStart_clicked)\n\t\twindow.connect(\"destroy\", Gtk.main_quit)\n\t\twindow.connect(\"delete-event\", Gtk.main_quit)\n\t\t\n\t\twindow.show_all()\n\t\t\n\t# function definition\n\tdef on_buttonStart_clicked(self, widget, *args):\n\t\t\"\"\" Handles \"clicked\" event of buttonStart. \"\"\"\n\t\tself.buttonStart.set_sensitive(False)\n\t\tself.start_clock_timer()\n\t\t\n\t# Displays Timer\n\tdef display_clock(self, *args, **kwargs):\n\t\t# putting our datetime into a var and setting our label to the result. \n\t\t# we need to return \"True\" to ensure the timer continues to run, otherwise it will only run once.\n\t\tnow = datetime.now()\n\t\tdatetimenow = str(now.strftime(\"%Y-%M-%d %X\"))\n\t\tself.label1.set_label(datetimenow)\n\t\treturn True\n\n\t# Initialize Timer\n\tdef start_clock_timer(self):\n\t\tGLib.timeout_add(250, self.display_clock, None)\n\n\n#-----------------------------------------------------------------------\n# main function\ndef main():\n\t\n\twindow = MyWindow()\n\t\n\t# GTK main loop\n\tGtk.main()\n#-----------------------------------------------------------------------\n\n\t\t\nif __name__ == '__main__':\n main()\n\n","repo_name":"mbahmani80/Python_QT_PyGObject","sub_path":"PyGObject_GTK_Glade_python/09_PyGObject_GTK_Glade_Clock.py","file_name":"09_PyGObject_GTK_Glade_Clock.py","file_ext":"py","file_size_in_byte":3231,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"10241210026","text":"from mapa_streamlit.cleaning import (\n _delete_files_in_dir,\n _get_data_size_of_dir,\n _get_disk_usage,\n _get_number_of_files_in_dir,\n run_cleanup_job,\n)\n\n\ndef test__get_disk_usage():\n usage = _get_disk_usage()\n assert isinstance(usage, float)\n assert 0.0 < usage <= 100.0\n\n\ndef test__get_data_size_of_dir(tmp_path):\n # get size of empty dir\n size = _get_data_size_of_dir(tmp_path)\n assert size == 0\n\n # put dummy file into dir\n file_1_size = 1234\n file_1 = tmp_path / \"file_1.stl\"\n f = open(file_1, \"wb\")\n f.seek(file_1_size - 1)\n f.write(b\"\\0\")\n f.close()\n\n size = _get_data_size_of_dir(tmp_path)\n assert size == round(file_1_size / 1024**2, 4)\n\n # put another file into the dir\n file_2_size = 2345\n file_2 = tmp_path / \"file_2.stl\"\n f = open(file_2, \"wb\")\n f.seek(file_2_size - 1)\n f.write(b\"\\0\")\n f.close()\n\n size = _get_data_size_of_dir(tmp_path)\n assert size == round((file_1_size + file_2_size) / 1024**2, 4)\n\n # verify files in nested dirs are also taken into account\n file_3_size = 3456\n d = tmp_path / \"sub\"\n d.mkdir()\n file_3 = d / \"file_3.stl\"\n f = open(file_3, \"wb\")\n f.seek(file_3_size - 1)\n f.write(b\"\\0\")\n f.close()\n\n size = _get_data_size_of_dir(tmp_path)\n assert size == round((file_1_size + file_2_size + file_3_size) / 1024**2, 4)\n\n\ndef test__delete_files_in_dir(tmp_path) -> None:\n # verify function deletes stl file\n p = tmp_path / \"baa.stl\"\n p.write_text(\"foo\")\n assert p.is_file()\n _delete_files_in_dir(tmp_path, \".stl\")\n assert not p.is_file()\n\n # running it again does not fail\n _delete_files_in_dir(tmp_path, \".stl\")\n\n # ensure files beside stl files won't be deleted\n p = tmp_path / \"baa.tiff\"\n p.write_text(\"foo\")\n assert p.is_file()\n _delete_files_in_dir(tmp_path, \".stl\")\n assert p.is_file()\n\n\ndef test__get_number_of_files_in_dir(tmp_path) -> None:\n num = _get_number_of_files_in_dir(tmp_path, \".stl\")\n assert num == 0\n\n stl = tmp_path / \"baa.stl\"\n stl.write_text(\"foo\")\n num = _get_number_of_files_in_dir(tmp_path, \".stl\")\n assert num == 1\n num = _get_number_of_files_in_dir(tmp_path, \".tiff\")\n assert num == 0\n\n tiff = tmp_path / \"baa.tiff\"\n tiff.write_text(\"foo\")\n num = _get_number_of_files_in_dir(tmp_path, \".stl\")\n assert num == 1\n num = _get_number_of_files_in_dir(tmp_path, \".tiff\")\n assert num == 1\n\n\ndef test_run_cleanup_job(tmp_path) -> None:\n # chose very low threshold to ensure files will be delted\n stl = tmp_path / \"baa.stl\"\n stl.write_text(\"foo\")\n assert stl.is_file()\n tiff = tmp_path / \"baa.tiff\"\n tiff.write_text(\"foo\")\n assert tiff.is_file()\n zip = tmp_path / \"baz.zip\"\n zip.write_text(\"foo\")\n assert zip.is_file()\n\n run_cleanup_job(tmp_path, disk_cleaning_threshold=0.0)\n assert not stl.is_file()\n assert tiff.is_file()\n assert not zip.is_file()\n\n # chose very high threshold to check that files won't get deleted\n stl = tmp_path / \"baa.stl\"\n stl.write_text(\"foo\")\n assert stl.is_file()\n tiff = tmp_path / \"baa.tiff\"\n tiff.write_text(\"foo\")\n assert tiff.is_file()\n\n run_cleanup_job(tmp_path, disk_cleaning_threshold=100.0)\n assert stl.is_file()\n assert tiff.is_file()\n","repo_name":"fgebhart/mapa-streamlit","sub_path":"tests/test_cleaning.py","file_name":"test_cleaning.py","file_ext":"py","file_size_in_byte":3297,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"13262249327","text":"import rospy\nimport serial\nimport time\nfrom std_msgs.msg import Int32\n\npy_serial = serial.Serial(port='/dev/ttyUSB0', baudrate=9600,)\n\ndef talker():\n pub = rospy.Publisher('jetson', Int32, queue_size=1)\n rospy.init_node('talker', anonymous=True)\n rate = rospy.Rate(10) # 10hz\n timer10 = 0\n\n while(1):\n if py_serial.readable():\n sending = py_serial.readline()\n sending_int = int(sending[:len(sending)-1].decode())\n pub.publish(sending_int)\n rospy.loginfo(sending_int) \n\n # if (sending_int==100):\n # sending2_int = sending_int\n # rospy.loginfo(sending2_int)\n # start_time = time.time()\n\n # while (timer10 <= 10 and sending_int == 100):# or sending2_int!=99):\n # rospy.loginfo(\"in while2\")\n \n # if py_serial2.readable():\n # sending = py_serial2.readligitne()\n # sending_int = int(sending[:len(sending)-1].decode())\n \n # rospy.loginfo(sending_int)\n # end_time = time.time()\n # rospy.loginfo(end_time)\n # timer10 = end_time - start_time\n # rospy.loginfo(timer10)\n\n #sending_int = sending2_int\n # rospy.loginfo(\"escape!\")\n\n # if(timer10 > 10):\n # #시리얼로 아두이노에 cmd='Z' 먹이기\n # command = \"Z\"\n # py_serial.write(command.encode())\n # time.sleep(0.1)\n \n #time.sleep(3)\n #pub.publish(sending_int)\n #time.sleep(0.1)\n #rate.sleep()\n\nif __name__ == '__main__':\n talker()\n","repo_name":"1213tnals/CoffeeRobot","sub_path":"sumin-communication/beginner_tutorials/scripts/talker.py","file_name":"talker.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35165326921","text":"import pandas as pd\r\nsites_table = pd.read_csv(\"PTM_Table.csv\", low_memory=False)\r\ndict_sites_PTM = {}\r\nfor row, column in sites_table.iterrows():\r\n UID = column[2]\r\n site_pos = int(column[0].split(\"-\")[1]) # this is the variable that holds the site for each row\r\n if UID in dict_sites_PTM:\r\n dict_sites_PTM[UID].append(site_pos)\r\n if UID not in dict_sites_PTM:\r\n dict_sites_PTM[UID] = [site_pos] # dictionary of all PTM sites from the deconv_jmp table(renamed to PTM table here\r\n\r\n# the dict_sites_PTM, the key is the UID and the value is the list of the positions/PTM sites for each UID\r\n\r\ninterpro_result = pd.read_table(\"interproscan_results.tsv\", sep = \"\\t\")\r\n#interpro_result.columns = [\"Protein_accession\", \"Sequence_MD5_Digest\", \"Length\", \"Analysis\", \"Signature_accession\", \"Description\", \"Start_pos\", \"End_pos\", \"E-Val\", \"Status\", \"Date\", \"Interpro_anno\", \"Interpro_anno_2\"]\r\ninterpro_result_pos = {}\r\nfor row, column in interpro_result.iterrows():\r\n UID = column[0].split(\"|\")[1]\r\n start = int(column[6])\r\n stop = int(column[7])\r\n eval = column[8]\r\n if eval != \"-\":\r\n pos_list = (start, stop, eval)\r\n if UID in interpro_result_pos:\r\n interpro_result_pos[UID].append(pos_list)\r\n if UID not in interpro_result_pos:\r\n interpro_result_pos[UID] = [pos_list] # this dictionary holds the start, stop and evalue for each motif entry\r\n# the interpro_result dictionary is the UID as the key, and the start stop and evalue for each motif entry as the value\r\n\r\nresult_appended = []\r\nfor UID_PTM in interpro_result_pos: # the two dictionaries are looped through to find the PTM sites within the start and stop positions of the motifs\r\n if UID_PTM in dict_sites_PTM:\r\n list_pos = dict_sites_PTM[UID_PTM]\r\n for pos in list_pos:\r\n for result_pos in interpro_result_pos[UID_PTM]:\r\n if pos in range(result_pos[0], result_pos[1]):\r\n result = []\r\n result.append(UID_PTM)\r\n result.append(pos)\r\n result.append(result_pos[0])\r\n result.append(result_pos[1])\r\n result.append(result_pos[2])\r\n result_appended.append(result)\r\n# result appended is the list of the UID, pos and the start position, stop position and e-value of the motif the site falls into.\r\nresult_df = pd.DataFrame(result_appended)\r\nresult_df.columns = [\"UID\",\"Site_position\",\"Motif_start_pos\", \"Motif_stop_pos\",\"E-value\"]\r\nresult_df.to_csv(\"Result_table.csv\") # final .csv file with the above mentioned columns.\r\n","repo_name":"ManasaUpadhyaya/Logistic_regression_model_statistical_significance_of-PTM_sites","sub_path":"motif_score_finder.py","file_name":"motif_score_finder.py","file_ext":"py","file_size_in_byte":2624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74144272082","text":"\"\"\"\nTask (1.1): Create a function named evenOdd that takes an integer n as input and print whether n is an even number or an odd number.\nTask (1.2): Use evenOdd function from Task 1.1 to print even/odd for each element in the list. [10,9,12,5,7]\n\"\"\"\n\ndef even_odd(n):\n if n%2==0:\n return \"Even\"\n else:\n return \"Odd\"\n\nls = [10,9,12,5,7]\nln = len(ls)\nfor i in range(0,ln):\n e = ls[i]\n print(f\"{e}: {even_odd(e)}\")","repo_name":"enigmatic-cipher/PLAB-S3-Task-1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33362706811","text":"def clear():\r\n from os import system, name \r\n # for windows\r\n if name == 'nt':\r\n _ = system('cls') \r\n # for mac and linux(here, os.name is 'posix')\r\n else:\r\n _ = system('clear')\r\nclear()\r\n\r\nfrom turtle import Turtle, Screen\r\nimport heroes\r\nimport random\r\n\r\ntimmy = Turtle()\r\nscreen = Screen()\r\nscreen.colormode(255)\r\n\r\n# Functions\r\ndef random_color():\r\n '''Make Timmy a random color'''\r\n timmy.pencolor( random.randint(0, 255),\r\n random.randint(0, 255),\r\n random.randint(0, 255))\r\n\r\ndef timmy_square(size):\r\n '''Draw a square of size'''\r\n timmy.begin_fill()\r\n for _ in range(4):\r\n timmy.forward(size)\r\n timmy.right(90)\r\n timmy.end_fill()\r\n\r\ndef dash_line(dash, length):\r\n '''Draw a line of length, with adjustable dash size'''\r\n for _ in range(length):\r\n timmy.forward(dash)\r\n timmy.pu()\r\n timmy.forward(dash)\r\n timmy.pd() \r\n\r\ndef all_shapes(sides, size):\r\n '''Draw all polygons up to the number of sides'''\r\n for i in range(3, sides):\r\n # Change colour\r\n random_color()\r\n for _ in range(i):\r\n timmy.forward(size)\r\n timmy.right(360 / i)\r\n i += 1\r\n\r\ndef random_walk(size, steps):\r\n '''Randomly move in a direction for x amount of steps'''\r\n timmy.pensize(15)\r\n direction = [0, 90, 180, 270]\r\n for _ in range(steps):\r\n # Choose random direction\r\n timmy.setheading(random.choice(direction))\r\n # Colour and move\r\n random_color()\r\n timmy.forward(size)\r\n\r\ndef spirograph(size, steps):\r\n for _ in range(steps):\r\n random_color()\r\n timmy.left(360/steps)\r\n timmy.circle(size)\r\n\r\n# Change shape and settings\r\ntimmy.pensize(2)\r\ntimmy.speed(28)\r\n \r\n# Do something\r\n# random_walk(30, 150)\r\nspirograph(100, 40)\r\n\r\n# Exit on click at the end of the code\r\nscreen.exitonclick()","repo_name":"heySanj/100-PYTHON","sub_path":"exercises/hirst/turtle_practice.py","file_name":"turtle_practice.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72117896402","text":"import pandas as pd \nimport numpy as np \nimport pickle\nimport json\nfrom Models.databaru import Database\n\ndb = Database()\n\nmodel = pickle.load(open(r\"C:\\Users\\ROG\\Documents\\mobile_price_classification\\experiment\\model_xgb.pkl\", 'rb'))\n\n# function yang digunakan untuk melihat semua data training\ndef showDataTrain():\n try:\n df = pd.read_csv(r\"C:\\Users\\ROG\\Documents\\mobile_price_classification\\dataset\\train.csv\")\n return df.to_dict(orient=\"records\")\n except Exception as e:\n print(f\"kesalahan function showDataTrain: {e}\")\n\n# function yang digunakan untuk memprediksi Data\ndef showPrediction(**params):\n try:\n data = [[\n int(params[\"battery_power\"]),\n int(params[\"blue\"]),\n float(params[\"clock_speed\"]),\n int(params[\"dual_sim\"]),\n int(params[\"fc\"]),\n int(params[\"four_g\"]),\n int(params[\"int_memory\"]),\n float(params[\"m_dep\"]),\n int(params[\"mobile_wt\"]),\n int(params[\"n_cores\"]),\n int(params[\"pc\"]),\n int(params[\"px_height\"]),\n int(params[\"px_width\"]),\n int(params[\"ram\"]),\n int(params[\"sc_h\"]),\n int(params[\"sc_w\"]),\n int(params[\"talk_time\"]),\n int(params[\"three_g\"]),\n int(params[\"touch_screen\"]),\n int(params[\"wifi\"])\n ]]\n data_test = pd.DataFrame(data, index=[0], columns=['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g','touch_screen', 'wifi'])\n hasilPrediksi = model.predict(data_test)\n \n dataToInsert = {\n \"battery_power\": int(params[\"battery_power\"]),\n \"blue\": int(params[\"blue\"]),\n \"clock_speed\": float(params[\"clock_speed\"]),\n \"dual_sim\": int(params[\"dual_sim\"]),\n \"fc\": int(params[\"fc\"]),\n \"four_g\": int(params[\"four_g\"]),\n \"int_memory\": int(params[\"int_memory\"]),\n \"m_dep\": float(params[\"m_dep\"]),\n \"mobile_wt\": int(params[\"mobile_wt\"]),\n \"n_cores\": int(params[\"n_cores\"]),\n \"pc\": int(params[\"pc\"]),\n \"px_height\": int(params[\"px_height\"]),\n \"px_width\": int(params[\"px_width\"]),\n \"ram\": int(params[\"ram\"]),\n \"sc_h\": int(params[\"sc_h\"]),\n \"sc_w\": int(params[\"sc_w\"]),\n \"talk_time\": int(params[\"talk_time\"]),\n \"three_g\": int(params[\"three_g\"]),\n \"touch_screen\": int(params[\"touch_screen\"]),\n \"wifi\": int(params[\"wifi\"]),\n \"price_range\" : str(hasilPrediksi[0])\n }\n db.insertDataBaru(**dataToInsert)\n return str(hasilPrediksi[0])\n except Exception as e:\n print(f\"kesalahan function showPrediction: {e}\")\n\n# function yang digunakan untuk convert ObjectId ke string\ndef ObjToStr(obj):\n try:\n return str(obj[\"_id\"][\"$oid\"])\n except Exception as e:\n print(f\"kesalahan fucntion ObjToStr: {e}\")\n\n# function yang digunakan untuk melihat databaru dari database\ndef showAllDataBaru():\n try:\n dbhasil = db.showAllDataBaru()\n hasil = json.loads(dbhasil)\n data_list = []\n for data in hasil:\n data[\"id\"] = ObjToStr(data)\n data_list.append(data)\n return data_list\n except Exception as e:\n print(f\"kesalahan function showAllDatabaru: {e}\")\n\n# function yang digunakan untuk melihat data hasil_prediksi\ndef showHasilPrediksi():\n try:\n return pd.read_feather(r\"C:\\Users\\ROG\\Documents\\mobile_price_classification\\experiment\\hasil_prediksi.feather\").to_dict(orient=\"records\")\n except Exception as e:\n print(f\"kesalahan function showHasilPrediksi: {e}\")","repo_name":"rshby/mobile_price_classification","sub_path":"app/Controllers/mobile.py","file_name":"mobile.py","file_ext":"py","file_size_in_byte":3846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7658368776","text":"#!/usr/bin/env python\nfrom setuptools import setup\nimport os, subprocess\nimport argparse\n\n\ndef cpu_version_build(cur_path, sanitaser=False):\n print(\"Build CPU version\")\n cpp_files = ['/src/cpu/cpu_solver.cpp',\n '/src/python_package/py_wrapper_cpu.cpp',\n '/src/model.cpp']\n\n # Build only CPU version\n\n sanitaser_setup = ['-Wall', '-Werror']\n input_cpp_files = [cur_path + path for path in cpp_files]\n\n o_files = [cur_path + '/src/python_package/build/' + path\n for path in ['cpu_solver.o', 'py_wrapper_cpu.o', 'model_cpu.o']]\n print(\"Using cpp files: \", cpp_files)\n\n for cpp_file, o_file in zip(input_cpp_files, o_files):\n get_object_command = ['g++', '-c', '-std=c++14','-fPIC',\n cpp_file, '-o', o_file]\n if sanitaser:\n get_object_command += sanitaser_setup\n subprocess.call([*get_object_command])\n\n so_cpu_path = cur_path + '/src/python_package/build/python_package_cpu.so'\n subprocess.call(['g++', '-shared', '-std=c++14', '-o', so_cpu_path, *o_files])\n print(\"Success!\")\n\n\ndef gpu_version_build(cur_path):\n # Build only GPU version\n print(\"Build GPU version\")\n so_gpu_path = cur_path + '/src/python_package/build/python_package_gpu.so'\n \n subprocess.call(['g++', '-c', '-std=c++14', \n cur_path + '/src/python_package/py_wrapper_gpu.cpp', \n '-o', \n cur_path + '/src/python_package/build/py_wrapper_gpu.o',\n '-fPIC'])\n subprocess.call(['nvcc', '-c', '-std=c++14',\n cur_path + '/src/gpu/gpu_solver.cu',\n '-o',\n cur_path +'/src/python_package/build/gpu_solver.o',\n '-Xcompiler',\n '-fPIC'])\n\n subprocess.call(['nvcc', '-c', '-std=c++14',\n cur_path + '/src/model.cpp',\n '-o',\n cur_path +'/src/python_package/build/model_cuda.o',\n '-Xcompiler',\n '-fPIC'])\n\n subprocess.call(['nvcc', '-shared', '-std=c++14',\n '-o', so_gpu_path,\n cur_path + '/src/python_package/build/model_cuda.o',\n cur_path + '/src/python_package/build/py_wrapper_gpu.o',\n cur_path + '/src/python_package/build/gpu_solver.o'])\n print(\"GPU build success!\", so_gpu_path)\n\n\nSANITASER = False\ncur_path = os.path.join(os.path.dirname(os.path.abspath(__file__)))\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--GPU\", help=\"Build GPU module\", default = None)\nargs = parser.parse_args()\n\nif args.GPU:\n cpu_version_build(cur_path)\n gpu_version_build(cur_path)\n\nelse:\n cpu_version_build(cur_path)\n\n\n\n\n\n# setup(\n# name='space_junk_simulator',\n# version='0.1',\n# packages=[''],\n# package_dir={'': 'src/python_package'},\n# url='',\n# license='',\n# author='ivan_kharitonov',\n# author_email='',\n# description=''\n# )\n","repo_name":"neer201/space_junk_simulator","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72767623441","text":"import requests\nimport os\nimport telebot\nfrom decoder import *\n\n\nTOKEN = os.environ['TELEGRAM_TOKEN']\nCLOUD_API = os.environ['CLOUD_TOKEN']\n\nbot = telebot.TeleBot(TOKEN)\n\n@bot.message_handler(commands=['start', 'help'])\ndef send_welcome(message):\n\tbot.reply_to(message, \"Привет, епта, я Гарик!\")\n\n@bot.message_handler(regexp=\"Поиск\")\ndef handle_message(message):\n bot.send_message(message, '{} данных в городе {}, по количеству {} запросов'.format(message, 'Алматы', '10'))\n\n@bot.message_handler(content_types=[\"text\"])\ndef repeat_all_messages(message):\n bot.send_message(message.chat.id, message.text)\n\n@bot.message_handler(content_types=['document', 'audio'])\ndef handle_docs_audio(message):\n\tbot.send_message(message.chat.id, \"Обработка документов и звука, ага блять!\")\n\n@bot.message_handler(content_types=['voice'])\ndef voice_processing(message):\n file_info = bot.get_file(message.voice.file_id)\n file = requests.get('https://api.telegram.org/file/bot{0}/{1}'.format(TOKEN, file_info.file_path))\n try:\n # обращение к нашему новому модулю\n text = speech_to_text(bytes=file.content)\n bot.send_message(message.chat.id, 'Распознал команду ' + text)\n except SpeechException:\n bot.send_message(message.chat.id, 'Извините, не удалось распознать запрос')\n else:\n bot.send_message(message.chat.id, 'Почему-то выпал в блок else!')\n\nif __name__ == '__main__':\n bot.polling(none_stop=True)\n","repo_name":"Uarabei/herokutry","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44429727199","text":"# Web imports\nfrom flask import request\nfrom flask_restful import Resource, abort\n\n# Helper functions\nfrom models import (\n get_features,\n get_feature_index,\n get_feature_names,\n OrganismNotFoundError,\n MeasurementTypeNotFoundError,\n)\nfrom api.v1.utils import (\n clean_feature_string,\n)\n\n\nclass HasFeatures(Resource):\n \"\"\"Get list of features for an organism\"\"\"\n\n def get(self):\n \"\"\"Get list of features (genes)\"\"\"\n args = request.args\n measurement_type = args.get(\"measurement_type\", \"gene_expression\")\n organism = args.get(\"organism\", None)\n if organism is None:\n abort(400, message='The \"organism\" parameter is required.')\n features = args.get(\"features\", None)\n if features is None:\n abort(400, message='The \"features\" parameter is required.')\n try:\n features = clean_feature_string(features, organism, measurement_type)\n except FeatureStringFormatError:\n abort(400, message=f\"Feature string not recognised: {features}.\")\n\n try:\n features_lowercase = get_features(\n organism=organism,\n measurement_type=measurement_type,\n )\n except OrganismNotFoundError:\n abort(400, message=f\"Organism not found: {organism}.\")\n except MeasurementTypeNotFoundError:\n abort(\n 400,\n message=f\"Measurement type not found: {measurement_type}.\",\n )\n\n is_found = []\n features_corrected = []\n features_all = get_feature_names(\n organism=organism,\n measurement_type=measurement_type,\n )\n for fea in features:\n if fea.lower() not in features_lowercase:\n is_found.append(False)\n features_corrected.append(fea)\n else:\n is_found.append(True)\n idx = get_feature_index(organism, fea.lower(), measurement_type=measurement_type)\n features_corrected.append(features_all[idx])\n\n return {\n \"measurement_type\": measurement_type,\n \"organism\": organism,\n \"features\": features_corrected,\n \"found\": is_found,\n }\n\n","repo_name":"fabilab/cell_atlas_approximations_API","sub_path":"web/api/v1/objects/check_features.py","file_name":"check_features.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22554407949","text":"####################################################################\n# Script to do basic stats and regression on behavior data. Including transitional RT analysis\n####################################################################\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom scipy.stats import zscore\nimport statsmodels.formula.api as smf\nsns.set_context('paper', font_scale=1)\nsns.set_style('white')\nsns.set_palette(\"colorblind\")\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\nimport scipy.cluster.hierarchy as sch\n\nincluded_subjects = ['128', '112', '108', '110', '120', '98', '86', '82', '115', '94', '76', '91', '80', '95', '121', '114', '125', '70',\n'107', '111', '88', '113', '131', '130', '135', '140', '167', '145', '146', '138', '147', '176', '122', '118', '103', '142']\nclasses = ['dcb', 'dcr', 'dpb', 'dpr', 'fcb', 'fcr', 'fpb', 'fpr']\n\n#these are paths for Argon cluster at UIowa, so specific to where we stored our data.\ndata_path = '/Shared/lss_kahwang_hpc/ThalHi_data/RSA/'\nROOT = '/Shared/lss_kahwang_hpc/ThalHi_data/' \n\n\ndef compile_behavior():\n\t''' conmpile dataframe for behavior data'''\n\tall_df = pd.DataFrame()\n\tfor sub in included_subjects:\n\n\t\tmetadata = pd.read_csv((ROOT+'RSA/%s_metadata.csv' %sub))\n\t\tmetadata.loc[metadata.rt=='none', 'rt'] = np.nan #recode no responses into nans\n\t\tmetadata.loc[metadata.Subject_Respo=='none', 'Subject_Respo'] = np.nan #recode no responses into nans\n\t\tmetadata.rt = metadata.rt.astype('float') #convert string to float\n\t\tmetadata.Subject_Respo = metadata.Subject_Respo.astype('float') \n\t\tmetadata['RT_zscore'] = (metadata.rt - np.nanmean(metadata.rt)) / np.nanstd(metadata.rt) #zcore of RT, in case it is useful\n\t\tmetadata['Accuracy'] = metadata.trial_Corr\n\n\t\t#determine swap or not swap version of context-feature mapping\n\t\tif 'version' in metadata.columns: # only the swapped version has the version column\n\t\t\tmetadata['swapped'] = 1\n\t\t\t### Dillan didnt code the Task info for the swapped version\n\t\t\tmetadata.loc[metadata['cue'] == 'dcb', 'Task'] = 'Face'\n\t\t\tmetadata.loc[metadata['cue'] == 'dcr', 'Task'] = 'Face'\n\t\t\tmetadata.loc[metadata['cue'] == 'dpb', 'Task'] = 'Scene'\n\t\t\tmetadata.loc[metadata['cue'] == 'dpr', 'Task'] = 'Scene'\n\t\t\tmetadata.loc[metadata['cue'] == 'fcb', 'Task'] = 'Face'\n\t\t\tmetadata.loc[metadata['cue'] == 'fcr', 'Task'] = 'Scene'\n\t\t\tmetadata.loc[metadata['cue'] == 'fpb', 'Task'] = 'Face'\n\t\t\tmetadata.loc[metadata['cue'] == 'fpr', 'Task'] = 'Scene'\n\t\telse:\n\t\t\tmetadata['swapped'] = 0\n\n\t\t\tmetadata['version'] = \"Donut=Color,Filled=Shape\"\n\t\tall_df = all_df.append(metadata)\n\tall_df = all_df.drop(columns=['pic_stim', 'img_path', 'index', 'cue_stim', 'trigs', 'Unnamed: 0']) #clear book keeping vars.\n\n\treturn all_df\n\n#behavior_df = compile_behavior()\n#behavior_df.to_csv('Data/behavior_data.csv')\nbehavior_df = pd.read_csv('Data/behavior_data.csv')\n\nprint('mean')\nprint(behavior_df.groupby(['sub']).mean().mean())\nprint('std')\nprint(behavior_df.groupby(['sub']).mean().std())\n\n\ndef rt_matrix(inputdf):\n ''' calcuate transtional RT matrix'''\n Subjects = inputdf['sub'].astype('int').unique()\n mat = np.zeros((8,8))\n subN = np.zeros((8,8))\n amat = np.zeros((8,8))\n all_mat = np.zeros((8,8, len(Subjects)))\n for idx, s in enumerate(Subjects):\n df = inputdf.loc[inputdf['sub'] == s]\n df = df.reset_index()\n\n #orgaznie matrix by texture, shape, color, return indx. Make sure the ordre here matches the \"classes variable above\"\n def get_positions(x):\n return {\n ('Filled', 'Polygon', 'red'): 7,\n ('Filled', 'Polygon', 'blue'): 6,\n ('Filled', 'Circle', 'red'): 5,\n ('Filled', 'Circle', 'blue'): 4,\n ('Donut', 'Polygon', 'red'): 3,\n ('Donut', 'Polygon', 'blue'): 2,\n ('Donut', 'Circle', 'red'): 1,\n ('Donut', 'Circle', 'blue'): 0,\n }[x]\n\n transitionRTs = np.zeros((8,8))\n trialN = np.zeros((8,8))\n for i in df.index:\n if i == 0:\n continue\n # if df.loc[i, 'cue']== df.loc[i-1, 'cue']: #remove cue repetition effect....?\n # continue\n else:\n if df.loc[i, 'trial_Corr']==1: # only correct trials\n if df.loc[i-1, 'trial_Corr']!=0: # exclude post error slowing trials\n if df.loc[i, 'RT_zscore']<3: #no overly slow trials\n previous_condition = get_positions((df.loc[i-1, 'Texture'], df.loc[i-1, 'Shape'], df.loc[i-1, 'Color']))\n current_condition = get_positions((df.loc[i, 'Texture'], df.loc[i, 'Shape'], df.loc[i, 'Color']))\n diff_rt = df.loc[i,'rt'] #- df.loc[i-1, 'rt']\n transitionRTs[previous_condition, current_condition] = transitionRTs[previous_condition, current_condition] + diff_rt\n trialN[previous_condition, current_condition] = trialN[previous_condition, current_condition] + 1\n\n smat = transitionRTs/trialN\n smat[np.isnan(smat)] = np.nanmean(smat)\n \n #here calcuate switch cost\n bmat = smat.copy()\n for k in np.arange(0,8):\n for j in np.arange(0,8):\n smat[k,j] = bmat[k,j] - bmat[j,j] #switch trial minus repeat trial\n\n #normalize\n smat = (smat - np.nanmean(smat)) / np.nanstd(smat)\n subaM = np.ones((8,8))\n subaM[np.isnan(smat)]=0\n subN = subN + subaM\n amat = amat + smat\n #stack subjects\n all_mat[:,:,idx] = smat\n #mat = np.hstack((mat,smat))\n\n amat = amat / subN #average across subjects\n mat = np.reshape(all_mat,(8,all_mat.shape[1]*all_mat.shape[2]))\n return mat, amat, all_mat\n\n# swapdf = behavior_df.loc[behavior_df['swapped']==1]\n# noswapdf = behavior_df.loc[behavior_df['swapped']==0]\n# mat, amat, all_mat = rt_matrix(behavior_df)\n#mat is stacked version\n#amat is ave across subjects\n#all_mat is the full 3D matrices\n# for s in np.arange(np.shape(all_mat)[2]):\n# sns.heatmap(all_mat[:,:,s])\n# plt.show()\n\n\nmat, amat, all_mat = rt_matrix(behavior_df)\ndiag_idx = np.eye(8)==1 # to get rid of eyes in the transition matrix which are repetition effects\namat[diag_idx] = np.nan \nsns.heatmap(amat, cmap = \"bwr\")\nplt.savefig('Figures/behav.png')\n#plt.show()\n\n### create regression model\nfrom gen_RSA import create_RSA_models\ncontext_model, shape_model, color_model, identity_model, swapped_dimension_model, nonswapped_dimension_model, swapped_task_model, nonswapped_task_model, swapped_feature_model, nonswapped_feature_model, alternative_model = create_RSA_models()\ndiag_idx = np.eye(8)==0 # to get rid of eyes in the transition matrix which are repetition effects\nrt_rsa_df = pd.DataFrame()\nfor s, subject in enumerate(behavior_df['sub'].astype('int').unique()):\n sdf = pd.DataFrame()\n sdf['TransitionRT'] = all_mat[:,:,s][diag_idx].flatten()\n sdf['Context'] = 1-context_model[diag_idx].flatten()\n sdf['Identity'] = 1-identity_model[diag_idx].flatten()\n sdf['alternative'] = 1-alternative_model[diag_idx].flatten()\n if behavior_df.loc[behavior_df['sub']==subject]['swapped'].unique()[0]==1:\n sdf['Feature'] = 1-swapped_dimension_model[diag_idx].flatten()\n sdf['Task'] = 1-swapped_task_model[diag_idx].flatten()\n sdf['Swap'] = 1\n else:\n sdf['Feature'] = 1-nonswapped_dimension_model[diag_idx].flatten()\n sdf['Task'] = 1-nonswapped_task_model[diag_idx].flatten()\n sdf['Swap'] = 0\n sdf['Subject'] = subject\n sdf['Steps'] = (sdf['Context'] + sdf['Feature'])\n \n rt_rsa_df = rt_rsa_df.append(sdf)\n\nmodel = \"TransitionRT ~ 0 + Identity + Task\" # identity is effectively intercept\nbase_model = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(base_model.summary())\n\nmodel = \"TransitionRT ~ 0 + Identity + Task + Context + Feature\"\ntest_model = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model.summary())\n\nmodel = \"TransitionRT ~ 0 + Identity + Task + Feature\"\ntest_model2 = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model2.summary())\n\nmodel = \"TransitionRT ~ 0 + Identity + Task + Context + Feature + alternative\"\ntest_model3 = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model3.summary())\n\nmodel = \"TransitionRT ~ 1 + alternative\"\ntest_model4 = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model4.summary())\n\nmodel = \"TransitionRT ~ 1 + Task + alternative\"\ntest_model4 = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model4.summary())\n\n### nested model comparison\nfrom statsmodels.stats.anova import anova_lm\nprint(anova_lm(base_model, test_model))\nprint(anova_lm(base_model, test_model2))\nprint(anova_lm(test_model, test_model2))\n\n\n\n# end of script","repo_name":"HwangLabNeuroCogDynamics/TaskRep","sub_path":"behav.py","file_name":"behav.py","file_ext":"py","file_size_in_byte":8920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32011718800","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nfrom wicketkeeping import *\n\ndef graph_wicketkeepers():\n wicketkeepers_name=list(wicket_keepers.NAME)\n\n mra_listforplot=[]\n avg_listforplot=[]\n stk_listforplot=[]\n brpi_listforplot=[]\n wka_listforplot=[]\n \n for i in wicketkeepers_name:\n mra_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].MRA_Rating))\n avg_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].BattingAverageRating))\n stk_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].StrikeRateRating))\n brpi_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].BRPI_Rating))\n wka_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].WKA_Rating))\n bar3=[]\n bar4=[]\n bar5=[]\n for i in range(len(mra_listforplot)):\n \tbar3.append(avg_listforplot[i]+mra_listforplot[i])\n \tbar4.append(bar3[i]+stk_listforplot[i])\n \tbar5.append(bar4[i]+brpi_listforplot[i])\n\n plt.bar(wicketkeepers_name,wka_listforplot,label='WKA Rating',color=\"c\",bottom=bar5)\n plt.bar(wicketkeepers_name,brpi_listforplot,label='BRPI Rating',color='y',bottom=bar4)\n plt.bar(wicketkeepers_name,stk_listforplot,label='Strike Rate Rating',color='g',bottom=bar3)\n plt.bar(wicketkeepers_name,mra_listforplot,label='MRA_Rating',color='r',bottom=avg_listforplot)\n plt.bar(wicketkeepers_name,avg_listforplot,label='Average_Rating',color='b')\n plt.title('WicketKeepers Statistical Attributes Rating')\n plt.xticks(rotation=45)\n plt.legend()\n plt.show()\n\n\ngraph_wicketkeepers()\n\n","repo_name":"sushantsabnis218/CriketPlayerRatings","sub_path":"graph_wicketkeeper.py","file_name":"graph_wicketkeeper.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21743227690","text":"from sklearn.model_selection import ParameterGrid\nfrom skopt import gp_minimize\nfrom skopt.space import Categorical\nfrom skopt.utils import use_named_args \nfrom src import style\nfrom src.config import config_template\nfrom src.constants import Directories\nfrom src.io import read_image\nfrom src.utils import convert_to_serializable\nfrom src.utils import parse\nfrom src.utils import parse_search_string \nfrom src import search_io \nfrom tqdm import tqdm\nimport json\nimport numpy as np\nimport os\nimport subprocess\nimport urllib.request\n\n\n#TODO: merge parameter search and training, setup is similar\ndef start_training(config):\n experiment_root = f\"{Directories.EXPERIMENTS}/{config['experiment_name']}\"\n config[\"experiment_root\"] = experiment_root\n checkpoint_dir = f\"{experiment_root}/checkpoints\"\n config[\"checkpoint_dir\"] = checkpoint_dir\n os.makedirs(checkpoint_dir, exist_ok=True)\n with open(f\"{experiment_root}/config.json\", \"w\") as f:\n f.write(json.dumps(config))\n with open(f\"{experiment_root}/user.json\", \"w\") as f:\n f.write(json.dumps({}))\n\n style.run(config)\n\n\n\ndef __start_search_experiment(idx, config_setup, search_config):\n experiment_root = f\"{Directories.SEARCHES}/{search_config['experiment_name']}/experiments/{idx}\" \n checkpoint_dir = f\"{experiment_root}/checkpoints\"\n os.makedirs(checkpoint_dir, exist_ok=True)\n\n with open(f\"{experiment_root}/config.json\", \"w\") as f:\n f.write(json.dumps(config_setup))\n with open(f\"{experiment_root}/user.json\", \"w\") as f:\n f.write(json.dumps({\"rating\": \"unrated\"}))\n\n config_setup.update(search_config)\n\n config_setup[\"experiment_root\"] = experiment_root\n \n\n output_file = f\"{experiment_root}/output.jpg\"\n config_setup[\"output_image_name\"] = output_file\n config_setup[\"checkpoint_path\"] = f\"{checkpoint_dir}/\"\n\n style.run(config_setup)\n\ndef start_parameter_search(search_config, search_space):\n search_root = f\"{Directories.SEARCHES}/{search_config['experiment_name']}\"\n os.makedirs(search_root)\n os.makedirs(search_root + \"/experiments\")\n with open(f\"{search_root}/search_config.json\", \"w\") as f:\n f.write(json.dumps(search_config))\n with open(f\"{search_root}/search_space.json\", \"w\") as f:\n f.write(json.dumps(search_space))\n\n key_order = list(search_space.keys())\n search_bounds = []\n for parameter_name in key_order:\n parameter_space_str = search_space[parameter_name]\n bound = parse_search_string(parameter_space_str, parameter_name)\n search_bounds.append(bound)\n\n \n n_calls = search_config[\"initial_searches\"]\n progress_bar = tqdm(total=n_calls)\n @use_named_args(dimensions=search_bounds)\n def f(content_weight, history_size, iterations, learning_rate, max_iter, style_weight):\n config = {\"iterations\": int(iterations),\n \"learning_rate\": float(learning_rate),\n \"style_weight\": int(style_weight),\n \"content_weight\": int(content_weight),\n \"history_size\": int(history_size),\n \"max_iter\": int(max_iter)}\n\n idx = search_io.get_experiment_index(search_config)\n __start_search_experiment(idx, config, search_config)\n progress_bar.update()\n\n return 1.0\n \n res = gp_minimize(f, # the function to minimize\n\t\t search_bounds, # the bounds on each dimension of x\n\t\t n_calls=n_calls, # the number of evaluations of f\n n_random_starts=n_calls,\n n_initial_points=n_calls)\n\n\ndef download_image(image_type, image_url, image_name):\n\n with urllib.request.urlopen(image_url) as response, \\\n open(f\"{image_type}/{image_name}\", 'wb') as out_file:\n data = response.read()\n out_file.write(data)\n\ndef annotated_search(search_name, optimize_runs, random, new_width):\n search_config, search_space = search_io.read_search_config(search_name)\n key_order = list(search_space.keys())\n parameter_runs = search_io.list_parameter_runs(search_name)\n prior_x = []\n prior_y = []\n for parameter_run in parameter_runs:\n run_config = search_io.read_parameter_config(search_name, parameter_run)\n prior_x.append([run_config[key] for key in key_order])\n experiment_root = f\"{Directories.SEARCHES}/{search_name}/experiments/{parameter_run}\" \n run_user = search_io.read_user(experiment_root)\n prior_y.append( 0.0 if run_user[\"rating\"] == \"good\" else 1.0)\n\n\n search_bounds = []\n for parameter_name in key_order:\n parameter_space_str = search_space[parameter_name]\n bound = parse_search_string(parameter_space_str, parameter_name)\n search_bounds.append(bound)\n\n calls = optimize_runs + random\n progress_bar = tqdm(total=calls)\n @use_named_args(dimensions=search_bounds)\n def f(content_weight, history_size, iterations, learning_rate, max_iter, style_weight):\n config = {\"iterations\": int(iterations),\n \"learning_rate\": float(learning_rate),\n \"style_weight\": int(style_weight),\n \"content_weight\": int(content_weight),\n \"history_size\": int(history_size),\n \"max_iter\": int(max_iter)}\n search_config[\"image_width\"] = new_width\n\n idx = search_io.get_experiment_index(search_config)\n __start_search_experiment(idx, config, search_config)\n progress_bar.update()\n\n return 0.5\n \n\n res = gp_minimize(f, # the function to minimize\n\t\t search_bounds, # the bounds on each dimension of x\n\t\t n_calls=calls, # the number of evaluations of f\n n_random_starts=random,\n n_initial_points=calls,\n\t\t x0=prior_x,\n\t\t y0=prior_y) # the random seed\n\n f(res.x)\n\n\n","repo_name":"gozmo/neural_style","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29061275767","text":"from dataclasses import replace\nfrom uuid import uuid4\n\nimport psycopg2.errors\nimport pytest\n\nfrom hamcrest import assert_that, contains_inanyorder, equal_to\n\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage import IntegrationMapper\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage.entities.enums import IntegrationStatus\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage.entities.integration import Integration\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage.entities.merchant import Merchant\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage.exceptions import (\n IntegrationDuplicateDeployedIntegrationError,\n IntegrationDuplicateMerchantPSPPerStage,\n)\nfrom billing.yandex_pay_admin.yandex_pay_admin.tests.unit.storage.mappers.base import BaseMapperTests\n\n\nclass TestIntegrationMapper(BaseMapperTests):\n @pytest.fixture\n def psp_id(self):\n return uuid4()\n\n @pytest.fixture\n def entity(self, psp_id, merchant: Merchant) -> Integration:\n assert merchant.merchant_id is not None\n\n return Integration(\n psp_id=psp_id,\n psp_external_id='payture',\n merchant_id=merchant.merchant_id,\n status=IntegrationStatus.DEPLOYED,\n )\n\n @pytest.fixture\n def another_entity(self, psp_id, merchant: Merchant) -> Integration:\n return Integration(\n psp_id=uuid4(),\n psp_external_id='uniteller',\n merchant_id=merchant.merchant_id,\n status=IntegrationStatus.READY,\n )\n\n @pytest.fixture\n def mapper(self, storage) -> IntegrationMapper:\n return storage.integration\n\n @pytest.mark.asyncio\n async def test_can_get_deployed_by_merchant_id(self, mapper, merchant, entity, another_entity):\n first_integration = await mapper.create(entity)\n await mapper.create(another_entity)\n\n found = await mapper.get_deployed_by_merchant_id(merchant.merchant_id, for_testing=True)\n\n assert_that(found, equal_to(first_integration))\n\n @pytest.mark.asyncio\n async def test_get_deployed_by_merchant_id_missing(self, mapper, entity, another_entity):\n integration = await mapper.create(entity)\n await mapper.create(another_entity)\n\n assert_that(integration.for_testing, equal_to(True))\n\n with pytest.raises(Integration.DoesNotExist):\n await mapper.get_deployed_by_merchant_id(integration.merchant_id, for_testing=False)\n\n @pytest.mark.asyncio\n async def test_list_by_merchant_id(self, mapper, merchant, entity, another_entity):\n first_integration = await mapper.create(entity)\n second_integration = await mapper.create(another_entity)\n\n found = await mapper.list_by_merchant_id(merchant.merchant_id)\n\n assert_that(found, contains_inanyorder(first_integration, second_integration))\n\n @pytest.mark.asyncio\n async def test_get_by_merchant_id_and_psp_id(self, mapper, merchant, entity, another_entity):\n first_integration = await mapper.create(entity)\n second_integration = await mapper.create(another_entity)\n\n loaded_first = await mapper.get_by_merchant_id_and_psp_id(\n merchant.merchant_id, first_integration.psp_id, for_testing=True\n )\n loaded_second = await mapper.get_by_merchant_id_and_psp_id(\n merchant.merchant_id, second_integration.psp_id, for_testing=True\n )\n\n assert_that(loaded_first, equal_to(first_integration))\n assert_that(loaded_second, equal_to(second_integration))\n\n @pytest.mark.asyncio\n async def test_get_by_merchant_id_and_psp_id__not_found(self, mapper, entity):\n integration = await mapper.create(entity)\n\n assert_that(integration.for_testing, equal_to(True))\n\n with pytest.raises(Integration.DoesNotExist):\n await mapper.get_by_merchant_id_and_psp_id(integration.merchant_id, integration.psp_id, for_testing=False)\n\n @pytest.mark.asyncio\n async def test_can_update(self, mapper, entity):\n created = await mapper.create(entity)\n created.status = IntegrationStatus.READY\n\n updated = await mapper.save(created)\n created.updated = updated.updated\n created.revision = updated.revision\n\n assert_that(\n updated,\n equal_to(created),\n )\n\n @pytest.mark.asyncio\n async def test_can_remove(self, mapper, entity, another_entity, merchant):\n created = await mapper.create(entity)\n second_integration = await mapper.create(another_entity)\n\n await mapper.delete(created)\n found = await mapper.list_by_merchant_id(merchant.merchant_id)\n\n assert_that(found, equal_to([second_integration]))\n\n @pytest.mark.asyncio\n async def test_update_bumps_revision(self, mapper, entity):\n created = await mapper.create(entity)\n assert_that(created.revision, equal_to(1))\n\n updated = await mapper.save(created)\n assert_that(updated.revision, equal_to(2))\n\n created.updated = updated.updated\n assert_that(updated, equal_to(created))\n\n @pytest.mark.asyncio\n async def test_has_foreign_key_on_merchant(self, mapper, entity):\n entity.merchant_id = uuid4()\n\n with pytest.raises(psycopg2.errors.ForeignKeyViolation):\n await mapper.create(entity)\n\n @pytest.mark.asyncio\n async def test_unique_merchant_psp_env_combination(self, rollback_after, mapper, entity, another_entity):\n first = await mapper.create(entity)\n second = await mapper.create(another_entity)\n\n async with rollback_after():\n with pytest.raises(IntegrationDuplicateMerchantPSPPerStage):\n await mapper.create(replace(entity, integration_id=uuid4()))\n\n second.psp_id = first.psp_id\n async with rollback_after():\n with pytest.raises(IntegrationDuplicateMerchantPSPPerStage):\n await mapper.save(second)\n\n second.for_testing = not second.for_testing\n await mapper.save(second)\n\n @pytest.mark.asyncio\n async def test_unique_deployed_integration_per_merchant(self, rollback_after, mapper, entity, another_entity):\n await mapper.create(entity)\n second = await mapper.create(another_entity)\n\n second.status = IntegrationStatus.DEPLOYED\n async with rollback_after():\n with pytest.raises(IntegrationDuplicateDeployedIntegrationError):\n await mapper.save(second)\n\n second.for_testing = not second.for_testing\n await mapper.save(second)\n\n @pytest.mark.parametrize('first_status', (IntegrationStatus.READY, IntegrationStatus.DEPLOYED))\n @pytest.mark.parametrize('second_status', (IntegrationStatus.READY, IntegrationStatus.DEPLOYED))\n @pytest.mark.parametrize('first_for_testing', (True, False))\n @pytest.mark.parametrize('second_for_testing', (True, False))\n @pytest.mark.asyncio\n async def test_list_by_partner_id(\n self,\n mapper,\n storage,\n entity,\n another_entity,\n partner,\n merchant,\n first_status,\n second_status,\n first_for_testing,\n second_for_testing,\n ):\n first = await mapper.create(replace(entity, for_testing=first_for_testing, status=first_status))\n second_merchant = await storage.merchant.create(replace(merchant, merchant_id=uuid4()))\n second = await mapper.create(\n replace(\n another_entity,\n for_testing=second_for_testing,\n status=second_status,\n merchant_id=second_merchant.merchant_id,\n )\n )\n # Extra integration\n outside_partner = await storage.partner.create(replace(partner, partner_id=uuid4()))\n outside_merchant = await storage.merchant.create(\n replace(merchant, partner_id=outside_partner.partner_id, merchant_id=uuid4())\n )\n await mapper.create(replace(entity, merchant_id=outside_merchant.merchant_id, for_testing=False))\n\n found = await mapper.list_by_partner_id(\n partner_id=merchant.partner_id, status=IntegrationStatus.DEPLOYED, for_testing=False\n )\n\n expected = []\n if first_status == IntegrationStatus.DEPLOYED and not first_for_testing:\n expected.append(first)\n if second_status == IntegrationStatus.DEPLOYED and not second_for_testing:\n expected.append(second)\n assert_that(found, contains_inanyorder(*expected))\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests/unit/storage/mappers/test_integration.py","file_name":"test_integration.py","file_ext":"py","file_size_in_byte":8477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14233057045","text":"import csv\nimport random\n\nnamefile = open(\"formatted_drinker_names.txt\", \"r\")\ncitystatefile = open(\"ddb+_city_state.txt\").read().splitlines()\naddrfile = open(\"ddb+_ppl_addr.txt\", \"r\")\nphonefile = open(\"formatted_phone_num.txt\", \"r\")\n\nwith open('drinkers.csv', 'w') as csvfile:\n filewriter = csv.writer(csvfile, delimiter=',')\n\n for nameline, phoneline, addrline in zip(namefile, phonefile, addrfile):\n cityline = random.choice(citystatefile)\n cstate = cityline.split(\", \")\n\n filewriter.writerow([nameline.strip(), \n cstate[0].strip(), cstate[1].strip(), \n phoneline.strip(), addrline.strip()])\n\nnamefile.close()\naddrfile.close()\nphonefile.close()\n\n ","repo_name":"nrf17/Restaurant_Database","sub_path":"table_data/Source Code/drinkerformat.py","file_name":"drinkerformat.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23848359739","text":"# %%\nimport sys, os\nimport torch\nimport numpy as np\nfrom torch.utils.data import random_split\nfrom torchvision.datasets import CIFAR10, CIFAR100\nimport torchvision.transforms as transforms\nfrom torch.utils.data import Dataset, DataLoader, SubsetRandomSampler\nfrom timm.data.transforms_factory import create_transform\n\n# %%\ndataset='cifar10'\ndata_path=f'/data1/lsj9862/data/{dataset}'\nbatch_size=256\nuse_validation=True\naug=True\nval_ratio=0.1\ndat_per_cls=1\nseed=1\n\n\n# %%\n## Set Seed\ndef set_seed(RANDOM_SEED=0):\n '''\n Set seed for reproduction\n '''\n torch.manual_seed(RANDOM_SEED)\n np.random.seed(RANDOM_SEED)\n torch.cuda.manual_seed(RANDOM_SEED)\n torch.cuda.manual_seed_all(RANDOM_SEED) # if use multi-GPU\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n import random\n random.seed(RANDOM_SEED)\n\nset_seed(seed)\n\n# %%\n## Get transform\nif aug:\n transform_train = create_transform(224, is_training=True)\n transform_test = create_transform(224)\nelse:\n transform_train = transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n ])\n\n transform_test = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n ]) \n\n# %%\n## Load Data\nif dataset=='cifar10':\n tr_data = CIFAR10(data_path, train=True, transform=transform_train,\n download=True)\n te_data = CIFAR10(data_path, train=False, transform = transform_test,\n download=True)\nelif dataset=='cifar100':\n tr_data = CIFAR100(data_path, train=True, transform=transform_train,\n download=True)\n te_data = CIFAR100(data_path, train=False, transform = transform_test,\n download=True)\n \nnum_classes = max(te_data.targets) + 1\nprint(f\"Number of classes : {num_classes} :: Data point per class : {dat_per_cls}\")\n\n\n# %%\n## Split Data\nval_len = int(len(tr_data) * val_ratio)\ntr_len = len(tr_data) - val_len\n\ntr_data, val_data = random_split(tr_data, [tr_len, val_len])\n\n# %%\n## Pre-Setting for Few-shot Setting\nclass_indices = [[] for _ in range(num_classes)]\nfor idx, (_, target) in enumerate(tr_data):\n class_indices[target].append(idx)\n\nfew_shot_indices = []\nfor indices in class_indices:\n few_shot_indices.extend(indices[:dat_per_cls])\n\nsampler = SubsetRandomSampler(few_shot_indices)\n\n# %%\n## Make Loader\ntr_loader = DataLoader(tr_data,\n batch_size=batch_size,\n num_workers=4,\n pin_memory=True,\n sampler=sampler)\n \n# %%\nos.makedirs(f'/mlainas/lsj9862/data/{dataset}/{dat_per_cls}shot', exist_ok=True)\ntorch.save(tr_loader, f'/mlainas/lsj9862/data/{dataset}/{dat_per_cls}shot/tr_loader_seed{seed}.pth')\n# %%\n","repo_name":"SungJun98/SA-BTL","sub_path":"utils/data/fewshot_loader.py","file_name":"fewshot_loader.py","file_ext":"py","file_size_in_byte":3149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20231692317","text":"import jwt\nimport psycopg2\nfrom fastapi import FastAPI, Depends, HTTPException, status\nfrom pydantic import BaseModel\nfrom config import DB_NAME, DB_HOST, DB_PORT, DB_PASS, DB_USER, SECRET_KEY\nfrom datetime import datetime\nfrom fastapi.security import HTTPBearer, HTTPAuthorizationCredentials\n\n\nclass NewSub(BaseModel):\n sub_name: str\n sub_price: int\n sub_currency: str\n sub_date: datetime\n sub_period: str\n fk_subs_users: int\n\n\napp = FastAPI()\nsecurity = HTTPBearer()\n\nconnection = psycopg2.connect(\n host=DB_HOST,\n user=DB_USER,\n password=DB_PASS,\n database=DB_NAME,\n port=DB_PORT,\n)\nconnection.autocommit = True\n\n\n@app.on_event(\"startup\")\nasync def startup_event():\n print(\"Сервер запущен\")\n\n\n@app.on_event(\"shutdown\")\nasync def shutdown_event():\n connection.close()\n print(\"Сервер остановлен\")\n\n\nasync def get_current_user_id(credentials: HTTPAuthorizationCredentials = Depends(security)):\n token = credentials.credentials\n\n try:\n secret_key = SECRET_KEY\n payload = jwt.decode(token, secret_key, algorithms=[\"HS256\"])\n user_id = payload.get(\"id\")\n return user_id\n except jwt.DecodeError:\n raise HTTPException(\n status_code=status.HTTP_401_UNAUTHORIZED,\n detail=\"Invalid token\",\n headers={\"WWW-Authenticate\": \"Bearer\"},\n )\n except jwt.ExpiredSignatureError:\n raise HTTPException(\n status_code=status.HTTP_401_UNAUTHORIZED,\n detail=\"Expired token\",\n headers={\"WWW-Authenticate\": \"Bearer\"}, )\n\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n@app.post(\"/subs\")\nasync def add_subscription(sub: NewSub, user_id: int = Depends(get_current_user_id)):\n try:\n with connection.cursor() as cursor:\n\n cursor.execute(\"SELECT * FROM users WHERE id = %s;\", (user_id,))\n user_data = cursor.fetchone()\n\n if user_data:\n\n cursor.execute(\n \"\"\"\n INSERT INTO subs (sub_name, sub_price, sub_currency, sub_date, sub_period, fk_subs_users)\n VALUES (%s, %s, %s, %s, %s, %s);\n \"\"\",\n (sub.sub_name, sub.sub_price, sub.sub_currency, sub.sub_date, sub.sub_period, user_id),\n )\n\n return {\"message\": \"Подписка успешно добавлена.\"}\n else:\n return {\"error\": \"Пользователь не найден.\"}\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n@app.put(\"/subs/{sub_id}\")\nasync def update_sub(sub_id: int, item: NewSub, user_id: int = Depends(get_current_user_id)):\n sub_name = item.sub_name\n sub_price = item.sub_price\n sub_currency = item.sub_currency\n sub_date = item.sub_date\n sub_period = item.sub_period\n\n try:\n with connection.cursor() as cursor:\n\n cursor.execute(\n \"SELECT * FROM subs WHERE id = %s AND fk_subs_users = %s;\",\n (sub_id, user_id),\n )\n sub_data = cursor.fetchone()\n\n if sub_data:\n cursor.execute(\n \"\"\"UPDATE subs SET sub_name = %s, sub_price = %s, sub_currency = %s, sub_date = %s,\n sub_period = %s, fk_subs_users = %s WHERE id = %s;\"\"\",\n (sub_name, sub_price, sub_currency, sub_date, sub_period, user_id, sub_id)\n )\n return {\"done\": f\"Подписка {sub_id} успешно обновлена!\"}\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n@app.get(\"/subs\")\nasync def get_user_subs(user_id: int = Depends(get_current_user_id)):\n try:\n with connection.cursor() as cursor:\n\n cursor.execute(\n \"\"\"SELECT * FROM subs WHERE fk_subs_users = %s;\"\"\",\n (user_id,)\n )\n\n subs = cursor.fetchall()\n\n if subs:\n\n subs_list = []\n\n for sub in subs:\n sub_data = {\n \"sub_name\": sub[1],\n \"sub_price\": sub[2],\n \"sub_currency\": sub[3],\n \"sub_date\": sub[4],\n \"sub_period\": sub[5],\n }\n subs_list.append(sub_data)\n\n return {\"subscriptions\": subs_list}\n else:\n return {\"subscriptions\": []}\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n@app.delete(\"/subs/{sub_id}\")\nasync def delete_sub(sub_id: int, user_id: int = Depends(get_current_user_id)):\n try:\n with connection.cursor() as cursor:\n\n cursor.execute(\n \"SELECT * FROM subs WHERE id = %s AND fk_subs_users = %s;\",\n (sub_id, user_id),\n )\n sub_data = cursor.fetchone()\n\n if sub_data:\n cursor.execute(\n \"DELETE FROM subs WHERE id = %s;\",\n (sub_id,),\n )\n\n return {\"message\": \"Подписка успешно удалена.\"}\n else:\n return {\"error\": \"Подписка не найдена или не принадлежит пользователю.\"}\n\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n\n","repo_name":"batyrio/fin-dev-subscriptionsAPI","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74853555920","text":"# -*- coding: utf-8 -*-\r\nimport binascii\r\nfrom urllib.parse import urlparse\r\n\r\n__author__ = 'gakki429'\r\n\r\nimport re\r\nimport os\r\nimport zlib\r\nimport hashlib\r\n\r\n\r\ndef _mkdir(path):\r\n path = os.path.dirname(path)\r\n if path and not os.path.exists(path):\r\n os.makedirs(path)\r\n\r\n\r\nclass GitPack(object):\r\n \"\"\"Git Parse Pack Format\"\"\"\r\n\r\n def __init__(self, git_path, pack_hash, git_leak_dict):\r\n self.git_path = git_path\r\n self.pack_hash = pack_hash\r\n self.objects = {} # hash: {type, length}\r\n self.objects_num = 0\r\n\r\n self.parsed = urlparse(self.git_path)\r\n self.base_path = './' + self.parsed.hostname + '.' + str(self.parsed.port) + self.parsed.path\r\n\r\n self.idx_path = self.base_path + 'objects/pack/pack-{}.idx'.format(self.pack_hash)\r\n self.pack_path = self.base_path + 'objects/pack/pack-{}.pack'.format(self.pack_hash)\r\n self.pack_data = ''\r\n self.git_leak_dict = git_leak_dict\r\n\r\n def pack_header(self):\r\n pack = open(self.pack_path, 'rb').read()\r\n signature = pack[:4]\r\n if signature == b'PACK':\r\n # print(22)\r\n version_number = pack[4:8]\r\n objects_number = pack[8:12]\r\n # print(int.from_bytes(objects_number,'big'))\r\n # print(binascii.b2a_hex(objects_number).decode())\r\n self.objects_num = int.from_bytes(objects_number, 'big')\r\n self.pack_data = pack\r\n\r\n def idx_header(self):\r\n pack_idx = open(self.idx_path, 'rb').read()\r\n magic_number = pack_idx[:4]\r\n if magic_number == b'\\xfftOc':\r\n version_number = pack_idx[4:8]\r\n fan_out_table = pack_idx[8:8 + 1024] # 网络字节序整数\r\n\r\n pack_hash = pack_idx[-40:-20]\r\n idx_hash = pack_idx[-20:]\r\n\r\n # Todo: 区分版本（v1，v2），区分idx大小，大于 2g 的 offset 为 8bytes\r\n idx_data = pack_idx[8 + 1024:-40]\r\n self.parse_idx(idx_data)\r\n\r\n def split_to_hex(self, length, data):\r\n length = length * 2\r\n data_hex_str = binascii.b2a_hex(data).decode()\r\n # print(data_hex_str[0:20])\r\n # print(data_hex_str)\r\n # print(len(data_hex_str))\r\n # print(length)\r\n # a7ab5d1025b971316024b84a1706a096b23e9c\r\n str_len = int(len(data_hex_str) / length)\r\n # print(len(data_hex_str))\r\n res_list = []\r\n # print(str_len)\r\n for i in range(str_len):\r\n res_list.append(data_hex_str[i * length:i * length + length])\r\n\r\n # print(len(res_list))\r\n return res_list\r\n\r\n def parse_idx(self, idx_data):\r\n num = self.objects_num\r\n # print(idx_data[:num*20])\r\n _hashs = self.split_to_hex(20, idx_data[:num * 20])\r\n # print(_hashs)\r\n\r\n _crcs = self.split_to_hex(4, idx_data[num * 20:num * 24])\r\n _offsets = self.split_to_hex(4, idx_data[num * 24:])\r\n for i in range(num):\r\n self.objects[_hashs[i]] = {'crc': _crcs[i], 'offset': int(_offsets[i], 16)}\r\n\r\n def extract_pack(self):\r\n # print(self.objects)\r\n sort_objects = sorted(self.objects.items(), key=lambda x: x[1]['offset'])\r\n for i in range(len(sort_objects)):\r\n _hash, _info = sort_objects[i]\r\n crc = _info['crc']\r\n offset = _info['offset']\r\n if i == len(sort_objects) - 1:\r\n next_offset = -20\r\n else:\r\n next_offset = sort_objects[i + 1][1]['offset']\r\n self.objects[_hash]['data'] = self.pack_data[offset:next_offset]\r\n self.parse_pack()\r\n\r\n def pack_type(self, num):\r\n _types = {\r\n '1': 'commit',\r\n '2': 'tree',\r\n '3': 'blob',\r\n '4': 'tag',\r\n '6': 'ofs_delta',\r\n '7': 'ref_delta',\r\n }\r\n return _types[str(int(num, 2))]\r\n\r\n def parse_pack(self):\r\n\r\n for _hash, _info in sorted(self.objects.items(), key=lambda x: x[1]['offset']):\r\n # print(_hash)\r\n # print(_info)\r\n _size = []\r\n try:\r\n flag, zlib_data = re.search(b'(.*?)(\\x78\\x9c.*)', _info['data'], re.S).groups()\r\n # print(flag)\r\n except AttributeError:\r\n return\r\n\r\n # for i in range(len(flag)):\r\n # # bin_info = bin(int(flag[i].encode('hex'), 16))[2:].rjust(8, '0')\r\n # # int.from_bytes(flag, 'big')\r\n # bin_info = bin(int.from_bytes(flag, 'big'))[2:].rjust(8, '0')\r\n # msb = bin_info[0]\r\n # if i == 0:\r\n # _type = bin_info[1:4]\r\n # _size.append(bin_info[-4:])\r\n # else:\r\n # _size.append(bin_info[-7:])\r\n # _length = int(''.join(_size[::-1]), 2) # 这里其实是小端，不是���端\r\n # _type = self.pack_type(_type)\r\n\r\n bin_info = bin(int.from_bytes(flag, 'big'))[2:].rjust(8, '0')\r\n # print('====')\r\n # print(bin_info)\r\n _length = bin_info[4:4 + (len(bin_info) // 8 - 1) * 7 + 4]\r\n # print(_length)\r\n # _type = int(bin_info[1:4],2)\r\n\r\n try:\r\n self.objects[_hash]['type'] = self.pack_type(bin_info[1:4])\r\n except:\r\n continue\r\n # self.objects[_hash]['length'] = int(_length,2) # 需要根据length和type来计算sha1 这里计算的不对 大端小端都试了 所以直接使用了解压后的data长度\r\n self.objects[_hash]['file'] = zlib.decompress(zlib_data)\r\n self.objects[_hash]['length'] = len(self.objects[_hash]['file'])\r\n self.objects[_hash]['bin_info'] = flag\r\n # print(self.objects[_hash]['file']) # 这个不知道文件名啊\r\n\r\n def pack_to_object_file(self):\r\n for _object in self.objects.values():\r\n try:\r\n if _object['type'] != 'blob':\r\n continue\r\n # object_format = '{} {}\\x00{}'.format(\r\n # _object['type'].encode(), _object['length'], _object['file'])\r\n object_format = _object['type'].encode() + b' ' + str(_object['length']).encode() + b'\\x00' + _object[\r\n 'file']\r\n # print(_object['type'], _object['length'], _object['bin_info'], _object['file'])\r\n # print(object_format)\r\n except KeyError:\r\n continue\r\n sha = hashlib.sha1(object_format).hexdigest()\r\n\r\n path = self.base_path+'objects/{}/{}'.format(sha[:2], sha[2:])\r\n zlib_object = zlib.compress(object_format)\r\n dir = path.rsplit('/', 1)[0]\r\n # print(dir)\r\n os.makedirs(dir, exist_ok=True)\r\n open(path, 'wb').write(zlib_object)\r\n\r\n if _object['type'] == 'blob' and sha in self.git_leak_dict:\r\n blob_path = self.git_leak_dict[sha]['path']\r\n dir=blob_path.rsplit('/', 1)[0]\r\n os.makedirs(dir, exist_ok=True)\r\n try:\r\n file = open(blob_path, 'rb').read()\r\n if file != _object['file']:\r\n filename = '{}.{}'.format(blob_path, sha[:6])\r\n with open(filename, 'wb') as f:\r\n f.write(_object['file'])\r\n f.close()\r\n # logger.info('find new file, save file to : {}'.format(filename))\r\n except FileNotFoundError:\r\n with open(blob_path, 'wb') as f:\r\n f.write(_object['file'])\r\n f.close()\r\n # logger.info('saving file to : {}'.format(blob_path))\r\n\r\n\r\n def pack_init(self):\r\n self.pack_header()\r\n self.idx_header()\r\n self.extract_pack()\r\n self.pack_to_object_file()\r\n # Todo: 重建 ofs_delta，ref_delta\r\n","repo_name":"Young873/gitdump","sub_path":"lib/git_packs_parse.py","file_name":"git_packs_parse.py","file_ext":"py","file_size_in_byte":7979,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"21394065529","text":"import os\r\nimport numpy as np\r\nfrom torch.utils.data import DataLoader, Dataset\r\nimport torch\r\nimport config\r\nfrom PIL import Image, ImageDraw2, ImageDraw\r\nimport logging\r\nfrom glob import glob\r\n\r\n\r\nclass xDataSet(Dataset):\r\n def __init__(self):\r\n self.OKroot = config.DataSetPath\r\n # self.NKroot = config.DataSetPath_\r\n self.images = [x.path for x in os.scandir(self.OKroot)]\r\n # self.images_ = [x.path for x in os.scandir(self.NKroot)]\r\n # print(self.images)\r\n # print(self.images)\r\n\r\n def __len__(self):\r\n return len(self.images)\r\n\r\n def __getitem__(self, index):\r\n img = Image.open(self.images[index]).convert('L')\r\n img1 = np.array(img) / 127.5 - 1\r\n\r\n img2 = img\r\n box, color = self.getbox()\r\n ImageDraw.Draw(img2).ellipse(box, fill=color)\r\n img2 = np.array(img2)\r\n img2 = img2 / 127.5 - 1\r\n\r\n return torch.from_numpy(img2).unsqueeze(0), torch.from_numpy(img1).unsqueeze(0)\r\n\r\n\r\n @staticmethod\r\n def getbox():\r\n # x1 = np.random.randint(1, 127)\r\n # y1 = np.random.randint(1, 127)\r\n # x2 = x1 + np.random.randint(1, 75)\r\n # y2 = y1 + np.random.randint(1, 75)\r\n x1 = torch.randint(low=1, high=127, size=[1]).squeeze(0)\r\n y1 = torch.randint(low=1, high=127, size=[1]).squeeze(0)\r\n x2 = x1 + torch.randint(low=1, high=75, size=[1]).squeeze(0)\r\n y2 = y1 + torch.randint(low=1, high=75, size=[1]).squeeze(0)\r\n color = np.random.randint(5, 250)\r\n if x2 > 127:\r\n x2 = 127\r\n if y2 > 127:\r\n y2 = 127\r\n\r\n return (x1, y1, x2, y2), color\r\n\r\nclass tDataSet(Dataset):\r\n def __init__(self):\r\n self.NKroot = config.DataSetPath_\r\n self.images_ = [x.path for x in os.scandir(self.NKroot)]\r\n\r\n def __len__(self):\r\n return len(self.images_)\r\n\r\n def __getitem__(self, index):\r\n img = Image.open(self.images_[index]).convert('L')\r\n img1 = np.array(img) / 127.5 - 1\r\n\r\n return torch.from_numpy(img1).unsqueeze(0)\r\n\r\nclass unetDataset(Dataset):\r\n def __init__(self, imgs_dir, scale = 1):\r\n self.imgs_dir = imgs_dir\r\n self.scale = scale\r\n assert 0 < scale <= 1, 'Scale must be between 0 and 1'\r\n\r\n self.ids = [os.path.splitext(file)[0] for file in os.listdir(imgs_dir)\r\n if not file.startswith('.')]\r\n logging.info(f'Creating dataset with {len(self.ids)} examples')\r\n\r\n def __len__(self):\r\n return len(self.ids)\r\n\r\n @classmethod\r\n def preprocess(cls, pil_img, scale):\r\n w, h = pil_img.size\r\n newW, newH = int(scale * w), int(scale * h)\r\n assert newW > 0 and newH > 0, 'Scale is too small'\r\n pil_img = pil_img.resize((newW, newH))\r\n\r\n x1 = np.random.randint(1, newW)\r\n y1 = np.random.randint(1, newH)\r\n x2 = x1 + np.random.randint(1, newW // 2)\r\n y2 = y1 + np.random.randint(1, newH // 2)\r\n color = np.random.randint(5, 250)\r\n if x2 > newW - 1:\r\n x2 = newW - 1\r\n if y2 > newH - 1:\r\n y2 = newH - 1\r\n box = (x1, y1, x2, y2)\r\n\r\n flaw = pil_img\r\n ImageDraw.Draw(flaw).ellipse(box, fill=color)\r\n img1 = np.array(flaw)\r\n\r\n mask = Image.new('L', (newW, newH), 0)\r\n ImageDraw.Draw(mask).ellipse(box, fill='white')\r\n img2 = np.array(mask)\r\n\r\n if len(img1.shape) == 2:\r\n img1 = np.expand_dims(img1, axis=2)\r\n if len(img2.shape) == 2:\r\n img2 = np.expand_dims(img2, axis=2)\r\n\r\n # HWC to CHW\r\n img_trans1 = img1.transpose((2, 0, 1))\r\n if img_trans1.max() > 1:\r\n img_trans1 = img_trans1 / 255\r\n img_trans2 = img2.transpose((2, 0, 1))\r\n if img_trans2.max() > 1:\r\n img_trans2 = img_trans2 / 255\r\n\r\n return img_trans1, img_trans2\r\n\r\n def __getitem__(self, i):\r\n idx = self.ids[i]\r\n img_file = glob(self.imgs_dir + idx + '.*')\r\n\r\n assert len(img_file) == 1, \\\r\n f'Either no image or multiple images found for the ID {idx}: {img_file}'\r\n img = Image.open(img_file[0]).convert('L')\r\n\r\n img, mask = self.preprocess(img, self.scale)\r\n\r\n return {\r\n 'image': torch.from_numpy(img).type(torch.FloatTensor),\r\n 'mask': torch.from_numpy(mask).type(torch.FloatTensor)\r\n }","repo_name":"lwshen/DCGAN","sub_path":"dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":4404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36332434538","text":"def main():\n X_train, Y_train = [], []\n with open('training_data.tsv', 'r') as f:\n for line in f:\n split = line.strip().split('\\t')\n # Get feature representation\n #embedding_1 = get_embedding(split[0], embeddings)\n #embedding_2 = get_embedding(split[1], embeddings)\n #X_train.append(embedding_1 + embedding_2)\n # Get label\n label = split[2]\n Y_train.append(label)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"leonwetzel/Learning-from-Data","sub_path":"Assignment 5/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38792864555","text":"import argparse\n\nfrom colcon_core.package_discovery import add_package_discovery_arguments\nfrom colcon_core.plugin_system import satisfies_version\nfrom colcon_core.verb import VerbExtensionPoint\nfrom colcon_ed.edit_tools import edit_target_file\nfrom colcon_ed.edit_tools import FileCompleter\nfrom colcon_ed.edit_tools import find_file\nfrom colcon_ed.edit_tools import get_package_path\n\n\nclass EditVerb(VerbExtensionPoint):\n \"\"\"Edit a file within a package.\"\"\"\n\n def __init__(self): # noqa: D107\n super().__init__()\n satisfies_version(VerbExtensionPoint.EXTENSION_POINT_VERSION, '^1.0')\n\n def add_arguments(self, *, parser): # noqa: D102\n parser.add_argument(\n 'package_name', metavar='PKG_NAME',\n type=argument_package_name,\n help='Package name')\n\n args = parser.add_argument(\n 'filename', metavar='FILE_NAME',\n type=argument_package_name,\n help='File name')\n args.completer = FileCompleter(package_name_key='package_name')\n\n add_package_discovery_arguments(parser)\n\n def main(self, *, context): # noqa: D102\n args = context.args\n package_path = get_package_path(args.package_name, args)\n if not package_path:\n return ((\"Package '{args.package_name}' \" +\n 'not found').format_map(locals()))\n\n file_paths = find_file(package_path, args.filename)\n if not file_paths:\n return ((\"File '{args.filename}' not found \" +\n 'in package {args.package_name}').format_map(locals()))\n\n # Ask the user to choose when there are more than one file found\n if len(file_paths) > 1:\n msg = 'Multiple files found:'\n for i in range(len(file_paths)):\n msg += '\\n ' + str(i + 1) + ') ' + file_paths[i]\n print(msg)\n try:\n index = input('Please enter a number: ')\n index = int(index) - 1\n except KeyboardInterrupt:\n return '\\n'\n except ValueError:\n return 'Not an integer'\n if index < 0 or index >= len(file_paths):\n return 'Index out of range'\n path = file_paths[index]\n else:\n path = file_paths[0]\n\n return edit_target_file(path)\n\n\ndef argument_package_name(value):\n \"\"\"\n Check if an argument is a valid package name.\n\n Used as a ``type`` callback in ``add_argument()`` calls.\n Package names starting with a dash must be prefixed with a space to avoid\n collisions with command line arguments.\n\n :param str value: The command line argument\n :returns: The package name\n :raises argparse.ArgumentTypeError: if the value starts with a dash\n \"\"\"\n if value.startswith('-'):\n raise argparse.ArgumentTypeError('unrecognized argument: ' + value)\n return value.lstrip()\n","repo_name":"colcon/colcon-ed","sub_path":"colcon_ed/verb/edit.py","file_name":"edit.py","file_ext":"py","file_size_in_byte":2912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15368333724","text":"import os\nimport requests\nimport re\n\nbase = 'http://sunlight.iis.sinica.edu.tw'\nfirst = '/cgi-bin/text.cgi'\nsecond = '/uwextract/show.php?type=tag&id='\n\ndef parseTerm(tag):\n split=tag.split('(')\n return {'term': split[0], 'tag': split[1][:-1]}\n\ndef getID(query):\n r = requests.post(base + first, data={'query': query.encode('big5')})\n regex = re.search('\\d+', r.text.split('URL=')[1])\n return regex.group(0)\n\ndef getTags(id):\n r = requests.get(base + second + id)\n r.encoding = 'Big5'\n regex = re.search('((.|\\n)*?)<\\/pre>', r.text)\n result = regex.group(1)\n\n pattern = re.compile('[^\\s!-]+')\n tags = pattern.findall(result)\n return map(parseTerm, tags)\n\ndef parse(query):\n id = getID(query)\n tags = getTags(id)\n return tags\n","repo_name":"renddiyeh/heroku-ckip-crawler","sub_path":"src/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74055391761","text":"# -*- encoding:utf-8 -*-\n_reload_all = True\n\nclass Record(object):\n\t__slots__ = ('ability_level','level_hp_rate','level_atk_rate','level_defence_rate','level_speed_rate',)\n\tdef __init__(self,k0,k1,k2,k3,k4):\n\t\tself.ability_level=k0\n\t\tself.level_hp_rate=k1\n\t\tself.level_atk_rate=k2\n\t\tself.level_defence_rate=k3\n\t\tself.level_speed_rate=k4\n\t\t\n\tdef get(self, key, default=None):\n\t\tret = getattr(self, key, None)\n\t\tif ret is None:\n\t\t\treturn default\n\t\telse:\n\t\t\treturn ret\n\t\t\t\n\t__getitem__ = object.__getattribute__\n\n\ndata = {\n\t1:Record(1,1.0,0.1,0.1,0.1),\n\t2:Record(2,2.0,0.2,0.2,0.2),\n\t3:Record(3,3.0,0.3,0.3,0.3),\n\t4:Record(4,4.0,0.4,0.4,0.4),\n\t5:Record(5,5.0,0.5,0.5,0.5),\n\t6:Record(6,6.0,0.6,0.6,0.6),\n\t7:Record(7,7.0,0.7,0.7,0.7),\n\t8:Record(8,8.0,0.8,0.8,0.8),\n\t9:Record(9,9.0,0.9,0.9,0.9),\n\t10:Record(10,10.0,1.0,1.0,1.0)\n}\n","repo_name":"shuitian/card_battle","sub_path":"datas/ability.py","file_name":"ability.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21537377965","text":"import tensorflow as tf\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom keras.layers import Dropout\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.utils import np_utils\n\nimport matplotlib.pyplot as pt\nimport numpy as np\n\nfrom keras.datasets import cifar10\nbatch_size = 20\nepochs = 4\nn_classes = 10\n\n(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n\nprint(x_train.shape)\n\nheight = x_train.shape[1]\nwidth = x_train.shape[2]\n\nx_val = x_train[:2000,:,:,:]\ny_val = y_train[:2000]\nx_train = x_train[2000:,:,:,:]\ny_train = y_train[2000:]\n\nprint('Train Dataset: ', x_train.shape, y_train.shape)\nprint('Validate dataset: ', x_val.shape, y_val.shape)\nprint('Test dataset: ', x_test.shape, y_test.shape)\n\nimage = x_train[4]\n\nlabel = y_train\nprint(label)\n\ny_train = np_utils.to_categorical(y_train, n_classes)\ny_val = np_utils.to_categorical(y_val, n_classes)\ny_test = np_utils.to_categorical(y_test, n_classes)\n\nprint(y_train)\n\nx_train = x_train.astype('float32')\nx_val = x_val.astype('float32')\nx_test = x_test.astype('float32')\n\nx_train /= 255\nx_val /= 255\nx_test /= 255\n\ndef create_model():\n model = Sequential()\n model.add(Conv2D(filters = 80, kernel_size = (4,4), input_shape=(height, width,4), strides=1, padding='same', activation='relu'))\n model.add(MaxPooling2D(pool_size=(2,2)))\n\n model.add(Conv2D(filters = 80, kernel_size=(4,4), strides=1, activation='relu'))\n model.add(MaxPooling2D(pool_size=(2,2)))\n model.add(Dropout(0.5))\n\n model.add(Flatten())\n model.add(Dense(40, activation='relu'))\n model.add(Dense(n_classes, activation='softmax'))\n\n return model\n\n model = create_model()\n\n model.compile(optimizer = 'adam',\n loss = 'categorical_crossentropy',\n metrics = ['accuracy'])\n \n model.fit(x_train, y_train, batch_size = 20, epochs = 4, validation_data=(x_val, y_val), verbose=2)\n\n model.summary()\n","repo_name":"GabiDuartte/Trabalho_final","sub_path":"rede.py","file_name":"rede.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13773360257","text":"from __future__ import absolute_import\n#Init has to be imported first because it has code to workaround the python bug where relative imports don't work if the module is imported as a main module.\nimport __init__\n\nfrom fabmetheus_utilities.fabmetheus_tools import fabmetheus_interpret\nfrom fabmetheus_utilities.vector3 import Vector3\nfrom fabmetheus_utilities import archive\nfrom fabmetheus_utilities import euclidean\nfrom fabmetheus_utilities import gcodec\nfrom fabmetheus_utilities import intercircle\nfrom fabmetheus_utilities import settings\nfrom skeinforge_application.skeinforge_utilities import skeinforge_craft\nfrom skeinforge_application.skeinforge_utilities import skeinforge_polyfile\nfrom skeinforge_application.skeinforge_utilities import skeinforge_profile\nimport math\nimport sys\n\n\n__author__ = 'Enrique Perez (perez_enrique@yahoo.com)'\n__date__ = '$Date: 2008/21/04 $'\n__license__ = 'GPL 3.0'\n\n\ndef getCraftedText( fileName, text = '', multiplyRepository = None ):\n\t\"Multiply the fill file or text.\"\n\treturn getCraftedTextFromText( archive.getTextIfEmpty( fileName, text ), multiplyRepository )\n\ndef getCraftedTextFromText( gcodeText, multiplyRepository = None ):\n\t\"Multiply the fill text.\"\n\tif gcodec.isProcedureDoneOrFileIsEmpty( gcodeText, 'multiply'):\n\t\treturn gcodeText\n\tif multiplyRepository == None:\n\t\tmultiplyRepository = settings.getReadRepository( MultiplyRepository() )\n\tif not multiplyRepository.activateMultiply.value:\n\t\treturn gcodeText\n\treturn MultiplySkein().getCraftedGcode( gcodeText, multiplyRepository )\n\ndef getNewRepository():\n\t\"Get the repository constructor.\"\n\treturn MultiplyRepository()\n\ndef writeOutput(fileName=''):\n\t\"Multiply a gcode linear move file.\"\n\tfileName = fabmetheus_interpret.getFirstTranslatorFileNameUnmodified(fileName)\n\tif fileName != '':\n\t\tskeinforge_craft.writeChainTextWithNounMessage( fileName, 'multiply')\n\n\nclass MultiplyRepository:\n\t\"A class to handle the multiply settings.\"\n\tdef __init__(self):\n\t\t\"Set the default settings, execute title & settings fileName.\"\n\t\tskeinforge_profile.addListsToCraftTypeRepository('skeinforge_application.skeinforge_plugins.craft_plugins.multiply.html', self )\n\t\tself.fileNameInput = settings.FileNameInput().getFromFileName( fabmetheus_interpret.getGNUTranslatorGcodeFileTypeTuples(), 'Open File for Multiply', self, '')\n\t\tself.openWikiManualHelpPage = settings.HelpPage().getOpenFromAbsolute('http://www.bitsfrombytes.com/wiki/index.php?title=Skeinforge_Multiply')\n\t\tself.activateMultiply = settings.BooleanSetting().getFromValue('Activate Multiply:', self, False )\n\t\tsettings.LabelSeparator().getFromRepository(self)\n\t\tsettings.LabelDisplay().getFromName('- Center -', self )\n\t\tself.centerX = settings.FloatSpin().getFromValue( - 100.0, 'Center X (mm):', self, 100.0, 0.0 )\n\t\tself.centerY = settings.FloatSpin().getFromValue( -100.0, 'Center Y (mm):', self, 100.0, 0.0 )\n\t\tsettings.LabelSeparator().getFromRepository(self)\n\t\tsettings.LabelDisplay().getFromName('- Number of Cells -', self )\n\t\tself.numberOfColumns = settings.IntSpin().getFromValue( 1, 'Number of Columns (integer):', self, 10, 1 )\n\t\tself.numberOfRows = settings.IntSpin().getFromValue( 1, 'Number of Rows (integer):', self, 10, 1 )\n\t\tsettings.LabelSeparator().getFromRepository(self)\n\t\tself.separationOverPerimeterWidth = settings.FloatSpin().getFromValue( 5.0, 'Separation over Perimeter Width (ratio):', self, 25.0, 15.0 )\n\t\tself.executeTitle = 'Multiply'\n\n\tdef execute(self):\n\t\t\"Multiply button has been clicked.\"\n\t\tfileNames = skeinforge_polyfile.getFileOrDirectoryTypesUnmodifiedGcode(self.fileNameInput.value, fabmetheus_interpret.getImportPluginFileNames(), self.fileNameInput.wasCancelled)\n\t\tfor fileName in fileNames:\n\t\t\twriteOutput(fileName)\n\n\nclass MultiplySkein:\n\t\"A class to multiply a skein of extrusions.\"\n\tdef __init__(self):\n\t\tself.distanceFeedRate = gcodec.DistanceFeedRate()\n\t\tself.isExtrusionActive = False\n\t\tself.layerCount = settings.LayerCount()\n\t\tself.layerIndex = 0\n\t\tself.layerLines = []\n\t\tself.lineIndex = 0\n\t\tself.lines = None\n\t\tself.oldLocation = None\n\t\tself.rowIndex = 0\n\t\tself.shouldAccumulate = True\n\n\tdef addElement( self, offset ):\n\t\t\"Add moved element to the output.\"\n\t\tfor line in self.layerLines:\n\t\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\t\tfirstWord = gcodec.getFirstWord(splitLine)\n\t\t\tif firstWord == 'G1':\n\t\t\t\tmovedLocation = self.getMovedLocationSetOldLocation( offset, splitLine )\n\t\t\t\tline = self.distanceFeedRate.getLinearGcodeMovement( movedLocation.dropAxis(2), movedLocation.z )\n\t\t\telif firstWord == '(':\n\t\t\t\tmovedLocation = self.getMovedLocationSetOldLocation( offset, splitLine )\n\t\t\t\tline = self.distanceFeedRate.getBoundaryLine( movedLocation )\n\t\t\tself.distanceFeedRate.addLine(line)\n\n\tdef addLayer(self):\n\t\t\"Add multiplied layer to the output.\"\n\t\tself.layerCount.printProgressIncrement('multiply')\n\t\tself.addRemoveThroughLayer()\n\t\toffset = self.centerOffset - self.arrayCenter - self.shapeCenter\n\t\tfor rowIndex in xrange( self.multiplyRepository.numberOfRows.value ):\n\t\t\tyRowOffset = float( rowIndex ) * self.extentPlusSeparation.imag\n\t\t\tif self.layerIndex % 2 == 1:\n\t\t\t\tyRowOffset = self.arrayExtent.imag - yRowOffset\n\t\t\tfor columnIndex in xrange( self.multiplyRepository.numberOfColumns.value ):\n\t\t\t\txColumnOffset = float( columnIndex ) * self.extentPlusSeparation.real\n\t\t\t\tif self.rowIndex % 2 == 1:\n\t\t\t\t\txColumnOffset = self.arrayExtent.real - xColumnOffset\n\t\t\t\telementOffset = complex( offset.real + xColumnOffset, offset.imag + yRowOffset )\n\t\t\t\tself.addElement( elementOffset )\n\t\t\tself.rowIndex += 1\n\t\tif len( self.layerLines ) > 1:\n\t\t\tself.layerIndex += 1\n\t\tself.layerLines = []\n\n\tdef addRemoveThroughLayer(self):\n\t\t'Parse gcode initialization and store the parameters.'\n\t\tfor layerLineIndex in xrange( len( self.layerLines ) ):\n\t\t\tline = self.layerLines[ layerLineIndex ]\n\t\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\t\tfirstWord = gcodec.getFirstWord(splitLine)\n\t\t\tself.distanceFeedRate.addLine(line)\n\t\t\tif firstWord == '(':\n\t\t\t\tself.layerLines = self.layerLines[ layerLineIndex + 1 : ]\n\t\t\t\treturn\n\n\tdef getCraftedGcode( self, gcodeText, multiplyRepository ):\n\t\t\"Parse gcode text and store the multiply gcode.\"\n\t\tself.centerOffset = complex( multiplyRepository.centerX.value, multiplyRepository.centerY.value )\n\t\tself.multiplyRepository = multiplyRepository\n\t\tself.numberOfColumns = multiplyRepository.numberOfColumns.value\n\t\tself.numberOfRows = multiplyRepository.numberOfRows.value\n\t\tself.lines = archive.getTextLines(gcodeText)\n\t\tself.parseInitialization()\n\t\tself.setCorners()\n\t\tfor line in self.lines[self.lineIndex :]:\n\t\t\tself.parseLine(line)\n\t\treturn self.distanceFeedRate.output.getvalue()\n\n\tdef getMovedLocationSetOldLocation( self, offset, splitLine ):\n\t\t\"Get the moved location and set the old location.\"\n\t\tlocation = gcodec.getLocationFromSplitLine(self.oldLocation, splitLine)\n\t\tself.oldLocation = location\n\t\treturn Vector3( location.x + offset.real, location.y + offset.imag, location.z )\n\n\tdef parseInitialization(self):\n\t\t'Parse gcode initialization and store the parameters.'\n\t\tfor self.lineIndex in xrange(len(self.lines)):\n\t\t\tline = self.lines[self.lineIndex]\n\t\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\t\tfirstWord = gcodec.getFirstWord(splitLine)\n\t\t\tself.distanceFeedRate.parseSplitLine(firstWord, splitLine)\n\t\t\tif firstWord == '()':\n\t\t\t\tself.distanceFeedRate.addLine('( multiply )')\n\t\t\t\tself.distanceFeedRate.addLine(line)\n\t\t\t\tself.lineIndex += 1\n\t\t\t\treturn\n\t\t\telif firstWord == '(':\n\t\t\t\tself.absolutePerimeterWidth = abs(float(splitLine[1]))\n\t\t\tself.distanceFeedRate.addLine(line)\n\n\tdef parseLine(self, line):\n\t\t\"Parse a gcode line and add it to the multiply skein.\"\n\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\tif len(splitLine) < 1:\n\t\t\treturn\n\t\tfirstWord = splitLine[0]\n\t\tif firstWord == '( )':\n\t\t\tself.addLayer()\n\t\t\tself.distanceFeedRate.addLine(line)\n\t\t\treturn\n\t\telif firstWord == '()':\n\t\t\tself.shouldAccumulate = False\n\t\tif self.shouldAccumulate:\n\t\t\tself.layerLines.append(line)\n\t\t\treturn\n\t\tself.distanceFeedRate.addLine(line)\n\n\tdef setCorners(self):\n\t\t\"Set maximum and minimum corners and z.\"\n\t\tcornerHighComplex = complex(-987654321.0, -987654321.0)\n\t\tcornerLowComplex = -cornerHighComplex\n\t\tfor line in self.lines[self.lineIndex :]:\n\t\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\t\tfirstWord = gcodec.getFirstWord(splitLine)\n\t\t\tif firstWord == 'G1':\n\t\t\t\tlocation = gcodec.getLocationFromSplitLine(self.oldLocation, splitLine)\n\t\t\t\tif self.isExtrusionActive:\n\t\t\t\t\tlocationComplex = location.dropAxis()\n\t\t\t\t\tcornerHighComplex = euclidean.getMaximum(locationComplex, cornerHighComplex)\n\t\t\t\t\tcornerLowComplex = euclidean.getMinimum(locationComplex, cornerLowComplex)\n\t\t\t\tself.oldLocation = location\n\t\t\telif firstWord == 'M101':\n\t\t\t\tself.isExtrusionActive = True\n\t\t\telif firstWord == 'M103':\n\t\t\t\tself.isExtrusionActive = False\n\t\tself.extent = cornerHighComplex - cornerLowComplex\n\t\tself.shapeCenter = 0.5 * ( cornerHighComplex + cornerLowComplex )\n\t\tself.separation = self.multiplyRepository.separationOverPerimeterWidth.value * self.absolutePerimeterWidth\n\t\tself.extentPlusSeparation = self.extent + complex( self.separation, self.separation )\n\t\tcolumnsMinusOne = self.numberOfColumns - 1\n\t\trowsMinusOne = self.numberOfRows - 1\n\t\tself.arrayExtent = complex( self.extentPlusSeparation.real * columnsMinusOne, self.extentPlusSeparation.imag * rowsMinusOne )\n\t\tself.arrayCenter = 0.5 * self.arrayExtent\n\n\ndef main():\n\t\"Display the multiply dialog.\"\n\tif len(sys.argv) > 1:\n\t\twriteOutput(' '.join(sys.argv[1 :]))\n\telse:\n\t\tsettings.startMainLoopFromConstructor( getNewRepository() )\n\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"makerbot/ReplicatorG","sub_path":"skein_engines/skeinforge-35/skeinforge_application/skeinforge_plugins/craft_plugins/multiply.py","file_name":"multiply.py","file_ext":"py","file_size_in_byte":9773,"program_lang":"python","lang":"en","doc_type":"code","stars":397,"dataset":"github-code","pt":"3"}
+{"seq_id":"23522218843","text":"import xgboost as xgb\nimport numpy as np\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, roc_auc_score\nimport matplotlib.pyplot as plt\n# Generate some example data\nX, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=42)\n\n# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Convert the data to a DMatrix format\ndtrain = xgb.DMatrix(X_train, label=y_train)\ndtest = xgb.DMatrix(X_test, label=y_test)\n\n# Set the hyperparameters\nparams = {\n \"max_depth\": 3,\n \"eta\": 0.1,\n \"objective\": \"binary:logistic\",\n \"eval_metric\": \"logloss\"\n}\n\n# Train the model\nnum_rounds = 100\nmodel = xgb.train(params, dtrain, num_rounds)\n\n# Make predictions on the test set\ny_pred = model.predict(dtest)\n\n# Evaluate the model\nprint(\"Accuracy:\", accuracy_score(y_test, np.round(y_pred)))\nprint(\"ROC AUC score:\", roc_auc_score(y_test, y_pred))\n\n# Print the feature importance\nimportances = model.get_score(importance_type='gain')\nimportances = sorted(importances.items(), key=lambda x: x[1], reverse=True)\nprint(\"Feature importance:\")\nfor i in importances:\n print(i)\n\n# visualize\n# Calculate ROC curve and AUC-ROC\nfpr, tpr, thresholds = roc_curve(y_test, y_pred)\nauc_roc = roc_auc_score(y_test, y_pred)\n\n# Visualize the ROC curve\nplt.plot(fpr, tpr, label=f\"AUC-ROC = {auc_roc:.3f}\")\nplt.plot([0, 1], [0, 1], linestyle=\"--\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"ROC Curve\")\nplt.legend()\nplt.show()\n","repo_name":"volom/statistics_tools","sub_path":"xgboost.py","file_name":"xgboost.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29652742201","text":"'''\n有一堆石头，用整数数组 stones 表示。其中 stones[i] 表示第 i 块石头的重量。\n每一回合，从中选出任意两块石头，然后将它们一起粉碎。\n假设石头的重量分别为 x 和 y，且 x <= y。那么粉碎的可能结果如下：\n如果 x == y，那么两块石头都会被完全粉碎；\n如果 x != y，那么重量为 x 的石头将会完全粉碎，而重量为 y 的石头新重量为 y-x。\n最后，最多只会剩下一块 石头。返回此石头 最小的可能重量 。如果没有石头剩下，就返回 0。\n\n思路：\n1.总重量Weight = sum(stones)\n 如果能完全粉碎，则Weight为偶数且两组石头的总量都是 Weight/2\n 就算不能完全粉碎，也应该使其中一组石头的重量尽可能接近 Weight/2\n2.令target = Weight//2\n 若stones中能选取一组石头，使其重量在其他选择中是最大的（在小于 target的情况下）\n 如果这组石头的重量为a，则所求的结果为(Weight-a) - a\n3.至此，转换成了背包问题\n dp[i+1][j]表示从stones[:i+1]中（即前i个石头）能否选出一组重量为j的石头组合\n 设stones长度为n，我们最终想要获得dp[n][target]（如果可以）\n 所以dp数组为n+1行，target+1列（不然取不到[n][target]下标）\n4.状态压缩\n i+1只和i有关，可以压缩。然后注意需要倒着遍历\n'''\nclass Solution:\n def lastStoneWeightII(self, stones: List[int]) -> int:\n total = sum(stones)\n n = len(stones)\n target = total // 2\n dp = [False] * (target+1)\n dp[0] = True\n\n for stone in stones:\n for j in range(target, stone-1, -1):\n dp[j] = dp[j] or dp[j-stone]\n \n ans = float('inf')\n for j in range(target, -1, -1):\n if dp[j]:\n ans = min(ans, total - 2*j)\n return int(ans)\n\n\n\n total = sum(stones)\n n = len(stones)\n target = total // 2\n dp = [[False]*(target+1) for _ in range(n+1)]\n dp[0][0] = True\n\n for i in range(n):\n for j in range(target + 1):\n if j < stones[i]:\n dp[i + 1][j] = dp[i][j]\n else:\n dp[i + 1][j] = dp[i][j] or dp[i][j - stones[i]]\n \n res= 0\n for j in range(target, -1, -1):\n if dp[-1][j] == True:\n res = total - 2*j\n break\n return res","repo_name":"miyagipipi/studying","sub_path":"bag/1049. 最后一块石头的重量 II.py","file_name":"1049. 最后一块石头的重量 II.py","file_ext":"py","file_size_in_byte":2466,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"3114665655","text":"\"\"\"\nTest estimators\n\"\"\"\n\nimport pytest\nfrom sklearn.datasets import load_iris, load_diabetes\n\nfrom discretefirstorder import DFORegressor\n\n\n@pytest.fixture\ndef iris_data():\n \"\"\"Load Iris dataset for testing\"\"\"\n return load_iris(return_X_y=True)\n\n\n@pytest.fixture\ndef diabetes_data():\n \"\"\"Load diabetes dataset for testing\"\"\"\n return load_diabetes(return_X_y=True)\n\n\ndef test_dfo_regressor_default():\n \"\"\"Test DFORegressor default params\"\"\"\n reg = DFORegressor()\n assert reg.loss == \"mse\"\n assert reg.learning_rate == \"auto\"\n assert reg.k == 1\n assert reg.polish is True\n assert reg.n_runs == 50\n assert reg.max_iter == 100\n assert reg.tol == 1e-3\n assert reg.fit_intercept is True\n assert reg.normalize is True\n\n\ndef test_dfo_regressor():\n \"\"\"Test DFORegressor params\"\"\"\n reg = DFORegressor(\n learning_rate=0.001,\n k=5,\n polish=False,\n n_runs=25,\n max_iter=50,\n tol=1e-4,\n fit_intercept=False,\n normalize=False,\n )\n assert reg.loss == \"mse\"\n assert reg.learning_rate == 0.001\n assert reg.k == 5\n assert reg.polish is False\n assert reg.n_runs == 25\n assert reg.max_iter == 50\n assert reg.tol == 1e-4\n assert reg.fit_intercept is False\n assert reg.normalize is False\n\n\ndef test_dfo_regressor_fit(iris_data):\n \"\"\"Test DFORegressor fit method\"\"\"\n reg = DFORegressor()\n reg.fit(*iris_data)\n assert hasattr(reg, \"is_fitted_\")\n\n\ndef test_invalid_loss():\n \"\"\"Test invalid loss\"\"\"\n with pytest.raises(NotImplementedError):\n _ = DFORegressor(loss=\"myloss\")\n\n\ndef test_invalid_value_learning_rate():\n \"\"\"Test invalid string value for learning rate\"\"\"\n with pytest.raises(ValueError):\n _ = DFORegressor(learning_rate=\"myrate\")\n\n\ndef test_invalid_k(iris_data):\n \"\"\"Test invalid k given data\"\"\"\n with pytest.raises(ValueError):\n reg = DFORegressor(k=10) # n_features = 4\n reg.fit(*iris_data)\n","repo_name":"miguelfmc/sklearn-discretefirstorder","sub_path":"discretefirstorder/tests/test_discrete_first_order.py","file_name":"test_discrete_first_order.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3837299096","text":"import calendar\nimport datetime\nimport unittest\nimport mock\nimport os\n\nfrom mip_convert_wrapper import TIME_UNIT, NEMO_SUBSTREAMS\nfrom mip_convert_wrapper.file_management import (\n _expected_ap, _expected_in, _expected_on,\n get_paths, copy_to_staging_dir, link_data,\n )\n\n\nclass TestMisc(unittest.TestCase):\n \"\"\"\n Test miscellaneous helper functions in the file_management.py module.\n \"\"\"\n\n def test_get_paths(self):\n \"\"\"\n Tests the file_management.get_paths function.\n \"\"\"\n suite_name = 'u-RUNID'\n stream = 'ap4'\n start_date = TIME_UNIT.num2date(0)\n length1 = 360 * 5\n end_date = TIME_UNIT.num2date(length1)\n step = 30\n input_dir = os.path.sep + os.path.join('path', 'to', 'input', 'dir')\n work_dir = os.path.sep + os.path.join('path', 'to', 'work', 'dir')\n\n (expected_files_test,\n old_input_location_test,\n new_input_location_test,) = get_paths(suite_name,\n stream,\n start_date,\n end_date,\n input_dir,\n work_dir)\n\n expected_files_exp = generate_expected(_ap_formatter_1,\n step,\n stream,\n length1,\n )\n\n old_input_exp = os.path.join(input_dir,\n suite_name,\n stream,\n )\n new_input_exp = os.path.join(work_dir,\n suite_name,\n stream,\n )\n\n self.assertEqual(old_input_location_test,\n old_input_exp,\n 'old_input_location not correct')\n\n self.assertEqual(new_input_location_test,\n new_input_exp,\n 'new_input_location not correct')\n\n self.assertEqual([l1 for l1 in expected_files_test],\n list(expected_files_exp))\n\n @mock.patch('os.path.exists')\n @mock.patch('shutil.copy')\n def test_copy_to_staging_dir(self,\n mock_shutil_copy2,\n mock_os_exists):\n \"\"\"\n Tests the file_management.copy_to_staging_dir function.\n \"\"\"\n src_dir = '/path/to/src/dir/'\n dest_dir = '/path/to/dest/dir/'\n start_dt = datetime.datetime(year=1850, month=1, day=1)\n end_dt = datetime.datetime(year=1854, month=12, day=30)\n expected_files = [(start_dt, end_dt, 'file1.nc'),\n (start_dt, end_dt, 'file2.nc'),\n (start_dt, end_dt, 'file3.nc')]\n\n mock_os_exists.return_value = True\n\n copy_to_staging_dir(expected_files, src_dir, dest_dir)\n\n copy2_calls_list = [mock.call(os.path.join(src_dir, f1),\n os.path.join(dest_dir, f1))\n for sdt, edt, f1 in expected_files]\n\n mock_shutil_copy2.assert_has_calls(copy2_calls_list)\n\n @mock.patch('glob.glob')\n @mock.patch('os.symlink')\n @mock.patch('os.path.exists')\n def test_link_data(self,\n mock_os_exists,\n mock_os_symlink,\n mock_glob_glob):\n \"\"\"\n Tests the file_management.link_data function.\n \"\"\"\n src_dir = '/path/to/src/dir/'\n dest_dir = '/path/to/dest/dir/'\n start_dt = datetime.datetime(year=1850, month=1, day=1)\n end_dt = datetime.datetime(year=1854, month=12, day=30)\n expected_files = [(start_dt, end_dt, 'file1.nc'),\n (start_dt, end_dt, 'file2.nc'),\n (start_dt, end_dt, 'file3.nc')]\n\n mock_glob_glob.return_value = []\n\n exists_returns = [True ,]\n for _ in expected_files:\n exists_returns += [False, True,]\n mock_os_exists.side_effect = exists_returns\n\n num_links, link_dir = link_data(expected_files, src_dir, dest_dir)\n\n self.assertEqual(3, num_links)\n symlink_calls = [mock.call(os.path.join(src_dir, f1),\n os.path.join(dest_dir, f1))\n for sdt, edt, f1 in expected_files]\n\n mock_os_symlink.assert_has_calls(symlink_calls)\n\n\nclass TestExpectedAP(unittest.TestCase):\n \"\"\"\n Test expected file names for UM output streams.\n \"\"\"\n def test_monthly(self):\n runid = 'RUNID'\n stream = 'ap4'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected(_ap_formatter_1, step, stream)\n actual = list(_expected_ap(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n def test_10day(self):\n runid = 'RUNID'\n stream = 'ap6'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 10\n expected = generate_expected(_ap_formatter_3, step, stream)\n actual = list(_expected_ap(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n\nclass TestExpectedIN(unittest.TestCase):\n \"\"\"\n Test expected file names for CICE output streams.\n \"\"\"\n def test_monthly(self):\n runid = 'RUNID'\n stream = 'inm'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected(_in_formatter, step, stream)\n actual = list(_expected_in(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n def test_1day(self):\n runid = 'RUNID'\n stream = 'ind'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected(_in_formatter, step, stream)\n\n actual = list(_expected_in(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n\nclass TestExpectedON(unittest.TestCase):\n \"\"\"\n Test expected files for NEMO output streams.\n \"\"\"\n def test_monthly(self):\n runid = 'RUNID'\n stream = 'onm'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected_ocean(step, stream)\n actual = list(_expected_on(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n def test_1day(self):\n runid = 'RUNID'\n stream = 'ond'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected_ocean(step, stream)\n actual = list(_expected_on(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n\ndef generate_expected(file_name_formatter, step, stream, length=360):\n \"\"\"\n Return a list of tuples representing the time bounds and name\n of each file expected.\n\n Parameters\n ----------\n file_name_formatter : function\n Function that takes the stream name (str) and the file start\n and end dates (datetimes) and returns a the file name expected\n as a string.\n step : int\n Number of days between files, e.g. 10\n stream : str\n Name of the stream, e.g. 'ap4'\n\n Returns\n -------\n : list\n start time, end time and file name for each expected file.\n \"\"\"\n expected = []\n for datenum in range(-step, length + step, step):\n file_start = TIME_UNIT.num2date(datenum)\n file_end = TIME_UNIT.num2date(datenum + step)\n file_name = file_name_formatter(stream, file_start, file_end)\n expected.append((file_start, file_end, file_name))\n return expected\n\n\ndef _ap_formatter_1(stream, file_start, file_end):\n month = calendar.month_abbr[file_start.month].lower()\n file_name = 'RUNIDa.p{}{}{}.pp'.format(\n stream[-1], file_start.year, month)\n return file_name\n\n\ndef _ap_formatter_3(stream, file_start, file_end):\n file_name = 'RUNIDa.p{}{}{:02d}{:02d}.pp'.format(\n stream[-1], file_start.year, file_start.month, file_start.day)\n return file_name\n\n\ndef _in_formatter(stream, file_start, file_end):\n file_name = 'cice_RUNIDi_1{}_{}-{}.nc'.format(\n stream[-1], file_start.strftime('%Y%m%d'),\n file_end.strftime('%Y%m%d'))\n return file_name\n\n\ndef generate_expected_ocean(step, stream):\n \"\"\"\n Return a list of tuples representing the time bounds and name\n of each file expected in ocean streams.\n\n Parameters\n ----------\n step : int\n Number of days between files, e.g. 10\n stream : str\n Name of the stream, e.g. 'ap4'\n\n Returns\n -------\n : list\n start time, end time and file name for each expected file.\n \"\"\"\n expected = []\n for substream in NEMO_SUBSTREAMS:\n for datenum in range(-step, 360 + step, step):\n file_start = TIME_UNIT.num2date(datenum)\n file_end = TIME_UNIT.num2date(datenum + step)\n file_name = 'nemo_RUNIDo_1{}_{}-{}_{}.nc'.format(\n stream[-1], file_start.strftime('%Y%m%d'),\n file_end.strftime('%Y%m%d'), substream\n )\n expected.append((file_start, file_end, file_name))\n return expected\n\n\ndef _on_formatter(stream, file_start, file_end, substream=None):\n file_name = 'nemo_RUNIDi_1{}_{}-{}_{}.nc'.format(\n stream[-1], file_start.strftime('%Y%m%d'),\n file_end.strftime('%Y%m%d'),\n substream)\n return file_name\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"NCAS-CMS/NCAS-Useful-Documentation","sub_path":"cdds_git_files/u-ak283_JSON_wip_first_run_23May2019/app/mip_convert/wrapper/mip_convert_wrapper/tests/test_file_management.py","file_name":"test_file_management.py","file_ext":"py","file_size_in_byte":9903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32389833342","text":"\"\"\"\nTo improve your defining function skills and get familiar with the recursion concept.\n\n -Define a function named my_fact to calculate factorial of the given number. Given a non-negative integer return the factorial of the integer.\n\n(Example: The factorial of 5 is: 5*4*3*2*1 = 120 and factorial of 0 is: 1)\n\"\"\"\n\ndef my_fact(n):\n multip = 1\n for i in range(1, n+1):\n if n == 0:\n return 1\n else:\n multip *= i\n return multip\n \n","repo_name":"msaidcevik/python_exercises","sub_path":"exercise-11-factorial.py","file_name":"exercise-11-factorial.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24742133678","text":"import numpy as np \n\n\ndef transform_data(data, values_key): \n import math\n def isNaN(num):\n return num != num\n def one_hot_embedding(labels, num_classes):\n \"\"\"Embedding labels to one-hot form.\n\n Args:\n labels: (LongTensor) class labels, sized [N,].\n num_classes: (int) number of classes.\n\n Returns:\n (tensor) encoded labels, sized [N, #classes].\n \"\"\"\n y = np.identity(num_classes) \n return y[labels] \n\n\n \n # split data into numeric and categoric\n\n data_num = data.select_dtypes(include=['float', 'int'])\n data_cat = data.select_dtypes(include='object')\n\n #replace values in dict by numeric \n for key, sub_dict in values_key.items():\n \n for sub_key, value in sub_dict.items():\n if isNaN(sub_key):\n data_cat.loc[data_cat[key].isna() , key] = value\n else:\n data_cat.loc[data_cat[key] == sub_key, key] = value\n\n # transform data to torch tensor\n numpy_num = data_num.values\n numpy_cat = data_cat.values\n\n\n\n def StandardizeData( data):\n \"\"\"Scales data to mean 0 and std 0\n\n Args:\n data\n\n Returns:\n (tensor) Normalised data\n \"\"\"\n \n return (data - np.mean( data, axis= 0))/np.std( data, axis = 0)\n \n \n\n print(np.sum(np.isnan(numpy_num) ))\n print(np.sum(np.isnan(numpy_cat) ))\n\n nr_data, num_features = np.shape(numpy_cat)\n print( nr_data, num_features)\n\n one_hot_data = [] \n for index,feat in enumerate( data_cat.columns):\n one_hot_data.append(one_hot_embedding( numpy_cat[:, index], len(values_key[feat])) )\n numpy_one_hot = np.concatenate( one_hot_data, axis= 1)\n numpy_num_standard = StandardizeData( numpy_num)\n\n\n all_data = np.concatenate(( numpy_num_standard , numpy_one_hot) , axis= 1)\n\n\n # fill torch num nan with -1000\n\n all_data[np.isnan( all_data)] = -1000\n\n print(all_data)\n return all_data\n\n","repo_name":"victor-armegioiu/Deep-Learning","sub_path":"Barebones Fully-Connected Nets/exercise_code/transform_house_data.py","file_name":"transform_house_data.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"72651791441","text":"import random, copy, sys, pygame, math\r\nfrom pygame.locals import *\r\n\r\nWIN_WIDTH = 640 # width of the program's window, in pixels\r\nWIN_HEIGHT = 480 # height in pixels\r\nCOLS = 7 # how many spaces wide the board is\r\nROWS = 6 # how many spaces tall the board is\r\nP_SIZE = 50 # size of the tokens and individual board spaces in pixels\r\nX_MARG = int((WIN_WIDTH - COLS * P_SIZE) / 2)\r\nY_MARG = int((WIN_HEIGHT - ROWS * P_SIZE) / 2)\r\n\r\nboard_width = COLS * P_SIZE\r\nboard_height = ROWS * P_SIZE\r\n\r\nNONE = None\r\nRED_TKN = 'R'\r\nYELLOW_TKN = 'Y'\r\nREQ_TO_WIN = 4\r\nassert COLS >= 4 and ROWS >= 4, 'Board must be at least 4x4.'\r\n\r\nFPS = 30 # frames per second to update the screen\r\n\r\n# Colours\r\nBRIGHTBLUE = (0, 50, 255)\r\nBLACK = (0, 0, 0)\r\nWHITE = (255, 255, 255)\r\nRED = (255, 0, 0)\r\nYELLOW = (255, 255, 0)\r\nBGCOLOR = BRIGHTBLUE\r\nTEXTCOLOR = WHITE\r\n\r\n\r\n\r\ndef main():\r\n '''\r\n Main function: calls user and computer turns\r\n '''\r\n global screen, clock, font\r\n pygame.init()\r\n clock = pygame.time.Clock()\r\n screen = pygame.display.set_mode((WIN_WIDTH, WIN_HEIGHT))\r\n font = pygame.font.SysFont('Calibri', 25, True, False)\r\n pygame.display.set_caption('C4')\r\n main_board = get_new_board()\r\n\r\n turn = \"player\"\r\n\r\n carry_on = True\r\n while carry_on:\r\n\r\n # --- Game logic ---\r\n\r\n for event in pygame.event.get():\r\n if event.type == QUIT or (event.type == KEYUP and event.key == K_ESCAPE):\r\n pygame.quit()\r\n sys.exit()\r\n # Draw board\r\n screen.fill(WHITE)\r\n draw_board(main_board)\r\n\r\n if turn == \"player\":\r\n # Human player's turn.\r\n # TEST FOR CORRECT COLOR TILE?\r\n xy_pos = get_player_move(main_board)\r\n \r\n # Check for win\r\n if is_position_winner(xy_pos[0], xy_pos[1], main_board):\r\n # Render some WInninG Text\r\n text = font.render(\"You WIN!\", True, BLACK)\r\n screen.blit(text, [WIN_WIDTH // 2, WIN_HEIGHT - P_SIZE])\r\n\r\n # break to outside Main Loop for reset or quit\r\n carry_on = False\r\n break\r\n # switch to other computers's turn\r\n turn = \"computer\"\r\n\r\n if turn == \"computer\":\r\n # index of poss_moves is COL, value is fitness\r\n computer_move = get_computer_move(main_board, YELLOW_TKN)\r\n comp_row = get_lowest_row(main_board, computer_move)\r\n main_board[computer_move][comp_row] = YELLOW_TKN\r\n\r\n if is_position_winner(computer_move, comp_row, main_board):\r\n text = font.render(\"You WIN!\", True, BLACK)\r\n screen.blit(text, [WIN_WIDTH // 2, WIN_HEIGHT - P_SIZE])\r\n # break to outside Main Loop for reset or quit\r\n carry_on = False\r\n break\r\n turn = \"player\"\r\n\r\n # --- Screen-clearing code goes here\r\n\r\n # Here, we clear the screen to white. Don't put other drawing commands\r\n # above this, or they will be erased with this command.\r\n\r\n # If you want a background image, replace this clear with blit'ing the\r\n # background image.\r\n screen.fill(WHITE)\r\n\r\n # --- Drawing code should go here\r\n draw_board(main_board)\r\n\r\n # --- Go ahead and update the screen with what we've drawn.\r\n\r\n pygame.display.flip()\r\n\r\n # --- Limit to 60 frames per second\r\n clock.tick(60)\r\n\r\n # Close the window and quit.\r\n pygame.quit()\r\n sys.exit()\r\n\r\n\r\ndef make_move(board, col, tile):\r\n '''\r\n takes duplicate/board, col, and tile color\r\n get's lowest row and sets board pos to color\r\n returns consecutive tiles with that board\r\n '''\r\n row = get_lowest_row(board, col)\r\n board[col][row] = tile\r\n return calc_consecutive(col, row, board)\r\n\r\n\r\n# basic algo to check each column move, and every counter move from that\r\ndef get_computer_move(board, color):\r\n # Set colors; irrelevant atm\r\n if color == YELLOW_TKN:\r\n other_color = RED_TKN\r\n else:\r\n other_color = YELLOW_TKN\r\n\r\n # possible_moves index position is col, value of index pos is strength\r\n possible_moves = [0] * COLS\r\n for col in range(COLS):\r\n dupe_board = copy.deepcopy(board)\r\n\r\n if not is_valid_move(board, col):\r\n possible_moves[col] = -1\r\n continue\r\n\r\n # Strength is consecutive tiles for that color\r\n lowest_row = get_lowest_row(dupe_board, col)\r\n dupe_board[col][lowest_row] = color\r\n initial_strength = calc_consecutive(col, lowest_row, dupe_board)\r\n initial_strength = make_move(dupe_board, col, color)\r\n\r\n # Check if winner\r\n if is_position_winner(col, lowest_row, dupe_board):\r\n possible_moves[col] = REQ_TO_WIN\r\n return col\r\n\r\n # Else, Check is winner for other color from that pos\r\n dupe_board2 = copy.deepcopy(dupe_board)\r\n dupe_board2[col][lowest_row] = other_color\r\n if is_position_winner(col, lowest_row, dupe_board2):\r\n return col\r\n\r\n # Check opponent's counter moves\r\n for counter_col in range(COLS):\r\n dupe_board2 = copy.deepcopy(dupe_board)\r\n\r\n if not is_valid_move(dupe_board2, counter_col):\r\n # counter move is worthless\r\n continue\r\n\r\n counter_row = get_lowest_row(dupe_board2, counter_col)\r\n\r\n # Test Play other_color tile for potential counter\r\n dupe_board2[counter_col][counter_row] = other_color\r\n counter_row = get_lowest_row(dupe_board2, counter_col)\r\n counter_strength = calc_consecutive(counter_col, counter_row, dupe_board2)\r\n\r\n # Check is_valid() for playing other_color\r\n if not is_valid_move(dupe_board2, counter_col):\r\n # Skip to next counter position\r\n continue\r\n\r\n # If counter is winner, then don't play init col\r\n # This can produce deviant results - where counter col is same as col\r\n if is_position_winner(counter_col, counter_row, dupe_board2):\r\n possible_moves[col] = -1\r\n # += poss_moves[col]?\r\n possible_moves[counter_col] = counter_strength\r\n else:\r\n if counter_strength >= initial_strength:\r\n # Defensive - Play counter col not init col\r\n possible_moves[counter_col] = counter_strength\r\n\r\n # After checking all counters\r\n if possible_moves[col] < initial_strength:\r\n possible_moves[col] = initial_strength\r\n\r\n best_moves = []\r\n max_str = max(possible_moves)\r\n\r\n for i in range(len(possible_moves)):\r\n if possible_moves[i] == max_str:\r\n # Append index value\r\n best_moves.append(i)\r\n\r\n return best_moves[random.randrange(len(best_moves))]\r\n\r\n\r\ndef get_player_move(board):\r\n '''\r\n Player move: click on column for tile placement\r\n Automatically drops tile to lowest row\r\n '''\r\n while True:\r\n # Check for exit, mouse click\r\n for event in pygame.event.get():\r\n if event.type == QUIT or (event.type == KEYUP and event.key == K_ESCAPE):\r\n pygame.quit()\r\n sys.exit()\r\n\r\n elif event.type == MOUSEBUTTONDOWN:\r\n pos_x, pos_y = event.pos\r\n\r\n elif event.type == MOUSEMOTION:\r\n pos_x, pos_y = event.pos\r\n\r\n elif event.type == MOUSEBUTTONUP:\r\n if X_MARG < pos_x < (WIN_WIDTH - X_MARG):\r\n\r\n column = int((pos_x - X_MARG) / P_SIZE)\r\n\r\n if is_valid_move(board, column):\r\n animate_falling_token(board, column, RED)\r\n row = get_lowest_row(board, column)\r\n\r\n # update board\r\n board[column][row] = RED_TKN\r\n draw_board(board)\r\n pygame.display.update()\r\n xy_human_pos = [column, row]\r\n return xy_human_pos\r\n draw_board(board)\r\n pygame.display.update()\r\n clock.tick()\r\n\r\n\r\n# returns max items in a line 1, 2, 3, or 4\r\n# use as strength of move\r\ndef calc_consecutive(pos_col, pos_row, board):\r\n # item = board[pos_col][pos_row]\r\n row_bound = len(board[0])\r\n col_bound = len(board)\r\n\r\n max_consecutive = 1\r\n temp_con = 1\r\n for delta_col, delta_row in [(0, 1), (1, 0), (1, 1), (-1, 1)]:\r\n consecutive_items = 1\r\n for delta in (1, -1):\r\n delta_col *= delta\r\n delta_row *= delta\r\n next_col = pos_col + delta_col\r\n next_row = pos_row + delta_row\r\n\r\n while 0 <= next_row < row_bound and 0 <= next_col < col_bound:\r\n if board[next_col][next_row] == board[pos_col][pos_row]:\r\n consecutive_items += 1\r\n temp_con = consecutive_items\r\n else:\r\n # if not consecutive, reset and break\r\n consecutive_items = 1\r\n break\r\n next_row += delta_row\r\n next_col += delta_col\r\n if consecutive_items == 4:\r\n return 4\r\n if temp_con > max_consecutive:\r\n max_consecutive = temp_con\r\n\r\n return max_consecutive\r\n\r\n# Almost duplicate code of consecutive_strength\r\ndef is_position_winner(pos_col, pos_row, board):\r\n # item = board[pos_col][pos_row]\r\n row_bound = len(board[0])\r\n col_bound = len(board)\r\n\r\n # pos_row = get_lowest_row(board, pos_col)\r\n\r\n for delta_col, delta_row in [(0, 1), (1, 0), (1, 1), (-1, 1)]:\r\n consecutive_items = 1\r\n\r\n for delta in (1, -1):\r\n delta_col *= delta\r\n delta_row *= delta\r\n next_col = pos_col + delta_col\r\n next_row = pos_row + delta_row\r\n\r\n while 0 <= next_row < row_bound and 0 <= next_col < col_bound:\r\n if board[next_col][next_row] == board[pos_col][pos_row]:\r\n consecutive_items += 1\r\n else:\r\n break\r\n if consecutive_items == 4:\r\n return True\r\n next_row += delta_row\r\n next_col += delta_col\r\n return False\r\n\r\n\r\ndef animate_falling_token(board, column, color):\r\n lowest_space = get_lowest_row(board, column)\r\n radius = 24\r\n # column += 1\r\n x = X_MARG + column * P_SIZE\r\n y = Y_MARG + radius\r\n drop_speed = 0.05\r\n # get_lowest already vetted in main\r\n\r\n while True:\r\n y += int(drop_speed)\r\n drop_speed += 0.01\r\n if int((y - Y_MARG) / P_SIZE) >= lowest_space:\r\n return\r\n draw_board(board)\r\n pygame.draw.circle(screen, color, (x + radius, y), radius)\r\n pygame.display.update()\r\n clock.tick()\r\n\r\n# Gravity call - will find lowest row for tile placement\r\ndef get_lowest_row(board, col):\r\n row = 5\r\n while board[col][row] != NONE and row > -1:\r\n row -= 1\r\n return row\r\n\r\n# Checks if tile can be placed\r\ndef is_valid_move(board, col):\r\n lowest_row = get_lowest_row(board, col)\r\n if lowest_row == -1:\r\n return False\r\n else:\r\n return True\r\n\r\n# Initialize board for new game\r\ndef get_new_board():\r\n board = [[NONE] * ROWS for _ in range(COLS)]\r\n return board\r\n\r\n\r\n# Board draw requires updated board\r\ndef draw_board(board):\r\n screen.fill(WHITE)\r\n # Start x, y\r\n x = int(X_MARG + (P_SIZE / 2))\r\n y = int(Y_MARG + (P_SIZE / 2))\r\n pygame.draw.rect(screen, BGCOLOR, [X_MARG, Y_MARG, COLS * P_SIZE, ROWS * P_SIZE])\r\n\r\n # Loop provides i, j of Board[i][j]\r\n for i in range(COLS): # i = 0,1,2,3...\r\n pygame.draw.circle(screen, WHITE, (x, y), 24)\r\n # init y coord\r\n y = int(Y_MARG + (P_SIZE / 2))\r\n\r\n for j in range(ROWS): # j = 0,1,2,3...\r\n # iterate rows downwards for first col\r\n # next centre at y += 52\r\n if board[i][j] == RED_TKN:\r\n pygame.draw.circle(screen, RED, (x, y), 24)\r\n elif board[i][j] == YELLOW_TKN:\r\n pygame.draw.circle(screen, YELLOW, (x, y), 24)\r\n else:\r\n pygame.draw.circle(screen, WHITE, (x, y), 24)\r\n y += 50\r\n\r\n # Render the text. \"True\" means anti-aliased text.\r\n\r\n text = font.render(str(i + 1), True, BLACK)\r\n\r\n # Put the image of the text on the screen at 250x250\r\n # screen.blit(text, [x, y])\r\n screen.blit(text, [x, y])\r\n x += 50\r\n\r\n\r\nmain()\r\n","repo_name":"Davvott/CP1401","sub_path":"C4_pygame.py","file_name":"C4_pygame.py","file_ext":"py","file_size_in_byte":12726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21175782950","text":"import re\nimport datetime\nimport inspect as ins\nfrom sys import stdout,stdin\nfrom time import sleep\nfrom debug import TDB,ploc\nfrom rig.ipython import embed\nfrom sqlalchemy.orm import object_session #FIXME vs database.imports?\nfrom database.decorators import Get_newest_id, datafile_maker, new_abf_DataFile, hardware_interface, is_mds, new_DataFile\nfrom threading import RLock\nfrom rig.gui import takeScreenCap #FIXME\n#from IPython import embed\ntry:\n import rpdb2\nexcept:\n pass\n\ntdb=TDB()\nprintD=tdb.printD\nprintFD=tdb.printFuncDict\ntdb.off()\n\n#file to consolidate all the different functions I want to execute using the xxx.Control classes\n#TODO this file needs a complete rework so that it can pass data to the database AND so that it can be used by keyboard AND so that it can be used by experiment scripts... means I may need to split stuff up? ;_;\n#TODO rig control vs experiment control... these are technically two different 'modes' one is keyboard controlled the other is keyboard initiated...\n#TODO ideally I want to do experiments the same way every time instead of allowing one part here and another there which is sloppy so those are highly ordered...\n#TODO BUT I need a way to fix things, for example if the slice moves and I need to recalibrate the slice position (FUCK, how is THAT going to work out in metadata)\n\n#TODO all of these are configured for terminal output only ATM, ideally they should be configged by whether they are called from keyboard or from experiment... that seems... reasonable??! not very orthogonal...\n#mostly because when I'm running an experiment I don't want to accientally hit something or cause an error\n\n#TODO split in to send and recieve?!?\n\n#TODO datasource/expected datasource mismatch\n\n#dec for telling the program to run the function in the same thread\ndef sameThread(function):\n def _sameThread(self,*args,**kwargs):\n out=function(self,*args,**kwargs)\n return out\n _sameThread.sameThread=True\n _sameThread.__name__=function.__name__\n return _sameThread\n\n\n#static method for adding a function as a key requester\ndef keyRequest(function): #FIXME sometimes we want to release a kr EARLY!\n def _keyRequest(self,*args,**kwargs):\n def wrapper():\n try:\n self.krdLock.acquire()\n self._kr_not_done+=1\n printD(self._kr_not_done)\n out=function(self,*args,**kwargs)\n except:\n #self._kr_not_done-=1\n raise\n #printD('an error has occured while calling func!')\n finally:\n self._kr_not_done-=1 #moved this here to prevent weird errors?\n printD(self._kr_not_done)\n self.krdLock.release()\n assert self._kr_not_done >= 0 ,'utoh! _kr_not_done < 0 !'\n printD('kr_not_done=',self._kr_not_done)\n if not self._kr_not_done: #TODO prevent multiple unlocks\n self._releaseKR()\n return out\n return wrapper() #FIXME this seems to be needed to prevent some other race condition >_<\n _keyRequest.keyRequester=True\n _keyRequest.__name__=function.__name__\n _keyRequest.__doc__=function.__doc__\n if hasattr(function,'is_mds'):\n _keyRequest.is_mds=True\n return _keyRequest\n\n###\n#class decorator\ndef hasKeyRequests(cls):\n cls.keyRequesters=[]\n cls._kr_not_done=0 #FIXME will we get nasty threading collisions here?\n def init_wrap(init):\n def __init__(self,modestate,*args):\n self._releaseKR=modestate.releaseKeyRequest\n for name in self.keyRequesters:\n modestate.registerKeyRequest(cls.__name__,name)\n init(self,modestate,*args)\n return __init__\n cls.__init__=init_wrap(cls.__init__)\n\n for name,method in cls.__dict__.items(): #register keyRequesters\n if hasattr(method,'keyRequester'):\n #printD('got one!',name)\n cls.keyRequesters.append(name)\n return cls\n\nclass kCtrlObj:\n \"\"\"key controller object\"\"\"\n def __init__(self, modestate):\n self.charBuffer=modestate.charBuffer\n #self.releaseKeyReq=modestate.releaseKeyRequest #FIXME don't even have to do this, @keyRequest can do it for me! :)\n #I probably do not need to pass key handler to thing outside of inputManager...\n #yep, not used anywhere, but I supose it could be used for submodes... we'll leave it in\n self.setMode=modestate.setMode\n self.__mode__=self.__class__.__name__\n self.keyThread=modestate.keyThread\n\n self._kr_not_done=0\n self._releaseKR=modestate.releaseKeyRequest\n self.krdLock=modestate.krdLock\n self.keyRequesters=[]\n for name,method in ins.getmembers(self):\n #printD(name,method)\n if ins.ismethod(method) and hasattr(method,'keyRequester'):\n self.keyRequesters.append(name)\n modestate.registerKeyRequest(self.__class__.__name__,name)\n\n #some way to pass the state along to other controllers XXX REQUIRED FOR CLASS DECORATORS TO WORK XXX\n def dat_get_write_targets():\n return modestate.ctrlDict['datFuncs'].getWriteTargets()\n def dat_get_df_subjects():\n return modestate.ctrlDict['datFuncs'].getDFSubjects()\n def dat_new_df():\n return modestate.ctrlDict['datFuncs'].newDataFileCallback()\n self._wt_getter=dat_get_write_targets\n self._sub_getter=dat_get_df_subjects #name needs to match for decorators to work >_<\n self._new_datafile=dat_new_df\n\n @keyRequest\n def __getChars__(self,prompt=''):\n \"\"\" replacement for input()\"\"\"\n\n class inputBuffer:\n char_list=[]\n pos=0\n _filler=0 #used to make the display update correct\n def put(self,char):\n self.char_list.insert(self.pos,char)\n #if self._filler > 0: #don't need it is ok to overfill\n #self._filler -= 1\n self.goR()\n def goR(self):\n if self.pos + 1 <= len(self.char_list):\n self.pos += 1\n def goL(self):\n if self.pos -1 >= 0:\n self.pos -= 1\n def home(self):\n self.pos = 0\n def end(self):\n self.pos = len(self.char_list)\n def backspace(self):\n if self.pos: #if we're already at zero dont go!\n self._filler+=1\n self.char_list.pop(self.pos-1)\n self.goL()\n def delete(self):\n try:\n self.char_list.pop(self.pos)\n self._filler+=1\n except IndexError:\n pass\n def __str__(self):\n return ''.join(self.char_list)\n @property\n def str_to_pos(self):\n return ''.join(self.char_list[:self.pos])\n def pop_filler(self):\n out=self._filler #ick this is terrible TODO FIXME\n self._filler=0\n return out\n\n ib=inputBuffer()\n\n cmddict={\n '[A':lambda:None,\n '[B':lambda:None,\n '[C':ib.goR,\n '[D':ib.goL,\n '\\x7f':ib.backspace,\n '[3~':ib.delete,\n '[7~':ib.home,\n '[8~':ib.end,\n 'esc':lambda:None,\n }\n\n def charHand(char):\n if cmddict.get(char):\n cmddict[char]()\n elif len(char) > 1:\n pass #prevents escape sequences from printing\n else:\n ib.put(char)\n\n #self.keyLock.acquire()\n stdout.write('\\r%s'%prompt)\n while self.keyThread.is_alive():\n char=self.charBuffer.get()\n #printD('got a key:', char) \n if char == '\\n' or char == '\\r':\n printD('done')\n stdout.write('\\n\\r')\n stdout.flush()\n break\n charHand(char)\n stdout.write('\\r%s'%prompt+str(ib)+' '*ib.pop_filler())\n stdout.write('\\r%s'%prompt+ib.str_to_pos)\n stdout.flush()\n if not self.keyThread.is_alive():\n raise IOError('Key thread is not alive!')\n else:\n #print(str(ib))\n return str(ib)\n\n @keyRequest\n def getString(self,prompt='string> '):\n print(\"Please enter a string.\",)\n return self.__getChars__(prompt)\n\n @keyRequest\n def getFloat(self,prompt='float> '):\n print('Please enter a floating point value.',)\n while self.keyThread.is_alive():\n string=self.__getChars__(prompt)\n try:\n out=float(string)\n return out\n except:\n print('Could not convert value to float, try again!')\n\n @keyRequest\n def getInt(self,prompt='int> '):\n print('please enter an integer',)\n while self.keyThread.is_alive():\n string=self.__getChars__(prompt)\n try:\n out=int(string)\n return out\n except ValueError as e:\n print(e,'Try again!')\n #print('could not convert value to int, try again!')\n\n @keyRequest\n def getBool(self,prompt='Boolean: hit space for True, anything else for False.'):\n print(prompt)\n true_key=' '\n #self.keyHandler(1) #requesting key passthrough\n out=self.charBuffer.get() == true_key\n return out\n\n def cleanup(self):\n pass\n\nfrom database.models import Person, Project, ExperimentType, Experiment, Cell, Slice, Mouse, DataFile\nfrom datetime import datetime\nfrom database.imports import NoResultFound\nclass datFuncs(kCtrlObj): \n #interface with the database TODO this should be able to run independently?\n \"\"\"Put ANYTHING permanent that might be data in here\"\"\"\n def __init__(self,modestate,*args,person_id=None,project_id=None):\n super().__init__(modestate)\n self.Session=modestate.Session #FIXME maybe THIS was the problem?\n session=self.Session()\n self.c_person=session.query(Person).filter_by(id=person_id).one()\n self.c_project=session.query(Project).filter_by(id=project_id).one() #FIXME the person could not be on the project\n self.getUnfinished(session)\n self.c_datafile=None\n self.session=session #FIXME\n self.sLock=RLock() #a lock for the session, see if we need it\n #session.close()\n\n def getUnfinished(self,session): #FIXME it is possible to get cells or slices w/o experiment...\n def queryUnf(obj):\n return session.query(obj).filter(obj.endDateTime==None)\n #experiemtns\n try:\n self.c_experiment=queryUnf(Experiment).one()\n self.c_target=self.c_experiment\n print('Got unfinished experiment ',repr(self.c_experiment))\n except NoResultFound:\n self.c_experiment=None\n self.c_target=None\n #slices\n try:\n self.c_slice=queryUnf(Slice).one()\n self.c_target=self.c_slice\n print('Got unfinished slice ',self.c_slice.strHelper())\n except NoResultFound:\n self.c_slice=None\n #cells\n try:\n cells=queryUnf(Cell).join(Experiment,Cell.experiments).filter(Experiment.id==self.c_experiment.id).join(Slice,Cell.parent).filter(Slice.id==self.c_slice.id).all()\n self.c_cells=cells\n if cells:\n print('Got unfinished cells ',[c.strHelper() for c in cells])\n self.c_target=self.c_cells[0]\n except AttributeError: #catch NoneType\n self.c_cells=[]\n #datafiles #TODO\n\n @keyRequest\n def setWriteTargets(self):\n print('slice','cell','exp','data')\n type_=self.__getChars__('target> ')\n if type_ == 'exp':\n self.c_target=self.c_experiment\n elif type_ == 'slice':\n self.c_target=self.c_slice\n elif type_ == 'cell':\n print('cell number 0-n amongst current cells n probably <=3')\n index=self.getInt('cell #> ')\n if index >= len(self.c_cells):\n index=len(self.c_cells)-1\n self.c_target=self.c_cells[index]\n elif type_ == 'data':\n self.c_target=self.c_datafile\n else:\n print('not a type, try fiddling with ipython if you really need to spec soemthing')\n\n\n def getDFSubjects(self):\n \"\"\" used to pass the current cells to the new_abf_DataFile decorator \"\"\"\n return self.c_cells\n \n def getWriteTargets(self): #XXX NOTE XXX there is only a SINGLE write target at a time now!\n targets=[] #FIXME change calling methods to match\n target=self.c_target\n #try:\n #iter(target)\n #targets.extend(target)\n #except TypeError:\n targets.append(target)\n\n return targets\n\n def get_df_statistics(self):\n \"\"\" calculate and rpint the test pulse statistics and hopefully save them to the database? \"\"\"\n from database.abf_analysis import print_tp_stats\n print_tp_stats(self.c_datafile.full_url[8:])\n \n def print_write_target(self):\n print(repr(self.c_target))\n try:\n iter(self.c_target)\n for t in self.c_target:\n self.session.refresh(t)\n print(t.metadata_)\n print(t.notes)\n try:\n print(t.datafiles)\n except:\n pass\n except:\n self.session.refresh(self.c_target)\n print(self.c_target.metadata_)\n print(self.c_target.notes)\n\n def set_slice_md(self,markDict): #FIXME seems like this might be related to our errors?\n self.c_slice.markDict=markDict\n self.session.add(self.c_slice)\n #print(self.c_slice.markDict)\n try:\n self.session.commit()\n except:\n session.rollback()\n raise\n\n def printAll(self):\n print(self.c_person.strHelper())\n print(repr(self.c_project))\n print(repr(self.c_experiment))\n print(repr(self.c_slice))\n print(self.c_cells)\n print(repr(self.c_datafile))\n print('target:',repr(self.c_target))\n\n def newDataFileCallback(self):\n new_df=self.session.query(DataFile).order_by(DataFile.creationDateTime.desc()).first()\n printD('c_datafile set to %s'%new_df)\n self.c_datafile=new_df\n self.c_target=new_df\n \n @keyRequest\n def newNote(self): #FIXME need a way to hit a single cell\n note=self.__getChars__('note> ')\n #try:\n #iter(self.c_target)\n #for t in self.c_target:\n #n=t.Note(note,t)\n #self.session.add(n)\n #except:\n n=self.c_target.Note(note,self.c_target)\n self.session.add(n)\n #finally:\n self.session.commit()\n\n @keyRequest\n def newExperiment(self): #FIXME this fails because of how dictMan works...\n session=self.session\n #session=self.Session()\n types=session.query(ExperimentType).all()\n print('please enter the type of experiment, available types are: %s'%[t.abbrev for t in types])\n type_=None\n type_string=self.__getChars__('type>')\n type_=[t for t in types if t.abbrev==type_string]\n if self.c_experiment: #FIXME this is here to prevent keyhandler from going haywire\n #if session.query(Experiment).get(self.c_experiment.id): #FIXME if an experiment is deled elsewhere\n print('[!] You already have an experiment, end the current one first!')\n return self\n #else:\n #self.c_experiment=None #FIXME actually noat an issue it would seem\n #print('[!] It would seem that you current expeirment got deleted...')\n if type_:\n if type_[0].abbrev=='patch':\n se=session.query(Experiment).join(ExperimentType,Experiment.type).filter(ExperimentType.abbrev=='slice').order_by(Experiment.id.desc()).first()\n if not se:\n print('[!] There is no slice experiment on record! Please add one!')\n return self\n experiment=Experiment(type_id=type_[0],project_id=self.c_project,person_id=self.c_person)\n session.add(experiment)\n try: \n session.commit()\n self.c_experiment=experiment\n self.c_target=experiment\n print('New experiment added = %s'%self.c_experiment.strHelper())\n except :\n print('[!] experiment NOT added')\n session.rollback() #FIXME could be dangerous? if others are in the session?\n finally:\n #session.close()\n pass\n else:\n print('Invalid type: experiment not created')\n return self\n #newExperiment.keyRequester=True\n\n def newSlice(self):\n if self.c_slice:\n #raise BaseException('You already have a slice on the rig!')\n print('[!] You already have a slice on the rig!')\n return self\n elif not self.c_experiment:\n print('[!] You have not added an experiment!')\n return self\n session=self.session\n #session=self.Session()\n ge=session.query(Experiment).join(ExperimentType,Experiment.type).filter(ExperimentType.abbrev=='slice').order_by(Experiment.id.desc()).first()\n if not ge:\n print('[!] There is no slice experiment on record! Please add one!')\n return self\n slice_=Slice(generating_experiment_id=ge)\n slice_.experiments.append(self.c_experiment)\n session.add(slice_)\n try:\n session.commit()\n self.c_slice=slice_\n self.c_target=slice_\n print('New slice added = %s'%repr(self.c_slice))\n except:\n session.rollback()\n finally:\n #session.close()\n pass\n\n def newCell(self):\n if not self.c_experiment:\n print('[!] Cells dont really belong in slice prep experiments')\n return self\n elif self.c_experiment.type.abbrev=='slice':\n print('[!] Cells dont really belong in slice prep experiments')\n return self\n if len(self.c_cells) >=2:\n print('[!] You already have 2 cells! New rig eh?')\n return self\n if not self.c_slice:\n print('You do not have a slice on the rig!')\n return self\n #raise BaseException('You do not have a slice on the rig!')\n #self.__getChars__('headstage>')\n cell=Cell(parent_id=self.c_slice.id,generating_experiment_id=self.c_experiment)\n cell.experiments.append(self.c_experiment)\n session=self.session\n #session=self.Session()\n session.add(cell)\n try:\n session.commit()\n self.c_cells.append(cell)\n self.c_target=cell #FIXME\n print('New cell added = %s'%repr(cell))\n except:\n session.rollback()\n finally:\n #session.close()\n pass\n #newCell.keyRequester=True\n \n @keyRequest #FIXME?\n def getBrokenIn(self):\n if self.getBool('Hit space if you broke in otherwise fail!'):\n #TODO metadata on breakin?\n print('Yay!')\n else:\n print('Your failure has been noted.')\n #self.c_target.notes.append('FAILURE MESSAGE') #TODO\n self.endCell(self.c_target) #FIXME make sure this works?\n return self\n\n def endDataFile(self):\n if not self.c_datafile:\n print('No datafile to end')\n return None\n self.c_datafile=None\n self.c_target=self.c_cells[0]\n\n def endCell(self,cell=None): #TODO I think it might be worth having a c_cell in addition??!?\n if not self.c_cells:\n print('No cell to end.')\n return self\n if not cell and type(self.c_target) is not Cell:\n print('Current target is not a cell!')\n return self\n elif not cell: #FIXME\n cell=self.c_target\n\n cell.endDateTime=datetime.now()\n self.session.commit()\n self.c_cells.remove(cell)\n print('Ended cell %s'%cell.strHelper())\n if self.c_cells:\n self.c_target=self.c_cells[0] #FIXME\n else:\n self.c_target=self.c_slice\n return self\n\n def endSlice(self): #TODO location in the pfa well?\n if not self.c_slice:\n print('No slice to end.')\n return self\n if self.c_cells:\n print('You still have cells! Please end them first!')\n return self\n self.c_slice.endDateTime=datetime.now()\n session=self.session\n #session=self.Session()\n #session.add(self.c_slice)\n session.commit()\n print('Ended slice %s'%self.c_slice.strHelper())\n self.c_slice=None\n self.c_target=self.c_experiment\n\n def endExperiment(self):\n if not self.c_experiment:\n print('No experiment to end.')\n return self\n if self.c_slice:\n print('[!] You still have slices! End them first!')\n return self\n self.c_experiment.endDateTime=datetime.now()\n session=self.session\n #session=self.Session()\n #session.add(self.c_experiment)\n session.commit()\n print('Ended experiment %s'%self.c_experiment.strHelper())\n self.c_experiment=None\n self.c_target=None\n \n@datafile_maker#@hardware_interface('Digidata 1322A')\nclass clxFuncs(kCtrlObj):\n def __init__(self, modestate, controller):\n try:\n if controller.__class__.__name__ is not 'clxControl':\n raise TypeError('wrong controller type')\n except:\n raise\n #from clx import clxControl\n super().__init__(modestate)\n self.clx=controller\n #self.initController(clxmsg)\n #printD('clx ctrl',self.clx)\n #self.clxCleanup=self.cleanup\n self.programDict={}\n self.current_program=''\n #self.wrapDoneCB()\n\n #class only\n def readProgDict(self,progDict):\n self.programDict=progDict\n return self\n\n def setProtocolPath(self,protoPath):\n print('PROTOCOL PATH SET %s'%protoPath)\n self.protocolPath=protoPath\n\n def cleanup(self):\n super().cleanup()\n try:\n self.clx.DestroyObject()\n print(self.clx.__class__,'handler destroyed')\n except:\n pass\n #print('this this works the way it is supposed to the we should never have to destory the object')\n\n\n #input with output\n def getStatus(self,outputs=None): #TODO outputs... should be able to output to as many things as I want... probably should be a callback to simplify things elsewhere? no?!?!\n status=self.clx.GetStatus()\n print(status)\n return self\n\n def wait_till_done(self):\n gs=self.clx.GetStatus\n while gs() != 'CLXMSG_ACQ_STATUS_IDLE' and self.keyThread.is_alive():\n sleep(.001) #FIXME\n print('Done recording')\n\n def loadfile(self,filename):\n try:\n filepath=self.protocolPath+filename\n self.clx.LoadProtocol(filepath.encode('ascii'))\n self.current_program=filepath\n print('%s loaded!'%filepath)\n except BaseException as e:\n print(e)\n\n #@keyRequest\n def load(self,key):\n #if not key:\n #print('Please enter the program to load')\n #key=self.charBuffer.get()\n try:\n filename=self.programDict[key]\n filepath=self.protocolPath+filename\n self.clx.LoadProtocol(filepath.encode('ascii'))\n self.current_program=filepath\n print('%s loaded!'%filepath)\n except BaseException as e:\n print(e)\n #print('Program not found')\n #raise\n return self\n\n @new_abf_DataFile()\n def record(self):\n #RECORD=109, RERECORD=110, VIEW=108\n try:\n self.clx.StartAcquisition(109)\n print('Running %s'%self.current_program)\n except:\n raise\n return self\n\n def view(self):\n #RECORD=109, RERECORD=110, VIEW=108\n try:\n self.clx.StartAcquisition(108)\n print('Viewing %s'%self.current_program)\n except:\n raise\n finally:\n return self\n \n def stop_rec(self):\n self.clx.StopAcquisition()\n print('Stopping curren acquistion')\n\n\n #input only\n def startMembTest(self):\n self.clx.StartMembTest(120)\n self.clx.StartMembTest(121)\n return self\n\n\n@hardware_interface('mc1') #FIXME or is it software interface?\nclass mccFuncs(kCtrlObj): #FIXME add a way to get the current V and I via... telegraph?\n def __init__(self, modestate, controller):\n try:\n if controller.__class__.__name__ is not 'mccControl':\n raise TypeError('wrong controller type')\n except:\n raise\n #from mcc import mccControl\n super().__init__(modestate) #better error messages?\n self.mcc=controller\n #self.initController(mccmsg)\n self.MCCstateDict={}\n #self.wrapDoneCB()\n \n #associated metadata sources\n #self.state1DataSource=None\n\n def setGain(self,value=1):\n self.mcc.SetPrimarySignalGain(value)\n print('Primary signal gain set to %s'%value)\n return self\n\n def allGain(self,value=1):\n self.mcc.selectMC(0)\n self.mcc.SetPrimarySignalGain(value)\n self.mcc.selectMC(1)\n self.mcc.SetPrimarySignalGain(value)\n return self\n\n\n def inpWait(self): #XXX depricated\n #wait for keypress to move to the next program, this may need to spawn its own thread?\n raise DeprecationWarning('please use trmFuncs.getKbdHit()')\n\n def getState(self): #FIXME this function and others like it should probably be called directly by dataman?\n printD('hMCCmsg outer',self.mcc.hMCCmsg)\n def base(state):\n state['HoldingEnable']=self.mcc.GetHoldingEnable()\n state['Holding']=self.mcc.GetHolding()\n state['PrimarySignal']=self.mcc.GetPrimarySignal()\n state['PrimarySignalGain']=self.mcc.GetPrimarySignalGain() #also in abf file and already handled by AxonIO\n state['PrimarySignalLPF']=self.mcc.GetPrimarySignalLPF() #also in abf file\n state['PipetteOffset']=self.mcc.GetPipetteOffset()\n\n #def vc(state):\n #base(state)\n #XXX ONLY RELEVANT FOR MODE 0\n state['FastCompCap']=self.mcc.GetFastCompCap()\n state['SlowCompCap']=self.mcc.GetSlowCompCap()\n state['FastCompTau']=self.mcc.GetFastCompTau()\n state['SlowCompTau']=self.mcc.GetSlowCompTau()\n state['SlowCTX20Enable']=self.mcc.GetSlowCompTauX20Enable()\n\n #def ic(state):\n #base(state)\n #XXX ONLY RELEVANT FOR MODE 1\n state['BridgeBalEnable']=self.mcc.GetBridgeBalEnable()\n state['BridgeBalResist']=self.mcc.GetBridgeBalResist()\n\n #def iez(state):\n #base(state)\n\n #modeDict={0:vc,1:ic,2:iez}\n stateList=[]\n for i in range(self.mcc.mcNum):\n self.mcc.selectMC(i)\n state={} #FIXME: make this a dict with keys as the name of the value? eh would probs complicate\n state['Channel']=i #might be suprflulous but it could simplify the code to read out stateList\n mode=self.mcc.GetMode()\n state['mode']=(mode)\n #modeDict[mode](state)\n base(state)\n stateList.append(state)\n print()\n print(state)\n\n self.MCCstateDict[datetime.utcnow()]=stateList\n #print(stateList)\n return stateList\n\n def stateToDataFile(self): #AAAAAAAAAAAHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH SO BAD FIXME\n datFunc=self.modestate.ctrlDict['datFuncs']\n df=datFunc.c_datafile\n stateList=self.getState()\n df.mddictlist=stateList\n datFunc.session.commit()\n return self #IF THIS WORKS I WILL BUY A HAT AND EAT IT WAIT I ALREADY HAVE A HAT\n\n\n def printMCCstate(self):\n print(re.sub('\\), ',')\\r\\n',str(self.MCCstateDict)))\n return self\n\n def setMCState(self,MC=None,Mode=None,Holding=None,HoldingEnable=None): #TODO\n #FIXME all of the experiment logic needs to be stored in one place instead of hidden in 10 files\n #selectMC,SetMode,SetHolding,SetHoldingEnable,\n #self.mcc.selectMC()\n return self\n\n def set_hs0(self):\n print('Setting headstage 0')\n self.mcc.selectMC(0)\n self.current_headstage=0\n return self\n def set_hs1(self):\n print('Setting headstage 1')\n self.mcc.selectMC(1)\n self.current_headstage=1 #TODO use this to link cells to the headstage!\n return self\n def set_hsAll(self): #FIXME\n self.ALL_HS=True\n\n def autoOffset(self):\n self.mcc.AutoPipetteOffset()\n def autoCap(self):\n self.mcc.AutoFastComp()\n self.mcc.AutoSlowComp()\n def setVCholdOFF(self):\n self.mcc.SetMode(0)\n self.mcc.SetHoldingEnable(0)\n def setVCholdON(self):\n self.mcc.SetMode(0)\n self.mcc.SetHoldingEnable(1)\n\n def setVChold(self,holding_volts):\n #print(holding_volts)\n self.mcc.SetMode(0)\n print(holding_volts)\n self.mcc.SetHolding(float(holding_volts))\n h=self.mcc.GetHolding()\n print(h)\n #sleep(.001) #FIXME trying to figure out why holding set to -1000mV\n #self.mcc.SetHoldingEnable(1)\n def setICnoHold(self):\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n\n def allVChold(self,holding_volts):\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(float(holding_volts))\n #self.mcc.SetHoldingEnable(1)\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(float(holding_volts))\n #self.mcc.SetHoldingEnable(1)\n return self\n\n def allIeZ(self):\n self.mcc.selectMC(0)\n self.mcc.SetMode(2)\n self.mcc.selectMC(1)\n self.mcc.SetMode(2)\n return self\n def allVCnoHold(self):\n #try:\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHoldingEnable(0)\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n self.mcc.SetHoldingEnable(0)\n #except:\n #raise BaseException\n return self\n def allVChold_60(self):\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(-.06)\n self.mcc.SetHoldingEnable(1)\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(-.06)\n self.mcc.SetHoldingEnable(1)\n return self\n def allICnoHold(self):\n self.mcc.selectMC(0)\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n self.mcc.selectMC(1)\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n return self\n def testZtO(self,holding_voltage):\n self.mcc.selectMC(0)\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(float(holding_voltage))\n self.mcc.SetHoldingEnable(1)\n return self\n def testOtZ(self,holding_voltage):\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(float(holding_voltage))\n self.mcc.SetHoldingEnable(1)\n self.mcc.selectMC(1)\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n return self\n def zeroVChold_60(self):\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(-.06)\n self.mcc.SetHoldingEnable(1)\n return self\n def oneVChold_60(self):\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n #self.mcc.poops(1) #awe, this is broken now due to something\n self.mcc.SetHolding(-.06)\n self.mcc.SetHoldingEnable(1)\n return self\n def cleanup(self):\n super().cleanup()\n try:\n self.mcc.DestroyObject()\n print(self.mcc.__class__,'handler destroyed')\n except:\n pass\n\n\n@hardware_interface('ESP300')\nclass espFuncs(kCtrlObj):\n def __init__(self, modestate, controller):\n try:\n if controller.__class__.__name__ is not 'espControl':\n raise TypeError('wrong controller type')\n except:\n raise\n #from esp import espControl\n\n\n super().__init__(modestate)\n self.esp=controller\n self.markDict={} #FIXME\n self.posDict={} #FIXME\n #self.initController(npControl)\n self.modestate=modestate\n self.setMoveDict()\n self.move_list=[]\n self.motor_on=False\n #self.event=modestate.event\n \n #associated metadatasources:\n #self.EspXDataSource=None\n #self.EspYDataSource=None\n\n @is_mds('m','m','ESP300',5)\n def getPos(self):\n \"\"\" get the (x,y) position of the eps300 \"\"\"\n #may want to demand a depth input (which can be bank)\n #try:\n pos=self.esp.getPos()\n #self.doneCB()\n self.posDict[datetime.now()]=pos #FIXME dat should handle ALL of this internally\n print(pos)\n #except:\n #printD('oops')\n #raise\n return list(pos)\n\n def motorToggle(self):\n self.esp.motorToggle()\n def printPosDict(self):\n #self.doneCB()\n print(re.sub('\\), ',')\\r\\n',str(self.posDict)))\n return self\n\n def set_move_list(self,move_list): #FIXME TODO should probably be saved at the... slice level?\n self.move_list=move_list\n self._current_move_list_pos=0\n\n def moveNext(self):\n if not self.move_list:\n print('No movelist! not moving')\n return None\n else:\n #stdout.write('\\rMoving to next position')\n #stdout.flush()\n self.esp.BsetPos(self.move_list[self._current_move_list_pos])\n if self._current_move_list_pos + 1 < len(self.move_list):\n self._current_move_list_pos+=1\n else:\n print('Move list is done! Resetting to the start!')\n self._current_move_list_pos=0\n\n @keyRequest\n def mark(self): #FIXME seems like this might be where the crashes are coming from?\n \"\"\"mark/store the position of a cell using a character sorta like vim\"\"\"\n try:\n slice_md=self.modestate.ctrlDict['datFuncs'].c_slice.markDict\n self.markDict=slice_md\n except AttributeError:\n pass\n stdout.write('\\rmark> ')\n stdout.flush()\n key=self.charBuffer.get()\n #printD('we got the key from charBuffer')\n if key in self.markDict:\n print('Mark %s is already being used, do you want to replace it? y/N'%(key))\n yeskey=self.charBuffer.get()\n #self._releaseKR() #XXX\n if yeskey=='y' or yeskey=='Y':\n self.markDict[key]=self.esp.getPos()\n print(key,'=',self.markDict[key])\n else:\n print('No mark set')\n elif key=='esc':\n self.unmark()\n else:\n #self._releaseKR() #XXX\n self.markDict[key]=self.esp.getPos()\n print(key,'=',self.markDict[key])\n #self.keyHandler(getMark) #fuck, this could be alot slower...\n try:\n self.modestate.ctrlDict['datFuncs'].set_slice_md(self.markDict)\n except AttributeError:\n pass\n #raise\n return self\n #mark.keyRequester=True\n\n @keyRequest\n def unmark(self):\n #self.doneCB()\n try:\n mark=self.charBuffer.get()\n pos=self.markDict.pop(mark)\n print(\"umarked '%s' at pos %s\"%(mark,pos))\n try:\n self.modestate.ctrlDict['datFuncs'].set_slice_md(self.markDict)\n except:\n raise\n except KeyError:\n stdout.write('\\n')\n stdout.flush()\n pass\n return self\n\n @keyRequest #FIXME we may actually want to use the manual version here due to BsetPos...\n def gotoMark(self): #FIXME\n #self.doneCB()\n stdout.write('\\rgoto:')\n stdout.flush()\n key=self.charBuffer.get()\n #self._releaseKR() #XXX\n if key in self.markDict:\n print('Moved to: ',self.esp.BsetPos(self.markDict[key])) #AH HA! THIS is what is printing stuff out as I mvoe FIXME I removed BsetPos at some point and now I need to add it... back? or what\n else:\n print('No position has been set for mark %s'%(key))\n return self\n #gotoMark.keyRequester=True\n\n @keyRequest\n def mark_to_movelist(self):\n names='origin','target'\n args=[]\n for i in range(2):\n stdout.write(names[i]+'> ')\n stdout.flush()\n key=self.charBuffer.get()\n stdout.write(key)\n stdout.flush()\n if key in self.markDict:\n args.extend(self.markDict[key]) #x,y,x,y\n stdout.write('\\n')\n stdout.flush()\n else:\n print('Mark not found exiting.')\n return None\n step=self.getFloat('step um>')\n number=self.getInt('number>')\n from rig.calcs import random_vector_points, vector_points, random_vector_ret_start\n #moves=vector_points(*args,number=number,spacing=step/1000) #.05 mm = 50um\n #moves=random_vector_points(*args,number=number,spacing=step/1000) #.05 mm = 50um\n moves=random_vector_ret_start(*args,number=number,spacing=step/1000)\n print(moves)\n self.set_move_list(moves)\n\n @keyRequest\n def mark_to_cardinal(self,step_um=25,number=8): #FIXME need a way to reload marks at startup!\n from numpy.random import shuffle\n from rig.calcs import vector_points, intersperse\n #step_um=self.getFloat('step um>') #FIXME stupid having to compile this at startup >_<\n #number=self.getInt('number>')\n\n start='origin'\n stdout.write(start+'> ')\n stdout.flush()\n key=self.charBuffer.get()\n stdout.write(key)\n stdout.flush()\n #base_args=[]\n if key in self.markDict:\n base_args=self.markDict[key] #x,y,x,y #XXX NOTE XXX this is the origin!\n stdout.write('\\n')\n stdout.flush()\n else:\n print('Mark not found!')\n return None\n\n dirs='dorsal','ventral','medial','lateral'\n base=[base_args] #include the origin somewhere in the list\n for i in range(len(dirs)):\n args=[]\n args.extend(base_args)\n stdout.write(dirs[i]+'> ')\n stdout.flush()\n key=self.charBuffer.get()\n stdout.write(key)\n stdout.flush()\n if key in self.markDict:\n args.extend(self.markDict[key]) #x,y,x,y\n stdout.write('\\n')\n stdout.flush()\n #print(base_args,args)\n #pts=vector_points(args[0],args[1],args[2],args[3],number=number,spacing=step_um/1000)\n pts=vector_points(*args,number=number,spacing=step_um/1000)[1:] #[1:] to prevent redundant origin calls\n #print(pts)\n base.extend(pts)\n else:\n print('Mark not found exiting.')\n return None\n shuffle(base)\n moves=[m for m in intersperse(base,base_args)]\n moves.append(base_args) #add the last validation origin point\n print(moves)\n print(len(moves))\n self.set_move_list(moves)\n return moves\n\n @keyRequest\n def mark_to_spline(self,number=10,step_um=100):\n from rig.calcs import get_moves_from_points\n from numpy.random import shuffle\n import pylab as plt\n\n marks='start','one','two','three','four','five'\n points=[]\n for i in range(len(marks)):\n stdout.write(marks[i]+'> ')\n stdout.flush()\n key=self.charBuffer.get()\n stdout.write(key)\n stdout.flush()\n if key in self.markDict:\n points.append(self.markDict[key]) #x,y,x,y\n stdout.write('\\n')\n stdout.flush()\n else:\n print('Mark not found exiting.')\n return None\n moves=get_moves_from_points(points,points[0],number,step_um/1000,switch_xy=True) #XXX NOTE THE SWITCH XXX\n plt.plot(-moves[0][0],moves[0][1]+.01,'yo')\n shuffle(moves)\n [plt.plot(-m[0],m[1],'ro') for m in points]\n [plt.plot(-m[0],m[1],'go') for m in moves]\n plt.axis('equal')\n plt.show()\n print(moves)\n print(len(moves))\n self.set_move_list(moves)\n\n\n def printMarks(self):\n \"\"\"print out all marks and their associated coordinates\"\"\"\n try:\n slice_md=self.modestate.ctrlDict['datFuncs'].c_slice.markDict\n self.markDict=slice_md\n except AttributeError:\n pass\n print(re.sub('\\), ','),\\r\\n ',str(self.markDict)))\n return self\n\n def fakeMove(self):\n #self.doneCB()\n if self.esp.sim:\n from numpy import random #only for testing remove later\n a,b=random.uniform(-10,10,2) #DO NOT SET cX or cY manually\n self.esp.setPos((a,b))\n #print(self.esp._cX,self.esp._cY)\n else:\n print('Not in fake mode! Not moving!')\n return self\n\n #callback to break out of display mode\n def _gd_break_callback(self): #XXX showDisp now\n print('leaving disp mode!')\n self._gd_exit=1\n self.modestate.keyActDict['esc']=self._gd_old_esc\n self._gd_old_esc=None\n del(self._gd_old_esc)\n self.modestate.keyActDict[self._gd_own_key]=self.showDisp\n self._gd_own_key=None\n del(self._gd_own_key)\n\n #@keyRequest #just added the getDisp.keyRequester=True at the bottom and we're good to go\n def showDisp(self):\n \"\"\"BLOCKING stream the displacement from a set point\"\"\"\n #self.doneCB()\n from numpy import sum as npsum #FIXME should these go here!?\n from numpy import array as arr\n print('entering disp mode')\n\n if not len(self.markDict):\n try:\n self.markDict.update(self.modestate.ctrlDict['datFuncs'].c_slice.markDict)\n except:\n pass #in the event there is no slice we don't really need to tell anyone\n if not len(self.markDict):\n self.mark()\n else:\n self._releaseKR() #XXX release whether we got it or not\n\n def get_key(_dict,func):\n for key,value in _dict.items():\n if value == func:\n return key\n raise KeyError('key not found!')\n\n\n self._gd_old_esc=self.modestate.keyActDict['esc']\n self._gd_own_key=get_key(self.modestate.keyActDict,self.showDisp)\n printD('get disp key:',self._gd_own_key)\n\n self._gd_exit=0\n self.modestate.keyActDict['esc']=self._gd_break_callback #FIXME probably going to need some try:finally:\n self.modestate.keyActDict[self._gd_own_key]=lambda:print('already in disp mode!')\n\n def format_str(string,width=9):\n missing=width-len(string)\n return string+' '*missing\n dist1=1\n print(list(self.markDict.keys()))\n while self.keyThread.is_alive() and not self._gd_exit: #FIXME may need a reentrant lock here to deal w/ keyboard control\n pos=self.markDict.values()\n dist=(npsum((arr(self.esp._BgetPos()-arr(list(pos))))**2,axis=1))**.5 #FIXME the problem here is w/ _BgetPos()\n if dist1!=dist[0]:\n #names='%s \\r'%(list(self.markDict.keys())) #FIXME sadly, it seems there is no way ;_;\n #stdout.write('\\r'+''.join(map('{:1.5f} '.format,dist)))\n stdout.write('\\r'+''.join([format_str('{:1.5f}'.format(d)) for d in dist]))\n stdout.flush()\n dist1=dist[0]\n\n #try:\n #key=self.charBuffer.get_nowait()\n ##FIXME this should go AFTER the other bit to allow proper mode changing\n #if key=='q' or key=='esc': #this should be dealt with through the key handler...\n #print('\\nleaving displacement mode')\n #break\n #elif key: #FIXME HOW DOES THIS EVEN WORK!?!??!\n ##printD('breaking shit')\n #try:\n #func=self.modestate.keyActDict[key] #FIXME WOW BAD\n #printD(func.__name__)\n #if func.__name__ !='getDisp': #FIXME this isnt catching it because lambda\n #from threading import Thread\n #a=Thread(target=func)\n #a.start()\n #except:\n #printD('should have keyHandled it')\n #except:\n #pass\n printD('done! loops broken!')\n return self\n showDisp.keyRequester=True\n\n def setSpeedDef(self):\n #self.doneCB()\n self.esp.setSpeedDefaults()\n return self\n\n def cleanup(self):\n super().cleanup()\n self.esp.cleanup()\n return self\n\n def setMoveDict(self,moveDict={'w':'up','s':'down','a':'left','d':'right'}):\n self.moveDict=moveDict\n return self\n\n def move(self): #FIXME this does not work in displacement mode because modestate.key is always i\n key=self.modestate.key\n #printD(key)\n if key.isupper(): #shit, this never triggers :(\n #printD('upper')\n self.esp.move(self.moveDict[key.lower()],.05) #FIXME\n else:\n #printD('lower')\n self.esp.move(self.moveDict[key.lower()],.1)\n return self\n\n def printError(self):\n print('Error:',self.esp.getErr().decode('utf-8'))\n return self\n def readProgram(self):\n #self.keyHandler(1)\n #num=self.charBuffer.get()\n print(self.esp.readProgram())\n return self\n\n\n\n@datafile_maker\nclass guiFuncs(kCtrlObj): #FIXME GOD DAMN IT now hardware interfaces and datafile makers are not sufficient because this gui could interface with a ton of stuff >_<\n @new_DataFile('jpg') #TODO integrate this with the device and stick it in a dedicated paths config file, could look it up by program but...\n def getCameraImage(self): #XXX: getSub_getCameraImage via df maker\n takeScreenCap()\n sleep(1) #ICK some day a callback FIXME\n\n\n\nclass keyFuncs(kCtrlObj):\n \"\"\"utility functions eg meta functions such as : cmd and the like\"\"\"\n def __init__(self, modestate):\n super().__init__(modestate)\n self.modestate=modestate\n def help(self):\n self.modestate.updateModeDict()\n tdb.on()\n printFD(self.modestate.helpDict)\n tdb.off()\n #self.doneCB()\n return self\n def esc(self):\n return 0\n\n\n@hardware_interface('keyboard')\nclass trmFuncs(kCtrlObj): #FIXME THIS NEEDS TO BE IN THE SAME THREAD\n def __init__(self, modestate):\n self._keyThread=modestate.keyThread\n self.keyLock=modestate.keyLock\n self.modestate=modestate #need this to det the current modestate\n super().__init__(modestate)\n #def printwrap(func):\n #def wrap(*args,**kwargs):\n #out=func()\n #printD(out)\n #return out\n #wrap.__name__=func.__name__\n #return wrap\n #for name in self.ctrl.__dir__():\n #if name[:3]=='get':\n #setattr(self,name,printwrap(getattr(self.ctrl,name)))\n #for name in self.__dir__():\n #if name[:3]=='get':\n #setattr(self,name,printwrap(getattr(self,name)))\n #setattr(getattr(self,name),'__name__',name)\n\n @is_mds('u','m','BX51WI',0)\n @keyRequest\n def getDistance_um(self):\n \"\"\" get a distance in um in this case read from the olympus bx51wi wheel \"\"\"\n value=self.getFloat('distance um> ')\n return value\n\n @keyRequest\n def getKbdHit(self,prompt='Hit any key to advance.'):\n print(prompt)\n self.charBuffer.get()\n return True\n\n def getDoneNB(self): #FIXME \n print('Hit space when you are done') #FIXME\n try:\n old_func=self.modestate.keyActDict[' ']\n except:\n old_func=None\n\n self._gdnb_cb_done=False\n def callback():\n self._gdnb_cb_done=True\n\n self.modestate.keyActDict[' ']=callback\n while not self._gdnb_cb_done and self.keyThread.is_alive(): \n sleep(.001) #FIXME\n printD('got it')\n \n if old_func:\n self.modestate.keyActDict[' ']=old_func #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(' ')\n return True\n \n def getDoneFailNB(self): #FIXME \n print('Hit space for success or \\ for failure') #FIXME use a reverse dict in keybinds\n #FIXME ick\n success=' '\n fail='\\\\'\n try:\n old_func=self.modestate.keyActDict[success]\n except:\n old_func=None\n try:\n old_fail=self.modestate.keyActDict[fail]\n except:\n old_fail=None\n\n self._gdnb_cb=0\n def callback():\n self._gdnb_cb=1 #'done'\n def fail_cb():\n self._gdnb_cb=2 #'fail'\n\n self.modestate.keyActDict[success]=callback\n self.modestate.keyActDict[fail]=fail_cb\n while not self._gdnb_cb and self.keyThread.is_alive():\n sleep(.001) #FIXME\n\n if self._gdnb_cb == 1:\n out=True\n printD('got success')\n else:\n out=False\n printD('got fail')\n \n if old_func:\n self.modestate.keyActDict[success]=old_func #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(success)\n if old_fail:\n self.modestate.keyActDict[fail]=old_fail #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(fail)\n return out\n\n def _getDoneFailNB(self): #FIXME \n print('Hit space for success or \\ for failure') #FIXME use a reverse dict in keybinds\n #FIXME ick\n succss=' '\n fail='\\\\'\n try:\n old_func=self.modestate.keyActDict[success]\n except:\n old_func=None\n try:\n old_fail=self.modestate.keyActDict[fail]\n except:\n old_fail=None\n\n self._gdnb_cb_done=False\n self._gdnb_cb_fail=False\n def callback():\n self._gdnb_cb_done=True\n def fail_cb():\n self._gdnb_cb_fail=True\n\n self.modestate.keyActDict[success]=callback\n while not self._gdnb_cb_done or not self._gdnb_cb_fail:\n sleep(.001) #FIXME\n printD('got it')\n\n if self._gdnb_cb_fail:\n out=False\n if self._gdnb_cb_done:\n out=True\n \n if old_func:\n self.modestate.keyActDict[success]=old_func #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(success)\n if old_fail:\n self.modestate.keyActDict[fail]=old_fail #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(fail)\n return out\n\n\n @keyRequest\n def command(self): #TODO\n \"\"\" vim : mode \"\"\"\n #TODO figure out where to move the command dict...\n #cmdDict={'ipython':'ipython'} #can't put ipython here due to race conditions\n cmdDict={}\n def parse_command(com_str): #TODO\n if com_str=='\\n':\n return None\n def match_key(com_str):\n printD(com_str)\n matches = [key for key in cmdDict.keys() if key[:len(com_str)]==com_str]\n matches.sort()\n return matches\n matches=match_key(com_str)\n printD(matches)\n try:\n if matches:\n func=getattr(self,matches[0],lambda:None)\n else:\n return None\n except:\n print('[!] no function found')\n return None\n func()\n #printD(com_str,'TODO FIXME')\n return com_str\n print('command opened')\n com_str=self.__getChars__(':')\n return parse_command(com_str)\n\n def ipython(self):\n #TODO how to deal with imports...\n try:\n self.keyLock.acquire() #XXX lock acquire so ipython can have input priority\n #embed()\n printD('Not Implemented here due to threading and needing to hit one extra key to break the lock in keyListener')\n finally:\n self.keyLock.release() #XXX lock release\n\n def test(self):\n print('testing testing 123')\n\ndef main():\n #esp=espFuncs()\n #clx=clxFuncs()\n #mcc=mccFuncs(None,None,None,None)\n pass\n\n__all__=(\n 'clxFuncs',\n 'mccFuncs',\n 'espFuncs',\n 'keyFuncs',\n 'trmFuncs',\n 'datFuncs',\n 'guiFuncs',\n)\nif __name__=='__main__':\n main()\n","repo_name":"tgbugs/mlab","sub_path":"rig/functions_seg.py","file_name":"functions_seg.py","file_ext":"py","file_size_in_byte":53699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29259095761","text":"'''\n494. 目标和\n给你一个整数数组 nums 和一个整数 target 。\n\n向数组中的每个整数前添加 '+' 或 '-' ，然后串联起所有整数，可以构造一个 表达式 ：\n\n例如，nums = [2, 1] ，可以在 2 之前添加 '+' ，在 1 之前添加 '-' ，然后串联起来得到表达式 \"+2-1\" 。\n返回可以通过上述方法构造的、运算结果等于 target 的不同 表达式 的数目。\n\n\n\n示例 1：\n\n输入：nums = [1,1,1,1,1], target = 3\n输出：5\n解释：一共有 5 种方法让最终目标和为 3 。\n-1 + 1 + 1 + 1 + 1 = 3\n+1 - 1 + 1 + 1 + 1 = 3\n+1 + 1 - 1 + 1 + 1 = 3\n+1 + 1 + 1 - 1 + 1 = 3\n+1 + 1 + 1 + 1 - 1 = 3\n示例 2：\n\n输入：nums = [1], target = 1\n输出：1\n\n\n提示：\n\n1 <= nums.length <= 20\n0 <= nums[i] <= 1000\n0 <= sum(nums[i]) <= 1000\n-1000 <= target <= 100\n'''\n\n\nclass Solution:\n def findTargetSumWays(self, nums, target: int) -> int:\n '''\n DFS全排列，搜索目标值，暴力解法O(2^n)，大用例会超时\n 考虑记忆化，减少重复运算 -> 类似场景都可以考虑DP\n '''\n self.res = 0\n # self.res_path = set()\n # self.visited = [0] * 10000\n\n def dfs(sum_in, i):\n print(sum_in, i)\n if i >= len(nums):\n if sum_in == target:\n self.res += 1\n return\n\n for sign in [-1, 1]:\n sum_in += (sign * nums[i])\n dfs(sum_in, i+1)\n sum_in -= (sign * nums[i])\n\n dfs(0, 0)\n\n return self.res\n\n def findTargetSumWays_dp(self, nums, S: int) -> int:\n '''设取＋的集合之和为x，取-的集合之和为y\n x+y = sum(nums)\n x-y = S\n 求得'x = (S+sum(nums))/2'\n 转换为标准01背包问题\n 目标值: 'x = (S+sum(nums))/2'\n dp[i][j]: i个数的组合能到j的方法数\n '''\n # 若目标大于集合总和返回0\n # 用例[1,2,7,9,981] 1000000000\n if S > sum(nums):\n return 0\n\n # 若为奇数返回0\n if (S+sum(nums)) % 2 == 1:\n return 0\n\n x = (S+sum(nums))//2\n\n dp = [0 for _ in range(x+1)]\n # base case，空集合有一种方法满足目标为0\n dp[0] = 1\n\n for i in range(1, len(nums)+1):\n for j in range(x, nums[i-1]-1, -1):\n # 状态转移方程：放->dp[j-cost_i]种方法+不放->dp[j]种方法\n dp[j] = dp[j] + dp[j - nums[i-1]]\n\n return dp[-1]\n# 示例 1：\n\n\n# 输入：\nnums = [1, 1, 1, 1, 1]\ntarget = 3\n# 输出：5\n# 解释：一共有 5 种方法让最终目标和为 3 。\n# -1 + 1 + 1 + 1 + 1 = 3\n# +1 - 1 + 1 + 1 + 1 = 3\n# +1 + 1 - 1 + 1 + 1 = 3\n# +1 + 1 + 1 - 1 + 1 = 3\n# +1 + 1 + 1 + 1 - 1 = 3\n# 示例 2：\nprint(Solution().findTargetSumWays(nums, target))\n\n# 输入：\nnums = [1]\ntarget = 1\n# 输出：1\nprint(Solution().findTargetSumWays(nums, target))\n","repo_name":"moshlwx/leetcode","sub_path":"CODE/494. 目标和.py","file_name":"494. 目标和.py","file_ext":"py","file_size_in_byte":2947,"program_lang":"python","lang":"zh","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"1858026608","text":"import pyrogue as pr\nimport rogue.interfaces.memory as rim\nimport threading\nfrom collections import OrderedDict as odict\n\n\nclass MemoryDevice(pr.Device):\n def __init__(self, *,\n name=None,\n description='',\n memBase=None,\n offset=0,\n size=0,\n hidden=False,\n expand=True,\n enabled=True,\n base=pr.UInt,\n wordBitSize=32,\n stride=4,\n verify=True):\n \n super().__init__(\n name=name,\n description=description,\n memBase=memBase,\n offset=offset,\n size=size,\n hidden=hidden,\n expand=expand,\n enabled=enabled,\n )\n\n self._lockCnt = 0\n self._base = base\n self._wordBitSize = wordBitSize\n self._stride = stride\n self._verify = verify\n\n self._setValues = odict()\n self._wrValues = odict() # parsed from yaml\n self._verValues = odict()\n self._wrData = odict() # byte arrays written\n self._verData = odict() # verify data wread back\n\n\n def _buildBlocks(self):\n pass\n\n def _setDict(self, d, writeEach, modes):\n # Parse comma separated values at each offset (key) in d\n with self._memLock:\n for offset, values in d.items():\n self._setValues[offset] = [self._base.fromString(s, self._wordBitSize) for s in values.split(',')]\n\n def _getDict(self,modes):\n return None\n\n def writeBlocks(self, force=False, recurse=True, variable=None, checkEach=False):\n if not self.enable.get(): return\n\n with self._memLock:\n self._wrValues = self._setValues\n \n for offset, values in self._setValues.items():\n wdata = self._rawTxnChunker(offset, values, self._base, self._stride, self._wordBitSize, rim.Write)\n if self._verify:\n self._wrData[offset] = wdata\n\n # clear out wrValues when done\n self._setValues = odict()\n \n\n def verifyBlocks(self, recurse=True, variable=None, checkEach=False):\n if not self.enable.get(): return\n\n with self._memLock:\n for offset, ba in self._wrData.items():\n self._verData[offset] = bytearray(len(ba))\n self._rawTxnChunker(offset, self._verData[offset], txnType=rim.Verify)\n\n self._wrData = odict()\n self._verValues = self._wrValues\n\n def checkBlocks(self, recurse=True, variable=None):\n with self._memLock:\n # Wait for all txns to complete\n self._waitTransaction(0)\n\n # Error check?\n error = self._getError()\n self._setError(0)\n\n # Convert the read verfiy data back to the natic type\n checkValues = odict()\n #print(self._verData.items())\n for offset, ba in self._verData.items():\n checkValues[offset] = [self._base.mask(self._base.fromBytes(ba[i:i+self._stride]), self._wordBitSize)\n for i in range(0, len(ba), self._stride)]\n\n # Do verify if necessary\n if len(self._verValues) > 0:\n # Compare wrData with verData\n if self._verValues != checkValues:\n msg = 'Verify error \\n'\n msg += f'Expected: \\n {self._verValues} \\n'\n msg += f'Got: \\n {checkValues}'\n print(msg)\n raise MemoryError(name=self.name, address=self.address, error=rim.VerifyError, msg=msg, size=self._size)\n\n\n # destroy the txn maps when done with verify\n self._verValues = odict()\n self._verData = odict()\n\n\n def readBlocks(self, recurse=True, variable=None, checkEach=False):\n pass\n\n","repo_name":"codacy-badger/rogue","sub_path":"python/pyrogue/_Memory.py","file_name":"_Memory.py","file_ext":"py","file_size_in_byte":3938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"12249321839","text":"from app.schemas import Build, GetTasksResponse\n\nfrom app.utils import get_order_tasks\nfrom app.errors import OperationError\nfrom redis_storage.connector import RedisConnector\nfrom config import SETTINGS\n\n\nclass Managers:\n @staticmethod\n def get_order_tasks(\n build: Build,\n connector: RedisConnector,\n ) -> GetTasksResponse:\n try:\n ordered_tasks: list[str] = get_order_tasks(\n build=build,\n connector=connector,\n )\n\n status = SETTINGS.STATUS_OK\n msg = {\"ordered_tasks\": ordered_tasks}\n except OperationError as error:\n status = error.status\n msg = error.msg\n\n response = {\"status\": status, \"msg\": msg}\n\n return GetTasksResponse(**response)\n","repo_name":"anonimneyshiy/TestTask","sub_path":"app/managers.py","file_name":"managers.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5745910671","text":"import codecs\n\nfor file_name in ['D_MEGA.HTM']:\n source_file = 'raw/{}'.format(file_name)\n dest_file = 'normalized/{}'.format(file_name)\n\n with codecs.open(source_file, \"r\",encoding='utf-8', errors='ignore') as fdata:\n html = fdata.read()\n\n with open(dest_file, 'w') as outfile:\n outfile.write(html)\n","repo_name":"samukasmk/bora_falar_de_python","sub_path":"2018-02-08/megasena/data/normalize.py","file_name":"normalize.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"21588184000","text":"#\n\n\n#\nimport numpy\nimport pandas\nfrom sklearn.metrics import matthews_corrcoef, confusion_matrix\n\n\n#\n\n\n#\ndef cm_adopted(data, clusters, categories):\n agg = pandas.crosstab(index=data[clusters], columns=data[categories])\n rates = agg / agg.sum(axis=0)\n\n clusters_re = {}\n categories_re = {}\n n = 0\n while rates.shape[0] > 0:\n n += 1\n ix_mx_clusters = numpy.where(rates.values == rates.values.max())[0][0]\n ix_mx_categories = numpy.where(rates.values == rates.values.max())[1][0]\n mx_cluster = rates.index[ix_mx_clusters]\n mx_category = rates.columns[ix_mx_categories]\n clusters_re[mx_cluster] = n\n categories_re[mx_category] = n\n\n rates = rates.drop(index=mx_cluster, columns=mx_category)\n\n def convert_clusters(x):\n return clusters_re[x]\n\n def convert_categories(x):\n return categories_re[x]\n\n data[clusters + '_RE'] = data[clusters].apply(func=convert_clusters)\n data[categories + '_RE'] = data[categories].apply(func=convert_categories)\n\n return data[clusters + '_RE'], data[categories + '_RE']\n\n\n# by https://en.wikipedia.org/wiki/Phi_coefficient\ndef mcc_final(c):\n ra = range(c.shape[0])\n num = sum([sum([sum([c[k, k] * c[l, m] - c[k, l] * c[m, k] for m in ra]) for l in ra]) for k in ra])\n den_p1 = sum([sum([c[k, l] for l in ra]) * sum([sum([c[k_, l_] for l_ in ra]) for k_ in ra if k_ != k]) for k in ra]) ** 0.5\n den_p2 = sum([sum([c[l, k] for l in ra]) * sum([sum([c[l_, k_] for l_ in ra]) for k_ in ra if k_ != k]) for k in ra]) ** 0.5\n return num / (den_p1 * den_p2)\n\n\ndef mcc_adopted(y_pred, y_true):\n data = pandas.DataFrame(data={'y_pred': y_pred, 'y_true': y_true})\n y_pred_new, y_true_new = cm_adopted(data=data, clusters='y_pred', categories='y_true')\n score = matthews_corrcoef(y_true=y_true_new, y_pred=y_pred_new)\n return score\n\n\na = [2, 1, 2, 2, 2]\nb = [1, 2, 2, 1, 1]\ndata = pandas.DataFrame(data={'a': a, 'b': b})\n\nr1 = mcc_final(confusion_matrix(y_true=a, y_pred=b))\nr2 = matthews_corrcoef(y_true=a, y_pred=b)\n\nr = cm_adopted(data=data.copy(), clusters='a', categories='b')\n\n","repo_name":"edazizovv/shock_pods","sub_path":"testing/mcc_adopt.py","file_name":"mcc_adopt.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27180938955","text":"import pygame\nimport random\nimport time\n\npygame.init()\npygame.display.set_caption(\"Inchaders\")\nScreenWidth = 800\nScreenHeight = 800\nscreen = pygame.display.set_mode((ScreenWidth,ScreenHeight))\nclock = pygame.time.Clock()\ngreen = (0, 255,0)\nblu = (0, 0, 255)\nplayer = pygame.image.load('I CAN1.gif')\n\n\ndoExit = False\nxpos = 0\nypos = 700\nVx = 0\n#constants\nLEFT = 0\nRIGHT = 1\nSPACE = 2\nkeys = [False, False, False]\ntimer = 0\nlives = 3\n\nmisslist = list()\njustShot = 0\n\n\nclass Wall:\n def __init__(self, xpos, ypos):\n self.xpos = xpos\n self.ypos = ypos\n self.numHits = 0\n def draw(self):\n if self.numHits == 0:\n pygame.draw.rect(screen, (250, 250, 20), (self.xpos, self.ypos, 30, 30))\n if self.numHits == 1:\n pygame.draw.rect(screen, (150, 150, 10), (self.xpos, self.ypos, 30, 30))\n if self.numHits == 2:\n pygame.draw.rect(screen, (50, 50, 0), (self.xpos, self.ypos, 30, 30))\n \n def collide(self, BulletX, BulletY):\n if self.numHits < 3:\n if BulletX > self.xpos:\n if BulletX < self.xpos + 30:\n if BulletY < self.ypos + 30:\n if BulletY > self.ypos:\n print(\"hit!\")\n self.numHits += 1\n return False\n return True\n \n\nwalls = []\nfor k in range(4):\n for i in range(2):\n for j in range(3):\n walls.append(Wall(j*30+200 * k + 50, i * 30 + 600))\n\nclass Alien:\n def __init__(self, xpos, ypos):\n self.xpos = xpos\n self.ypos = ypos\n self.isAlive = True\n self.direction = 1\n def draw(self):\n if self.isAlive == True:\n pygame.draw.rect(screen, (250, 250, 250), (self.xpos, self.ypos, 40, 40))\n \n def move(self, time):\n \n #reset what direction you're moving every 8 moves:\n if time % 800 == 0:\n self.ypos += 100\n self.direction *= -1\n return 0\n \n if timer%100 == 0:\n self.xpos += 50 * self.direction\n \n return time\n \n def collide(self, BulletX, BulletY):\n if self.isAlive:\n if BulletX > self.xpos:\n if BulletX < self.xpos + 50:\n if BulletY < self.ypos + 20:\n if BulletY > self.ypos:\n print(\"hit!\")\n self.isAlive = False\n return False\n return True\n\narmada = []\nfor i in range (4):\n for j in range(12):\n armada.append(Alien(j*60 + 70, i *50 + 70))\nclass Missile:\n def __init__(self):\n self.xpos = -10\n self.ypos = -10\n self.isAlive = False\n \n \n\nclass Bullet:\n def __init__(self, xpos, ypos):\n self.xpos = xpos\n self.ypos = ypos\n self.isAlive = False\n \n def move(self, xpos, ypos):\n if self.isAlive == True:\n self.ypos -=5\n if self.ypos < 0:\n self.isAlive = False\n self.xpos = xpos\n self.ypos = ypos\n \n def draw(self):\n if self.isAlive == True:\n pygame.draw.rect(screen, (250, 250, 250), (self.xpos, self.ypos, 3, 20))\n#instantiate bullet object\nbullet = Bullet(xpos+28, ypos)\n\nclass Missile:\n def __init__(self):\n self.xpos = -10\n self.ypos = -10\n self.isAlive = False\n def move(self):\n if self.isAlive == True:\n self.ypos +=5\n if self.ypos > 800:\n self.isAlive = False\n self.xpos = xpos\n self.ypos = ypos\n def draw(self):\n if self.isAlive == True:\n pygame.draw.rect(screen, (250, 250, 250), (self.xpos, self.ypos, 2, 10))\n\nmisslies = []\n\nfor i in range(10):\n misslies.append(Missile())\n\n \n\n\nwhile lives > 0:\n clock.tick(60)\n timer += 1;\n \n for event in pygame.event.get(): #quit game if x is pressed in top corner\n if event.type == pygame.QUIT:\n gameover = True\n \n if event.type == pygame.KEYDOWN: #keyboard input\n if event.key == pygame.K_LEFT:\n keys[LEFT]=True\n elif event.key == pygame.K_RIGHT:\n keys[RIGHT]=True\n if event.key == pygame.K_SPACE:\n keys[SPACE] = True\n\n \n if event.type == pygame.KEYUP: #keyboard input\n if event.key == pygame.K_LEFT:\n keys[LEFT]=False\n elif event.key == pygame.K_RIGHT:\n keys[RIGHT]=False\n if event.key == pygame.K_SPACE:\n keys[SPACE] = False\n \n if keys[SPACE] == True:\n bullet.isAlive = True\n \n if bullet.isAlive == True:\n bullet.move(xpos+28, ypos)\n if bullet.isAlive == True:\n #alien collision\n for i in range(len(armada)):\n bullet.isAlive = armada[i].collide(bullet.xpos, bullet.ypos)\n if bullet.isAlive == False:\n break\n if bullet.isAlive == True:\n for i in range(len(walls)):\n bullet.isAlive = walls[i].collide(bullet.xpos, bullet.ypos)\n if bullet.isAlive == False:\n break\n \n \n \n \n \n else:\n bullet.xpos = xpos+28\n bullet.ypos = ypos\n \n \n for i in range(len(walls)):\n for j in range(len(misslies)):\n if misslies[j].isAlive == True:\n if walls[i].collide(misslies[j].xpos, misslies[j].ypos) == False:\n misslies[j].isAlive = False\n break\n \n buffa = random.randrange(100)\n if buffa < 2:\n armbruh = random.randrange(len(armada))\n if armada[armbruh].isAlive == True:\n for i in range(len(misslies)):\n if misslies[i].isAlive == False:\n misslies[i].isAlive = True\n misslies[i].xpos = armada[i].xpos + 5\n misslies[i].ypos = armada[i].ypos\n break\n \n #player collision\n for i in range(len(misslies)):\n if misslies[i].isAlive:\n if misslies[i].xpos > xpos:\n if misslies[i].xpos < xpos + 40:\n if misslies[i].ypos < ypos + 40:\n if misslies[i].ypos > ypos:\n lives -= 1\n time.sleep(1)\n xpos = 0\n \n for i in range(len(armada)):\n if armada[i].ypos > ypos:\n lives -= 3\n print(\"Bruh how did they get this low\")\n time.sleep(1)\n break\n \n \n \n \n #left movement \n if keys[LEFT]==True:\n Vx = -5\n direction = LEFT\n \n #Right Movement\n elif keys[RIGHT]==True:\n Vx = 5\n direction = RIGHT\n \n else:\n Vx = 0\n #updating player\n xpos += Vx\n \n for i in range(len(armada)):\n armada[i].move(timer)\n for i in range(len(misslies)):\n misslies[i].move()\n\n \n screen.fill((0,0,0))\n \n \n my_font = pygame.font.SysFont('Comic Sans MS', 30)\n text_surface = my_font.render('LIVES:', False, (255, 0, 0))\n number = my_font.render(str(lives), False, (255,0,0))\n \n \n screen.blit(player, (xpos, ypos))\n for i in range(len(armada)):\n armada[i].draw()\n for i in range(len(walls)):\n walls[i].draw()\n bullet.draw()\n for i in range(len(misslies)):\n misslies[i].draw()\n \n screen.blit(text_surface, (0,0))\n screen.blit(number, (100,0))\n \n \n pygame.display.flip()\n \nprint(\"RIP\")\n\npygame.quit()\n","repo_name":"Shadowtheepic/I-M-GONNA-MAKE-ALL-OF-FORTNITE-irl","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39168635955","text":"\"\"\"holosongranker URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/4.0/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom django.conf.urls.i18n import i18n_patterns\nfrom django.views.generic.base import RedirectView\nfrom django.contrib.staticfiles.storage import staticfiles_storage\nfrom . import homepage\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path(\n \"ads.txt\",\n RedirectView.as_view(url=staticfiles_storage.url(\"ads.txt\")),\n ),\n]\n\nurlpatterns += i18n_patterns(\n path('', homepage.Index, name='homepage'),\n path('about', homepage.About, name='about'),\n path('songs/', include('songs.urls', namespace='songs')),\n path('singers/', include('singers.urls', namespace='singers')),\n)\n\nhandler404 = 'holosongranker.error_handler.view_404'\n\n# if settings.DEBUG:\n# import debug_toolbar\n# DEBUG_TOOLBAR_CONFIG = {\n# 'INTERCEPT_REDIRECTS': False,\n# }\n# urlpatterns += [\n# path('__debug__/', include(debug_toolbar.urls)),\n# ]\n","repo_name":"gift-li/holosongranker","sub_path":"holosongranker/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"2550806519","text":"# -*- coding: utf-8 -*-\n# purchasesDemo4\n\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier\n\n# 使用 Pandas 读取数据\ndf = pd.read_excel(\"user_fit.xlsx\", header=0)\n\n# 特征\nx = df.iloc[:, 0:8]\n\n# 目标\ny = df['用户是否为会员'.decode('utf-8')]\n\n# 安装 3:7 切分验证集和训练集\ntrain_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.3)\n\n# 构建神经网络分类器\n\nmodel_MLPClassifier= MLPClassifier(activation='logistic',max_iter=50,hidden_layer_sizes=(50,50,50))\n\n# 使用训练集训练模型\nmodel_MLPClassifier.fit(train_x, train_y)\n\n# 使用验证集评估准确度\nscore_trainset = model_MLPClassifier.score(train_x, train_y)*100\nscore_testset = model_MLPClassifier.score(test_x, test_y)*100\n\nprint(\"训练集预测准确率: %.2f%%\" % score_trainset)\nprint(\"测试集预测准确率: %.2f%%\" % score_testset)","repo_name":"sunshinelu/pythonDemo","sub_path":"shiyanlou/PythonDataAnalysisAndAdvanced/Purchases/purchasesDemo4.py","file_name":"purchasesDemo4.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37055323705","text":"from Predictions import *\r\n\r\nReactor_Shutdown = Linear_Combo()\r\nReactor_Shutdown.Wavelets += [Hubbert( x0=2035.000, s0=0.30586, s1=0.46300, peak=31.700)]\r\nReactor_Shutdown.Wavelets += [Sigmoid( x0=2053.000, s0=0.64954, left=0.000, right=10.000)]\r\nReactor_Shutdown.Wavelets += [Hubbert( x0=2050.000, s0=0.33832, s1=0.34093, peak=5.469)]\r\n\r\n# Needed for phenomenological model later\r\n_Nuclear_Functions = Linear_Combo()\r\n_Nuclear_Functions.Wavelets += [Weibull( 1968, .018, 2.2, 40000)]\r\n_Nuclear_Functions.Wavelets += [Hubbert( 2013, 0.3, 0.3, -100)]\r\n_Nuclear_Functions.Wavelets += [Hubbert( 2028, 0.4, 0.29, 70)]\r\n_Nuclear_Functions.Wavelets += [Hubbert( 2038, 0.4, 0.4, -70)]\r\n_Nuclear_Functions.Wavelets += [Hubbert( 2052, 0.4, 0.4, 40)]\r\n_Nuclear_Functions.Wavelets += [Sigmoid( 2063, .08, 0, 1550)]\r\n\r\n_Nuclear_Functions_2 = Linear_Combo()\r\n_Nuclear_Functions_2.Wavelets += [Weibull( 1968, .018, 2.2, 40000)]\r\n_Nuclear_Functions_2.Wavelets += [Hubbert( 2013, 0.3, 0.3, -100)]\r\n_Nuclear_Functions_2.Wavelets += [Hubbert( 2028, 0.4, 0.29, 70)]\r\n_Nuclear_Functions_2.Wavelets += [Hubbert( 2038, 0.4, 0.4, -70)]\r\n_Nuclear_Functions_2.Wavelets += [Hubbert( 2052, 0.4, 0.4, 40)]\r\n\r\nyears = np.linspace(1800, 2300, 501)\r\nMW_connected = np.zeros( len(years))\r\nf_start = open(\"./Data/Nuclear_Reactor_Commissioning.txt\")\r\nfor i in range(1000):\r\n s = f_start.readline()\r\n if len(s) <= 0: break\r\n ss = s.split(\"MW(e)\")\r\n n = int(s[0:4])\r\n ss1 = ss[0].split('(')\r\n w = float( ss1[-1])\r\n MW_connected[n-1800] += w/1000\r\nf_start.close()\r\nprd = 10\r\ngrowth = 1.02\r\nfor i in range(219,len(years)):\r\n MW_connected[i] += prd\r\n prd *= growth\r\n\r\nMW_disconnected = np.zeros( len(years))\r\nf_end = open(\"./Data/Nuclear_Reactor_Shutdown.txt\")\r\nfor i in range(1000):\r\n s = f_end.readline()\r\n if len(s) <= 0: break\r\n ss = s.split(\"MW(e)\")\r\n n = int(s[0:4])\r\n ss1 = ss[0].split('(')\r\n w = float( ss1[-1])\r\n MW_disconnected[n-1800] += w/1000\r\nf_end.close()\r\nfor i in range(219,270):\r\n MW_disconnected[i] += Reactor_Shutdown.Compute(years[i])\r\nfor i in range(270,len(years)):\r\n MW_disconnected[i] += MW_connected[i-50]\r\n\r\nw = 0\r\nMW_operating = np.zeros( len(years))\r\nfor i in range( len(years)):\r\n w += MW_connected[i]\r\n w -= MW_disconnected[i]\r\n if w < 0:\r\n w = 0\r\n MW_disconnected[i] = 0\r\n MW_operating[i] = w\r\n #print( \"{:g}\\t{:.0f}\\t{:.0f}\\t{:.0f}\".format(years[i],\r\n # MW_connected[i],MW_disconnected[i],w/1000))\r\n\r\nProduction = MW_operating * 0.77\r\nPop = Population().UN_Medium.GetVector( years)\r\n\r\ny, nuc = Load_Calibration(\"../Global Data/Energy_Calibration.csv\", [\"Year\", \"Nuclear\"])\r\nnuc *= 4419.2 / 24 / 365.25\r\nfor i in range(len(y)):\r\n j = int(y[i]-years[0])\r\n if j<0: continue\r\n Production[j] = nuc[i]\r\n\r\nx_start, x_end = 1950, 2150\r\n\r\nfig = plt.figure( figsize=(15,10))\r\nfig.suptitle( 'Гражданские генерирующие мощности ядерной энергетики', fontsize=22)\r\ngs = plt.GridSpec(2, 1, height_ratios=[2,1]) \r\nax1 = plt.subplot(gs[0])\r\nax2 = plt.subplot(gs[1])\r\n\r\nax1.set_title(\"Ввод и вывод мощностей\")\r\nax1.bar( years, MW_connected*10, 1, alpha=0.4, color=\"g\", label=\"Ввод (x10)\")\r\nax1.bar( years, -MW_disconnected*10, 1, alpha=0.4, color=\"r\", label=\"Вывод (x10)\")\r\nax1.bar( years[:219], MW_connected[:219]*10, 1, alpha=1, color=\"g\")\r\nax1.bar( years[:219], -MW_disconnected[:219]*10, 1, alpha=1, color=\"r\")\r\nax1.plot( years, MW_operating, \"-\", color=\"k\", label=\"Рабочих мощностей\")\r\nax1.errorbar( y, nuc, yerr=nuc*0.03, fmt=\".\", color=\"m\", label=\"Выработка АЭС по данным ВР\")\r\nax1.plot( years, Production, \"--\", color=\"m\", label=\"Выработка АЭС (модель)\")\r\n#ax1.plot( years, _Nuclear_Functions.GetVector(years)*0.39/31.6*42, \"--\", color=\"y\", label=\"Phenomenological Model 1\")\r\n#ax1.plot( years, _Nuclear_Functions_2.GetVector(years)*0.39/31.6*42, \"-.\", color=\"y\", label=\"Phenomenological Model 2\")\r\nax1.set_xlim(x_start, x_end)\r\nax1.set_ylim( -400, 1000)\r\nax1.text( 2030, 50, \"Рост/снижение по {:.1f}% в год\".format((growth-1)*100))\r\nax1.set_ylabel(\"Гигаватт\")\r\nax1.grid(True)\r\nax1.legend(loc=0)\r\n\r\nax2.set_title(\"Мощность на душу населения\")\r\nax2.plot( years, Production*1000/Pop, \"-\", color=\"m\")\r\nax2.set_xlim(x_start, x_end)\r\nax2.set_ylim( 0, 60)\r\nax2.set_xlabel(\"год\")\r\nax2.set_ylabel(\"Ватт(электрич.)\")\r\nax2.grid(True)\r\n\r\nplt.savefig( \"./Graphs/figure_12_11.png\")\r\nif InteractiveModeOn: plt.show(True)\r\n","repo_name":"myak555/LIMITS_TO_LIMITS","sub_path":"Chapter 12/Test_10_Nuclear_Reactors_Projection.py","file_name":"Test_10_Nuclear_Reactors_Projection.py","file_ext":"py","file_size_in_byte":4584,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"19754289952","text":"import os\r\nimport sys\r\nimport cv2\r\nimport PIL.Image\r\n\r\nchars = \" .:*+o0O\"\r\nnewWidth=100\r\n\r\ndef resize(image):\r\n width, height = image.size\r\n ratio = height/width\r\n newHeight = int(newWidth * ratio)\r\n resized_image = image.resize((newWidth, newHeight))\r\n return(resized_image)\r\n\r\ndef grayscale(image):\r\n grayscaleImage = image.convert(\"L\")\r\n return(grayscaleImage)\r\n \r\ndef ascii(image):\r\n pixels = image.getdata()\r\n characters = \"\".join([chars[pixel//25] for pixel in pixels])\r\n return(characters)\r\n\r\ndef framer(image):\r\n newImage = ascii(grayscale(resize(image)))\r\n pixelCount = len(newImage) \r\n asciiImage = \"\\n\".join([newImage[index:(index+newWidth)] for index in range(0, pixelCount, newWidth)])\r\n sys.stdout.write(asciiImage)\r\n os.system('cls' if os.name == 'nt' else 'clear') \r\n\r\ndef main(new_width=100):\r\n run = True\r\n while run:\r\n path = str(input(\"Paste the image path: \"))\r\n try:\r\n video = cv2.VideoCapture(path)\r\n except Exception:\r\n print(path, \" is not a valid pathname to an image.\")\r\n pass\r\n\r\n play = True \r\n while play:\r\n try:\r\n ret, frame = video.read()\r\n cv2.imshow(\"frame\",frame)\r\n framer(PIL.Image.fromarray(frame))\r\n cv2.waitKey(10)\r\n except:\r\n play = False\r\n\r\n redo = str(input(\"Have another file? (y/n): \"))\r\n if redo == 'y' or redo == 'Y':\r\n print(\"______________________________________________________________________________\")\r\n run = True\r\n else:\r\n run = False\r\n \r\nmain()\r\n","repo_name":"theeemanuel/ascii","sub_path":"video rendering in ascii/videoRendering.py","file_name":"videoRendering.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41687877453","text":"'''\nYou are climbing a staircase. It takes n steps to reach the top.\nEach time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top?\n\nn=2\n1+1\n2\n\nn=3\n1+1+1\n2+1\n1+2\n\nn=4\n1+1+1+1\n1+1+2\n2+1+1\n2+2\n\nn=5\n\n1+1+1+1+1\n1+1+1+2\n1+2+2\n2+2+1\n2+1+1+1\n2+1+2\n\nn=6\n1+1+1+1+1+1\n1+1+1+1+2\n1+1+2+2\n2+2+2\n2+1+1+1+1\n2+2+1+1\n1+2+1+1+1\n1+1+2+1+1\n\n\n\n# '''\n# #c = {}\n#\n# def steps(n):\n# c={}\n# if n ==1:\n# return 1\n#\n# if n ==2:\n# return 2\n#\n#\n# elif str(n) in c.keys():\n# return c[str(n)]\n# else:\n# c[str(n)]= steps(n-2)+steps(n-1)\n# return steps(n-2)+steps(n-1)\n#\n# step_count = steps(105)\n#\n# print (step_count)\n\n\nclass Solution:\n def climbStairs(self, n: int) -> int:\n if n == 1:\n return 1\n\n if n == 2:\n return 2\n\n else:\n # c[str(n)]= steps(n-2)+steps(n-1)\n return self.climbStairs(n - 2) + self.climbStairs(n - 1)\n\n\nS = Solution()\nt=S.climbStairs(10)\nprint(t)\n","repo_name":"pauyang/Interview_questions","sub_path":"steps.py","file_name":"steps.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70146171602","text":"from __future__ import print_function, division, absolute_import\n\n# Copyright (c) 2011 Red Hat, Inc.\n#\n# This software is licensed to you under the GNU General Public License,\n# version 2 (GPLv2). There is NO WARRANTY for this software, express or\n# implied, including the implied warranties of MERCHANTABILITY or FITNESS\n# FOR A PARTICULAR PURPOSE. You should have received a copy of GPLv2\n# along with this software; if not, see\n# http://www.gnu.org/licenses/old-licenses/gpl-2.0.txt.\n#\n# Red Hat trademarks are not licensed under GPLv2. No permission is\n# granted to use or replicate Red Hat trademarks that are incorporated\n# in this software or its documentation.\n#\ntry:\n import unittest2 as unittest\nexcept ImportError:\n import unittest\n\nfrom subscription_manager.gui.storage import MappedTreeStore, MappedListStore\n\n\nclass StorageTests(unittest.TestCase):\n\n def test_mapped_list_store(self):\n self._run_test(MappedListStore, lambda data, store: store.add_map(data))\n\n def test_mapped_tree_store(self):\n self._run_test(MappedTreeStore, lambda data, store: store.add_map(None, data))\n\n def _run_test(self, store_class, add_data_to_map_funct):\n expected_c1 = \"C1_VAL\"\n expected_c2 = True\n expected_c3 = \"C3_val\"\n store = self.create_store(store_class)\n data = self.create_data_map(expected_c1, expected_c2, expected_c3)\n add_data_to_map_funct(data, store)\n\n tree_iter = store.get_iter_first()\n self.assertEqual(expected_c1, store.get_value(tree_iter, store['c1']))\n self.assertEqual(expected_c2, store.get_value(tree_iter, store['c2']))\n self.assertEqual(expected_c3, store.get_value(tree_iter, store['c3']))\n\n def create_store(self, store_class):\n type_map = {\n 'c1': str,\n 'c2': bool,\n 'c3': str\n }\n\n return store_class(type_map)\n\n def create_data_map(self, c1_str, c2_bool, c3_str):\n return {\n 'c1': c1_str,\n 'c2': c2_bool,\n 'c3': c3_str\n }\n","repo_name":"Lorquas/rhsm","sub_path":"test/test_storage.py","file_name":"test_storage.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13406188537","text":"\"\"\"User model tests.\"\"\"\n\n# run these tests like:\n#\n# python -m unittest test_user_model.py\n\n\nimport os\nfrom unittest import TestCase\nfrom sqlalchemy.exc import IntegrityError\nfrom models import db, User, Message, Follows\n\n# BEFORE we import our app, let's set an environmental variable\n# to use a different database for tests (we need to do this\n# before we import our app, since that will have already\n# connected to the database\n\nos.environ['DATABASE_URL'] = \"postgresql:///warbler-test\"\n\n\n# Now we can import app\n\nfrom app import app\n\n# Create our tables (we do this here, so we only create the tables\n# once for all tests --- in each test, we'll delete the data\n# and create fresh new clean test data\n\ndb.create_all()\n\n\nclass UserModelTestCase(TestCase):\n \"\"\"Test views for messages.\"\"\"\n\n def setUp(self):\n \"\"\"Create test client, add sample data.\"\"\"\n\n User.query.delete()\n Message.query.delete()\n Follows.query.delete()\n with app.app_context():\n db.session.commit()\n self.u1 = User.signup(\"uniqueusername1\", \"uniqueemail1@email.com\", None)\n self.u2 = User.signup(\"uniqueusername2\", \"uniqueemail2@email.com\", None)\n\n with app.app_context():\n db.session.commit()\n\n self.client = app.test_client()\n\n def tearDown(self):\n db.session.rollback()\n\n def test_repr(self):\n self.u1.id = 1\n\n self.assertEqual(\n repr(self.u1), \"\")\n\n def test_user_model(self):\n \"\"\"Does basic model work?\"\"\"\n\n u = User(\n email=\"test@test.com\",\n username=\"testuser\",\n password=\"HASHED_PASSWORD\"\n )\n\n db.session.add(u)\n db.session.commit()\n\n # User should have no messages & no followers\n self.assertEqual(len(u.messages), 0)\n self.assertEqual(len(u.followers), 0)\n def test_is_following(self):\n \"\"\"Does is_following work as intended?\"\"\"\n\n self.u1.following.append(self.u2)\n self.u2.following.append(self.u3)\n\n self.assertEqual(self.u1.is_following(self.u2), True)\n self.assertEqual(self.u2.is_following(self.u3), True)\n self.assertEqual(self.u1.is_following(self.u3), False)\n\n def test_is_followed_by(self):\n \"\"\"Does is_followed_by work as intended?\"\"\"\n\n self.u2.followers.append(self.u1)\n self.u3.followers.append(self.u2)\n\n self.assertEqual(self.u2.is_followed_by(self.u1), True)\n self.assertEqual(self.u3.is_followed_by(self.u2), True)\n self.assertEqual(self.u3.is_followed_by(self.u1), False)\n\n def test_user_creation(self):\n\n count = User.query.count()\n self.u5 = User.signup(\n \"uniqueusername5\", \"uniqueemail5@email.com\", None)\n db.session.commit()\n\n self.assertEqual(User.query.count(), count+1)\n\n def test_user_creation_fail(self):\n # Duplicate username overlap with self.u1\n\n with self.assertRaises(IntegrityError):\n self.u6 = User.signup(\n \"uniqueusername1\", \"uniqueemail6@email.com\", None)\n db.session.commit()\n\n def test_authenticate(self):\n\n authentication = User.authenticate(self.u2.username)\n\n self.assertTrue(authentication)\n\n\n def test_authenticate_bad_username(self):\n\n authentication = User.authenticate(self.u3.username, \"wrongpass\")\n\n self.assertFalse(authentication)\n\n def test_authenticate_bad_pass(self):\n authentication = User.authenticate(\"wrongusername\")\n\n self.assertFalse(authentication)","repo_name":"ILikeeggs313/Twitter-clone-project","sub_path":"test_user_model.py","file_name":"test_user_model.py","file_ext":"py","file_size_in_byte":3603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23907052765","text":"# -*- coding: utf-8 -*-\nimport time\n\nclass TimeDetector():\n def __init__(self):\n self.times = []\n self.targetWords = ['시', '분', '반', '후', '뒤', ]\n self.timeWords = [\n '퇴근 후', '점심시간', '쉬는시간', '아침', '점심', '저녁', '밤', '새벽',\n '한시', '두시', '세시', '네시', '다섯시', '여섯시', '일곱시', '여덟시', '아홉시', '열시', '열한시', '열두시'\n ]\n\n def getTime(self):\n return self.times\n\n def addTime(self, word):\n if word: self.times.append(word)\n\n def isTimeFormat(self, input):\n try:\n time.strptime(input, '%H:%M')\n return True\n except ValueError:\n return False\n\n def hasNumbers(self, str):\n return any(char.isdigit() for char in str)\n\n # 단어에 포함된 특수문자 제거\n def deleteSpecialCharacters(self, word):\n word = ''.join(char for char in word if char.isalnum())\n return word\n\n # 시간과 관련된 단어 외의 글자들은 삭제\n def getOnlyTimeWord(self, _word):\n word = []\n def slice_list_by_targetWord(targetWord, word):\n idx = word.index(targetWord)\n return word[:idx+1]\n def slice_list_from_targetWord(targetWord, word):\n idx = word.index(targetWord)\n return word[idx:]\n\n #후, 뒤 -> 반 -> 분 -> 시 우선순위대로 자른다.\n targetWords = ['후', '뒤', '반', '분', '시']\n for targetWord in targetWords:\n if targetWord in _word:\n word = slice_list_by_targetWord(targetWord, _word)\n break\n # 첫번째 숫자 이전의 글자들 삭제\n for character in _word:\n if self.hasNumbers(character):\n word = slice_list_from_targetWord(character, _word)\n break\n return word\n\n # target이 포함된 word가 시간과 관련된 단어인지 유효성 검사\n def isValid(self, word, idx):\n if word == '':\n return False\n elif '시' in word or '분' in word:\n return self.hasNumbers(word)\n elif word in '반' or word in '후' or word in '뒤':\n if self.hasNumbers(word) :\n return True\n elif self.words[idx-1] in self.times:\n return True\n else: return False\n return False\n\n def time_detector(self, sentence):\n words = sentence.split(' ')\n\n # sentence 안에 시간과 관련된 단어만 times에 추가.\n for word in words:\n # 정규식 검사\n index = word.index('~') if '~' in word else word.index('-') if '-' in word else 0\n if index == 0:\n if self.isTimeFormat(word):\n self.addTime(word)\n continue\n start, end = word[:index], word[index+1:]\n if self.isTimeFormat(start) and self.isTimeFormat(end):\n self.addTime(start+\"-\"+end)\n continue\n\n #targetWords 단어 검사\n done = False\n for targetWord in self.targetWords:\n if targetWord in word:\n idx = words.index(word)\n if self.isValid(word,idx):\n # _word = self.deleteSpecialCharacters(word)\n _word = self.getOnlyTimeWord(word)\n self.addTime(_word)\n done = True\n if done: continue\n\n #timeWords 단어 검사\n for timeWord in self.timeWords:\n if timeWord in word:\n # _word = self.deleteSpecialCharacters(word)\n _word = self.getOnlyTimeWord(word)\n self.addTime(_word)\n\n return self.times\n\n def disaster_message_time_detector(self, times, message):\n time = ''\n\n # 불필요한 데이터 삭제\n _times = ''\n for _time in times:\n if _times: _times += ' '\n _times += _time\n _times = _times.replace('에서', '')\n _times = _times.replace('까지', '')\n _times = _times.replace('(', '')\n _times = _times.replace(')', '')\n # print(_times)\n\n # 첫번째 데이터가 구간인 경우 해당 데이터만 추출\n split_times = _times.split(' ')\n if '~' in split_times[0]: return split_times[0]\n\n # 첫번째 데이터가 정규표현식 형태의 시간일 경우 해당 데이터만 추출\n if self.isTimeFormat(split_times[0].split('-')[0]):\n return split_times[0]\n\n return time\n\nif __name__ == '__main__':\n timeDetector = TimeDetector()\n message = '[서산시청] 5.27.목 19시~21시까지 서산중앙병원 장례식장 별실 방문자는 6월 1일까지 보건소 선별진료소(09~18시까지)에서 검사 바랍니다.'\t\t\n message = '[서산시청] 5월 27일(목) 19시~21시까지 서산중앙병원 장례식장 별실 방문자는 6월 1일까지 보건소 선별진료소(09~18시까지)에서 검사 바랍니다.'\t\t\n message = '[서산시청] 5.27.목 19:30 서산중앙병원 장례식장 별실 방문자는 6월 1일까지 보건소 선별진료소(09~18시까지)에서 검사 바랍니다.'\t\t\n message = '[서산시청] 5.27.목 19:30~20:30 서산중앙병원 장례식장 별실 방문자는 6월 1일까지 보건소 선별진료소(09~18시까지)에서 검사 바랍니다.'\t\t\n times = timeDetector.time_detector(message)\n # print(times)\n disaster_times = timeDetector.disaster_message_time_detector(times, message)\n print(disaster_times)","repo_name":"gyuree-kim/auto-schedule-updater","sub_path":"server/sms_ner_pkg/sms_ner_pkg/time_detector.py","file_name":"time_detector.py","file_ext":"py","file_size_in_byte":5688,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"6229004917","text":"import io\nimport pathlib\n\nimport sentencepiece as spm\n\n\"\"\" This is just a demonstration code for the Medium article. Uncomment the piece of code you want to use. \"\"\"\n\nCURRENT_PATH = pathlib.Path(__file__).parent\nSPLITTED_ALICE_FILE_PATH = CURRENT_PATH.joinpath('dataset').joinpath('splitted_alice_in_wonderland.txt')\n\n\"\"\" 1) Train from a corpus file. \"\"\"\n\n# # Train from a corpus file\n# spm.SentencePieceTrainer.Train(input=SPLITTED_ALICE_FILE_PATH,\n# model_prefix='sp_alice',\n# vocab_size=1500,\n# pad_id=0, # Let's re-define the special tokens for TensorFlow!\n# unk_id=1,\n# bos_id=2,\n# eos_id=3\n# )\n\n\n\"\"\" 2) Train from a corpus in memory. \"\"\"\n\n# # Pretend we have the sentences in memory\n# with open(SPLITTED_ALICE_FILE_PATH, encoding='utf-8') as infile:\n# corpus = [line for line in infile]\n#\n# spm.SentencePieceTrainer.Train(sentence_iterator=iter(corpus),\n# model_prefix='sp_alice',\n# vocab_size=1500,\n# pad_id=0, # Let's re-define the special tokens for TensorFlow!\n# unk_id=1,\n# bos_id=2,\n# eos_id=3\n# )\n\n\n\"\"\" 3) Leave the model in memory. \"\"\"\n\nmodel = io.BytesIO()\n\n# Pretend we have the sentences in our memory\nwith open(SPLITTED_ALICE_FILE_PATH, encoding='utf-8') as infile:\n corpus = [line for line in infile]\n\nspm.SentencePieceTrainer.Train(sentence_iterator=iter(corpus),\n model_writer=model,\n vocab_size=1500,\n pad_id=0, # Let's re-define the special tokens for TensorFlow!\n unk_id=1,\n bos_id=2,\n eos_id=3\n )\n\n# Optionally, save it\nwith open(CURRENT_PATH.joinpath('sp_alice.model'), 'wb') as outfile:\n outfile.write(model.getvalue())\n\n# Optionally, visualize it\nsp = spm.SentencePieceProcessor(model_proto=model.getvalue())\nencoded_input = sp.Encode(\"'Yes, that's it,' said the Hatter with a sigh: 'it's always tea-time'\")\n\ntokenized_input = [sp.IdToPiece(id) for id in encoded_input]\nprint(*tokenized_input)\n\n","repo_name":"maxtlw/sp_tf2_medium","sub_path":"train_sentencepiece.py","file_name":"train_sentencepiece.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18832392146","text":"import json\nfrom django.views.generic import ListView\nfrom django.views.generic.detail import DetailView\nfrom catalog.models import Product, Gallery\nfrom card.forms import CartAddProductForm\nfrom django.http import JsonResponse\nfrom django.core import serializers\nfrom django.core.paginator import Paginator\nfrom django.db.models import Q\n\n\nclass ProductView(ListView):\n model = Product\n paginate_by = 1\n template_name = \"catalog/product.html\"\n context_object_name = 'product_list'\n queryset = Product.objects.all()\n\n\n\n # def sorting_price_text(self, text):\n # self.queryset = Product.objects.order_by(text)\n # data = {}\n # return JsonResponse(data, safe=False)\n #\n # def sorting_price_number(self, number):\n # self.queryset = Product.objects.filter(price__gt=number-50, price__lte=number)\n # data = {}\n # return JsonResponse(data, safe=False)\n\n # def get_context_data(self, **kwargs):\n # context = super().get_context_data(**kwargs)\n # product = Product.objects.get(id=1)\n # images = product.gallery_set.all()[:1]\n # context['images'] = images\n # return context\n\n\n# def sort(request):\n# print(1)\n# number_sort = request.POST['number']\n# print(number_sort)\n# if number_sort == 1:\n# queryset = Product.objects.order_by('-price')\n# data = serializers.serialize(\"json\", queryset)\n# print(queryset)\n# return JsonResponse(data, safe=False)\n# elif number_sort == -1:\n# queryset = Product.objects.order_by('price')\n# data = serializers.serialize(\"json\", queryset)\n# print(queryset)\n# return JsonResponse(data, safe=False)\n# elif number_sort is None:\n# queryset = Product.objects.all()\n# data = serializers.serialize(\"json\", queryset)\n# return JsonResponse(data, safe=False)\n# else:\n# min = int(number_sort)-50\n# max = int(number_sort)+50\n# queryset = Product.objects.filter(price__gt=min, price__lte=max)\n# data = serializers.serialize(\"json\", queryset)\n# return JsonResponse(data, safe=False)\n\n\nclass ProductDetailView(DetailView):\n model = Product\n pk_url_kwarg = 'product_id'\n template_name = \"catalog/product-detail.html\"\n context_object_name = 'product_detail'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n products = Product.objects.all()[:8]\n context['products'] = products\n context['selector_colors'] = Product.objects.filter(title=self.get_object().title)\n context['cart_product_form'] = CartAddProductForm()\n return context\n\n\nclass FilterProductView(ProductView):\n\n def get_queryset(self):\n data = self.request.GET\n print(data)\n queryset = Product.objects.all()\n if 'price_text_sort' in data:\n queryset = queryset.order_by(self.request.GET['price_text_sort'])\n\n if 'price_interval_sort' in data:\n queryset = queryset.filter(price__gt=int(self.request.GET['price_interval_sort'])-50,\n price__lte=int(self.request.GET['price_interval_sort']))\n\n if 'category_sort' in data:\n queryset = queryset.filter(categories__name__in=[self.request.GET['category_sort'], ])\n\n if 'between_sort_min' in data and 'between_sort_max' in data:\n queryset = queryset.filter(price__gt=int(self.request.GET['between_sort_min']),\n price__lte=int(self.request.GET['between_sort_max']))\n\n if 'title_sort' in data:\n queryset = queryset.filter(title__icontains=self.request.GET['title_sort'])\n print(self.request.GET['title_sort'])\n print(queryset)\n\n return queryset\n\n def get(self, request, *args, **kwargs):\n data = self.request.GET\n print(data)\n number_page = self.request.GET[\"number_page\"]\n change_sort = self.request.GET.get(\"change_sort\", False)\n queryset = self.get_queryset()\n p = Paginator(queryset, 1)\n num_pages = p.num_pages\n print(num_pages)\n if bool(change_sort) is True:\n number_page = 1\n page = p.page(number_page)\n page_queryset = page.object_list\n has_previous = page.has_previous()\n has_next = page.has_next()\n if has_next:\n next_page_number = page.next_page_number()\n previous_page_number = -1\n elif has_previous:\n next_page_number = -1\n previous_page_number = page.previous_page_number()\n elif has_next and has_previous:\n next_page_number = page.next_page_number()\n previous_page_number = page.previous_page_number()\n else:\n previous_page_number = -1\n next_page_number = -1\n json1 = serializers.serialize(\"json\", page_queryset)\n data = json.loads(json1)\n if len(queryset) > 0:\n k = 0\n else:\n k = 1\n\n for product in page_queryset:\n img = product.gallery.all()[:1]\n data[k]['fields']['img'] = str(img[0].image)\n data[k]['get_absolute_url'] = str(product.get_absolute_url())\n k += 1\n try:\n data[k-1]['num_pages'] = str(num_pages)\n data[k-1]['next_page_number'] = str(next_page_number)\n data[k-1]['previous_page_number'] = str(previous_page_number)\n data[k-1]['number_page'] = str(number_page)\n except:\n data={}\n return JsonResponse(data, safe=False)\n","repo_name":"Alexsandr007/djangoShop","sub_path":"djangoShop/catalog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72326868243","text":"# Join DataFrames together, using mappings that specify how\n#\n# N.B. join_with_mappings and join_with_mapping are two different functions\n\n# Python\nimport copy\n# Forklift\nfrom .joinable import are_dataframes_joinable\nfrom ..column_renamer import ColumnRenamer\n\n# This function takes in two DataFrames, and joins them using many mapping rules\n# It does this by recursing over each mapping, joining decorate_dataframe to with_dataframe for each mapping\ndef join_with_mappings(decorate_dataframe, with_dataframe, mappings):\n try:\n # Step 1: Get a mapping\n mapping = mappings.pop(0)\n # Step 2: Join the decorate_dataframe and with_dataframe DataFrames using the mapping\n decorated_dataframe = join_with_mapping(decorate_dataframe, with_dataframe, mapping)\n # Step 3: Call this function again, with the newly decorated_dataframe and the remaining mappings\n return join_with_mappings(decorated_dataframe, with_dataframe, mappings)\n except IndexError:\n # There are no more mappings left to join with, so we're done\n return decorate_dataframe\n\n# This function narrows down mappings to the ones that actually exist in the dataframe\ndef mappings_that_exist_on_dataframe(dataframe, mappings, mapping_key_for_dataframe):\n requested_columns_names = [column_mapping[mapping_key_for_dataframe] for column_mapping in mappings]\n requested_columns_that_actually_exist = list(set(requested_columns_names).intersection(set(dataframe.columns)))\n return [column_mapping for column_mapping in mappings if column_mapping[mapping_key_for_dataframe] in requested_columns_that_actually_exist]\n\n# This function narrows down mappings to the ones that don't exist in the dataframe\ndef mappings_that_dont_exist_on_dataframe(dataframe, mappings, mapping_key_for_dataframe):\n requested_columns_names = [column_mapping[mapping_key_for_dataframe] for column_mapping in mappings]\n requested_columns_that_dont_exist = [column_name for column_name in requested_columns_names if column_name not in dataframe.columns]\n return [column_mapping for column_mapping in mappings if column_mapping[mapping_key_for_dataframe] in requested_columns_that_dont_exist]\n\n# This function collects the values for one key in the mapping\ndef get_list_from_mappings(mappings, mapping_key):\n return [mapping[mapping_key] for mapping in mappings]\n\n# This function takes in two DataFrames, and joins them using a mapping rule\ndef join_with_mapping(decorate_dataframe, with_dataframe, mapping):\n # Step 1: Ensure we can even join these two DataFrames\n # Check that the decorate_dataframe and the with_dataframe both have the columns that the\n # mapping wishes to join on (otherwise we can't join, so return immediately)\n if not are_dataframes_joinable(decorate_dataframe, mapping[\"this_join_column_name\"], with_dataframe, mapping[\"with_join_column_name\"]):\n return decorate_dataframe\n # Step 2: Get the valid mappings\n # Make a deep copy of the mappings, since we edit the hash destructively\n mappings_dereferenced = copy.deepcopy(mapping[\"add_from_with_to_this\"])\n # Remove mappings that don't exist on with_dataframe\n compatible_mappings = mappings_that_exist_on_dataframe(with_dataframe, mappings_dereferenced, \"current_with_column_name\")\n # Remove mappings that would collide decorate_dataframe and with_dataframe\n compatible_mappings = mappings_that_dont_exist_on_dataframe(decorate_dataframe, compatible_mappings, \"becomes_this_column_name\")\n # Make sure there's at least 1 mapping left, otherwise joining would be pointless\n if len(compatible_mappings) < 1:\n return decorate_dataframe\n # Step 3: Narrow down with_dataframe to what is needed\n # Get the columns the user requested from with_dataframe\n with_dataframe = with_dataframe.select(get_list_from_mappings(compatible_mappings, \"current_with_column_name\") + [mapping[\"with_join_column_name\"]])\n # Rename the columns in with_dataframe_only_columns_needed\n column_renamer = ColumnRenamer({a_mapping[\"current_with_column_name\"]: a_mapping[\"becomes_this_column_name\"] for a_mapping in compatible_mappings})\n with_dataframe = column_renamer.rename_columns(with_dataframe)\n # Give the join column a predefined name, so that it doesn't collide with an existing column in decorate_dataframe\n with_dataframe = with_dataframe.withColumnRenamed(mapping[\"with_join_column_name\"], \"__temp_with_join_column\")\n # Step 4: Return the joined DataFrame,\n # getting rid of the uniquely-named join column along the way\n return decorate_dataframe.join(with_dataframe, decorate_dataframe[mapping[\"this_join_column_name\"]] == with_dataframe[\"__temp_with_join_column\"], \"left_outer\").drop(\"__temp_with_join_column\")\n","repo_name":"jerzygangi/forklift","sub_path":"forklift/join/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4653,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"3"}
+{"seq_id":"30289029515","text":"from typing import Callable, Iterable, List, Tuple, Union\n\nimport numpy as np\n\n\ndef vaidate_input_types_is_str(inputs: List[Union[str, np.ndarray]]) -> bool:\n \"\"\"Validates that the inputs are all the same type and one of str or np.ndarray.\n\n\n Args:\n inputs (List[Union[str, np.ndarray]]): List of inputs to validate\n Returns:\n true if the inputs are str and false if the inputs are np.ndarray\n Raises:\n ValueError if all inputs are not the same type\n ValueError if the types are other than np.ndarray or str\n\n \"\"\"\n types = set(map(type, inputs))\n if len(types) > 1:\n raise ValueError(\n \"Mixed input type usuage. Ensure all are numpy arrays or strings.\"\n )\n\n t = types.pop()\n\n if t in [str, np.ndarray]:\n return t == str\n else:\n raise ValueError(\"Input type must either be np.ndarray or string\")\n\n\ndef validate_parallel_params(\n gpus: List[int] = None, cpus: int = None, out_dir: str = None\n) -> Tuple[List[int], bool]:\n \"\"\"Validates that the parallel params.\n\n Args:\n gpus (List[int]): list GPU ids to use for parallel classification\n cpus (int): number of cpus to use for parallel classification\n Returns:\n A tuple where the first element is a List of integer id values for each\n worker. The second element is true if the ids are gpu ids and false if\n they are cpu ids\n Raises:\n ValueError if both gpus and cpus are given\n ValueError is cpus or gpus are given, but out_dir is not given\n ValueError if len(gpus)==1\n ValueError if cpus<2\n \"\"\"\n if (gpus is not None) and (cpus is not None):\n raise ValueError(\"Please only give a value cpus or gpus, not both.\")\n\n if (gpus is None) and (cpus is None):\n return [0], False\n\n if (gpus is not None or cpus is not None) and (out_dir is None):\n raise ValueError(\"Parallel classification requires an out_dir\")\n\n if gpus is not None:\n if len(gpus) == 1:\n err = \" \".join(\n [\n \"Only one gpu indicated. If you are trying to select\",\n \"a single gpu, then use the CUDA_VISIBLE_DEVICES environment\",\n \"variable. For more information visit:\",\n \"https://devblogs.nvidia.com/cuda-pro-tip-control-gpu-visibility-cuda_visible_devices/\",\n ]\n )\n\n raise ValueError(err)\n else:\n return gpus, True\n else:\n if cpus < 2:\n raise ValueError(\"If passing cpus please indicate a value greater than 1.\")\n\n return np.arange(cpus), False\n\n\ndef arrays_not_same_size(inputs: List[np.ndarray]) -> bool:\n \"\"\"Validates that all input arrays are the same size.\n\n Args:\n inputs (List[np.ndarray]): Input arrays to validate\n Returns:\n true if the arrays are the same size and false if they are not\n \"\"\"\n shapes = [i.shape for i in inputs]\n shp_first = shapes[0]\n shp_rest = shapes[1:]\n\n return not np.array_equiv(shp_first, shp_rest)\n\n\ndef apply(f: Callable, args: Iterable, kwargs: Iterable[dict] = None) -> None:\n \"\"\"Applies the function f to the args and kwargs.\n\n Args:\n f (Callable): fucntion to apply\n args (Iterable): iterable to apply f to\n kwargs (Iterable[dict]): iterable of a dict of kwargs to apply with\n each element in args\n Returns:\n None\n \"\"\"\n try:\n i_args = iter(args)\n i_kwargs = iter(kwargs) if kwargs else None\n while True:\n a = next(i_args)\n k = next(i_kwargs) if kwargs else {}\n if (type(a) is tuple) or (type(a) is list):\n f(*a, **k)\n else:\n f(a, **k)\n except StopIteration:\n pass\n","repo_name":"morpheus-project/morpheus-core","sub_path":"morpheus_core/helpers/misc_helper.py","file_name":"misc_helper.py","file_ext":"py","file_size_in_byte":3831,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29000220925","text":"\nfrom django.contrib import admin\nfrom django.urls import path,include\nfrom . import views\nfrom . import api_view\nurlpatterns = [\n path('invitedevents',api_view.invited_events_home),\n path('generalevents',api_view.general_events_home),\n path('generalevents/detail/',api_view.general_events_detail),\n path('generalevents/comment/',api_view.general_event_comment),\n path('inviteuser//',api_view.invite_users),\n path('invitedevent/details/',api_view.invited_event_details),\n path('invitedevent/comments/',api_view.invited_event_comment),\n \n path('signup',views.signup),\n\n \n path('login',views.login)\n \n \n]\n","repo_name":"utkarshparkhi/IMGSched","sub_path":"IMGbackend/IMGsched/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4249780930","text":"#string\ncamping_stuff = \"tent, sleeping bags, water, raspberry pi, coffee, knife, ethernet cable, flash drive, beard oil, marshmallows\"\n\n#list\n#ordered\n#zero-indexed\ncamping_list = [\"tent\", \"sleeping bags\", \"water\", \"raspberry pi\", \"coffee\", \"knife\", \"ethernet cable\", \"flash drive\", \"beard oil\", \"marshmallows\"]\n\n#who brings what?\nme = camping_list[4]\n\nyou = camping_list[3]\n\n#negative index\n#access last item in list\nthem = camping_list[-1]\n\nprint(me)\n\nprint(you)\n\nprint(them)\n\nprint(camping_list)\n\n#methods\nsupplies = [\"tent\", \"sleeping bags\", \"water\", \"raspberry pi\", \"coffee\", \"knife\", \"ethernet cable\", \"flash drive\", \"beard oil\", \"marshmallows\"]\n\ncamp_site = [\"Crystal Lake\", 404, 95.5, False]\n\n#add to list (one at a time) to end of list\nsupplies.append(\"toilet paper\")\n\nsupplies.append(\"bidet\")\n\n#add to list(multiple pieces of data) to end of list\nsupplies.extend([\"formula\", \"diapers\"])\n\nsupplies = supplies + [\"dog food\", \"treats\"]\n\n#add to list to beginning\nsupplies.insert(0,\"blankets\")\n\n#add to list second to last\nsupplies.insert(-1, \"leashes\")\n\n#delete from list \nsupplies.remove(\"tent\")\n\n#delete from list and return deleted item\nprint(\"This item was just deleted: \" + supplies.pop(0))\n\n#delete entire list\n#empty list\n#supplies.clear()\n\nprint(supplies)\n","repo_name":"paigejananne/NetworkChuck","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37125505714","text":"class Solution(object):\n def productExceptSelf(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[int]\n \"\"\"\n total = 1\n doesZeroExist = False\n # If 2 zeroes exist, then the nums list will be 0\n # If only 1 0 exist, then only the value with 0 will have the total value, and the rest of the variables will have 0\n doesTwoZeroExist = False\n for num in nums: \n if num == 0:\n if doesZeroExist == True:\n doesTwoZeroExist = True\n doesZeroExist = True\n continue\n total = total * num\n \n for count, num in enumerate(nums):\n if doesTwoZeroExist == True:\n nums[count] = 0\n continue\n\n if num == 0:\n nums[count] = total\n else:\n if doesZeroExist == True:\n nums[count] = 0\n continue\n nums[count] = total/num \n \n print(nums)\n return nums\n \ns = Solution()\ns.productExceptSelf([0, 0])\n","repo_name":"tanshiyu1999/LeetCode","sub_path":"238.Product_of_Array_Except_Self/productOfArrayExceptSelf.py","file_name":"productOfArrayExceptSelf.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2132506305","text":"import json\nimport re\nimport requests\nfrom app.models1 import Indicates\n\nclass Drugs(object):\n def __init__(self, indicate_name, client_snp):\n self.client_snp = client_snp\n self.indicate_name = indicate_name\n self.results = self.drug_get_results(self.indicate_name, self.client_snp)\n\n def split_job(self, indicate_info, clinet_SNP):\n \"\"\"\n module_dict:{ 2:{ATM:[{siteinfo1},{siteinfo2}}}\n \"\"\"\n conclusions = {}\n module_dict = {}\n gene_results = []\n advices = []\n level = 0\n conclusion = ''\n for siteinfo in indicate_info['site_result']:\n try:\n module_dict.setdefault(siteinfo['interpretation_label'], {}).\\\n setdefault(siteinfo['gene'], []).append(siteinfo)\n except:\n pass\n for module in module_dict:\n if int(module) == 2:\n ins = DrugModule2(drug_id=indicate_info['indicate_name'], client_SNP=clinet_SNP, sites_dict=module_dict[module])\n elif int(module) == 1:\n ins = DrugModule1(drug_id=indicate_info['indicate_name'], client_SNP=clinet_SNP, sites_dict=module_dict[module], conclusion_result=indicate_info['conclusion_result'])\n\n if ins.level > level:\n conclusion = ins.conclusion\n level = ins.level\n conclusions = {}\n conclusions[conclusion] = ins.level\n\n gene_results.extend(ins.gene_result)\n advices.extend(ins.advices)\n \n return conclusions, gene_results, advices\n\n def drug_get_results(self, indicate_name, client_SNP): \n indicates = Indicates.query.filter_by(name = indicate_name).first()\n if indicates:\n each_info = {}\n indicate_info = indicates.json_info\n indicate_info = json.loads(indicate_info)\n \n try:\n indicate_info['conclusion_result'] = json.loads(indicate_info['conclusion_result'])\n except:\n pass\n\n indicate_info['site_result'] = json.loads(indicate_info['site_result'])\n\n conclusions, gene_results, advices = self.split_job(indicate_info, client_SNP) \n each_info['name'] = indicate_name\n each_info['conclusion'] = list(conclusions.keys())[0]\n each_info['tableData'] = []\n each_info['explanation'] = []\n each_info['medication_advice'] = []\n\n for each in gene_results:\n table={}\n table['geneName'] = each[0]\n if type(each[1]) == list:\n table.setdefault('geneSite', each[1])\n else:\n table.setdefault('geneSite', []).append(each[1])\n table['GT'] = each[2]\n table['explain'] = each[3]\n table['tips'] = each[4]\n each_info['tableData'].append(table)\n \n \n for each in advices:\n tmp = {}\n tmp.setdefault('content',each)\n each_info['medication_advice'].append(tmp)\n\n each_info['explanation'].append({'content':indicate_info['explanation']})\n \n return each_info\n \n\nclass DrugModuleCommon(object):\n\n def __init__(self, drug_id, client_SNP, sites_dict):\n self.indicate_name = drug_id\n self.client_SNP = client_SNP\n self.sites_dict = sites_dict\n\n self.advices = []\n self.conclusion = None #\n self.level = None #\n self.gene_result = [] #表格中:基因 检测位点 基因型 表型 用药提示\n \n def mut_type(self, site, ref, alt, GT):\n \"\"\"\n 'rs_name': 'rs730012', 'ref': 'A', 'alt': 'C,T'\n GT: client SNP : C/C\n \"\"\"\n def len_base(alt):#判断Alt是否存在indel 缺失的情况。暂不考虑插入缺失\n len_base = 1\n for base in alt.split(','):\n if len(base) != 1:\n len_base = len(base)\n return len_base\n\n mutype = ''\n gt = set(tuple(GT)) # GG 变为（'G'）,AG变为（'A','G'）\n if (len(gt) == 1): #纯合\n if len(gt & set(['A', 'T', 'C' ,'G'])) == 1: #SNP，根据碱基来判断处于什么型别\n if ref in gt: #纯合野生型\n mutype = '野生型'\n elif len(gt & set(alt.split(','))) == 1: #纯合突变型\n mutype = '突变型,纯合'\n else:\n print (\"Error {} not exists\".format(GT))\n #print (siteinfo)\n elif len(gt & set(['I', 'D'])) == 1: #InDel，I表示野生型,D表示突变型\n if len(ref) != 1 or len_base(alt) != 1: #判断该位点在数据库中是否有InDel\n if list(gt)[0] == 'I':\n mutype = '野生型'\n elif list(gt)[0] == 'D':\n mutype = '缺失,纯合'\n else:\n print ('Error not exists InDel mutation, ref:{} alt{}'.format(ref, alt))\n elif (len(gt) == 2): #杂合\n if len(gt & set(['A', 'T', 'C' , 'G'])) == 2: #SNP，根据碱基来判断处于什么型别 \n if ref in gt or len(gt & set(alt.split(','))) == 1:\n mutype = '突变型,杂合'\n else:\n print (\"Error {} not exits\".format(GT))\n elif len(gt & set(['I', 'D'])) == 2: #InDel，I表示野生型,D表示突变型\n if len(ref) != 1 or len_base(alt) != 1: #判断该位点在数据库中是否有InDel\n mutype = '缺失,杂合'\n return mutype\n \n def get_user_muttype(self, siteinfo):\n '''\n get user and ref GT, and return mutype\n '''\n site = siteinfo['rs_name']\n ref = siteinfo['ref'] #碱基唯一\n #alt = siteinfo['alt_db'] #暂用alt_db替代\n alt = siteinfo['alt'] #可能存在多个碱基，用','分割\n try:\n GT = self.client_SNP[site].replace('/', '').replace('|', '') #提取用户的基因型，并去除分隔符号\n except:\n print (\"Error {} not exists Genotypes info\").format(site)\n return site, GT, self.mut_type(site, ref, alt, GT)\n\n\nclass DrugModule2(DrugModuleCommon):\n \"\"\"\n Site Depended Results\n \"\"\"\n def __init__(self, drug_id, client_SNP, sites_dict):\n super(DrugModule2, self).__init__(drug_id, client_SNP, sites_dict)\n self.get_result_model() #function，get genotype info\n\n def get_result_model(self):\n '''\n Get geneotype and add each SNP result to detail.\n :return: gene_types: Dict with gene, dbSNP and genotype. Example: {'SJDK9': {'rs2898': '突变，杂合'}}\n '''\n priority_gene_module = 0\n medication_tips_module = ''\n medication_advice_module = ''\n\n\n for gene, Gene in self.sites_dict.items():\n priority_gene = 0\n medication_tips_gene = ''\n medication_advice_gene = ''\n\n #判断用户的基因型对应数据位点数据库的哪一条数据 \n for siteinfo in Gene:\n priority_site = 0 #每一次位点的priority，初始值为0\n site, GT, mutype = self.get_user_muttype(siteinfo)\n if mutype == siteinfo['phenotype'].replace('，', ','):\n #ATM rs11212617 AC 突变型,杂合 提高疗效\n self.gene_result.append([gene, siteinfo['rs_name'], GT, siteinfo['phenotype'], siteinfo['medication_tips']])\n priority_site = int(siteinfo['priority'])\n if priority_gene < priority_site: #比较大小，site的priority高则用该site的值\n priority_gene = priority_site\n medication_advice_gene = siteinfo['medication_advice']\n medication_tips_gene = siteinfo['medication_tips']\n\n self.advices.append(medication_advice_gene)\n if priority_gene_module < priority_gene : #比较大小，gene的priority则用该gene的值\n priority_gene_module = priority_gene\n medication_tips_module = medication_tips_gene\n #medication_advice_module = medication_advice_gene\n\n self.level = priority_gene_module\n self.conclusion = medication_tips_module\n\n #self.advices.append(medication_advice_module)\n \n\nclass DrugModule14(DrugModuleCommon):\n def __init__(self, drug_id, client_SNP, sites_dict):\n super(DrugModule14, self).__init__(drug_id, client_SNP, sites_dict)\n \n def get_pheo_geno(self, gene, genotype):\n '''\n get IM, PM, PM from genotype \n '''\n ##json_data = requests.get(main_config['remote_api']['genotype_phenotype'], params=param).json() \n type_dict = json.load(open('app/rpt/config/{}.json'.format(gene), 'r'))\n\n type_m = type_dict[genotype[0]][genotype[1]]\n return type_m\n\n def _type2info(self, conclusion_result):\n type2info = {}\n for each in conclusion_result:\n type_m = each['phenotype'].replace(' ', '')\n type2info.setdefault(type_m, {}).setdefault('medication_tips', each['medication_tips'])\n type2info.setdefault(type_m, {}).setdefault('medication_advice', each['medication_advice'])\n type2info.setdefault(type_m, {}).setdefault('priority', each['priority'])\n return type2info\n\nclass DrugModule1(DrugModule14):\n def __init__(self, drug_id, client_SNP, sites_dict, conclusion_result):\n super(DrugModule1, self).__init__(drug_id, client_SNP, sites_dict)\n self.__conclusion_result = conclusion_result\n self.get_result_model() #function，get genotype info\n\n def bubble_sort(self, lst, priority):\n '''\n 根据priority进行排序, 冒泡排序法\n '''\n n = len(lst)\n if n <= 1:\n return lst\n for i in range(0, n):\n for j in range(0, n-i-1):\n if priority[lst[j]] < priority[lst[j+1]]:\n lst[j], lst[j+1] = lst[j+1], lst[j]\n return lst\n\n def get_result_model(self):\n '''\n Get gene subtypes.\n :return: gene_types: python dict with gene name and subtypes. {gene, subtypes},\n example {'CYP2D6', ('CYP2D6*1', 'CYP2D6*2')}\n '''\n try:\n type_info = self._type2info(self.__conclusion_result) # get infomation for each coclusion type\n except:\n print ('Error: conclusion result something error {}'.format(self.__conclusion_result))\n\n priority_gene_module = 0\n medication_tips_module = ''\n medication_advice_module = ''\n \n for gene, Gene in self.sites_dict.items():\n priority_gene = 0\n medication_tips_gene = ''\n medication_advice_gene = ''\n priority_type = {} # this is gene type priority, for each type 'CYP2D6*1', 'CYP2D6*2'\n\n wild_type = gene + '_1' # each genetype, _1 set to wild type\n genotype = [wild_type, wild_type] # \n priority_type[wild_type] = 0\n sites = []\n \n for siteinfo in Gene:\n site, GT, mutype = self.get_user_muttype(siteinfo)\n sites.append(site)\n priority_type.setdefault(siteinfo['phenotype'],int(siteinfo['priority']))\n if re.search(r'杂合', mutype):\n genotype.append(siteinfo['phenotype'])\n elif re.search(r'纯合',mutype):\n genotype.append(siteinfo['phenotype'])\n genotype.append(siteinfo['phenotype'])\n\n genotype = self.bubble_sort(genotype, priority_type)[0:2] #降序排序，取前两位\n type_m = self.get_pheo_geno(gene, genotype)\n genotype_simple = '*' + genotype[0].split('_')[1] + '/' + '*' + genotype[1].split('_')[1]\n try:\n self.gene_result.append([gene, sites, genotype_simple, type_m, type_info[type_m]['medication_tips']])\n except:\n self.gene_result.append([gene, sites, genotype_simple, type_m, \"\"]) \n try:\n priority_gene_module < type_info[type_m]['priority'] #this is medication tips priority\n priority_gene_module = type_info[type_m]['priority']\n medication_tips_module = type_info[type_m]['medication_tips']\n medication_advice_module = type_info[type_m]['medication_advice']\n except:\n pass\n\n #如果结论中有priority且priority大于1,那么就按照priority的方式，取prority为大的结论。\n #如果priority为0，则表示priority失效，则按照基因与基因之间(2个)的矩阵取值\n ##针对与两个基因决定一个结论的情况\n if priority_gene_module == 0:\n if len(self.gene_result) == 2:\n #\n\n gene1 = self.gene_result[0][0]\n gene2 = self.gene_result[1][0]\n con1 = self.gene_result[0][3]\n con2 = self.gene_result[1][3]\n genename = gene1 + '_' + gene2 #CYP2D6_CYP2C19\n genotype = [gene1 + '_' + con1, gene2 + '_' + con2] # CYP2D6_IM，CYP2C19_PM\n type_m = self.get_pheo_geno(genename, genotype) # 指定conclusions\n self.gene_result[0][4] = type_info[type_m]['medication_tips']\n self.gene_result[1][4] = type_info[type_m]['medication_tips']\n self.level = 1 #为了避免后续错误，如果level为0，则是初始化值，不会替换初始化值\n self.conclusion = type_info[type_m]['medication_tips']\n self.advices.append(type_info[type_m]['medication_advice'])\n\n else:\n self.level = priority_gene_module\n self.conclusion = medication_tips_module\n self.advices.append(medication_advice_module)","repo_name":"afei2357/bird_strike","sub_path":"backend/app/rpt/Drugs.py","file_name":"Drugs.py","file_ext":"py","file_size_in_byte":14215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22120387047","text":"#! /usr/bin/python\n# -*- coding: utf-8 -*-\n'''\nImplementing watchdogs for others fsm.\n\n@date: Oct 2016\n@author: Davide Marcato\n@email: davide.marcato@lnl.infn.it\n'''\n\nfrom random import uniform\nfrom . import fsmBase\n\n\nclass fsmWatchdog(fsmBase):\n '''A FSM that implements watchdogs for others fsm.'''\n\n def __init__(self, name: str, fsms: list, **args) -> None:\n super(fsmWatchdog, self).__init__(name, **args)\n\n # a list of timers with timer name and input linked\n self.timers = {}\n # connect input for each fsm\n # the input is the pv where to write each time to say the fsm is alive\n for fsm in fsms:\n wd = fsm.getWatchdogInput()\n if wd is not None:\n inp = wd[0]\n self.timers[inp.ioname()] = fsm\n\n statesWithIOs = {\n \"run\": []\n }\n self.setSensLists(statesWithIOs)\n self.gotoState('run')\n\n def run_entry(self):\n for tmrName, fsm in self.timers.items():\n # start with a random delay not to write all pvs at the same instant\n interval = fsm.getWatchdogInput()[2]\n randDelay = uniform(0, interval)\n self.tmrSet(tmrName, randDelay)\n self.logD(\"set %d watchdogs\" % len(self.timers))\n\n def run_eval(self):\n for tmrName, fsm in self.timers.items():\n if self.tmrExpired(tmrName):\n inp, mode, interval = fsm.getWatchdogInput()\n self.tmrSet(tmrName, interval)\n if inp.connected():\n if mode == \"on\":\n inp.put(int(fsm.is_alive()))\n elif mode == \"off\":\n inp.put(int(not fsm.is_alive()))\n elif mode == \"on-off\":\n if fsm.is_alive():\n inp.put(int(not inp.val()))\n else:\n self.logE(\"Unknown watchdog mode\")\n","repo_name":"darcato/pysmlib","sub_path":"smlib/fsmWatchdog.py","file_name":"fsmWatchdog.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"34320518904","text":"import os\n\nimport stat_attributes as attributes\nfrom build_tools_crawler import BuildToolsCrawler\nfrom msvs_tools import MsvsTools\nfrom ide_writer import IdeXmlWriter, IdeCompositeWriter\nfrom services import locateResource, toNativePath\n\n\nclass MsvsSolutionWriter(object):\n _TEMPLATE_FILENAME = 'vs_solution.tsln'\n _SOLUTION_FILENAME = \"vs_{name}.sln\"\n\n def __init__(self, contents, tools, projectGuid, projectFilePath):\n \"\"\"\n :type contents: StatMakefileProject\n :type tools: MsvsTools\n \"\"\"\n self.__contents = contents\n self.__tools = tools\n self.__projectFilePath = projectFilePath\n self.__projectGuid = projectGuid\n pass\n\n def write(self):\n fileContents = self.__readSolutionFileTemplate()\n fileContents = fileContents.format(\n format=self.__tools.solutionFormat, version=self.__tools.versionId, year=self.__tools.year,\n name=self.__contents.name, project_file=self.__projectFilePath, guid=self.__projectGuid)\n filename = self._SOLUTION_FILENAME.format(name=self.__contents.name)\n filePath = \"./{0}/{1}\".format(attributes.IDE_DIRECTORY, filename)\n _file = open(filePath, \"w\")\n _file.write(fileContents)\n _file.flush()\n _file.close()\n print('\\nVisual-Studio solution \"{0}\" has been created successfully.'.format(os.path.abspath(filePath)))\n\n def __readSolutionFileTemplate(self):\n filePath = locateResource(self._TEMPLATE_FILENAME)\n _file = open(filePath)\n _template = _file.read()\n _file.close()\n return _template\n\n\nclass MsvsLegacyWriter(IdeXmlWriter):\n _PROJECT_GUID = '86FC28D1-F4DE-4209-B544-10B5415D0C20'\n _PROJECT_FILENAME = \"vs_{0}.vcproj\"\n\n def __init__(self, contents, tools):\n \"\"\"\n :type contents: StatMakefileProject\n :type tools: MsvsTools\n \"\"\"\n super(MsvsLegacyWriter, self).__init__(contents)\n self._filename = self._PROJECT_FILENAME.format(self._contents.name)\n self.__tools = tools\n output = os.path.join(\"..\", attributes.OUTPUT_DIRECTORY, self._contents.outputName,\n \"ide_{0}\".format(self._contents.name))\n self.__includePath = os.path.join(output, \"inc\")\n self.__executable = os.path.join(output, \"bin\", \"{0}.exe\".format(self._contents.outputName))\n root = self.__composeBodyBase()\n for component in self.__composeComponents():\n root.appendChild(component)\n\n def __composeComponents(self):\n self.__rootDirectoryTag = self.composeElement(\"Files\")\n return (\n self.__composePlatforms(),\n self.composeElement(\"ToolFiles\"),\n self.__composeConfigurations(),\n self.composeElement(\"References\"),\n self.__rootDirectoryTag,\n self.composeElement(\"Globals\")\n )\n\n def __composeConfigurations(self):\n tool = self.composeElement(\n \"Tool\",\n Name=\"VCNMakeTool\",\n BuildCommandLine=self.formatCommandLine(\"build\"),\n ReBuildCommandLine=self.formatCommandLine(\"rebuild\"),\n CleanCommandLine=self.formatCommandLine(\"clean\"),\n Output=self.__executable,\n PreprocessorDefinitions=\"WIN32;_DEBUG;{0}\".format(';'.join(self._contents.definitions)),\n IncludeSearchPath=self.__includePath,\n ForcedIncludes=\"\",\n AssemblySearchPath=\"\",\n ForcedUsingAssemblies=\"\",\n CompileAsManaged=\"\"\n )\n configuration = self.composeElement(\n \"Configuration\", Name=\"Debug|Win32\", OutputDirectory=\".\", IntermediateDirectory=\".\", ConfigurationType=\"0\")\n configuration.appendChild(tool)\n configurations = self.composeElement(\"Configurations\")\n configurations.appendChild(configuration)\n return configurations\n\n def __composePlatforms(self):\n platforms = self.composeElement(\"Platforms\")\n platforms.appendChild(self.composeElement(\"Platform\", Name=\"Win32\"))\n return platforms\n\n def __composeBodyBase(self):\n body = self.composeElement(\n \"VisualStudioProject\",\n ProjectType=\"Visual C++\",\n Version=\"{0:.2f}\".format(self.__tools.versionId),\n Name=self._contents.name,\n ProjectGUID=\"{{{0}}}\".format(self._PROJECT_GUID),\n Keyword=\"MakeFileProj\",\n RootNamespace=self._contents.name\n )\n self._doc.appendChild(body)\n return body\n\n def createRootToken(self):\n return self.__rootDirectoryTag\n\n def createDirectoryToken(self, name, parentDirectoryToken):\n childNodes = parentDirectoryToken.childNodes if parentDirectoryToken.hasChildNodes() else ()\n for element in [node for node in childNodes if node.tagName == \"Filter\"]:\n if element.getAttribute(\"Name\") == name:\n break\n else:\n element = self._doc.createElement(\"Filter\")\n element.setAttribute(\"Name\", name)\n parentDirectoryToken.appendChild(element)\n return element\n\n def addFile(self, filePath, parentDirectoryToken):\n element = self._doc.createElement(\"File\")\n element.setAttribute(\"RelativePath\", os.path.join(\"..\", toNativePath(filePath)))\n parentDirectoryToken.appendChild(element)\n\n def write(self):\n super(MsvsLegacyWriter, self).write()\n solution = MsvsSolutionWriter(self._contents, self.__tools, self._PROJECT_GUID, self._filename)\n solution.write()\n\n\nclass Msvs2010ProjectWriter(IdeXmlWriter):\n _PROJECT_GUID = '86FC28D1-F4DE-4209-B544-10B5415D0C20'\n _PROJECT_FILENAME = \"vs_{0}.vcxproj\"\n\n def __init__(self, contents, tools):\n \"\"\"\n :type contents: StatMakefileProject\n :type tools: MsvsTools\n \"\"\"\n super(Msvs2010ProjectWriter, self).__init__(contents)\n self._filename = self._PROJECT_FILENAME.format(self._contents.name)\n self.__tools = tools\n output = os.path.join(\"..\", attributes.OUTPUT_DIRECTORY, self._contents.outputName,\n \"ide_{0}\".format(self._contents.name))\n self.__executable = os.path.join(output, \"bin\", \"{0}.exe\".format(self._contents.outputName))\n self.__includePath = os.path.join(output, \"inc\")\n self.__sources = self.composeElement(\"ItemGroup\")\n self.__headers = self.composeElement(\"ItemGroup\")\n body = self.__composeBodyBase()\n for element in self.__composeBodyElements():\n body.appendChild(element)\n\n def __composeBodyElements(self):\n return [\n self.___composeConfigGroup(),\n self.composeElement(\"PropertyGroup\",\n context=dict(ProjectName=self._contents.name,\n ProjectGuid=\"{{{0}}}\".format(self._PROJECT_GUID),\n Keyword=\"MakeFileProj\",\n RootNamespace=self._contents.name),\n Label=\"Globals\"),\n self.composeElement(\"Import\", Project=r\"$(VCTargetsPath)\\Microsoft.Cpp.Default.props\"),\n self.composeElement(\"PropertyGroup\",\n context=dict(ConfigurationType=\"Makefile\", ),\n Condition=\"'$(Configuration)|$(Platform)'=='Debug|Win32'\",\n Label=\"Configuration\"),\n self.composeElement(\"Import\", Project=r\"$(VCTargetsPath)\\Microsoft.Cpp.props\"),\n self.composeElement(\"ImportGroup\", Label=\"ExtensionSettings\"),\n self.__composePropertySheets(),\n self.composeElement(\"PropertyGroup\", Label=\"UserMacros\"),\n self.composeElement(\"PropertyGroup\",\n context=dict(_ProjectFileVersion=\"{0:.2f}\".format(self.__tools.versionId))),\n self.__composeCommandLineGroup(),\n self.composeElement(\"ItemDefinitionGroup\"),\n self.__sources,\n self.__headers,\n self.composeElement(\"Import\", Project=r\"$(VCTargetsPath)\\Microsoft.Cpp.targets\"),\n self.composeElement(\"ImportGroup\", Label=\"ExtensionTargets\"),\n ]\n\n def __composeCommandLineGroup(self):\n includePath = \"{0};$(NMakeIncludeSearchPath)\".format(self.__includePath)\n definitions = \"WIN32;_DEBUG;{0};$(NMakePreprocessorDefinitions)\".format(';'.join(self._contents.definitions))\n commandLineGroup = self.composeElement(\n \"PropertyGroup\",\n dict(OutDir=\".\",\n IntDir=\".\",\n NMakeBuildCommandLine=self.formatCommandLine(\"build\"),\n NMakeReBuildCommandLine=self.formatCommandLine(\"rebuild\"),\n NMakeCleanCommandLine=self.formatCommandLine(\"clean\"),\n NMakeOutput=self.__executable,\n NMakePreprocessorDefinitions=definitions,\n NMakeIncludeSearchPath=includePath\n ),\n Condition=\"'$(Configuration)|$(Platform)'=='Debug|Win32'\")\n return commandLineGroup\n\n def __composePropertySheets(self):\n propertySheets = self.composeElement(\"ImportGroup\",\n Condition=\"'$(Configuration)|$(Platform)'=='Debug|Win32'\",\n Label=\"PropertySheets\")\n propertySheets.appendChild(self.composeElement(\n \"Import\",\n Project=r\"$(UserRootDir)\\Microsoft.Cpp.$(Platform).user.props\",\n Condition=r\"exists('$(UserRootDir)\\Microsoft.Cpp.$(Platform).user.props')\",\n Label=\"LocalAppDataPlatform\"))\n return propertySheets\n\n def ___composeConfigGroup(self):\n configGroup = self.composeElement(\"ItemGroup\", Label='ProjectConfigurations')\n projectConfig = self.composeElement(\"ProjectConfiguration\",\n context=dict(Configuration=\"Debug\", Platform=\"Win32\"),\n Include=\"Debug|Win32\")\n configGroup.appendChild(projectConfig)\n return configGroup\n\n def __composeBodyBase(self):\n body = self.composeElement(\n \"Project\", DefaultTargets=\"Build\",\n ToolsVersion=\"{0:.2f}\".format(self.__tools.versionId),\n xmlns=\"http://schemas.microsoft.com/developer/msbuild/2003\"\n )\n self._doc.appendChild(body)\n return body\n\n def createRootToken(self):\n pass\n\n def createDirectoryToken(self, name, parentDirectoryToken):\n pass\n\n def addFile(self, filePath, parentDirectoryToken):\n if os.path.splitext(filePath)[1] == \".c\":\n self.__sources.appendChild(self.composeElement(\"ClCompile\", Include=os.path.join(\"..\", filePath)))\n if os.path.splitext(filePath)[1] == \".h\":\n self.__headers.appendChild(self.composeElement(\"ClInclude\", Include=os.path.join(\"..\", filePath)))\n\n def write(self):\n super(Msvs2010ProjectWriter, self).write()\n solution = MsvsSolutionWriter(self._contents, self.__tools, self._PROJECT_GUID, self._filename)\n solution.write()\n\n\nclass MsvsWriter(IdeCompositeWriter):\n IDE = 'MSVS'\n writers = []\n\n def __init__(self, contents, *args):\n \"\"\"\n :type contents: StatMakefileProject\n \"\"\"\n super(MsvsWriter, self).__init__(contents, args)\n tools = BuildToolsCrawler().retrieveMsvs()\n if isinstance(tools, MsvsTools):\n writer = Msvs2010ProjectWriter if tools.year >= 2010 else MsvsLegacyWriter\n self._instances.append(writer(contents, tools))\n else:\n raise MsvsIdeWriterException(\"No workspace was created due to lack of MSVS.\")\n\n\nclass MsvsIdeWriterException(Exception):\n \"\"\"\n Custom exception for STAT MSVS IDE Writer\n \"\"\"","repo_name":"westerndigitalcorporation/stat","sub_path":"msvs_ide_writer.py","file_name":"msvs_ide_writer.py","file_ext":"py","file_size_in_byte":11863,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"40605886815","text":"import os\nimport streamlit as st\nfrom streamlit_router import StreamlitRouter\nfrom streamlit_option_menu import option_menu\nfrom supabase import create_client, Client\nimport streamlit as st\n\nimport datetime\nimport cv2\nimport csv\nimport tempfile\nfrom tensorflow.keras.models import load_model\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom collections import Counter\nimport mediapipe as mp\nfrom mediapipe.python.solutions.drawing_utils import DrawingSpec\nfrom mediapipe.framework.formats import landmark_pb2\nfrom FaceDetection import FaceDetector\nimport datetime,time\nimport pandas as pd\n\nurl = \"https://fzpvefiyomazymkowund.supabase.co\"\nkey = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6ImZ6cHZlZml5b21henlta293dW5kIiwicm9sZSI6ImFub24iLCJpYXQiOjE2ODI4NTYyNjQsImV4cCI6MTk5ODQzMjI2NH0.qxmzEFCys_1i1Auln9xO1GRMpMYlznSo-qNjbLYK6cI\"\nsupabase = create_client(url, key)\n\n@st.cache_resource\ndef load_face_detection():\n return FaceDetector()\n\nfd = load_face_detection()\n\n@st.cache_resource\ndef load_classifier_model():\n model = load_model(\"model/model.h5\")\n model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n return model\n\nmodel = load_classifier_model()\n\nmp_drawing = mp.solutions.drawing_utils\ndrawing_spec = DrawingSpec(color=(255, 255, 255), thickness=1, circle_radius=0)\nmp_face_mesh = mp.solutions.face_mesh\n\nface_mesh = mp_face_mesh.FaceMesh(\n max_num_faces=1,\n refine_landmarks=True,\n min_detection_confidence=0.5,\n min_tracking_confidence=0.5)\n\n\n\n\n@st.cache_data\ndef detect_face(frame, _faceNet,threshold=0.5):\n\t# grab the dimensions of the frame and then construct a blob\n\t# from it\n\tglobal detections \n\t(h, w) = frame.shape[:2]\n\tblob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),(104.0, 177.0, 123.0))\n\n\t# pass the blob through the network and obtain the face detections\n\t_faceNet.setInput(blob)\n\tdetections = _faceNet.forward()\n\n\n\tlocs = []\n\n\tfor i in range(0, detections.shape[2]):\n\t\t# extract the confidence (i.e., probability) associated with\n\t\tconfidence = detections[0, 0, i, 2]\n\n\t\t# filter out weak detections by ensuring the confidence is\n\t\t# greater than the minimum confidence\n\t\tif confidence >threshold:\n\t\t\t# compute the (x, y)-coordinates of the bounding box for\n\t\t\t# the object\n\t\t\tbox = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n\t\t\t(startX, startY, endX, endY) = box.astype(\"int\")\n\n\t\t\t# ensure the bounding boxes fall within the dimensions of\n\t\t\t# the frame\n\t\t\t(startX, startY) = (max(0, startX), max(0, startY))\n\t\t\t(endX, endY) = (min(w - 1, endX), min(h - 1, endY))\n \n\t\t\t# add the face and bounding boxes to their respective\n\t\t\t# lists\n\t\t\tlocs.append((startX, startY, endX, endY))\n\n\treturn (locs)\n\n\nfacenet = cv2.dnn.readNet(\"model/deploy.prototxt\",\"model/res10_300x300_ssd_iter_140000.caffemodel\")\n\ntry:\n user = supabase.auth.get_user()\nexcept:\n user = None\npage = None\ndef index(router):\n \n if user is not None:\n user_email = user.user.dict()['email']\n user_data = supabase.table('users').select(\"*\").eq(\"email\",user_email).execute()\n if len(user_data.data) == 0:\n supabase.auth.sign_out()\n router.redirect(*router.build(\"index\"))\n\n if user_data.data[0]['role'] == 0:\n with st.sidebar:\n page = option_menu(None, [\"Home\", \"Detect From Video\",\"Detect From Webcam\", \"Logout\"],\n icons=['house',\"film\",\"webcam-fill\",\"box-arrow-right\"],key=\"menu\")\n elif user_data.data[0]['role'] == 1:\n with st.sidebar:\n page = option_menu(None, [\"Admin Panel\",\"Manage Users\", \"Logout\"],icons=[\"speedometer2\",\"people-fill\",\"box-arrow-right\"],key=\"menu\")\n \n else:\n with st.sidebar:\n page = option_menu(None, [\"Home\", \"Login\", \"Signup\"],icons=[\"house\",\"door-open\",\"person-plus-fill\"],key=\"menu\")\n\n\n if page == \"Home\":\n st.write(\"# Human Behaviour Detection\")\n st.write(\"\"\"\n \n #### The Human Behaviour Recognition System is an application that uses deep learning classifier models to detect and classify human facial expressions in real-time\n\n \"\"\")\n\n st.video(\"video\\Media1.mp4\",start_time=0)\n elif page == \"Login\":\n router.redirect(*router.build(\"login\"))\n\n elif page == \"Signup\":\n router.redirect(*router.build(\"signup\"))\n\n elif page == \"Detect From Video\":\n router.redirect(*router.build(\"detect_from_video\"))\n\n elif page == \"Logout\":\n supabase.auth.sign_out()\n router.redirect(*router.build(\"index\"))\n \n elif page == \"Detect From Webcam\":\n router.redirect(*router.build(\"detect_from_webcam\"))\n\n elif page == \"Manage Users\":\n router.redirect(*router.build(\"manage_users\"))\n\n elif page == \"Admin Panel\":\n st.write(\"# Admin Panel\")\n st.write(f\"\"\"\n #### Welcome {user_data.data[0]['name'].capitalize()} !!\n \"\"\")\n users = supabase.table(\"users\").select(\"*\").eq('role',0).execute()\n df= pd.DataFrame(users.data)\n df['date_joined'] = pd.to_datetime(df['created_at']).dt.date\n st.area_chart(df['date_joined'].value_counts())\n\n\n\n\n\n\ndef signup(router):\n with st.form(\"Signup\"):\n st.write(\"# Signup\")\n name = st.text_input(\"Name\")\n email = st.text_input(\"Email\")\n password = st.text_input(\"Password\", type=\"password\")\n submit_button = st.form_submit_button(label=\"Signup\")\n\n if submit_button:\n try:\n res = supabase.auth.sign_up({\n \"email\": email,\n \"password\": password,\n })\n user = supabase.auth.sign_in_with_password({\n \"email\": email,\n \"password\": password,\n })\n supabase.table('users').insert({\"name\": name,\"email\":email}).execute()\n st.success(\"Signup successful\")\n time.sleep(1)\n router.redirect(*router.build(\"index\"))\n except Exception as e:\n st.error(\"Signup failed\")\n st.write(e)\n if st.button(\"Back to Homepage\"):\n router.redirect(*router.build(\"index\"))\n\n\ndef login(router):\n \n with st.form(\"Login\"):\n st.write(\"# Login\")\n email = st.text_input(\"Email\")\n password = st.text_input(\"Password\", type=\"password\")\n submit_button = st.form_submit_button(label=\"Login\")\n\n if submit_button:\n try:\n supabase.auth.sign_in_with_password({\"email\": email, \"password\": password}) \n user = supabase.table('users').select(\"*\").eq(\"email\",email).execute() \n if len(user.data) ==0:\n raise Exception(\"User not found\")\n router.redirect(*router.build(\"index\"))\n except Exception as e: \n st.error(e.__str__())\n \n if st.button(\"Back to Homepage\"):\n router.redirect(*router.build(\"index\"))\n\ndef show_pie_chart():\n\n try:\n with open(\"results/data.csv\", 'r') as f:\n reader = csv.reader(f)\n data = list(reader)\n df = pd.DataFrame(data, columns = ['Emotion', 'data/time'])\n df = df['Emotion']\n count = Counter(df)\n fig, ax = plt.subplots()\n ax.pie(count.values(), labels=count.keys(), autopct='%1.1f%%', shadow=True, startangle=90)\n ax.axis('equal')\n st.pyplot(fig)\n os.remove(\"results/data.csv\")\n except:\n pass\n\n\n\n \n\ndef detect_from_video(router):\n global emotions_detected\n emotion_labels = ['Angry','Disgust','Fear','Happy','Neutral', 'Sad', 'Surprise']\n\n with st.sidebar:\n st.subheader(\"Settings\")\n video_file = st.file_uploader(\"Upload a video\",type=[\"mp4\",\"avi\"])\n st.sidebar.markdown(\"## Settings\")\n show_face_mesh = st.sidebar.checkbox(\"Show Face Mesh\",True)\n\n st.divider()\n if st.button(\"Go to Homepage\"):\n router.redirect(*router.build(\"index\"))\n\n\n if video_file is not None:\n tffile = tempfile.NamedTemporaryFile(delete=False)\n tffile.write(video_file.read())\n cap = cv2.VideoCapture(tffile.name)\n emotions_detected = []\n st.write(\"## Emotion Detection From Video\")\n stframe = st.empty()\n stop_detection = False\n\n col = st.columns(1)\n col[0] = st.empty()\n with col[0]:\n if st.button(\"STOP\"):\n stop_detection = True \n col[0].empty()\n\n \n ret=True\n while stop_detection != True and ret==True:\n ret,frame = cap.read()\n \n if not ret:\n \n break\n\n faces = detect_face(frame,facenet,0.5)\n for (x,y,x2,y2) in faces:\n cv2.rectangle(frame,(x,y),(x2,y2),(255,255,255),2)\n roi = frame[y:x2,x:y2]\n roi = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n roi = cv2.resize(roi,(48,48),interpolation=cv2.INTER_AREA)\n roi = np.array(roi)\n roi = roi/255.0\n roi = np.expand_dims(roi, axis=0)\n prediction = np.argmax(model.predict(roi))\n emotions_detected.append(emotion_labels[prediction])\n cv2.putText(frame,f\"{emotion_labels[prediction]}\",(x,y-10),0,0.5,(255,255,255),2)\n\n with open(\"results/data.csv\",\"a\", newline=\"\\n\") as f:\n csvwriter = csv.writer(f)\n csvwriter.writerow([emotion_labels[prediction], datetime.datetime.now()])\n\n if show_face_mesh:\n results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n if results.multi_face_landmarks:\n for face_landmarks in results.multi_face_landmarks:\n mp_drawing.draw_landmarks(\n image=frame,\n landmark_list=face_landmarks,\n connections=mp_face_mesh.FACEMESH_TESSELATION,\n landmark_drawing_spec=None,\n connection_drawing_spec=drawing_spec ) \n \n \n stframe.image(frame,channels=\"BGR\")\n\n show_pie_chart()\n\ndef detect_from_webcam(router):\n \n emotion_labels = ['Angry','Disgust','Fear','Happy','Neutral', 'Sad', 'Surprise']\n\n with st.sidebar:\n st.subheader(\"Settings\")\n st.sidebar.markdown(\"## Settings\")\n show_face_mesh = st.sidebar.checkbox(\"Show Face Mesh\",True)\n \n\n st.divider()\n if st.button(\"Go to Homepage\"):\n router.redirect(*router.build(\"index\"))\n\n st.write(\"## Emotion Detection From Webcam\")\n if st.button(\"Start\"):\n cap = cv2.VideoCapture(0)\n emotions_detected = []\n stframe = st.empty()\n stop_detection = False\n\n ret,frame = cap.read()\n\n if ret == False:\n st.error(\"Can't Access Webcam\")\n st.stop()\n\n \n col = st.columns(1)\n col[0] = st.empty()\n with col[0]:\n if st.button(\"STOP\"):\n stop_detection = True\n \n col[0].empty()\n\n\n while stop_detection != True:\n \n if not ret:\n break\n\n faces = detect_face(frame,facenet,0.5)\n for (x,y,x2,y2) in faces:\n cv2.rectangle(frame,(x,y),(x2,y2),(255,255,255),2)\n roi = frame[y:x2,x:y2]\n roi = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n roi = cv2.resize(roi,(48,48),interpolation=cv2.INTER_AREA)\n roi = np.array(roi)\n roi = roi/255.0\n roi = np.expand_dims(roi, axis=0)\n prediction = np.argmax(model.predict(roi))\n emotions_detected.append(emotion_labels[prediction])\n cv2.putText(frame,f\"{emotion_labels[prediction]}\",(x,y-10),0,0.5,(255,255,255),2)\n\n with open(\"results/data.csv\",\"+a\", newline=\"\\n\") as f:\n csvwriter = csv.writer(f)\n csvwriter.writerow([emotion_labels[prediction], datetime.datetime.now()])\n\n if show_face_mesh:\n results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n if results.multi_face_landmarks:\n for face_landmarks in results.multi_face_landmarks:\n mp_drawing.draw_landmarks(\n image=frame,\n landmark_list=face_landmarks,\n connections=mp_face_mesh.FACEMESH_TESSELATION,\n landmark_drawing_spec=None,\n connection_drawing_spec=drawing_spec ) \n\n ret,frame = cap.read()\n \n \n stframe.image(frame,channels=\"BGR\")\n \n show_pie_chart()\n \n\n \ndef manage_users(router):\n st.write(\"## Manage Users\")\n \n users = supabase.table(\"users\").select(\"*\").eq('role',0).execute()\n df= pd.DataFrame(users.data)\n cols = st.columns(4)\n cols[0].write(\"Name\")\n cols[1].write(\"Email\")\n cols[2].write(\"Action\")\n\n notification = st.empty()\n for user in users.data:\n cols = st.columns(4)\n cols[0].write(f\"{user['name']}\")\n cols[1].write(f\"{user['email']}\")\n cols[2].button(\"Delete\",key=f\"del{user['id']}\", on_click=delete_user, args=(user['id'],notification))\n cols[3].button(\"Make Admin\",key=f\"adm{user['id']}\", on_click=make_admin, args=(user['id'],notification))\n\n if st.button(\"Go to Homepage\"):\n supabase.auth.sign_out()\n router.redirect(*router.build(\"index\"))\n\ndef make_admin(userid,notification):\n try:\n supabase.table(\"users\").update({\"role\":1}).eq('id',userid).execute()\n with notification:\n st.success(\"User made admin successfully\")\n time.sleep(1)\n st.experimental_rerun()\n except:\n pass\n\ndef delete_user(userid,notification):\n try:\n supabase.table(\"users\").delete().eq('id',userid).execute()\n with notification:\n st.success(\"User deleted successfully\")\n time.sleep(1)\n st.experimental_rerun()\n except:\n pass\n\n\n \n\nrouter = StreamlitRouter()\nrouter.register(index, '/')\nrouter.register(login, '/login')\nrouter.register(signup, '/signup')\nrouter.register(detect_from_video, '/detect-from-video')\nrouter.register(detect_from_webcam, '/detect-from-webcam')\nrouter.register(manage_users, '/manage-users')\n\nrouter.serve()","repo_name":"meetxvii/HumanBehaviourRecognition","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":14784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26885410005","text":"\"\"\"\nClass for Pikachu\n\"\"\"\nimport pygame\nfrom spritesheet import SpriteSheet\nfrom player import Player\n\nvec = pygame.math.Vector2\n\n\nclass Pikachu(Player):\n \"\"\"\n Class for Pikachu character\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Creates Player object with Pikachu's attributes\n\n Room for expansion with custom functions/attacks\n \"\"\"\n pygame.init()\n self.spritesheet = SpriteSheet(\"resources/pikachu_sheet.png\")\n right = pygame.transform.scale(\n self.spritesheet.image_at((5, 40, 35, 28), (134, 255, 104)),\n (70, 56),\n )\n left = pygame.transform.flip(right, flip_x=True, flip_y=False)\n tilt_r = pygame.transform.scale(\n self.spritesheet.image_at((14, 3067, 55, 28), (134, 255, 104)),\n (120, 52),\n )\n tilt_l = pygame.transform.flip(tilt_r, flip_x=True, flip_y=False)\n smash_r = self.spritesheet.image_at(\n (316, 1093, 120, 309), (255, 255, 255)\n )\n smash_l = pygame.transform.flip(smash_r, flip_x=True, flip_y=False)\n self.images = {\n \"left\": left,\n \"right\": right,\n \"tilt_r\": tilt_r,\n \"tilt_l\": tilt_l,\n \"smash_r\": smash_r,\n \"smash_l\": smash_l,\n }\n super().__init__()\n\n self.name = \"pikachu\"\n self.weight = 3\n self.speed = 4.5\n self.smash_cooldown=60\n self.hurtbox = pygame.Rect(self.rect.x, self.rect.y, 70, 56)\n self.attacks = {\n \"tilt\": {\"damage\": 5, \"base\": 2, \"ratio\": 1 / 2},\n \"smash\": {\"damage\": 20, \"base\": 8, \"ratio\": 1},\n }\n\n def set_boxes(self):\n \"\"\"\n Update Pikachu's hitboxes and hurtboxes\n \"\"\"\n if self.attack_cooldown > 0:\n if self.attack == \"tilt\":\n if self.direction == \"left\":\n self.hurtbox = pygame.Rect(\n self.rect.x + 58, self.rect.y, 62, 56\n )\n self.hitbox = pygame.Rect(\n self.rect.x, self.rect.y + 10, 68, 42\n )\n else:\n self.hurtbox = pygame.Rect(self.rect.x, self.rect.y, 62, 56)\n self.hitbox = pygame.Rect(\n self.rect.x + 52, self.rect.y + 10, 68, 42\n )\n if self.attack == \"smash\":\n self.hurtbox = pygame.Rect(\n self.rect.x + 50, self.rect.y + 236, 68, 52\n )\n self.hitbox = pygame.Rect(\n self.rect.x + 37, self.rect.y - 15, 49, 322\n )\n self.rect = self.image.get_rect()\n else:\n self.hurtbox = pygame.Rect(self.rect.x, self.rect.y, 70, 56)\n self.hitbox = pygame.Rect(0, 0, 0, 0)\n self.rect = self.image.get_rect()\n\n def tilt(self):\n \"\"\"\n Perform a tilt attack\n \"\"\"\n self.attack = \"tilt\"\n self.attack_cooldown = 15\n if self.direction == \"left\":\n self.pos.x -= 30\n\n def smash(self):\n \"\"\"\n Perform a smash attack\n \"\"\"\n self.attack = \"smash\"\n self.attack_cooldown = 60\n if self.direction == \"left\":\n self.pos.x -= 30\n\n def gravity(self):\n \"\"\"\n Apply acceleration due to gravity to player object except if on a\n platform\n Changes jump count to 3\n \"\"\"\n self.acc = vec(0, 0.5)\n if 185 < self.pos.x < (185 + 861) and 405 < (self.pos.y - 70) < 430:\n self.vel.y = 1\n if self.knockback_counter <= 0 and 405 > (self.pos.y - 70):\n hits = pygame.sprite.spritecollide(self, self.platforms, False)\n if hits:\n self.pos.y = hits[0].rect.top + 1\n self.vel.y = 0\n self.jump_count = 3\n","repo_name":"olincollege/pysmash","sub_path":"characters/pikachu.py","file_name":"pikachu.py","file_ext":"py","file_size_in_byte":3897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39303620230","text":"#P3.py\n#Continuous Assesment Test main file\n\nimport random\nimport analyzer\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom deap import creator,base,tools,algorithms\nfrom functools import reduce\n\ndef fulfill_constraints(max_imagery=1000, min_imagery_type=10):\n def decorator(func):\n def wrapper(*args, **kargs):\n offspring = func(*args, **kargs)\n\n for child in offspring:\n\n decoded_child = list(analyzer.decode(child))\n #decoded_child = list(analyzer.decode_trivial(child))\n\n\n ####################\n ####Exercise 2c#####\n ####################\n\n # TO DO: here 'child' is an individual generated by DEAP (child[i] is a 'gene'\n # of this individual). You need to force\n # it to fulfill the problem requirements, taking care of the coding you chose\n # At the end, every child must contain a coded individual that fulfills\n # the requirements of max_imagery and min_imagery_type\n\n # HINT: use the decoded_child to work with it with ease.\n # First, focus on the min_imagery_type constraint\n # Second, focus on max_imagery constraint\n # Finally, you will need to encode back the individual before modifying the child array\n # You can use as many vars as you want, of course\n new_child = []\n \n\n #firstly, minimum 10 images per image type\n for i in range(0,len(decoded_child)):\n # remove the 'pass' instruction and complete this loop as you need\n if decoded_child[i] < 10:\n #print(\"MinDectedted: \",decoded_child[i])\n \n #print(\"Decoded_child. N_Images[\"\n #,sum(decoded_child),\"] \"\n #,decoded_child[0],\" \"\n #,decoded_child[1],\" \"\n #,decoded_child[2],\" \"\n #,decoded_child[3],\" \"\n #,decoded_child[4],\" \")\n \n decoded_child[i] = random.randint(10, 255)\n \n # print(\"Decoded_child_revised. N_Images[\"\n #,sum(decoded_child),\"] \"\n #,decoded_child[0],\" \"\n #,decoded_child[1],\" \"\n #,decoded_child[2],\" \"\n #,decoded_child[3],\" \"\n #,decoded_child[4],\" \")\n \n\n #secondly, max amount of images: 1000\n while sum(decoded_child) > max_imagery:\n # print(\"MaxDectedted: \",sum(decoded_child))\n \n #print(\"Decoded_child. N_Images[\"\n # ,sum(decoded_child),\"] \"\n # ,decoded_child[0],\" \"\n # ,decoded_child[1],\" \"\n # ,decoded_child[2],\" \"\n # ,decoded_child[3],\" \"\n # ,decoded_child[4],\" \")\n \n # remove the 'pass' instruction and complete this loop as you need\n #Recorre els diferents números d'imatges i assignar valors aleatoris entre 10 i 255\n for i in range(0,len(decoded_child)):\n decoded_child[i] = random.randint(10, 255)\n\n #print(\"Decoded_child_revised. N_Images[\"\n # ,sum(decoded_child),\"] \"\n # ,decoded_child[0],\" \"\n # ,decoded_child[1],\" \"\n # ,decoded_child[2],\" \"\n # ,decoded_child[3],\" \"\n # ,decoded_child[4],\" \")\n \n #for i in range(0, len(child)):\n # remove the 'pass' instruction and\n # do the necessary actions (inside and/or outside this loop) to get the new_child\n # as the encoded representation of the new individual, which fulfills the requirements\n \n \n new_child = analyzer.encode(decoded_child) \n\n # offspring's child modification\n # encode back your decoded individual before entering this loop!!!\n for i in range(0, len(child)):\n child[i] = new_child[i]\n \n\n return offspring\n return wrapper\n return decorator\n\n\n#Implement GA. Prepare deap: initialization, definition of strategies\n\n#GA initialization\n\n# Create the item dictionary: item name is an integer, and value is\n# a (weight, value) 2-uple.\n#ITEMS_NAME = \"RGB\", \"term\", \"IR\", \"RADAR\", \"LiDAR\"\n#ITEMS_NAME2 = 1,2,3,4,5\n\n#items = {}\n#Create the item dictionary: item name is an integer an value is the number of images\n#for i in ITEMS_NAME2:\n# items[i] = (ITEMS_NAME2[i], random.randint(0, 255))\n \n\nITEMS_NAME = \"RGB\", \"term\", \"IR\", \"RADAR\", \"LiDAR\"\n#IMAGES = (1, 2, 3, 3, 4)\n#ITEMS = dict(\n# (name, (images, random.uniform(1, 5))) \n# for name, images in zip(ITEMS_NAME, IMAGES)\n# ) \n\ncreator.create('FitnessMin', base.Fitness, weights=(-1.0,))\ncreator.create('Individual', list, fitness=creator.FitnessMin)\n#creator.create('Individual', set, fitness=creator.FitnessMin)\n#creator.create('Individual', np.ndarray, fitness=creator.FitnessMin)\n\ntoolbox = base.Toolbox()\n\n####################\n####Exercise 2a#####\n####################\n\n#TO DO: Complete the following sentences to prepare your GA algorithm\n#Register the attributes, individuals, population... according to your individual coding\n#toolbox.register('attrImageType', random.randint, 0, 4)\ntoolbox.register('attr_numberImages', random.randint, 0, 255)\n#toolbox.register(, , , )\ntoolbox.register('individual', tools.initRepeat, creator.Individual, \n #(toolbox.attr_typeImage,toolbox.attr_numberImages),\n toolbox.attr_numberImages,\n n=10)\ntoolbox.register('population', tools.initRepeat, list, toolbox.individual)\n\n#GA strategies definition\n#Objective function is analyzer.analyze_performance\nobjective_function = analyzer.analyze_performance\n\n#TO DO: Complete the following sentences to prepare your GA algorithm\n#Register the GA operations\n\ntoolbox.register('evaluate', objective_function)\ntoolbox.register('mate', tools.cxTwoPoint)\ntoolbox.register('mutate',tools.mutShuffleIndexes , indpb=0.1)\ntoolbox.register('select', tools.selTournament, tournsize=3)\n\n\n#GA decoration\n\n###############################\n####Exercise 2 - final step####\n###############################\n# TO DO: uncomment the following 3 lines\ntoolbox.decorate('population', fulfill_constraints())\ntoolbox.decorate('mate', fulfill_constraints())\ntoolbox.decorate('mutate', fulfill_constraints())\n\n#run the GA\npopulation = toolbox.population(25)\nprint(population)\n\nNGEN = 20\ncx_prob = 0.5\nmut_prob = 0.2\n\nfor gen in range(NGEN):\n\n offspring = algorithms.varAnd(population, toolbox, cxpb=cx_prob, mutpb=mut_prob)\n fits = toolbox.map(toolbox.evaluate, offspring)\n\n for fit,ind in zip(fits, offspring):\n ind.fitness.values = fit\n\n population = toolbox.select(offspring, k=len(population))\n top = tools.selBest(population, k=1)\n fitness = objective_function(top[0])\n ####################\n ####Exercise 2######\n ####################\n # TO DO: replace the call to 'decode_trivial' with a call to the function 'decode' that you just implemented\n #print(\"Decode_Trivial: \", gen, fitness, analyzer.decode_trivial(top[0]), top[0])\n print(\"Decode: \", gen, fitness, analyzer.decode(top[0]), top[0])\n ####################\n ####Exercise 4######\n ####################\n # TO DO: Save the best fitness somehow, so that you can plot them later\n\n# TO DO: plot some charts to assess how the error behaves over generations\n# you can use x as the x axis list (this may help you plot)\nx = list(range(NGEN))\n#...\n\n\n\n\n","repo_name":"manelzz/uoc-exercises-iaa","sub_path":"PAC3/P3.py","file_name":"P3.py","file_ext":"py","file_size_in_byte":8151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4258865133","text":"class stack():\n def __init__(self) -> None:\n self.st=list()\n def push(self,value:int)->None:\n self.st.append(value)\n def top(self)->int:\n return self.st[-1] if self.st else None\n def pop(self)->None:\n if self.st:\n self.st.pop()\n def is_empty(self)->bool:\n return len(self.st)==0\nclass solution():\n def is_equal(self,char1:str,char2:str)->bool:\n if char1 =='(' and char2==')':\n return True\n elif char1=='{' and char2=='}':\n return True\n elif char1=='[' and char2==']':\n return True\n else:\n return False\n\n def valid_parenthis(self,string:str)->bool:\n st=stack()\n for chr in string:\n curr=st.top()\n if self.is_equal(curr,chr):\n st.pop()\n continue\n st.push(chr)\n return st.is_empty()\n\nif __name__=='__main__':\n string=input().strip()\n sol=solution()\n print(sol.valid_parenthis(string))\n ","repo_name":"farhan1503001/Hackerrank-Python-Language-Problems","sub_path":"Python datastructuresleetcode/validparenthesis.py","file_name":"validparenthesis.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"10633799292","text":"import os\nimport glob\nimport csv\ntry:\n import junction_quality\n import svg_arclengths_statistics\n import files\nexcept:\n from tools import junction_quality, files\n from tools import svg_arclengths_statistics\n\nfrom numpy import mean, median, percentile\n\ndef get_gt_by_sketch(sketch, gt_list):\n '''\n Given:\n sketch, name of a single sketch\n gt_list, full list of all gt sketches\n Return:\n a dict as retrieves that contains all gt sketch with same base name of that given sketch\n all gts are also grouped by author name, such as {author1: [gt1, gt2,...], ...}\n '''\n \n gt_dict_by_test = {}\n name, _ = splitext(sketch)\n base_sketch_name = '_'.join(name.split('_')[0:4])\n for gt in gt_list:\n gt_name, _ = splitext(gt)\n author = gt_name.split('_')[4]\n if base_sketch_name in gt:\n if author in gt_dict_by_test:\n gt_dict_by_test[author].append(gt)\n else:\n gt_dict_by_test[author] = [gt]\n return gt_dict_by_test\n\ndef generate_stroke_analyze_csv(config, path_to_table = None, alg = None, p = None, \n path_to_csv = None, path_to_svg_folder = None):\n '''\n '''\n # initialize path information\n if path_to_table != None and alg != None and p != None:\n path_to_csv = os.path.join(path_to_table, \"Stroke_Analysis\", \"SA_%s_%s.csv\"%(alg, p[0]))\n path_to_svg_folder = p[1]\n elif path_to_csv != None and path_to_svg_folder != None:\n print(\"Log:\\tworking in command line mode\")\n else:\n print(\"Error:\\tincorrect parameter\")\n\n # initalize header\n # let's use messiness instead of number strokes!\n header = [\"sketch\", \"arc_len_min\", \"arc_len_25-th\", \"arc_len_median\", \n \"arc_len_75-th\", \"arc_len_max\", \"arc_len_mean\", \"junc_min_dist_sum\", \"junc_dist_count_0.1\", \"used time\"]\n\n if os.path.exists(path_to_csv):\n print(\"Log:\\tfind %s exists, skip stroke analyze\"%path_to_csv)\n return None\n else:\n print(\"Log:\\tcreating stroke analysis table: %s\"%path_to_csv)\n\n row_list = []\n\n log = os.path.join(config['log_dir'], \"%s_%s.csv\"%(alg, p[0]))\n\n alg_input_full_tag = files.open_sketch_list(log, basemode = True)\n h = files.get_header(row = alg_input_full_tag[0])\n \n \n\n for i in range(len(alg_input_full_tag)):\n \n \n file = alg_input_full_tag[i][h[0]]\n name, extension = os.path.splitext(file)\n file = name + \".svg\"\n path_to_svg = os.path.join(path_to_svg_folder, file)\n running_time = alg_input_full_tag[i][h[1]]\n \n \n if os.path.exists(path_to_svg):\n print(\"Log:\\topen %s\"%path_to_svg)\n \n # evaluate svg paths, there may have some exception case that interrupt analysis\n # so any expection will be caught and recorded\n try:\n lengths = svg_arclengths_statistics.arc_length_statistics(path_to_svg)\n arc_num = len(lengths)\n arc_mean = mean( lengths )\n arc_min = min( lengths )\n arc_25 = percentile( lengths, 25 )\n arc_50 = median( lengths )\n arc_75 = percentile( lengths, 75 )\n arc_max = max( lengths )\n \n except ValueError as e:\n print(\"Log:\\t\" + str(e))\n arc_num = \"svgpathtool open error\"\n arc_mean = \"svgpathtool open error\"\n arc_min = \"svgpathtool open error\"\n arc_25 = \"svgpathtool open error\"\n arc_50 = \"svgpathtool open error\"\n arc_75 = \"svgpathtool open error\"\n arc_max = \"svgpathtool open error\"\n except Exception as e:\n print(\"Log:\\t\" + str(e))\n arc_num = \"unknown error\"\n arc_mean = \"unknown error\"\n arc_min = \"unknown error\"\n arc_25 = \"unknown error\"\n arc_50 = \"unknown error\"\n arc_75 = \"unknown error\"\n arc_max = \"unknown error\"\n \n \n try:\n minimum_distances = junction_quality.endpoint_statistics(path_to_svg)\n junc_sum = minimum_distances.sum()\n junc_count = ( minimum_distances > 0.0001 ).sum()\n except ValueError as e:\n print(\"Log:\\t\" + str(e))\n junc_sum = \"svgpathtool open error\"\n junc_count = \"svgpathtool open error\"\n except RecursionError as e:\n print(\"Log:\\t\" + str(e))\n junc_sum = \"excess of points\"\n junc_count = \"excess of points\"\n except Exception as e:\n print(\"Log:\\t\" + str(e))\n if \"has not yet been implemented\" in str(e):\n junc_sum = \"svgpathtool func error\"\n junc_count = \"svgpathtool func error\"\n else:\n junc_sum = \"unknown error\"\n junc_count = \"unknown error\"\n \n \n value = [file, arc_min, arc_25, arc_50, arc_75, arc_max, arc_mean,\n junc_sum, junc_count, running_time]\n row = {}\n for i in range(len(header)):\n row[header[i]] = value[i]\n row_list.append(row)\n # write results\n \n # write csv\n folder, _ = os.path.split(path_to_csv)\n if os.path.isdir(folder) is False:\n os.makedirs(folder)\n \n with open(path_to_csv, 'w', newline = '') as f:\n dict_writer = csv.DictWriter(f, fieldnames = header)\n dict_writer.writeheader()\n for row in row_list:\n dict_writer.writerow(row)\n\nif __name__ == \"__main__\":\n\n import argparse\n parser = argparse.ArgumentParser( description = \"Compute svg stroke statistics and write to csv\" )\n parser.add_argument( \"svg\", help = \"The root folder of algorithm output (SVG file) to analyze.\" )\n parser.add_argument( \"csv\", help = \"Path to save the analyze csv results\" )\n args = parser.parse_args()\n\n generate_stroke_analyze_csv(path_to_csv = args.csv, path_to_svg_folder = args.svg)\n","repo_name":"Nauhcnay/A-Benchmark-for-Rough-Sketch-Cleanup","sub_path":"tools/metrics_stroke.py","file_name":"metrics_stroke.py","file_ext":"py","file_size_in_byte":6237,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"3"}
+{"seq_id":"23168177180","text":"import os\nfrom torch_geometric.data import InMemoryDataset, DataLoader, Batch\nfrom torch_geometric import data as DATA\nimport torch\n\n\n# initialize the dataset\nclass DTADataset(InMemoryDataset):\n def __init__(self, root='/tmp', dataset='davis',\n xd=None, y=None, transform=None,\n pre_transform=None, smile_graph=None, target_key=None, target_graph=None):\n\n super(DTADataset, self).__init__(root, transform, pre_transform)\n self.dataset = dataset\n self.process(xd, target_key, y, smile_graph, target_graph)\n\n @property\n def raw_file_names(self):\n pass\n # return ['some_file_1', 'some_file_2', ...]\n\n @property\n def processed_file_names(self):\n return [self.dataset + '_data_mol.pt', self.dataset + '_data_pro.pt']\n\n def download(self):\n # Download to `self.raw_dir`.\n pass\n\n def _download(self):\n pass\n\n def _process(self):\n if not os.path.exists(self.processed_dir):\n os.makedirs(self.processed_dir)\n\n def process(self, xd, target_key, y, smile_graph, target_graph):\n assert (len(xd) == len(target_key) and len(xd) == len(y)), 'The three lists must be the same length!'\n data_list_mol = []\n data_list_pro = []\n data_len = len(xd)\n for i in range(data_len):\n smiles = xd[i]\n tar_key = target_key[i]\n labels = y[i]\n # convert SMILES to molecular representation using rdkit\n c_size, features, edge_index = smile_graph[smiles]\n target_size, target_features, target_edge_index = target_graph[tar_key]\n # print(np.array(features).shape, np.array(edge_index).shape)\n # print(target_features.shape, target_edge_index.shape)\n # make the graph ready for PyTorch Geometrics GCN algorithms:\n GCNData_mol = DATA.Data(x=torch.Tensor(features),\n edge_index=torch.LongTensor(edge_index).transpose(1, 0),\n y=torch.FloatTensor([labels]))\n GCNData_mol.__setitem__('c_size', torch.LongTensor([c_size]))\n\n GCNData_pro = DATA.Data(x=torch.Tensor(target_features),\n edge_index=torch.LongTensor(target_edge_index).transpose(1, 0),\n y=torch.FloatTensor([labels]))\n GCNData_pro.__setitem__('target_size', torch.LongTensor([target_size]))\n # print(GCNData.target.size(), GCNData.target_edge_index.size(), GCNData.target_x.size())\n data_list_mol.append(GCNData_mol)\n data_list_pro.append(GCNData_pro)\n\n if self.pre_filter is not None:\n data_list_mol = [data for data in data_list_mol if self.pre_filter(data)]\n data_list_pro = [data for data in data_list_pro if self.pre_filter(data)]\n if self.pre_transform is not None:\n data_list_mol = [self.pre_transform(data) for data in data_list_mol]\n data_list_pro = [self.pre_transform(data) for data in data_list_pro]\n self.data_mol = data_list_mol\n self.data_pro = data_list_pro\n\n def __len__(self):\n return len(self.data_mol)\n\n def __getitem__(self, idx):\n return self.data_mol[idx], self.data_pro[idx]\n\n\n# training function at each epoch\ndef train(model, device, train_loader, optimizer, epoch):\n print('Training on {} samples...'.format(len(train_loader.dataset)))\n model.train()\n LOG_INTERVAL = 10\n TRAIN_BATCH_SIZE = 512\n loss_fn = torch.nn.MSELoss()\n for batch_idx, data in enumerate(train_loader):\n data_mol = data[0].to(device)\n data_pro = data[1].to(device)\n optimizer.zero_grad()\n output = model(data_mol, data_pro)\n loss = loss_fn(output, data_mol.y.view(-1, 1).float().to(device))\n loss.backward()\n optimizer.step()\n if batch_idx % LOG_INTERVAL == 0:\n print('Train epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(epoch,\n batch_idx * TRAIN_BATCH_SIZE,\n len(train_loader.dataset),\n 100. * batch_idx / len(train_loader),\n loss.item()))\n\n# predict\ndef predicting(model, device, loader):\n model.eval()\n total_preds = torch.Tensor()\n total_labels = torch.Tensor()\n print('Make prediction for {} samples...'.format(len(loader.dataset)))\n with torch.no_grad():\n for data in loader:\n data_mol = data[0].to(device)\n data_pro = data[1].to(device)\n output = model(data_mol, data_pro)\n total_preds = torch.cat((total_preds, output.cpu()), 0)\n total_labels = torch.cat((total_labels, data_mol.y.view(-1, 1).cpu()), 0)\n return total_labels.numpy().flatten(), total_preds.numpy().flatten()\n\n\n#prepare the protein and drug pairs\ndef collate(data_list):\n batchA = Batch.from_data_list([data[0] for data in data_list])\n batchB = Batch.from_data_list([data[1] for data in data_list])\n return batchA, batchB\n","repo_name":"595693085/DGraphDTA","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5269,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"3"}
+{"seq_id":"33417464455","text":"from django.db import models\n\n\nclass Appointment(models.Model):\n date_appointment = models.DateField(verbose_name=\"Дата записи\")\n time_appointment = models.TimeField(verbose_name=\"Время записи\")\n last_name = models.CharField(max_length=255, verbose_name=\"Фамилия, имя, отчество\", blank=True, null=True)\n date_of_birth = models.CharField(max_length=20, verbose_name=\"Дата рождения\", blank=True, null=True)\n email = models.EmailField(verbose_name=\"E-mail\", blank=True, null=True)\n date_of_write = models.DateTimeField(verbose_name=\"Дата записи\", blank=True, null=True, auto_now=True)\n is_booked = models.BooleanField(verbose_name=\"Забронировано\", default=False)\n comment = models.TextField(verbose_name=\"Комментарий к записи\", blank=True, null=True)\n\n def __str__(self):\n return str(self.date_appointment) + ' ' + str(self.time_appointment) + ' ' + str(self.last_name)\n\n class Meta:\n ordering = ('pk',)\n verbose_name = 'Запись на тестирование'\n verbose_name_plural = 'Записи на тестирование'\n","repo_name":"levenkoevgeny/amia_queue","sub_path":"amia/appointment/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9931187527","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport csv\nimport formz.models.db as db\nfrom datetime import timedelta\nfrom sqlobject import AND, IN\nfrom mx.DateTime.mxDateTime.mxDateTime import now\nfrom collections import Counter\n\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\nos.chdir(os.getcwd() + '/tools/stats/temp/')\n\nHEADERS = [\n 'Логин',\n 'Эл.почта',\n 'Дата регистрации',\n 'Бизнес Плюс/Лайт',\n 'Дата окончания подписки',\n 'Последний вход в систему',\n 'Общее количество документов',\n 'Товарный чек',\n 'Счет на оплату',\n 'Расходная накладная',\n 'УПД',\n 'ТТН (форма 1-Т)',\n 'Транспортная накладная',\n 'Акт',\n 'Счет-фактура',\n 'Ценники',\n 'ПКО',\n 'РКО',\n 'Платежное поручение',\n 'Дата создания последнего документа',\n 'Часовой пояс',\n 'Организация/ ИП',\n 'Наименование',\n 'ИНН',\n 'Адрес',\n 'Эл.почта',\n 'Телефон',\n 'Веб-сайт',\n 'Название банка',\n 'БИК банка',\n 'Местонахождение банка',\n 'ФИО руководителя',\n 'Тип учета НДС'\n]\n\n\ndef timed(fn):\n def decorated(*args):\n start = now()\n result = fn(*args)\n print(\"Executing %s took %d sec\" % (fn.__name__, (now() - start)))\n return result\n\n return decorated\n\n\nclass ReportConstructor:\n \"\"\"Отчёт по всем пользователям Formz\"\"\"\n\n def __init__(self):\n self.data = ReportDataAggregator(\n loginCount=1,\n loginPeriod=30,\n docsCount=2,\n docsPeriod=60\n ).data\n\n @staticmethod\n def validateSubscribed(row):\n \"\"\"Валидация поля subscribed\"\"\"\n\n subs = row.get('subscribed', '')\n subsUntil = row.get('subscribedUntil', '')\n\n if subs and subsUntil:\n if subsUntil > now():\n if row.get('createdOn') > now() - timedelta(days=30):\n subs = u'Бизнес-плюс'\n else:\n subs = u'Триал'\n else:\n subs = u'Лайт'\n else:\n subs = u'Лайт'\n\n row['subscribed'] = subs\n return row\n\n @staticmethod\n def timeZone(row):\n \"\"\"Больше читабельности часовому поясу\"\"\"\n\n timezone = row.get('timezone', '')\n\n row['timezone'] = u'МСК {0:+d}'.format(timezone)\n return row\n\n @staticmethod\n def nds(row):\n \"\"\"Валидируем значения поля НДС\"\"\"\n\n nds = row.get('doc_nds_type', '')\n\n enumDict = {\n '': u'не задано',\n '0': u'без НДС',\n '1': u'НДС в сумме',\n '2': u'НДС сверху'\n }\n\n row['doc_nds_type'] = enumDict.get(nds)\n return row\n\n @staticmethod\n def companyPhone(row):\n \"\"\"Убираем лишние символы в строке телефон что бы не сбивались колонки\"\"\"\n\n phone = row.get('company_phone', '')\n\n if ',' in phone:\n phone = '; '.join(phone.split(','))\n\n row['company_phone'] = phone\n return row\n\n @staticmethod\n def statusCompany(row):\n \"\"\"Валидируем статус\"\"\"\n\n status = row.get('company_status')\n\n if status:\n if not int(status):\n status = u'Организация'\n else:\n status = u'ИП'\n else:\n status = u'Физ. лицо'\n\n row['company_status'] = status\n\n return row\n\n @staticmethod\n def extends(row):\n \"\"\"Обновляем словарь из вложенного словаря\"\"\"\n\n data = row.pop('data', {})\n row.update(data)\n\n return row\n\n @staticmethod\n def calculateDocsCount(userDocs, docTypes=None):\n \"\"\"Возвращает количество документов переданного типа или общее количество\"\"\"\n\n count = 0\n if docTypes:\n for docType in docTypes:\n count += userDocs.get(docType, 0)\n else:\n count = sum(userDocs.values())\n\n return count\n\n @staticmethod\n def calculationLastLogin(logs, userId):\n \"\"\"Вычисляем последний логин в систему\"\"\"\n\n sortedLogs = [i.createdOn for i in logs if i.userID == userId]\n\n return max(sortedLogs)\n\n @staticmethod\n def convertDictToList(dictRow):\n \"\"\"Преобразуем словарь в лист перед записью\"\"\"\n\n listRow = list()\n\n sequence = (\n 'login', 'email', 'createdOn', 'subscribed', 'subscribedUntil', 'lastLogin', 'documentCount',\n 'check', 'invoice', 'torg12', 'upd', 'ttn', 'ttn-2011', 'act', 'sfv_2012', 'cen', 'pko', 'rko', 'platezhka',\n 'lastDocCreate', 'timezone', 'company_status', 'company_name', 'company_inn', 'company_address',\n 'company_email',\n 'company_phone', 'company_website', 'company_bank_name', 'company_bank_bik', 'company_bank_address',\n 'company_director', 'doc_nds_type'\n )\n\n for key in sequence:\n listRow.append(dictRow.get(key, ''))\n\n return listRow\n\n @staticmethod\n def sortedUniqueUsers(logs):\n \"\"\"Из записей логов фильтрует данные пользователей возвращает уникальных\"\"\"\n\n userList = [log.user for log in logs]\n\n uniqIds = list()\n uniqUsers = list()\n\n for user in userList:\n if user.id not in uniqIds:\n uniqIds.append(user.id)\n uniqUsers.append(user)\n\n return uniqUsers\n\n @timed\n def prepareData(self):\n \"\"\"Готовит стуктуру данных перед валидацией\"\"\"\n\n logs = self.data.get('logs')\n docs = self.data.get('documents')\n rawListData = list()\n\n validFunc = (\n self.validateSubscribed,\n self.timeZone,\n self.extends,\n self.nds,\n self.companyPhone,\n self.statusCompany,\n self.convertDictToList\n )\n\n for user in self.sortedUniqueUsers(logs):\n\n row = {\n 'login': user.login,\n 'email': user.email,\n 'createdOn': user.createdOn,\n 'subscribed': user.subscribed,\n 'subscribedUntil': user.subscribedUntil,\n 'lastLogin': self.calculationLastLogin(logs, user.id),\n 'documentCount': self.calculateDocsCount(docs[user.id]),\n\n 'check': self.calculateDocsCount(docs[user.id], ['check']),\n 'invoice': self.calculateDocsCount(docs[user.id], ['invoice']),\n 'torg12': self.calculateDocsCount(docs[user.id], ['torg12']),\n 'upd': self.calculateDocsCount(docs[user.id], ['upd']),\n 'ttn': self.calculateDocsCount(docs[user.id], ['ttn']),\n 'ttn-2011': self.calculateDocsCount(docs[user.id], ['ttn-2011']),\n 'act': self.calculateDocsCount(docs[user.id], ['act', 'ks2', 'act_sverki', 'mx1', 'torg16', 'mx3']),\n 'sfv_2012': self.calculateDocsCount(docs[user.id], ['sfv_2012', 'sfv']),\n 'cen': self.calculateDocsCount(docs[user.id], ['cen']),\n 'pko': self.calculateDocsCount(docs[user.id], ['pko']),\n 'rko': self.calculateDocsCount(docs[user.id], ['rko']),\n 'platezhka': self.calculateDocsCount(docs[user.id], ['platezhka']),\n\n 'lastDocCreate': self.calculationLastLogin(logs, user.id),\n 'timezone': user.timezone,\n 'data': user.data\n }\n\n for func in validFunc:\n row = func(row)\n\n rawListData.append(row)\n\n return rawListData\n\n def generate(self):\n \"\"\"Подгатавливаем и записываем данные в файл\"\"\"\n\n rawData = self.prepareData()\n\n # пишем построчно в файл с заголовками\n with open('stats' + '.csv', \"w\") as f:\n writer = csv.writer(f)\n writer.writerow(HEADERS)\n writer.writerows(rawData)\n\n\nclass ReportDataAggregator:\n \"\"\"Класс для формирования данных по заданным измирениям\"\"\"\n\n listDocuments = [\n 'check',\n 'invoice',\n 'torg12',\n 'upd',\n 'ttn',\n 'ttn_2011',\n 'act',\n 'ks2',\n 'act_sverki',\n 'mx1',\n 'torg16',\n 'mx3',\n 'sfv_2012',\n 'sfv',\n 'cen',\n 'pko',\n 'rko',\n 'platezhka'\n ]\n\n def __init__(self, loginCount=None, loginPeriod=None, dateStart=None, dateEnd=None, docsCount=None, docsPeriod=None,\n listDocuments=None):\n self.logins = loginCount\n self.docsCount = docsCount\n self.loginPeriod = loginPeriod\n self.docsPeriod = docsPeriod\n self.dateStart = dateStart\n self.dateEnd = dateEnd\n\n self.logs = list()\n self.docs = list()\n\n if listDocuments:\n self.listDocuments = listDocuments\n\n @property\n def data(self):\n \"\"\"Фомирует сагрегированные данные по заданным параметрам\"\"\"\n validUserIds = self.__getUsers()\n dictDocsCount = self.__getCountDocuments(validUserIds)\n logs = [i for i in self.logs if i.userID in validUserIds]\n\n return {\n 'logs': logs,\n 'documents': dictDocsCount\n }\n\n @staticmethod\n def __selectedItems(items, sortedField, minValue):\n \"\"\"Отбирает значения по заданному полю и порогу вхождения\"\"\"\n\n countedDict = Counter([i.sqlmeta.asDict().get(sortedField) for i in items])\n\n return [i for i in countedDict.keys() if countedDict[i] >= minValue]\n\n @timed\n def __getCountDocuments(self, validUserIds):\n \"\"\"Список документов c их количеством для каждого пользователя\"\"\"\n\n dictDocsCount = dict()\n\n for userId in validUserIds:\n forms = \", \".join(\"'{0}'\".format(doc) for doc in self.listDocuments)\n\n query = \"\"\"\n SELECT form, count(form)\n FROM document\n WHERE user_id = '{0}' AND form IN ({1})\n GROUP BY form\n \"\"\".format(userId, forms)\n\n query_result = db.db_conn.queryAll(query)\n\n dictDocsCount[userId] = {i[0]: i[1] for i in query_result}\n\n return dictDocsCount\n\n @timed\n def __getUsers(self):\n \"\"\"Список пользователей для отчёта\"\"\"\n\n if self.loginPeriod:\n userQuery = AND(\n db.UsersLog.q.createdOn >= (now() - timedelta(days=self.loginPeriod))\n )\n self.logs = list(db.UsersLog.select(userQuery))\n else:\n self.logs = list(db.UsersLog.select())\n\n if self.docsPeriod:\n users = [i.userID for i in self.logs]\n docsQuery = AND(\n db.Document.q.createdOn >= (now() - timedelta(days=self.docsPeriod)),\n IN(db.Document.q.user, users)\n )\n self.docs = list(db.Document.select(docsQuery))\n\n userIds = self.__selectedItems(self.logs, 'userID', self.logins)\n userDocIds = self.__selectedItems(self.docs, 'userID', self.docsCount)\n\n if userIds and userDocIds:\n validUserId = list(set(userIds) & set(userDocIds))\n else:\n validUserId = userIds\n\n return validUserId\n\n\nif __name__ == '__main__':\n ReportConstructor().generate()\n","repo_name":"Artem-Ivanov/python_scripts","sub_path":"stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":12274,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72801567760","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport time\nfrom collections import defaultdict\nfrom .base import BaseCache\n\n\nclass DictCache(BaseCache):\n\n def __init__(self, config=None):\n super(DictCache, self).__init__(config)\n self._data = None\n\n @property\n def data(self):\n if self._data is None:\n self._data = defaultdict(lambda: ('', 0)) \\\n if self.config.get('max_age') else defaultdict(str)\n return self._data\n\n def get(self, key):\n if self.config.get('max_age'):\n value, expiry_time = self.data[key]\n if time.time() > expiry_time:\n self.delete(key)\n raise KeyError(key)\n else:\n value = self.data[key]\n return value\n\n def set(self, key, value):\n max_age = self.config.get('max_age')\n if max_age:\n _, expiry_time = self.data[key]\n if expiry_time == 0:\n # ie: if we got the default value\n expiry_time = time.time() + max_age\n self.data[key] = (value, expiry_time)\n else:\n self.data[key] = value\n\n def delete(self, key):\n del(self.data[key])\n\n def clear(self):\n return self.data.clear()\n","repo_name":"lonetwin/supycache","sub_path":"supycache/backends/dict_cache.py","file_name":"dict_cache.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"3"}
+{"seq_id":"71506242962","text":"\"\"\"Unittests for propagation of linker inputs through Rust libraries\"\"\"\n\nload(\"@bazel_skylib//lib:unittest.bzl\", \"analysistest\", \"asserts\")\n\ndef _shared_lib_is_propagated_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n link_action = [action for action in tut.actions if action.mnemonic == \"CppLink\"][0]\n\n lib_name = _get_lib_name(ctx, name = \"foo_shared\")\n asserts.true(env, _contains_input(link_action.inputs, lib_name))\n\n return analysistest.end(env)\n\ndef _static_lib_is_not_propagated_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n link_action = [action for action in tut.actions if action.mnemonic == \"CppLink\"][0]\n\n lib_name = _get_lib_name(ctx, name = \"foo\")\n asserts.false(env, _contains_input(link_action.inputs, lib_name))\n\n return analysistest.end(env)\n\ndef _contains_input(inputs, name):\n for input in inputs.to_list():\n # We cannot check for name equality because rlib outputs contain\n # a hash in their name.\n if input.basename.startswith(name):\n return True\n return False\n\ndef _get_lib_name(ctx, name):\n if ctx.target_platform_has_constraint(ctx.attr._windows_constraint[platform_common.ConstraintValueInfo]):\n return name\n else:\n return \"lib{}\".format(name)\n\nstatic_lib_is_not_propagated_test = analysistest.make(\n _static_lib_is_not_propagated_test_impl,\n attrs = {\n \"_windows_constraint\": attr.label(default = Label(\"@platforms//os:windows\")),\n },\n)\n\nshared_lib_is_propagated_test = analysistest.make(\n _shared_lib_is_propagated_test_impl,\n attrs = {\n \"_macos_constraint\": attr.label(default = Label(\"@platforms//os:macos\")),\n \"_windows_constraint\": attr.label(default = Label(\"@platforms//os:windows\")),\n },\n)\n\ndef _linker_inputs_propagation_test():\n static_lib_is_not_propagated_test(\n name = \"depends_on_foo_via_staticlib\",\n target_under_test = \"//test/linker_inputs_propagation:depends_on_foo_via_staticlib\",\n )\n\n shared_lib_is_propagated_test(\n name = \"depends_on_shared_foo_via_staticlib\",\n target_under_test = \"//test/linker_inputs_propagation:depends_on_shared_foo_via_staticlib\",\n )\n\n static_lib_is_not_propagated_test(\n name = \"depends_on_foo_via_sharedlib\",\n target_under_test = \"//test/linker_inputs_propagation:depends_on_foo_via_sharedlib\",\n )\n\n shared_lib_is_propagated_test(\n name = \"depends_on_shared_foo_via_sharedlib\",\n target_under_test = \"//test/linker_inputs_propagation:depends_on_shared_foo_via_sharedlib\",\n )\n\ndef linker_inputs_propagation_test_suite(name):\n \"\"\"Entry-point macro called from the BUILD file.\n\n Args:\n name: Name of the macro.\n \"\"\"\n _linker_inputs_propagation_test()\n\n native.test_suite(\n name = name,\n tests = [\n \":depends_on_foo_via_staticlib\",\n \":depends_on_shared_foo_via_staticlib\",\n \":depends_on_foo_via_sharedlib\",\n \":depends_on_shared_foo_via_sharedlib\",\n ],\n )\n","repo_name":"bazelbuild/rules_rust","sub_path":"test/unit/linker_inputs_propagation/linker_inputs_propagation_test.bzl","file_name":"linker_inputs_propagation_test.bzl","file_ext":"bzl","file_size_in_byte":3117,"program_lang":"python","lang":"en","doc_type":"code","stars":568,"dataset":"github-code","pt":"3"}
+{"seq_id":"25815710063","text":"#!/usr/bin/py\n\nimport argparse\nfrom Bio import SeqIO\nfrom collections import Counter\nimport glob\nimport gzip\nimport logging\n#import numpy as np\nimport os\nimport pandas as pd\nfrom plotnine import ggplot, aes, geom_bar, theme, element_text, xlab, ggtitle\nimport psutil\nimport subprocess\nimport sys\nimport time\nimport xlsxwriter\n\n\ndef parseArgs(argv):\n '''\n Parsing the arguments\n '''\n parser = argparse.ArgumentParser(description='Takes the output from taxprofiler and parses it')\n parser.add_argument(\"--Tools\", dest = 'toolfolders', required=True, help =\"All tools you want to parse together from the nextflow parser (Required), could be [Diamond, Kraken2, KrakenUniq, Metaphlan4], presented in a space seperatated string\", nargs=\"+\")\n arguments = parser.parse_args(argv)\n return arguments\n\n\n\n\ndef SummarizeInExcel(toolfolders):\n \"\"\"\n One sheet per sample\n \"\"\"\n\n OutExcel=\"Out_ParsedExcel.xlsx\"\n writer = pd.ExcelWriter(OutExcel, engine='xlsxwriter')\n\n Countfiles={}\n\n # Generate 1 dataframe per sample! \n for t in toolfolders:\n Countfiles[t]=[]\n countstoplot=glob.glob(t+\"/*_CountsForplotting.txt\")\n for c in countstoplot:\n samplename=c.split(\"/\")[-1]\n Countfiles[t].append(samplename)\n \n uniqueCountFiles=list(set(sum(list(Countfiles.values()),[]))) # Get the unique counts for plotting name to be able to loop through them!\n\n for c in uniqueCountFiles:\n #df=pd.DataFrame() # For every sample generate a dataframe \n counter=0\n for tool, reports in Countfiles.items():\n counter+=1\n if c in reports:\n samplename=c.split(\"_CountsForplotting.txt\")[0]\n d={}\n fullpath=tool+\"/\"+c\n \n with open(fullpath, \"r\") as inf: # Check amount of rows to make sure you can loop through it! \n nrowsCounts=len(inf.readlines())\n if nrowsCounts > 1:\n with open(fullpath, \"r\") as inf:\n next(inf) # Skip headerline\n for l in inf:\n l=l.strip()\n taxnr=l.split(\"\\t\")[0]\n taxname=l.split(\"\\t\")[1]\n counts=int(l.split(\"\\t\")[2])\n d[taxname]=counts\n if counter == 1: # First tool for the sample generate the dataframe\n df=pd.DataFrame.from_dict(d, orient='index', columns=[tool])\n else: # If we already had a tool append to the existing dataframe\n df2=pd.DataFrame.from_dict(d, orient='index', columns=[tool])\n df=pd.merge(df,df2, how='outer', left_index=True, right_index=True)\n\n df=df.astype('Int64') # To convert columns with NAN to int we use int64\n # Print calculate the detection rate, if 1 in 4 the detection is 25%\n df[\"DetectionRate\"]=(len(df.columns)-df.isna().sum(axis=1))/len(df.columns)\n df=df.sort_values('DetectionRate', ascending=False)\n\n # We cannot have sheet name longer than 30 characters in excel\n if len([*samplename]) > 30:\n new_samplename=\"\".join([*samplename][0:30])\n\n else:\n new_samplename=samplename\n\n # Create a Pandas Excel writer using XlsxWriter as the engine.\n df.to_excel(writer, sheet_name=new_samplename)\n writer.close()\n \ndef main(toolfolders):\n SummarizeInExcel(toolfolders)\n \nif __name__ == '__main__':\n args=parseArgs(sys.argv[1:])\n main(args.toolfolders)\n\n \n","repo_name":"ClinicalGenomicsGBG/Taxprofiler-Metagenomics","sub_path":"Scripts/ParserScripts/WriteExcel.py","file_name":"WriteExcel.py","file_ext":"py","file_size_in_byte":3666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32934041950","text":"import sys\nimport pandas as pd\n\n\ndef vowel_count(text):\n text = text.lower()\n count = 0\n vowels = set(\"aeiou\")\n for char in text:\n if char in vowels:\n count += 1\n return count\n\n\ndef consonant_count(text):\n text = text.lower()\n count = 0\n vowels = set(\"bcdfghjklmnpqrstvxyz\")\n for char in text:\n if char in vowels:\n count += 1\n return count\n\n\ndef fileToArray(path):\n file = open(path, 'r')\n lines = file.read().split('\\n')\n file.close()\n return lines\n\n\ndef commonFactor(num1, num2):\n small = min(num1, num2)\n for i in range(2, small+1):\n if (num1 % i == 0 and num2 % i == 0):\n return True\n return False\n\n\ndef topSecretAlgoritm(destinationArry, driverArr):\n\n table_destination = []\n table_drivers = []\n table_ss = []\n for destination in destinationArry:\n driver_dest_ss = []\n for driver in driverArr[:]:\n destinationLength = len(destination.replace(\" \", \"\"))\n driverLength = len(driver.replace(\" \", \"\"))\n if destinationLength % 2 == 0:\n vcount = vowel_count(driver)\n ss = vcount*1.5\n else:\n ccount = consonant_count(driver)\n ss = ccount\n\n _commonFactor = commonFactor(destinationLength, driverLength)\n if _commonFactor:\n ss = ss+ss*.5\n\n driver_dest_ss.append(ss)\n # GETTING THE MAX SS VALUE\n max_value = max(driver_dest_ss)\n max_index = driver_dest_ss.index(max_value)\n\n # ADDING DESTINATION TO THE FINAL DESTINATION ARRAY\n table_destination.append(destination)\n # ADDING DRIVER TO THE FINAL DRIVERS ARRAY\n table_drivers.append(driverArr[max_index])\n # ADDING SS VALUE TO THE FINAL SS ARRAY\n table_ss.append(max_value)\n del driverArr[max_index]\n\n data = {'Destination': table_destination,\n 'Driver': table_drivers,\n 'SS': table_ss}\n return data\n\n\ndef main():\n try:\n destination_path = sys.argv[1]\n drivers_path = sys.argv[2]\n destination_array = fileToArray(destination_path)\n drivers_array = fileToArray(drivers_path)\n data = topSecretAlgoritm(destination_array, drivers_array)\n\n table = pd.DataFrame(data)\n print(table)\n except Exception as e:\n print(\n \"An error occurred please check that you have entered the file paths correctly\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"javiervar/SDE-Code-Exercise","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33896050421","text":"# 공유기 설치\n# https://www.acmicpc.net/problem/2110\n\n# 5 3\n# 1\n# 2\n# 8\n# 4\n# 9\n\nimport sys\n\nn,c=map(int, sys.stdin.readline().split())\nnums=[]\nfor _ in range(n):\n nums.append(int(sys.stdin.readline()))\nnums.sort()\n\nstart=1\nend=nums[-1]-nums[0]\nresult=0\nwhile start<=end:\n center=(start+end)//2\n cnt=1\n temp=nums[0]\n for i in range(1, n):\n if temp+center<=nums[i]:\n temp=nums[i]\n cnt+=1\n if cnt>=c:\n result=center\n start=center+1\n else:\n end=center-1\nprint(result)\n","repo_name":"Greek-and-Roman-God/Athena","sub_path":"codingtest/week19/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38944174450","text":"from datetime import datetime\n\nclass Pessoas:\n def __init__(self,nome: str,cpf: str,idade: int,endereco: str,telefone: int):\n self.nome = nome\n self.cpf = cpf\n self.idade = idade\n self.endereco = endereco\n self.telefone = telefone\n\nclass Funcionarios(Pessoas):\n def __init__(self, nome: str, cpf: str, idade: int, endereco: str, telefone: int, salario: float,data_de_contratacao: datetime , numero_de_alugueis: int, status:bool = True ):\n super().__init__(nome, cpf, idade, endereco, telefone)\n self.salario = salario\n self.data_de_contratacao = data_de_contratacao\n self.numero_de_alugueis = numero_de_alugueis\n self._status = status\n\n @property\n def status(self):\n if self._status == True:\n return 'Ativado'\n return 'Desativado'\n\n @property\n def ativar(self):\n if self._status == False:\n self._status = True\n \n @property\n def desativar(self):\n if self._status == True:\n self._status = False\n \n @property\n def tempo_de_servico(self):\n return datetime.now() - self.data_de_contratacao \n \nclass Clientes(Pessoas):\n def __init__(self, nome: str, cpf: str, idade: int, endereco: str, telefone: int, numero_carteira_motorista: int,ano_vencimento_carteira_motorista: int):\n super().__init__(nome, cpf, idade, endereco, telefone)\n self.numero_carteira_motorista = numero_carteira_motorista\n self.ano_vencimento_carteira_motorista = ano_vencimento_carteira_motorista ","repo_name":"Thiagomrad/Locadora_de_carros","sub_path":"pessoas.py","file_name":"pessoas.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35197889303","text":"from proglog import ProgressBarLogger\nimport moviepy.editor as mp\nimport os\nimport random\nimport string\n\n\nclass MyBarLogger(ProgressBarLogger):\n progress_message = None\n parametr_message = None\n sticker_message = None\n total_message = None\n prev_percent = 0\n\n def __init__(self, user_id, bot):\n super().__init__()\n self.user_id = user_id\n self.bot = bot\n\n # калбак каждого вызова logger message\n def callback(self, **changes):\n if self.total_message is None:\n self.showSticker()\n self.total_message = self.bot.send_message(self.user_id, f'Кол-во параметров: {len(changes.items())}')\n\n for (_, value) in changes.items():\n print(\"> bot work status:\", value)\n if self.parametr_message is None:\n self.parametr_message = self.bot.send_message(self.user_id, 'Текущий: %s' % (value))\n else:\n self.bot.edit_message_text(chat_id=self.user_id, message_id=self.parametr_message.message_id,\n text='Текущий: %s' % (value))\n self.prev_percent = 0\n\n def showSticker(self):\n self.sticker_message = self.bot.send_animation(self.user_id,\n r'https://media.tenor.com/-2me41xDUsIAAAAC/working-cat-cat.gif')\n\n def hideSticker(self):\n self.bot.delete_message(chat_id=self.user_id, message_id=self.sticker_message.message_id)\n self.progress_message = None\n self.parametr_message = None\n self.sticker_message = None\n self.total_message = None\n self.prev_percent = 0\n\n # калбак каждого обновления logger progress\n def bars_callback(self, bar, _, value, old_value=None):\n percent = (value / self.bars[bar]['total']) * 100\n if self.progress_message is None:\n self.progress_message = self.bot.send_message(self.user_id, 'Загрузка: 0%')\n else:\n if round(percent - self.prev_percent) >= 30:\n self.bot.edit_message_text(chat_id=self.user_id, message_id=self.progress_message.message_id,\n text=f'Загрузка: {percent:.2f}%')\n self.prev_percent = percent\n\n\nclass Error:\n error_gif_url = 'https://goo.su/t8mNi'\n\n def __init__(self, bot, markup):\n super().__init__()\n self.bot = bot\n self.markup = markup\n\n def sendError(self, user_id):\n self.bot.send_animation(user_id, self.error_gif_url)\n self.bot.send_message(user_id, 'Что-то пошло не так..❌\\nПовтори пожалуйста попытку', reply_markup=self.markup)\n\n\nclass Loader:\n loading_message = None\n isLoading = False\n loader_gif_url = r'https://media.tenor.com/fjdydcAjFo8AAAAi/capoo-blue.gif'\n\n # todo сделать выбор оформления темы типо (котики, строгий вуз, универсальное)\n\n def __init__(self, user_id, bot):\n super().__init__()\n self.user_id = user_id\n self.bot = bot\n\n def showLoader(self):\n if not self.isLoading:\n self.isLoading = True\n self.loading_message = self.bot.send_animation(self.user_id, self.loader_gif_url)\n\n def hideLoader(self):\n if self.isLoading:\n self.isLoading = False\n self.bot.delete_message(chat_id=self.user_id, message_id=self.loading_message.message_id)\n self.bot.send_message(self.user_id, \"✅\")\n\n\ndef downloadVideo(bot, message):\n user_id = message.chat.id\n loader = Loader(user_id, bot)\n loader.showLoader()\n file_info = bot.get_file(message.video.file_id)\n downloaded_file = bot.download_file(file_info.file_path)\n local_file_name = f'{user_id}_{getUniqID()}_video.mp4'\n with open(local_file_name, 'wb') as new_file:\n new_file.write(downloaded_file)\n loader.hideLoader()\n return local_file_name\n\n\ndef getUniqID():\n # генерирует случайную строку из букв и цифр длиной 5 символов\n letters_and_digits = string.ascii_letters + string.digits\n random_string = ''.join(random.choice(letters_and_digits) for _ in range(5))\n return random_string\n\n\ndef extractAudio(bot, message, local_file_name):\n logger = MyBarLogger(message.chat.id, bot)\n try:\n video = mp.VideoFileClip(local_file_name)\n audio = video.audio\n local_audio_file_name = f'{message.chat.id}_{getUniqID()}_audio.mp3'\n audio.write_audiofile(local_audio_file_name, logger=logger)\n logger.hideSticker()\n return local_audio_file_name\n except:\n return ''\n finally:\n audio.close()\n video.close()\n\n\ndef downloadConvertVoice(bot, message):\n user_id = message.chat.id\n loader = Loader(user_id, bot)\n loader.showLoader()\n file_info = bot.get_file(message.voice.file_id)\n downloaded_file = bot.download_file(file_info.file_path)\n local_ogg_file_name = f'{user_id}_{getUniqID()}_voice-audio.ogg'\n with open(local_ogg_file_name, 'wb') as new_file:\n new_file.write(downloaded_file)\n audio = mp.AudioFileClip(local_ogg_file_name)\n local_mp3_file_name = f'{user_id}_{getUniqID()}_audio.mp3'\n audio.write_audiofile(local_mp3_file_name)\n audio.close()\n loader.hideLoader()\n return local_mp3_file_name\n\n\ndef downloadAudio(bot, message):\n user_id = message.chat.id\n loader = Loader(user_id, bot)\n loader.showLoader()\n file_info = bot.get_file(message.audio.file_id)\n downloaded_file = bot.download_file(file_info.file_path)\n local_file_name = f'{user_id}_{getUniqID()}_audio.mp3'\n with open(local_file_name, 'wb') as new_file:\n new_file.write(downloaded_file)\n\n loader.hideLoader()\n return local_file_name\n\n\ndef overlayMedia(bot, user_id, local_file_name, local_mp3_file_name):\n logger = MyBarLogger(user_id, bot)\n try:\n video = mp.VideoFileClip(local_file_name)\n audio = mp.AudioFileClip(local_mp3_file_name)\n final_video = video.set_audio(audio)\n local_final_video_name = f'{user_id}_{getUniqID()}_final.mp4'\n final_video.write_videofile(local_final_video_name, codec='libx264', logger=logger)\n logger.hideSticker()\n return local_final_video_name\n except:\n return ''\n finally:\n audio.close()\n video.close()\n\n\ndef concatenateAudio(bot, user_id, local_file_name, local_file_name2):\n logger = MyBarLogger(user_id, bot)\n try:\n audio_clip1 = mp.AudioFileClip(local_file_name)\n audio_clip2 = mp.AudioFileClip(local_file_name2)\n final_audio = mp.concatenate_audioclips([audio_clip1, audio_clip2])\n local_final_audio = f'{user_id}_{getUniqID()}_final_audio.mp3'\n final_audio.write_audiofile(local_final_audio, logger=logger)\n logger.hideSticker()\n return local_final_audio\n except:\n return ''\n finally:\n audio_clip1.close()\n audio_clip2.close()\n\n\ndef deleteUserFiles(user_id):\n files = os.listdir()\n for file_name in files:\n if str(user_id) in file_name:\n os.remove(file_name)\n print(\"> files deleted\")\n","repo_name":"psevdo-progger/audio_bot","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":7289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30910233816","text":"from bs4 import BeautifulSoup\nimport urllib.request\n\nurl = \"https://en.wikipedia.org/wiki/Colt_Python\"\n\nresponse = urllib.request.urlopen(url)\nsoup = BeautifulSoup(response,\"html.parser\")\n\nprint(response.geturl())\nprint(\"*********************************\")\ndef getSoupTitle(soup):\n return soup.title\n\ndef printList(list,number):\n print(\"length of the list=\",len(list))\n for i in list[:number]:\n print (i,\"\\n\")\n\ndef getUrls(soup):\n urls=soup.find_all(\"a\")\n url_list=[]\n for a in urls:\n #excluding tel ,mailto, and #(same page) links\n if (a.get(\"href\"))and(\"tel\" not in a.get(\"href\"))and(\"mailto\" not in a.get(\"href\")) and (\"#\" not in a.get(\"href\")):\n # part of URLs on wiki presented as \\\\url without http start and part of the URLs presented as \\w\\index or \\wiki\\,\n # so we need to check and to \"repair\" those links\n if str(a.get(\"href\"))[:2]==\"//\":\n url_list.append(\"https:\"+a.get(\"href\"))\n elif str(a.get(\"href\"))[:2]==\"/w\":\n url_list.append(\"https://en.wikipedia.org\" + a.get(\"href\"))\n else:\n url_list.append(a.get(\"href\"))\n return url_list\n\n\n\n\n\n\n\n\n\ndef main():\n\n\n printList((getUrls(soup)),280)\nif __name__ == \"__main__\":\n main()","repo_name":"StasLi/src","sub_path":"002.py","file_name":"002.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"69873640402","text":"import sys\nfrom collections import deque\nN = int(sys.stdin.readline().strip())\nboard = [[0]*N for _ in range(N)]\nbsx, bsy = 0, 0 # 아기상어 좌표\nbs = 2 # 아기상어 크기 = 2\nfor i in range(N):\n a = list(map(int, sys.stdin.readline().strip().split()))\n for j in range(N):\n if a[j] == 9:\n bsx, bsy = i, j\n board[i][j] = 9\n elif 1 <= a[j] <= 6:\n board[i][j] = a[j]\n\ndx = (0, 1, 0, -1)\ndy = (1, 0, -1, 0)\n\ndef bfs(bsx, bsy):\n dq = deque()\n dq.append((bsx, bsy, 0))\n dist = []\n chk = [[False]*N for _ in range(N)]\n chk[bsx][bsy] = True\n while dq:\n x, y, n = dq.popleft()\n if 1 <= board[x][y] <= 6 and bs > board[x][y]: # 먹을 수 있는 물고기\n dist.append((n, x, y))\n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n if 0 <= nx < N and 0 <= ny < N and not chk[nx][ny] and board[nx][ny] <= bs: # 지나갈 수 있는지\n dq.append((nx, ny, n+1))\n chk[nx][ny] = True\n\n dist.sort(key = lambda x : (x[0], x[1], x[2]))\n # print(dist)\n return dist\n\ntime = 0\ncnt = 0 # 같은 크기 물고기 잡아먹는 경우 카운트\nwhile True:\n fish = bfs(bsx, bsy)\n if len(fish) == 0:\n print(time)\n break\n\n board[bsx][bsy] = 0\n bsx, bsy = fish[0][1], fish[0][2]\n\n board[fish[0][1]][fish[0][2]] = 0\n time += fish[0][0]\n cnt += 1\n if cnt == bs: # 크기 갱신\n bs += 1\n cnt = 0\n\n\n\n\n\n\n\n","repo_name":"auddus16/Algorithm_python","sub_path":"백준_문제집_삼성기출/16236_아기상어.py","file_name":"16236_아기상어.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20956999785","text":"import math\nimport threading\nimport time\n\nfrom selenium.common.exceptions import TimeoutException\nfrom bs4 import BeautifulSoup as bs4\nimport requests as rq\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.chrome.options import Options\n\n\n# noinspection PyMethodMayBeStatic,PyBroadException\nclass Heart:\n def __init__(self):\n self.website_url = 'https://dramacool.cr'\n self.browser_exe_path = 'C:\\\\Program Files\\\\BraveSoftware\\\\Brave-Browser\\\\Application\\\\brave.exe'\n\n def search(self, name: str):\n user_looking_for = name.replace(' ', '+')\n response = rq.get(f'{self.website_url}/search?type=movies&keyword={user_looking_for}')\n soup = bs4(response.text, 'html.parser')\n names = [i.string for i in soup.select('ul li a h3')]\n img_url = [i.get('data-original') for i in soup.select('ul li a img')[1:]]\n urls = [self.website_url + ((i.get('onclick')).replace('window.location = \\'', '')).replace('\\'', '') for i in\n soup.select('ul li a h3')]\n result = [{'name': names[i], 'img': img_url[i], 'url': urls[i]} for i in range(len(names))]\n\n return result\n\n def home_pg(self):\n chrome_driver_path = '.\\\\chromedriver.exe'\n options = Options()\n # browser exe file\n options.binary_location = self.browser_exe_path\n\n options.add_argument('headless')\n driver = webdriver.Chrome(options=options, service=Service(chrome_driver_path))\n driver.get(self.website_url)\n a = driver.find_elements(By.CSS_SELECTOR, '.views1 ul li')\n b = [{'name': i.find_element(By.CSS_SELECTOR, 'a').get_attribute('title'),\n 'url': i.find_element(By.CSS_SELECTOR, 'a').get_attribute('href'),\n 'img': i.find_element(By.CSS_SELECTOR, 'a div').get_attribute('style')[23:-3]} for i in a]\n return b\n\n def get_all_ep_pg(self, media_page: str):\n response = rq.get(media_page)\n soup = bs4(response.text, 'html.parser')\n some_useless_data = soup.select('.all-episode li a h3')\n ep_pg = [self.website_url + ((ep.get('onclick')).replace('window.location = \\'', '').replace('\\'', '')) for ep\n in some_useless_data]\n ep_pg.reverse()\n return ep_pg\n\n def get_ep_download_links(self, ep_to_download):\n ep_download = []\n for ep in ep_to_download:\n response = rq.get(ep)\n soup = bs4(response.text, 'html.parser')\n some_useless_data = soup.select('.download a')\n for i in some_useless_data:\n ep_download.append(\"https://\" + str(i).split('\\n')[0][11:-18])\n return ep_download\n\n # noinspection PyShadowingNames,PyBroadException\n def downloading(self, ep):\n chrome_driver_path = '.\\\\chromedriver.exe'\n options = Options()\n options.binary_location = self.browser_exe_path\n # fdm\n options.add_extension('.\\\\fdm.crx')\n # buster\n options.add_extension('.\\\\buster.crx')\n options.add_experimental_option('detach', True)\n\n driver = webdriver.Chrome(options=options, service=Service(chrome_driver_path))\n driver.minimize_window()\n driver.get(ep)\n if self.check_captcha(driver):\n driver.maximize_window()\n\n WebDriverWait(driver, 100).until(EC.frame_to_be_available_and_switch_to_it(\n (By.CSS_SELECTOR, \"iframe[name^='a-'][src^='https://www.google.com/recaptcha/api2/anchor?']\")))\n WebDriverWait(driver, 100).until(\n EC.element_to_be_clickable((By.XPATH, \"//span[@id='recaptcha-anchor']\"))).click()\n try:\n WebDriverWait(driver, 5).until(\n EC.presence_of_element_located(\n (By.XPATH, \"//span[@id='recaptcha-anchor' and @aria-checked='true']\")))\n except TimeoutException:\n self.solve_captcha(driver)\n\n driver.switch_to.default_content()\n driver.find_element(By.ID, 'btn-submit').click()\n driver.minimize_window()\n time.sleep(5)\n\n for i in range(4, 0, -1):\n try:\n btn = driver.find_element(By.XPATH, f'/html/body/section/div/div[2]/div/div[4]/div[1]/div[{i}]')\n except Exception:\n pass\n else:\n btn.click()\n driver.minimize_window()\n break\n\n time.sleep(1)\n driver.quit()\n return\n\n # noinspection PyBroadException\n def solve_captcha(self, driver):\n driver.switch_to.default_content()\n WebDriverWait(driver, 50).until(EC.frame_to_be_available_and_switch_to_it(\n (By.CSS_SELECTOR, \"iframe[name^='c-'][src^='https://www.google.com/recaptcha/api2/bframe?']\")))\n\n WebDriverWait(driver, 10).until(\n EC.element_to_be_clickable((By.CSS_SELECTOR, '.help-button-holder'))).click()\n time.sleep(3)\n\n driver.switch_to.default_content()\n try:\n WebDriverWait(driver, 5).until(EC.frame_to_be_available_and_switch_to_it(\n (By.CSS_SELECTOR, \"iframe[name^='a-'][src^='https://www.google.com/recaptcha/api2/anchor?']\")))\n WebDriverWait(driver, 4).until(EC.presence_of_element_located(\n (By.XPATH, \"//span[@id='recaptcha-anchor' and @aria-checked='true']\")))\n return\n except Exception:\n driver.switch_to.default_content()\n WebDriverWait(driver, 50).until(EC.frame_to_be_available_and_switch_to_it(\n (By.CSS_SELECTOR, \"iframe[name^='c-'][src^='https://www.google.com/recaptcha/api2/bframe?']\")))\n WebDriverWait(driver, 10).until(\n EC.element_to_be_clickable((By.CSS_SELECTOR, '.image-button-holder'))).click()\n time.sleep(2)\n self.solve_captcha(driver)\n\n def check_captcha(self, driver):\n \"\"\"Returns true if captcha found on page\"\"\"\n try:\n WebDriverWait(driver, 5).until(\n EC.presence_of_element_located((By.ID, \"captcha-v2\"))\n )\n except Exception:\n return False\n else:\n return True\n\n\ndef partition(ls: list):\n x = math.ceil(len(ls) / 6) if 6 < len(ls) else 1\n n = 0\n ls_for_challenge = []\n for i in range(x):\n if n + 6 > len(ls):\n ls_for_challenge.append(ls[n:len(ls)])\n else:\n ls_for_challenge.append(ls[n:n + 6])\n n += 6\n\n return ls_for_challenge\n\n\ndef handle_download(download_links: list, func):\n thread_list, ls = [], []\n for a in partition(download_links):\n for link in a:\n t = threading.Thread(target=func, kwargs={'ep': link})\n t.start()\n time.sleep(1)\n thread_list.append(t)\n # Wait for all threads to complete\n for thread in thread_list:\n thread.join()\n return\n","repo_name":"IMFinallyHere/Dramacool_download_GUI","sub_path":"heart.py","file_name":"heart.py","file_ext":"py","file_size_in_byte":7124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31829635467","text":"from selenium import webdriver\nimport os\nimport time\nimport argparse\n\n\ndef file_is_downloaded():\n \"\"\"Check if video is being downloaded\n\n Returns\n ---------\n True\n \"\"\"\n time.sleep(3)\n downloaded = False\n\n while not downloaded:\n files = os.listdir(args.location)\n downloaded = True\n for file in files:\n if \".crdownload\" in file:\n downloaded = False\n print(\"STILL DOWNLOADING\")\n time.sleep(1)\n\n return True\n\n\nif __name__ == \"__main__\":\n \"\"\"Downloads videos from the sentence_urls.csv list\n\n Returns\n ---------\n Saves videos to downloads path\n \"\"\"\n\n # Arguments\n parser = argparse.ArgumentParser(description=\"\")\n parser.add_argument(\n \"-l\", \"--location\", help=\"Location to where the videos will be downloaded to\", type=str, required=True\n )\n args = parser.parse_args()\n\n with open(\"sentence_urls.csv\") as f:\n data = f.readlines()[1:]\n\n urls = []\n for item in data:\n erf, mov = item.split(\",\")\n urls.append(erf)\n urls.append(mov)\n\n options = webdriver.ChromeOptions()\n options.add_argument(\"--start-maximized\")\n prefs = {\"download.default_directory\": args.location}\n options.add_experimental_option(\"prefs\", prefs)\n driver = webdriver.Chrome(options=options)\n i = 0\n\n for url in urls:\n driver.get(url)\n driver.switch_to.frame(driver.find_element_by_name(\"MainFrame\"))\n driver.switch_to.frame(driver.find_element_by_name(\"SideMain\"))\n driver.find_element_by_xpath('//*[text()=\"Continue >>\"]').click()\n\n assert file_is_downloaded()\n print(i)\n i += 1\n\n driver.close()\n","repo_name":"KristinaG5/AI-Sign-Language-Translator","sub_path":"dataset/webscraper/sentence_clip_fetcher.py","file_name":"sentence_clip_fetcher.py","file_ext":"py","file_size_in_byte":1712,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"29026531727","text":"# coding: utf-8\n\n\nimport pytest\n\nfrom idm.users.models import Department\nfrom idm.tests.utils import create_user\n\npytestmark = [pytest.mark.django_db, pytest.mark.robotless]\n\n\ndef test_subdepartments():\n assert Department.objects.count() == 0\n\n yandex = Department.objects.create(\n id=3,\n name='Яндекс',\n )\n akadem = Department.objects.create(\n id=392,\n name='Отдел по работе с академическими программами',\n parent=yandex,\n )\n Department.objects.create(\n id=450,\n name='Группа практики школы анализа данных',\n parent=akadem,\n )\n\n assert Department.objects.count() == 3\n\n\ndef test_bind_user():\n user = create_user('art')\n yandex = Department.objects.create(name='Яндекс')\n\n assert yandex.users2.count() == 0\n yandex.users2.add(user)\n assert yandex.users2.count() == 1\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/models/users/test_departments.py","file_name":"test_departments.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19039425301","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\" calculate estimate xg values for a range if weightening parameters and dumps the best estimates into json files \"\"\"\nimport os\nimport sys\nimport numpy as np\nsys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))\nsys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)))\n\n# pylint: disable=E0401, C0413\nfrom rest.functions.corsi import goals5v5_get, gameshots5v5_get\nfrom rest.functions.helper import config_load, logger_setup, json_load, json_store, uts_now\nfrom rest.functions.match import match_info_get, match_list_get\nfrom rest.functions.periodevent import periodevent_get\nfrom rest.functions.shift import shift_get\nfrom rest.functions.shot import shot_list_get\nfrom rest.functions.xg import xgmodel_get, shotlist_process, xgf_calculate, xgscore_get\n\ndef _config_load(logger, cfg_file=os.path.dirname(__file__)+'/'+'hockeygraphs.cfg'):\n \"\"\"\" load config from file \"\"\"\n logger.debug('_config_load()')\n\n rebound_interval = 0\n break_interval = 0\n config_dic = config_load(cfg_file=cfg_file)\n if 'Shots' in config_dic:\n if 'rebound_interval' in config_dic['Shots']:\n rebound_interval = int(config_dic['Shots']['rebound_interval'])\n if 'break_interval' in config_dic['Shots']:\n break_interval = int(config_dic['Shots']['break_interval'])\n logger.debug('_config_load() ended.')\n\n return (rebound_interval, break_interval)\n\ndef dump_it(g_result_dic_, filename='result.json', limit=20):\n \"\"\" dump and shrink result dictionary \"\"\"\n ele_list = ['home_correct', 'visitor_correct', 'winloss_correct', 'result_correct']\n\n # new_result_dic = {'home_correct': {}, 'visitor_correct': {}, 'winloss_correct': {}, 'result_correct': {}}\n new_result_dic = {}\n\n for ele in ele_list:\n new_result_dic[ele] = []\n cnt = 0\n # pylint: disable=W0640\n for result in sorted(g_result_dic_[ele], key=lambda i: (i[ele], i['result_correct'], i['winloss_correct'], i['g_cnt']), reverse=True):\n cnt += 1\n new_result_dic[ele].append(result)\n if cnt >= limit:\n break\n\n # dump dictionary to store data\n # print('########## dump-disabled ##############')\n json_store(filename, new_result_dic)\n\n return new_result_dic\n\ndef _inputparameters_get(logger, parameter_file, start_val, max_val, step):\n \"\"\"\" load config from file \"\"\"\n logger.debug('inputparameters_get()')\n\n # quantifier dictionary\n input_parameters_ = {\n 'shots_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'handness_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'handness_shots_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'rb_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'rb_shots_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'br_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'br_shots_pctg': {'start': start_val, 'max': max_val, 'step': step}\n }\n\n # process parameters file if exists\n if os.path.exists(parameter_file):\n # load start values from file\n parameter_dic = json_load(parameter_file)\n\n # overwrite start_value we have it in parameter file\n for parameter in input_parameters_:\n if parameter in parameter_dic:\n input_parameters_[parameter]['start'] = parameter_dic[parameter]\n\n return input_parameters_\n\ndef shotstats_get(logger, match_list):\n \"\"\"\" gets shots per match \"\"\"\n logger.debug('stats_get()')\n\n g_shotstat_dict = {}\n g_goal_dic_ = {}\n\n for match_id_ in match_list:\n\n # get match information\n match_dic = match_info_get(LOGGER, match_id_, None, ['date', 'home_team_id', 'home_team__shortcut', 'visitor_team_id', 'visitor_team__shortcut', 'result'])\n\n # get list of shots\n vlist = ['shot_id', 'match_id', 'match_shot_resutl_id', 'player_id', 'player__first_name', 'player__last_name', 'player__jersey', 'player__stick', 'team_id', 'coordinate_x', 'coordinate_y', 'match__home_team_id', 'match__visitor_team_id', 'timestamp', 'real_date']\n shot_list = shot_list_get(LOGGER, 'match_id', match_id_, vlist)\n shift_list = shift_get(logger, 'match_id', match_id, ['shift'])\n periodevent_list = periodevent_get(logger, 'match_id', match_id, ['period_event'])\n\n # get corsi statistics\n (shots_for_5v5, shots_against_5v5, shots_ongoal_for_5v5, shots_ongoal_against_5v5, shot_list_5v5) = gameshots5v5_get(logger, match_id_, match_dic, 'home', shot_list, shift_list, periodevent_list)\n\n # 5v5 goals from periodevents\n goals5v5_dic = goals5v5_get(logger, match_id_, match_dic)\n\n # convert shots and goals into structure we can process later on\n # we also need the XGMODEL_DIC to check if we have the shotcoordinates in our structure\n (shotstat_dic, goal_dic) = shotlist_process(LOGGER, shot_list_5v5, XGMODEL_DIC, REBOUND_INTERVAL, BREAK_INTERVAL)\n g_shotstat_dict[match_id_] = shotstat_dic\n\n # g_goal_dic_[match_id_] = {'home': len(goal_dic['home']), 'visitor': len(goal_dic['visitor'])}\n # we need to compare against 5v5 goals\n g_goal_dic_[match_id_] = {'home': goals5v5_dic['home'], 'visitor': goals5v5_dic['visitor']}\n\n return(g_shotstat_dict, g_goal_dic_)\n\nif __name__ == \"__main__\":\n\n # DEBUG mode on/off\n DEBUG = False\n\n MODEL_FILE = 'model_data.json'\n PARAMETER_FILE = 'model_parameters.json'\n RESULT_FILE = 'result.json'\n\n # initialize logger\n LOGGER = logger_setup(DEBUG)\n\n # load rebound and break interval from config file\n (REBOUND_INTERVAL, BREAK_INTERVAL) = _config_load(LOGGER)\n\n # get model from database\n # XGMODEL_DIC = xgmodel_get(LOGGER)\n XGMODEL_DIC = json_load(MODEL_FILE)\n\n # get list of matches to investigate\n MATCH_LIST = match_list_get(LOGGER, 'season_id', 1, ['match_id'])\n # MATCH_LIST = [1820]\n\n START_VAL = 1.0\n MAX_VAL = 3.5\n STEP = 0.5\n\n # START_VAL = 1.0\n # MAX_VAL = 1.1\n # STEP = 0.1\n\n # quantifier dictionary\n input_parameters = _inputparameters_get(LOGGER, PARAMETER_FILE, START_VAL, MAX_VAL, STEP)\n\n from pprint import pprint\n pprint(input_parameters)\n\n # get global shot and goal stats\n (g_shotstat_dic, g_goal_dic) = shotstats_get(LOGGER, MATCH_LIST)\n\n # load result dic from file to continue on a certain point\n if os.path.exists(RESULT_FILE):\n g_result_dic = json_load(RESULT_FILE)\n else:\n g_result_dic = {'home_correct': [], 'visitor_correct': [], 'winloss_correct': [], 'result_correct': []}\n\n # set startpoint for measurement\n uts_start = uts_now()\n\n # initialze counter\n G_CNT = 0\n for shots_pctg in np.arange(START_VAL, input_parameters['shots_pctg']['max'] + input_parameters['shots_pctg']['step'], input_parameters['shots_pctg']['step']):\n if shots_pctg < input_parameters['shots_pctg']['start']:\n continue\n else:\n input_parameters['shots_pctg']['start'] = START_VAL\n\n for handness_pctg in np.arange(START_VAL, input_parameters['handness_pctg']['max'] + input_parameters['handness_pctg']['step'], input_parameters['handness_pctg']['step']):\n if handness_pctg < input_parameters['handness_pctg']['start']:\n continue\n else:\n input_parameters['handness_pctg']['start'] = START_VAL\n\n for handness_shots_pctg in np.arange(START_VAL, input_parameters['handness_shots_pctg']['max'] + input_parameters['handness_shots_pctg']['step'], input_parameters['handness_shots_pctg']['step']):\n if handness_shots_pctg < input_parameters['handness_shots_pctg']['start']:\n continue\n else:\n input_parameters['handness_shots_pctg']['start'] = START_VAL\n\n for rb_pctg in np.arange(START_VAL, input_parameters['rb_pctg']['max'] + input_parameters['rb_pctg']['step'], input_parameters['rb_pctg']['step']):\n if rb_pctg < input_parameters['rb_pctg']['start']:\n continue\n else:\n input_parameters['rb_pctg']['start'] = START_VAL\n\n for rb_shots_pctg in np.arange(START_VAL, input_parameters['rb_shots_pctg']['max'] + input_parameters['rb_shots_pctg']['step'], input_parameters['rb_shots_pctg']['step']):\n if rb_shots_pctg < input_parameters['rb_shots_pctg']['start']:\n continue\n else:\n input_parameters['rb_shots_pctg']['start'] = START_VAL\n\n for br_pctg in np.arange(START_VAL, input_parameters['br_pctg']['max'] + input_parameters['br_pctg']['step'], input_parameters['br_pctg']['step']):\n if br_pctg < input_parameters['br_pctg']['start']:\n continue\n else:\n input_parameters['br_pctg']['start'] = START_VAL\n\n for br_shots_pctg in np.arange(START_VAL, input_parameters['br_shots_pctg']['max'] + input_parameters['br_shots_pctg']['step'], input_parameters['br_shots_pctg']['step']):\n if br_shots_pctg < input_parameters['br_shots_pctg']['start']:\n continue\n else:\n input_parameters['br_shots_pctg']['start'] = START_VAL\n\n G_CNT += 1\n # print(round(shots_pctg, 1), round(handness_pctg, 1), round(handness_shots_pctg, 1), round(rb_pctg, 1), round(rb_shots_pctg, 1), round(br_pctg, 1), round(br_shots_pctg, 1))\n quantifier_dic = {\n 'home': {\n 'shots_pctg': round(shots_pctg, 1),\n 'handness_pctg': round(handness_pctg, 1),\n 'handness_shots_pctg': round(handness_shots_pctg, 1),\n 'rb_pctg': round(rb_pctg, 1),\n 'rb_shots_pctg': round(rb_shots_pctg, 1),\n 'br_pctg': round(br_pctg, 1),\n 'br_shots_pctg': round(br_shots_pctg, 1),\n },\n 'visitor': {\n 'shots_pctg': round(shots_pctg, 1),\n 'handness_pctg': round(handness_pctg, 1),\n 'handness_shots_pctg': round(handness_shots_pctg, 1),\n 'rb_pctg': round(rb_pctg, 1),\n 'rb_shots_pctg': round(rb_shots_pctg, 1),\n 'br_pctg': round(br_pctg, 1),\n 'br_shots_pctg': round(br_shots_pctg, 1),\n }\n }\n\n MATCH_CNT = 0\n HOME_CORRECT = 0\n VISITOR_CORRECT = 0\n WINLOSS_CORRECT = 0\n RESULT_CORRECT = 0\n\n for match_id in g_shotstat_dic:\n MATCH_CNT += 1\n\n playerxgf_dic = xgf_calculate(LOGGER, g_shotstat_dic[match_id], quantifier_dic)\n xgf_dic = xgscore_get(LOGGER, playerxgf_dic)\n # convert xg to make them comparable with 5v5 goals\n xgf_dic['home'] = int(round(xgf_dic['home'], 0))\n xgf_dic['visitor'] = int(round(xgf_dic['visitor'], 0))\n # print(xgf_dic['home'], xgf_dic['visitor'], g_goal_dic[match_id]['home'], g_goal_dic[match_id]['visitor'])\n\n # check homescore\n if xgf_dic['home'] == g_goal_dic[match_id]['home']:\n HOME_CORRECT += 1\n # check visitor score\n if xgf_dic['visitor'] == g_goal_dic[match_id]['visitor']:\n VISITOR_CORRECT += 1\n\n # check full score\n if xgf_dic['home'] == g_goal_dic[match_id]['home'] and xgf_dic['visitor'] == g_goal_dic[match_id]['visitor']:\n RESULT_CORRECT += 1\n\n # check for winloss\n if xgf_dic['home'] < xgf_dic['visitor'] and g_goal_dic[match_id]['home'] < g_goal_dic[match_id]['visitor']:\n WINLOSS_CORRECT += 1\n elif xgf_dic['home'] > xgf_dic['visitor'] and g_goal_dic[match_id]['home'] > g_goal_dic[match_id]['visitor']:\n WINLOSS_CORRECT += 1\n elif xgf_dic['home'] == xgf_dic['visitor'] and g_goal_dic[match_id]['home'] == g_goal_dic[match_id]['visitor']:\n WINLOSS_CORRECT += 1\n\n tmp_dic = {'g_cnt': G_CNT, 'match_cnt': MATCH_CNT, 'home_correct': HOME_CORRECT, 'visitor_correct': VISITOR_CORRECT, 'winloss_correct': WINLOSS_CORRECT, 'result_correct': RESULT_CORRECT, 'quantifier_dic': quantifier_dic}\n # store results in different trees\n g_result_dic['home_correct'].append(tmp_dic)\n g_result_dic['visitor_correct'].append(tmp_dic)\n g_result_dic['winloss_correct'].append(tmp_dic)\n g_result_dic['result_correct'].append(tmp_dic)\n\n print('{0}/{4}: dump shots_pctg: {1}, handness_pctg: {2}, handness_shots_pctg: {3}'.format(uts_now()-uts_start, round(shots_pctg, 1), round(handness_pctg, 1), round(handness_shots_pctg, 1), G_CNT))\n # dump here\n g_result_dic = dump_it(g_result_dic, RESULT_FILE)\n # store quantifier dic\n json_store(PARAMETER_FILE, quantifier_dic['home'])\n\n # print status\n print('g_cnt', G_CNT, 'match_cnt', MATCH_CNT, 'home_correct', HOME_CORRECT, 'visitor_correct', VISITOR_CORRECT, 'winloss_correct', WINLOSS_CORRECT, 'result_correct', RESULT_CORRECT)\n # dump with uts at the end\n uts = uts_now()\n g_result_dic = dump_it(g_result_dic, 'result_handness_{0}.json'.format(uts))\n","repo_name":"grindsa/hockey_graphs","sub_path":"rest/tools/xg_weightener.py","file_name":"xg_weightener.py","file_ext":"py","file_size_in_byte":15044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29933602502","text":"import numpy as np\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.datasets import fetch_openml\n\n\n# Network fcn class.\nclass NeuralNetwork:\n # config data.\n TOTAL_EPOCH = 15\n BATCH_SIZE = 1000\n LEARNING_RATE = 0.001\n SEED = 42\n TRAIN_DATASET_SIZE = 60000\n TEST_DATASET_SIZE = 10000\n\n def __init__(self, learning_rate):\n # for batch count.\n self._i = 0\n\n # weight initialize\n self.W1 = np.random.randn(784, 100)\n self.W2 = np.random.randn(100, 10)\n self.learningRate = learning_rate\n\n # load data using scikit-learn.\n self.X_train, self.X_test, self.y_train, self.y_test = self.load_data(one_hot=True)\n\n def sigmoid(self, x):\n return 1.0 / (1.0 + np.exp(-x))\n\n def back_sigmoid(self, x):\n return x * (1. - x)\n\n def softmax(self, x):\n if x.ndim == 1:\n x = x.reshape([1, x.size])\n modifiedX = x - np.max(x, 1).reshape([x.shape[0], 1])\n sigmoid = np.exp(modifiedX)\n return sigmoid / np.sum(sigmoid, axis=1).reshape([sigmoid.shape[0], 1])\n\n def getCrossEntropy(self, predictY, labelY):\n return np.mean(-np.sum(labelY * np.log(self.softmax(predictY)), axis=1))\n\n def feedForward(self, x):\n y1 = np.dot(x, self.W1)\n sigmoidY1 = self.sigmoid(y1)\n\n y2 = np.dot(sigmoidY1, self.W2)\n softmaxY2 = self.softmax(y2)\n\n return sigmoidY1, softmaxY2\n\n def backpropagation(self, x, labelY, predictY1, predictY2):\n dW2 = predictY1.T.dot((predictY2 - labelY) / NeuralNetwork.BATCH_SIZE)\n dW1 = x.T.dot(np.matmul(predictY2 - labelY, self.W2.T) * self.back_sigmoid(predictY1))\n\n return dW1, dW2\n\n def update_weight(self, dW1, dW2):\n self.W1 -= self.learningRate * dW1\n self.W2 -= self.learningRate * dW2\n\n def load_data(self, one_hot=True):\n\n print(\"loading data...\")\n # only use scikit-learn when load MNIST data for convenience\n mnist = fetch_openml('mnist_784')\n X, y = mnist[\"data\"], mnist[\"target\"]\n\n if one_hot:\n one_hot = OneHotEncoder()\n y = one_hot.fit_transform(y.reshape(-1, 1))\n y = y.toarray()\n print(\"y are one-hot encoded..\")\n\n return X[:NeuralNetwork.TRAIN_DATASET_SIZE], X[NeuralNetwork.TRAIN_DATASET_SIZE:], y[\n :NeuralNetwork.TRAIN_DATASET_SIZE], y[\n NeuralNetwork.TRAIN_DATASET_SIZE:]\n\n # print next batch.\n def next_batch(self, batch_size):\n x, y = self.X_train[self._i:self._i + batch_size], self.y_train[self._i: self._i + batch_size]\n self._i = (self._i + batch_size) % len(self.X_train)\n return x, y\n\n def shake_data(self):\n rnd_index = np.random.permutation(len(self.X_train))\n self.X_train = self.X_train[rnd_index]\n self.y_train = self.y_train[rnd_index]\n\n\n# fix the random value.\nnp.random.seed(NeuralNetwork.SEED)\n\n# define network fcn.\nnetwork_model = NeuralNetwork(learning_rate=NeuralNetwork.LEARNING_RATE)\n\n# using mini-batch\nfor i in range(NeuralNetwork.TOTAL_EPOCH):\n print(\"============== EPOCH {} START ==============\".format(i + 1))\n for j in range(NeuralNetwork.TRAIN_DATASET_SIZE // NeuralNetwork.BATCH_SIZE):\n print(\"-------------- batch {} training...\".format(j))\n batch_x, batch_y = network_model.next_batch(NeuralNetwork.BATCH_SIZE)\n\n # TODO: feed-forward term.\n y1, y2 = network_model.feedForward(batch_x)\n\n # TODO: back-propagation term.\n dW1, dW2 = network_model.backpropagation(batch_x, batch_y, y1, y2)\n network_model.update_weight(dW1, dW2)\n print(\"============== EPOCH {} END ================\".format(i + 1))\n\n # shake data.\n network_model.shake_data()\n\n y1, y2 = network_model.feedForward(network_model.X_test)\n correct_prediction = np.equal(np.argmax(y2, axis=1), np.argmax(network_model.y_test, axis=1))\n accuracy = np.mean(correct_prediction)\n print(accuracy)\n","repo_name":"hololee/Implement-neural-network-from-scratch","sub_path":"test/question_2layer_sc.py","file_name":"question_2layer_sc.py","file_ext":"py","file_size_in_byte":4169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12612337263","text":"\"\"\"Tests covering utilities for integrating with the catalog service.\"\"\"\n# pylint: disable=missing-docstring\n\n\nfrom collections import defaultdict\nfrom datetime import timedelta\nfrom unittest import mock\n\nfrom django.contrib.auth import get_user_model\nfrom django.core.cache import cache\nfrom django.test import TestCase, override_settings\nfrom django.test.client import RequestFactory\nfrom django.utils.timezone import now\nfrom opaque_keys.edx.keys import CourseKey\n\nfrom common.djangoapps.course_modes.helpers import CourseMode\nfrom common.djangoapps.course_modes.tests.factories import CourseModeFactory\nfrom common.djangoapps.entitlements.tests.factories import CourseEntitlementFactory\nfrom openedx.core.constants import COURSE_UNPUBLISHED\nfrom openedx.core.djangoapps.catalog.cache import (\n CATALOG_COURSE_PROGRAMS_CACHE_KEY_TPL,\n COURSE_PROGRAMS_CACHE_KEY_TPL,\n PATHWAY_CACHE_KEY_TPL,\n PROGRAM_CACHE_KEY_TPL,\n PROGRAMS_BY_TYPE_CACHE_KEY_TPL,\n PROGRAMS_BY_TYPE_SLUG_CACHE_KEY_TPL,\n SITE_PATHWAY_IDS_CACHE_KEY_TPL,\n SITE_PROGRAM_UUIDS_CACHE_KEY_TPL\n)\nfrom openedx.core.djangoapps.catalog.models import CatalogIntegration\nfrom openedx.core.djangoapps.catalog.tests.factories import (\n CourseFactory,\n CourseRunFactory,\n PathwayFactory,\n ProgramFactory,\n ProgramTypeFactory,\n ProgramTypeAttrsFactory\n)\nfrom openedx.core.djangoapps.catalog.tests.mixins import CatalogIntegrationMixin\nfrom openedx.core.djangoapps.catalog.utils import (\n child_programs,\n course_run_keys_for_program,\n is_course_run_in_program,\n get_course_run_details,\n get_course_runs,\n get_course_runs_for_course,\n get_currency_data,\n get_localized_price_text,\n get_owners_for_course,\n get_pathways,\n get_program_types,\n get_programs,\n get_programs_by_type,\n get_programs_by_type_slug,\n get_visible_sessions_for_entitlement,\n normalize_program_type,\n)\nfrom openedx.core.djangoapps.content.course_overviews.tests.factories import CourseOverviewFactory\nfrom openedx.core.djangoapps.site_configuration.tests.factories import SiteFactory\nfrom openedx.core.djangolib.testing.utils import CacheIsolationTestCase, skip_unless_lms\nfrom common.djangoapps.student.tests.factories import CourseEnrollmentFactory, UserFactory\nfrom openedx.core.djangoapps.site_configuration.tests.test_util import with_site_configuration_context\n\nUTILS_MODULE = 'openedx.core.djangoapps.catalog.utils'\nUser = get_user_model() # pylint: disable=invalid-name\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.logger.info')\n@mock.patch(UTILS_MODULE + '.logger.warning')\nclass TestGetPrograms(CacheIsolationTestCase):\n ENABLED_CACHES = ['default']\n\n def setUp(self):\n super().setUp()\n self.site = SiteFactory()\n\n def test_get_many(self, mock_warning, mock_info):\n programs = ProgramFactory.create_batch(3)\n\n # Cache details for 2 of 3 programs.\n partial_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in programs[:2]\n }\n cache.set_many(partial_programs, None)\n\n # When called before UUIDs are cached, the function should return an\n # empty list and log a warning.\n with with_site_configuration_context(domain=self.site.name, configuration={'COURSE_CATALOG_API_URL': 'foo'}):\n assert get_programs(site=self.site) == []\n mock_warning.assert_called_once_with(\n f'Failed to get program UUIDs from the cache for site {self.site.domain}.'\n )\n mock_warning.reset_mock()\n\n # Cache UUIDs for all 3 programs.\n cache.set(\n SITE_PROGRAM_UUIDS_CACHE_KEY_TPL.format(domain=self.site.domain),\n [program['uuid'] for program in programs],\n None\n )\n\n actual_programs = get_programs(site=self.site)\n\n # The 2 cached programs should be returned while info and warning\n # messages should be logged for the missing one.\n assert {program['uuid'] for program in actual_programs} == \\\n {program['uuid'] for program in partial_programs.values()}\n mock_info.assert_called_with('Failed to get details for 1 programs. Retrying.')\n mock_warning.assert_called_with(\n 'Failed to get details for program {uuid} from the cache.'.format(uuid=programs[2]['uuid'])\n )\n mock_warning.reset_mock()\n\n # We can't use a set comparison here because these values are dictionaries\n # and aren't hashable. We've already verified that all programs came out\n # of the cache above, so all we need to do here is verify the accuracy of\n # the data itself.\n for program in actual_programs:\n key = PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid'])\n assert program == partial_programs[key]\n\n # Cache details for all 3 programs.\n all_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in programs\n }\n cache.set_many(all_programs, None)\n\n actual_programs = get_programs(site=self.site)\n\n # All 3 programs should be returned.\n assert {program['uuid'] for program in actual_programs} ==\\\n {program['uuid'] for program in all_programs.values()}\n assert not mock_warning.called\n\n for program in actual_programs:\n key = PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid'])\n assert program == all_programs[key]\n\n @mock.patch(UTILS_MODULE + '.cache')\n def test_get_many_with_missing(self, mock_cache, mock_warning, mock_info):\n programs = ProgramFactory.create_batch(3)\n\n all_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in programs\n }\n\n partial_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in programs[:2]\n }\n\n def fake_get_many(keys):\n if len(keys) == 1:\n return {PROGRAM_CACHE_KEY_TPL.format(uuid=programs[-1]['uuid']): programs[-1]}\n else:\n return partial_programs\n\n mock_cache.get.return_value = [program['uuid'] for program in programs]\n mock_cache.get_many.side_effect = fake_get_many\n\n with with_site_configuration_context(domain=self.site.name, configuration={'COURSE_CATALOG_API_URL': 'foo'}):\n actual_programs = get_programs(site=self.site)\n\n # All 3 cached programs should be returned. An info message should be\n # logged about the one that was initially missing, but the code should\n # be able to stitch together all the details.\n assert {program['uuid'] for program in actual_programs} ==\\\n {program['uuid'] for program in all_programs.values()}\n assert not mock_warning.called\n mock_info.assert_called_with('Failed to get details for 1 programs. Retrying.')\n\n for program in actual_programs:\n key = PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid'])\n assert program == all_programs[key]\n\n def test_get_one(self, mock_warning, _mock_info):\n expected_program = ProgramFactory()\n expected_uuid = expected_program['uuid']\n\n assert get_programs(uuid=expected_uuid) is None\n mock_warning.assert_called_once_with(\n f'Failed to get details for program {expected_uuid} from the cache.'\n )\n mock_warning.reset_mock()\n\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=expected_uuid),\n expected_program,\n None\n )\n\n actual_program = get_programs(uuid=expected_uuid)\n assert actual_program == expected_program\n assert not mock_warning.called\n\n def test_get_from_course(self, mock_warning, _mock_info):\n expected_program = ProgramFactory()\n expected_course = expected_program['courses'][0]['course_runs'][0]['key']\n\n assert get_programs(course=expected_course) == []\n\n cache.set(\n COURSE_PROGRAMS_CACHE_KEY_TPL.format(course_run_id=expected_course),\n [expected_program['uuid']],\n None\n )\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=expected_program['uuid']),\n expected_program,\n None\n )\n\n actual_program = get_programs(course=expected_course)\n assert actual_program == [expected_program]\n assert not mock_warning.called\n\n def test_get_via_uuids(self, mock_warning, _mock_info):\n first_program = ProgramFactory()\n second_program = ProgramFactory()\n\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=first_program['uuid']),\n first_program,\n None\n )\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=second_program['uuid']),\n second_program,\n None\n )\n\n results = get_programs(uuids=[first_program['uuid'], second_program['uuid']])\n\n assert first_program in results\n assert second_program in results\n assert not mock_warning.called\n\n def test_get_from_catalog_course(self, mock_warning, _mock_info):\n expected_program = ProgramFactory()\n expected_catalog_course = expected_program['courses'][0]\n\n assert get_programs(catalog_course_uuid=expected_catalog_course['uuid']) == []\n\n cache.set(\n CATALOG_COURSE_PROGRAMS_CACHE_KEY_TPL.format(course_uuid=expected_catalog_course['uuid']),\n [expected_program['uuid']],\n None\n )\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=expected_program['uuid']),\n expected_program,\n None\n )\n\n actual_program = get_programs(catalog_course_uuid=expected_catalog_course['uuid'])\n\n assert actual_program == [expected_program]\n assert not mock_warning.called\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.logger.info')\n@mock.patch(UTILS_MODULE + '.logger.warning')\nclass TestGetPathways(CacheIsolationTestCase):\n ENABLED_CACHES = ['default']\n\n def setUp(self):\n super().setUp()\n self.site = SiteFactory()\n\n def test_get_many(self, mock_warning, mock_info):\n pathways = PathwayFactory.create_batch(3)\n\n # Cache details for 2 of 3 programs.\n partial_pathways = {\n PATHWAY_CACHE_KEY_TPL.format(id=pathway['id']): pathway for pathway in pathways[:2]\n }\n cache.set_many(partial_pathways, None)\n\n # When called before pathways are cached, the function should return an\n # empty list and log a warning.\n assert get_pathways(self.site) == []\n mock_warning.assert_called_once_with('Failed to get credit pathway ids from the cache.')\n mock_warning.reset_mock()\n\n # Cache all 3 pathways\n cache.set(\n SITE_PATHWAY_IDS_CACHE_KEY_TPL.format(domain=self.site.domain),\n [pathway['id'] for pathway in pathways],\n None\n )\n\n actual_pathways = get_pathways(self.site)\n\n # The 2 cached pathways should be returned while info and warning\n # messages should be logged for the missing one.\n assert {pathway['id'] for pathway in actual_pathways} ==\\\n {pathway['id'] for pathway in partial_pathways.values()}\n mock_info.assert_called_with('Failed to get details for 1 pathways. Retrying.')\n mock_warning.assert_called_with(\n 'Failed to get details for credit pathway {id} from the cache.'.format(id=pathways[2]['id'])\n )\n mock_warning.reset_mock()\n\n # We can't use a set comparison here because these values are dictionaries\n # and aren't hashable. We've already verified that all pathways came out\n # of the cache above, so all we need to do here is verify the accuracy of\n # the data itself.\n for pathway in actual_pathways:\n key = PATHWAY_CACHE_KEY_TPL.format(id=pathway['id'])\n assert pathway == partial_pathways[key]\n\n # Cache details for all 3 pathways.\n all_pathways = {\n PATHWAY_CACHE_KEY_TPL.format(id=pathway['id']): pathway for pathway in pathways\n }\n cache.set_many(all_pathways, None)\n\n actual_pathways = get_pathways(self.site)\n\n # All 3 pathways should be returned.\n assert {pathway['id'] for pathway in actual_pathways} ==\\\n {pathway['id'] for pathway in all_pathways.values()}\n assert not mock_warning.called\n\n for pathway in actual_pathways:\n key = PATHWAY_CACHE_KEY_TPL.format(id=pathway['id'])\n assert pathway == all_pathways[key]\n\n @mock.patch(UTILS_MODULE + '.cache')\n def test_get_many_with_missing(self, mock_cache, mock_warning, mock_info):\n pathways = PathwayFactory.create_batch(3)\n\n all_pathways = {\n PATHWAY_CACHE_KEY_TPL.format(id=pathway['id']): pathway for pathway in pathways\n }\n\n partial_pathways = {\n PATHWAY_CACHE_KEY_TPL.format(id=pathway['id']): pathway for pathway in pathways[:2]\n }\n\n def fake_get_many(keys):\n if len(keys) == 1:\n return {PATHWAY_CACHE_KEY_TPL.format(id=pathways[-1]['id']): pathways[-1]}\n else:\n return partial_pathways\n\n mock_cache.get.return_value = [pathway['id'] for pathway in pathways]\n mock_cache.get_many.side_effect = fake_get_many\n\n actual_pathways = get_pathways(self.site)\n\n # All 3 cached pathways should be returned. An info message should be\n # logged about the one that was initially missing, but the code should\n # be able to stitch together all the details.\n assert {pathway['id'] for pathway in actual_pathways} ==\\\n {pathway['id'] for pathway in all_pathways.values()}\n assert not mock_warning.called\n mock_info.assert_called_with('Failed to get details for 1 pathways. Retrying.')\n\n for pathway in actual_pathways:\n key = PATHWAY_CACHE_KEY_TPL.format(id=pathway['id'])\n assert pathway == all_pathways[key]\n\n def test_get_one(self, mock_warning, _mock_info):\n expected_pathway = PathwayFactory()\n expected_id = expected_pathway['id']\n\n assert get_pathways(self.site, pathway_id=expected_id) is None\n mock_warning.assert_called_once_with(\n f'Failed to get details for credit pathway {expected_id} from the cache.'\n )\n mock_warning.reset_mock()\n\n cache.set(\n PATHWAY_CACHE_KEY_TPL.format(id=expected_id),\n expected_pathway,\n None\n )\n\n actual_pathway = get_pathways(self.site, pathway_id=expected_id)\n assert actual_pathway == expected_pathway\n assert not mock_warning.called\n\n\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetProgramTypes(CatalogIntegrationMixin, TestCase):\n \"\"\"Tests covering retrieval of program types from the catalog service.\"\"\"\n @override_settings(COURSE_CATALOG_API_URL='https://api.example.com/v1/')\n def test_get_program_types(self, mock_get_edx_api_data):\n \"\"\"Verify get_program_types returns the expected list of program types.\"\"\"\n program_types = ProgramTypeFactory.create_batch(3)\n mock_get_edx_api_data.return_value = program_types\n\n # Catalog integration is disabled.\n data = get_program_types()\n assert data == []\n\n catalog_integration = self.create_catalog_integration()\n UserFactory(username=catalog_integration.service_username)\n data = get_program_types()\n assert data == program_types\n\n program = program_types[0]\n data = get_program_types(name=program['name'])\n assert data == program\n\n\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetCurrency(CatalogIntegrationMixin, TestCase):\n \"\"\"Tests covering retrieval of currency data from the catalog service.\"\"\"\n @override_settings(COURSE_CATALOG_API_URL='https://api.example.com/v1/')\n def test_get_currency_data(self, mock_get_edx_api_data):\n \"\"\"Verify get_currency_data returns the currency data.\"\"\"\n currency_data = {\n \"code\": \"CAD\",\n \"rate\": 1.257237,\n \"symbol\": \"$\"\n }\n mock_get_edx_api_data.return_value = currency_data\n\n # Catalog integration is disabled.\n data = get_currency_data()\n assert data == []\n\n catalog_integration = self.create_catalog_integration()\n UserFactory(username=catalog_integration.service_username)\n data = get_currency_data()\n assert data == currency_data\n\n\n@mock.patch(UTILS_MODULE + '.get_currency_data')\nclass TestGetLocalizedPriceText(TestCase):\n \"\"\"\n Tests covering converting prices to a localized currency\n \"\"\"\n def test_localized_string(self, mock_get_currency_data):\n currency_data = {\n \"BEL\": {\"rate\": 0.835621, \"code\": \"EUR\", \"symbol\": \"\\u20ac\"},\n \"GBR\": {\"rate\": 0.737822, \"code\": \"GBP\", \"symbol\": \"\\u00a3\"},\n \"CAN\": {\"rate\": 2, \"code\": \"CAD\", \"symbol\": \"$\"},\n }\n mock_get_currency_data.return_value = currency_data\n\n request = RequestFactory().get('/dummy-url')\n request.session = {\n 'country_code': 'CA'\n }\n expected_result = '$20 CAD'\n assert get_localized_price_text(10, request) == expected_result\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetCourseRuns(CatalogIntegrationMixin, CacheIsolationTestCase):\n \"\"\"\n Tests covering retrieval of course runs from the catalog service.\n \"\"\"\n def setUp(self):\n super().setUp()\n\n self.catalog_integration = self.create_catalog_integration(cache_ttl=1)\n self.user = UserFactory(username=self.catalog_integration.service_username)\n\n def assert_contract(self, call_args):\n \"\"\"\n Verify that API data retrieval utility is used correctly.\n \"\"\"\n args, kwargs = call_args\n\n for arg in (self.catalog_integration, 'course_runs'):\n assert arg in args\n\n assert kwargs['base_api_url'] == self.catalog_integration.get_internal_api_url() # pylint: disable=protected-access, line-too-long\n\n querystring = {\n 'page_size': 20,\n 'exclude_utm': 1,\n }\n\n assert kwargs['querystring'] == querystring\n\n return args, kwargs\n\n def test_config_missing(self, mock_get_edx_api_data):\n \"\"\"\n Verify that no errors occur when catalog config is missing.\n \"\"\"\n CatalogIntegration.objects.all().delete()\n self.clear_caches()\n\n data = get_course_runs()\n assert not mock_get_edx_api_data.called\n assert data == []\n\n @mock.patch(UTILS_MODULE + '.logger.error')\n def test_service_user_missing(self, mock_log_error, mock_get_edx_api_data):\n \"\"\"\n Verify that no errors occur when the catalog service user is missing.\n \"\"\"\n catalog_integration = self.create_catalog_integration(service_username='nonexistent-user')\n\n data = get_course_runs()\n mock_log_error.any_call(\n 'Catalog service user with username [%s] does not exist. Course runs will not be retrieved.',\n catalog_integration.service_username,\n )\n assert not mock_get_edx_api_data.called\n assert data == []\n\n def test_get_course_runs(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of course runs.\n \"\"\"\n catalog_course_runs = CourseRunFactory.create_batch(10)\n mock_get_edx_api_data.return_value = catalog_course_runs\n\n data = get_course_runs()\n assert mock_get_edx_api_data.called\n self.assert_contract(mock_get_edx_api_data.call_args)\n assert data == catalog_course_runs\n\n def test_get_course_runs_by_course(self, mock_get_edx_api_data):\n \"\"\"\n Test retrievals of run from a Course.\n \"\"\"\n catalog_course_runs = CourseRunFactory.create_batch(10)\n catalog_course = CourseFactory(course_runs=catalog_course_runs)\n mock_get_edx_api_data.return_value = catalog_course\n\n data = get_course_runs_for_course(course_uuid=str(catalog_course['uuid']))\n assert mock_get_edx_api_data.called\n assert data == catalog_course_runs\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetCourseOwners(CatalogIntegrationMixin, TestCase):\n \"\"\"\n Tests covering retrieval of course runs from the catalog service.\n \"\"\"\n def setUp(self):\n super().setUp()\n\n self.catalog_integration = self.create_catalog_integration(cache_ttl=1)\n self.user = UserFactory(username=self.catalog_integration.service_username)\n\n def test_get_course_owners_by_course(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of course runs.\n \"\"\"\n catalog_course_runs = CourseRunFactory.create_batch(10)\n catalog_course = CourseFactory(course_runs=catalog_course_runs)\n mock_get_edx_api_data.return_value = catalog_course\n\n data = get_owners_for_course(course_uuid=str(catalog_course['uuid']))\n assert mock_get_edx_api_data.called\n assert data == catalog_course['owners']\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestSessionEntitlement(CatalogIntegrationMixin, TestCase):\n \"\"\"\n Test Covering data related Entitlements.\n \"\"\"\n def setUp(self):\n super().setUp()\n\n self.catalog_integration = self.create_catalog_integration(cache_ttl=1)\n self.user = UserFactory(username=self.catalog_integration.service_username)\n self.tomorrow = now() + timedelta(days=1)\n\n def test_get_visible_sessions_for_entitlement(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of visible session entitlements.\n \"\"\"\n catalog_course_run = CourseRunFactory.create()\n catalog_course = CourseFactory(course_runs=[catalog_course_run])\n mock_get_edx_api_data.return_value = catalog_course\n course_key = CourseKey.from_string(catalog_course_run.get('key'))\n course_overview = CourseOverviewFactory.create(id=course_key, start=self.tomorrow)\n CourseModeFactory.create(mode_slug=CourseMode.VERIFIED, min_price=100, course_id=course_overview.id)\n course_enrollment = CourseEnrollmentFactory(\n user=self.user, course=course_overview, mode=CourseMode.VERIFIED\n )\n entitlement = CourseEntitlementFactory(\n user=self.user, enrollment_course_run=course_enrollment, mode=CourseMode.VERIFIED\n )\n\n session_entitlements = get_visible_sessions_for_entitlement(entitlement)\n assert session_entitlements == [catalog_course_run]\n\n def test_get_visible_sessions_for_entitlement_expired_mode(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of visible session entitlements.\n \"\"\"\n catalog_course_run = CourseRunFactory.create()\n catalog_course = CourseFactory(course_runs=[catalog_course_run])\n mock_get_edx_api_data.return_value = catalog_course\n course_key = CourseKey.from_string(catalog_course_run.get('key'))\n course_overview = CourseOverviewFactory.create(id=course_key, start=self.tomorrow)\n CourseModeFactory.create(\n mode_slug=CourseMode.VERIFIED,\n min_price=100,\n course_id=course_overview.id,\n expiration_datetime=now() - timedelta(days=1)\n )\n course_enrollment = CourseEnrollmentFactory(\n user=self.user, course=course_overview, mode=CourseMode.VERIFIED\n )\n entitlement = CourseEntitlementFactory(\n user=self.user, enrollment_course_run=course_enrollment, mode=CourseMode.VERIFIED\n )\n\n session_entitlements = get_visible_sessions_for_entitlement(entitlement)\n assert session_entitlements == [catalog_course_run]\n\n def test_unpublished_sessions_for_entitlement_when_enrolled(self, mock_get_edx_api_data):\n \"\"\"\n Test unpublished course runs are part of visible session entitlements when the user\n is enrolled.\n \"\"\"\n catalog_course_run = CourseRunFactory.create(status=COURSE_UNPUBLISHED)\n catalog_course = CourseFactory(course_runs=[catalog_course_run])\n mock_get_edx_api_data.return_value = catalog_course\n course_key = CourseKey.from_string(catalog_course_run.get('key'))\n course_overview = CourseOverviewFactory.create(id=course_key, start=self.tomorrow)\n CourseModeFactory.create(\n mode_slug=CourseMode.VERIFIED,\n min_price=100,\n course_id=course_overview.id,\n expiration_datetime=now() - timedelta(days=1)\n )\n course_enrollment = CourseEnrollmentFactory(\n user=self.user, course=course_overview, mode=CourseMode.VERIFIED\n )\n entitlement = CourseEntitlementFactory(\n user=self.user, enrollment_course_run=course_enrollment, mode=CourseMode.VERIFIED\n )\n\n session_entitlements = get_visible_sessions_for_entitlement(entitlement)\n assert session_entitlements == [catalog_course_run]\n\n def test_unpublished_sessions_for_entitlement(self, mock_get_edx_api_data):\n \"\"\"\n Test unpublished course runs are not part of visible session entitlements when the user\n is not enrolled and upgrade deadline is passed.\n \"\"\"\n catalog_course_run = CourseRunFactory.create(status=COURSE_UNPUBLISHED)\n catalog_course = CourseFactory(course_runs=[catalog_course_run])\n mock_get_edx_api_data.return_value = catalog_course\n course_key = CourseKey.from_string(catalog_course_run.get('key'))\n course_overview = CourseOverviewFactory.create(id=course_key, start=self.tomorrow)\n CourseModeFactory.create(\n mode_slug=CourseMode.VERIFIED,\n min_price=100,\n course_id=course_overview.id,\n expiration_datetime=now() - timedelta(days=1)\n )\n entitlement = CourseEntitlementFactory(\n user=self.user, mode=CourseMode.VERIFIED\n )\n\n session_entitlements = get_visible_sessions_for_entitlement(entitlement)\n assert not session_entitlements\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetCourseRunDetails(CatalogIntegrationMixin, TestCase):\n \"\"\"\n Tests covering retrieval of information about a specific course run from the catalog service.\n \"\"\"\n def setUp(self):\n super().setUp()\n self.catalog_integration = self.create_catalog_integration(cache_ttl=1)\n self.user = UserFactory(username=self.catalog_integration.service_username)\n\n def test_get_course_run_details(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of details about a specific course run\n \"\"\"\n course_run = CourseRunFactory()\n course_run_details = {\n 'content_language': course_run['content_language'],\n 'weeks_to_complete': course_run['weeks_to_complete'],\n 'max_effort': course_run['max_effort']\n }\n mock_get_edx_api_data.return_value = course_run_details\n data = get_course_run_details(course_run['key'], ['content_language', 'weeks_to_complete', 'max_effort'])\n assert mock_get_edx_api_data.called\n assert data == course_run_details\n\n\nclass TestProgramCourseRunCrawling(TestCase):\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n cls.grandchild_1 = {\n 'title': 'grandchild 1',\n 'curricula': [{'is_active': True, 'courses': [], 'programs': []}],\n }\n cls.grandchild_2 = {\n 'title': 'grandchild 2',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [{\n 'course_runs': [\n {'key': 'course-run-4'},\n ],\n }],\n 'programs': [],\n },\n ],\n }\n cls.grandchild_3 = {\n 'title': 'grandchild 3',\n 'curricula': [{'is_active': False}],\n }\n cls.child_1 = {\n 'title': 'child 1',\n 'curricula': [{'is_active': True, 'courses': [], 'programs': [cls.grandchild_1]}],\n }\n cls.child_2 = {\n 'title': 'child 2',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [{\n 'course_runs': [\n {'key': 'course-run-3'},\n ],\n }],\n 'programs': [cls.grandchild_2, cls.grandchild_3],\n },\n ],\n }\n cls.complex_program = {\n 'title': 'complex program',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [{\n 'course_runs': [\n {'key': 'course-run-2'},\n ],\n }],\n 'programs': [cls.child_1, cls.child_2],\n },\n ],\n }\n cls.simple_program = {\n 'title': 'simple program',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [{\n 'course_runs': [\n {'key': 'course-run-1'},\n ],\n }],\n 'programs': [cls.grandchild_1]\n },\n ],\n }\n cls.empty_program = {\n 'title': 'notice that I have a curriculum, but no programs inside it',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [],\n 'programs': [],\n },\n ],\n }\n\n def test_child_programs_no_curriculum(self):\n program = {\n 'title': 'notice that I do not have a curriculum',\n }\n assert not child_programs(program)\n\n def test_child_programs_no_children(self):\n assert not child_programs(self.empty_program)\n\n def test_child_programs_one_child(self):\n assert [self.grandchild_1] == child_programs(self.simple_program)\n\n def test_child_programs_many_children(self):\n expected_children = [\n self.child_1,\n self.grandchild_1,\n self.child_2,\n self.grandchild_2,\n self.grandchild_3,\n ]\n assert expected_children == child_programs(self.complex_program)\n\n def test_course_run_keys_for_program_no_courses(self):\n assert set() == course_run_keys_for_program(self.empty_program)\n\n def test_course_run_keys_for_program_one_course(self):\n assert {'course-run-1'} == course_run_keys_for_program(self.simple_program)\n\n def test_course_run_keys_for_program_many_courses(self):\n expected_course_runs = {\n 'course-run-2',\n 'course-run-3',\n 'course-run-4',\n }\n assert expected_course_runs == course_run_keys_for_program(self.complex_program)\n\n def test_is_course_run_in_program(self):\n assert is_course_run_in_program('course-run-4', self.complex_program)\n assert not is_course_run_in_program('course-run-5', self.complex_program)\n assert not is_course_run_in_program('course-run-4', self.simple_program)\n\n\n@skip_unless_lms\nclass TestGetProgramsByType(CacheIsolationTestCase):\n \"\"\" Test for the ``get_programs_by_type()`` and the ``get_programs_by_type_slug()`` functions. \"\"\"\n ENABLED_CACHES = ['default']\n\n @classmethod\n def setUpClass(cls):\n \"\"\" Sets up program data. \"\"\"\n super().setUpClass()\n cls.site = SiteFactory()\n cls.other_site = SiteFactory()\n cls.masters_program_1 = ProgramFactory.create(\n type='Masters',\n type_attrs=ProgramTypeAttrsFactory.create(slug=\"masters\")\n )\n cls.masters_program_2 = ProgramFactory.create(\n type='Masters',\n type_attrs=ProgramTypeAttrsFactory.create(slug=\"masters\")\n )\n cls.masters_program_other_site = ProgramFactory.create(\n type='Masters',\n type_attrs=ProgramTypeAttrsFactory.create(slug=\"masters\")\n )\n cls.bachelors_program = ProgramFactory.create(\n type='Bachelors',\n type_attrs=ProgramTypeAttrsFactory.create(slug=\"bachelors\")\n )\n cls.no_type_program = ProgramFactory.create(\n type=None,\n type_attrs=None\n )\n\n def setUp(self):\n \"\"\" Loads program data into the cache before each test function. \"\"\"\n super().setUp()\n self.init_cache()\n\n def init_cache(self):\n \"\"\" This function plays the role of the ``cache_programs`` management command. \"\"\"\n all_programs = [\n self.masters_program_1,\n self.masters_program_2,\n self.bachelors_program,\n self.no_type_program,\n self.masters_program_other_site\n ]\n cached_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in all_programs\n }\n cache.set_many(cached_programs, None)\n\n programs_by_type = defaultdict(list)\n programs_by_type_slug = defaultdict(list)\n for program in all_programs:\n program_type = normalize_program_type(program.get('type'))\n program_type_slug = (program.get('type_attrs') or {}).get('slug')\n site_id = self.site.id\n\n if program == self.masters_program_other_site:\n site_id = self.other_site.id\n\n program_type_cache_key = PROGRAMS_BY_TYPE_CACHE_KEY_TPL.format(\n site_id=site_id,\n program_type=program_type\n )\n program_type_slug_cache_key = PROGRAMS_BY_TYPE_SLUG_CACHE_KEY_TPL.format(\n site_id=site_id,\n program_slug=program_type_slug\n )\n programs_by_type[program_type_cache_key].append(program['uuid'])\n programs_by_type_slug[program_type_slug_cache_key].append(program['uuid'])\n\n cache.set_many(programs_by_type, None)\n cache.set_many(programs_by_type_slug, None)\n\n def test_get_masters_programs(self):\n expected_programs = [self.masters_program_1, self.masters_program_2]\n self.assertCountEqual(expected_programs, get_programs_by_type(self.site, 'masters'))\n self.assertCountEqual(expected_programs, get_programs_by_type_slug(self.site, 'masters'))\n\n def test_get_bachelors_programs(self):\n expected_programs = [self.bachelors_program]\n assert expected_programs == get_programs_by_type(self.site, 'bachelors')\n assert expected_programs == get_programs_by_type_slug(self.site, 'bachelors')\n\n def test_get_no_such_type_programs(self):\n expected_programs = []\n assert expected_programs == get_programs_by_type(self.site, 'doctorate')\n assert expected_programs == get_programs_by_type_slug(self.site, 'doctorate')\n\n def test_get_masters_programs_other_site(self):\n expected_programs = [self.masters_program_other_site]\n assert expected_programs == get_programs_by_type(self.other_site, 'masters')\n assert expected_programs == get_programs_by_type_slug(self.other_site, 'masters')\n\n def test_get_programs_null_type(self):\n expected_programs = [self.no_type_program]\n assert expected_programs == get_programs_by_type(self.site, None)\n assert expected_programs == get_programs_by_type_slug(self.site, None)\n\n def test_get_programs_false_type(self):\n expected_programs = []\n assert expected_programs == get_programs_by_type(self.site, False)\n assert expected_programs == get_programs_by_type_slug(self.site, False)\n\n def test_normalize_program_type(self):\n assert 'none' == normalize_program_type(None)\n assert 'false' == normalize_program_type(False)\n assert 'true' == normalize_program_type(True)\n assert '' == normalize_program_type('')\n assert 'masters' == normalize_program_type('Masters')\n assert 'masters' == normalize_program_type('masters')\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/catalog/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":36458,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"15747812182","text":"from collections import deque\n\n\ndef dfs(graph, v, visited):\n #현재노드 방문처리\n visited[v] = 1\n print(v, end=' ')\n for i in graph[v]:\n if visited[i] != 1:\n dfs(graph, i, visited)\n\n\ndef bfs(graph, start, visited):\n #시작 방문노드 큐 추가 및 방문처리\n queue = deque([start])\n visited[start] = 1\n while queue:\n v = queue.popleft()\n print(v, end=' ')\n for i in graph[v]:\n if visited[i] != 1:\n queue.append(i)\n visited[i] = 1\n\n\nn, m, v = map(int, input().split())\ngraph = [[] for _ in range(n + 1)]\nfor i in range(m):\n a, b = map(int, input().split())\n graph[a].append(b)\n graph[b].append(a)\n\n#번호가 작은것 번저 방문하기 위한 정렬\nfor i in range(n+1):\n graph[i].sort()\n\nvisited = [0] * (n + 1)\ndfs(graph, v, visited)\nprint('')\nvisited = [0] * (n + 1)\nbfs(graph, v, visited)\n\n\n#https://www.acmicpc.net/problem/1260\n\n\n\n\n\n\n\n\n\n\n","repo_name":"agmon5959/TS_Study","sub_path":"BOJ_STUDY/DFS & BFS/박도현/BOJ_1260.py","file_name":"BOJ_1260.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"45074991112","text":"import os\n\nimport filetype\n\nfrom apps.data_source.enums.type import DataTypeChoices\nfrom apps.data_source.models import AIServices\nfrom apps.users.models import UserRoles\n\n\nclass RequestHandler:\n @classmethod\n def get_user_id(cls, request):\n user_id = None\n try:\n user_id = request.user.id\n except:\n user_id = None\n finally:\n return user_id\n\n @classmethod\n def get_user(cls, request):\n user = None\n try:\n user = request.user\n except:\n user = None\n finally:\n return user\n\n @classmethod\n def get_user_role(cls, request):\n role = None\n try:\n role_key = cls.get_request_data(request=request, key=\"role_key\")\n role = UserRoles.get_role(role_key=role_key)\n except:\n role = None\n finally:\n return role\n\n @classmethod\n def get_files(cls, request):\n file_list = None\n try:\n file_list = request.FILES.getlist(\"file\")\n except:\n file_list = None\n finally:\n return file_list\n\n\n\n @classmethod\n def get_request_data(cls, request, key):\n file_type = None\n try:\n file_type = request.data.get(key)\n except:\n file_type = None\n finally:\n return file_type\n\n @classmethod\n def get_service_type(cls, request):\n service = None\n try:\n service_type_key = cls.get_request_data(request=request, key=\"service_type\")\n service_obj = AIServices.get_service(service_key=service_type_key)\n if service_obj:\n service = service_obj.pk\n\n except:\n service = None\n finally:\n return service\n\n\ndef get_file_name_type(file):\n filename = None\n file_type = None\n try:\n filename, file_extension = os.path.splitext(file.name)\n\n kind = filetype.guess(file).mime.split(\"/\")[0]\n if kind == \"image\":\n if file_extension in [\".tiff\", \".tif\"]:\n file_type = DataTypeChoices.SATELLITE\n else:\n file_type = DataTypeChoices.IMAGE\n elif kind == \"video\":\n file_type = DataTypeChoices.VIDEO\n elif kind == \"audio\":\n file_type = DataTypeChoices.AUDIO\n else:\n file_type = None\n except:\n filename = None\n file_type = None\n finally:\n if file_type is None:\n raise Exception\n else:\n return filename, file_type\n","repo_name":"AliCheema77/aix_project","sub_path":"customutils/request_handlers.py","file_name":"request_handlers.py","file_ext":"py","file_size_in_byte":2583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74377588562","text":"import randominfo as ri\nimport random\nimport string\nimport randomtimestamp as rt\n\nnum = random.random()\nprint('hello')\noutput = open(\"sampledata.txt\", 'w')\n\n# for i in range(220):\n# user1 = random.randrange(0,110)\n# user2 = random.randrange(0,110)\n# while user2 == user1:\n# user2 = random.randrange(0,110)\n# Jdate = ri.get_birthdate(startAge = 0, endAge = 2, _format = \"%Y-%m-%d\")\n# output.write(\"INSERT INTO friend VALUES (\" + str(user1) + \",\" + str(user2) + \",'\" + Jdate + \"',\" + \"\\'Lets be friends!\\'\" +\");\\n\")\n\n# for i in range(10):\n# letters = string.ascii_lowercase\n# gId = i\n# gName = ''.join(random.choice(letters) for i in range(10))\n# size = 20\n# description = ''.join(random.choice(letters) for i in range(75))\n# output.write(\"INSERT INTO groupInfo VALUES (\" + str(gId) + \",\" + gName + \",\" + str(size) + \",\\'\" + description +\"\\');\\n\")\n\n\n# for i in range(10):\n# member_list = random.sample(range(0,110), 20)\n# for j in member_list:\n# output.write(\"INSERT INTO groupMember VALUES (\" + str(i) + \",\" + str(j) + \",\" + \"user\" + \",\\'\" + str(rt.randomtimestamp()) + \"\\');\\n\" )\n\nfor i in range(1000):\n user1 = random.randrange(0,110)\n user2 = random.randrange(0,110)\n while user2 == user1:\n user2 = random.randrange(0,110)\n\n Jdate = ri.get_birthdate(startAge = 0, endAge = 2, _format = \"%Y-%m-%d\")\n output.write(\"INSERT INTO message VALUES (\" + str(i) + \",\" + str(user1) + \",'\" + \"Bing bong\" + \"',\" + str(user2) + \",\"+ str(0) +\",'\" + str(rt.randomtimestamp()) +\"');\\n\")\n","repo_name":"rhn5/1555-project","sub_path":"Sample Data Maker/sampleData.py","file_name":"sampleData.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34541260299","text":"#!/usr/bin/env python\r\n# -*- coding: UTF-8 -*-\r\n\r\n# block_retriever.py\r\n\r\n\"\"\"\r\n%prog <.align_file> \r\n\r\nretrieve blocks from MCscan output .aligns file\r\n\r\noutput format:\r\nblock_ID qstart_gene qend_gene sstart_gene send_gene block_size score\r\n\r\n\"\"\"\r\n\r\n\r\nimport os,sys\r\nfrom optparse import OptionParser\r\nfrom itertools import groupby\r\n\r\n\r\ndef retrieve_blocks(fi):\r\n '''function to parse MCSCAN .align file for geenrating block summary.'''\r\n with open(fi,'r') as f:\r\n for block_id, block in groupby(f, lambda x: x.split()[2].rstrip(':') if \"Alignment\" in x else x.split('-')[0].strip()): # hard coded\r\n block=list(block)\r\n try: size = block[0].split(\"N=\")[1].split()[0]\r\n except IndexError: continue\r\n score = block[0].split(\"score=\")[1].split()[0]\r\n flag=0 # initialize\r\n for align in block[1:]:\r\n align = align.strip('\\n').split('\\t')\r\n end1, end2 = align[1:3]\r\n if (not flag):\r\n flag=1\r\n start1, start2 = align[1:3]\r\n if start1 > end1:\r\n start1,end1=end1,start1\r\n if start2 > end2:\r\n start2,end2=end2,start2\r\n yield (block_id, start1, end1, start2, end2, size, score)\r\n \r\n \r\nif __name__ == \"__main__\":\r\n \r\n usage = \"usage: %prog <.align_file> \"\r\n parser = OptionParser(usage)\r\n args=parser.parse_args()[1]\r\n try:\r\n fi = args[0]\r\n fo = args[1]\r\n except:\r\n sys.exit(parser.print_help())\r\n\r\n fo = open(fo,'w')\r\n for block in retrieve_blocks(fi):\r\n fo.write('\\t'.join(block)+'\\n')\r\n fo.close()\r\n \r\n \r\n","repo_name":"Jingping/BiteTools","sub_path":"mcscan_block_getter.py","file_name":"mcscan_block_getter.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"873529653","text":"from lib2to3.pgen2 import token\nfrom turtle import pos\nimport pandas as pd\nimport numpy as np\nimport torch.nn.functional as F\nfrom torch.utils import data\nimport os\nimport torch\nfrom torch.utils.data import DataLoader,Dataset\nimport matplotlib.pyplot as plt\nimport jieba\nfrom transformers import BertTokenizer\nfrom bert.make_data import cut_sent\n\nclass ChSentiDataSet(Dataset):\n def __init__(self, data_path, embedding) -> None:\n # 由于数据比较少, 我们直接一次加载进来就好.\n # label, review\n self.data_all = pd.read_csv(data_path, keep_default_na=False, header=0)\n self.embedding = embedding\n super().__init__()\n pass\n\n def __len__(self):\n # 返回数据集大小\n return self.data_all.shape[0]\n\n def __getitem__(self, index):\n # 返回下标为index的数据项\n review = self.data_all.iloc[index,1]\n review = self.embedding.toTensor(review)\n label =int(self.data_all.iloc[index,0])\n return review, label\n\nclass embedding:\n # 不太了解embedding, 先使用所有训练数据中的句子拆出的词表, 把出现次数少(<100)的词语视为other \n # 拆出的词表需要去掉停止词\n def __init__(self, all_sentence_path,minfr=100) -> None:\n # 读取所有句子\n all_sentence = pd.read_csv(all_sentence_path,header=0,names=['label','sentence'],keep_default_na=False)\n #读取停止词\n self.stop_words = pd.read_csv(\"data/stop_words.csv\",header=None,sep='!')\n\n # 计算词频\n # FIXME: 性能有待提升\n self.word_dict = {}\n for sentence in all_sentence['sentence']:\n words = jieba.lcut(sentence)\n for wd in words:\n self.word_dict[wd]= self.word_dict[wd]+1 if self.word_dict.__contains__(wd) else 1\n # 得到所有单词\n self.words = list(self.word_dict.keys())\n\n # 删除停止词和低频词\n for wd in self.word_dict:\n if self.word_dict[wd]<=minfr:\n self.words.remove(wd)\n\n # 添加other\n self.words.append(\"@other\")\n self.words.append(\"@pad\")\n self.words.append(\"@cls\")\n self.words.append(\"@sep\")\n\n def __len__(self):\n return len(self.words)\n\n def toTensor(self, sentence):\n # 得到一个句子的向量化结果\n # 词袋\n try:\n st_words = jieba.lcut(sentence)\n except:\n # print(sentence)\n st_words = [' ']\n wd_dict = dict(zip(self.words, np.zeros(len(self.words))))\n for wd in st_words:\n # if wd_dict.__contains__(wd):\n if wd in self.stop_words:\n continue\n if wd in self.words:\n wd_dict[wd] = wd_dict[wd]+1\n else:\n wd_dict[\"@other\"]+=1\n \n ret = torch.tensor(list(wd_dict.values()),dtype=float)\n ret = F.softmax(ret,dim=-1)\n return ret\n\nclass oneHotEmbedding(embedding):\n def __init__(self, all_sentence_path, length, minfr=100) -> None:\n super().__init__(all_sentence_path, minfr)\n # 得到词典\n self.wordsindex = dict((_,i) for i,_ in enumerate(self.words))\n\n self.length = length\n\n def toTensor(self, sentence):\n sent_words = jieba.lcut(sentence)\n sent_index = []\n # 把词语转换维字典对应的序号\n for word in sent_words:\n if self.wordsindex.__contains__(word):\n sent_index.append(self.wordsindex[word]) # 已存在的\n else:\n sent_index.append(self.wordsindex['@other']) # unk\n while len(sent_index) < self.length: # padding\n sent_index.append(self.wordsindex['@pad'])\n\n # 将id转换为独热码\n id = torch.tensor(sent_index[:self.length]).reshape(self.length,1) \n sent_tensor = torch.zeros((self.length,len(self.words)), dtype=torch.float).scatter_(dim=1, index=id, value=1)\n\n return sent_tensor.unsqueeze(0) # 为了后面卷积方便调用接口, 因此加一层channel维\n\nclass indexDictEmbedding(oneHotEmbedding):\n def __init__(self, all_sentence_path, length, minfr=100) -> None:\n super().__init__(all_sentence_path, length, minfr)\n \n def toTensor(self, sentence):\n sent_words = jieba.lcut(sentence)\n sent_index = []\n # 把词语转换维字典对应的序号\n for word in sent_words:\n if self.wordsindex.__contains__(word):\n sent_index.append(self.wordsindex[word]) # 已存在的\n else:\n sent_index.append(self.wordsindex['@other']) # unk\n while len(sent_index) < self.length: # padding\n sent_index.append(self.wordsindex['@pad'])\n\n # 得到句子中每个单词的id\n id = torch.tensor(sent_index[:self.length])\n\n return id.unsqueeze(0) # 为了后面卷积方便调用接口, 因此加一层channel维\n\nclass bertEmbedding(embedding):\n def __init__(self, all_sentence_path, length, minfr=100) -> None:\n super().__init__(all_sentence_path, minfr)\n self.length = length\n # 使用的预训练模型：https://github.com/ymcui/Chinese-BERT-wwm\n # self.tokenizer = BertTokenizer.from_pretrained('hfl/chinese-bert-wwm',padding=True, truncation=True, return_tensors=\"pt\")\n self.tokenizer = BertTokenizer('data/bert_min/vocab.txt')\n\n def __len__(self):\n return self.tokenizer.vocab_size\n\n def toTensor(self, sentence):\n token_words = self.tokenizer.encode_plus(sentence, max_length=self.length, padding='max_length',truncation=True)\n token_words['attention_mask'] = (np.array(token_words['attention_mask'])-1)*([-1]) # if my bert\n return torch.tensor([token_words['input_ids'], token_words['token_type_ids'], token_words['attention_mask']])\n # return torch.tensor(token_words['input_ids'])\n\nclass bertEmbeddingv1(embedding):\n def __init__(self, all_sentence_path, length, minfr=100) -> None:\n super().__init__(all_sentence_path, minfr)\n # 得到词典\n self.wordsindex = dict((_,i) for i,_ in enumerate(self.words))\n self.length = length\n \n def toTensor(self, sentence):\n sent_words = jieba.lcut(sentence)\n sent_index = []\n # 把词语转换维字典对应的序号\n sent_index.append(self.wordsindex['@cls'])\n for word in sent_words:\n if self.wordsindex.__contains__(word):\n sent_index.append(self.wordsindex[word]) # 已存在的\n else:\n sent_index.append(self.wordsindex['@other']) # unk\n sent_index.append(self.wordsindex['@sep'])\n\n while len(sent_index) < self.length: # padding\n sent_index.append(self.wordsindex['@pad'])\n\n # 得到句子中每个单词的id\n input_ids = torch.tensor(sent_index[:self.length])\n attention_mask = torch.tensor([i!=self.wordsindex['@pad'] for i in sent_index])\n token_type_ids = torch.tensor([0]*self.length)\n position_ids = torch.tensor([i for i in range(self.length)])\n return torch.tensor(input_ids, attention_mask, token_type_ids, position_ids)\n\nclass HANembedding(embedding):\n def __init__(self, all_sentence_path, doc_len, sent_len, minfr=100) -> None:\n super().__init__(all_sentence_path, minfr)\n self.doc_len = doc_len\n self.sent_len = sent_len\n self.wordsindex = dict((_,i) for i,_ in enumerate(self.words))\n \n def toTensor(self, sentence):\n doc = sentence\n ret = []\n for sent in cut_sent(doc):\n sent_words = jieba.lcut(sent)\n sent_index = []\n # 把词语转换维字典对应的序号\n for word in sent_words:\n if self.wordsindex.__contains__(word):\n sent_index.append(self.wordsindex[word]) # 已存在的\n else:\n sent_index.append(self.wordsindex['@other']) # unk\n while len(sent_index) < self.sent_len: # padding\n sent_index.append(self.wordsindex['@pad'])\n\n ret.append(sent_index[:self.sent_len])\n while len(ret) < self.doc_len:\n ret.append([self.wordsindex['@pad']]*self.sent_len) \n ret = ret[:self.doc_len]\n\n return torch.tensor(ret) # [doc_len, sent_len]的二维数组\n \nclass corpusInfo:\n def __init__(self, path) -> None:\n self.data = pd.read_csv(path, keep_default_na=False, header=0, names=['label','sentence'])\n\n def maxWords(self):\n max_cnt = 0\n max_sent =''\n for sent in self.data['sentence']:\n word_sent = jieba.lcut(sent)\n if max_cnt < len(word_sent):\n max_cnt = len(word_sent)\n max_sent = sent\n return (max_sent, max_cnt)\n \n def minWords(self):\n min_cnt = 1000000\n min_sent =''\n for sent in self.data['sentence']:\n word_sent = jieba.lcut(sent)\n if min_cnt > len(word_sent) and len(word_sent) != 0:\n min_cnt = len(word_sent)\n min_sent = sent\n return (min_sent, min_cnt)\n\n def meanWords(self):\n mean_cnt = 0.0\n for sent in self.data['sentence']:\n word_sent = jieba.lcut(sent)\n mean_cnt += len(word_sent)\n return mean_cnt/3200.0\n\n def histWords(self):\n cnt = []\n for sent in self.data['sentence']:\n cnt.append(len(jieba.lcut(sent)))\n cnt.sort()\n plt.hist(cnt,bins=300)\n plt.show()\n print(cnt[round(len(cnt)*0.9)])\n\n def HANstatics(self):\n doc_all = self.data['sentence']\n sent_len = []\n word_len = []\n sent = []\n word = []\n for doc in doc_all:\n sents = cut_sent(doc)\n sent.append(sents)\n sent_len.append(len(sents))\n words = []\n for st in sents:\n token = jieba.lcut(st)\n words.append(token)\n word_len.append(len(token))\n word.append(words)\n \n print(f\"mean length of the doc:{np.mean(sent_len)}\")\n print(f\"mean length of the sentence:{np.mean(word_len)}\")\n print(f\"max length of the doc:{np.max(sent_len)}\")\n print(f\"max length of the sentence:{np.max(word_len)}\")\n print(f\"min length of the doc:{np.min(sent_len)}\")\n print(f\"min length of the sentence:{np.min(word_len)}\")\n sent_len.sort()\n word_len.sort()\n plt.subplot(2, 1, 1)\n plt.hist(sent_len,bins=300)\n plt.subplot(2, 1, 2)\n plt.hist(word_len,bins=300)\n plt.show()\n\nif __name__ == \"__main__\":\n info = corpusInfo(\"data/ChnSentiCorp_htl_all/ChnSentiCorp_htl_all.csv\")\n info.HANstatics()\n # embedding = bertEmbedding(\"data/ChnSentiCorp_htl_all/ChnSentiCorp_htl_all.csv\", 128)\n # train_data = ChSentiDataSet(\"data/ChnSentiCorp_htl_all/train_1600+1600.csv\", embedding)\n # print(train_data.__getitem__(2))\n","repo_name":"Hsyy04/NLP_elementary","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":11039,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"33149726067","text":"from argparse import ArgumentParser\nfrom pathlib import Path\n\n\ndef solve_example(parser: ArgumentParser) -> None:\n \"\"\"Adds arguments to parser for solving a single example\"\"\"\n parser.add_argument(\"--word\", type=str, help=\"Word representing the one relator\", required=True)\n\n\ndef solve_examples(parser: ArgumentParser) -> None:\n \"\"\"adds arguments to parse for solving multiple examples\"\"\"\n parser.add_argument(\n \"--word-size-range\",\n type=int,\n nargs=2,\n help=\"range of word sizes to run examples on\",\n required=True\n )\n\n parser.add_argument(\n \"--sample-size\",\n type=int,\n help=\"Size of sample to run on each word size\",\n )\n\n\ndef get_polytope(parser: ArgumentParser) -> None:\n \"\"\"Adds arguments to parser for outputting polytope of single example\"\"\"\n parser.add_argument(\"--word\", type=str, help=\"Word representing the one relator\", required=True)\n \n\ndef get_polytopes(parser: ArgumentParser) -> None:\n \"\"\"\n Adds arguments to parse for outputting poltyopes for multiple examples\n \"\"\"\n parser.add_argument(\n \"--words\", type=str, nargs=\"+\", help=\"List of words to get polytopes for\", required=True\n )\n\n\ndef get_all_cycle_data(parser: ArgumentParser) -> None:\n \"\"\"Adds arguments for reading database files\"\"\"\n parser.add_argument(\n \"--input-dir\", type=Path, help=\"Directory containing database files\"\n )\n","repo_name":"antonydellavecchia/one_relator_curvature","sub_path":"one_relator_curvature/parsers.py","file_name":"parsers.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37681665834","text":"import os\r\nimport os.path as osp\r\nimport argparse\r\nimport utils\r\nimport face_alignment\r\nfrom facenet_pytorch import MTCNN\r\nimport numpy as np\r\nimport mmcv, cv2\r\nfrom mmengine import ProgressBar\r\nfrom PIL import Image, ImageDraw\r\n\r\nimport torch\r\n\r\n\r\ndef face2head(boxes, scale=1.5):\r\n new_boxes = []\r\n for box in boxes:\r\n width = box[2] - box[0]\r\n height = box[3] - box[1]\r\n width_center = (box[2] + box[0]) / 2\r\n height_center = (box[3] + box[1]) / 2\r\n square_width = int(max(width, height) * scale)\r\n new_box = [width_center - square_width / 2, height_center - square_width / 2, width_center + square_width / 2,\r\n height_center + square_width / 2]\r\n new_boxes.append(new_box)\r\n return new_boxes\r\n\r\n\r\ndef bb_intersection_over_union(boxA, boxB):\r\n # determine the (x, y)-coordinates of the intersection rectangle\r\n xA = max(boxA[0], boxB[0])\r\n yA = max(boxA[1], boxB[1])\r\n xB = min(boxA[2], boxB[2])\r\n yB = min(boxA[3], boxB[3])\r\n # compute the area of intersection rectangle\r\n interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)\r\n # compute the area of both the prediction and ground-truth\r\n # rectangles\r\n boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)\r\n boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)\r\n # compute the intersection over union by taking the intersection\r\n # area and dividing it by the sum of prediction + ground-truth\r\n # areas - the interesection area\r\n iou = interArea / float(boxAArea + boxBArea - interArea)\r\n # return the intersection over union value\r\n return iou\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('--video_root', type=str, required=True, help=\"The root directory of the videos' path\")\r\n parser.add_argument('--faces', type=str, required=True, help=\"The root directory of output face videos' path\")\r\n parser.add_argument('--csv_root', type=str, required=True, help=\"The root directory of output csv file for each video\")\r\n parser.add_argument('--landmark', type=str, required=True, help=\"The root directory of output videos' path\")\r\n parser.add_argument('--tracked_video', type=str, required=True, help=\"The root directory of output tracked videos' path\")\r\n\r\n parser.add_argument('--detect_every_N_frame', type=int, default=8, help=\"detect face for every N frames\")\r\n parser.add_argument('--scalar_face_detection', type=float, default=1.5)\r\n parser.add_argument('--number_of_speakers', type=int, default=1, help=\"number of speakers in videos\")\r\n args = parser.parse_args()\r\n\r\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\r\n print('Running on device: {}'.format(device))\r\n os.makedirs(args.faces, exist_ok=True)\r\n os.makedirs(args.csv_root, exist_ok=True)\r\n os.makedirs(args.landmark, exist_ok=True)\r\n os.makedirs(args.tracked_video, exist_ok=True)\r\n\r\n videos = os.listdir(args.video_root)\r\n\r\n pb = ProgressBar(len(videos))\r\n pb.start()\r\n\r\n for video_input in videos:\r\n landmarks_dic = {}\r\n faces_dic = {}\r\n boxes_dic = {}\r\n for i in range(args.number_of_speakers):\r\n landmarks_dic[i] = []\r\n faces_dic[i] = []\r\n boxes_dic[i] = []\r\n\r\n mtcnn = MTCNN(keep_all=True, device=device)\r\n\r\n video = mmcv.VideoReader(osp.join(args.video_root, video_input))\r\n print(\"Video statistics: \", video.width, video.height, video.resolution, video.fps)\r\n frames = [Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) for frame in video]\r\n print('Number of frames in video: ', len(frames))\r\n fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False)\r\n\r\n try:\r\n for i, frame in enumerate(frames):\r\n print('\\rTracking frame: {}'.format(i + 1), end='')\r\n\r\n # Detect faces\r\n if i % args.detect_every_N_frame == 0:\r\n boxes, _ = mtcnn.detect(frame)\r\n boxes = boxes[:args.number_of_speakers]\r\n boxes = face2head(boxes, args.scalar_face_detection)\r\n else:\r\n boxes = [boxes_dic[j][-1] for j in range(args.number_of_speakers)]\r\n\r\n # Crop faces and save landmarks for each speaker\r\n if len(boxes) != args.number_of_speakers:\r\n boxes = [boxes_dic[j][-1] for j in range(args.number_of_speakers)]\r\n\r\n for j, box in enumerate(boxes):\r\n face = frame.crop((box[0], box[1], box[2], box[3])).resize((224, 224))\r\n preds = fa.get_landmarks(np.array(face))\r\n if i == 0:\r\n faces_dic[j].append(face)\r\n landmarks_dic[j].append(preds)\r\n boxes_dic[j].append(box)\r\n else:\r\n iou_scores = []\r\n for b_index in range(args.number_of_speakers):\r\n last_box = boxes_dic[b_index][-1]\r\n iou_score = bb_intersection_over_union(box, last_box)\r\n iou_scores.append(iou_score)\r\n box_index = iou_scores.index(max(iou_scores))\r\n faces_dic[box_index].append(face)\r\n landmarks_dic[box_index].append(preds)\r\n boxes_dic[box_index].append(box)\r\n\r\n for s in range(args.number_of_speakers):\r\n frames_tracked = []\r\n for i, frame in enumerate(frames):\r\n # Draw faces\r\n frame_draw = frame.copy()\r\n draw = ImageDraw.Draw(frame_draw)\r\n draw.rectangle(boxes_dic[s][i], outline=(255, 0, 0), width=6)\r\n # Add to frame list\r\n frames_tracked.append(frame_draw)\r\n dim = frames_tracked[0].size\r\n fourcc = cv2.VideoWriter_fourcc(*'FMP4')\r\n\r\n video_tracked = cv2.VideoWriter(osp.join(args.tracked_video, video_input), fourcc, 25.0, dim)\r\n for frame in frames_tracked:\r\n video_tracked.write(cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))\r\n video_tracked.release()\r\n\r\n # Save landmarks\r\n for i in range(args.number_of_speakers):\r\n utils.save2npz(osp.join(args.landmark, osp.splitext(video_input)[0] + '.npz'), data=landmarks_dic[i])\r\n dim = face.size\r\n fourcc = cv2.VideoWriter_fourcc(*'FMP4')\r\n speaker_video = cv2.VideoWriter(osp.join(args.faces, video_input), fourcc, 25.0, dim)\r\n for frame in faces_dic[i]:\r\n speaker_video.write(cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))\r\n speaker_video.release()\r\n\r\n csvfile = open(osp.join(args.csv_root, osp.splitext(video_input)[0] + '.csv'), 'w')\r\n for i in range(args.number_of_speakers):\r\n csvfile.write('speaker' + str(i + 1) + ',0\\n')\r\n csvfile.close()\r\n except:\r\n continue\r\n\r\n pb.update()\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"MCG-NJU/BFRNet","sub_path":"utils/detect_faces.py","file_name":"detect_faces.py","file_ext":"py","file_size_in_byte":7281,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"23145852713","text":"#!/usr/bin/python3\ndef list_division(my_list_1, my_list_2, list_length):\n '''\n Divides the elements of 2 lists with the same index\n Parameters:\n my_list_1 (list): The first list\n my_list_2 (list): The second list\n list_length (int): The length of the items to divide\n Returns:\n A new list consisting of the result of the divisions\n '''\n new_list = []\n for i in range(list_length):\n result = 0\n try:\n a, b = (my_list_1[i], my_list_2[i])\n result = a / b\n except TypeError:\n print(\"wrong type\")\n except ZeroDivisionError:\n print(\"division by 0\")\n except IndexError:\n print(\"out of range\")\n finally:\n new_list.append(result)\n return new_list\n","repo_name":"OBigVee/alx-higher_level_programming","sub_path":"0x05-python-exceptions/4-list_division.py","file_name":"4-list_division.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"11225174361","text":"def parse_geology():\n\twith open('input.txt', 'r') as f:\n\t\tgeology_matrix = f.read().splitlines()\n\n\treturn geology_matrix\n\ndef calculate_num_trees(num_right, num_down, geology_matrix):\n\tmatrix_width = len(geology_matrix[0])\n\n\tx_pos = 0\n\ty_pos = 0\n\ttrees_count = 0\n\n\twhile y_pos < len(geology_matrix):\n\t\tif geology_matrix[y_pos][x_pos] == \"#\":\n\t\t\ttrees_count += 1\n\t\tx_pos += num_right\n\t\tx_pos = x_pos % matrix_width\n\t\t\n\t\ty_pos += num_down\n\n\treturn trees_count \n\nif __name__ == \"__main__\":\n\tparsed_board = parse_geology()\n\ttree_count_3_1 = calculate_num_trees(3, 1, parsed_board)\n\tprint('The answer to day 3 part 1 is {}.'.format(tree_count_3_1))\n\n\ttree_count_1_1 = calculate_num_trees(1, 1, parsed_board)\n\ttree_count_5_1 = calculate_num_trees(5, 1, parsed_board)\n\ttree_count_7_1 = calculate_num_trees(7, 1, parsed_board)\n\ttree_count_1_2 = calculate_num_trees(1, 2, parsed_board)\n\n\ttree_product = tree_count_3_1 * tree_count_1_1 * tree_count_5_1 * \\\n\t\ttree_count_7_1 * tree_count_1_2\n\tprint('The answer to day 3 part 2 is {}.'.format(tree_product))","repo_name":"keing1/advent-of-code-2020","sub_path":"day_03/toboggan_trajectory.py","file_name":"toboggan_trajectory.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31569117120","text":"from turbogears.widgets.base import JSSource, CoreWD, RenderOnlyWD\n\nclass RPyJSSource(JSSource):\n def __init__(self, src, location=None):\n #print 'RPyJSSource: python:', src\n mod = 'RPyJSSourceTmp.py'\n f = open(mod, 'w')\n f.write(src)\n f.close()\n function_names = []\n from rpython2javascript.pypy.translator.js.main import rpython2javascript_main\n jssrc = rpython2javascript_main([mod] + function_names)\n #print 'RPyJSSource: javascript:', jssrc\n super(RPyJSSource, self).__init__(jssrc)\n \nclass RPyJSSourceDesc(CoreWD, RenderOnlyWD):\n name = \"RPyJSSource\"\n for_widget = RPyJSSource(\"def main(): return 42\")\n","repo_name":"camillobruni/pygirl","sub_path":"pypy/translator/js/turbogears/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"23587534646","text":"import os\nimport csv\n\ncsvpath = os.path.join(\"..\", \"Resources\", \"budget_data.csv\")\n\nmonthly_change = []\ndate = []\n\nmonth = 0\nfirst_mo_profit = 0\ntotal_profit = 0\nprevious_profit = 0\n\nwith open(csvpath,'r', newline=\"\") as csvfile:\n csvreader = csv.reader(csvfile, delimiter=\",\")\n next(csvreader)\n\n for row in csvreader:\n date.append(row[0])\n month = month + 1\n total_profit += int(row[1])\n profit_change = int(row[1])-previous_profit\n previous_profit = int(row[1])\n monthly_change_profit = profit_change\n monthly_change.append(profit_change)\n \n monthly_change.pop(0)\n \n max_increase_profit = max(monthly_change)\n max_increase_date = date[monthly_change.index(max_increase_profit)]\n max_decrease_profit = min(monthly_change)\n max_decrease_date = date[monthly_change.index(max_decrease_profit)]\n\naverage_change_profit = round(sum(monthly_change)/(month-1),2)\n \nprint(\"Financial Analysis\")\nprint(\"--------------------\")\nprint(f\"Total # of Months: {month}\")\nprint(f\"Total Profits: ${total_profit}\")\nprint(f\"Average Monthly Change in Profits: ${average_change_profit}\")\nprint(f\"Greatest Increase in Profits: {max_increase_date} ${max_increase_profit}\")\nprint(f\"Greatest Decrease in Profits: {max_decrease_date} ${max_decrease_profit}\")\n\ntextpath = os.path.join(\"..\", \"Resources\", \"main.txt\")\n\nwith open(textpath,'w') as text:\n\n text.write(\"Financial Analysis\" + \"\\n\")\n text.write(\"--------------------\\n\")\n text.write(f\"Total # of Months: {month} \\n\")\n text.write(f\"Total Profits: ${total_profit} \\n\")\n text.write(f\"Average Monthly Change in Profits: ${average_change_profit} \\n\")\n text.write(f\"Greatest Increase in Profits: {max_increase_date} ${max_increase_profit} \\n\")\n text.write(f\"Greatest Decrease in Profits: {max_decrease_date} ${max_decrease_profit} \\n\") ","repo_name":"captainjamesmisson/Homework","sub_path":"3-Python/PyBank/Resources/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11577230801","text":"\"\"\"Groups are a named collection of series and tests. They can be manipulated\nwith the `pav group` command.\"\"\"\n\nfrom pathlib import Path\nimport re\nimport shutil\nfrom typing import NewType, List, Tuple, Union, Dict\nimport uuid\n\nfrom pavilion import config\nfrom pavilion.errors import TestGroupError\nfrom pavilion.series import TestSeries, list_series_tests, SeriesInfo\nfrom pavilion.test_run import TestRun, TestAttributes\nfrom pavilion.utils import is_int\n\nGroupMemberDescr = NewType('GroupMemberDescr', Union[TestRun, TestSeries, \"TestGroup\", str])\nFlexDescr = NewType('FlexDescr', Union[List[GroupMemberDescr], GroupMemberDescr])\n\n\nclass TestGroup:\n \"\"\"A named collection tests and series.\"\"\"\n\n GROUPS_DIR = 'groups'\n TESTS_DIR = 'tests'\n SERIES_DIR = 'series'\n EXCLUDED_DIR = 'excluded'\n\n TEST_ITYPE = 'test'\n SERIES_ITYPE = 'series'\n GROUP_ITYPE = 'group'\n EXCL_ITYPE = 'test*'\n\n group_name_re = re.compile(r'^[a-zA-Z][a-zA-Z0-9_-]+$')\n\n def __init__(self, pav_cfg: config.PavConfig, name: str):\n\n self.pav_cfg = pav_cfg\n\n self._check_name(name)\n\n self.name = name\n\n self.path = self.pav_cfg.working_dir/self.GROUPS_DIR/self.name\n\n if self.path.exists():\n self.created = True\n else:\n self.created = False\n\n def create(self):\n \"\"\"Actually create the group.\"\"\"\n\n try:\n self.path.mkdir(parents=True, exist_ok=True)\n except OSError as err:\n raise TestGroupError(\"Could not create group dir at '{}'\"\n .format(self.path), prior_error=err)\n\n for category in self.TESTS_DIR, self.SERIES_DIR, self.GROUPS_DIR, self.EXCLUDED_DIR:\n cat_dir = self.path/category\n try:\n cat_dir.mkdir(exist_ok=True)\n except OSError as err:\n raise TestGroupError(\"Could not create group category directory '{}'\"\n .format(cat_dir.as_posix()),\n prior_error=err)\n\n self.created = True\n\n def exists(self) -> bool:\n \"\"\"Whether this group exists.\"\"\"\n\n return self.path.exists()\n\n def info(self) -> Dict:\n \"\"\"Return some basic group info. Number of tests, series, sub-groups, creation time.\"\"\"\n\n info = {\n 'name': self.name,\n 'created': self.path.stat().st_mtime,\n }\n for cat_type, cat_dir in (\n (self.TEST_ITYPE, self.TESTS_DIR),\n (self.SERIES_ITYPE, self.SERIES_DIR),\n (self.GROUP_ITYPE, self.GROUPS_DIR),):\n cat_path = self.path/cat_dir\n\n if not cat_path.exists():\n info[cat_dir] = 0\n continue\n info[cat_dir] = len(list(cat_path.iterdir()))\n\n return info\n\n def tests(self, seen_groups=None) -> List[Path]:\n \"\"\"Returns a list of paths to all tests in this group. Use with\n cmd_utils.get_tests_by_paths to convert to real test objects. Bad links are ignored.\n Groups are recursively examined (loops are allowed, but not followed).\n \"\"\"\n\n seen_groups = seen_groups if seen_groups is not None else []\n seen_groups.append(self.name)\n\n tests = []\n\n if not self.exists():\n return []\n\n # Get all the tests directly added to the group.\n try:\n if (self.path/self.TESTS_DIR).exists():\n for test_dir in (self.path/self.TESTS_DIR).iterdir():\n try:\n if not test_dir.exists():\n continue\n except OSError as err:\n raise TestGroupError(\n \"Error getting test '{}' from group '{}'\"\n .format(test_dir.name, self.name),\n prior_error=err)\n\n tests.append(test_dir)\n\n except OSError as err:\n raise TestGroupError(\"Error getting tests for group '{}'\"\n .format(self.name), prior_error=err)\n\n # Get all the tests from each series\n try:\n if (self.path/self.SERIES_DIR).exists():\n for series_dir in (self.path/self.SERIES_DIR).iterdir():\n try:\n if not series_dir.exists():\n continue\n except OSError as err:\n raise TestGroupError(\n \"Error getting test series '{}' from group '{}'\"\n .format(series_dir.name, self.name),\n prior_error=err)\n\n tests.extend(list_series_tests(self.pav_cfg, series_dir.name))\n\n except OSError as err:\n raise TestGroupError(\n \"Error getting series for group '{}'\"\n .format(self.name), prior_error=err)\n\n # Finally, recursively get the tests for each group that's under this group.\n try:\n if (self.path/self.GROUPS_DIR).exists():\n for group_file in (self.path/self.GROUPS_DIR).iterdir():\n group_name = group_file.name\n sub_group = TestGroup(self.pav_cfg, group_name)\n if group_name not in seen_groups:\n tests.extend(sub_group.tests(seen_groups=seen_groups))\n\n except OSError as err:\n raise TestGroupError(\n \"Error getting sub groups for group '{}'\"\n .format(self.name), prior_error=err)\n\n # Filter out any excluded tests.\n excluded_paths = list(self._excluded().values())\n tests = [test_path.resolve() for test_path in tests]\n return [test_path for test_path in tests if test_path not in excluded_paths]\n\n def _has_test(self, test_path: Path) -> bool:\n \"\"\"Return True if the given test path is included anywhere in the group.\"\"\"\n\n return test_path in self.tests()\n\n def add(self, items: FlexDescr) -> Tuple[List[str], List[TestGroupError]]:\n \"\"\"Add each of the given items to the group. Accepts TestRun, TestSeries, and TestGroup\n objects, as well as just the test/series(sid)/group names as strings.\n\n :returns: A list of the item names added, and a list of errors\n \"\"\"\n\n if not isinstance(items, (list, tuple)):\n items = [items]\n\n if not self.created:\n self.create()\n\n warnings = []\n added = []\n for item in items:\n # Get the type of item we're dealing with, and where it will be put in the group.\n try:\n itype, item_path = self._get_member_info(item)\n except TestGroupError as err:\n warnings.append(\n TestGroupError(\"Could not add unknown item to test group '{}': '{}' '{}'\"\n .format(self.name, type(item), item), prior_error=err))\n continue\n\n if item_path.exists():\n # Don't try to add items that are already in the group.\n continue\n\n # Get a string a name for the item, and the path to the actual item.\n try:\n if itype == self.TEST_ITYPE:\n iname, dest_path = self._get_test_info(item)\n elif itype == self.SERIES_ITYPE:\n iname, dest_path = self._get_series_info(item)\n elif itype == self.GROUP_ITYPE:\n if isinstance(item, TestGroup):\n agroup = item\n else:\n agroup = TestGroup(self.pav_cfg, item)\n\n # Don't add a test group to itself.\n if agroup.name == self.name:\n continue\n\n if not agroup.exists():\n warnings.append(\n TestGroupError(\"Group '{}' does not exist.\".format(agroup.name)))\n continue\n\n iname = agroup.name\n\n except TestGroupError as err:\n warnings.append(\n TestGroupError(\"Could not add to test group '{}'\".format(self.name),\n prior_error=err))\n continue\n\n if itype == self.TEST_ITYPE:\n if iname in self._excluded():\n try:\n self._remove_excluded(iname)\n except TestGroupError as err:\n warnings.append(\n TestGroupError(\n \"Could not remove exclusion for test {}.\".format(iname),\n prior_error=err))\n\n if self._has_test(dest_path):\n added.append((self.EXCL_ITYPE, iname))\n continue\n\n try:\n # For tests and series, symlink to their directories.\n if itype in (self.TEST_ITYPE, self.SERIES_ITYPE):\n # Add the item, unless it just needed to be un-excluded.\n item_path.symlink_to(dest_path)\n # For groups, just touch a file of that name (prevents symlink loops).\n else:\n item_path.touch()\n added.append((itype, iname))\n except OSError as err:\n warnings.append(\n TestGroupError(\"Could not add {} '{}' to test group '{}'\"\n .format(itype, iname, self.name), prior_error=err))\n continue\n\n return added, warnings\n\n def remove(self, items: FlexDescr) -> Tuple[List[str], List[TestGroupError]]:\n \"\"\"Remove all of the given items from the group. Returns a list of warnings.\"\"\"\n\n removed = []\n warnings = []\n\n if not isinstance(items, list):\n items = [items]\n\n all_tests = None\n\n for item in items:\n if isinstance(item, int):\n item = str(item)\n\n itype, rmpath = self._get_member_info(item)\n\n if not rmpath.exists():\n if itype == self.TEST_ITYPE:\n try:\n t_full_id, t_path = self._get_test_info(rmpath.name)\n except TestGroupError as err:\n warnings.append(\n TestGroupError(\n \"Could not exclude item {}.\".format(item),\n prior_error=err))\n continue\n\n # Check to see if this is in our list of tests at all.\n if all_tests is None:\n all_tests = self.tests()\n\n if t_path in all_tests:\n self._add_excluded(t_full_id, t_path)\n removed.append((self.EXCL_ITYPE, t_full_id))\n continue\n\n warnings.append(\n \tTestGroupError(\"Given {} '{}' to remove, but it is not in group '{}'.\"\n .format(itype, item, self.name)))\n continue\n\n try:\n rmpath.unlink()\n removed.append((itype, rmpath.name))\n except OSError:\n warnings.append(\n TestGroupError(\"Could not remove {} '{}' from group '{}'.\"\n .format(itype, item, self.name)))\n continue\n\n return removed, warnings\n\n def members(self, recursive=False, seen_groups=None) -> List[Dict]:\n \"\"\"Return a list of dicts of member info, keys 'itype', 'name'.\"\"\"\n\n seen_groups = seen_groups if seen_groups is not None else []\n seen_groups.append(self.name)\n\n if not self.exists():\n return []\n\n members = []\n\n for itype, type_dir in (\n (self.TEST_ITYPE, self.TESTS_DIR),\n (self.SERIES_ITYPE, self.SERIES_DIR),\n (self.GROUP_ITYPE, self.GROUPS_DIR)):\n\n try:\n for path in (self.path/type_dir).iterdir():\n abs_path = None\n try:\n if path.exists():\n abs_path = path.resolve()\n except OSError:\n pass\n\n members.append({\n 'group': self.name,\n 'itype': itype,\n 'path': abs_path,\n 'id': path.name,})\n\n if recursive and itype == self.GROUP_ITYPE and path.name not in seen_groups:\n try:\n subgroup = self.__class__(self.pav_cfg, path.name)\n except TestGroupError:\n continue\n\n members.extend(subgroup.members(recursive=True, seen_groups=seen_groups))\n\n except OSError as err:\n raise TestGroupError(\n \"Could not list {} for group '{}'\".format(type_dir, self.name),\n prior_error=err)\n\n for mem_info in members:\n path = mem_info['path']\n if path is None:\n continue\n\n if mem_info['itype'] == self.TEST_ITYPE:\n test_attrs = TestAttributes(mem_info['path'])\n mem_info['name'] = test_attrs.name\n mem_info['created'] = test_attrs.created\n elif mem_info['itype'] == self.SERIES_ITYPE:\n series_info = SeriesInfo(self.pav_cfg, path)\n mem_info['name'] = series_info.name\n mem_info['created'] = series_info.created\n else: # Groups\n path = self.path.parents[1]/path.name\n if path.exists():\n mem_info['created'] = path.stat().st_mtime\n return members\n\n def member_tuples(self) -> List[Tuple[str,str]]:\n \"\"\"As per 'members', but return a list of (item_type, item_id) tuples.\"\"\"\n\n tups = []\n for item in self.members():\n tups.append((item['itype'], item['id']))\n return tups\n\n def clean(self) -> List[TestGroupError]:\n \"\"\"Remove all dead links and group files, then delete the group if it's empty.\n Returns a list of errors/warnings.\"\"\"\n\n keepers = False\n warnings = []\n\n # Cleanup items for each item type (tests, series, groups)\n for itype, type_dir in (\n (self.TEST_ITYPE, self.TESTS_DIR),\n (self.SERIES_ITYPE, self.SERIES_DIR),\n (self.GROUP_ITYPE, self.GROUPS_DIR)):\n\n try:\n for item_path in (self.path/type_dir).iterdir():\n # Skip that items that still exist.\n if itype == self.GROUP_ITYPE:\n if (self.path.parent/item_path.name).exists():\n keepers = True\n continue\n elif item_path.exists():\n # Note - this tests both if the target of the symlink and the symlink\n # itself exit. (Absent a race condition of some sort, the\n # symlink will exist.)\n keepers = False\n continue\n\n try:\n item_path.unlink()\n except OSError as err:\n warnings.append(\n TestGroupError(\n \"Could not remove test '{}' from group '{}'.\"\n .format(test_link.name, self.name),\n prior_error=err))\n except OSError as err:\n warnings.append(\n TestGroupError(\n \"Could not cleanup {} for group '{}'\"\n .format(self.GROUPS_DIR, self.name),\n prior_error=err))\n\n if not keepers:\n try:\n self.delete()\n except TestGroupError as err:\n warnings.append(err)\n\n return warnings\n\n def delete(self):\n \"\"\"Delete this group.\"\"\"\n\n try:\n # Symlinks are just removed, not followed.\n shutil.rmtree(self.path.as_posix())\n except OSError as err:\n raise TestGroupError(\n \"Could not delete group '{}'\".format(self.name),\n prior_error=err)\n\n self.created = False\n\n def rename(self, new_name, redirect_parents=True):\n \"\"\"Rename this group.\n\n :param redirect_parents: Search other test groups for inclusion of this group,\n and point them at the new name.\n \"\"\"\n\n self._check_name(new_name)\n\n new_path = self.path.parent/new_name\n\n if new_path.exists():\n raise TestGroupError(\"Renaming group '{}' to '{}' but a group already exists \"\n \"under that name.\".format(self.name, new_name))\n\n try:\n self.path.rename(new_path)\n except OSError as err:\n raise TestGroupError(\n \"Could not rename group '{}' to '{}'\".format(self.name, new_name),\n prior_error=err)\n\n if redirect_parents:\n try:\n for group_path in self.path.parent.iterdir():\n for sub_group in (group_path/self.GROUPS_DIR).iterdir():\n if sub_group.name == self.name:\n new_sub_path = sub_group.parent/new_name\n sub_group.rename(new_sub_path)\n except OSError as err:\n raise TestGroupError(\"Failed to redirect parents of group '{}' to the new name.\"\n .format(self.name), prior_error=err)\n\n self.name = new_name\n self.path = new_path\n\n def _check_name(self, name: str):\n \"\"\"Make sure the given test group name complies with the naming standard.\"\"\"\n\n if self.group_name_re.match(name) is None:\n raise TestGroupError(\n \"Invalid group name '{}'\\n\"\n \"Group names must start with a letter, but can otherwise have any \"\n \"combination of letters, numbers, underscores and dashes.\"\n .format(name))\n if name[0] in ('s', 'S') and is_int(name[1:]):\n raise TestGroupError(\n \"Invalid group name '{}'\\n\"\n \"Group name looks too much like a series ID.\"\n .format(name))\n\n def _get_test_info(self, test: Union[TestRun, str]) -> Tuple[str, Path]:\n \"\"\"Find the test full id and path from the given test information.\"\"\"\n\n if isinstance(test, TestRun):\n if not test.path.exists():\n raise TestGroupError(\"Test '{}' does not exist.\".format(test.full_id))\n return test.full_id, test.path\n\n if isinstance(test, str):\n if '.' in test:\n cfg_label, test_id = test.split('.', maxsplit=1)\n else:\n cfg_label = config.DEFAULT_CONFIG_LABEL\n test_id = test\n\n elif isinstance(test, int):\n cfg_label = config.DEFAULT_CONFIG_LABEL\n test_id = str(int)\n # We'll use this as our full_id too.\n test = test_id\n\n if not is_int(test_id):\n raise TestGroupError(\n \"Invalid test id '{}' from test id '{}'.\\n\"\n \"Test id's must be a number, like 27.\"\n .format(test_id, test))\n if cfg_label not in self.pav_cfg.configs:\n raise TestGroupError(\n \"Invalid config label '{}' from test id '{}'.\\n\"\n \"No such Pavilion configuration directory exists. Valid config \"\n \"labels are:\\n {}\"\n .format(cfg_label, test,\n '\\n'.join([' - {}'.format(lbl for lbl in self.pav_cfg.configs)])))\n\n rel_cfg = self.pav_cfg.configs[cfg_label]\n tpath = rel_cfg.working_dir/'test_runs'/test_id\n\n if not tpath.is_dir():\n raise TestGroupError(\n \t\"Could not add test '{}' to group, test directory could not be found.\\n\"\n \"Looked at '{}'\".format(test, tpath))\n\n return test, tpath\n\n def _get_series_info(self, series: Union[TestSeries, str]) -> Tuple[str, Path]:\n \"\"\"Get the sid and path for a series, given a flexible description.\"\"\"\n\n if isinstance(series, TestSeries):\n if not series.path.exists():\n raise TestGroupError(\"Series '{}' at '{}' does not exist.\"\n .format(series.sid, series.path))\n return series.sid, series.path\n\n series = str(series)\n if series.startswith(\"s\"):\n series_id = series[1:]\n sid = series\n else:\n sid = 's{}'.format(series)\n\n if not is_int(series_id):\n raise TestGroupError(\"Invalid series id '{}', not numeric id.\"\n .format(series))\n\n series_dir = self.pav_cfg.working_dir/'series'/series_id\n\n if not series_dir.is_dir():\n raise TestGroupError(\"Series directory for sid '{}' does not exist.\\n\"\n \"Looked at '{}'\".format(sid, series_dir))\n\n return sid, series_dir\n\n def _get_member_info(self, item: GroupMemberDescr) -> Tuple[str, Path]:\n \"\"\"Figure out what type of item 'item' is, and return its type name and path in\n the group.\"\"\"\n\n if isinstance(item, TestRun):\n return self.TEST_ITYPE, self.path/self.TESTS_DIR/item.full_id\n elif isinstance(item, TestSeries):\n return self.SERIES_ITYPE, self.path/self.SERIES_DIR/item.sid\n elif isinstance(item, self.__class__):\n return self.GROUP_ITYPE, self.path/self.GROUPS_DIR/item.name\n elif isinstance(item, str):\n if is_int(item) or '.' in item:\n # Looks like a test id\n return self.TEST_ITYPE, self.path/self.TESTS_DIR/item\n elif item[0] == 's' and is_int(item[1:]):\n # Looks like a sid\n return self.SERIES_ITYPE, self.path/self.SERIES_DIR/item\n else:\n # Anything can only be a group\n return self.GROUP_ITYPE, self.path/self.GROUPS_DIR/item\n else:\n raise TestGroupError(\"Invalid group item '{}' given for removal.\".format(item))\n\n def _excluded(self) -> Dict[str, Path]:\n \"\"\"Excluded items are items that are in series or sub-groups that were deleted\n from this group.\n\n Returns a dict of excluded test id's and the test directories they point to.\n \"\"\"\n\n excluded = {}\n try:\n for test_path in (self.path/self.EXCLUDED_DIR).iterdir():\n full_id = test_path.name\n test_path = test_path.resolve()\n if test_path.exists():\n excluded[full_id] = test_path\n except (OSError, FileNotFoundError):\n pass\n\n return excluded\n\n def _add_excluded(self, full_id: str, test_path: Path):\n \"\"\"Add the given test path to the excluded directory.\"\"\"\n\n path = self.path/self.EXCLUDED_DIR/full_id\n\n try:\n if not path.exists():\n path.symlink_to(test_path)\n except (OSError, FileNotFoundError) as err:\n raise TestGroupError(\n \"Could not create test exclusion record at {}\".format(path),\n prior_error=err)\n\n def _remove_excluded(self, full_id: str):\n \"\"\"Remove the test from the exclusion records.\"\"\"\n\n path = self.path/self.EXCLUDED_DIR/full_id\n\n try:\n if path.exists():\n path.unlink()\n except (OSError, FileNotFoundError) as err:\n raise TestGroupError(\n \"Could not remove test exclusion record at {}\".format(path),\n prior_error=err)\n\n def _clean_excluded(self):\n \"\"\"Remove any dead links from the exclusion records.\"\"\"\n\n root_path = self.path/self.EXCLUDED_DIR\n\n for full_id, path in self._excluded().items():\n if not path.exists():\n ex_path = root_path/full_id\n try:\n ex_path.unlink()\n except (OSError, FileNotFoundError) as err:\n pass\n","repo_name":"hpc/pavilion2","sub_path":"lib/pavilion/groups.py","file_name":"groups.py","file_ext":"py","file_size_in_byte":24554,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"3"}
+{"seq_id":"71382521040","text":"import autodynatrace\nfrom ddtrace import tracer\nfrom fastapi import APIRouter, Depends, Header\nfrom fastapi.security import HTTPAuthorizationCredentials, HTTPBearer\nfrom shared.infrastructure import HttpResponse\nfrom shared.infrastructure.settings import get_settings\nfrom worker.domain import (responses_layaway, responses_liveness,\n responses_readiness)\nfrom worker.domain.entities import Filter, Layaway\nfrom worker.infrastructure import WorkerController\n\n# layaway: /orders/api/v1/layaway/docs\n\nsettings = get_settings()\n\nversion = settings.API_VERSION\nnamespace = settings.NAMESPACE\nresource = settings.RESOURCE\nprefix = f'/{namespace}/api/{version}/{resource}'\n\ndescriptions = {\n 'liveness': \"Verifica que el servicio se encuentre disponible.\",\n 'readiness': \"Verifica que existan conexiones activas a MONGO/REDIS/FIREBASE.\",\n 'set_layaway': \"Agrega un cómic al apartado.\",\n 'get_layaway': \"Obtiene el apartado de cómics del usuario.\"\n}\n\nrouter = APIRouter(prefix=prefix)\n\n\n@router.get('/liveness', tags=['Health Checks'], responses=responses_liveness,\n summary=descriptions['liveness'])\n@autodynatrace.trace(f'{prefix}/liveness')\n@tracer.wrap(service='userauth', resource=f'GET {prefix}/liveness')\ndef liveness() -> dict:\n return {'status': 'Success'}\n\n\n@router.get('/readiness', tags=[\"Health Checks\"], responses=responses_readiness,\n summary=descriptions['readiness'])\n@autodynatrace.trace(f'{prefix}/readiness')\n@tracer.wrap(service='userauth', resource=f'GET {prefix}/liveness')\ndef readiness() -> HttpResponse:\n return WorkerController.readiness()\n\n\n@router.post(f'', tags=[\"Layaway\"], responses=responses_layaway,\n summary=descriptions['set_layaway'])\n@autodynatrace.trace(f'{prefix}')\n@tracer.wrap(service='userauth', resource=f'POST {prefix}/{resource}')\ndef set_layaway(body: Layaway,\n authorization: HTTPAuthorizationCredentials = Depends(HTTPBearer())) -> HttpResponse:\n \"\"\" Agrega un cómic al apartado. \"\"\"\n token = authorization.credentials\n return WorkerController.set_layaway(token, body)\n\n\n@router.get(f'', tags=[\"Layaway\"], responses=responses_layaway,\n summary=descriptions['get_layaway'])\n@autodynatrace.trace(f'{prefix}')\n@tracer.wrap(service='userauth', resource=f'GET {prefix}/{resource}')\ndef get_layaway(filter: Filter = Depends(),\n authorization: HTTPAuthorizationCredentials = Depends(HTTPBearer())) -> HttpResponse:\n \"\"\" Obtiene el apartado de cómics del usuario. \"\"\"\n token = authorization.credentials\n return WorkerController.get_layaway(token, filter)\n","repo_name":"LawlietJH/ComicShop","sub_path":"layaway/src/worker/infrastructure/worker_routes.py","file_name":"worker_routes.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38583838100","text":"#Author: Guido S. Nickels \n#Based on the utorrent maraschino module\n\nfrom flask import render_template\nfrom datetime import timedelta\nfrom maraschino import app, logger\nfrom maraschino.tools import *\nfrom rtorrent import RTorrent\n\ndef log_error(ex):\n\tlogger.log('RTORRENTDL :: EXCEPTION - %s' % ex, 'DEBUG')\n\n@app.route('/xhr/rtorrentdl/')\n@requires_auth\n\ndef xhr_rtorrentdl():\n\n\t# url qualification\n\tdef url_qualify(url_proto,url_host,url_port):\n\n\t\turl_host_part=str(url_host).partition('/')\n\n\t\t# validate host (kinda... just make sure it's not empty)\n\t\tif url_host_part[0]:\n\n\t\t\t# validate port\n\t\t\tif not url_port:\n\n\t\t\t\t# for http and https we can assume default service ports\n\t\t\t\tif url_proto == 'http':\n\t\t\t\t\turl_port = 80\n\t\t\t\telif url_proto == 'https':\n\t\t\t\t\turl_port = 443\n\t\t\t\telse:\n\t\t\t\t\traise Exception('port must be defined for protocol %s' % (url_proto))\n\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\turl_port=int(url_port)\n\t\t\t\texcept ValueError:\n\t\t\t\t\traise Exception('port must be a numeric value')\n\n\t\t\turl_qualified='%s://%s:%i%s%s' % (\n\t\t\t\turl_proto,\n\t\t\t\turl_host_part[0],\n\t\t\t\turl_port,\n\t\t\t\turl_host_part[1],\n\t\t\t\turl_host_part[2]\n\t\t\t)\n\n\t\t\treturn url_qualified\n\n\t\telse:\n\t\t\traise Exception('invalid host: %s' % (url_host[0]))\n\n\t# initialize empty list, which will be later populated with listing\n\t# of active torrents\n\ttorrentlist = list()\n\n\t# connection flag\n\tconnected = False\n\n\t# global rates\n\tdown_rate = 0.0\n\tup_rate = 0.0\n\n\trtorrent_url = None\n\trtorrent_user = None\n\trtorrent_password = None\n\n\ttry:\n\t\tif get_setting_value('rtorrent_host') is not None:\n\t\t\trtorrent_url = url_qualify(\n\t\t\t\tget_setting_value('rtorrent_proto'),\n\t\t\t\tget_setting_value('rtorrent_host'),\n\t\t\t\tget_setting_value('rtorrent_port')\n\t\t\t)\n\texcept Exception as ex:\n\t\tlog_error(ex)\n\n\ttry:\n\t\tif rtorrent_url:\n\t\t\t# user/password login is not implemented for scgi\n\t\t\tif get_setting_value('rtorrent_proto') != 'scgi':\n\t\t\t\trtorrent_user = get_setting_value('rtorrent_user')\n\t\t\t\trtorrent_password = get_setting_value('rtorrent_password')\n\n\t\t\tclient = RTorrent(rtorrent_url,rtorrent_user,rtorrent_password,True)\n\n\t\t\tif client is not None:\n\t\t\t\tconnected = True\n\t\t\t\tdown_rate = client.get_down_rate()\n\t\t\t\tup_rate = client.get_up_rate()\n\n\t\t\t# loop through each job and add all torrents to torrentlist()\n\t\t\tfor torrent in client.get_torrents():\n\t\t\t\t# friendly status and time left\n\t\t\t\ttime_left = -1\n\t\t\t\tif torrent.complete:\n\t\t\t\t\tif torrent.active:\n\t\t\t\t\t\tstatus = 'seeding'\n\t\t\t\t\telse:\n\t\t\t\t\t\tstatus = 'done'\n\t\t\t\telse:\n\t\t\t\t\tif torrent.active:\n\t\t\t\t\t\tif torrent.down_rate > 0:\n\t\t\t\t\t\t\ttime_left = str(timedelta(seconds = round(float(torrent.left_bytes) / torrent.down_rate)))\n\t\t\t\t\t\t\tstatus = 'leeching'\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tstatus = 'waiting'\n\t\t\t\t\telse:\n\t\t\t\t\t\tstatus = 'inactive'\n\n\t\t\t\t# get torrent file list\n\t\t\t\t# FIXME takes too much time and is not used anyway for now\n\t\t\t\t#torrent_filelist = []\n\t\t\t\t#for file_current in torrent.get_files():\n\t\t\t\t#\ttorrent_filelist.append(os.path.join(torrent.directory,file_current.path))\n\n\t\t\t\t# what's left?\n\t\t\t\tprogress = float(100.0 / torrent.size_bytes * (torrent.size_bytes-torrent.left_bytes))\n\n\t\t\t\t# append to list\n\t\t\t\ttorrentlist.append({\n\t\t\t\t\t'name': torrent.name,\n\t\t\t\t\t'info_hash': torrent.info_hash,\n\t\t\t\t\t'status': status,\n\t\t\t\t\t'state': torrent.state,\n\t\t\t\t\t'progress': progress,\n\t\t\t\t\t'time_left': time_left,\n\t\t\t\t\t'down_rate': torrent.down_rate,\n\t\t\t\t\t'up_rate': torrent.up_rate,\n\t\t\t\t\t'ratio': torrent.ratio\n\t\t\t\t#\t'folder': torrent.directory,\n\t\t\t\t#\t'files': '|'.join(torrent_filelist)\n\t\t\t\t})\n\n\t\t\t# no torrents -> empty list\n\t\t\tif not torrentlist.__len__():\n\t\t\t\ttorrentlist = None\n\n\texcept Exception as ex:\n\t\tlog_error(ex)\n\t\ttorrentlist = None\n\n\treturn render_template('rtorrentdl.html',\n\t\tconnected = connected,\n\t\ttorrentlist_scroll = get_setting_value('rtorrent_list_scroll'),\n\t\ttorrentlist = torrentlist,\n\t\tdown_rate = down_rate,\n\t\tup_rate = up_rate\n\t)\n","repo_name":"mrkipling/maraschino","sub_path":"modules/rtorrentdl.py","file_name":"rtorrentdl.py","file_ext":"py","file_size_in_byte":3861,"program_lang":"python","lang":"en","doc_type":"code","stars":426,"dataset":"github-code","pt":"3"}
+{"seq_id":"34070260296","text":"import requests\n\nfrom licenseware.common.constants import envs\nfrom licenseware.utils.dramatiq_redis_broker import broker\nfrom licenseware.utils.logger import log\n\n\nclass RegistrationFailed(Exception):\n ...\n\n\ndef registration_failed(retries_so_far: int, exception):\n return isinstance(exception, RegistrationFailed)\n\n\n@broker.actor(retry_when=registration_failed, queue_name=envs.QUEUE_NAME)\ndef register_all(payload: dict):\n\n if envs.DESKTOP_ENVIRONMENT:\n return\n\n log.info(\"Sending payload to registry-service\")\n\n registration = requests.post(\n url=envs.REGISTER_ALL_URL,\n json=payload,\n headers={\n \"Authorization\": envs.get_auth_token(),\n \"Content-type\": \"application/json\",\n \"Accept\": \"application/json\",\n },\n )\n\n if registration.status_code != 200:\n log.warning(registration.content)\n raise RegistrationFailed(\"Registration failed\")\n\n log.success(\"Registration successful!\")\n","repo_name":"licenseware/licenseware-sdk-v2","sub_path":"licenseware/registry_service/register_all.py","file_name":"register_all.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"26544875004","text":"# myAgents.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).\nfrom tracemalloc import start\nfrom game import Agent\nfrom util import PriorityQueue\nfrom searchProblems import PositionSearchProblem\nfrom collections import namedtuple\nimport util\nimport time\nimport search\nimport random\nfrom functools import partial\n\n\ndef find_shortest_food(position, gridList):\n val_list = []; least_element = 0; min_val = 99999; result = 0; \n for element in gridList:\n val = 0;\n val += abs(position[0] - element[0])\n val += abs(position[1] - element[1])\n val_list.append(val)\n if min_val > val:\n min_val = val\n least_element = element\n if min_val != 99999:\n return least_element, min_val\n else: \n return 0\n\ndef foodHeuristic(state, problem):\n\n position = state\n val = 0\n val += abs(position[0] - problem.goal[0])\n val += abs(position[1] - problem.goal[1])\n\n return val\n\ndef weirdHeuristic(state, problem):\n position = state\n val = 100\n for i in range(problem.Num):\n pac = problem.gameState.getPacmanPosition(i)\n val += 0.5 ** abs(position[0] - pac[0])\n val += 0.5 ** abs(position[1] - pac[1])\n return val\n\ndef manhattanHeuristic(goal, position, info={}):\n \"The Manhattan distance heuristic for a PositionSearchProblem\"\n xy1 = position\n xy2 = goal\n return abs(xy1[0] - xy2[0]) + abs(xy1[1] - xy2[1])\n\n\n\n\n\n\n\"\"\"\nIMPORTANT\n`agent` defines which agent you will use. By default, it is set to ClosestDotAgent,\nbut when you're ready to test your own agent, replace it with MyAgent\n\"\"\"\ndef createAgents(num_pacmen, agent_list= ['Pioneer']):\n ret_list = []\n for i in range(num_pacmen):\n index = i%len(agent_list)\n ret_list.append(eval(agent_list[index])(index=i) )\n return ret_list\n\n\nclass Pioneer(Agent):\n \"\"\"\n Implementation of your agent.\n \"\"\"\n def findPathToClosestDot(self, gameState):\n \"\"\"\n Returns a path (a list of actions) to the closest dot, starting from\n gameState.\n \"\"\"\n # Here are some useful elements of the startState\n numAgent = gameState.getNumAgents()\n pacPosList = []\n \n for i in range(numAgent):\n pacPosList.append(gameState.getPacmanPosition(i))\n startPosition = pacPosList[self.index]\n pacPosList[self.index] = (999,999)\n \n global food\n gridList = food.asList()\n length = len(gridList)\n\n \"*** YOUR CODE HERE ***\"\n \n if length > 0:\n least_element, min_val = find_shortest_food(startPosition, gridList)\n\n\n self.destination[0] = least_element\n\n # for i in range(numAgent):\n # pacPosList[i] = manhattanHeuristic(pacPosList[i], startPosition)\n # min_pac = min(pacPosList)\n # if min_pac < 7:\n # decision_goAway = random.randint(0,2)\n # if decision_goAway == 2:\n # ran = random.randint(0, length-1)\n # least_element = gridList[ran]\n # self.destination[0] = least_element\n # problem = AnyFoodSearchProblem(gameState = gameState, food = gridList, agentIndex = self.index, goal = self.destination, goal_val = min_val, agentLocation=pacPosList)\n # result = search.astar(problem, foodHeuristic)\n # return result\n\n problem = AnyFoodSearchProblem(gameState = gameState, food = gridList, agentIndex = self.index, goal = self.destination, goal_val = min_val, agentLocation=pacPosList)\n\n before = time.time()\n result = search.astar(problem, foodHeuristic)\n after = time.time()\n return result\n \n\n\n def getAction(self, state):\n global food\n food = state.getFood()\n if len(self.actionList) == 0:\n self.actionList = self.findPathToClosestDot(state)\n if food[self.destination[0][0]][self.destination[0][1]] == False:\n self.actionList = self.findPathToClosestDot(state)\n ret_val = self.actionList[0]\n self.actionList = self.actionList[1:]\n return ret_val\n\n\n\n def initialize(self):\n self.actionList = []\n self.destination = [(999,999)]\n self.dest_list = []\n \"\"\"\n Intialize anything you want to here. This function is called\n when the agent is first created. If you don't need to use it, then\n leave it blank\n \"\"\"\n\n\n\n\"\"\"\nPut any other SearchProblems or search methods below. You may also import classes/methods in\nsearch.py and searchProblems.py. (ClosestDotAgent as an example below)\n\"\"\"\n\nclass ClosestDotAgent(Agent):\n\n def findPathToClosestDot(self, gameState):\n \"\"\"\n Returns a path (a list of actions) to the closest dot, starting from\n gameState.\n \"\"\"\n # Here are some useful elements of the startState\n startPosition = gameState.getPacmanPosition(self.index)\n food = gameState.getFood()\n gridList = food.asList()\n \n \"*** YOUR CODE HERE ***\"\n if len(gridList) > 0:\n least_element, min_val = find_shortest_food(startPosition, gridList)\n problem = AnyFoodSearchProblem(gameState = gameState, food = gridList, agentIndex = self.index, goal = least_element)\n startPosition = least_element\n result = search.ucs(problem)\n\n return result\n\n def getAction(self, state):\n return self.findPathToClosestDot(state)[0]\n\n\nclass AnyFoodSearchProblem(PositionSearchProblem):\n \"\"\"\n A search problem for finding a path to any food.\n This search problem is just like the PositionSearchProblem, but has a\n different goal test, which you need to fill in below. The state space and\n successor function do not need to be changed.\n The class definition above, AnyFoodSearchProblem(PositionSearchProblem),\n inherits the methods of the PositionSearchProblem.\n You can use this search problem to help you fill in the findPathToClosestDot\n method.\n \"\"\"\n\n def __init__(self, food, gameState, goal, agentIndex, goal_val, agentLocation):\n \"Stores information from the gameState. You don't need to change this.\"\n # Store the food for later reference\n self.food = food\n self.gameState = gameState\n self.goal = goal[0]\n self.goal_list = goal\n self.goal_val = goal_val\n self.Num = self.gameState.getNumAgents()\n # Store info for the PositionSearchProblem (no need to change this)\n self.walls = gameState.getWalls()\n self.agentIndex = agentIndex\n self.agentLocation = agentLocation\n self.startState = gameState.getPacmanPosition(agentIndex)\n self.foundPacman = False\n self.costFn = lambda pos: 1\n # if self.agentIndex == 2:\n # self.costFn = lambda pos: .8 ** pos[1]\n # if self.agentIndex == 3:\n # self.costFn = lambda pos: 1.2 ** pos[1]\n # if self.agentIndex == 6:\n # self.costFn = lambda pos: .8 ** pos[0]\n # if self.agentIndex == 7:\n # self.costFn = lambda pos: 1.2 ** pos[0]\n self._visited, self._visitedlist, self._expanded = {}, [], 0 # DO NOT CHANGE\n self.counter = 0\n\n def isGoalState(self, state):\n \"\"\"\n The state is Pacman's position. Fill this in with a goal test that will\n complete the problem definition.\n \"\"\"\n\n (x,y) = state\n \n \n self.counter += 1\n if (x,y) in self.food:\n\n self.goal_list[0] = (x,y)\n return True\n else: \n return False\n \n # if self.foundPacman == True:\n # return False\n \n \n\n # if (x,y) in self.agentLocation:\n # self.foundPacman == True\n\n \"*** YOUR CODE HERE ***\"","repo_name":"xlhuang37/midterm-contest","sub_path":"myAgents.py","file_name":"myAgents.py","file_ext":"py","file_size_in_byte":8518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12520255945","text":"\"\"\"\n Created by Dhaif BEKHA, (dhaif.bekha@cea.fr).\n\n \"\"\"\nimport pandas as pd\nfrom subprocess import Popen, PIPE\nimport os\nimport numpy as np\n\"\"\"\n\nGoal\n----\nCreate a Fractional Anisotropy (FA) template image based \non the NAIS controls children group. After that, we compute\nthe white matter skeleton based on the FA template. The FA template and\nthe corresponding white matter skeleton can be directly use with\nthe TBSS methodology.\n\nThis code use python wrapper's code around FSL and DTI-TK,\nsome of A. Grigis (antoine.grigis@cea.fr), and some of mine. Please visit :\nhttps://github.com/neurospin/pyconnectome/tree/master/pyconnectome.\n\n\nAll the steps for the creation of the template are done with the help\nof DTI-TK and can be found at:\nhttp://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.HomePage\n\nAll the DTI-TK template preparation steps for the TBSS pipeline can be found at:\nhttp://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.TBSS\n\nAll the steps of the TBSS pipeline can be found at:\nhttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS/UserGuide\n----\n\nStep 1\n------\nConvert the DTIFIT FSL output to the DTI-TK tensors\nfile format.\n\nStep 2\n------\nCompute a tensors template from a subset group of \nsubjects. This step increase in time with the number\nof subject.\n\nStep 3\n------\nWarp all subject in the tensor template space \npreviously computed\n\nStep 4\n------\nGenerate a final refine template by taking\nthe mean of all normalized tensors image \npreviously computed. \n\nStep 5\n------\nOn the final template, compute the 1mm3\nFA template map.\n\nStep 6\n------\nOn the previously computed FA map, compute\nthe skeleton of major tracks.\n\n\"\"\"\n# read subjects list\nsubjects = list(pd.read_csv('/neurospin/grip/protocols/MRI/Ines_2018/controls_list.txt',\n header=None)[0])\n# main TBSS analysis directory\ntbss_directory = '/media/db242421/db242421_data/DTI_TBSS_M2Ines/controls_dtitk/dtitk_native_tensors'\n\n# PyConnectome script directory: Change according to you're installation !!\npyconnectome_script = '/media/db242421/db242421_data/pyconnectome/pyconnectome/scripts'\n\n# Number of controls used for the template creation\nsubset_of_controls = 12\n\n# Template output directory\ntemplate_outdir = '/media/db242421/db242421_data/DTI_TBSS_M2Ines/controls_dtitk/' \\\n 'controls_dti_template'\n\n# Normalized tensors output directory\nnormalized_tensors_outdir = '/media/db242421/db242421_data/DTI_TBSS_M2Ines/controls_dtitk/normalized_tensors'\n\n# Final template output directory for the TBSS analysis\nfinal_high_res_template_outdir = '/media/db242421/db242421_data/DTI_TBSS_M2Ines/controls_dtitk/final_template'\n\n\"\"\"\nConvert DTIFIT fsl output to DTI-TK tensors file format.\n\"\"\"\nfor subject in subjects:\n print('Converting FSL tensor to DTI-TK tensor file format for {}'.format(subject))\n\n # Build the tensor file format requested for dti-tk\n fsl_to_dtitk = [\"python\",\n \"{}\".format(os.path.join(pyconnectome_script,\n 'pyconnectome_dtitk_import_tensors')),\n \"-b\", \"{}\".format(os.path.join(tbss_directory,\n subject, \"dtifit\", \"dtifit_\" + subject)),\n \"-s\", \"{}\".format(subject),\n \"-o\", \"{}\".format(os.path.join(tbss_directory, subject))]\n fsl_to_dtitk_process = Popen(fsl_to_dtitk, stdout=PIPE)\n fsl_to_dtitk_output, fsl_to_dtitk_error = fsl_to_dtitk_process.communicate()\n\n\n\"\"\"\nRandomly choose control subject to create a bootstrapped sample.\n\"\"\"\n# Extract a subset of controls for the creation of the template\nsubset_of_controls_nip = list(np.random.choice(a=subjects, size=subset_of_controls))\n# build full path to the dti-tk tensors file of the subset\nsubset_controls_tensors_path = [os.path.join(tbss_directory, s_subset,\n s_subset, 'dtifit_' + s_subset + '_dtitk.nii.gz')\n for s_subset in subset_of_controls_nip]\n\n\"\"\"\nBuild the DTI template based on the bootstrapped sample.\n\"\"\"\n# For formatting purposes, I have to write down the command in a text\nwith open(os.path.join(template_outdir, \"cmd_create_template.txt\"), 'w') as command_txt:\n command_txt.writelines('python {}/pyconnectome_dtitk_create_templates '.format(pyconnectome_script))\n command_txt.writelines('-t ')\n for tensor_path in range(len(subset_controls_tensors_path)):\n command_txt.writelines('{} '.format(subset_controls_tensors_path[tensor_path]))\n command_txt.writelines('-o {} '.format(template_outdir))\n command_txt.writelines('-V 2')\n\nwith open(os.path.join(template_outdir, \"cmd_create_template.txt\"), 'r') as command_txt:\n dtitk_create_template = command_txt.read()\n\ndtitk_create_template_process = Popen(dtitk_create_template.split(), stdout=PIPE)\ndtitk_create_template_output, dtitk_create_template_error = \\\n dtitk_create_template_process.communicate()\n\n# Write command output\nwith open(os.path.join(template_outdir, \"output_create_template.txt\"), 'w') as create_template_out:\n create_template_out.write(dtitk_create_template_output.decode('utf-8'))\n\n\"\"\"\nWarp all controls tensors in the template space, with final resolution \nof 1mm3 \n\"\"\"\n# Loop over all controls subject\nfor subject in subjects:\n dtitk_register = [\"python\", os.path.join(pyconnectome_script,\n \"pyconnectome_dtitk_register\"),\n \"-t\", os.path.join(tbss_directory, subject, subject,\n \"dtifit_\" + subject + \"_dtitk.nii.gz\"),\n \"-s\", subject,\n \"-e\", os.path.join(template_outdir,\n \"mean_diffeomorphic_initial6.nii.gz\"),\n \"-o\", normalized_tensors_outdir,\n \"-b\", os.path.join(template_outdir, \"mask.nii.gz\"),\n \"-V 2\"]\n dtitk_register_process = Popen(dtitk_register, stdout=PIPE)\n dtitk_register_output, dtitk_register_error = dtitk_register_process.communicate()\n\n\"\"\"\nGenerate the population specific DTI template with\nisotropic 1mm3 spacing\n\"\"\"\n# Write down in a text file the path to the normalized\n# tensors for all controls\nwith open(os.path.join(final_high_res_template_outdir,\n \"subjects_normalized.txt\"), \"w\") as tensors_path_file:\n for subject in subjects:\n tensors_path_file.write(os.path.join(normalized_tensors_outdir, subject,\n \"dtifit_\" + subject + \"_dtitk_diffeo.nii.gz\"))\n tensors_path_file.write(\"\\n\")\n# Full path to final tensor template to be written\nmean_final_high_res = os.path.join(final_high_res_template_outdir,\n \"mean_final_high_res.nii.gz\")\n# Compute the final template with the TVMean command\ncompute_final_template = [\"TVMean\",\n \"-in\", os.path.join(final_high_res_template_outdir,\n \"subjects_normalized.txt\"),\n \"-out\", mean_final_high_res,\n \"-type\", \"ORIGINAL\",\n \"-interp\", \"LEI\"\n ]\nprint(\"Computing the final tensor template with isotropic resolution...\")\ncompute_final_template_process = Popen(compute_final_template, stdout=PIPE)\ncompute_final_template_output, compute_final_template_error = \\\n compute_final_template_process.communicate()\n\n# Write command output\nwith open(os.path.join(final_high_res_template_outdir,\n \"TVMean_output.txt\"), \"w\") as TVMean_output:\n TVMean_output.write(compute_final_template_output.decode('utf-8'))\n\n\"\"\"\nCompute the high resolution FA template map from\nthe previously computed tensor template\n\"\"\"\ngenerate_FA_template = [\"TVtool\",\n \"-in\", mean_final_high_res,\n \"-fa\"]\ngenerate_FA_template_process = Popen(generate_FA_template, stdout=PIPE)\ngenerate_FA_template_output, generate_FA_template_error = \\\n generate_FA_template_process.communicate()\n# Write command output\nwith open(os.path.join(final_high_res_template_outdir,\n \"TVtool_FA_output.txt\"), \"w\") as TVtool_output:\n TVtool_output.write(compute_final_template_output.decode('utf-8'))\n\n\n\"\"\"\nOptional: Compute the RGB image from\nthe final tensor template. Very convenient\nwith ITK-SNAP viewer for example.\n\n\"\"\"\ngenerate_RGB_template = [\"TVtool\",\n \"-in\", mean_final_high_res,\n \"-rgb\"]\ngenerate_RGB_template_process = Popen(generate_RGB_template, stdout=PIPE)\ngenerate_RGB_template_output, generate_RGB_template_error = \\\n generate_RGB_template_process.communicate()\n# Write command output\nwith open(os.path.join(final_high_res_template_outdir,\n \"TVtool_RGB_output.txt\"), \"w\") as TVtool_RGB_output:\n TVtool_RGB_output.write(generate_RGB_template_output.decode('utf-8'))\n\n\"\"\"\nCompute the skeleton from\nthe FA map template\n\n\"\"\"\nskeletonize_FA = [\"tbss_skeleton\",\n \"-i\", os.path.join(final_high_res_template_outdir,\n \"mean_final_high_res_fa.nii.gz\"),\n \"-o\", os.path.join(final_high_res_template_outdir,\n \"mean_final_high_res_fa_skeleton.nii.gz\")\n ]\nskeletonize_FA_process = Popen(skeletonize_FA, stdout=PIPE)\nskeletonize_FA_output, skeletonize_FA_error = \\\n skeletonize_FA_process.communicate()\n# Write command output\nwith open(os.path.join(final_high_res_template_outdir,\n \"tbss_skeleton_RGB_output.txt\"), \"w\") as skeleton_output:\n skeleton_output.write(skeletonize_FA_output.decode('utf-8'))\n\n\"\"\"\nEnd of the creation of the FA and corresponding skeleton template.\nThe output of this script can now replace the standard templates provide by \nthe FSL software for the TBSS analysis.\n\"\"\"\n","repo_name":"ConPagnon/conpagnon","sub_path":"conpagnon/examples_and_test/tbss_create_controls_template.py","file_name":"tbss_create_controls_template.py","file_ext":"py","file_size_in_byte":9948,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"71626089682","text":"from access_token import TOKEN\nimport requests\nfrom time import sleep\n\n\ndef write_csv(list_groups: list) -> None:\n \"\"\"Записывает id групп в файл (они могут повторяться)\"\"\"\n with open('groups_id.csv', 'a', encoding='utf-8') as file:\n for group in list_groups:\n if not group['is_closed']:\n file.write(f\"{group['id']}\\n\")\n\n\ndef get_a_list_of_groups(key_word: str) -> list:\n \"\"\"по ключевому слову возвращает список групп\"\"\"\n params = {\n 'q': key_word,\n 'access_token': TOKEN,\n 'v': 5.103,\n 'type': 'group',\n 'sort': 0,\n \"count\": 1000,\n 'offset': 0\n }\n response = requests.get(f'https://api.vk.com/method/groups.search', params)\n print(response.status_code)\n list_groups = response.json()['response']['items']\n return list_groups\n\n\ndef main():\n key_words = [\n \"Путин\",\n \"политика\",\n \"протест\",\n \"революция\",\n \"россия\",\n \"полика новости\",\n \"путин новости\",\n \"путин бог\",\n \"путин госсовет\",\n \"политика новости\",\n \"политика россии\",\n \"политика сегодня\",\n \"политика в россии\",\n 'Единая Россия',\n 'КПРФ',\n 'ЛДПР',\n 'Справедливая Россия'\n 'Коммунисты России',\n 'Яблоко',\n 'Российская партия пенсионеров за социальную справедливость',\n 'Родина',\n 'Партия Роста',\n 'Зеленые',\n 'ПАРНАС',\n 'Патриоты России',\n 'Гражданская платформа и Гражданская сила',\n \"навальный митинг\",\n \"навальный новости\"\n ]\n for key_word in key_words:\n sleep(1)\n write_csv(get_a_list_of_groups(key_word))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"DenZaytsev/vk_cluster","sub_path":"search_groups.py","file_name":"search_groups.py","file_ext":"py","file_size_in_byte":2173,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22512645702","text":"import config\nfrom distexprunner import *\n\n\nparameter_grid = ParameterGrid(\n dramGB=[150],\n factor_mem_ssd=[10],\n # numberNodes= range(1,6),\n numberNodes= [5],\n pp=[2],\n fp=[1],\n workers = range(1,25),\n partitioned= [\"OLAP_partitioned\", \"noOLAP_partitioned\"],\n # partitioned= [\"OLAP_partitioned\"], \n # partitioned= [\"noOLAP_partitioned\"],\n)\n\n\n@reg_exp(servers=config.server_list)\ndef compile(servers):\n servers.cd(\"/home/tziegler/scalestore/build\")\n cmake_cmd = f'cmake -DSANI=OFF -DCMAKE_BUILD_TYPE=Release ..'\n procs = [s.run_cmd(cmake_cmd) for s in servers]\n assert(all(p.wait() == 0 for p in procs))\n\n make_cmd = f'make -j'\n procs = [s.run_cmd(make_cmd) for s in servers]\n assert(all(p.wait() == 0 for p in procs))\n\n \n\n@reg_exp(servers=config.server_list, params=parameter_grid, raise_on_rc=False)\ndef ycsbBenchmark(servers, dramGB, factor_mem_ssd, numberNodes ,pp,fp,workers, partitioned):\n servers.cd(\"/home/tziegler/scalestore/build/frontend\")\n\n cmds = []\n for i in range(0,numberNodes):\n print(i)\n cmd = f'blkdiscard {servers[i].ssdPath}'\n cmds += [servers[i].run_cmd(cmd)]\n \n if not all(cmd.wait() == 0 for cmd in cmds):\n return Action.RESTART\n \n cmds = []\n for i in range(0, numberNodes):\n cmd = f'numactl --membind=0 --cpunodebind=0 ./OLAP -worker={workers} -dramGB={dramGB} -nodes={numberNodes} -messageHandlerThreads=2 -ownIp={servers[i].ibIp} -pageProviderThreads={pp} -coolingPercentage=10 -freePercentage={fp} -csvFile=olap_scale_server.csv -OLAP_run_for_seconds=30 --ssd_path={servers[i].ssdPath} --ssd_gib=2000 --OLAP_factor_mem_ssd={factor_mem_ssd} -{partitioned} -tag={partitioned} -evictCoolestEpochs=0.5'\n cmds += [servers[i].run_cmd(cmd)]\n \n if not all(cmd.wait() == 0 for cmd in cmds):\n return Action.RESTART\n","repo_name":"DataManagementLab/ScaleStore","sub_path":"distexperiments/experiments/olap_scale_servers.py","file_name":"olap_scale_servers.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"3"}
+{"seq_id":"43179614330","text":"def play():\n for i in range(1, N + 1):\n if li[1][i] == 1:\n stack.append(i)\n li[1][i] = 0\n li[i][1] = 0\n\n for j in range(len(stack)):\n for i in range(1, N + 1):\n if li[stack[j]][i] == 1 and i not in stack:\n stack.append(i)\n li[j][i] = 0\n li[i][j] = 0\n\n\nT = int(input())\n\nfor TC in range(1, T + 1):\n N, M = map(int, input().split())\n li = [[0] * (N + 1) for _ in range(N + 1)]\n v = [0] * (N + 1)\n for i in range(M):\n a, b = map(int, input().split())\n li[a][b] = 1\n li[b][a] = 1\n stack = []\n play()\n print('#%d %d' %(TC, len(stack)))\n","repo_name":"sondongmin0419/study","sub_path":"python/s_5521 상원이의 생일파티.py","file_name":"s_5521 상원이의 생일파티.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35691353823","text":"import psycopg2\r\nimport os\r\nfrom os import environ\r\n\r\n#Example python code to connect to a PG Database, pull back the table and add new records\r\n#really basic\r\n\r\n#execute using account equinox on RHEL8 VM\r\n\r\ndef insert_record(cur,last_id):\r\n cur.execute(\"INSERT INTO event (id, app, ac3,ac4,submit,staffid) VALUES \\\r\n (%s, %s, %s, %s, %s, %s)\", ((str(int(last_id)+1)),\"Jira\",\"BigData\",\"MSS\",\"2021-01-31\",\"04992466\"))\r\n\r\n\r\ndef print_table(results):\r\n\r\n for row in results:\r\n print('id: %s , app: %s , ac3: %s , ac4: %s, submit: %s, staffid %s' % (row[0],row[1],row[2],row[3],row[4],row[5]))\r\n last_id=row[0]\r\n return last_id \r\n\r\n\r\ndef main():\r\n \r\n #Connect\r\n conn = psycopg2.connect(\"dbname=equinox_db user=equinox\")\r\n cur = conn.cursor()\r\n \r\n \r\n #extract data\r\n cur.execute(\"SELECT * FROM event;\")\r\n results=cur.fetchall() \r\n \r\n \r\n #print table and get last ID\r\n last_id=print_table(results)\r\n \r\n #insert a new record\r\n insert_record(cur,last_id)\r\n\r\n #Close connection\r\n conn.commit() \r\n cur.close()\r\n conn.close()\r\n\r\nif __name__ == \"__main__\":\r\n\r\n main()","repo_name":"lewchenko/pgconnector","sub_path":"pgconnector.py","file_name":"pgconnector.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70940832403","text":"import netCDF4 as nc\nimport numpy as np\nimport os\nimport scipy.stats as st\nimport operator\nfrom collections import OrderedDict\nimport constants as CC\nimport scipy as sp\nfrom scipy import ndimage as ndi\nimport time\nfrom skimage import measure\nfrom scipy.spatial.distance import pdist\nfrom matplotlib import pyplot as plt\n\n\ndef stats(tab):\n return [np.mean(tab),np.min(tab),np.max(tab),np.std(tab),np.median(tab),np.percentile(tab,5),np.percentile(tab,95)]\n\ndef do_cyclic(label, choice=(-2,-1), rename=True) :\n if isinstance(choice,int): choice=(choice,)\n if not isinstance(choice,tuple): raise NameError(choice+\" is not a valid choice for cyclic conditions.\\nUse None or a tuple\")\n objects = label\n shape = objects.shape\n listObj = np.unique(objects[objects>0]).tolist()\n nbr0 = len(listObj)\n tmp = np.moveaxis(objects, list(choice), range(-len(choice),0)) # swap cyclic axes to last axes\n dim = len(shape)\n # reshape to 4D if needed\n if dim==4 :\n n0,n1,n2,n3 = shape\n elif dim==3 :\n n1,n2,n3 = shape\n n0 = 1\n tmp = tmp[None,:,:,:]\n elif dim==2 :\n n2,n3 = shape\n n0 = 1 ; n1 = 1\n tmp = tmp[None,None,:,:]\n for i0 in range(n0) :\n for i1 in range(n1) :\n for i2 in range(n2) :\n # last dim is cyclic\n if (tmp[i0,i1,i2,n3-1] and tmp[i0,i1,i2,0] and (tmp[i0,i1,i2,n3-1]!=tmp[i0,i1,i2,0])) :\n num = tmp[i0,i1,i2,n3-1]\n tmp[tmp==num] = tmp[i0,i1,i2,0]\n if rename : tmp[tmp==listObj.pop(-1)]=num\n if len(choice)==2 :\n for i3 in range(n3) :\n # also one before last dim is cyclic\n if (tmp[i0,i1,n2-1,i3] and tmp[i0,i1,0,i3] and (tmp[i0,i1,n2-1,i3]!=tmp[i0,i1,0,i3])) :\n num = tmp[i0,i1,n2-1,i3]\n tmp[tmp==num] = tmp[i0,i1,0,i3]\n if rename : tmp[tmp==listObj.pop(-1)]=num\n tmp = tmp.reshape(shape)\n objects = np.moveaxis(tmp, range(-len(choice),0), list(choice))\n nbr = len(np.unique(objects[objects>0]))\n #print \"\\t', nbr0 - nbr, 'objects on the borders'\n #self.nbr=nbr\n return objects\n\n\"\"\" Statistical functions for binary cloud masks. \"\"\"\n\"\"\" Extracted from Typhon website \"\"\"\ndef get_cloudproperties(cloudmask, connectivity=1):\n \"\"\"Calculate basic cloud properties from binary cloudmask.\n\n Note:\n All parameters are calculated in pixels!!\n\n See also:\n :func:`skimage.measure.label`:\n Used to find different clouds. \n :func:`skimage.measure.regionprops`:\n Used to calculate cloud properties.\n\n Parameters:\n cloudmask (ndarray): 2d binary cloud mask.\n connectivity (int): Maximum number of orthogonal hops to consider\n a pixel/voxel as a neighbor (see :func:`skimage.measure.label`).\n\n Returns:\n list:\n List of :class:`RegionProperties`\n (see :func:`skimage.measure.regionprops`)\n \"\"\"\n cloudmask[np.isnan(cloudmask)] = 0\n\n labels = measure.label(cloudmask, connectivity=connectivity)\n #print labels.shape\n #plt.contourf(cloudmask);plt.colorbar();plt.show()\n #plt.contourf(labels);plt.title('cloud properties');plt.colorbar();plt.show()\n return measure.regionprops(labels)\n\n\ndef neighbor_distance(cloudproperties, mindist=0):\n \"\"\"Calculate nearest neighbor distance for each cloud.\n periodic boundaries\n\n Note: \n Distance is given in pixels.\n\n See also: \n :class:`scipy.spatial.cKDTree`:\n Used to calculate nearest neighbor distances. \n\n Parameters: \n cloudproperties (list[:class:`RegionProperties`]):\n List of :class:`RegionProperties`\n (see :func:`skimage.measure.regionprops` or\n :func:`get_cloudproperties`).\n mindist\n Minimum distance to consider between centroids.\n If dist < mindist: centroids are considered the same object\n\n Returns: \n ndarray: Nearest neighbor distances in pixels.\n \"\"\"\n centroids = [prop.centroid for prop in cloudproperties]\n indices = np.arange(len(centroids))\n neighbor_distance = np.zeros(len(centroids))\n centroids_array = np.asarray(centroids)\n\n for n, point in enumerate(centroids):\n #print n, point\n # use all center of mass coordinates, but the one from the point\n mytree = sp.spatial.cKDTree(centroids_array[indices != n])\n dist, indexes = mytree.query(point,k=len(centroids)-1)\n #print n,dist\n #print n,point,indexes,dist\n #ball = mytree.query_ball_point(point,mindist)\n distsave=dist[dist>mindist] \n #print distsave\n\n #if abs(centroids_array[indexes[0]][0]-point[0])>100.:\n # print centroids_array[indexes[0]]\n # print n,point#,[centroids_array[ij] for ij in indexes]\n\n neighbor_distance[n] = distsave[0]\n\n return neighbor_distance\n\ndef iorg(neighbor_distance, cloudmask):\n \"\"\"Calculate the cloud cluster index 'I_org'.\n\n See also: \n :func:`scipy.integrate.trapz`:\n Used to calculate the integral along the given axis using\n the composite trapezoidal rule.\n\n Parameters: \n neighbor_distance (list or ndarray): Nearest neighbor distances. \n Output of :func:`neighbor_distance`. \n cloudmask (ndarray): 2d binary cloud mask.\n\n Returns:\n float: cloud cluster index I_org.\n\n References: \n Tompkins, A. M., and A. G. Semie (2017), Organization of tropical \n convection in low vertical wind shears: Role of updraft entrainment, \n J. Adv. Model. Earth Syst., 9, 1046-1068, doi:10.1002/2016MS000802.\n \"\"\"\n nn_sorted = np.sort(neighbor_distance)\n NL = len(neighbor_distance)\n \n nncdf = np.array(range(NL)) / float(NL)\n #print len(neighbor_distance),nncdf,nn_sorted\n \n # theoretical nearest neighbor cumulative frequency\n # distribution (nncdf) of a random point process (Poisson)\n lamb = (NL /\n float(cloudmask.shape[0] * cloudmask.shape[1]))\n nncdf_poisson = 1 - np.exp(-lamb * np.pi * nn_sorted**2)\n\n return sp.integrate.trapz(y=nncdf, x=nncdf_poisson)\n\ndef scai(cloudproperties, cloudmask, connectivity=1):\n \"\"\"Calculate the 'Simple Convective Aggregation Index (SCAI)'. \n\n The SCAI is defined as the ratio of convective disaggregation\n to a potential maximal disaggregation.\n\n See also: \n :func:`scipy.spatial.distance.pdist`:\n Used to calculate pairwise distances between cloud entities. \n :func:`scipy.stats.mstats.gmean`:\n Used to calculate the geometric mean of all clouds in pairs. \n\n Parameters:\n cloudproperties (list[:class:`RegionProperties`]):\n Output of function :func:`get_cloudproperties`. \n cloudmask (ndarray): 2d binary cloud mask.\n connectivity (int): Maximum number of orthogonal hops to consider\n a pixel/voxel as a neighbor (see :func:`skimage.measure.label`).\n mask (ndarray): 2d mask of non valid pixels.\n\n Returns:\n float: SCAI.\n\n References: \n Tobin, I., S. Bony, and R. Roca, 2012: Observational Evidence for \n Relationships between the Degree of Aggregation of Deep Convection, \n Water Vapor, Surface Fluxes, and Radiation. J. Climate, 25, 6885-6904,\n https://doi.org/10.1175/JCLI-D-11-00258.1\n\n \"\"\"\n centroids = [prop.centroid for prop in cloudproperties]\n\n # number of cloud clusters\n N = len(centroids)\n\n # potential maximum of N depending on cloud connectivity\n if connectivity == 1:\n chessboard = np.ones(cloudmask.shape).flatten()\n # assign every second element with \"0\"\n chessboard[np.arange(1, len(chessboard), 2)] = 0\n # reshape to original cloudmask.shape\n chessboard = np.reshape(chessboard, cloudmask.shape)\n # inlcude NaNmask\n chessboard[np.isnan(cloudmask)] = np.nan\n N_max = np.nansum(chessboard)\n elif connectivity == 2: # it doesn't work\n #chessboard = np.ones(cloudmask.shape).flatten() # add by Florent\n chessboard[np.arange(1, cloudmask.shape[0], 2), :] = 0\n chessboard = np.reshape(chessboard, cloudmask.shape)\n chessboard[np.isnan(cloudmask)] = np.nan\n N_max = np.sum(chessboard)\n else:\n raise ValueError('Connectivity argument should be `1` or `2`.')\n\n # distance between points (center of mass of clouds) in pairs\n di = pdist(centroids, 'euclidean')\n # order-zero diameter\n D0 = sp.stats.mstats.gmean(di)\n\n # characteristic length of the domain (in pixels): diagonal of box\n L = np.sqrt(cloudmask.shape[0]**2 + cloudmask.shape[1]**2)\n\n return N / N_max * D0 / L * 1000\n\ndef adjust_spines(ax, spines):\n for loc, spine in ax.spines.items():\n if loc in spines:\n spine.set_position(('outward', 0)) # outward by 10 points\n spine.set_smart_bounds(True)\n else:\n spine.set_color('none') # don't draw spine\n\n # turn off ticks where there is no spine\n if 'left' in spines:\n ax.yaxis.set_ticks_position('left')\n else:\n # no yaxis ticks\n ax.yaxis.set_ticks([])\n\n if 'bottom' in spines:\n ax.xaxis.set_ticks_position('bottom')\n else:\n # no xaxis ticks\n ax.xaxis.set_ticks([])\n\ndef timing(f):\n def wrap(*args):\n time1 = time.time()\n ret = f(*args)\n time2 = time.time()\n #print '%s function took %0.3f ms' % (f.func_name, (time2-time1)*1000.0)\n return ret\n return wrap\n\ndef nc_dataset_list(files):\n data = {}\n for ss in files.keys():\n #print files\n #print ss\n #print files[ss]\n data[ss]=nc.Dataset(files[ss],'r')\n return data\n\ndef mkdir(path):\n try:\n os.mkdir(path)\n except:\n pass\n\ndef mask999(data):\n NPV = 999.\n data = np.ma.masked_values(data, NPV)\n return data\n\ndef mask0(data):\n NPV = 0.\n data = np.ma.masked_values(data, NPV)\n return data\n\ndef createoffset(offset):\n offset['LWP']=1000.\n rho0=1.14; RLVTT=2.5e6;RCP=1004.\n offset['E0']=rho0*RLVTT\n offset['Q0']=rho0*RCP\n offset['RC']=1000.\n offset['RV']=1000.\n offset['RT']=1000.\n offset['RCT']=1000.\n offset['RVT']=1000.\n offset['DTHRAD']=86400.\n offset['RNPM']=1000.\n return offset\n\ndef readvalues(file,typ=None,over=None):\n if typ is None:\n typ = '\\r'\n if over is None:\n over= 1\n f = open(file, 'r'); data = f.read().split(typ)\n dataout = [ij.split('\\t') for ij in data[over:]];f.close()\n return dataout\n\ndef resiz(tmp): # should be removed by pre-treatment\n tmp = np.squeeze(tmp)\n return tmp\n\ndef removebounds(tmp):\n if len(tmp.shape) == 1.:\n tmp = tmp[1:-1]\n elif len(tmp.shape) == 3.:\n tmp = tmp[1:-1,1:-1,1:-1]\n else:\n #print 'Problem removebounds'\n pass\n return tmp\n\ndef cond2plot(case,prefix,boxch=False):\n boxes = None\n if boxch:\n offbox = 15\n boxes = [0,200,0,200] # x1,x2,y1,y2\n boxes = [ij+offbox for ij in boxes]\n\n xycas = {}\n xycas['IHOP'] = {'008':([50,250],[410,410])} #[[x1,x2],[y1,y2]]\n try:\n xy = xy[case][prefix]\n except:\n #print 'Error in xy for ',case,prefix\n xy = None\n return boxes,xy\n\n\ndef selboxes(tmp,boxes,idx=None):\n if len(tmp.shape) == 1. and idx is not None:\n tmp = tmp[idx[0]:idx[1]]\n elif len(tmp.shape) == 3.:\n idxy=(boxes[2],boxes[3])\n idxx=(boxes[0],boxes[1])\n tmp = tmp[:,idxy[0]:idxy[1],idxx[0]:idxx[1]]\n else:\n #print 'Problem selboxes ',tmp.shape\n pass\n return tmp\n\n\ndef anomcalc(tmp,ip=0):\n mean = np.mean(np.mean(tmp,axis=ip+2),axis=ip+1)\n if ip == 0:\n mean = np.repeat(np.repeat(mean[ :,np.newaxis, np.newaxis],tmp.shape[ip+1],axis=ip+1),tmp.shape[ip+2],axis=ip+2)\n data = tmp-mean\n return data\n \ndef giveminmaxflux(vars):\n # vars need to be 2D at least\n levels,vmin,vmax,vdiff = None,None,None,None\n if vars[1] == \"WT\" or vars[1] == \"W\":\n if vars[0] == \"THLM\":\n #vmin,vmax,vdiff = -0.04,0.01,0.01 # old\n vmin,vmax,vdiff = -0.02,0.005,0.005\n if vars[0] == \"RNPM\":\n vmin,vmax,vdiff = -0.005,0.02,0.005\n\n if vmin is not None:\n nb = abs(vmax-vmin)/vdiff+1\n levels = [vmin + float(ij)*vdiff for ij in range(int(nb))]\n return levels\n\ndef giveminmax(var,aa,offset):\n levels,vmin,vmax,vdiff,cmap = None,None,None,None,None\n if var == \"WT\" or var == \"W\":\n if aa=='Anom' or aa=='Mean':\n vmin,vmax,vdiff = -0.8,0.8,0.2 #0.05\n elif var == \"WTMEAN\":\n if aa=='Anom' or aa=='Mean':\n vmin,vmax,vdiff = -0.3,0.3,0.05\n elif var[0:2] == \"TH\": # or var == \"THLM\" or var == \"THV\":\n if aa=='Anom':\n #vmin,vmax,vdiff = -3.0,3.0,0.25\n vmin,vmax,vdiff = -0.5,0.5,0.1\n elif var == \"LWP\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0.00,0.14,0.002\n if aa=='Anom':\n vmin,vmax,vdiff = -0.08,0.08,0.005\n elif var == \"SVT004\" or var == \"SVT001\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0,26,2\n if aa=='Anom':\n vmin,vmax,vdiff = -12,12,4\n elif var == \"SVT005\" or var == \"SVT002\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0,800,100\n if aa=='Anom':\n vmin,vmax,vdiff = -400,400,50\n elif var == \"SVT006\" or var == \"SVT003\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0,900,100\n if aa=='Anom':\n vmin,vmax,vdiff = -200,200,50\n elif var == \"RVT\" or var == \"RNPM\":\n if aa=='Anom':\n vmin,vmax,vdiff = -6e-4,6e-4,1e-4\n elif var == \"RCT\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0,5e-4,5e-5 #0,5e-4,1e-5 (used for 3D)\n if aa=='Anom':\n cmap = 'RdBu'\n vmin,vmax,vdiff = -4e-4,4e-4,1e-4\n elif var == \"DTHRAD\":\n if aa=='Anom':\n vmin,vmax,vdiff = -10,10,1\n vmin,vmax,vdiff = [ij/offset[var] for ij in (vmin,vmax,vdiff)]\n elif var == \"DIVUV\":\n if aa=='Anom' or aa=='Mean':\n vmin,vmax,vdiff = -0.02,0.02,0.005 #0.05\n elif var == \"UT\" or var == \"VT\":\n if aa=='Anom':\n vmin,vmax,vdiff = -2.,2.,0.5 #0.05\n\n\n if vmin is not None:\n if var in offset.keys():\n vmin,vmax,vdiff = [ij*offset[var] for ij in (vmin,vmax,vdiff)]\n nb = abs(vmax-vmin)/vdiff+1\n levels = [vmin + float(ij)*vdiff for ij in range(int(nb))]\n #print var,aa,levels\n\n \n return levels,vmin,vmax,vdiff,cmap\n\n\ndef selectdata(data,zyx,x,y,z,xy=None):\n tmpdata = [x,y,z]\n nbsum = sum([ij==None for ij in tmpdata])\n title = ''\n\n if len(data.shape)==3 and (nbsum != 3 or nbsum != 0): # number of None\n if xy is not None:\n axis = [zyx[0],np.arange(xy.shape[1])]\n tmp = np.zeros((len(axis[0]),len(axis[1])))\n for ij in range(xy.shape[1]):\n tmp[:,ij]=data[:,xy[1,ij],xy[0,ij]]\n data = tmp\n elif x is not None:\n data=data[:,:,x];axis=[zyx[0],zyx[1]];title+='_x'+str(x)\n if y is not None:\n data=data[:,y];axis=[zyx[0]];title+='_y'+str(y)\n if z is not None:\n data=data[z,:];axis=[zyx[1]];title+='_z'+str(z)\n elif y is not None:\n data=data[:,y,:];axis=[zyx[0],zyx[2]];title+='_y'+str(y)\n if z is not None:\n data=data[z,:];axis=[zyx[2]];title+='_z'+str(z)\n elif z is not None:\n data=data[z,:,:];axis=[zyx[1],zyx[2]];title+='_z'+str(z)\n elif len(data.shape)==2:\n axis=[zyx[1],zyx[2]];title+='_z'+str(z) # 2D is x,y data\n elif len(data.shape)==1:\n axis=[zyx[0]] # 1D is z data\n else:\n data = None; axis = None\n #print data.shape,axis\n return data,axis,title\n\ndef selectdiag(zyx,xy1,xy2):\n # diagonal that start on (x1,y1) and ends on (x2,y2)\n # xy1 : [x1,y1]\n # make diagonal\n #print xy2[0]==xy1[0],xy2[0],xy1[0],xy2[1],xy1[1]\n xx = np.mgrid[xy1[0]:xy2[0]:len(zyx[-1])*2*1j] # a lot of points\n if xy2[0]==xy1[0]:\n aa = 0\n yy = np.mgrid[xy1[1]:xy2[1]:len(zyx[-2])*2*1j] # a lot of points\n else:\n aa = float(xy2[1]-xy1[1])/float(xy2[0]-xy1[0])\n bb = xy1[1]-aa*xy1[0]\n yy = aa*xx+bb\n #print aa\n xy = np.zeros((2,len(xx)))*np.nan\n xorig= np.arange(len(zyx[2]))\n yorig= np.arange(len(zyx[1]))\n ik = -1\n for ij in range(len(xx)):\n ik += 1\n xy[0,ik] = xorig[near(xorig,xx[ij])] \n xy[1,ik] = yorig[near(yorig,yy[ij])]\n if ik>0:\n if (xy[0,ik]==xy[0,ik-1]) and (xy[1,ik]==xy[1,ik-1]):\n ik = ik-1\n xy = xy[:,:ik+1]\n return xy\n \n\n# Find nearrest point \ndef near(array,value):\n idx=(abs(array-value)).argmin()\n return idx+1 # +1 because it starts at index=0\n\n# sign character\ndef signch(data):\n signch='p'\n if np.sign(data)<0:\n signch='m'\n return signch\n \ndef unitch(case):\n switcher = {\n 'WT': \"m/s\",\n 'RCT': \"kg/kg\",\n 'RVT': \"kg/kg\",\n 'THT': \"K\",\n 'TKET': \"m2/s2\",\n 'SVT004': \"-\",\n }\n return switcher.get(case, \"Invalid units\")\n\n\ndef findbasetop(data,z,epsilon):\n # Find cloud base and cloud top\n # Define as the first layer where RCT (ql) > epsilon\n cloudbase = np.nan; cloudtop = np.nan;\n for ij in range(len(z)):\n if np.isnan(cloudbase) and data[ij] > epsilon:\n cloudbase = ij\n if ~np.isnan(cloudbase) and np.isnan(cloudtop) and data[ij] < epsilon:\n cloudtop = ij\n #print 'base,top : ',cloudbase,cloudtop,data*1000.\n return cloudbase,cloudtop\n\ndef cloudinfo(nx,ny,z,rct,epsilon):\n # Find cloud base and cloud top and middle of cloud\n\n # First computation : Use the mean \n meanql = np.mean(np.mean(rct,axis=2),axis=1)\n cloudbase,cloudtop = findbasetop(meanql,z,epsilon)\n \"\"\"\n print cloudbase,cloudtop\n print 'cloudbase : ij='+str(cloudbase)+' for z='+str(z[cloudbase])\n print 'cloudtop : ij='+str(cloudtop)+' for z='+str(z[cloudtop]) \n \"\"\"\n # Second computation : Use all data and compute its mean\n cloudbase0 = np.zeros((ny,nx))\n cloudtop0 = np.zeros((ny,nx))\n for ix in range(nx):\n for iy in range(ny):\n data = rct[:,iy,ix]\n cloudbase0[iy,ix],cloudtop0[iy,ix] = findbasetop(data,z,epsilon) \n \n \"\"\"\n print np.nanmean(cloudbase0[:]) \n print np.nanmean(cloudtop0[:])\n \"\"\"\n cloudbase2 = np.nanmean(cloudbase0[:]) # real\n cloudtop2 = np.nanmean(cloudtop0[:]) # real\n # Make integer\n cloudbase2 = int(cloudbase2)\n cloudtop2 = int(cloudtop2)+1\n \n # Choice the second computation\n baseandtop = 1 # hard choice\n #baseandtop = 2 # hard choice\n if baseandtop == 2:\n cloudbase = cloudbase2\n cloudtop = cloudtop2\n elif baseandtop == 3:\n cloudbase = min(cloudbase,cloudbase2)\n cloudtop = min(cloudtop,cloudtop2) # not sure, max or min\n\n cloudmiddle = int(round((cloudbase+cloudtop)/2))\n \"\"\"\n print 'Compute final :'\n print 'cloudbase : ij='+str(cloudbase)+' for z='+str(z[cloudbase])\n print 'cloudtop : ij='+str(cloudtop)+' for z='+str(z[cloudtop])\n \"\"\"\n return cloudbase,cloudmiddle,cloudtop,cloudbase0,cloudtop0\n\n\ndef qs(temp,pres,order) :\n esat = CC.es0 * np.exp(-(CC.RLVTT*CC.mv/CC.RU)*(1./temp - 1./CC.T0) )\n qstmp = CC.epsilon * esat/ (pres - esat)\n if order >= 1 :\n dqsP = -1*qstmp/pres # change of qsat for a change of P, with fixed T\n dqsT = CC.RLVTT*CC.epsilon*qstmp/ (CC.RD*temp*temp)# change of qsat for a change of T, with fixed P\n else :\n dqsP = 0; dqsT =0\n if order >=2 :\n d2qsP = 2*qstmp/(pres*pres) # change of dqsat/dP for a change of P, with fixed T\n d2qsT = CC.RLVTT *CC.epsilon*CC.epsilon*qstmp*(2/temp -1 )/(CC.RD*temp*temp*temp ) # change of dqsat/dT for a change of T, with fixed P\n else :\n d2qsP = 0; d2qsT =0\n\n return qstmp,dqsP,dqsT,d2qsP,d2qsT\n\n\ndef findlcl(q0,t0,temp,rvt,pres,z):\n # Find Lifting Condensation Level\n # surface humidity q0 (2D)\n # temp 3D, pres 3D\n # z 3D\n gradsec = -0.0098 # K/m z in meter\n gradhum = -0.0065 # K/m z in meter\n order = 0\n ss = pres.shape\n q2D = np.repeat(q0[np.newaxis,:,:],ss[0],axis=0)\n lcl = np.ones((ss[1],ss[2]))*np.nan\n lfc = np.ones((ss[1],ss[2]))*np.nanmax(z,axis=0) #*np.nan\n for i1 in range(ss[1]):\n for i2 in range(ss[2]):\n conditionlcl = True\n conditionlfc = True\n i0 = -1\n while(conditionlfc and i0 temp[i0,i1,i2]:\n conditionlfc=False\n lfc[i1,i2] = z[i0,i1,i2]\n #print i1,i2,i0,lcl[i1,i2],lfc[i1,i2]\n if conditionlcl and qsat1 < q0[i1,i2]:\n conditionlcl=False\n lcl[i1,i2] = z[i0,i1,i2]\n tlcl = t1\n #print i1,i2,i0,lcl[i1,i2]\n #print i1,i2,i0,t1,temp[i0,i1,i2]\n return lcl,lfc\n\ndef makethv(theta,qv,ql):\n return theta*(1.0+0.61*qv/1000.-ql/1000.)\n\ndef makethl(theta,ql):\n return theta-(CC.RLVTT/CC.RCP)*ql/1000.\n\ndef makeths1(thlm,rnpm):\n a1 = 5.87\n QT = rnpm/(np.ones((rnpm.shape))+rnpm)\n return thlm * np.exp(a1 * QT)\n\ndef makeths2(thlm,rv,rnpm):\n a1 = 5.87; a2 = -0.46\n rstar = np.ones((rv.shape))*0.0124\n QT = rnpm/(np.ones((rnpm.shape))+rnpm)\n QV = rv/(np.ones((rnpm.shape))+rnpm)\n tmp = thlm * np.exp( a1 * QT)\\\n * np.exp( a2 * np.log(QV/rstar) * QT )\\\n * np.exp( a2 * (QT-QV) )\n return tmp\n \n\n\ndef group_variables(data, files, varread, varwrite, offset, resizb=0):\n # Create the variable vars with all information in files\n vars = {}\n extraname = []; minusname =[]\n for ss in files.keys():\n vars[ss] = {}\n for v in np.arange(len(varread)):\n #print ss,v,varread[v]\n try:\n tmp = data[ss][varread[v]][:]\n except:\n tmp = None\n #if 'SV' in varread[v]: \n # print 'SV1 ',tmp.shape,data[ss][varread[v]].shape\n if resizb and tmp is not None:\n tmp = resiz(tmp)\n\n tmpsave = None\n varwriteall = [varwrite[v]]\n if tmp is not None:\n if len(tmp.shape)==3: # traceurs\n varwriteall=[]\n minusname += [varwrite[v]]\n for ij in range(tmp.shape[0]):\n aa = varwrite[v].split('_')\n #print 'aa ',aa \n varwriteall += [aa[0]+'_'+aa[1]+str(ij+1)+'_'+aa[2]]\n extraname += varwriteall\n tmpsave = tmp\n\n ik = -1\n for namevar in varwriteall:\n ik+=1\n if tmpsave is not None:\n tmp = tmpsave[ik,:,:]\n offch = [ij in namevar for ij in offset.keys()]\n if sum(offch) and tmp is not None: # namevar in offset.keys() and tmp is not None:\n #print offch,np.where(offch)[0][0],offset.keys()[np.where(offch)[0][0]]\n tmp = tmp*offset[offset.keys()[np.where(offch)[0][0]]]\n vars[ss][namevar] = tmp\n return vars,extraname,minusname\n\n\ndef quadrants(data,data2=None,data3=None,threshold=0.0,threshold2=0.0,threshold3=0.0):\n # Make quadrants or octants\n # data should be one arrow (e.g. 40000 points)\n # By default\n # data is WT\n # data2 is SVT004\n # data3 is SVT006\n # Outputs : slct(4D index), frac0,frac1 (fraction of quadrants), colors (index), colorall (each color info)\n thr = [-1*abs(threshold),abs(threshold)]\n thr2 = [-1*abs(threshold2),abs(threshold2)]\n thr3 = [-1*abs(threshold3),abs(threshold3)]\n\n idx1a = (data >= thr[1]);idx1b = (data <= thr[0]) #updraft/downdraft\n idx1 = [idx1a,idx1b]\n idx2a,idx2b,idx3a,idx3b = None,None,None,None\n if data2 is not None:\n idx2a = (data2 >= thr2[1]);idx2b = (data2 <= thr2[0])\n if data3 is not None:\n idx3a = (data3 >= thr3[1]);idx3b = (data3 <= thr3[0])\n \n idx1 = [idx1a,idx1b]; idx2 = [idx2a,idx2b]; idx3 = [idx3a,idx3b];\n if data2 is not None:\n slct = quadindex(idx1,idx2)\n else:\n slct = idx1\n if data3 is not None:\n slct = octaindex(idx1,idx2,idx3)\n\n #print 'sum ',quad.shape,sum(quad)\n #print 'Sum : ',[100*sum(ij)/len(data) for ij in quad],len(data)\n\n frac0,frac1 = percentquad(data,slct)\n colors,colorall = makecolors(data,slct)\n return slct,frac0,frac1,colors,colorall\n\ndef quadindex(idx1,idx2):\n # updraft that bring SVT004 anomaly\n idxA = idx1[0][:] & idx2[0][:]\n # updraft that has lower than average SVT004\n idxB = idx1[0][:] & idx2[1][:]\n # downdraft that has higher than average SVT004\n idxC = idx1[1][:] & idx2[0][:]\n # downdraft that bring clear air downwards\n idxD = idx1[1][:] & idx2[1][:]\n # Quadrant index\n quad = [idxA,idxB,idxC,idxD]\n return quad\n\ndef octaindex(idx1,idx2,idx3):\n\n # updraft that bring SVT004 anomaly + positive cloud-top tracer anomaly\n idxA = idx1[0][:] & idx2[0][:] & idx3[0][:]\n # updraft that has lower than average SVT004 + positive cloud-top tracer anomaly\n idxB = idx1[0][:] & idx2[1][:] & idx3[0][:]\n # downdraft that has higher than average SVT004 + positive cloud-top tracer anomaly\n idxC = idx1[1][:] & idx2[0][:] & idx3[0][:]\n # downdraft that bring clear air downwards + positive cloud-top tracer anomaly\n idxD = idx1[1][:] & idx2[1][:] & idx3[0][:]\n\n # updraft that bring SVT004 anomaly + negative cloud-top tracer anomaly\n idxE = idx1[0][:] & idx2[0][:] & idx3[1][:]\n # updraft that has lower than average SVT004 + negative cloud-top tracer anomaly\n idxF = idx1[0][:] & idx2[1][:] & idx3[1][:]\n # downdraft that has higher than average SVT004 + negative cloud-top tracer anomaly\n idxG = idx1[1][:] & idx2[0][:] & idx3[1][:]\n # downdraft that bring clear air downwards + negative cloud-top tracer anomaly\n idxH = idx1[1][:] & idx2[1][:] & idx3[1][:]\n\n # Octant index\n #print 'idxA ',idxA.shape # nx*ny\n octa = [idxA,idxB,idxC,idxD,idxE,idxF,idxG,idxH]\n return octa\n\ndef percentquad(data,quad):\n # Give relative fraction of quadrant\n # frac0 : relative to the grid-box\n # frac1 : relative the selection\n # size of data?\n ss = len(data)\n tmp = [sum(ij) for ij in quad]\n frac0 = [100.0*ij/ss for ij in tmp] \n if sum(tmp)!=0:\n frac1 = [100.0*ij/sum(tmp) for ij in tmp]\n else:\n frac1 = None \n\n return frac0,frac1\n\n\ndef makecolors(data,quad): #data4=None,threshold=0.0):\n # Make colors bar for coloring scatter plot in plotdistrib\n # By default\n # data is Wt\n\n colorblack = (0.6,0.6,0.6) # grey\n color1,color2 = (0.,0.,1.),(1.,0.,0.) # blue if negative and red if positive\n colorall = [color2,color1]\n colors = np.array([colorblack]*len(data))\n if len(quad) == 2:\n colors[quad[0][:]] = colorall[0]\n colors[quad[1][:]] = colorall[1]\n elif len(quad) == 4:\n #print 'Lengths ',len(data)\n color3,color4 = (0.,0.8,1.),(1.,0.5,0.) # bleu ciel : (-,+) ; orange (+,-)\n colorall = [color2,color4,color3,color1]\n # updraft that bring SVT004 anomaly\n colors[quad[0][:]] = colorall[0]\n # updraft that has lower than average SVT004\n colors[quad[1][:]] = colorall[1]\n # downdraft that has higher than average SVT004\n colors[quad[2][:]] = colorall[2]\n # downdraft that bring clear air downwards\n colors[quad[3][:]] = colorall[3]\n elif len(quad) == 8:\n # As before, but with negative cloud-top tracer anomaly\n # (w+,s1+,s2+),(w+,s1-,s2+),(w-,s1+,s2+),(w-,s1-,s2+),(w+,s1+,s2-),(w+,s1-,s2-),(w-,s1+,s2-),(w-,s1-,s2-)\n # the closer to Park or Davini\n color1,color2,color3,color4 = (1.,1.,0.),(1.,0.7,0.7),(0.2,0.4,0.1),(0.6,1.0,0.6) # yellow, pink, dark green, light green\n color5,color6,color7,color8 = (1.,0.,0.),(1.,0.5,0.),(0.,0.8,1.),(0.,0.,1.) # red,orange,light blue,blue\n # new\n #color1,color2,color3,color4 = (1.,1.,0.),(0.9,0.9,0.9),(0.2,1.0,0.),(0.2,0.6,0) # light yellow, white, light green, green\n #color5,color6,color7,color8 = (1.,0.,0.),(0.9,0.4,1.),(0.,0.6,1.),(0.,0.,1.) # red,light red/purple,light blue,blue\n colorall = [color1,color2,color3,color4,color5,color6,color7,color8]\n for ij in range(len(quad)):\n colors[quad[ij][:]] = colorall[ij]\n \n return colors,colorall\n \n\ndef infos(case,sens,prefix='000',vtype='V0301',concat=''):\n #files={}\n files = OrderedDict() #dict([(sens[i],'i') for i in range(len(sens))])\n # local path with simulations\n path0 = '/cnrm/tropics/user/brientf/MESONH/'\n path0 = path0 + case +'/'\n # list with the names of files\n for ss in sens:\n #print 'ss ',ss, case\n # path of the selected experience\n path=path0+ss+'/'\n # name of the file (e.g. sel_HTFIR.1.V0301.000.nc4)\n #vtype = 'V0301'\n \n #prefix1 = '000'\n #if prefix is not 'None':\n # prefix1 = prefix\n prefix += concat+\".nc4\"\n\n # Too heavy file, pre-selection before\n # e.g. ncks -O -v XHAT,YHAT,ZHAT,RHOREFZ,RCT,THLM HTFIR.1.V0301.012.nc4 HTFIR.1.V0301.012.nc4.out\n prefix2 = prefix\n if ss == 'L25.6km':\n prefix2 = prefix #+ '.out'\n\n if ss == 'SMALL':\n prefix2 = prefix #+ '.MNH' #'.save' #'.test2'\n\n if ss == 'SMALR':\n prefix2 = prefix + '.MOCR.FIRE' #'.T150' \n\n if (ss == 'REF' or ss == 'L25.6km') and case == 'FIRE':\n name = 'HTFIR'\n else:\n name = ss\n\n filename = name+\".1.\"+vtype+\".\"+prefix2\n\n files[ss]=path+filename\n #print 'files ',files\n\n return path0,files\n\ndef makepdf(tmprh,tmpcf,ltsrange):\n # Binning information\n ltstable = np.arange(ltsrange[0],ltsrange[1]+ltsrange[2],ltsrange[2])+ltsrange[2]/2\n\n kde = st.gaussian_kde(tmprh)\n pdftmp = kde(ltstable)\n pdftmp = 100.*pdftmp/sum(pdftmp)\n\n statstmp, bin_edges, binnumber = st.binned_statistic(tmprh, tmpcf, 'mean', bins=len(ltstable),range=(ltsrange[0],ltsrange[1]))\n stdtmp = [ np.std(tmpcf[binnumber==ij]) for ij in np.arange(1,len(ltstable)+1) ]\n #print ' binnumber ',binnumber\n #for ij in np.arange(1,len(ltstable)+1):\n # print ij,np.std(tmpcf[binnumber==ij]),tmpcf[binnumber==ij]\n \n return pdftmp,statstmp,stdtmp\n\ndef do_unique(tmp):\n tmp[tmp>0]=1\n return tmp\n\ndef do_delete(objects,nbmin,rename=True) :\n listObj = np.unique(objects[objects>0]).tolist()\n if len(listObj):\n nbmax = max(listObj)\n #print 'nbmax ', nbmax\n while (len(listObj)):\n num = listObj.pop(0) \n nbcels = len(objects[objects==num])\n if (nbcels < nbmin):\n objects[objects==num] = 0\n if rename and len(listObj) : \n lastnum = listObj.pop(-1)\n while (len(objects[objects==lastnum]) 0:\n mems_loco[0].update(self._anchor_position_signal)\n \n self.lock_drones.release()\n\n #la seconda callback avviene quando i dati provenienti dai droni sono pronti\n def _anchor_position_signal(self,mem):\n \n self.lock_anchor_writing_data.acquire()\n for anchor_number, anchor_data in enumerate(mem.anchor_data):\n if anchor_data.is_valid:\n self.anchor_pos[anchor_number]=anchor_data.position\n self.lock_anchor_data_are_ready.release()\n\n\n #questa funzione permette di creare i percorsi per i droni\n def add_route(self, data):\n\n eventi=EventEmitter()\n \n self.lock_drones.acquire()\n lista_drones=self.drones.items()\n self.lock_drones.release()\n\n self.lock_route.acquire()\n\n for url,drone in lista_drones: \n self.route_collection[drone.uri]=Route_class(data[url],self.drones,url,eventi)\n\n self.lock_route.release()\n\n return \"ok\"\n \n\n def start_all_drones(self):\n\n self.lock_drones.acquire()\n\n for url,drone in self.drones.items():\n if drone.mc!=None:\n self.launchthread(drone)\n \n else:\n print(\"drone:\",drone.uri,\"not ready\")\n \n self.lock_drones.release()\n\n def movement(self,uav: UAV):\n print(\"mi muovo\")\n time.sleep(1)\n \n percorso=self.route_collection[uav.uri]\n \n punto=percorso.pop()\n percorso.passed(self.route_collection)\n \n punto=percorso.pop()\n \n \n if punto[1]==\"takeoff\":\n print(\"decollo\")\n uav.take_off(float(punto[0][1]))\n percorso.passed(self.route_collection)\n else:\n print(\"errore posizione di decollo\")\n return 0\n \n while True:\n punto=percorso.pop()\n \n if punto[1]==\"landing\":\n \n print(\"atteraggio\")\n uav.land()\n return\n \n elif(punto[1]==\"base\"):\n print(\"vado al punto:\",punto[0][0],punto[0][2],punto[0][1])\n uav.go_to(float(punto[0][0]),float(punto[0][2]),float(punto[0][1]))\n else:\n print(punto[1])\n print(\"vado al punto:\",punto[0][0],punto[0][2],punto[0][1])\n uav.go_to(float(punto[0][0]),float(punto[0][2]),float(punto[0][1]))\n \n \n percorso.passed(self.route_collection)\n \n def launchthread(self,uav: UAV):\n th = threading.Thread(target=self.movement, args=(uav,))\n th.start()\n\n def set_anchor_position(self, data):\n \n self.lock_drones.acquire()\n \n json={}\n if len(self.drones)==0:\n self.lock_drones.release()\n json[\"status\"]=\"no drone connected\"\n return json\n\n \n ancore = LoPoAnchor(list(self.drones.values())[0].scf.cf)\n \n for id, pos in data.items():\n ancore.set_position(int(id), (float(pos[\"x\"]), float(pos[\"z\"]), float(pos[\"y\"])))\n\n self.lock_drones.release()\n json[\"status\"]=\"successfully anchor setted\"\n return json\n\n\n def reset_estimation_for_all_drone_connected(self):\n for url,drone in self.drones.items():\n drone.reset_estimator() ","repo_name":"UniCT-ARSLab/Twinflie","sub_path":"python server/classe refactoring/classes/drones_collection.py","file_name":"drones_collection.py","file_ext":"py","file_size_in_byte":8345,"program_lang":"python","lang":"it","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"29497351586","text":"'''\n给定一个非空的整数数组，返回其中出现频率前 k 高的元素。\n\n示例 1:\n\n输入: nums = [1,1,1,2,2,3], k = 2\n输出: [1,2]\n示例 2:\n\n输入: nums = [1], k = 1\n输出: [1]\n \n\n提示：\n\n你可以假设给定的 k 总是合理的，且 1 ≤ k ≤ 数组中不相同的元素的个数。\n你的算法的时间复杂度必须优于 O(n log n) , n 是数组的大小。\n题目数据保证答案唯一，换句话说，数组中前 k 个高频元素的集合是唯一的。\n你可以按任意顺序返回答案。\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/top-k-frequent-elements\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n'''\n\n'''\n思路：1.字典统计次数\n 2.建大顶堆\n 要点：两个函数percUp和percDown\n percUp————对每一个非叶节点进行percDown\n percDown————将某一个节点向下调整到“合适”位置\n 3.弹出k个堆顶值：（每弹一个就要维护堆）\n'''\nclass Solution:\n def topKFrequent(self, nums: [int], k: int) -> [int]:\n\n #从末位的第一个非叶节点开始，逐个进行percDown\n def percUp(lst):\n new_lst = [-1] + lst\n n = len(new_lst)-1\n for i in range(n//2,0,-1):\n percDown(new_lst,i)\n\n return new_lst\n #将某一位置的节点向下调整到合适的位置\n def percDown(lst, i):\n n = len(lst)-1\n j = 2*i\n while j <= n:\n if j+1 <= n and lst[j+1][1] > lst[j][1]:\n j = j+1\n if lst[i][1] < lst[j][1]:\n lst[i],lst[j] = lst[j],lst[i]\n i = j\n j = 2*i\n else:\n break\n # 用字典统计数字的次数\n d_cnt = {}\n for a in nums:\n if a not in d_cnt:\n d_cnt[a] = 1\n else:\n d_cnt[a] += 1\n lst_cnt = list(d_cnt.items())\n print(lst_cnt)\n\n #建大顶堆\n heap = percUp(lst_cnt)\n #取前K个元素\n res = []\n for _ in range(k):\n heap[1],heap[-1] = heap[-1],heap[1]\n res.append(heap.pop()[0])\n percDown(heap,1)\n return res\n\n#测试\nnums = [1]\nk = 1\nres = Solution().topKFrequent(nums, k)\nprint(res)\n","repo_name":"Da1anna/Data-Structed-and-Algorithm_python","sub_path":"leetcode/其它题型/哈希表/common/前K个高频元素.py","file_name":"前K个高频元素.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2720203890","text":"class TweetData(object):\n def __init__(self, tweet_id, created, text, geo, coordinates, place, retweet_count, favorite_count, lang,\n user_location, user_description):\n self.tweet_id = tweet_id\n self.created = created\n self.text = text\n self.geo = geo\n self.coordinates = coordinates\n self.place = place\n self.retweet_count = retweet_count\n self.favorite_count = favorite_count\n self.lang = lang\n self.user_location = user_location\n self.user_description = user_description\n\n def __repr__(self):\n return str(self.__dict__)\n","repo_name":"ShawonAshraf/text-tech-project-winter20","sub_path":"view/tweet_data.py","file_name":"tweet_data.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"4616126724","text":"from django.http import HttpResponse\nfrom django.template import loader\nfrom .models import Member\nimport requests\nfrom django.shortcuts import render\nimport pandas as pd\n\n#Lấy ip public\nip = requests.get('https://api.ipify.org').text\n\n#API lấy data thời tiết\nGetLocal = \"https://weatherapi-com.p.rapidapi.com/ip.json\"\nGetMappoint = \"https://weatherapi-com.p.rapidapi.com/astronomy.json\"\nGetWeather = \"https://weatherapi-com.p.rapidapi.com/current.json\"\nGetForecast = \"https://weatherapi-com.p.rapidapi.com/forecast.json\"\n\n# Query của api\nquerystring = {\"q\":ip}\n\n# Key sử dụng api\nheaders = {\n \"X-RapidAPI-Key\": \"5761b749b1msh63b3d30b91858ddp173a64jsna65a320effd1\",\n \"X-RapidAPI-Host\": \"weatherapi-com.p.rapidapi.com\"\n }\n\n#Lấy data vị trí từ ip public\nlocals = requests.get(GetLocal, headers=headers, params=querystring)\n\nquerystringMappoint = {\"q\":locals.json()['city']}\n\n#Lấy vị trí cụ thể trên map\nMappoint = requests.get(GetMappoint, headers=headers, params=querystring)\n\nStringmappoint = str(Mappoint.json()['location']['lat']) +',' +str(Mappoint.json()['location']['lon'])\n\nquerystringWeather = {\"q\": Stringmappoint}\n\n#Lấy data thời tiết trong thời gian thực\nWeather = requests.get(GetWeather, headers=headers, params=querystringWeather)\n\n#Lấy data thời gian\ndate=pd.Timestamp(Weather.json()['location']['localtime'])\n\nquerystringForecast = {\"q\":locals.json()['city'],\"days\":\"3\"}\n\n#Lấy data dự báo thời tiết\nForecast = requests.get(GetForecast, headers=headers, params=querystringForecast)\n\n#Hiển thị thời tiết trang chủ, auto chọn vị trí từ IP\ndef weather(request): \n info = {\n 'forecastday' : [],\n 'avgtemp_c' : [],\n 'maxwind_kph' :[],\n 'date' :[],\n }\n\n #Tại api xài free nên chỉ có cho xem dự báo thời tiết trong 3 ngày :'(\n for i in range(3):\n info['forecastday'].append(Forecast.json()['forecast']['forecastday'][i]['day']['condition']['icon']) \n info['avgtemp_c'].append(Forecast.json()['forecast']['forecastday'][i]['day']['avgtemp_c'])\n info['maxwind_kph'].append(Forecast.json()['forecast']['forecastday'][i]['day']['maxwind_kph'])\n info['date'].append(pd.Timestamp(Forecast.json()['forecast']['forecastday'][i]['date']).day_name())\n \n return render(request,'template.html',{'city':locals.json()['city'],\n 'temp_c':int(Weather.json()['current']['temp_c']),\n 'humidity':int(Weather.json()['current']['humidity']),\n 'wind_kph':int(Weather.json()['current']['wind_kph']),\n 'icon':Weather.json()['current']['condition']['icon'],\n 'time':date.time(),\n 'date':date.day_name(),'month':date.month_name(),'datenumber':date.strftime(\"%d\"),\n 'info':info,'n':range(3)})\n\n# Dự báo thời tiết trong 24 giờ của ngày cụ thể nào đó\ndef weather24(request):\n weather24 ={\n 'date':[],\n 'time':[],\n 'temp_c':[],\n 'icon' :[],\n 'wind_kph':[],\n 'chance_of_rain':[],\n 'humidity':[]\n }\n for i in range(3):\n #Lưu data trong 24 giờ vào direction\n for x in range(24):\n weather24['date'].append(pd.Timestamp(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['time']).day_name())\n weather24['time'].append(pd.Timestamp(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['time']).time())\n weather24['temp_c'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['temp_c'])\n weather24['icon'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['condition']['icon'])\n weather24['wind_kph'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['wind_kph'])\n weather24['chance_of_rain'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['chance_of_rain'])\n weather24['humidity'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['humidity'])\n\n return render(request,'weather24.html',{'weather24':weather24,\n 'n':range(12)})\n\n# Tìm theo tên thành phố\ndef search(request):\n #Lấy data từ form input của HTML\n city = request.POST['city']\n querystringForecast1 = {\"q\":city,\"days\":\"3\"}\n\n #Lấy data dự báo thời tiết dựa theo tên thành phố lấy từ form\n ForecastSearch = requests.get(GetForecast, headers=headers, params=querystringForecast1)\n \n info = {\n 'forecastday' : [],\n 'avgtemp_c' : [],\n 'maxwind_kph' :[],\n 'date' :[],\n }\n\n for i in range(3):\n info['forecastday'].append(ForecastSearch.json()['forecast']['forecastday'][i]['day']['condition']['icon']) \n info['avgtemp_c'].append(ForecastSearch.json()['forecast']['forecastday'][i]['day']['avgtemp_c'])\n info['maxwind_kph'].append(ForecastSearch.json()['forecast']['forecastday'][i]['day']['maxwind_kph'])\n info['date'].append(pd.Timestamp(ForecastSearch.json()['forecast']['forecastday'][i]['date']).day_name())\n \n return render(request, 'search.html',{'info':info,'n':range(3),'city':ForecastSearch.json()['location']['name'],\n 'temp_c':int(ForecastSearch.json()['current']['temp_c']),\n 'humidity':int(ForecastSearch.json()['current']['humidity']),\n 'wind_kph':int(ForecastSearch.json()['current']['wind_kph']),\n 'date':pd.Timestamp(ForecastSearch.json()['location']['localtime']).day_name(),\n 'time':pd.Timestamp(ForecastSearch.json()['location']['localtime']).time(),\n 'month':pd.Timestamp(ForecastSearch.json()['location']['localtime']).month_name(),\n 'datenumber':pd.Timestamp(ForecastSearch.json()['location']['localtime']).strftime(\"%d\")})\n\n","repo_name":"PnThinh/weather","sub_path":"members/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6217,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5415661973","text":"import itertools\nfrom copy import deepcopy\nfrom dataclasses import dataclass\nfrom typing import Optional, AnyStr\n\nfrom lxml.etree import _Element as Element\n\nfrom .find_utils import uses, flatten_list\nfrom .grade_code_found import GradeCodeFound, get_node_source\nfrom .ast_to_xml import xml\nfrom .xml_utils import (\n compare_xml_nodes, literal, expr_xml_node\n)\n\n@dataclass\nclass Arg:\n \"\"\"A dataclass to represent a function's positional argument\"\"\"\n name: Optional[str] = ''\n code: AnyStr = None\n xml_node: AnyStr = None\n\n def __str__(self) -> str:\n if self.name != '':\n return f\"positional argument: {self.name} = {self.code}\"\n else:\n return f\"positional argument: {self.code}\"\n\n@dataclass\nclass KWArg:\n \"\"\"A dataclass to represent a function's keyword argument\"\"\"\n name: Optional[str] = ''\n code: AnyStr = None\n value_xml_node: AnyStr = None\n # this field is so we can refer to originating keyword XML Element\n # when it comes to match and store keyword arguments for `find_arguments()`\n keyword_xml_node: AnyStr = None\n \n def __str__(self) -> str:\n return f\"keyword argument: {self.name} = {self.code}\"\n\n@dataclass\nclass ArgList:\n \"\"\"A dataclass to represent a function's argument or keyword argument\"\"\"\n args: list[Arg]\n\n def __init__(self, args: list[Arg] = None):\n self.args = [] if args is None else args\n\ndef args(*args: AnyStr, **kwargs: AnyStr) -> ArgList:\n \"\"\"Returns a list of Arg objects for positional (unnamed) argument values \n or keyword arguments to facilitate finding arguments in function calls.\n\n Returns\n -------\n ArgList\n an ArgList object that holds a list of Arg(s)\n \"\"\"\n all_args = []\n\n # first add the positional arguments\n if len(args) > 0:\n positional = list(args)\n all_args.extend([\n # because Python eagerly evaluates all arguments passed in to args()\n # we have to specially handle the quoting of them: \n # - if the argument was meant to be a `str` we double quote `\"` it\n # - anything else we just wrap into a normal string\n Arg(\n code = arg.source if isinstance(arg, literal) else arg,\n xml_node = expr_xml_node(arg)\n )\n for arg in positional\n ])\n\n # then, add the keyword arguments\n if len(kwargs) > 0:\n all_kwargs = list(kwargs.items())\n # this is extracting the name = value\n all_args.extend(\n # similar logic to positional arguments here, where kwargs value strings\n # are quoted if it's meant to be a `str`\n KWArg(\n name = kwarg[0],\n code = kwarg[1].source if isinstance(kwarg[1], literal) else kwarg[1],\n value_xml_node = expr_xml_node(kwarg[1])\n )\n for kwarg in all_kwargs\n )\n\n return ArgList(all_args)\n\ndef make_kwargs(code: str, kwarg_xml_nodes: list[Element]) -> list[KWArg]:\n \"\"\"Return KWArg(s) based on XML Element(s)\n\n Parameters\n ----------\n code : str\n the source code text\n kwarg_xml_nodes : list[Element]\n list of XML Elements representing keyword\n\n Returns\n -------\n list[KWArg]\n a list of KWArgs representing those keywords\n \"\"\"\n code_kwargs_list = []\n for kwarg_node in kwarg_xml_nodes:\n # keyword argument name\n kwarg_names = [\n x.text for x in kwarg_node.xpath(\"./arg\")\n ]\n\n # get the textual form for values\n kwarg_values = [v for v in kwarg_node.xpath(\"./value/*\")]\n kwargs_values_strings = [\n get_node_source(code, kw)\n for kw in kwarg_values\n ]\n\n # combine them into pairs\n kwargs_pairs = list(zip(kwarg_names, kwargs_values_strings))\n\n # construct the list of Arg(s) from the keyword pairs\n code_kwargs_list.append([\n KWArg(\n name = k,\n code = v,\n value_xml_node = expr_xml_node(v),\n keyword_xml_node = kwarg_node\n )\n for k, v in kwargs_pairs\n ])\n return flatten_list(code_kwargs_list)\n\ndef find_arguments(\n code: str | GradeCodeFound,\n match: ArgList = ArgList(),\n recurse: bool = True\n) -> GradeCodeFound:\n \"\"\"Find arguments of function calls in the code.\n\n Parameters\n ----------\n code : str | GradeCodeFound\n a string for the source code, or a GradeCodeFound for chaining queries\n match : ArgList\n an ArgList representing the arguments to match against\n recurse : bool\n a flag that requests whether or not to look for arguments\n within function call arguments for a previous 'function' request, by default False\n\n Returns\n -------\n GradeCodeFound\n a GradeCoundFound object that holds the list of previous results\n and the current query results if any, by default True\n \n Examples\n --------\n >>> code = \"sum([1, round(2.5), 3])\\nprint('Hello', 'World!', sep=', ')\"\n >>> find_arguments(code)\n ── pygradecode found ──\n print(1, \"2\", sep=\", \", end=foo(x = [\"\\n\"]))\n\n ── Request ──\n arguments \n Found 5 results.\n\n ── Result 1 ──\n 1\n\n ── Result 2 ──\n \"2\"\n\n ── Result 3 ──\n sep=\", \"\n\n ── Result 4 ──\n end=foo(x = [\"\\n\"]))\n\n ── Result 5 ──\n x = [\"\\n\"])\n \"\"\"\n if not isinstance(code, str) and not isinstance(code, GradeCodeFound):\n raise Exception(\"`code` should be a `str` or `GradeCodeFound`\")\n \n gcf = deepcopy(code) if isinstance(code, GradeCodeFound) else GradeCodeFound(code)\n xml_tree = xml(gcf.source) if isinstance(code, GradeCodeFound) else xml(code)\n\n request_type = 'arguments'\n # TODO create a __str__ representation for ArgList\n request = ''\n results = []\n\n # Note: we have to encode the code to escape it properly\n match_args = match.args\n\n # get previous function calls in results if any, otherwise get all function calls\n all_call_xml_nodes = []\n if gcf.has_previous_request():\n for node in gcf.last_result:\n if recurse:\n all_call_xml_nodes.append(node.xpath('//Call'))\n else:\n all_call_xml_nodes.append(node)\n all_call_xml_nodes = list(itertools.chain(*all_call_xml_nodes))\n else:\n all_call_xml_nodes = xml_tree.xpath(\"//Call\")\n\n # if there are no arguments to look for in particular, look for\n # all of the arguments in code\n if len(match_args) == 0:\n results = [\n node.xpath(\"./args/*|./keywords/*\")\n for node in all_call_xml_nodes\n ]\n results = list(itertools.chain(*results))\n else:\n # Extracting each keyword pair is a bit tricky because a value\n # could be any ast node so there is no way to have one query\n # satisfy all types of nodes. So, instead we look at the\n # and its node source text to reconstruct them.\n # TODO if a gcf was passed when there was a previous request\n # we need to iterate through gcf.results to repeat this process\n # so we can make all of the Arg/KWArg\n\n # extract the positional args\n all_arg_xml_nodes = flatten_list([\n call.xpath(\"./args/*\")\n for call in all_call_xml_nodes\n ])\n # extract the kwargs args\n all_kwarg_xml_nodes = flatten_list([\n call.xpath(\"./keywords/*\")\n for call in all_call_xml_nodes\n ])\n\n # construct Arg() for positional arguments\n code_args_list = [\n Arg(\n code = get_node_source(code, arg),\n xml_node = deepcopy(arg)\n )\n for arg in all_arg_xml_nodes\n ]\n\n # extract the keyword args\n code_kwargs_list = make_kwargs(code, all_kwarg_xml_nodes)\n # combine the args with the kwargs\n all_code_args = code_args_list + code_kwargs_list\n \n # check that each match Arg is in code's [Arg]\n results = get_matched_arguments(match_args, all_code_args)\n\n # TODO make the chaining work well\n return gcf.push(request_type=request_type, request=request, result=results)\n\ndef get_matched_arguments(\n match_args: list[Arg | KWArg],\n all_code_args: list[Arg | KWArg]\n ) -> list[Element]:\n \"\"\"Return all code XML elements that match given the list of match and code arguments.\n\n Parameters\n ----------\n match_args : list[Arg | KWArg]\n a list with Arg and KWArgs representing positional and keyword arguments\n to match against\n all_code_args : list[Arg | KWArg]\n a list with Arg and KWArgs representing positional and keyword arguments\n of the code\n\n Returns\n -------\n list[Element]\n a list of XML Element(s)\n \"\"\"\n results = []\n for match_arg in match_args:\n # for argument, element in args_spec:\n for argument in all_code_args:\n # if match Arg Element is the same as the code Arg Element\n # add the code Arg Element as a result\n if type(match_arg) != type(argument):\n continue\n elif isinstance(match_arg, KWArg) and isinstance(argument, KWArg):\n # if we're comparing keyword arguments \n # check value nodes match\n value_nodes_match = compare_xml_nodes(\n match_arg.value_xml_node,\n argument.value_xml_node\n )\n # check the kwarg name too\n arg_names_match = match_arg.name == argument.name\n if arg_names_match and value_nodes_match:\n results.append(argument.value_xml_node)\n elif compare_xml_nodes(match_arg.xml_node, argument.xml_node):\n results.append(argument.xml_node)\n return results\n\ndef uses_argument(code: str, match: str = \"\") -> bool:\n \"\"\"Check if the code has arguments.\n\n Parameters\n ----------\n code : str\n the source code\n match : str, optional\n argument name(s), by default None\n\n Returns\n -------\n bool\n True if found, False otherwise\n\n Examples\n --------\n >>> code = \"print('Hello', 'World!', sep=', ')\"\n >>> uses_argument(code, \"sum\")\n True\n >>> uses_argument(code, \"round\")\n False\n \"\"\"\n return uses(find_arguments, code, match)\n","repo_name":"rstudio/pygradethis","sub_path":"python/pygradecode/find_arguments.py","file_name":"find_arguments.py","file_ext":"py","file_size_in_byte":9587,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"18281746363","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[9]:\n\n\n#由于基于原图每个像素生成锚框，数量级过大，计算量太大\n#将原图输入卷积层后得到特征图，基于特征图生成锚框，在不同的特征图中生成的锚框对应原图中不同尺寸的感受野\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport d2lzh as d2l\nfrom mxnet import contrib, image, nd\n\nimg = image.imread('../img/catdog.jpg')\nh, w = img.shape[0: 2]\nh, w\n\n\n# In[17]:\n\n\n#定义display_anchors函数\nd2l.set_figsize()\n\ndef display_anchors(fmap_w, fmap_h, s):\n fmap = nd.zeros((1, 10, fmap_w, fmap_h))\n anchors = contrib.nd.MultiBoxPrior(fmap, sizes=s, ratios=[1, 2, 0.5])\n bbox_scale = nd.array((w, h, w, h))\n d2l.show_bboxes(d2l.plt.imshow(img.asnumpy()).axes,\n anchors[0] * bbox_scale)\n\n\n# In[18]:\n\n\ndisplay_anchors(fmap_w=4, fmap_h=4, s=[0.15])\n\n\n# In[20]:\n\n\ndisplay_anchors(fmap_w=2, fmap_h=2, s=[0.4])\n\n\n# In[21]:\n\n\ndisplay_anchors(fmap_w=1, fmap_h=1, s=[0.8])\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"HaitaoWang97/training7","sub_path":"multi_scale.py","file_name":"multi_scale.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12616119403","text":"\"\"\"\nCustom routers used by both lms and cms when routing tasks to worker queues.\n\nFor more, see https://celery.readthedocs.io/en/latest/userguide/routing.html#routers\n\"\"\"\n\nimport logging\n\nfrom django.conf import settings\n\n\nlog = logging.getLogger(__name__)\n\n\ndef route_task(name, args, kwargs, options, task=None, **kw): # pylint: disable=unused-argument\n \"\"\"\n Celery-defined method allowing for custom routing logic.\n\n If None is returned from this method, default routing logic is used.\n \"\"\"\n if name in settings.EXPLICIT_QUEUES:\n return settings.EXPLICIT_QUEUES[name]\n\n alternate_env = settings.ALTERNATE_ENV_TASKS.get(name, None)\n if alternate_env:\n return ensure_queue_env(alternate_env)\n\n\ndef ensure_queue_env(desired_env):\n \"\"\"\n Helper method to get the desired type of queue.\n\n If no such queue is defined, default routing logic is used.\n \"\"\"\n queues = getattr(settings, 'CELERY_QUEUES', None)\n return next(\n (\n queue\n for queue in queues\n if f'.{desired_env}.' in queue\n ),\n None\n )\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/lib/celery/routers.py","file_name":"routers.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"38110607849","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 5 15:05:35 2019\n\n@author: david\n\"\"\"\n\n\n# Chromatic Scale\nCH_SCALE = {\n \"fr\" : [\"Do\", \"Do#\", \"Re\", \"Re#\", \"Mi\", \"Fa\", \"Fa#\", \"Sol\", \"Sol#\", \"La\", \"La#\", \"Si\"],\n \"en\" : [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"],\n \"id\" : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], # Sur gamme chromatique de Do\n }\n\nclass Accord:\n \n def __init__(self):\n \n self._ind = []\n \n @property\n def fr(self):\n return self.get_chord('fr')\n \n @fr.setter\n def fr(self, chord):\n self.set_chord(chord, 'fr')\n \n @property\n def en(self):\n return self.get_chord('en')\n \n @en.setter\n def en(self, chord):\n self.set_chord(chord, 'en')\n \n @property\n def id(self):\n return self.get_chord('id')\n \n @id.setter\n def id(self, chord):\n self.set_chord(chord, 'id')\n \n def get_chord(self, lang):\n chord = [CH_SCALE[lang][x] for x in self._ind] \n return chord\n \n def set_chord(self, chord, lang):\n \n self._ind = [CH_SCALE[lang].index(note) for note in chord] \n \n\nif __name__ == \"__main__\" :\n \n accord = Accord()\n print(\"accord = Accord()\")\n accord.fr = ['Mi', 'Sol', 'Do', 'Si']\n print(\"accord.fr = ['Mi', 'Sol', 'Do', 'Si']\")\n print(\" \")\n \n print(\"accord.fr : \" + str(accord.fr))\n print(\"accord.en : \" + str(accord.en))\n print(\"accord.id : \" + str(accord.id))\n print(\" \")\n accord.en = ['C','B','G']\n print(\"accord.en = ['C','B','G']\")\n print(\" \")\n print(\"accord.fr : \" + str(accord.fr))\n print(\"accord.en : \" + str(accord.en))\n print(\"accord.id : \" + str(accord.id))\n \n \n \n \n \n \n \n \n ","repo_name":"Daguhh/GuitarChordGeneratorVrac","sub_path":"class_accord.py","file_name":"class_accord.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71560796243","text":"import collections\nn = int(input())\nlst = [int(i) for i in input().split()]\nL = collections.Counter(lst)\nx = n\nans = 0\n\nfor k in sorted(L.keys()):\n x -= L[k]\n ans += x*L[k]\nprint(ans)","repo_name":"dakkenkd/AtCoder","sub_path":"ABC/ABC206/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24816980241","text":"from bael import project\nfrom baelfire.core import Core\nfrom baelfire.filedict import FileDict\nfrom os.path import abspath\nfrom os.path import dirname\nfrom os.path import exists\nfrom os.path import join\n\n\nclass ProjectCore(Core):\n\n def phase_settings(self):\n super().phase_settings()\n data = self.get_data_settings()\n\n self.settings['project_name'] = data['name']\n self.settings['package_name'] = data['package name']\n\n self.paths.set('cwd', dirname(self.find_pyproject_file()))\n self.paths.set('report', '.bael.yml', 'cwd')\n self.paths.set('pyproject', dirname(project.__file__))\n self.paths.set('pyptemplates', 'templates', 'pyproject')\n\n self.paths.set('package:src', 'src', 'cwd')\n self.paths.set_generator('package:main', lambda paths: self.settings['package_name'], 'package:src')\n self.paths.set(\n 'template_setuppy',\n 'setuppy.jinja2',\n 'pyptemplates',\n )\n self.paths.set('setuppy', 'setup.py', 'cwd')\n\n self.paths.set_generator(\n 'virtualenv:base',\n lambda paths: 'venv_{0}'.format(self.settings['package_name']),\n 'cwd',\n )\n self.paths.set('virtualenv:bin', 'bin', 'virtualenv:base')\n self.paths.set('exe:python', 'python', 'virtualenv:bin')\n self.paths.set('exe:pip', 'pip', 'virtualenv:bin')\n\n def get_data_settings(self):\n path = self.find_pyproject_file()\n data = FileDict(path)\n data.load(True)\n data.ensure_key_exists('name', 'Project name')\n data.ensure_key_exists('package name', 'Package name')\n data.save()\n return data\n\n def find_pyproject_file(self, path=None):\n if not path:\n path = abspath('./')\n file_path = join(path, '.pyproject.yaml')\n if exists(file_path):\n return file_path\n else:\n toppath = dirname(path)\n if toppath == path:\n return '.pyproject.yaml'\n return self.find_pyproject_file(toppath)\n","repo_name":"socek/bael.project","sub_path":"src/bael/project/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":2073,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"43227595955","text":"import errno\nimport time\nfrom time import sleep\nimport json\nimport logging\nimport os\nimport re\nimport requests\nimport selenium\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nfrom banned_exception import BannedException\nfrom constants import AMAZON_BASE_URL\nimport pandas\n\nOUTPUT_DIR = 'comments'\n\nif not os.path.exists(OUTPUT_DIR):\n os.makedirs(OUTPUT_DIR)\n\n\ndef get_reviews_filename(product_id):\n filename = os.path.join(OUTPUT_DIR, '{}.csv'.format(product_id))\n exist = os.path.isfile(filename)\n return filename, exist\n\n\ndef mkdir_p(path):\n try:\n os.makedirs(path)\n except OSError as exc:\n if exc.errno == errno.EEXIST and os.path.isdir(path):\n pass\n else:\n raise\n\n\ndef persist_comment_to_disk(reviews):\n if len(reviews) == 0:\n return False\n product_id_set = set([r['product_id'] for r in reviews])\n assert len(product_id_set) == 1, 'all product ids should be the same in the reviews list.'\n product_id = next(iter(product_id_set))\n output_filename, exist = get_reviews_filename(product_id)\n if exist:\n print(\"File with same pid is there...\")\n return False\n mkdir_p(OUTPUT_DIR)\n # https://stackoverflow.com/questions/18337407/saving-utf-8-texts-in-json-dumps-as-utf8-not-as-u-escape-sequence/18337754\n with open(output_filename, 'w', encoding='utf-8') as fp:\n json.dump(reviews, fp, sort_keys=True, indent=4, ensure_ascii=False)\n\n pandas.read_json(output_filename).to_csv(output_filename)\n #return True\n return output_filename\n\n\ndef extract_product_id(link_from_main_page):\n # e.g. B01H8A7Q42\n p_id = -1\n tags = ['/dp/', '/gp/product/']\n for tag in tags:\n try:\n p_id = link_from_main_page[link_from_main_page.index(tag) + len(tag):].split('/')[0]\n except:\n pass\n m = re.match('[A-Z0-9]{10}', p_id)\n if m:\n return m.group()\n else:\n return None\n\n\n'''\nfrom lxml.html import fromstring\nfrom itertools import cycle\nimport traceback\n\ndef get_proxies():\n url = 'https://free-proxy-list.net/'\n response = requests.get(url)\n parser = fromstring(response.text)\n proxie = list()\n for i in parser.xpath('//tbody/tr')[:100]:\n if i.xpath('.//td[7][contains(text(),\"yes\")]'):\n proxy = \":\".join([i.xpath('.//td[1]/text()')[0], i.xpath('.//td[2]/text()')[0]])\n proxie.append(proxy)\n return proxie\n\n\n#If you are copy pasting proxy ips, put in the list below\n#proxies = ['121.129.127.209:80', '124.41.215.238:45169', '185.93.3.123:8080', '194.182.64.67:3128', '106.0.38.174:8080', '163.172.175.210:3128', '13.92.196.150:8080']\nproxie = get_proxies()\nprint(proxie)\n#proxy_pool = cycle(proxies)\n\n\nclass myclass:\n c = 7\n'''\ndriver = webdriver.Chrome('D:/chromedriver')\n\ndef get_soup(url):\n if AMAZON_BASE_URL not in url:\n url = AMAZON_BASE_URL + url\n #nap_time_sec = 1\n #logging.debug('Script is going to sleep for {} (Amazon throttling). ZZZzzzZZZzz.'.format(nap_time_sec))\n #sleep(nap_time_sec)\n #url = \"www.google.com\"\n driver.get('https://www.proxysite.com/')\n url_input_box = driver.find_element_by_name('d')\n url_input_box.send_keys(url)\n #time.sleep(10)\n #go_submit_button = driver.find_element_by_xpath('//*[@id=\"url-form-wrap\"]/form/div[2]/button').click()\n try:\n go_submit_button = WebDriverWait(driver, 20).until(EC.element_to_be_clickable((By.XPATH, '//*[@id=\"url-form-wrap\"]/form/div[2]/button')))\n time.sleep(1)\n go_submit_button.click()\n print(\"YOU link found and returned\")\n except TimeoutException:\n print(\"YOU link not found ... breaking out\")\n \n time.sleep(1)\n html = driver.page_source\n soup = BeautifulSoup(html, \"lxml\")\n return soup\n\n\n'''\ndef get_soup(url):\n if AMAZON_BASE_URL not in url:\n url = AMAZON_BASE_URL + url\n nap_time_sec = 1\n logging.debug('Script is going to sleep for {} (Amazon throttling). ZZZzzzZZZzz.'.format(nap_time_sec))\n sleep(nap_time_sec)\n #header = requests.utils.default_headers()\n header = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36'\n }\n logging.debug('-> to Amazon : {}'.format(url))\n # Get a proxy from the pool\n #proxy = next(proxy_pool)\n m = myclass()\n m.c += 1\n proxy = proxie[m.c]\n print(proxy)\n #proxy = next(i)\n try:\n print('\\na')\n sleep(2)\n out = requests.get(url, headers=header ,proxies={'https': 'http://213.32.5.165:443' ,'http': 'http://213.32.5.165:443'})\n assert out.status_code == 200\n soup = BeautifulSoup(out.content, 'lxml')\n if 'captcha' in str(soup):\n raise BannedException('Your bot has been detected. Please wait a while.')\n return soup\n\n except requests.exceptions.ConnectionError as error:\n print(error)\n\n \n except BaseException as error:\n print(error)\n # Most free proxies will often get connection errors. You will have retry the entire request using another proxy to work.\n # We will just skip retries as its beyond the scope of this tutorial and we are only downloading a single url\n print(\"Skipping. Connnection error\")\n'''\n","repo_name":"sherlvick/sentimental-analysis_SVM","sub_path":"amazon-reviews-scraper-master/core_utils.py","file_name":"core_utils.py","file_ext":"py","file_size_in_byte":5513,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"8731789061","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('', views.IndexView.as_view(), name='index'),\n path(\"contacts\", views.ContactsView.as_view(), name=\"contacts\"),\n path(\"about\", views.AboutView.as_view(), name=\"about\"),\n path('kurzy/', views.CourseListView.as_view(), name='course_list'),\n path('pobocky/', views.BranchListView.as_view(), name='branch_list'),\n path('tym/', views.PersonListView.as_view(), name='branch_list'),\n # Verze z první poloviny\n #path('prihlaska/', views.ApplicationCreateView.as_view(), name='application_create'),\n path('prihlaska//', views.ApplicationCreateView.as_view(), name='application_create'),\n path('prihlaska/potvrzeni/', views.ApplicationConfirmation.as_view(), name='application_confirmation'),\n path('prihlaska-clena-tymu/', views.PersonRegisterView.as_view(), name='person_register'),\n path('kurz/', views.CourseDetailView.as_view(), name='course_detail'),\n path('pobocka/', views.BranchDetailView.as_view(), name='course_detail'),\n]\n","repo_name":"pesikj/progr2-python","sub_path":"python-pro-web/reseni/formulare/projekt/czechitas/courses/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"22419579775","text":"class Solution:\n def coinChange(self, coins: List[int], amount: int) -> int:\n if not coins:\n return -1\n numCoins = [float('inf')]*(amount+1)\n numCoins[0] = 0\n for i in range(1,len(numCoins)):\n for j in range(len(coins)):\n if coins[j] <= i:\n numCoins[i] = min(numCoins[i], 1+numCoins[i-coins[j]])\n if numCoins[-1] < float('inf'):\n return numCoins[-1]\n return -1\n","repo_name":"abhinay-b/Leetcode-Submissions","sub_path":"accepted_codes/Coin_Change/Coin Change_292911169.py","file_name":"Coin Change_292911169.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"27276447399","text":"def sum_power(a, b):\n num = a ** b\n total = 0\n for c in str(num):\n total += int(c)\n return total\n\n\nif __name__ == '__main__':\n maximum = 0\n for a in range(100):\n for b in range(100):\n s = sum_power(a, b)\n if s > maximum:\n maximum = s\n print(maximum)\n","repo_name":"Rylan12/Project-Euler","sub_path":"solved/p56.py","file_name":"p56.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25511511500","text":"import osmnx as ox\nimport math\nimport networkx as nx\nimport geopy\nimport json\nfrom shapely import geometry\nimport sys\n\n\nlchs_lat = 39.12\nlchs_long = -77.58\ndouglass_lat = 39.11\ndouglass_long = -77.55\n\n# G = ox.graph_from_place(\"Leesburg, Virginia, USA\", network_type='drive')\n# G2 = ox.graph_from_bbox(lchs_lat, douglass_lat, douglass_long, lchs_long, network_type='drive')\n# G is a multidigraph created by the networkx python package\n\n# for (u, v, l) in G.edges(data='length'):\n# print(u, v, l)\n\n# lchs = ox.get_nearest_node(G, (lchs_lat, lchs_long))\n# douglass = ox.get_nearest_node(G, (douglass_lat, douglass_long))\n\n\ndef generate_int(n):\n for i in range(n):\n yield i\n\n\ndef get_travel_time(edge):\n \"\"\"\n :param edge: dictionary containing edge data\n :return: approximate time in minutes to travel along the edge\n \"\"\"\n\n # dictionary of default speed limits to be used when no speed limit is given\n # based off of descriptions found on https://wiki.openstreetmap.org/wiki/Key:highway\n default_speed_limits = {\n 'motorway': 55,\n 'trunk': 55,\n 'primary': 45,\n 'secondary': 45,\n 'tertiary': 35,\n 'unclassified': 30,\n 'residential': 25,\n 'motorway_link': 45,\n 'trunk_link': 45,\n 'primary_link': 35,\n 'secondary_link': 35,\n 'tertiary_link': 25,\n 'living_street': 15,\n }\n\n distance_mi = edge['length'] / 1609.0 # convert from meters to miles\n try:\n edge['maxspeed']\n if type(edge['maxspeed']) == str:\n maxspeed = int(edge['maxspeed'][:-3])\n elif type(edge['maxspeed']) == list:\n maxspeed = int(edge['maxspeed'][-1][:-3]) # get highest speed, remove last three characters (mpg)\n except KeyError:\n road_type = edge['highway']\n try:\n maxspeed = default_speed_limits[road_type]\n except KeyError:\n maxspeed = 25\n\n travel_time = (distance_mi / maxspeed) * 60\n return travel_time\n\n\ndef dijkstra(start, graph):\n print(\"Dijkstra\", type(graph))\n infinity = math.inf\n distances = {start: (0, None)}\n seen = []\n\n for node in list(graph.nodes()):\n if node != start:\n distances[node] = (infinity, None)\n\n q = list(graph.nodes())\n while q:\n u = min((distances[i][0], i) for i in (distances.keys() - seen))[1]\n seen.append(u)\n try:\n q.remove(u)\n except ValueError:\n print(\"{} not in list: {}\".format(u, u not in q))\n break\n neighbors = graph.neighbors(u)\n for n in neighbors:\n travel_time = get_travel_time(graph.get_edge_data(u, n)[0])\n if distances[n][0] > distances[u][0] + travel_time:\n distances[n] = (distances[u][0] + travel_time, u)\n\n return distances\n\n\ntest_graph = nx.Graph()\ntest_graph.add_nodes_from(\"abcdefg\")\ntest_graph.add_edges_from([(\"a\", \"b\", {0: {'length': 1600, 'maxspeed': '55 mph'}}),\n (\"a\", \"c\", {0: {'length': 1810, 'maxspeed': '45 mph'}}),\n (\"a\", \"g\", {0: {'length': 300, 'maxspeed': '25 mpg'}}),\n (\"b\", \"d\", {0: {'length': 800, 'maxspeed': '45 mpg'}}),\n (\"c\", \"d\", {0: {'length': 1650, 'maxspeed': '45 mpg'}}),\n (\"c\", \"e\", {0: {'length': 510, 'maxspeed': '35 mpg'}}),\n (\"d\", \"e\", {0: {'length': 743, 'maxspeed': '25 mpg'}}),\n (\"e\", \"f\", {0: {'length': 621, 'maxspeed': '25 mpg'}}),\n (\"f\", \"g\", {0: {'length': 750, 'maxspeed': '25 mpg'}})])\n\n\ndef dijkstra_brute_force(graph, start_nodes):\n print(\"BF\",type(graph))\n dijkstra_results = []\n for node in start_nodes:\n dijkstra_results.append(dijkstra(node, graph))\n\n node_sums = {}\n for node in graph.nodes():\n node_sum = 0\n for result in dijkstra_results:\n node_sum += result[node][0]\n node_sums[node] = node_sum\n\n meeting_node = min((node_sums[i], i) for i in node_sums.keys())[1]\n\n paths = {}\n for i in range(len(start_nodes)):\n path = [meeting_node]\n res = dijkstra_results[i]\n current = meeting_node\n\n while res[current][1] is not None:\n path.insert(0, res[current][1])\n current = res[current][1]\n paths[start_nodes[i]] = path\n\n return meeting_node, paths\n\n\ndef to_coords(locations):\n \"\"\"\n Get coordinates of given locations\n :param locations: List of strings representing geographical locations\n :return: list of tuples containing coordinates of locations\n \"\"\"\n coordinates = []\n locator = geopy.Nominatim(user_agent=\"myGeocoder\", scheme='http')\n\n for location in locations:\n try:\n location_coords = locator.geocode(location)[1]\n except TypeError:\n print(\"Invalid location: {}\".format(location), file=sys.stderr)\n return None\n coordinates.append(location_coords)\n return coordinates\n\n\ndef create_graph(initial_locations):\n \"\"\"\n Creates a graph to perform meeting place finding algorithm\n :param initial_locations: List of strings containing initial locations\n :return: Networkx graph and list of coordinate tuples\n \"\"\"\n coordinates = to_coords(initial_locations)\n lat = []\n long = []\n\n for c in coordinates:\n lat.append(c[0])\n long.append(c[1])\n\n north = max(lat)\n south = min(lat)\n east = max(long)\n west = min(long)\n\n return ox.graph_from_bbox(north, south, east, west, network_type='drive')\n\n\ndef find_meeting_place(initial_locations, algorithm=\"bf\"):\n \"\"\"\n Finds meeting place on graph between initial locations\n :param initial_locations: List of strings containing initial locations\n :param algorithm: String describing which algorithm to use\n :return: Returns dictionary of relevant data of meeting place\n \"\"\"\n\n G = create_graph(initial_locations)\n print(\"init\",type(G))\n coordinates = to_coords(initial_locations)\n\n if G is None:\n print(\"Invalid location was specified\", file=sys.stderr)\n return None\n\n if algorithm == \"bf\":\n initial_nodes = []\n for location in coordinates:\n print(\"Adding node for {}\".format(location))\n initial_nodes.append(ox.get_nearest_node(G, location))\n meeting_place, _ = dijkstra_brute_force(G, initial_nodes)\n else:\n return None\n\n return meeting_place\n\n\nlocations = [\n \"Loudoun County High School, Leesburg, VA\",\n \"407 E Market St, Leesburg, VA\",\n \"18 E Market St, Leesburg, VA\"\n]\n\nprint(find_meeting_place(locations))\n\n\n# locator = geopy.Nominatim(user_agent=\"myGeocoder\", scheme='http')\n# lchs_coords = locator.geocode(\"Loudoun County High School, Leesburg, VA\")[1]\n# douglass_coords = locator.geocode(\"407 E Market St, Leesburg, VA\")[1]\n# court_coords = locator.geocode(\"18 E Market St, Leesburg, VA\")[1]\n#\n# large_test_graph = ox.graph_from_place(\"Leesburg, Virginia, USA\", network_type='drive')\n#\n# lchs = ox.get_nearest_node(large_test_graph, (lchs_coords[0], lchs_coords[1]))\n# douglass = ox.get_nearest_node(large_test_graph, (douglass_coords[0], douglass_coords[1]))\n# courthouse = ox.get_nearest_node(large_test_graph, (court_coords[0], court_coords[1]))\n#\n#\n# meeting_place, paths = dijkstra_brute_force([lchs, douglass, courthouse], large_test_graph)\n# node_data = dict(large_test_graph.nodes(data=True))\n# print(node_data[meeting_place])\n\n\n\n#meeting_node, paths = dijkstra_brute_force(['a', 'd', 'f'], test_graph)\n#print(meeting_node)\n#print(paths)\n\n\ndef floyd_warshall(graph):\n infinity = math.inf\n distances = {}\n q = list(graph.nodes())\n print(q)\n\n num_nodes = graph.number_of_nodes()\n for i in generate_int(num_nodes):\n for j in generate_int(num_nodes):\n if (q[i], q[j]) in graph.edges():\n road_length = graph.get_edge_data(q[i], q[j])[0]['length']\n try:\n max_speed = graph.get_edge_data(q[i], q[j])[0]['max_speed']\n except KeyError:\n road_type = graph.get_edge_data(q[i], q[j])[0]['highway']\n if road_type == 'residential':\n max_speed = 25\n else:\n print(road_type)\n\n travel_time = get_travel_time(road_length, max_speed)\n distances[(q[i], q[j])] = travel_time\n else:\n distances[(q[i], q[j])] = infinity\n\n for k in generate_int(num_nodes):\n for i in generate_int(num_nodes):\n for j in generate_int(num_nodes):\n if (q[i], q[k]) in graph.edges():\n length_i_k = graph.get_edge_data(q[i], q[k])[0]['length']\n try:\n max_speed_i_k = graph.get_edge_data(q[i], q[k])[0]['max_speed']\n except KeyError:\n road_type = graph.get_edge_data(q[i], q[k])[0]['highway']\n if road_type == 'residential':\n max_speed_i_k = 25\n else:\n print(road_type)\n travel_time_i_k = get_travel_time(length_i_k, max_speed_i_k)\n\n if (q[k], q[j]) in graph.edges():\n length_k_j = graph.get_edge_data(q[k], q[j])[0]['length']\n try:\n max_speed_k_j = graph.get_edge_data(q[k], q[j])[0]['max_speed']\n except KeyError:\n road_type = graph.get_edge_data(q[k], q[j])[0]['highway']\n if road_type == 'residential':\n max_speed_k_j = 25\n else:\n print(road_type)\n travel_time_k_j = get_travel_time(length_k_j, max_speed_k_j)\n\n new_dist = travel_time_i_k + travel_time_k_j\n if distances[(q[i], q[j])] > new_dist:\n distances[(q[i], q[j])] = new_dist\n\n for item in distances:\n print(\"{} --> {}\".format(item, distances[item]))\n\n for edge in graph.edges():\n print(graph.get_edge_data(edge[0], edge[1]))\n\n\n\n\n","repo_name":"APettit98/Senior-IS","sub_path":"PracticeCode/practice2.py","file_name":"practice2.py","file_ext":"py","file_size_in_byte":10313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21904893837","text":"#Django imports\nfrom django import urls\nfrom django.urls import path, include\n\n\n\n# Standard Package Imports\n\n# Project Imports\nfrom . import views\n\n\n# Third Party Imports\n\n\napp_name = 'event'\n\nurlpatterns = [\n path('official_dashboard', views.dashboard, name='dashboard'),\n path('official_dashboard/add_social', views.dashboard_add_social, name='dashboard_add_social'),\n path('official_dashboard/view_social/(?P[0-9a-f-]+)', views.dashboard_view_social, name='dashboard_view_social'),\n path('official_dashboard/del_social/(?P[0-9a-f-]+)', views.dashboard_del_social, name='dashboard_del_social'),\n path('official_dashboard/add_sports', views.dashboard_add_sports, name='dashboard_add_sports'),\n path('official_dashboard/view_sports/(?P[0-9a-f-]+)', views.dashboard_view_sports, name='dashboard_view_sports'),\n path('official_dashboard/del_sports/(?P[0-9a-f-]+)', views.dashboard_del_sports, name='dashboard_del_sports'), \n path('official_dashboard/add_studentwelfare', views.dashboard_add_studentwelfare, name='dashboard_add_studentwelfare'),\n path('official_dashboard/view_studentwelfare/(?P[0-9a-f-]+)', views.dashboard_view_studentwelfare, name='dashboard_view_studentwelfare'),\n path('official_dashboard/del_studentwelfare/(?P[0-9a-f-]+)', views.dashboard_del_studentwelfare, name='dashboard_del_studentwelfare'),\n path('official_dashboard/add_technology', views.dashboard_add_technology, name='dashboard_add_technology'),\n path('official_dashboard/view_technology/(?P[0-9a-f-]+)', views.dashboard_view_technology, name='dashboard_view_technology'),\n path('official_dashboard/del_technology/(?P[0-9a-f-]+)', views.dashboard_del_technology, name='dashboard_del_technology'),\n path('technology', views.technology, name='technology'),\n path('SocialandCulture', views.social, name='social'),\n path('SportsandGames', views.sports, name='SportsAndGames'),\n path('Studentswelfare', views.studentWelfare, name='studentWelfare'),\n path('technology/', views.technology_details, name='technology'),\n path('studentWelfare/', views.studentWelfare_details, name='studentWelfare'),\n path('SocialandCulture/', views.social_details, name='social'),\n path('SportsandGames/', views.sports_details, name='sports'),\n path('Results', views.result, name='result'),\n path('interIIT', views.interIIT, name='interIIT'),\n path('interIIT-Technology', views.interIITtechnology,name='interIIT-Technology'),\n path('interIITSocult', views.interIITsocult, name='interIIT-socult'),\n path('GCTechnology', views.GCTechnology, name='GCTechnology'),\n path('GCSocult', views.GCSocult, name='GCSocult'),\n]\n","repo_name":"bhagyashree201/TSGhackathon","sub_path":"event/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37013999836","text":"from random import randint\nfrom time import sleep\n\ndef sorteia(sorte):\n for c in range(1, 6):\n sorte.append(randint(1, 10))\n print(f'Sorteando os valores da lista:', end=' ')\n for v in sorte:\n print(f'{v}', end=' ')\n sleep(0.3)\n print()\n\n\ndef somaPar(n2):\n soma = 0\n for valor in numeros:\n if valor % 2 ==0:\n soma += valor\n print(f'Somando os valores pares de {numeros}, temos {soma}.')\n\n#Programa principal\nnumeros = []\nsorteia(numeros)\nsomaPar(numeros)\n","repo_name":"maisalobao/Python","sub_path":"pythonProject/desafio100.py","file_name":"desafio100.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"42057623406","text":"import os\nfrom glob import glob\n\nimport numpy as np\n\nfrom yt.utilities.grid_data_format.conversion.conversion_abc import Converter\nfrom yt.utilities.on_demand_imports import _h5py as h5py\n\ntranslation_dict = {}\ntranslation_dict[\"density\"] = \"density\"\ntranslation_dict[\"total_energy\"] = \"specific_energy\"\ntranslation_dict[\"velocity_x\"] = \"velocity_x\"\ntranslation_dict[\"velocity_y\"] = \"velocity_y\"\ntranslation_dict[\"velocity_z\"] = \"velocity_z\"\ntranslation_dict[\"cell_centered_B_x\"] = \"mag_field_x\"\ntranslation_dict[\"cell_centered_B_y\"] = \"mag_field_y\"\ntranslation_dict[\"cell_centered_B_z\"] = \"mag_field_z\"\n\n\nclass AthenaDistributedConverter(Converter):\n def __init__(self, basename, outname=None, source_dir=None, field_conversions=None):\n self.fields = []\n self.current_time = 0.0\n name = basename.split(\".\")\n self.ddn = int(name[1])\n if source_dir is None:\n source_dir = \"./\"\n self.source_dir = source_dir\n self.basename = name[0]\n if outname is None:\n outname = self.basename + \".%04i\" % self.ddn + \".gdf\"\n self.outname = outname\n if field_conversions is None:\n field_conversions = {}\n self.field_conversions = field_conversions\n self.handle = None\n\n def parse_line(self, line, grid):\n # grid is a dictionary\n splitup = line.strip().split()\n if \"vtk\" in splitup:\n grid[\"vtk_version\"] = splitup[-1]\n elif \"Really\" in splitup:\n grid[\"time\"] = splitup[-1]\n self.current_time = grid[\"time\"]\n elif \"PRIMITIVE\" in splitup:\n grid[\"time\"] = float(splitup[4].rstrip(\",\"))\n grid[\"level\"] = int(splitup[6].rstrip(\",\"))\n grid[\"domain\"] = int(splitup[8].rstrip(\",\"))\n self.current_time = grid[\"time\"]\n elif \"DIMENSIONS\" in splitup:\n grid[\"dimensions\"] = np.array(splitup[-3:], dtype=\"int64\")\n elif \"ORIGIN\" in splitup:\n grid[\"left_edge\"] = np.array(splitup[-3:], dtype=\"float64\")\n elif \"SPACING\" in splitup:\n grid[\"dds\"] = np.array(splitup[-3:], dtype=\"float64\")\n elif \"CELL_DATA\" in splitup:\n grid[\"ncells\"] = int(splitup[-1])\n elif \"SCALARS\" in splitup:\n field = splitup[1]\n grid[\"read_field\"] = field\n grid[\"read_type\"] = \"scalar\"\n elif \"VECTORS\" in splitup:\n field = splitup[1]\n grid[\"read_field\"] = field\n grid[\"read_type\"] = \"vector\"\n\n def write_gdf_field(self, fn, grid_number, field, data):\n f = self.handle\n ## --------- Store Grid Data --------- ##\n if \"grid_%010i\" % grid_number not in f[\"data\"].keys():\n g = f[\"data\"].create_group(\"grid_%010i\" % grid_number)\n else:\n g = f[\"data\"][\"grid_%010i\" % grid_number]\n name = field\n try:\n name = translation_dict[name]\n except Exception:\n pass\n # print 'Writing %s' % name\n if name not in g.keys():\n g.create_dataset(name, data=data)\n\n def read_and_write_index(self, basename, ddn, gdf_name):\n \"\"\"Read Athena legacy vtk file from multiple cpus\"\"\"\n proc_names = glob(os.path.join(self.source_dir, \"id*\"))\n # print('Reading a dataset from %i Processor Files' % len(proc_names))\n N = len(proc_names)\n grid_dims = np.empty([N, 3], dtype=\"int64\")\n grid_left_edges = np.empty([N, 3], dtype=\"float64\")\n grid_dds = np.empty([N, 3], dtype=\"float64\")\n grid_levels = np.zeros(N, dtype=\"int64\")\n grid_parent_ids = -1 * np.ones(N, dtype=\"int64\")\n grid_particle_counts = np.zeros([N, 1], dtype=\"int64\")\n\n for i in range(N):\n if i == 0:\n fn = os.path.join(\n self.source_dir, f\"id{i}\", basename + f\".{ddn:04d}.vtk\"\n )\n else:\n fn = os.path.join(\n self.source_dir, f\"id{i}\", basename + f\"-id{i}.{ddn:04d}.vtk\"\n )\n\n print(f\"Reading file {fn}\")\n f = open(fn, \"rb\")\n grid = {}\n grid[\"read_field\"] = None\n grid[\"read_type\"] = None\n line = f.readline()\n while grid[\"read_field\"] is None:\n self.parse_line(line, grid)\n if \"SCALAR\" in line.strip().split():\n break\n if \"VECTOR\" in line.strip().split():\n break\n if \"TABLE\" in line.strip().split():\n break\n if len(line) == 0:\n break\n del line\n line = f.readline()\n\n if len(line) == 0:\n break\n\n if np.prod(grid[\"dimensions\"]) != grid[\"ncells\"]:\n grid[\"dimensions\"] -= 1\n grid[\"dimensions\"][grid[\"dimensions\"] == 0] = 1\n if np.prod(grid[\"dimensions\"]) != grid[\"ncells\"]:\n print(\n \"product of dimensions %i not equal to number of cells %i\"\n % (np.prod(grid[\"dimensions\"]), grid[\"ncells\"])\n )\n raise TypeError\n\n # Append all hierarchy info before reading this grid's data\n grid_dims[i] = grid[\"dimensions\"]\n grid_left_edges[i] = grid[\"left_edge\"]\n grid_dds[i] = grid[\"dds\"]\n # grid_ncells[i]=grid['ncells']\n del grid\n\n f.close()\n del f\n f = self.handle\n\n ## --------- Begin level nodes --------- ##\n g = f.create_group(\"gridded_data_format\")\n g.attrs[\"format_version\"] = np.float32(1.0)\n g.attrs[\"data_software\"] = \"athena\"\n f.create_group(\"data\")\n f.create_group(\"field_types\")\n f.create_group(\"particle_types\")\n pars_g = f.create_group(\"simulation_parameters\")\n\n gles = grid_left_edges\n gdims = grid_dims\n dle = np.min(gles, axis=0)\n dre = np.max(gles + grid_dims * grid_dds, axis=0)\n glis = ((gles - dle) / grid_dds).astype(\"int64\")\n\n ddims = (dre - dle) / grid_dds[0]\n\n # grid_left_index\n f.create_dataset(\"grid_left_index\", data=glis)\n # grid_dimensions\n f.create_dataset(\"grid_dimensions\", data=gdims)\n\n # grid_level\n f.create_dataset(\"grid_level\", data=grid_levels)\n\n ## ----------QUESTIONABLE NEXT LINE--------- ##\n # This data needs two dimensions for now.\n f.create_dataset(\"grid_particle_count\", data=grid_particle_counts)\n\n # grid_parent_id\n f.create_dataset(\"grid_parent_id\", data=grid_parent_ids)\n\n ## --------- Done with top level nodes --------- ##\n\n pars_g.attrs[\"refine_by\"] = np.int64(1)\n pars_g.attrs[\"dimensionality\"] = np.int64(3)\n pars_g.attrs[\"domain_dimensions\"] = ddims\n pars_g.attrs[\"current_time\"] = self.current_time\n pars_g.attrs[\"domain_left_edge\"] = dle\n pars_g.attrs[\"domain_right_edge\"] = dre\n pars_g.attrs[\"unique_identifier\"] = \"athenatest\"\n pars_g.attrs[\"cosmological_simulation\"] = np.int64(0)\n pars_g.attrs[\"num_ghost_zones\"] = np.int64(0)\n pars_g.attrs[\"field_ordering\"] = np.int64(1)\n pars_g.attrs[\"boundary_conditions\"] = np.int64([0] * 6) # For Now\n\n # Extra pars:\n # pars_g.attrs['n_cells'] = grid['ncells']\n pars_g.attrs[\"vtk_version\"] = 1.0\n\n # Add particle types\n # Nothing to do here\n\n # Add particle field attributes\n # f.close()\n\n def read_and_write_data(self, basename, ddn, gdf_name):\n proc_names = glob(os.path.join(self.source_dir, \"id*\"))\n # print('Reading a dataset from %i Processor Files' % len(proc_names))\n N = len(proc_names)\n for i in range(N):\n if i == 0:\n fn = os.path.join(\n self.source_dir, f\"id{i}\", basename + f\".{ddn:04d}.vtk\"\n )\n else:\n fn = os.path.join(\n self.source_dir, +f\"id{i}\", basename + f\"-id{i}.{ddn:04d}.vtk\"\n )\n f = open(fn, \"rb\")\n # print('Reading data from %s' % fn)\n line = f.readline()\n while line != \"\":\n if len(line) == 0:\n break\n splitup = line.strip().split()\n\n if \"DIMENSIONS\" in splitup:\n grid_dims = np.array(splitup[-3:], dtype=\"int64\")\n line = f.readline()\n continue\n elif \"CELL_DATA\" in splitup:\n grid_ncells = int(splitup[-1])\n line = f.readline()\n if np.prod(grid_dims) != grid_ncells:\n grid_dims -= 1\n grid_dims[grid_dims == 0] = 1\n if np.prod(grid_dims) != grid_ncells:\n print(\n \"product of dimensions %i not equal to number of cells %i\"\n % (np.prod(grid_dims), grid_ncells)\n )\n raise TypeError\n break\n else:\n del line\n line = f.readline()\n read_table = False\n while line != \"\":\n if len(line) == 0:\n break\n splitup = line.strip().split()\n if \"SCALARS\" in splitup:\n field = splitup[1]\n if not read_table:\n line = f.readline() # Read the lookup table line\n read_table = True\n data = np.fromfile(f, dtype=\">f4\", count=grid_ncells).reshape(\n grid_dims, order=\"F\"\n )\n if i == 0:\n self.fields.append(field)\n # print('writing field %s' % field)\n self.write_gdf_field(gdf_name, i, field, data)\n read_table = False\n\n elif \"VECTORS\" in splitup:\n field = splitup[1]\n data = np.fromfile(f, dtype=\">f4\", count=3 * grid_ncells)\n data_x = data[0::3].reshape(grid_dims, order=\"F\")\n data_y = data[1::3].reshape(grid_dims, order=\"F\")\n data_z = data[2::3].reshape(grid_dims, order=\"F\")\n if i == 0:\n self.fields.append(field + \"_x\")\n self.fields.append(field + \"_y\")\n self.fields.append(field + \"_z\")\n\n # print('writing field %s' % field)\n self.write_gdf_field(gdf_name, i, field + \"_x\", data_x)\n self.write_gdf_field(gdf_name, i, field + \"_y\", data_y)\n self.write_gdf_field(gdf_name, i, field + \"_z\", data_z)\n del data, data_x, data_y, data_z\n del line\n line = f.readline()\n f.close()\n del f\n\n f = self.handle\n field_g = f[\"field_types\"]\n # Add Field Attributes\n for name in self.fields:\n tname = name\n try:\n tname = translation_dict[name]\n except Exception:\n pass\n this_field = field_g.create_group(tname)\n if name in self.field_conversions.keys():\n this_field.attrs[\"field_to_cgs\"] = self.field_conversions[name]\n else:\n this_field.attrs[\"field_to_cgs\"] = np.float64(\"1.0\") # For Now\n\n def convert(self, index=True, data=True):\n self.handle = h5py.File(self.outname, mode=\"a\")\n if index:\n self.read_and_write_index(self.basename, self.ddn, self.outname)\n if data:\n self.read_and_write_data(self.basename, self.ddn, self.outname)\n self.handle.close()\n\n\nclass AthenaConverter(Converter):\n def __init__(self, basename, outname=None, field_conversions=None):\n self.fields = []\n self.basename = basename\n name = basename.split(\".\")\n fn = \"%s.%04i\" % (name[0], int(name[1]))\n self.ddn = int(name[1])\n self.basename = fn\n if outname is None:\n outname = fn + \".gdf\"\n self.outname = outname\n if field_conversions is None:\n field_conversions = {}\n self.field_conversions = field_conversions\n\n def parse_line(self, line, grid):\n # grid is a dictionary\n splitup = line.strip().split()\n if \"vtk\" in splitup:\n grid[\"vtk_version\"] = splitup[-1]\n elif \"Really\" in splitup:\n grid[\"time\"] = splitup[-1]\n elif \"DIMENSIONS\" in splitup:\n grid[\"dimensions\"] = np.array(splitup[-3:], dtype=\"int64\")\n elif \"ORIGIN\" in splitup:\n grid[\"left_edge\"] = np.array(splitup[-3:], dtype=\"float64\")\n elif \"SPACING\" in splitup:\n grid[\"dds\"] = np.array(splitup[-3:], dtype=\"float64\")\n elif \"CELL_DATA\" in splitup:\n grid[\"ncells\"] = int(splitup[-1])\n elif \"SCALARS\" in splitup:\n field = splitup[1]\n grid[\"read_field\"] = field\n grid[\"read_type\"] = \"scalar\"\n elif \"VECTORS\" in splitup:\n field = splitup[1]\n grid[\"read_field\"] = field\n grid[\"read_type\"] = \"vector\"\n\n def read_grid(self, filename):\n \"\"\"Read Athena legacy vtk file from single cpu\"\"\"\n f = open(filename, \"rb\")\n # print('Reading from %s'%filename)\n grid = {}\n grid[\"read_field\"] = None\n grid[\"read_type\"] = None\n table_read = False\n line = f.readline()\n while line != \"\":\n while grid[\"read_field\"] is None:\n self.parse_line(line, grid)\n if grid[\"read_type\"] == \"vector\":\n break\n if not table_read:\n line = f.readline()\n if \"TABLE\" in line.strip().split():\n table_read = True\n if len(line) == 0:\n break\n\n if len(line) == 0:\n break\n if np.prod(grid[\"dimensions\"]) != grid[\"ncells\"]:\n grid[\"dimensions\"] -= 1\n if np.prod(grid[\"dimensions\"]) != grid[\"ncells\"]:\n print(\n \"product of dimensions %i not equal to number of cells %i\"\n % (np.prod(grid[\"dimensions\"]), grid[\"ncells\"])\n )\n raise TypeError\n\n if grid[\"read_type\"] == \"scalar\":\n grid[grid[\"read_field\"]] = np.fromfile(\n f, dtype=\">f4\", count=grid[\"ncells\"]\n ).reshape(grid[\"dimensions\"], order=\"F\")\n self.fields.append(grid[\"read_field\"])\n elif grid[\"read_type\"] == \"vector\":\n data = np.fromfile(f, dtype=\">f4\", count=3 * grid[\"ncells\"])\n grid[grid[\"read_field\"] + \"_x\"] = data[0::3].reshape(\n grid[\"dimensions\"], order=\"F\"\n )\n grid[grid[\"read_field\"] + \"_y\"] = data[1::3].reshape(\n grid[\"dimensions\"], order=\"F\"\n )\n grid[grid[\"read_field\"] + \"_z\"] = data[2::3].reshape(\n grid[\"dimensions\"], order=\"F\"\n )\n self.fields.append(grid[\"read_field\"] + \"_x\")\n self.fields.append(grid[\"read_field\"] + \"_y\")\n self.fields.append(grid[\"read_field\"] + \"_z\")\n else:\n raise TypeError\n grid[\"read_field\"] = None\n grid[\"read_type\"] = None\n line = f.readline()\n if len(line) == 0:\n break\n grid[\"right_edge\"] = grid[\"left_edge\"] + grid[\"dds\"] * (grid[\"dimensions\"])\n return grid\n\n def write_to_gdf(self, fn, grid):\n f = h5py.File(fn, mode=\"a\")\n\n ## --------- Begin level nodes --------- ##\n g = f.create_group(\"gridded_data_format\")\n g.attrs[\"format_version\"] = np.float32(1.0)\n g.attrs[\"data_software\"] = \"athena\"\n data_g = f.create_group(\"data\")\n field_g = f.create_group(\"field_types\")\n f.create_group(\"particle_types\")\n pars_g = f.create_group(\"simulation_parameters\")\n\n dle = grid[\"left_edge\"] # True only in this case of one grid for the domain\n gles = np.array([grid[\"left_edge\"]])\n gdims = np.array([grid[\"dimensions\"]])\n glis = ((gles - dle) / grid[\"dds\"]).astype(\"int64\")\n\n # grid_left_index\n f.create_dataset(\"grid_left_index\", data=glis)\n # grid_dimensions\n f.create_dataset(\"grid_dimensions\", data=gdims)\n\n levels = np.array([0], dtype=\"int64\") # unigrid example\n # grid_level\n f.create_dataset(\"grid_level\", data=levels)\n\n ## ----------QUESTIONABLE NEXT LINE--------- ##\n # This data needs two dimensions for now.\n n_particles = np.array([[0]], dtype=\"int64\")\n # grid_particle_count\n f.create_dataset(\"grid_particle_count\", data=n_particles)\n\n # Assume -1 means no parent.\n parent_ids = np.array([-1], dtype=\"int64\")\n # grid_parent_id\n f.create_dataset(\"grid_parent_id\", data=parent_ids)\n\n ## --------- Done with top level nodes --------- ##\n\n f.create_group(\"index\")\n\n ## --------- Store Grid Data --------- ##\n\n g0 = data_g.create_group(\"grid_%010i\" % 0)\n for field in self.fields:\n name = field\n if field in translation_dict.keys():\n name = translation_dict[name]\n if name not in g0.keys():\n g0.create_dataset(name, data=grid[field])\n\n ## --------- Store Particle Data --------- ##\n\n # Nothing to do\n\n ## --------- Attribute Tables --------- ##\n\n pars_g.attrs[\"refine_by\"] = np.int64(1)\n pars_g.attrs[\"dimensionality\"] = np.int64(3)\n pars_g.attrs[\"domain_dimensions\"] = grid[\"dimensions\"]\n try:\n pars_g.attrs[\"current_time\"] = grid[\"time\"]\n except Exception:\n pars_g.attrs[\"current_time\"] = 0.0\n pars_g.attrs[\"domain_left_edge\"] = grid[\"left_edge\"] # For Now\n pars_g.attrs[\"domain_right_edge\"] = grid[\"right_edge\"] # For Now\n pars_g.attrs[\"unique_identifier\"] = \"athenatest\"\n pars_g.attrs[\"cosmological_simulation\"] = np.int64(0)\n pars_g.attrs[\"num_ghost_zones\"] = np.int64(0)\n pars_g.attrs[\"field_ordering\"] = np.int64(0)\n pars_g.attrs[\"boundary_conditions\"] = np.int64([0] * 6) # For Now\n\n # Extra pars:\n pars_g.attrs[\"n_cells\"] = grid[\"ncells\"]\n pars_g.attrs[\"vtk_version\"] = grid[\"vtk_version\"]\n\n # Add Field Attributes\n for name in g0.keys():\n tname = name\n try:\n tname = translation_dict[name]\n except Exception:\n pass\n this_field = field_g.create_group(tname)\n if name in self.field_conversions.keys():\n this_field.attrs[\"field_to_cgs\"] = self.field_conversions[name]\n else:\n this_field.attrs[\"field_to_cgs\"] = np.float64(\"1.0\") # For Now\n\n # Add particle types\n # Nothing to do here\n\n # Add particle field attributes\n f.close()\n\n def convert(self):\n grid = self.read_grid(self.basename + \".vtk\")\n self.write_to_gdf(self.outname, grid)\n\n\n# import sys\n# if __name__ == '__main__':\n# n = sys.argv[-1]\n# n = n.split('.')\n# fn = '%s.%04i'%(n[0],int(n[1]))\n# grid = read_grid(fn+'.vtk')\n# write_to_hdf5(fn+'.gdf',grid)\n","repo_name":"yt-project/yt","sub_path":"yt/utilities/grid_data_format/conversion/conversion_athena.py","file_name":"conversion_athena.py","file_ext":"py","file_size_in_byte":19713,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"3"}
+{"seq_id":"71483738000","text":"\r\nfrom OpenGL.GL import *\r\nfrom OpenGL.GLU import *\r\nfrom OpenGL.GLUT import *\r\nimport sys\r\n\r\n\r\n\r\nimport math\r\n\r\nsys.setrecursionlimit(8000)\r\n\r\n\r\ndef init():\r\n glClearColor(1.0, 1.0, 1.0, 1.0)\r\n glPointSize(1.0)\r\n glMatrixMode(GL_PROJECTION)\r\n glLoadIdentity()\r\n gluOrtho2D(-250, 250.0, -250.0, 250.0)\r\n\r\n\r\ndef Display():\r\n\r\n glClear(GL_COLOR_BUFFER_BIT)\r\n glColor3f(0, 1, 1)\r\n glWindowPos2d(250,250)\r\n for ch in \"Hello World\":\r\n glutBitmapCharacter(GLUT_BITMAP_9_BY_15, ctypes.c_int(ord(ch)))\r\n print(ch)\r\n\r\n\r\n\r\n glBegin(GL_POINTS)\r\n glVertex2f(0,0)\r\n glEnd()\r\n glFlush()\r\n glFlush()\r\n\r\n\r\n\r\ndef main():\r\n glutInit(sys.argv)\r\n glutInitDisplayMode(GLUT_SINGLE | GLUT_RGB )\r\n glutInitWindowSize(500, 500)\r\n glutInitWindowPosition(50, 50)\r\n glutCreateWindow(\"Hello World\")\r\n glutDisplayFunc(Display)\r\n init()\r\n glutMainLoop()\r\n\r\n\r\nmain()\r\n","repo_name":"akulsanthosh/randomAlgos","sub_path":"CG LAB/Hello.py","file_name":"Hello.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"70745806803","text":"import os\n\nfrom flask import (\n current_app,\n render_template,\n Blueprint,\n request,\n jsonify,\n send_from_directory,\n)\n\nfrom werkzeug.utils import secure_filename\n\nassignment_form = Blueprint(\"assignment_form\", __name__, template_folder=\"templates\")\n\n\n@assignment_form.route(\"/\")\ndef hello_world():\n return jsonify(hello=\"from the other side\")\n\n\n@assignment_form.route(\"/static/\")\ndef staticfiles(filename):\n return send_from_directory(current_app.config[\"STATIC_FOLDER\"], filename)\n\n\n@assignment_form.route(\"/media/\")\ndef mediafiles(filename):\n return send_from_directory(current_app.config[\"MEDIA_FOLDER\"], filename)\n\n\n@assignment_form.route(\"/upload\", methods=[\"GET\", \"POST\"])\ndef upload_file():\n if request.method == \"POST\":\n file = request.files[\"file\"]\n filename = secure_filename(file.filename)\n file.save(os.path.join(current_app.config[\"MEDIA_FOLDER\"], filename))\n return \"\"\"\n \n upload new File \n \n \"\"\"\n","repo_name":"danhagg/flask-on-docker","sub_path":"services/web/project/assignment_form/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28195101143","text":"import os\n\ndef load_input(file):\n with open(file, 'r') as lines:\n return [str(line).split() for line in lines]\n\ndef count_easy(outputs):\n count = 0\n for line in outputs:\n for digit in line:\n if len(digit) == 7 or (len(digit)>1 and len(digit)<5):\n count += 1\n return count\n\ndef decode_easy(digits, decoded):\n for digit in digits:\n if len(digit) == 2:\n decoded[1] = digit\n elif len(digit) == 3:\n decoded[7] = digit\n elif len(digit) == 4:\n decoded[4] = digit\n elif len(digit) == 7:\n decoded[8] = digit\n return decoded\n\ndef decode_remaining(digits, decoded):\n decoded[3] = find_3(digits, decoded)\n decoded[5] = find_5(digits, decoded)\n decoded[2] = find_2(digits, decoded)\n decoded[0] = find_0(digits, decoded)\n decoded[9] = find_9(digits, decoded)\n decoded[6] = find_6(digits, decoded)\n return decoded\n\ndef find_0(digits, decoded):\n for digit in digits:\n if len(digit) == 6:\n for x in decoded[5]:\n if not x in digit:\n return digit\n\ndef find_2(digits, decoded):\n for digit in digits:\n if len(digit) == 5:\n if not (digit==decoded[3] or digit==decoded[5]):\n return digit\n\ndef find_3(digits, decoded):\n for digit in digits:\n if len(digit) == 5:\n is3 = True\n for x in decoded[1]:\n if not x in digit:\n is3 = False\n break\n if is3 == True:\n return digit\n\ndef find_5(digits, decoded):\n for digit in digits:\n if len(digit) == 5 and not digit==decoded[3]:\n common = 0\n for x in decoded[4]:\n if x in digit:\n common += 1 \n if common == 3:\n return digit\n\ndef find_6(digits, decoded):\n for digit in digits:\n if len(digit) == 6:\n if not (digit==decoded[0] or digit==decoded[9]):\n return digit\n\ndef find_9(digits, decoded):\n for digit in digits:\n if len(digit) == 6 and not digit==decoded[0]:\n is9 = True\n for x in decoded[1]:\n if not x in digit:\n is9 = False\n break\n if is9 == True:\n return digit\n\ndef read_output(decoded, output):\n output_digits = [0]*4\n for i in range(4):\n for j in range(10):\n if len(decoded[j]) == len(output[i]):\n same = True\n for x in decoded[j]:\n if not x in output[i]:\n same = False\n break\n if same == True:\n output_digits[i] = j\n return output_digits[0]*1000 + output_digits[1]*100 + output_digits[2]*10 + output_digits[3]\n\ndef main():\n # load file\n file_name = os.path.join(os.path.dirname(__file__), \"input.txt\")\n lines = load_input(file_name) \n\n #part 1\n outputs = [line[11:] for line in lines]\n print(\"Part 1: \" + str(count_easy(outputs)))\n\n #part 2\n patterns = [line[:10] for line in lines]\n sum = 0\n for i in range(len(patterns)):\n decoded = [\"\"]*10\n decoded = decode_easy(patterns[i], decoded)\n decoded = decode_remaining(patterns[i], decoded)\n sum += read_output(decoded, outputs[i])\n print(\"Part 2: \" + str(sum))\n\n\nif __name__ == '__main__':\n main()","repo_name":"MartynaBorak/AOC_2021","sub_path":"Day 08/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":3483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10969707663","text":"from datetime import datetime\n\nfrom src.db.db import DataBaseService\nfrom src.entities.compositor import CompositorEntity\nfrom src.service.per_musical_service import PerMusicalService\nfrom src.entities.per_musical import PerMusicalEntity\n\n\nclass CompositorService:\n def __init__(self):\n pass\n\n # relational = true: estamos criando um periodo musical em conjunto\n # relational = false: o periodo musical do compositor ja existe\n def add_to_db(self, relational=False):\n compositor = CompositorEntity()\n\n if relational:\n per_music = PerMusicalEntity()\n PerMusicalService().add_to_db_relational(per_music.id, per_music.descr, per_music.start_date, per_music.end_date)\n if compositor.death_date != None:\n sql_query_compositor = f\"compositor (id_compositor, id_periodo_musical, nome, local_nasc, data_nasc, data_morte) VALUES ('{compositor.id}', '{per_music.id}', '{compositor.name}', '{compositor.birth_place}', '{compositor.birthday}', '{compositor.death_date}')\"\n else:\n sql_query_compositor = f\"compositor (id_compositor, id_periodo_musical, nome, local_nasc, data_nasc) VALUES ('{compositor.id}', '{per_music.id}', '{compositor.name}', '{compositor.birth_place}', '{compositor.birthday}')\" \n DataBaseService().insert(sql_query_compositor)\n else:\n print(\"\\n\")\n PerMusicalService().show_per_music()\n\n per_music_name = str(input(\"Digite o nome do periodo musical do compositor: \"))\n id_per_music = PerMusicalService().find_by_name(per_music_name)\n\n if compositor.death_date != None:\n sql_query_compositor = f\"compositor (id_compositor, id_periodo_musical, nome, local_nasc, data_nasc, data_morte) VALUES ('{compositor.id}', '{id_per_music}', '{compositor.name}', '{compositor.birth_place}', '{compositor.birthday}', '{compositor.death_date}')\"\n else:\n sql_query_compositor = f\"compositor (id_compositor, id_periodo_musical, nome, local_nasc, data_nasc) VALUES ('{compositor.id}', '{id_per_music}', '{compositor.name}', '{compositor.birth_place}', '{compositor.birthday}')\"\n DataBaseService().insert(sql_query_compositor)\n return compositor.id\n\n def show_compositors(self):\n sql_query = f\"nome FROM compositor\"\n rows = DataBaseService().search(sql_query)\n print(\"\\n--------- COMPOSITORES ---------\")\n for row in rows:\n print(row[0])\n \n def find_by_name(self, name):\n db_service = DataBaseService()\n sql_query = f\"id_compositor FROM compositor WHERE nome = '{name}'\"\n return db_service.search(sql_query)[0][0]\n\n def update(self):\n CompositorService().show_compositors()\n comp_name = str(input(\"\\n\\nIdentifique o nome do compositor: \"))\n\n new_death_date = str(input(\"\\nDigite a nova data de morte: \"))\n death_date = datetime.strptime(new_death_date, \"%Y-%m-%d\")\n\n sql_query = f\"compositor SET data_morte = '{death_date}' WHERE nome = '{comp_name}'\"\n DataBaseService().update(sql_query)\n\n def view_compositor_q3(self):\n sql_query = f\"comp.nome, COUNT(fp.id_faixa) AS Contagem FROM compositor comp INNER JOIN faixa_compositor fc ON comp.id_compositor = fc.id_compositor INNER JOIN faixa f ON fc.id_faixa = f.id_faixa INNER JOIN faixa_playlist fp ON f.id_faixa = fp.id_faixa GROUP BY comp.nome ORDER BY Contagem DESC LIMIT 1\"\n rows = DataBaseService().search(sql_query)\n print(\"\\n------------- Questao 3 -------------\")\n for row in rows:\n print(\"NOME | CONTAGEM\")\n print(f\"{row[0]} - {row[1]}\")\n","repo_name":"erikbayerlein/fbd-spotify","sub_path":"src/service/compositor_service.py","file_name":"compositor_service.py","file_ext":"py","file_size_in_byte":3691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25065162326","text":"# remove the dups without allocating another array in the memory\n# return the length of the value and also the fixed array that does not have any duplicates\n\nnums = [0, 4, 4]\n\nclass Solution:\n def removeDuplicates(self, nums):\n if len(nums) in [0, 1]:\n return len(nums)\n\n j = 0\n for i in range(len(nums)-1):\n if nums[i] != nums[i+1]:\n nums[j] = nums[i]\n j += 1\n nums[j] = nums[-1]\n nums = nums[:j+1]\n\n return j+1\n\n\nL = Solution()\nx = L.removeDuplicates(nums)\nprint(x)\n","repo_name":"jkfer/LeetCode","sub_path":"Remove_dups_from_sorted_array.py","file_name":"Remove_dups_from_sorted_array.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18622106681","text":"from flask import request\n\n\ndef get_safe_max_id(v):\n if not v:\n return None\n\n try:\n rv = int(v)\n if rv <= 0:\n return None\n\n except ValueError:\n rv = None\n\n return rv\n\n\ndef get_safe_count(v, default=10, max=100):\n if not v:\n return default\n\n try:\n rv = int(v)\n if not (1 <= rv <= max):\n rv = default\n except ValueError:\n rv = default\n\n return rv","repo_name":"yoophi/flaskygram","sub_path":"flaskygram/library/get_params.py","file_name":"get_params.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71071338643","text":"# Uses python3\nimport sys\n\ndef fibonacci_sum(n):\n if n > 2:\n fibonacci = [0, 1, 1]\n sequence = [0, 1, 1]\n\n m = 10\n\n for i in range(3, n + 1):\n fibonacci.append(((fibonacci[-2] ) + (fibonacci[-1] )) % m)\n\n sequence[0] = fibonacci[-3]\n sequence[1] = fibonacci[-2]\n sequence[2] = fibonacci[-1]\n\n if sequence == [0, 1, 1]:\n n_mod = n // (i - 2)\n sequence_sum = sum(fibonacci[0:(i - 2)])\n ramainder_sum = sum(fibonacci[0:(n + 1 - ((i - 2) * n_mod))])\n _sum = (sequence_sum * n_mod) + ramainder_sum\n return _sum % m\n\n return sum(fibonacci) % m\n else:\n return n\n\nif __name__ == '__main__':\n # input = input()\n input = sys.stdin.read()\n n = int(input)\n print(fibonacci_sum(n))\n","repo_name":"HPaulowicz/algorithmic-toolbox","sub_path":"introduction_starter/fibonacci_sum_last_digit/fibonacci_sum_last_digit.py","file_name":"fibonacci_sum_last_digit.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23309883609","text":"import heapq\n\ndef minimum_classrooms(N, schedule):\n if N == 0:\n return 0\n\n # 수업을 시작 시간(Si)을 기준으로 오름차순 정렬\n schedule.sort(key=lambda x: x[0])\n\n # 우선순위 큐를 사용하여 강의실 관리\n classrooms = []\n\n # 첫 번째 수업을 첫 번째 강의실에 배정\n heapq.heappush(classrooms, schedule[0][1])\n\n for i in range(1, N):\n if schedule[i][0] >= classrooms[0]:\n # 현재 강의실의 종료 시간(Ti)가 다음 수업의 시작 시간(Si)보다 작거나 같으면\n # 같은 강의실에서 이어서 들을 수 있으므로 우선순위 큐를 업데이트\n heapq.heappop(classrooms)\n # 다음 수업을 해당 강의실로 배정\n heapq.heappush(classrooms, schedule[i][1])\n\n # 필요한 강의실의 개수 반환\n return len(classrooms)\n\n# 입력 받기\nN = int(input())\nschedule = []\nfor _ in range(N):\n Si, Ti = map(int, input().split())\n schedule.append((Si, Ti))\n\n# 결과 출력\nresult = minimum_classrooms(N, schedule)\nprint(result)","repo_name":"1234jienf/TIL","sub_path":"0830/problem/BAEKJOON_11000_강의실배정.py","file_name":"BAEKJOON_11000_강의실배정.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20027690860","text":"#!/usr/bin/env python3\n# -*- encoding: utf-8\n\nimport boto3\nimport argparse\nimport maya\nfrom datetime import datetime, timedelta\n\n\ndef get_log_events(log_group, start_time=None, end_time=None):\n \"\"\"Generate all the log events from a CloudWatch group.\n\n :param log_group: Name of the CloudWatch log group.\n :param start_time: Only fetch events with a timestamp after this time.\n Expressed as the number of milliseconds after midnight Jan 1 1970.\n :param end_time: Only fetch events with a timestamp before this time.\n Expressed as the number of milliseconds after midnight Jan 1 1970.\n\n \"\"\"\n client = boto3.client(\"logs\")\n kwargs = {\n \"logGroupName\": log_group,\n \"limit\": 10000,\n }\n\n if start_time is not None:\n kwargs[\"startTime\"] = start_time\n if end_time is not None:\n kwargs[\"endTime\"] = end_time\n\n while True:\n resp = client.filter_log_events(**kwargs)\n yield from resp[\"events\"]\n try:\n kwargs[\"nextToken\"] = resp[\"nextToken\"]\n except KeyError:\n break\n\n\ndef milliseconds_since_epoch(time_string):\n dt = maya.when(time_string)\n seconds = dt.epoch\n return seconds * 1000\n\n\ndef today_start_end():\n now = datetime.now()\n start = now.replace(hour=0, minute=0, second=0, microsecond=0)\n end = start + timedelta(days=1, microseconds=-1)\n return (start.isoformat(), end.isoformat())\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description=\"Print log event messages from a CloudWatch log group.\"\n )\n parser.add_argument(\"log_group\", type=str, help=\"Name of the CloudWatch log group.\")\n parser.add_argument(\n \"--start\",\n type=str,\n default=None,\n help=\"Only print events with a timestamp after this time. (defaults to the start of today)\",\n )\n parser.add_argument(\n \"--end\",\n type=str,\n default=None,\n help=\"Only print events with a timestamp before this time. (defaults to the end of today)\",\n )\n\n args = parser.parse_args()\n\n log_group = args.log_group\n\n if args.start:\n try:\n start_time = milliseconds_since_epoch(args.start)\n except ValueError:\n exit(f\"Invalid datetime input as --start: {args.start}\")\n else:\n start_time, _ = today_start_end()\n start_time = milliseconds_since_epoch(start_time)\n\n if args.end:\n try:\n end_time = milliseconds_since_epoch(args.end)\n except ValueError:\n exit(f\"Invalid datetime input as --end: {args.end}\")\n else:\n _, end_time = today_start_end()\n end_time = milliseconds_since_epoch(end_time)\n\n with open(\"cloudwatch.log\", \"w\") as f:\n f.write(f\"start_time: {start_time}\\\\n\")\n f.write(f\"end_time: {end_time}\\\\n\")\n\n print(f\"start_time: {start_time}\")\n print(f\"end_time: {start_time}\")\n\n logs = get_log_events(\n log_group=log_group, start_time=start_time, end_time=end_time\n )\n\n for event in logs:\n message = event[\"message\"].rstrip()\n print(event[\"message\"].rstrip())\n f.write(message + \"\\n\")\n","repo_name":"agentgill/cloudwatch-utils","sub_path":"get-logs.py","file_name":"get-logs.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70491196243","text":"from urllib.parse import quote\nfrom selectolax.parser import HTMLParser\nimport aiohttp\n\nfrom utils import user_agent\n\nbase_query = 'https://google.com/search?q='\n\n\nasync def google_it(query: str, how_many: int = 1) -> str:\n async with aiohttp.request('GET', base_query + quote(query), headers={'User-Agent': user_agent}) as resp:\n text = await resp.text()\n i = 0\n\n results = []\n search_result_node = HTMLParser(text).css_first('div[eid]')\n if search_result_node is None:\n return 'Ничего не нашел'\n\n nodes = search_result_node.css_first('div > div.srg').css_first('div.srg').css('div.g')\n for node in nodes:\n node = node.css_first('div[data-ved]').css_first('div.rc')\n header_node = node.css_first('div.r').css_first('a')\n\n url = header_node.attributes['href']\n\n header_node = node.css_first('h3').css_first('div')\n\n title = header_node.text().strip()\n\n print(f'{i}: {title} {url}')\n results.append(f'Описание: {title}\\nСсылка: {url}\\n')\n i += 1\n\n if len(results) > 0:\n return '\\n'.join(results[:how_many])\n else:\n return 'Ничего не нашел'\n","repo_name":"AntonyMoes/cyberhelper","sub_path":"bot/google.py","file_name":"google.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"16322439227","text":"# License: GNU Affero General Public License v3 or later\n# A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.\n\n\"\"\" Manages HTML construction for the Lanthipeptide module\n\"\"\"\n\nfrom jinja2 import FileSystemLoader, Environment, StrictUndefined\n\nfrom antismash.common import path\nfrom antismash.common.layers import ClusterLayer\nfrom antismash.common.secmet import Prepeptide\n\n\ndef will_handle(products) -> bool:\n \"\"\" Returns True if the products provided are relevant to the module \"\"\"\n return 'lanthipeptide' in products\n\n\nclass LanthipeptideLayer(ClusterLayer):\n \"\"\" An extended ClusterLayer for holding a list of LanthipeptideMotifs \"\"\"\n def __init__(self, record, cluster_feature):\n ClusterLayer.__init__(self, record, cluster_feature)\n self.motifs = []\n for motif in self.record.seq_record.get_cds_motifs():\n if not isinstance(motif, Prepeptide):\n continue\n if not motif.is_contained_by(self.cluster_rec):\n continue\n if motif.peptide_class == \"lanthipeptide\":\n self.motifs.append(motif)\n\n\ndef generate_details_div(cluster_layer, results, record_layer, options_layer) -> str:\n \"\"\" Generates a HTML div for the main page of results \"\"\"\n lanthi_layer = LanthipeptideLayer(record_layer, cluster_layer.cluster_rec)\n if not results:\n return \"\"\n env = Environment(loader=FileSystemLoader(path.get_full_path(__file__, \"templates\")),\n autoescape=True, undefined=StrictUndefined)\n template = env.get_template('details.html')\n motifs_in_cluster = {}\n for locus in results.clusters.get(cluster_layer.get_cluster_number(), []):\n motifs_in_cluster[locus] = results.motifs_by_locus[locus]\n details_div = template.render(record=record_layer,\n cluster=lanthi_layer,\n options=options_layer,\n results=motifs_in_cluster)\n return details_div\n\n\ndef generate_sidepanel(cluster_layer, results, record_layer, options_layer) -> str:\n \"\"\" Generates a div for the sidepanel results \"\"\"\n env = Environment(\n loader=FileSystemLoader(path.get_full_path(__file__, \"templates\")),\n autoescape=True, undefined=StrictUndefined)\n template = env.get_template('sidepanel.html')\n cluster = LanthipeptideLayer(record_layer, cluster_layer.cluster_rec)\n if not results:\n return \"\"\n record = record_layer\n motifs_in_cluster = {}\n for locus in results.clusters.get(cluster_layer.get_cluster_number(), []):\n motifs_in_cluster[locus] = results.motifs_by_locus[locus]\n sidepanel = template.render(record=record,\n cluster=cluster,\n options=options_layer,\n results=motifs_in_cluster)\n return sidepanel\n","repo_name":"agolfinos/antismash","sub_path":"antismash/modules/lanthipeptides/html_output.py","file_name":"html_output.py","file_ext":"py","file_size_in_byte":2915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"13856241758","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport random\n\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport numpy as np\n\nN = 20\ncenter = np.array([0.5, 0.5])\n\n\nfig = plt.figure() \n\n\np = np.random.rand(N, 2)\n#p = np.array([[5,5],[70,100],[160,180]])\n\nax = fig.add_subplot(111)\nax.grid(True, linestyle = '-', color = '0.75')\nax.set_xlim([0, 1])\nax.set_ylim([0, 1])\n\ncolors = np.random.rand(N)\nscat = plt.scatter(p[:, 0], p[:, 1], s = 100, c=colors)\nscat.set_alpha(0.5)\n\n\ndef update_plot(i,fig, scat):\n global p\n for i in range(0, N):\n cmp = p[i] != center\n if cmp[0] or cmp[1]:\n ran = random.random()\n p[i] += ran*(center-p[i])\n\n scat.set_offsets(p)\n #print('Frames: %d' %i)\n print(p)\n return scat,\n\nanim = animation.FuncAnimation(fig, update_plot, fargs = (fig, scat),frames = 100, interval = 500)\n \nplt.show()","repo_name":"youzipi/Language-Implementation-Partterns-note","sub_path":"complier/num/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29670759827","text":"\"\"\"\n * ***** BEGIN GPL LICENSE BLOCK *****\n *\n * This program is free software; you can redistribute it and/or\n * modify it under the terms of the GNU General Public License\n * as published by the Free Software Foundation; either version 2\n * of the License, or (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 Foundation,\n * Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n *\n * Contributor(s): Julien Duroure.\n *\n * ***** END GPL LICENSE BLOCK *****\n * This development is done in strong collaboration with Airbus Defence & Space\n \"\"\"\n\nfrom .primitive import *\nfrom ..rig import *\n\nclass Mesh():\n def __init__(self, index, json, gltf):\n self.index = index\n self.json = json # Mesh json\n self.gltf = gltf # Reference to global glTF instance\n self.primitives = []\n self.target_weights = []\n self.name = None\n self.skin = None\n\n\n def read(self):\n if 'name' in self.json.keys():\n self.name = self.json['name']\n self.gltf.log.debug(\"Mesh \" + self.json['name'])\n else:\n self.gltf.log.debug(\"Mesh index \" + str(self.index))\n\n cpt_idx_prim = 0\n for primitive_it in self.json['primitives']:\n primitive = Primitive(cpt_idx_prim, primitive_it, self.gltf)\n primitive.read()\n self.primitives.append(primitive)\n primitive.debug_missing()\n cpt_idx_prim += 1\n\n # reading default targets weights if any\n if 'weights' in self.json.keys():\n for weight in self.json['weights']:\n self.target_weights.append(weight)\n\n def rig(self, skin_id, mesh_id):\n if skin_id not in self.gltf.skins.keys():\n self.skin = Skin(skin_id, self.gltf.json['skins'][skin_id], self.gltf)\n self.skin.mesh_ids.append(mesh_id)\n self.gltf.skins[skin_id] = self.skin\n self.skin.read()\n self.skin.debug_missing()\n else:\n self.skin = self.gltf.skins[skin_id]\n self.skin.mesh_ids.append(mesh_id)\n\n def debug_missing(self):\n keys = [\n 'name',\n 'primitives',\n 'weights'\n ]\n\n for key in self.json.keys():\n if key not in keys:\n self.gltf.log.debug(\"MESH MISSING \" + key)\n","repo_name":"sketchfab/gltf2-blender-importer","sub_path":"gltf/mesh/mesh.py","file_name":"mesh.py","file_ext":"py","file_size_in_byte":2719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"29062386737","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom future import standard_library\n\nstandard_library.install_aliases()\n\nimport pytest\nimport mock\nimport http.client as http\nfrom decimal import Decimal as D\nfrom hamcrest import assert_that, equal_to\n\nfrom balance import constants as cst\n\nfrom brest.core.tests import security\nfrom yb_snout_api.utils import context_managers as ctx_util\nfrom yb_snout_api.tests_unit.base import TestCaseApiAppBase\nfrom yb_snout_api.tests_unit.fixtures.resource import mock_client_resource\nfrom yb_snout_api.tests_unit.fixtures.client import create_role_client, create_client\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.permissions import create_admin_role, create_role\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.invoice import create_invoice, create_custom_invoice\n\n\n@pytest.fixture(name='close_inv_role')\ndef create_close_inv_role():\n return create_role(\n (\n cst.PermissionCode.CLOSE_INVOICE,\n {cst.ConstraintTypes.firm_id: None, cst.ConstraintTypes.client_batch_id: None},\n ),\n )\n\n\n@pytest.mark.smoke\nclass TestCaseInvoiceClose(TestCaseApiAppBase):\n BASE_API = '/v1/invoice/close'\n\n @pytest.mark.parametrize(\n 'func',\n [\n pytest.param(lambda inv: setattr(inv, 'total_act_sum', inv.total_act_sum + inv.total_sum),\n id='total_act_sum < total_sum'),\n pytest.param(lambda inv: setattr(inv, 'receipt_sum', D('0')),\n id='receipt_sum == 0'),\n pytest.param(lambda inv: setattr(inv.invoice_orders[0].order, 'service_id', cst.ServiceId.DIRECT),\n id='service_id != ONE_TIME_SALE'),\n ],\n )\n @mock.patch('yb_snout_api.utils.context_managers._new_transactional_session', ctx_util.new_rollback_session)\n def test_cannot_close_invoice(self, admin_role, close_inv_role, func):\n security.set_roles([admin_role, close_inv_role])\n\n invoice = create_custom_invoice(service_id=cst.ServiceId.ONE_TIME_SALE)\n invoice.manual_turn_on(D('1000'))\n func(invoice) # нарушаем одно из условий закрытия счёта\n\n response = self.test_client.secure_post(\n self.BASE_API,\n data={'invoice_id': invoice.id, 'close_dt': self.test_session.now().strftime('%Y%m%d%H%M%S')},\n )\n assert_that(\n response.status_code,\n equal_to(http.INTERNAL_SERVER_ERROR),\n 'response code must be INTERNAL_SERVER_ERROR',\n )\n assert_that(response.get_json()['error'], 'CANNOT_CLOSE_INVOICE')\n\n def test_invalid_month(self, invoice):\n session = self.test_session\n invoice.turn_on_rows()\n\n with mock.patch('balance.mncloselib.get_task_last_status', return_value='resolved'):\n # Нельзя формировать акты, если закрыта вторая стадия\n response = self.test_client.secure_post(\n self.BASE_API,\n data={'invoice_id': invoice.id, 'close_dt': session.now().strftime('%Y%m%d%H%M%S')},\n )\n\n assert_that(\n response.status_code,\n equal_to(http.INTERNAL_SERVER_ERROR),\n 'response code must be INTERNAL_SERVER_ERROR',\n )\n assert_that(response.get_json()['error'], equal_to('CLOSED_MONTH'))\n\n\n@pytest.mark.permissions\n@pytest.mark.smoke\nclass TestCaseInvoiceClosePermissions(TestCaseApiAppBase):\n BASE_API = '/v1/invoice/close'\n\n @mock_client_resource('yb_snout_api.resources.v1.invoice.routes.close.InvoiceClose')\n @mock.patch('yb_snout_api.utils.context_managers._new_transactional_session', ctx_util.new_rollback_session)\n def test_close_by_user(self, client):\n security.set_passport_client(client)\n security.set_roles([])\n invoice = create_invoice(client=client)\n response = self.test_client.secure_post(\n self.BASE_API,\n data={\n 'invoice_id': invoice.id,\n 'close_dt': self.test_session.now().strftime('%Y%m%d%H%M%S'),\n },\n is_admin=False,\n )\n assert_that(response.status_code, equal_to(http.FORBIDDEN))\n\n @pytest.mark.parametrize(\n 'role_firm_id, res',\n [\n pytest.param(cst.SENTINEL, http.FORBIDDEN,\n id='wo role'),\n pytest.param(None, http.OK,\n id='role wo constraints'),\n pytest.param(cst.FirmId.YANDEX_OOO, http.OK,\n id='role constraints matches firm_id'),\n pytest.param(cst.FirmId.CLOUD, http.FORBIDDEN,\n id='role constraints don\\'t matches firm_id'),\n ],\n )\n @mock.patch('yb_snout_api.utils.context_managers._new_transactional_session', ctx_util.new_rollback_session)\n def test_firm_id(self, admin_role, close_inv_role, role_firm_id, res):\n roles = [admin_role]\n if role_firm_id is not cst.SENTINEL:\n roles.append((close_inv_role, {cst.ConstraintTypes.firm_id: role_firm_id}))\n security.set_roles(roles)\n\n invoice = create_custom_invoice(firm_id=cst.FirmId.YANDEX_OOO, service_id=cst.ServiceId.ONE_TIME_SALE)\n invoice.manual_turn_on(D('1000'))\n\n response = self.test_client.secure_post(\n self.BASE_API,\n data={\n 'invoice_id': invoice.id,\n 'close_dt': self.test_session.now().strftime('%Y%m%d%H%M%S'),\n },\n )\n assert_that(response.status_code, equal_to(res))\n\n @pytest.mark.parametrize(\n 'is_allowed',\n [True, False],\n )\n @mock.patch('yb_snout_api.utils.context_managers._new_transactional_session', ctx_util.new_rollback_session)\n def test_client_id(self, client, admin_role, close_inv_role, is_allowed):\n firm_id = cst.FirmId.DRIVE\n\n client_batch_id = create_role_client(client if is_allowed else None).client_batch_id\n roles = [\n admin_role,\n (\n close_inv_role,\n {cst.ConstraintTypes.firm_id: firm_id, cst.ConstraintTypes.client_batch_id: client_batch_id},\n ),\n ]\n security.set_roles(roles)\n\n invoice = create_custom_invoice(service_id=cst.ServiceId.ONE_TIME_SALE, client=client, firm_id=firm_id)\n invoice.manual_turn_on(D('1000'))\n\n response = self.test_client.secure_post(\n self.BASE_API,\n data={\n 'invoice_id': invoice.id,\n 'close_dt': self.test_session.now().strftime('%Y%m%d%H%M%S'),\n },\n )\n assert_that(response.status_code, equal_to(http.OK if is_allowed else http.FORBIDDEN))\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests_unit/resources/v1/invoice/test_invoice_close.py","file_name":"test_invoice_close.py","file_ext":"py","file_size_in_byte":6875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2379028572","text":"from __future__ import print_function\n\nimport os\nimport argparse\nimport json\nimport time\nimport collections\n\nfrom copy import deepcopy\n\nimport numpy as np\n\nfrom allennlp.data import Vocabulary, DataIterator, DatasetReader, Tokenizer, TokenIndexer\nfrom allennlp.common import Params\nfrom allennlp.data.dataset import Batch\nfrom allennlp.models import Model, archive_model, archival\nfrom allennlp.training import Trainer\n\nfrom cmv.preprocessing.cmvReader import CMVReader\nfrom cmv.rnn.cmvPredictor import CMVPredictor\nfrom cmv.rnn.cmvMultiChannelPredictor import CMVMultiChannelPredictor\n#from cmv.rnn.cmvExtractor import CMVExtractor\n\nimport argparse\nimport logging\nimport sys\nimport json\nimport os\n\nlogger = logging.getLogger(__name__) \n\ndef train_model(data_path, params, serialization_dir, cuda_device=-1, use_validation_data=True):\n\n os.makedirs(serialization_dir, exist_ok=True)\n\n handler = logging.FileHandler(os.path.join(serialization_dir, \"python_logging.log\"))\n handler.setLevel(logging.INFO)\n handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(name)s - %(message)s'))\n logging.getLogger().addHandler(handler)\n \n serialization_params = deepcopy(params).as_dict(quiet=True)\n\n with open(os.path.join(serialization_dir, \"model_params.json\"), \"w\") as param_file:\n json.dump(serialization_params, param_file, indent=4)\n \n ds_params = params.pop('dataset_reader', {})\n data_params = ds_params.pop('data', {})\n dataset_reader = CMVReader.from_params(ds_params)\n '''\n dataset_reader = CMVReader(data_path,\n tokenizer=Tokenizer.from_params(ds_params.pop('tokenizer', {})),\n token_indexers=TokenIndexer.from_params(ds_params.pop('token_indexers', {})))\n '''\n\n logger.info('Reading training data...')\n \n train_data = dataset_reader.read('train',\n **data_params)\n\n #train_data_response_only_for_vocab = dataset_reader.read('train', response_only=True)\n #train_data_op_only_for_vocab = dataset_reader.read('train', op_only=True)\n #all_datasets = [train_data_response_only_for_vocab, train_data_op_only_for_vocab]\n all_datasets = [train_data]\n datasets_in_vocab = ['train'] #_response_only_for_vocab']\n\n if use_validation_data:\n logger.info('Reading validation data...')\n validation_data = dataset_reader.read('val',\n **data_params)\n all_datasets.append(validation_data)\n datasets_in_vocab.append('val')\n else:\n validation_data = None\n\n logger.info('Creating a vocabulary using %s data.', ', '.join(datasets_in_vocab))\n vocab_params = params.pop('vocabulary', {})\n dataset = None\n if 'directory_path' not in vocab_params:\n dataset = Batch([instance for dataset in all_datasets\n for instance in dataset.instances])\n\n vocab = Vocabulary.from_params(vocab_params,\n dataset)\n vocab.save_to_files(os.path.join(serialization_dir, 'vocabulary'))\n \n model = Model.from_params(params=params.pop('model'), vocab=vocab)\n iterator = DataIterator.from_params(params.pop('iterator'))\n\n train_data.index_instances(vocab)\n if validation_data:\n validation_data.index_instances(vocab)\n\n trainer_params = params.pop(\"trainer\")\n if cuda_device is not None:\n trainer_params[\"cuda_device\"] = cuda_device\n trainer = Trainer.from_params(model,\n serialization_dir,\n iterator,\n train_data,\n validation_data,\n trainer_params)\n\n trainer.train()\n\n # Now tar up results\n archive_model(serialization_dir)\n\n return model\n \n \nparser = argparse.ArgumentParser()\n\nparser.add_argument('data_path')\nparser.add_argument('param_path',\n type=str,\n help='path to parameter file describing the model to be trained')\n \nparser.add_argument(\"logdir\",type=str)\n\nparser.add_argument(\"--cuda-device\", type=int, default=None, help='id of GPU to use (if any)')\n\nargs = parser.parse_args()\n\nparams = Params.from_file(args.param_path)\n\ntrain_model(args.data_path, params, args.logdir, args.cuda_device)\n \n","repo_name":"chridey/cmv","sub_path":"cmv/bin/train_cmv_torch.py","file_name":"train_cmv_torch.py","file_ext":"py","file_size_in_byte":4439,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"36412047773","text":"from contextlib import contextmanager\nimport os\nimport time\nimport yaml\n\nimport pytest\n\nimport libnmstate\nfrom libnmstate.schema import Interface\nfrom libnmstate.schema import LLDP\n\nfrom .testlib import assertlib\nfrom .testlib import cmdlib\nfrom .testlib import ifacelib\nfrom .testlib import statelib\nfrom .testlib.veth import create_veth_pair\nfrom .testlib.veth import remove_veth_pair\n\n\nLLDPTEST = \"lldptest\"\nLLDPTEST_PEER = \"lldptest.peer\"\n\nLLDP_TEST_NS = \"nmstate_lldp_test\"\n\nLLDP_SYSTEM_DESC = (\n \"Summit300-48 - Version 7.4e.1 (Build 5) by Release_Master \"\n \"05/27/05 04:53:11\"\n)\n\nEXPECTED_LLDP_NEIGHBOR = \"\"\"\n- system-name: Summit300-48\n type: 5\n- system-description: Summit300-48 - Version 7.4e.1 (Build 5) by Release_Master\n 05/27/05 04:53:11\n type: 6\n- system-capabilities:\n - MAC Bridge component\n - Router\n type: 7\n- _description: MAC address\n chassis-id: 00:01:30:F9:AD:A0\n chassis-id-type: 4\n type: 1\n- _description: Interface name\n port-id: 1/1\n port-id-type: 5\n type: 2\n- ieee-802-1-vlans:\n - name: v2-0488-03-0505\n vid: 488\n oui: 00:80:c2\n subtype: 3\n type: 127\n- ieee-802-3-mac-phy-conf:\n autoneg: true\n operational-mau-type: 16\n pmd-autoneg-cap: 27648\n oui: 00:12:0f\n subtype: 1\n type: 127\n- ieee-802-1-ppvids:\n - 0\n oui: 00:80:c2\n subtype: 2\n type: 127\n- management-addresses:\n - address: 00:01:30:F9:AD:A0\n address-subtype: MAC\n interface-number: 1001\n interface-number-subtype: 2\n type: 8\n- ieee-802-3-max-frame-size: 1522\n oui: 00:12:0f\n subtype: 4\n type: 127\n\"\"\"\n\nLLDP_CAPS = [\"MAC Bridge component\", \"Router\"]\n\nCHASSIS_ID = \"chassis-id\"\nCHASSIS_ID_TYPE = \"chassis-id-type\"\nMANAGEMENT_ADDRESSES = \"management-addresses\"\nADDRESS = \"address\"\nADDRESS_SUBTYPE = \"address-subtype\"\nINTERFACE_NUMBER = \"interface-number\"\nINTERFACE_NUMBER_SUBTYPE = \"interface-number-subtype\"\nAUTONEG = \"autoneg\"\nOPERATIONAL_MAU_TYPE = \"operational-mau-type\"\nPMD_AUTONEG_CAP = \"pmd-autoneg-cap\"\nPPVIDS_SUBTREE = \"ieee-802-1-ppvids\"\nPPVID = \"ppvid\"\nVLANS_SUBTREE = \"ieee-802-1-vlans\"\nNAME = \"name\"\nPORT_ID = \"port-id\"\nPORT_ID_TYPE = \"port-id-type\"\nSYSTEM_NAME = \"system-name\"\nSYSTEM_DESCRIPTION = \"system-description\"\nSYSTEM_CAPABILITIES = \"system-capabilities\"\nMFS_KEY = \"ieee-802-3-max-frame-size\"\nMAC_PHY_SUBTREE = \"ieee-802-3-mac-phy-conf\"\n\nLLDP_TEST_SYSTEM_NAME = \"Summit300-48\"\n\n\n@pytest.fixture(scope=\"module\")\ndef lldpiface_env():\n try:\n create_veth_pair(LLDPTEST, LLDPTEST_PEER, LLDP_TEST_NS)\n yield\n finally:\n remove_veth_pair(LLDPTEST, LLDP_TEST_NS)\n\n\n@pytest.fixture\ndef lldptest_up(lldpiface_env):\n with ifacelib.iface_up(LLDPTEST) as ifstate:\n yield ifstate\n\n\ndef test_enable_lldp(lldptest_up):\n with lldp_enabled(lldptest_up) as dstate:\n assertlib.assert_state_match(dstate)\n\n\ndef test_lldp_yaml(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n assert test_neighbor == yaml.safe_load(EXPECTED_LLDP_NEIGHBOR)\n\n\ndef test_lldp_system(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n seen = set()\n for tlv in test_neighbor:\n seen.add(tlv[LLDP.Neighbors.TLV_TYPE])\n if tlv[LLDP.Neighbors.TLV_TYPE] == 5:\n assert tlv[SYSTEM_NAME] == \"Summit300-48\"\n elif tlv[LLDP.Neighbors.TLV_TYPE] == 6:\n assert tlv[SYSTEM_DESCRIPTION] == LLDP_SYSTEM_DESC\n elif tlv[LLDP.Neighbors.TLV_TYPE] == 7:\n assert tlv[SYSTEM_CAPABILITIES] == LLDP_CAPS\n assert set([5, 6, 7]).issubset(seen)\n\n\ndef test_lldp_chassis(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 1, test_neighbor\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][CHASSIS_ID] == \"00:01:30:F9:AD:A0\"\n assert tlvs[0][CHASSIS_ID_TYPE] == 4\n\n\ndef test_lldp_management_addresses(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 8, test_neighbor\n )\n )\n assert len(tlvs) == 1\n test_mngt = tlvs[0][MANAGEMENT_ADDRESSES][0]\n assert test_mngt[ADDRESS] == \"00:01:30:F9:AD:A0\"\n assert test_mngt[ADDRESS_SUBTYPE] == \"MAC\"\n assert test_mngt[INTERFACE_NUMBER] == 1001\n assert test_mngt[INTERFACE_NUMBER_SUBTYPE] == 2\n\n\ndef test_lldp_macphy(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 127\n and tlv[LLDP.Neighbors.ORGANIZATION_CODE] == \"00:12:0f\"\n and tlv[LLDP.Neighbors.TLV_SUBTYPE] == 1,\n test_neighbor,\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][MAC_PHY_SUBTREE][AUTONEG] is True\n assert tlvs[0][MAC_PHY_SUBTREE][OPERATIONAL_MAU_TYPE] == 16\n assert tlvs[0][MAC_PHY_SUBTREE][PMD_AUTONEG_CAP] == 27648\n\n\ndef test_lldp_port(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 2, test_neighbor\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][PORT_ID] == \"1/1\"\n assert tlvs[0][PORT_ID_TYPE] == 5\n assert tlvs[0][LLDP.Neighbors.DESCRIPTION] == \"Interface name\"\n\n\ndef test_lldp_port_vlan(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 127\n and tlv[LLDP.Neighbors.ORGANIZATION_CODE] == \"00:80:c2\"\n and tlv[LLDP.Neighbors.TLV_SUBTYPE] == 2,\n test_neighbor,\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][PPVIDS_SUBTREE][0] == 0\n\n\ndef test_lldp_vlan(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 127\n and tlv[LLDP.Neighbors.ORGANIZATION_CODE] == \"00:80:c2\"\n and tlv[LLDP.Neighbors.TLV_SUBTYPE] == 3,\n test_neighbor,\n )\n )\n assert len(tlvs) == 1\n test_vlan = tlvs[0][VLANS_SUBTREE][0]\n assert test_vlan[NAME] == \"v2-0488-03-0505\"\n\n\ndef test_lldp_mfs(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 127\n and tlv[LLDP.Neighbors.ORGANIZATION_CODE] == \"00:12:0f\"\n and tlv[LLDP.Neighbors.TLV_SUBTYPE] == 4,\n test_neighbor,\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][MFS_KEY] == 1522\n\n\ndef test_lldp_empty_neighbors(lldptest_up):\n with lldp_enabled(lldptest_up):\n dstate = statelib.show_only((LLDPTEST,))\n lldp_state = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert not lldp_state.get(LLDP.NEIGHBORS_SUBTREE, [])\n\n\ndef test_show_running_config_has_no_lldp_neighbor(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n running_config = libnmstate.show_running_config()\n for iface_config in running_config[Interface.KEY]:\n if iface_config[Interface.NAME] == LLDPTEST:\n lldp_iface_config = iface_config\n break\n assert lldp_iface_config[LLDP.CONFIG_SUBTREE][LLDP.ENABLED]\n assert (\n LLDP.NEIGHBORS_SUBTREE\n not in lldp_iface_config[LLDP.CONFIG_SUBTREE]\n )\n\n\n@contextmanager\ndef lldp_enabled(ifstate):\n lldp_config = ifstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n lldp_config[LLDP.ENABLED] = True\n libnmstate.apply(ifstate)\n try:\n yield ifstate\n finally:\n lldp_config[LLDP.ENABLED] = False\n libnmstate.apply(ifstate)\n\n\ndef _send_lldp_packet():\n test_dir = os.path.dirname(os.path.realpath(__file__))\n cmdlib.exec_cmd(\n f\"ip netns exec {LLDP_TEST_NS} \"\n f\"tcpreplay --intf1={LLDPTEST_PEER} \"\n f\"{test_dir}/test_captures/lldp.pcap\".split(),\n check=True,\n )\n time.sleep(1)\n","repo_name":"nmstate/nmstate","sub_path":"tests/integration/lldp_test.py","file_name":"lldp_test.py","file_ext":"py","file_size_in_byte":10737,"program_lang":"python","lang":"en","doc_type":"code","stars":225,"dataset":"github-code","pt":"3"}
+{"seq_id":"73968011280","text":"## Feito por Léo\n## 20/08/22 Mod 29/08/22\n## Lib feita para Calculo Numérico\n\ndef GaussElimination(A):\n import numpy as np\n A = np.array(A)\n\n nl = len(A) \n\n nc = len(A[0])\n\n if nl>nc:\n exit(\"Nao posso resolver isso....\")\n\n for i in range(nl):\n if A[i][i] !=0:\n pivot = A[i][i]\n else:\n for j in range(i+1,nl):\n if A[j][i]:\n ans = list(A[i])\n A[i] = -A[j]\n A[j] = np.array(ans)\n pivot = A[i][i]\n break\n\n for j in range(i+1,nl):\n if not A[j][i]:\n continue\n else:\n A[j] = A[j]*pivot - A[j][i]*A[i]\n \n if A[nl-1][nl-1] == 0:\n if A[nl-1][nc-1] == 0:\n exit('Esse sistema possui raizes indeterminadas!!')\n\n else:\n exit('Esse sistema não possui raizes!!')\n \n #implementação da parte que pegar as raizes.\n\n nr = list()\n \n for i in range(nl):\n sm = 0\n rt = 0\n for j in range(i):\n sm += nr[j]*A[nl-1-i][nc-2-j]\n\n if A[nl-1-i][nl-1-i]:\n rt = (A[nl-1-i][nc-1] - sm)/A[nl-1-i][nl-1-i]\n\n nr.append(rt)\n\n nr.reverse()\n\n return nr\n\ndef LUdecomposition(A):\n import numpy as np\n \n A = np.array(A) \n nl = np.shape(A)[0]\n L = np.eye(nl)\n \n for k in np.arange(nl):\n L[k+1:nl,k] = A[k+1:nl,k]/A[k,k] \n for l in np.arange(k+1,nl):\n A[l,:] = A[l,:] - np.dot((L[l,k]),A[k,:])\n \n U = A\n \n return [L, U]\n\n\ndef gaussjacobi(A,b,x0,kmax):\n import numpy as np\n for i,line in enumerate(A):\n b[i] /= line[i]\n line /= line[i]\n M = A - np.eye(len(A))\n print(M)\n print(b)\n for i in range(kmax):\n x0 = b - M@x0\n if np.linalg.norm(x0) < 1e-3:\n break\n return [x0,kmax]\n\n\n\n","repo_name":"engleovictor/calpy","sub_path":"linsys.py","file_name":"linsys.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17112965576","text":"from collections import deque\nimport bisect\n\nn,k = map(int, input().split())\ncards = []\nfor _ in range(n):\n s = input()\n card = \"\"\n for i in range(k):\n if s[i] == \"E\":\n card += \"1\"\n elif s[i] == \"S\":\n card += \"2\"\n else:\n card += \"3\"\n cards.append(int(card))\n\ncards.sort()\n\nans = 0\n\ndq = deque([])\n\nfor i in range(n-2):\n dq.append(str(cards[i]))\n j = i+1\n while j < n-1:\n dq.append(str(cards[j]))\n tmps = \"\"\n for kk in range(k):\n if dq[0][kk] == dq[1][kk]:\n tmps += dq[0][kk]\n else:\n if dq[0][kk] != \"1\" and dq[1][kk] != \"1\":\n tmps += \"1\"\n elif dq[0][kk] != \"2\" and dq[1][kk] != \"2\":\n tmps += \"2\"\n else:\n tmps += \"3\"\n ind = bisect.bisect_left(cards, int(tmps))\n if ind < n and 0 <= ind:\n if cards[ind] == int(tmps):\n ans += 1\n d = dq.pop()\n j += 1\n d = dq.pop()\n\nprint(ans)","repo_name":"outk/competitive-programming","sub_path":"codeforces/Python/612div2b.py","file_name":"612div2b.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72342923921","text":"# use activation function\n\n# https://blog.csdn.net/u011630575/article/details/78063641\n\n# 可视化 tensorboard --logdir='logs/'\n\nimport tensorflow as tf\nimport numpy as np\n\n\ndef add_layer(intputs, in_size, out_size, n_layer, activation_function=None): # add new laryer\n layer_name = \"layer%s\" % n_layer\n with tf.name_scope(layer_name):\n\n with tf.name_scope(\"Weight\"):\n Weight = tf.Variable(tf.random_normal([in_size, out_size]), name=\"W\")\n tf.summary.histogram(layer_name+'/Weights', Weight)\n\n with tf.name_scope(\"biases\"):\n biases = tf.Variable(tf.zeros([1, out_size]), name=\"b\") + 0.1 # 推荐大于0\n tf.summary.histogram(layer_name + '/biases', biases)\n\n with tf.name_scope(\"Wx_plus_b\"):\n Wx_plus_b = tf.matmul(intputs, Weight) + biases\n\n if activation_function is None:\n outputs = Wx_plus_b\n else:\n outputs = activation_function(Wx_plus_b)\n tf.summary.histogram(layer_name + '/outputs', outputs)\n\n return outputs\n\n\nx_data = np.linspace(-1, 1, 300)[:, np.newaxis] # 创建等差数列\nnoise = np.random.normal(0, 0.05, x_data.shape) # 模拟噪点\ny_data = np.square(x_data) - 0.5 + noise # 创建结果数据集\n\nwith tf.name_scope(\"inputs\"):\n xs = tf.placeholder(tf.float32, [None, 1], name=\"input_x\") # 创建x_data占位符\n ys = tf.placeholder(tf.float32, [None, 1], name=\"input_y\") # 创建y_data占位符\n\nlayer1 = add_layer(xs, 1, 10, 1, activation_function=tf.nn.relu) # 创建输入层\nprediction = add_layer(layer1, 10, 1, 2, activation_function=None) # 创建输出层\n\nwith tf.name_scope(\"loss\"):\n loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1])) # 计算损失\n tf.summary.scalar('loss', loss)\n\nwith tf.name_scope(\"train\"):\n train_setps = tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 最小化loss\n\nwith tf.Session() as sess:\n merged = tf.summary.merge_all()\n writer = tf.summary.FileWriter(\"logs/\", sess.graph) # 将整个可视化文件写入本地\n\n sess.run(tf.global_variables_initializer())\n\n for step in range(1000):\n sess.run(train_setps, feed_dict={xs: x_data, ys: y_data})\n if step % 50 == 0:\n result = sess.run(merged, feed_dict={xs: x_data, ys: y_data})\n writer.add_summary(result, step)\n print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))\n","repo_name":"AndyandViky/ML-study","sub_path":"tensorflow/5-activation-function.py","file_name":"5-activation-function.py","file_ext":"py","file_size_in_byte":2446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28195157633","text":"def load_input(file):\n with open(file, 'r') as lines:\n return [str(line).strip() for line in lines]\n\ndef get_image(input):\n image = []\n for line in input[2:]:\n image.append([0 if x==\".\" else 1 for x in line])\n return image\n\ndef add_frame(image, background):\n framed = []\n width = len(image)+4\n framed.extend([[background]*width for i in range(2)])\n for line in image:\n temp = [background]*2\n temp.extend(line)\n temp.extend([background]*2)\n framed.append(temp)\n framed.extend([[background]*width for i in range(2)])\n return framed\n\ndef enhance(enhancement, image, background):\n filtered = [[0]*(len(image[0])-2) for i in range(len(image)-2)]\n for y in range(1, len(image)-1):\n for x in range(1, len(image[0])-1):\n square = []\n for a in range(-1,2):\n for b in range(-1,2):\n square.append(image[y+a][x+b])\n i = int(\"\".join(str(a) for a in square), 2)\n filtered[y-1][x-1] = 0 if enhancement[i] == \".\" else 1\n\n i = int(\"\".join(str(background)*9), 2)\n background = 0 if enhancement[i]==\".\" else 1\n return filtered, background\n\ndef main():\n #load file\n input = load_input(\"input.txt\")\n enhancement = input[0]\n image = get_image(input)\n background = 0\n\n #part 1\n filtered = image\n for _ in range(2):\n filtered = add_frame(filtered, background)\n filtered, background = enhance(enhancement, filtered, background)\n total = 0\n for y in range(len(filtered)):\n for x in range(len(filtered[0])):\n if filtered[y][x]==1:\n total+=1\n print(\"Part 1: \" + str(total))\n\n #part 2\n filtered = image\n for _ in range(50):\n filtered = add_frame(filtered, background)\n filtered, background = enhance(enhancement, filtered, background)\n total = 0\n for y in range(len(filtered)):\n for x in range(len(filtered[0])):\n if filtered[y][x]==1:\n total+=1\n print(\"Part 2: \" + str(total))\n \n\nif __name__ == '__main__':\n main()","repo_name":"MartynaBorak/AOC_2021","sub_path":"Day 20/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12079569383","text":"from time import time\nimport os\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\n\nfrom loss.Dice_loss import DiceLoss\nfrom net.YNet_kernel3_dropout import net\nfrom dataset.stage1_dataset import train_fix_ds\n\nif not os.path.exists(r'/openbayes/home/Module/'):\n os.makedirs(r'/openbayes/home/Module/')\n\ncudnn.benchmark = True\nEpoch = 1000\nleaing_rate_base = 1e-5\n\nbatch_size = 1\nnum_workers = 2\npin_memory = True\n\nnet = torch.nn.DataParallel(net).cuda()\n\n# 定义数据加载\ntrain_dl = DataLoader(train_fix_ds, batch_size, True, num_workers=num_workers, pin_memory=pin_memory)\n\n# 定义损失函数\nloss_func = DiceLoss()\n\n# 定义优化器\nopt = torch.optim.Adam(net.parameters(), lr=leaing_rate_base)\n\n# 学习率衰减\nlr_decay = torch.optim.lr_scheduler.MultiStepLR(opt, [400])\n\n# 训练网络\nstart = time()\n\nfor epoch in range(Epoch):\n\n lr_decay.step()\n\n loss_step = []\n\n for step, (ct, seg, pet) in enumerate(train_dl):\n\n ct = ct.cuda()\n seg = seg.cuda()\n pet = pet.cuda()\n\n # 如果一个正样本都没有就直接结束\n if torch.numel(seg[seg > 0]) == 0:\n continue\n\n ct = Variable(ct)\n seg = Variable(seg)\n pet = Variable(pet)\n\n outputs, outputs_temp = net(ct, pet)\n loss = loss_func(outputs, outputs_temp, seg)\n\n opt.zero_grad()\n loss.backward()\n opt.step()\n\n # xzl\n loss_step.append(loss.item())\n\n loss_mean = (sum(loss_step)/len(loss_step))\n\n print(epoch,loss_mean)\n\n # if step % 20 is 0:\n # print('epoch:{}, step:{}, loss:{:.3f}, time:{:.3f} min'\n # .format(epoch, step, loss.item(), (time() - start) / 60))\n\n # 每十个个epoch保存一次模型参数\n if epoch >= 150:\n torch.save(net.state_dict(), '/openbayes/home/Module/net{}.pth'.format(epoch))\n","repo_name":"ETVP/Multi-Modal-Co-learning","sub_path":"ours/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1925,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"}
+{"seq_id":"22753459721","text":"import typing\n\nimport pytest\nfrom ellar.socket_io import TestGateway\nfrom ellar.socket_io.testing.module import RunWithServerContext\n\nfrom .sample import EventGateway, GatewayOthers, GatewayWithGuards\n\n# @pytest.fixture()\n# from asgi_lifespan import LifespanManager\n# from httpx import AsyncClient\n# async def client() -> AsyncIterator[AsyncClient]:\n# async with LifespanManager(app):\n# async with AsyncClient(app=app, base_url=\"http://localhost\") as client:\n# yield client\n\n\n@pytest.mark.asyncio\nclass TestEventGateway:\n test_client = TestGateway.create_test_module(controllers=[EventGateway])\n\n async def test_socket_connection_work(self):\n my_response_message = []\n connected_called = False\n disconnected_called = False\n\n async with self.test_client.run_with_server() as ctx:\n\n @ctx.sio.event\n async def my_response(message):\n my_response_message.append(message)\n\n @ctx.sio.event\n async def disconnect():\n nonlocal disconnected_called\n disconnected_called = True\n\n @ctx.sio.event\n async def connect(*args):\n nonlocal connected_called\n await ctx.sio.emit(\"my_event\", {\"data\": \"I'm connected!\"})\n connected_called = True\n\n await ctx.connect(socketio_path=\"/ws/\")\n await ctx.wait()\n\n assert len(my_response_message) == 2\n assert my_response_message == [\n {\"data\": \"Connected\", \"count\": 0},\n {\"data\": \"I'm connected!\"},\n ]\n assert disconnected_called and connected_called\n\n async def test_broadcast_work(self):\n sio_1_response_message = []\n sio_2_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n ctx_2 = ctx.new_socket_client_context()\n\n @ctx.sio.on(\"my_response\")\n async def my_response_case_1(message):\n sio_1_response_message.append(message)\n\n @ctx_2.sio.on(\"my_response\")\n async def my_response_case_2(message):\n sio_2_response_message.append(message)\n\n await ctx.connect(socketio_path=\"/ws/\")\n await ctx_2.connect(socketio_path=\"/ws/\")\n\n await ctx.sio.emit(\n \"my_broadcast_event\", {\"data\": \"Testing Broadcast\"}\n ) # both sio_1 and sio_2 would receive this message\n\n await ctx.wait()\n await ctx_2.wait()\n\n assert len(sio_1_response_message) == 2\n assert sio_1_response_message == [\n {\"data\": \"Connected\", \"count\": 0},\n {\"data\": \"Testing Broadcast\"},\n ]\n\n assert len(sio_2_response_message) == 2\n assert sio_2_response_message == [\n {\"data\": \"Connected\", \"count\": 0},\n {\"data\": \"Testing Broadcast\"},\n ]\n\n\n@pytest.mark.asyncio\nclass TestGatewayWithGuards:\n test_client = TestGateway.create_test_module(controllers=[GatewayWithGuards])\n\n async def test_socket_connection_work(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n\n @ctx.sio.event\n async def my_response(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecret\"}\n )\n await ctx.sio.emit(\"my_event\", {\"data\": \"Testing Broadcast\"})\n await ctx.wait()\n\n assert my_response_message == [\n {\"auth-key\": \"supersecret\", \"data\": \"Testing Broadcast\"}\n ]\n\n async def test_event_with_header_work(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n\n @ctx.sio.event\n async def my_response(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecret\"}\n )\n await ctx.sio.emit(\"my_event_header\", {\"data\": \"Testing Broadcast\"})\n await ctx.wait()\n\n assert my_response_message == [\n {\"data\": \"Testing Broadcast\", \"x_auth_key\": \"supersecret\"}\n ]\n\n async def test_event_with_plain_response(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n\n @ctx.sio.on(\"my_plain_response\")\n async def message_receive(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecret\"}\n )\n await ctx.sio.emit(\"my_plain_response\", {\"data\": \"Testing Broadcast\"})\n await ctx.wait()\n\n assert my_response_message == [\n {\"data\": \"Testing Broadcast\", \"x_auth_key\": \"supersecret\"}\n ]\n\n async def test_failed_to_connect(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n ctx = typing.cast(RunWithServerContext, ctx)\n\n @ctx.sio.on(\"error\")\n async def error(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecre\"}\n )\n await ctx.sio.emit(\"my_event_header\", {\"data\": \"Testing Broadcast\"})\n await ctx.wait()\n\n assert my_response_message == [{\"code\": 1011, \"reason\": \"Authorization Failed\"}]\n\n async def test_failed_process_message_sent(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n ctx = typing.cast(RunWithServerContext, ctx)\n\n @ctx.sio.on(\"error\")\n async def error(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecret\"}\n )\n await ctx.sio.emit(\n \"my_event_header\", {\"doesnt_exist_key\": \"Testing Broadcast\"}\n )\n await ctx.wait()\n\n assert my_response_message == [\n {\n \"code\": 1007,\n \"reason\": [\n {\n \"loc\": [\"body\", \"data\"],\n \"msg\": \"none is not an allowed value\",\n \"type\": \"type_error.none.not_allowed\",\n }\n ],\n }\n ]\n\n\n@pytest.mark.asyncio\nclass TestGatewayExceptions:\n @pytest.mark.parametrize(\n \"debug, result\",\n [\n (\n True,\n [\n {\"code\": 1011, \"reason\": \"I dont have anything to run.\"},\n {\"code\": 1009, \"reason\": \"Message is too big\"},\n ],\n ),\n (\n False,\n [\n {\"code\": 1011, \"reason\": \"Something went wrong\"},\n {\"code\": 1009, \"reason\": \"Message is too big\"},\n ],\n ),\n ],\n )\n async def test_exception_handling_works_debug_true_or_false(self, debug, result):\n test_client = TestGateway.create_test_module(\n controllers=[GatewayOthers], config_module={\"DEBUG\": debug}\n )\n my_response_message = []\n\n async with test_client.run_with_server() as ctx:\n ctx = typing.cast(RunWithServerContext, ctx)\n ctx2 = ctx.new_socket_client_context()\n\n @ctx.sio.on(\"error\")\n async def error(message):\n my_response_message.append(message)\n\n @ctx2.sio.on(\"error\")\n async def error_2(message):\n my_response_message.append(message)\n\n await ctx.connect(socketio_path=\"/ws-others/\")\n await ctx2.connect(socketio_path=\"/ws-others/\")\n\n await ctx.sio.emit(\"my_event\", {})\n await ctx.wait()\n\n await ctx2.sio.emit(\"my_event_raise\", {})\n await ctx2.wait()\n\n assert my_response_message == result\n\n async def test_message_with_extra_args(self):\n test_client = TestGateway.create_test_module(controllers=[GatewayOthers])\n my_response_message = []\n\n async with test_client.run_with_server() as ctx:\n ctx = typing.cast(RunWithServerContext, ctx)\n\n @ctx.sio.on(\"error\")\n async def error(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-others/?query1=some-query1&query2=some-query2&?\"\n )\n\n await ctx.sio.emit(\"extra_args\", {})\n await ctx.wait()\n\n assert my_response_message == [\n {\"code\": 1009, \"reason\": {\"query1\": \"some-query1\", \"query2\": \"some-query2\"}}\n ]\n","repo_name":"eadwinCode/ellar","sub_path":"tests/test_socket_io/test_operation.py","file_name":"test_operation.py","file_ext":"py","file_size_in_byte":8925,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"3"}
+{"seq_id":"11558900976","text":"import asyncio\nimport contextlib\nimport functools\nimport logging\nimport os\nimport time\nimport unittest\n\n\nclass AsyncioTestCase(unittest.TestCase):\n \"\"\"\n Base class for tests that sets up an isolated event loop for each test.\n\n \"\"\"\n\n def __init_subclass__(cls, **kwargs):\n \"\"\"\n Convert test coroutines to test functions.\n\n This supports asychronous tests transparently.\n\n \"\"\"\n super().__init_subclass__(**kwargs)\n for name in unittest.defaultTestLoader.getTestCaseNames(cls):\n test = getattr(cls, name)\n if asyncio.iscoroutinefunction(test):\n setattr(cls, name, cls.convert_async_to_sync(test))\n\n @staticmethod\n def convert_async_to_sync(test):\n \"\"\"\n Convert a test coroutine to a test function.\n\n \"\"\"\n\n @functools.wraps(test)\n def test_func(self, *args, **kwargs):\n return self.loop.run_until_complete(test(self, *args, **kwargs))\n\n return test_func\n\n def setUp(self):\n super().setUp()\n self.loop = asyncio.new_event_loop()\n asyncio.set_event_loop(self.loop)\n\n def tearDown(self):\n self.loop.close()\n super().tearDown()\n\n def run_loop_once(self):\n # Process callbacks scheduled with call_soon by appending a callback\n # to stop the event loop then running it until it hits that callback.\n self.loop.call_soon(self.loop.stop)\n self.loop.run_forever()\n\n @contextlib.contextmanager\n def assertNoLogs(self, logger=\"websockets\", level=logging.ERROR):\n \"\"\"\n No message is logged on the given logger with at least the given level.\n\n \"\"\"\n with self.assertLogs(logger, level) as logs:\n # We want to test that no log message is emitted\n # but assertLogs expects at least one log message.\n logging.getLogger(logger).log(level, \"dummy\")\n yield\n\n level_name = logging.getLevelName(level)\n self.assertEqual(logs.output, [f\"{level_name}:{logger}:dummy\"])\n\n def assertDeprecationWarnings(self, recorded_warnings, expected_warnings):\n \"\"\"\n Check recorded deprecation warnings match a list of expected messages.\n\n \"\"\"\n self.assertEqual(len(recorded_warnings), len(expected_warnings))\n for recorded, expected in zip(recorded_warnings, expected_warnings):\n actual = recorded.message\n self.assertEqual(str(actual), expected)\n self.assertEqual(type(actual), DeprecationWarning)\n\n\n# Unit for timeouts. May be increased on slow machines by setting the\n# WEBSOCKETS_TESTS_TIMEOUT_FACTOR environment variable.\nMS = 0.001 * int(os.environ.get(\"WEBSOCKETS_TESTS_TIMEOUT_FACTOR\", 1))\n\n# asyncio's debug mode has a 10x performance penalty for this test suite.\nif os.environ.get(\"PYTHONASYNCIODEBUG\"): # pragma: no cover\n MS *= 10\n\n# Ensure that timeouts are larger than the clock's resolution (for Windows).\nMS = max(MS, 2.5 * time.get_clock_info(\"monotonic\").resolution)\n","repo_name":"WebKit/WebKit","sub_path":"LayoutTests/imported/w3c/web-platform-tests/tools/third_party/websockets/tests/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3031,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"30282379670","text":"from typing import Optional\n\nfrom django_tgbot.state_manager import message_types\nfrom . import BasicType\n\n\nclass Message(BasicType):\n \"\"\"\n Represents Telegram Message type:\n https://core.telegram.org/bots/api#message\n \"\"\"\n fields = {\n 'message_id': str,\n 'date': str,\n 'forward_from_message_id': str,\n 'forward_signature': str,\n 'forward_sender_name': str,\n 'forward_date': str,\n 'edit_date': str,\n 'media_group_id': str,\n 'author_signature': str,\n 'text': str,\n 'caption': str,\n 'new_chat_title': str,\n 'delete_chat_photo': BasicType.bool_interpreter,\n 'group_chat_created': BasicType.bool_interpreter,\n 'supergroup_chat_created': BasicType.bool_interpreter,\n 'channel_chat_created': BasicType.bool_interpreter,\n 'migrate_to_chat_id': str,\n 'migrate_from_chat_id': str,\n 'connected_website': str,\n }\n\n def __init__(self, obj=None):\n super(Message, self).__init__(obj)\n\n def type(self):\n field_checks = {\n 'text': message_types.Text,\n 'audio': message_types.Audio,\n 'document': message_types.Document,\n 'animation': message_types.Animation,\n 'game': message_types.Game,\n 'photo': message_types.Photo,\n 'sticker': message_types.Sticker,\n 'video': message_types.Video,\n 'voice': message_types.Voice,\n 'video_note': message_types.VideoNote,\n 'contact': message_types.Contact,\n 'dice': message_types.Dice,\n 'location': message_types.Location,\n 'venue': message_types.Venue,\n 'poll': message_types.Poll,\n 'new_chat_members': message_types.NewChatMembers,\n 'left_chat_member': message_types.LeftChatMember,\n 'new_chat_title': message_types.NewChatTitle,\n 'new_chat_photo': message_types.NewChatPhoto,\n 'delete_chat_photo': message_types.DeleteChatPhoto,\n 'group_chat_created': message_types.GroupChatCreated,\n 'supergroup_chat_created': message_types.SupergroupChatCreated,\n 'channel_chat_created': message_types.ChannelChatCreated,\n 'migrate_to_chat_id': message_types.MigrateToChatId,\n 'migrate_from_chat_id': message_types.MigrateFromChatId,\n 'pinned_message': message_types.PinnedMessage,\n 'invoice': message_types.Invoice,\n 'successful_payment': message_types.SuccessfulPayment,\n 'passport_data': message_types.PassportData\n }\n\n for key in field_checks.keys():\n if getattr(self, key, None) is not None:\n return field_checks[key]\n return None\n\n def get_message_id(self) -> str:\n return getattr(self, 'message_id', None)\n\n def get_chat(self):\n return getattr(self, 'chat', None)\n\n def get_user(self):\n return getattr(self, 'from', None)\n\n def get_from(self):\n return self.get_user()\n\n def get_reply_to_message(self):\n return getattr(self, 'reply_to_message', None)\n\n def get_text(self) -> Optional[str]:\n return getattr(self, 'text', None)\n\n def get_caption(self) -> Optional[str]:\n return getattr(self, 'caption', None)\n\n def get_reply_markup(self):\n return getattr(self, 'reply_markup', None)\n \n\n def get_photo(self):\n return getattr(self, 'photo', None)\n\n\n# Placed here to avoid import cycles\nfrom . import user, chat, messageentity, audio, document, animation, game, photosize, \\\n inlinekeyboardmarkup, passportdata, successfulpayment, invoice, poll, \\\n venue, location, contact, videonote, voice, video, sticker, dice\n\n\nMessage.fields.update({\n 'reply_to_message': Message,\n 'via_bot': user.User,\n 'pinned_message': Message,\n 'from': user.User,\n 'chat': chat.Chat,\n 'forward_from': user.User,\n 'forward_from_chat': chat.Chat,\n 'entities': {\n 'class': messageentity.MessageEntity,\n 'array': True\n },\n 'caption_entities': {\n 'class': messageentity.MessageEntity,\n 'array': True\n },\n 'audio': audio.Audio,\n 'document': document.Document,\n 'animation': animation.Animation,\n 'game': game.Game,\n 'photo': {\n 'class': photosize.PhotoSize,\n 'array': True\n },\n 'sticker': sticker.Sticker,\n 'video': video.Video,\n 'voice': voice.Voice,\n 'video_note': videonote.VideoNote,\n 'contact': contact.Contact,\n 'dice': dice.Dice,\n 'location': location.Location,\n 'venue': venue.Venue,\n 'poll': poll.Poll,\n 'new_chat_members': {\n 'class': user.User,\n 'array': True\n },\n 'left_chat_member': user.User,\n 'new_chat_photo': {\n 'class': photosize.PhotoSize,\n 'array': True\n },\n 'invoice': invoice.Invoice,\n 'successful_payment': successfulpayment.SuccessfulPayment,\n 'passport_data': passportdata.PassportData,\n 'reply_markup': inlinekeyboardmarkup.InlineKeyboardMarkup\n})\n","repo_name":"Ali-Toosi/django-tgbot","sub_path":"django_tgbot/types/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":5067,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"3"}
+{"seq_id":"16602936191","text":"def solution(citations):\n\n # citations 내림차순으로 정렬\n citations.sort(reverse = True)\n \n for i in range(len(citations)):\n if citations[i] < i + 1: # 논문의 인용수가 논문수보다 작아질 때\n return i\n elif citations[i] == i + 1: # 논문의 인용수와 논문수가 같을 때\n return i + 1\n \n return len(citations) # 논문의 인용수와 논문수가 같거나 작아지지 않을 때\n","repo_name":"eeeeeddy/Coding_Test_Practice","sub_path":"프로그래머스/lv2/42747. H－Index/H－Index.py","file_name":"H－Index.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8026201987","text":"import cv2\nimport math\nimport numpy as np\nimport pywt\nimport bandelette_1\nimport bandelette_inverse\nimport huff\nimport algo_RLE\nimport quantif_scalaire\nimport matplotlib.pyplot as plt\n\ndef afficher(img) :\n i = 0\n shape = img.shape\n while i < shape[0] :\n j = 0\n while j < shape[1] :\n print(\"img[\",i,\"]\",\"[\",j,\"]\",img[i][j])\n if img[i][j] != 0 :\n return False\n j += 1\n i += 1\n return True\ndef calcule_PSNR(max,mse) :\n pnsr = 10 * math.log(math.pow(max,2)/mse,10)\n return pnsr\ndef s(i1,i2) :\n i=0\n val = 0\n shape = i1.shape\n while i < abs(shape[0]) :\n j = 0\n while j < abs(shape[1]) :\n val += pow(i1[i][j]-i2[i][j],2)\n j += 1\n i += 1\n return val\ndef calcule_mse(img1,img2,n):\n mse = s(img1,img2)/n\n return mse\ndef calcule_taille_rle(list):\n taille= len(list) * 8\n return taille\n\ndef array_list(mat,l,c):\n i=0\n list=[]\n while i bool:\n try:\n self._sm_client.describe_endpoint(EndpointName=endpoint_name)\n except ClientError:\n return False\n return True\n\n def connect_to_endpoint(self, endpoint_name: str) -> None:\n if not self._does_endpoint_exist(endpoint_name):\n raise Exception(f\"Endpoint {endpoint_name} does not exist.\")\n self._endpoint_name = endpoint_name\n self._variant_name = \"AllTraffic\"\n self._resource_id = \"endpoint/{}/variant/{}\".format(\n self._endpoint_name, self._variant_name\n )\n\n def create_endpoint(\n self,\n arn: str,\n endpoint_name: str,\n instance_type: str,\n n_instances: int = 1,\n recreate: bool = False,\n role: Optional[str] = None,\n ) -> None:\n import sagemaker\n\n if role is None:\n role = get_role()\n\n if self._does_endpoint_exist(endpoint_name):\n if recreate:\n self.connect_to_endpoint(endpoint_name)\n self.delete_endpoint()\n else:\n raise Exception(\n f\"Endpoint {endpoint_name} already exists and recreate={recreate}.\"\n )\n\n model = sagemaker.ModelPackage(\n role=role,\n model_data=None,\n sagemaker_session=self._sm_session, # makes sure the right region is used\n model_package_arn=arn,\n )\n\n validation_params = dict(\n model_data_download_timeout=2400,\n container_startup_health_check_timeout=2400,\n )\n\n try:\n model.deploy(\n n_instances,\n instance_type,\n endpoint_name=endpoint_name,\n **validation_params,\n )\n except ParamValidationError:\n model.deploy(n_instances, instance_type, endpoint_name=endpoint_name)\n\n self.connect_to_endpoint(endpoint_name)\n\n def register_scalable_target(self, max_capacity, min_capacity=1):\n return self._aas_client.register_scalable_target(\n ServiceNamespace=\"sagemaker\",\n ResourceId=self._resource_id,\n ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n MinCapacity=min_capacity,\n MaxCapacity=max_capacity,\n )\n\n def set_step_autoscaling(self, policy_name, policy_configuration: Dict):\n return self._aas_client.put_scaling_policy(\n PolicyName=policy_name,\n ServiceNamespace=\"sagemaker\",\n ResourceId=self._resource_id,\n ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n PolicyType=\"StepScaling\",\n StepScalingPolicyConfiguration=policy_configuration,\n )\n\n def set_metric_alarm(self, policy_arn, **kwargs):\n kwargs[\"Dimensions\"] = [\n {\"Name\": \"EndpointName\", \"Value\": self._endpoint_name},\n {\"Name\": \"VariantName\", \"Value\": self._variant_name},\n ]\n kwargs[\"AlarmActions\"] = [policy_arn]\n\n return self._cw_client.put_metric_alarm(**kwargs)\n\n def create_transform_job(\n self,\n arn: str,\n n_instances: int,\n instance_type: str,\n input_path: str,\n output_path: str,\n role: Optional[str] = None,\n wait: bool = True,\n logs: bool = True,\n ):\n import sagemaker\n\n if role is None:\n role = get_role()\n\n model = sagemaker.ModelPackage(\n name=arn.split(\"/\")[-1],\n role=role,\n model_data=None,\n sagemaker_session=self._sm_session, # makes sure the right region is used\n model_package_arn=arn,\n )\n\n uid = uuid.uuid4().hex\n # if input path is a local path, upload to default s3 bucket\n if not input_path.startswith(\"s3://\") and os.path.exists(input_path):\n csv_path_with_ids = prefix_csv_with_ids(input_path=input_path)\n s3_input_path = self._sm_session.upload_data(\n path=csv_path_with_ids, key_prefix=f\"input/{uid}\"\n )\n print(f\"Input file uploaded to {s3_input_path}.\")\n\n download_output_path = None\n # if output path is a local path, change to default s3 bucket,\n # add job name and random uuid\n if not output_path.startswith(\"s3://\"):\n download_output_path = output_path\n output_path = os.path.join(\n \"s3://\",\n self._sm_session.default_bucket(),\n \"output\",\n model.name,\n uid,\n )\n\n transformer = model.transformer(\n instance_count=n_instances,\n instance_type=instance_type,\n output_path=output_path,\n strategy=\"MultiRecord\",\n )\n\n transformer.transform(\n data=s3_input_path,\n content_type=\"text/csv\",\n split_type=\"Line\",\n wait=wait,\n logs=logs,\n )\n\n if download_output_path is not None:\n download_s3_folder(\n path=output_path,\n local_dir=download_output_path,\n )\n print(f\"Output downloaded to {download_output_path}.\")\n\n def _invoke_endpoint(self, texts: Union[str, List[str]]):\n if self._endpoint_name is None:\n raise Exception(\n \"No endpoint connected. \" \"Run connect_to_endpoint() first.\"\n )\n\n if isinstance(texts, str):\n data = json.dumps({\"data\": {\"text\": texts}})\n else:\n data = json.dumps({\"data\": [{\"text\": text} for text in texts]})\n\n return self._sm_runtime_client.invoke_endpoint(\n EndpointName=self._endpoint_name,\n ContentType=\"application/json\",\n Body=data,\n )\n\n def embed(self, texts: Union[str, List[str]]):\n response = self._invoke_endpoint(texts)\n resp = json.loads(response[\"Body\"].read().decode())\n return resp[\"data\"]\n\n def delete_endpoint(self) -> None:\n if self._endpoint_name is None:\n raise Exception(\"No endpoint connected.\")\n try:\n self._sm_client.delete_endpoint(EndpointName=self._endpoint_name)\n except ClientError:\n print(\"Endpoint not found, skipping deletion.\")\n\n try:\n self._sm_client.delete_endpoint_config(\n EndpointConfigName=self._endpoint_name\n )\n except ClientError:\n print(\"Endpoint config not found, skipping deletion.\")\n\n def close(self) -> None:\n try:\n self._sm_runtime_client.close()\n self._sm_client.close()\n except AttributeError:\n print(\n \"SageMaker client could not be closed. \"\n \"This might be because you are using an old version of SageMaker.\"\n )\n raise\n","repo_name":"jina-ai/jina-sagemaker","sub_path":"jina_sagemaker/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":7989,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71405486483","text":"import numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import hsv_to_rgb\nfrom celluloid import Camera\nfrom ast import literal_eval\nfrom matplotlib.ticker import (MultipleLocator, FormatStrFormatter, AutoMinorLocator)\nimport matplotlib.ticker as ticker\nimport nibabel as nib\nimport matplotlib as mpl\n\nimport argparse\n\nif __name__ == '__main__':\n\tparser = argparse. ArgumentParser ()\n\trequiredNamed = parser.add_argument_group('Required arguments')\n\trequiredNamed. add_argument ('--input_dir','-in', help = \"path of input directory\")\n\targs = parser.parse_args()\n\n\t# READ PREDICTIONS\n\tdf = pd. read_csv (\"%s/Outputs/predictions.csv\"%args.input_dir, sep = ';', header = 0, index_col = 0)\n\tregions = list (df. columns)\n\n\tindex = df. index\n\ttitle = 'Brain activity predictions'\n\n\tROIS_desc = pd. read_csv (\"brain_areas.tsv\", sep = '\\t', header = 0)\n\n\n\tcolors_of_rois = []\n\n# 1000 distinct colors:\n\tcolors = [hsv_to_rgb([(i * 0.618033988749895) % 1.0, 1, 1]) for i in range(1000)]\n\n\tfor k in range (1, len (regions) + 1):\n\t\tcolors_of_rois. append (colors[k])\n\n\t# SAVE LEGENDS SEPARATELY\n\tfig = plt.figure()\n\tfig_legend = plt.figure(figsize=(6, 1))\n\tax = fig.add_subplot(111)\n\tbars = ax.bar(range(len (regions)), range(len (regions)), color=colors_of_rois, label=regions)\n\tfig_legend.legend(bars.get_children(), regions, loc='center', frameon=False, ncol = 3)\n\tfig_legend. savefig (\"%s/Outputs/legend.png\"%args.input_dir)\n\tplt.clf ()\n\tplt. cla ()\n\tplt. close ()\n\t#exit (1)\n\n\tmpl.style.use('seaborn')\n\t# SAVE PREDICTIONS AS A VIDEO\n\tfig, ax = plt.subplots (nrows = len (regions), ncols = 1, figsize=(10,5.6), sharex=True)\n\t#fig.text(0.5, 0.04, 'Time (s)', ha='center')\n\tfig.text(0.5, 0.04, 'Time (s)', ha='center')\n\tfig.subplots_adjust(\n\t top=0.981,\n\t bottom=0.09,\n\t left=0.03,\n\t right=0.8,\n\t hspace=0.2,\n\t wspace=0.2\n\t)\n\t#plt.tight_layout(pad=0.05)\n\t#fig.text(0.04, 0.5, title, va='center', rotation='vertical')\n\n\tcamera = Camera(fig)\n\tlegend_image = plt. figure (figsize = (3,5))\n\n\n\tfor j in range (len (regions)):\n\t\tax [j]. set_xlim (np.min (index), np. max (index) + 1)\n\t\tax [j]. xaxis.set_minor_locator(MultipleLocator(5))\n\t\tax [j]. set_ylim (0, 1.1)\n\t\tax [j]. yaxis. set_major_locator (ticker. MultipleLocator (1))\n\t\tax [j]. set_yticklabels([])\n\n\tfor i in range (1,len (index)):\n\t\tfor j in range (len (regions)):\n\t\t\tax[j]. plot (index [:i], df. iloc [:i, j], linewidth = 2, color = colors_of_rois [j], alpha = 1, label = regions[j])\n\t\t\tax[j]. legend(['%s'%regions[j]], bbox_to_anchor=(1, 1), loc=2, borderaxespad=0.05, markerscale = 0.5, handlelength=0.5, fontsize = 10)\n\t\tcamera. snap()\n\n\n\tanim = camera.animate (repeat = False, interval = 1205)\n\n\tanim.save(\"%s/Outputs/predictions_video.mp4\"%args.input_dir, extra_args=['-vcodec', 'h264', '-pix_fmt', 'yuv420p'])\n\tfig. savefig (\"%s/Outputs/predictions.png\"%args.input_dir)\n","repo_name":"Hmamouche/NeuroTSConvers","sub_path":"demo/animation.py","file_name":"animation.py","file_ext":"py","file_size_in_byte":2886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34906992866","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\nfrom base64 import b64encode\nimport requests\nimport upyun\n\ntry:\n import urllib.parse\n import queue\nexcept Exception as e:\n import Queue as queue\n import urllib\n\n# ----------待拉取的服务名操作员信息-------------\norigin_bucket = '' # (必填) 待拉取的服务名\norigin_username = '' # (必填) 待拉取的服务名下授权的操作员名\norigin_password = '' # (必填) 待拉取服务名下授权操作员的密码\nhost = '' # (必填) 待拉取的服务名的访问域名, 请使用 http// 或者 https:// 开头, 比如 'http://techs.upyun.com'\norigin_path = '' # (必填) 待拉取的资源路径 \n# --------------------------------------------\n\n# ----------目标迁移服务名, 操作员信息-------------\ntarget_bucket = '' # (必填) 文件迁移的目标服务名\ntarget_username = '' # (必填) 文件迁移的目标服务名的授权操作员名\ntarget_password = '' # (必填) 文件迁移的目���服务名的授权操作员的密码\nsave_as_prefix = '' # (必填) 目标服务名的保存路径的前置路径\n# --------------------------------------------\n\nnotify_url = '' # 将回调地址改成自己的服务器地址, 用来接收又拍云 POST 过来的异步拉取结果\n\n# --------------------------------------------\n\n\nqueue = queue.LifoQueue()\n\n\ndef push_tasks(url, up):\n fetch_data = [\n {\n 'url': host + url, # 需要拉取文件的 URL\n 'random': False, # 是否追加随机数, 默认 false\n 'overwrite': True, # 是否覆盖，默认 True\n 'save_as': save_as_prefix + url\n }\n ]\n if not notify_url:\n print('第 19 行 notify_url 参数不能为空')\n sys.exit(0)\n result = up.put_tasks(fetch_data, notify_url, 'spiderman')\n return result\n\n\ndef do_http_request(method, key, upyun_iter):\n uri = '/' + origin_bucket + (lambda x: x[0] == '/' and x or '/' + x)(key)\n try:\n uri = urllib.parse.quote(uri)\n except Exception as e:\n if isinstance(uri, unicode):\n uri = uri.encode('utf-8')\n uri = urllib.quote(uri)\n headers = {\n 'Authorization': 'Basic ' + b64encode(\n (origin_username + ':' + origin_password).encode()).decode(),\n 'User-Agent': 'up-python-script',\n 'X-List-Limit': '300'\n }\n if upyun_iter is not None or upyun_iter is not 'g2gCZAAEbmV4dGQAA2VvZg':\n headers['x-list-iter'] = upyun_iter\n\n url = \"http://v0.api.upyun.com\" + uri\n requests.adapters.DEFAULT_RETRIES = 5\n session = requests.session()\n try:\n response = session.request(method, url, headers=headers, timeout=30)\n status = response.status_code\n if status == 200:\n try:\n content = response.content.decode()\n except Exception as e:\n content = response.content\n try:\n iter_header = response.headers['x-upyun-list-iter']\n except Exception as e:\n iter_header = 'g2gCZAAEbmV4dGQAA2VvZg'\n data = {\n 'content': content,\n 'iter_header': iter_header\n }\n return data\n else:\n return None\n except Exception as e:\n return None\n\n\ndef sort_data(key, upyun_iter):\n result = do_http_request('GET', key, upyun_iter)\n if not result:\n return None\n content = result['content']\n items = content.split('\\n')\n content = [dict(zip(['name', 'type', 'size', 'time'],\n x.split('\\t'))) for x in items] + result['iter_header'].split()\n return content\n\n\ndef get_list(path):\n upyun_iter = None\n up = upyun.UpYun(target_bucket, target_username, target_password)\n while True:\n while upyun_iter != 'g2gCZAAEbmV4dGQAA2VvZg':\n res = sort_data(path, upyun_iter)\n if res:\n upyun_iter = res[-1]\n for i in res[:-1]:\n try:\n if not i['name']:\n continue\n new_path = '/' + i['name'] if path == '/' else path + '/' + i['name']\n if i['type'] == 'F':\n queue.put(new_path)\n elif i['type'] == 'N':\n print(new_path)\n push_tasks(new_path, up)\n else:\n sys.exit(0)\n except Exception as e:\n print(e)\n else:\n if not queue.empty():\n path = queue.get()\n upyun_iter = None\n queue.task_done()\n else:\n if not queue.empty():\n path = queue.get()\n upyun_iter = None\n queue.task_done()\n else:\n break\n\n\nif __name__ == '__main__':\n get_list(origin_path)\n print(\"Done!\")","repo_name":"upyun/upyun-uss-script","sub_path":"migrate_file_between_buckets/transfer_file.py","file_name":"transfer_file.py","file_ext":"py","file_size_in_byte":5001,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"16182354690","text":"# This is a code for WebApp for predicting the sales.\n\nimport pickle\nimport streamlit as st\nwith open('sales_predictor.pickle', 'rb') as file:\n model = pickle.load(file)\n\nprint(model)\n\nst.title('Sales Predictor')\n\nTV = st.number_input(label ='Enter how many ads were broadcasted through TV: ')\nradio = st.number_input(label = 'Enter how many ads were broadcasted through radio: ')\nnewspaper = st.number_input(label = 'Enter how many ads were posted in newspaper: ')\n\n\nif st.button('Submit'):\n \n y_pred = model.predict([[TV, radio, newspaper]])\n st.write('Sales: ',y_pred[0])","repo_name":"Kushal-Shr/Sales-Predictor-WebApp","sub_path":"predict_sales.py","file_name":"predict_sales.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36716273320","text":"li# -*- coding: utf-8 -*-\n\"\"\"\n@author: 东风\n@file: fec.py\n@time: 2019/11/29 17:40\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame, Series\nfrom pprint import pprint\n\npath = 'D:/data/pydata_book2/datasets/fec/P00000001-ALL.csv'\nfec = pd.read_csv(path, low_memory=False)\n\n# 信息加载\npprint(fec.info())\n\n# 打印一条样本记录\n# print(fec.iloc[123456])\n\n# 使用unique获得所有不同的政治候选人名单\n# unique_cands = fec.cand_nm.unique()\n# pprint(unique_cands)\n# pprint(unique_cands[2])\n\nparties = {\n 'Bachmann, Michelle': 'Republican',\n 'Cain, Herman': 'Republican',\n 'Gingrich, Newt': 'Republican',\n 'Huntsman, Jon': 'Republican',\n 'Johnson, Gary Earl': 'Republican',\n 'McCotter, Thaddeus G': 'Republican',\n 'Obama, Barack': 'Democrat',\n 'Paul, Ron': 'Republican',\n 'Pawlenty, Timothy': 'Republican',\n 'Perry, Rick': 'Republican',\n \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n 'Romney, Mitt': 'Republican',\n 'Santorum, Rick': 'Republican'\n}\n\n# print(fec.cand_nm[123456: 123461])\n# print(fec.cand_nm[123456: 123461].map(parties))\n","repo_name":"cuibinghua84/pydata","sub_path":"python_data_analysis/14-数据分析案例/fec.py","file_name":"fec.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17089105277","text":"# Is the actual A* algorithm\n# Code adapted from https://www.youtube.com/watch?v=ob4faIum4kQ&t=562s\n# 4/4/22\n# author = Charlotte Miller\n\nimport pygame\nimport random\nimport queue\nfrom MazeGeneration import *\n\nclass State(object):\n def __init__(self, value, parent, start = 0, goal = 0):\n self.children = []\n self.parent = parent\n self.value = value\n self.dist = 0\n if parent:\n self.path = parent.path[:]\n self.path.append(value)\n self.start = parent.start\n self.goal = parent.goal\n else:\n self.path = [value]\n self.start = start\n self.goal = goal\n\nclass StateString(State):\n def __init__(self, value, parent, start = 0, goal = 0):\n super(StateString, self).__init__(value, parent, start, goal)\n self.dist = self.GetDist()\n\n def GetDist(self):\n if self.value == self.goal:\n return 0\n return self.goal[0] - self.value[0], self.goal[1] - self.value[1]\n\n def CreateChildren(self, maze):\n parentcell = maze.cell_at(self.value[0], self.value[1])\n parentx = self.value[0]\n parenty = self.value[1]\n if not self.children:\n if not parentcell.walls['N']:\n self.children.append(maze.cell_at(parentx, parenty + 1))\n if not parentcell.walls['S']:\n self.children.append(maze.cell_at(parentx, parenty - 1))\n if not parentcell.walls['E']:\n self.children.append(maze.cell_at(parentx + 1, parenty))\n if not parentcell.walls['W']:\n self.children.append(maze.cell_at(parentx - 1, parenty))\n print(\"parentcell:\", parentcell)\n\nclass AStarSolver:\n def __init__(self, start, goal, nx, ny):\n self.path = []\n self.visitedQueue = []\n self.priorityQueue = queue.PriorityQueue()\n self.start = start\n self.goal = goal\n self.maze = maze\n self.maze.gen_maze()\n print(self.maze)\n self.maze.draw_background()\n\n def Solve(self):\n startState = StateString(self.start, 0, self.start, self.goal)\n # number of child nodes that have been visited\n count = 0\n self.priorityQueue.put((0, count, startState))\n while(not self.path and self.priorityQueue.qsize()):\n closestChild = self.priorityQueue.get()[2]\n closestChild.CreateChildren(self.maze)\n self.visitedQueue.append(closestChild.value)\n for child in closestChild.children:\n dist = goal[0] - child.x, goal[1] - child.y\n print(dist)\n if (child.x, child.y) not in self.visitedQueue:\n count += 1\n if not dist:\n self.path = child.path\n break\n self.priorityQueue.put((dist, count, child))\n if not self.path:\n print(\"Goal is impossible to reach\")\n return self.path\n\nif __name__ == \"__main__\":\n start = 0, 0\n goal = 14, 14\n a = AStarSolver(start, goal, 15, 15)\n a.Solve()","repo_name":"RavenclawLunatic/MazeGenerating","sub_path":"A*Pathfinder.py","file_name":"A*Pathfinder.py","file_ext":"py","file_size_in_byte":3117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31365927810","text":"import datetime\nimport random\nimport sys\nimport Agencia as Ag\nimport Correntista as Co\nimport Devedor\nimport Disponivel\n\n\nclass ContaBancaria:\n def __init__(self, nome_do_correntista: str, cpf: str,) -> None:\n\n self.__estado = Disponivel.Disponivel()\n self.agencia = Ag.Agencia(f\"Agência P{random.randint(100, 999)}\", random.randint(100, 999))\n self.correntista = Co.Correntista(nome_do_correntista, cpf)\n self.numero_da_conta = random.randint(10000, 99999)\n self.saldo = 0\n self.limite = 50\n self.data_abertura = datetime.datetime.now()\n self.data_encerramento = None\n self.dinheiro_disponivel = self.saldo + self.limite\n\n def realizar_saque(self, quantidade) -> None:\n quantidade_sacada = self.__estado.realizar_saque(self.dinheiro_disponivel, quantidade)\n self.saldo -= quantidade_sacada\n self.__verificar_estado()\n\n def realizar_deposito(self, quantidade) -> None:\n quantidade_depositada = self.__estado.realizar_deposito(quantidade)\n self.saldo += quantidade_depositada\n self.__verificar_estado()\n\n def encerrar_conta(self) -> None:\n self.data_encerramento = self.__estado.encerrar_conta()\n if self.data_encerramento is not None:\n sys.exit()\n\n def __verificar_estado(self) -> None:\n if self.limite + self.saldo < 0:\n self.__estado = Devedor.Devedor()\n elif self.saldo + self.limite >= 0:\n self.__estado = Disponivel.Disponivel\n\n self.dinheiro_disponivel = self.saldo + self.limite\n","repo_name":"SFaguiar/analise_e_projeto_de_software_2av","sub_path":"2q/ContaBancaria.py","file_name":"ContaBancaria.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17592245487","text":"#WAP: addition of two nos with command more precise \r\n#with filtering\r\n\r\nimport sys\r\ndef Addition(no1,no2):\r\n return no1 + no2\r\n \r\ndef main():\r\n if (len(sys.argv) < 3) or (len(sys.argv) > 3):\r\n print(\"Insufficient aruments\")\r\n return\r\n \r\n ret = Addition(int(sys.argv[1]),int(sys.argv[2]))\r\n print(\"Addition of {} and {} is {} :\".format(sys.argv[1],sys.argv[2],ret))\r\n \r\nif __name__ == \"__main__\":\r\n main()\r\n ","repo_name":"PrathameshDhumal/Python-programming","sub_path":"commands/command3.py","file_name":"command3.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6378875580","text":"import threading\nimport datetime\nimport pytz\nimport time\nimport copy\nimport re\nfrom ibapi.wrapper import EWrapper\nfrom ibapi.client import EClient\nfrom ibapi import contract\nfrom StockValues_Google import StockValues_Google\n\n'''\nCreated on 11th November 2017\n\n@author: rob dobson\n'''\n\nclass MarketDataWrapper(EWrapper):\n \"\"\"\n The wrapper deals with the action coming back from the IB gateway or TWS instance\n We override methods in EWrapper that will get called when this action happens\n \"\"\"\n\n def __init__(self):\n pass\n\nclass MarketDataClient(EClient):\n \"\"\"\n The client method\n We don't override native methods, but instead call them from our own wrappers\n \"\"\"\n def __init__(self, wrapper):\n ## Set up with a wrapper inside\n EClient.__init__(self, wrapper)\n\nclass PriceGetter(MarketDataWrapper, MarketDataClient):\n _REQ_ID_PRICE_BASE = 10000000\n _REQ_ID_DETAILS_BASE = 200000\n _nextReqId = 1\n _mapPriceReqIdToStockInfo = {}\n _mapSymbolToPriceReqId = {}\n _mapDetailsReqIdToPriceReqId = {}\n _IB_SymbolMappings = {\n \"INDEXSP:.INX\": { \"symbol\": \"SPX\", \"exchange\": \"CBOE\", \"currency\":\"USD\", \"secType\":\"IND\" },\n \"INDEXDJX:.DJI\": {\"symbol\": \"INDU\", \"exchange\": \"NYSE\", \"currency\": \"USD\", \"secType\": \"IND\"},\n # \"INDEXFTSE:UKX\": {\"symbol\": \"TICK-LSE\", \"exchange\": \"LSE\", \"currency\": \"GBP\", \"secType\": \"IND\"},\n \"AV-B.L\": {\"symbol\": \"AV.B\", \"exchange\": \"LSE\", \"currency\": \"GBP\", \"secType\": \"STK\"},\n }\n\n def __init__(self, ipaddress, portid, clientid, symbolChangedCallback):\n self._symbolChangedCallback = symbolChangedCallback\n # Initialise both base classes\n MarketDataWrapper.__init__(self)\n MarketDataClient.__init__(self, wrapper=self)\n # Connect using the passed params\n self.connect(ipaddress, portid, clientid)\n # Start a thread which runs the run() function inside EClient in the IB API\n self.getterThread = threading.Thread(target = self.run)\n self.getterThread.start()\n setattr(self, \"_thread\", self.getterThread)\n # A thread for adding the market requests\n self.requestAddStockList = []\n self.requestRemoveStockList = []\n self.requestStockListChanged = False\n self.requestListLock = threading.Lock()\n self.requestThreadRunning = True\n self.requestThread = threading.Thread(target = self.requestMarketData)\n self.requestThread.start()\n # A lock for the dictionary used to access symbol values\n self.mapsLock = threading.Lock()\n # Start the google getter too\n self._stockValuesFromGoogle = StockValues_Google(self.symbolChangedCallback)\n\n def error(self, reqId:int, errorCode:int, errorString:str):\n if errorCode == 200 or errorCode == 504: # security not found OR not connected\n with self.mapsLock:\n if reqId in self._mapPriceReqIdToStockInfo:\n sym = self._mapPriceReqIdToStockInfo[reqId][\"ySymbol\"]\n print(\"StockValues: Symbol not matched\", sym, \"errorMsg\", errorString)\n self._mapPriceReqIdToStockInfo.pop(reqId)\n if sym in self._mapSymbolToPriceReqId:\n self._mapSymbolToPriceReqId.pop(sym)\n # Add to list of symbols to get from google\n self._stockValuesFromGoogle.addStock(sym)\n else:\n print(\"StockValues: Unknown symbol not found\")\n else:\n if reqId == -1:\n print(\"StockValues: IB INFO msgCode\", errorCode, \"msgStr\", errorString)\n else:\n print(\"StockValues: IB INFO reqId\", reqId, \"msgCode\", errorCode, \"msgStr\", errorString)\n\n def stop(self):\n # Setting this flag avoids calling disconnect() twice (which throws an error)\n self._stockValuesFromGoogle.stop()\n self.done = True\n self.requestThreadRunning = False\n\n def setStocks(self, stockList):\n # Send full list to google getter (for slow rate updates - e.g. when market closed)\n self._stockValuesFromGoogle.setAllStocks(stockList)\n # Record list to be got\n with self.requestListLock:\n self.requestAddStockList = []\n with self.mapsLock:\n for sym in stockList:\n if sym not in self._mapSymbolToPriceReqId:\n self.requestAddStockList.append(sym)\n for sym, reqId in self._mapSymbolToPriceReqId.items():\n if sym not in stockList:\n self.requestRemoveStockList.append(sym)\n if len(self.requestAddStockList) > 0 or len(self.requestRemoveStockList) > 0:\n self.requestStockListChanged = True\n\n def clear(self):\n # Stop getting each symbol from the market data feed\n for reqId, stockItem in self._mapPriceReqIdToStockInfo.items():\n self.cancelMktData(reqId)\n # Lock here as we're changing the mappings\n self.mapsLock.acquire()\n # Clear maps\n self._mapSymbolToPriceReqId.clear()\n self._mapPriceReqIdToStockInfo.clear()\n self._mapDetailsReqIdToPriceReqId.clear()\n # Release lock\n self.mapsLock.release()\n # Set reqId back\n self._nextReqId = 1\n\n def addYSymbol(self, ySymbol):\n # Check if this stock is already being acquired\n if ySymbol in self._mapSymbolToPriceReqId:\n return\n # Get the contract (stockInfo) in the form needed for interactiveBrokers API\n stockInfo = self.makeStockInfo(ySymbol)\n # Ticker Id is used to communicate with the API - called reqId in the API\n curPriceReqId = self._REQ_ID_PRICE_BASE + self._nextReqId\n stockInfo[\"priceReqId\"] = curPriceReqId\n # Info used by the IB API\n contractInfo = self.getContractInfo(stockInfo)\n stockInfo[\"contractInfo\"] = contractInfo\n # Add to the lookups for the symbol and tickId\n self.mapsLock.acquire()\n self._mapSymbolToPriceReqId[ySymbol] = curPriceReqId\n self._mapPriceReqIdToStockInfo[curPriceReqId] = stockInfo\n self.mapsLock.release()\n # Start the API getting this symbol\n self.reqMktData(curPriceReqId, contractInfo, \"\", False, False, [])\n # No reuse of tickIds currently\n self._nextReqId += 1\n\n def removeSymbol(self, ySymbol):\n # Check we have the symbol\n if ySymbol in self._mapSymbolToPriceReqId:\n # Remove from maps\n tickId = self._mapSymbolToPriceReqId[ySymbol]\n self.mapsLock.acquire()\n self._mapSymbolToPriceReqId.pop(ySymbol, None)\n self._mapPriceReqIdToStockInfo.pop(tickId, None)\n self.mapsLock.release()\n # Stop the API getting this symbol\n self.cancelMktData(tickId)\n\n def makeStockInfo(self, ySymbol):\n nowInUk = datetime.datetime.now(pytz.timezone('GB'))\n # Base record\n stkInfo = {\"ySymbol\": ySymbol, \"symbol\": ySymbol, \"name\": \"\", \"exchange\": \"SMART\",\n \"currency\": \"USD\", \"secType\": \"STK\", \"includeExpired\": False,\n \"failCount\": 0, \"time\": nowInUk}\n if ySymbol in self._IB_SymbolMappings:\n for key, val in self._IB_SymbolMappings[ySymbol].items():\n stkInfo[key] = val\n else:\n # Split YSymbol apart to find underlying exchange and symbol\n # Attempt to handle Yahoo style ticker symbols\n dotPos = ySymbol.rfind(\".\")\n if dotPos > 0:\n if ySymbol[dotPos+1:] == \"L\":\n stkInfo[\"exchange\"] = \"LSE\"\n stkInfo[\"currency\"] = \"GBP\"\n symb = ySymbol[:dotPos]\n if len(symb) == 2:\n symb += \".\"\n stkInfo[\"symbol\"] = symb\n else:\n atPos = ySymbol.rfind(\"@\")\n if atPos > 0:\n stkInfo[\"primaryExchange\"] = ySymbol[atPos+1:]\n symb = ySymbol[:atPos]\n if len(symb) == 2:\n symb += \".\"\n stkInfo[\"symbol\"] = symb\n return stkInfo\n\n def getContractInfo(self, stockInfo):\n c = contract.Contract()\n c.conId = 0\n c.symbol = stockInfo[\"symbol\"]\n c.exchange = stockInfo[\"exchange\"]\n c.currency = stockInfo[\"currency\"]\n c.secType = stockInfo[\"secType\"]\n if \"primaryExchange\" in stockInfo:\n c.primaryExchange = stockInfo[\"primaryExchange\"]\n c.includeExpired = stockInfo[\"includeExpired\"]\n return c\n\n def requestMarketData(self):\n while self.requestThreadRunning:\n # Check if there is new data\n if self.requestStockListChanged:\n self.requestStockListChanged = False\n # Add new stocks\n stockList = []\n with self.requestListLock:\n for stk in self.requestAddStockList:\n stockList.append(stk)\n # Go through adding stocks\n for stk in stockList:\n self.addYSymbol(stk)\n # Remove unwanted stocks\n stockList = []\n with self.requestListLock:\n for stk in self.requestRemoveStockList:\n stockList.append(stk)\n # Go through adding stocks\n for stk in stockList:\n self.removeSymbol(stk)\n time.sleep(1)\n\n def marketDataCallback(self, reqId, tickType, price, attrib):\n pass\n\n def setValInStockDict(self, sym, elemName, val, stockDict):\n if elemName not in stockDict or stockDict[elemName] is None or stockDict[elemName] != val:\n stockDict[elemName] = val\n return sym\n return None\n\n def setChangeInStockDict(self, sym, stockDict):\n if \"close\" in stockDict and stockDict[\"close\"] is not None and \"price\" in stockDict and stockDict[\"price\"] is not None:\n last = stockDict[\"price\"]\n stockDict[\"change\"] = priceChange = last - stockDict[\"close\"]\n if last != 0:\n stockDict[\"chg_percent\"] = 100 * (priceChange) / last\n\n def tickPrice(self, reqId, tickType:int, price:float, attrib):\n # Acquire lock on maps\n symbolDataChanged = None\n with self.mapsLock:\n if reqId in self._mapPriceReqIdToStockInfo:\n stockInfo = self._mapPriceReqIdToStockInfo[reqId]\n sym = stockInfo[\"ySymbol\"]\n if tickType == 1: # bid_price\n # Not this does not set symbolDataChanged\n self.setValInStockDict(sym, \"bid_price\", price, stockInfo)\n elif tickType == 2: # ask_price\n # Not this does not set symbolDataChanged\n self.setValInStockDict(sym, \"ask_price\", price, stockInfo)\n elif tickType == 4: # last\n symbolDataChanged = self.setValInStockDict(sym, \"price\", price, stockInfo)\n if symbolDataChanged:\n self.setChangeInStockDict(sym, stockInfo)\n elif tickType == 6: # high\n symbolDataChanged = self.setValInStockDict(sym, \"high\", price, stockInfo)\n elif tickType == 7: # low\n symbolDataChanged = self.setValInStockDict(sym, \"low\", price, stockInfo)\n elif tickType == 9: # close\n symbolDataChanged = self.setValInStockDict(sym, \"close\", price, stockInfo)\n if \"price\" not in stockInfo or stockInfo[\"price\"] is None:\n self.setValInStockDict(sym, \"price\", price, stockInfo)\n if symbolDataChanged:\n self.setChangeInStockDict(sym, stockInfo)\n elif tickType == 14: # open\n symbolDataChanged = self.setValInStockDict(sym, \"open\", price, stockInfo)\n if \"price\" not in stockInfo or stockInfo[\"price\"] is None:\n self.setValInStockDict(sym, \"price\", price, stockInfo)\n else:\n print(\"StockValues: Unhandled tickPrice\", tickType, \"price\", price)\n if symbolDataChanged:\n nowInUk = datetime.datetime.now(pytz.timezone('GB'))\n stockInfo[\"time\"] = nowInUk\n # Check if detailed info (long name of stock etc) already requested\n if \"detailsReqId\" not in stockInfo:\n # Request contract details for this symbol\n detailsReqId = self._REQ_ID_DETAILS_BASE + self._nextReqId\n self.reqContractDetails(detailsReqId, stockInfo[\"contractInfo\"])\n self._mapDetailsReqIdToPriceReqId[detailsReqId] = stockInfo[\"priceReqId\"]\n stockInfo[\"detailsReqId\"] = detailsReqId\n self._nextReqId += 1\n # Callback if data has changed\n if symbolDataChanged is not None:\n self._symbolChangedCallback(symbolDataChanged)\n\n def tickSize(self, reqId:int, tickType:int, size:int):\n # Acquire lock on maps\n symbolDataChanged = None\n with self.mapsLock:\n if reqId in self._mapPriceReqIdToStockInfo:\n stockInfo = self._mapPriceReqIdToStockInfo[reqId]\n sym = stockInfo[\"ySymbol\"]\n if tickType == 0: # bid_size\n self.setValInStockDict(sym, \"bid_size\", size, stockInfo)\n elif tickType == 3: # ask_size\n self.setValInStockDict(sym, \"ask_size\", size, stockInfo)\n elif tickType == 5: # last_size\n self.setValInStockDict(sym, \"last_size\", size, stockInfo)\n elif tickType == 8: # volume\n symbolDataChanged = self.setValInStockDict(sym, \"volume\", size, stockInfo)\n else:\n print(\"StockValues: Unhandled tickSize\", tickType, \"size\", size)\n if symbolDataChanged:\n nowInUk = datetime.datetime.now(pytz.timezone('GB'))\n stockInfo[\"time\"] = nowInUk\n # Callback if data has changed\n if symbolDataChanged is not None:\n self._symbolChangedCallback(symbolDataChanged)\n\n def tickString(self, reqId:int, tickType:int, value:str):\n if tickType == 32: # ask_exch\n pass\n elif tickType == 33: # bid_exch\n pass\n elif tickType == 45: # last_timestamp\n pass\n elif tickType == 84: # last_exch\n pass\n else:\n print(\"StockValues: Unhandled tickString\", tickType, \"str\", value)\n\n def tickGeneric(self, reqId:int, tickType:int, value:float):\n print(\"StockValues: Unhandled tickGeneric\", tickType, \"float\", value)\n\n def tickEFP(self, reqId:int, tickType:int, basisPoints:float,\n formattedBasisPoints:str, totalDividends:float,\n holdDays:int, futureLastTradeDate:str, dividendImpact:float,\n dividendsToLastTradeDate:float):\n print(\"StockValues: Unhandled tickEFP\", tickType, \"basisPoints\", basisPoints,\n \"formattedBasisPoints\", formattedBasisPoints, \"totalDividends\", totalDividends,\n \"holdDays\", holdDays, \"futureLastTradeDate\", futureLastTradeDate,\n \"dividendImpact\", dividendImpact, \"dividendsToLastTradeDate\", dividendsToLastTradeDate)\n\n def tickSnapshotEnd(self, reqId:int):\n print(\"StockValues: Unhandled tickSnapshotEnd\", reqId)\n\n def contractDetails(self, reqId:int, contractDetails):\n self.mapsLock.acquire()\n if reqId in self._mapDetailsReqIdToPriceReqId:\n priceReqId = self._mapDetailsReqIdToPriceReqId[reqId]\n if priceReqId in self._mapPriceReqIdToStockInfo:\n stockInfo = self._mapPriceReqIdToStockInfo[priceReqId]\n stockInfo[\"name\"] = contractDetails.longName\n self._symbolChangedCallback(stockInfo[\"ySymbol\"])\n self.mapsLock.release()\n\n def contractDetailsEnd(self, reqId:int):\n pass\n\n def getStockInfoData(self, ySymbol):\n if ySymbol not in self._mapSymbolToPriceReqId:\n return self._stockValuesFromGoogle.getStockInfoData(ySymbol)\n stockInfo = self._stockValuesFromGoogle.getStockInfoData(ySymbol)\n if stockInfo is None:\n stockInfo = {}\n reqId = self._mapSymbolToPriceReqId[ySymbol]\n with self.mapsLock:\n for k,v in self._mapPriceReqIdToStockInfo[reqId].items():\n if k != \"price\" or v != 0:\n stockInfo[k] = v\n return stockInfo\n\n def symbolChangedCallback(self, symbol):\n self._symbolChangedCallback(symbol)\n\nclass StockValues_InteractiveBrokers:\n bOnlyUpdateWhileMarketOpen = False\n \n def __init__(self):\n self.openhour = 8\n self.openmin = 0\n self.closehour = 16\n self.closemin = 30\n self.tradingdow = 0, 1, 2, 3, 4\n self._priceGetter = PriceGetter(\"127.0.0.1\", 4001, 10, self.symbolDataChanged)\n self._dictOfStocksChangedSinceUIUpdate = {}\n self._lockOnStockChangeList = threading.Lock()\n \n def setStocks(self, stockList):\n # Set list of symbols got by the API\n self._priceGetter.setStocks(stockList)\n\n def symbolDataChanged(self, symbol):\n with self._lockOnStockChangeList:\n self._dictOfStocksChangedSinceUIUpdate[symbol] = True\n # print(\"UpdateTo\", symbol, \"len\", len(self._dictOfStocksChangedSinceUIUpdate))\n\n def getMapOfStocksChangedSinceUIUpdated(self):\n changedStockDict = {}\n with self._lockOnStockChangeList:\n changedStockDict = copy.copy(self._dictOfStocksChangedSinceUIUpdate)\n self._dictOfStocksChangedSinceUIUpdate = {}\n return changedStockDict\n\n def getStockData(self, sym):\n return self._priceGetter.getStockInfoData(sym)\n\n def setOnlyUpdateWhenMarketOpen(self, onlyWhenOpen):\n self.bOnlyUpdateWhileMarketOpen = onlyWhenOpen\n \n def run(self):\n pass\n\n def stop(self):\n self._priceGetter.stop()\n\n def getMarketOpenStatus(self):\n nowInUk = datetime.datetime.now(pytz.timezone('GB'))\n open_time = nowInUk.replace( hour=self.openhour, minute=self.openmin, second=0 )\n close_time = nowInUk.replace( hour=self.closehour, minute=self.closemin, second=0 )\n open_day = False\n for day in self.tradingdow:\n if ( day == nowInUk.weekday() ):\n open_day = True\n marketOpen = (nowInUk > open_time) and (nowInUk < close_time) and open_day\n return \"Market Open\" if marketOpen else \"Market Closed\"\n\n","repo_name":"robdobsn/QtStockTicker","sub_path":"StockValues_InteractiveBrokers.py","file_name":"StockValues_InteractiveBrokers.py","file_ext":"py","file_size_in_byte":18904,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"40259223904","text":"import tensorflow as tf\r\nfrom tensorflow.keras import backend as K\r\n\r\n#---------------------------------------------------#\r\n# ボックスを調整し、実際の画像に合わせる\r\n#---------------------------------------------------#\r\ndef yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image):\r\n #-----------------------------------------------------------------#\r\n # y軸を前に置くのは、予測ボックスと画像の幅・高さを簡単にかけるためです\r\n #-----------------------------------------------------------------#\r\n box_yx = box_xy[..., ::-1]\r\n box_hw = box_wh[..., ::-1]\r\n input_shape = K.cast(input_shape, K.dtype(box_yx))\r\n image_shape = K.cast(image_shape, K.dtype(box_yx))\r\n\r\n if letterbox_image:\r\n #-----------------------------------------------------------------#\r\n # ここで求められるoffsetは、画像の有効領域が画像の左上角からのオフセットです\r\n # new_shapeは幅と高さの縮小を示します\r\n #-----------------------------------------------------------------#\r\n new_shape = K.round(image_shape * K.min(input_shape/image_shape))\r\n offset = (input_shape - new_shape)/2./input_shape\r\n scale = input_shape/new_shape\r\n\r\n box_yx = (box_yx - offset) * scale\r\n box_hw *= scale\r\n\r\n box_mins = box_yx - (box_hw / 2.)\r\n box_maxes = box_yx + (box_hw / 2.)\r\n boxes = K.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]])\r\n boxes *= K.concatenate([image_shape, image_shape])\r\n return boxes\r\n\r\n#---------------------------------------------------#\r\n# 各特徴レイヤーの予測値を実数に調整する\r\n#---------------------------------------------------#\r\ndef get_anchors_and_decode(feats, anchors, num_classes, input_shape, calc_loss=False):\r\n num_anchors = len(anchors)\r\n #------------------------------------------#\r\n # grid_shapeは特徴レイヤーの高さと幅を示します\r\n #------------------------------------------#\r\n grid_shape = K.shape(feats)[1:3]\r\n #--------------------------------------------------------------------#\r\n # 各特徴点の座標情報を生成する。生成されるshapeは (13, 13, num_anchors, 2)\r\n #--------------------------------------------------------------------#\r\n grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, num_anchors, 1])\r\n grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], num_anchors, 1])\r\n grid = K.cast(K.concatenate([grid_x, grid_y]), K.dtype(feats))\r\n #---------------------------------------------------------------#\r\n # 各特徴点の座標情報を生成する。生成されるshapeは (13, 13, num_anchors, 2)\r\n #---------------------------------------------------------------#\r\n anchors_tensor = K.reshape(K.constant(anchors), [1, 1, num_anchors, 2])\r\n anchors_tensor = K.tile(anchors_tensor, [grid_shape[0], grid_shape[1], 1, 1])\r\n\r\n #---------------------------------------------------#\r\n # 予測結果を(batch_size,13,13,3,85)に調整する\r\n # 85は、4 + 1 + 80に分解できます\r\n # 4は中心の幅と高さの調整パラメータを示します\r\n # 1はボックスの信頼度を示します\r\n # 80はカテゴリの信頼度を示します\r\n #---------------------------------------------------#\r\n feats = K.reshape(feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])\r\n #------------------------------------------#\r\n #事前に設定されたボックスをデコードし、正規化する\r\n #------------------------------------------#\r\n box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[::-1], K.dtype(feats))\r\n box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats))\r\n #------------------------------------------#\r\n #予測ボックスの信頼度を取得\r\n #------------------------------------------#\r\n box_confidence = K.sigmoid(feats[..., 4:5])\r\n box_class_probs = K.sigmoid(feats[..., 5:])\r\n\r\n #---------------------------------------------------------------------#\r\n # 損失計算時にはgrid, feats, box_xy, box_whを返します\r\n # 予測時にはbox_xy, box_wh, box_confidence, box_class_probsを返します\r\n #---------------------------------------------------------------------#\r\n if calc_loss == True:\r\n return grid, feats, box_xy, box_wh\r\n return box_xy, box_wh, box_confidence, box_class_probs\r\n\r\n#---------------------------------------------------#\r\n# 画像の予測\r\n#---------------------------------------------------#\r\ndef DecodeBox(outputs,\r\n anchors,\r\n num_classes,\r\n input_shape,\r\n #-----------------------------------------------------------#\r\n # 13x13の特徴レイヤーに対応するアンカーは[116,90],[156,198],[373,326]\r\n # 26x26の特徴レイヤーに対応するアンカーは[30,61],[62,45],[59,119]\r\n # 52x52の特徴レイヤーに対応するアンカーは[10,13],[16,30],[33,23]\r\n #-----------------------------------------------------------#\r\n anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]],\r\n max_boxes = 100,\r\n confidence = 0.5,\r\n nms_iou = 0.3,\r\n letterbox_image = True):\r\n\r\n image_shape = K.reshape(outputs[-1],[-1])\r\n\r\n box_xy = []\r\n box_wh = []\r\n box_confidence = []\r\n box_class_probs = []\r\n for i in range(len(anchor_mask)):\r\n sub_box_xy, sub_box_wh, sub_box_confidence, sub_box_class_probs = \\\r\n get_anchors_and_decode(outputs[i], anchors[anchor_mask[i]], num_classes, input_shape)\r\n box_xy.append(K.reshape(sub_box_xy, [-1, 2]))\r\n box_wh.append(K.reshape(sub_box_wh, [-1, 2]))\r\n box_confidence.append(K.reshape(sub_box_confidence, [-1, 1]))\r\n box_class_probs.append(K.reshape(sub_box_class_probs, [-1, num_classes]))\r\n box_xy = K.concatenate(box_xy, axis = 0)\r\n box_wh = K.concatenate(box_wh, axis = 0)\r\n box_confidence = K.concatenate(box_confidence, axis = 0)\r\n box_class_probs = K.concatenate(box_class_probs, axis = 0)\r\n\r\n #------------------------------------------------------------------------------------------------------------#\r\n # 画像がネットワークに入力される前に、letterbox_imageを使用して画像周りにグレーのバーが追加されます。\r\n # したがって、生成されたbox_xy、box_whはグレーのバーがある画像に対応しています。\r\n # これらを修正し、グレーのバー部分を取り除く必要があります。box_xy、およびbox_whをy_min, y_max, xmin, xmaxに調整します。\r\n # letterbox_imageを使用していない場合でも、正規化されたbox_xy、box_whを元の画像サイズに対して調整する必要があります。\r\n #------------------------------------------------------------------------------------------------------------#\r\n boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)\r\n\r\n box_scores = box_confidence * box_class_probs\r\n\r\n #-----------------------------------------------------------#\r\n # スコアがscore_thresholdよりも大きいかどどうかを判断する必要があります\r\n #-----------------------------------------------------------#\r\n mask = box_scores >= confidence\r\n max_boxes_tensor = K.constant(max_boxes, dtype='int32')\r\n boxes_out = []\r\n scores_out = []\r\n classes_out = []\r\n for c in range(num_classes):\r\n #-----------------------------------------------------------#\r\n # すべてのbox_scores >= score_thresholdのボックスとスコアを取り出します。\r\n #-----------------------------------------------------------#\r\n class_boxes = tf.boolean_mask(boxes, mask[:, c])\r\n class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c])\r\n\r\n #-----------------------------------------------------------#\r\n # 非極大抑制：\r\n # ある範囲内でスコアが最大のボックスを保持します。\r\n #-----------------------------------------------------------#\r\n nms_index = tf.image.non_max_suppression(class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=nms_iou)\r\n\r\n #-----------------------------------------------------------#\r\n # 非極大抑制後の結果を取得します。\r\n # 以下の3つは、それぞれボックスの位置、スコア、カテゴリを示しています。\r\n #-----------------------------------------------------------#\r\n class_boxes = K.gather(class_boxes, nms_index)\r\n class_box_scores = K.gather(class_box_scores, nms_index)\r\n classes = K.ones_like(class_box_scores, 'int32') * c\r\n\r\n boxes_out.append(class_boxes)\r\n scores_out.append(class_box_scores)\r\n classes_out.append(classes)\r\n boxes_out = K.concatenate(boxes_out, axis=0)\r\n scores_out = K.concatenate(scores_out, axis=0)\r\n classes_out = K.concatenate(classes_out, axis=0)\r\n\r\n return boxes_out, scores_out, classes_out\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import matplotlib.pyplot as plt\r\n import numpy as np\r\n\r\n def sigmoid(x):\r\n s = 1 / (1 + np.exp(-x))\r\n return s\r\n #---------------------------------------------------#\r\n # 各特徴層の予測値を実際の値に調整します。\r\n #---------------------------------------------------#\r\n def get_anchors_and_decode(feats, anchors, num_classes):\r\n # feats [batch_size, 13, 13, 3 * (5 + num_classes)]\r\n # anchors [3, 2]\r\n # num_classes\r\n # 3\r\n num_anchors = len(anchors)\r\n #------------------------------------------#\r\n # grid_shapeは特徴層の高さと幅を示しています\r\n # grid_shape [13, 13]\r\n #------------------------------------------#\r\n grid_shape = np.shape(feats)[1:3]\r\n #--------------------------------------------------------------------#\r\n # 各特徴点の座標情報を取得します。生成されるshapeは（13, 13, num_anchors, 2）\r\n #--------------------------------------------------------------------#\r\n grid_x = np.tile(np.reshape(np.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, num_anchors, 1])\r\n grid_y = np.tile(np.reshape(np.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], num_anchors, 1])\r\n grid = np.concatenate([grid_x, grid_y], -1)\r\n #---------------------------------------------------------------#\r\n # 各特徴点の座標情報を生成する。生成されるshapeは (13, 13, num_anchors, 2)\r\n # [1, 1, 3, 2]\r\n # [13, 13, 3, 2]\r\n #---------------------------------------------------------------#\r\n anchors_tensor = np.reshape(anchors, [1, 1, num_anchors, 2])\r\n anchors_tensor = np.tile(anchors_tensor, [grid_shape[0], grid_shape[1], 1, 1])\r\n\r\n #---------------------------------------------------#\r\n # 予測結果を（batch_size, 13, 13, 3, 85）に調整します。\r\n # 85は、4 + 1 + 80に分割できます。\r\n # 4は中心の幅と高さの調整パラメータを表します。\r\n # 1はボックスの信頼度を表します。\r\n # 80はクラスの信頼度を表します。\r\n # [batch_size, 13, 13, 3 * (5 + num_classes)]\r\n # [batch_size, 13, 13, 3, 5 + num_classes]\r\n #---------------------------------------------------#\r\n feats = np.reshape(feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])\r\n #------------------------------------------#\r\n # 先行バウンディングボックスをデコードし、正規化します。\r\n #------------------------------------------#\r\n box_xy = sigmoid(feats[..., :2]) + grid\r\n box_wh = np.exp(feats[..., 2:4]) * anchors_tensor\r\n #------------------------------------------#\r\n # 予測ボックスの信頼度を取得します。\r\n #------------------------------------------#\r\n box_confidence = sigmoid(feats[..., 4:5])\r\n box_class_probs = sigmoid(feats[..., 5:])\r\n\r\n box_wh = box_wh / 32\r\n anchors_tensor = anchors_tensor / 32\r\n fig = plt.figure()\r\n ax = fig.add_subplot(121)\r\n plt.ylim(-2,15)\r\n plt.xlim(-2,15)\r\n plt.scatter(grid_x,grid_y)\r\n plt.scatter(5,5,c='black')\r\n plt.gca().invert_yaxis()\r\n\r\n anchor_left = grid_x - anchors_tensor/2\r\n anchor_top = grid_y - anchors_tensor/2\r\n print(np.shape(anchors_tensor))\r\n print(np.shape(box_xy))\r\n rect1 = plt.Rectangle([anchor_left[5,5,0,0],anchor_top[5,5,0,1]],anchors_tensor[0,0,0,0],anchors_tensor[0,0,0,1],color=\"r\",fill=False)\r\n rect2 = plt.Rectangle([anchor_left[5,5,1,0],anchor_top[5,5,1,1]],anchors_tensor[0,0,1,0],anchors_tensor[0,0,1,1],color=\"r\",fill=False)\r\n rect3 = plt.Rectangle([anchor_left[5,5,2,0],anchor_top[5,5,2,1]],anchors_tensor[0,0,2,0],anchors_tensor[0,0,2,1],color=\"r\",fill=False)\r\n\r\n ax.add_patch(rect1)\r\n ax.add_patch(rect2)\r\n ax.add_patch(rect3)\r\n\r\n ax = fig.add_subplot(122)\r\n plt.ylim(-2,15)\r\n plt.xlim(-2,15)\r\n plt.scatter(grid_x,grid_y)\r\n plt.scatter(5,5,c='black')\r\n plt.scatter(box_xy[0,5,5,:,0],box_xy[0,5,5,:,1],c='r')\r\n plt.gca().invert_yaxis()\r\n\r\n pre_left = box_xy[...,0] - box_wh[...,0]/2\r\n pre_top = box_xy[...,1] - box_wh[...,1]/2\r\n\r\n rect1 = plt.Rectangle([pre_left[0,5,5,0],pre_top[0,5,5,0]],box_wh[0,5,5,0,0],box_wh[0,5,5,0,1],color=\"r\",fill=False)\r\n rect2 = plt.Rectangle([pre_left[0,5,5,1],pre_top[0,5,5,1]],box_wh[0,5,5,1,0],box_wh[0,5,5,1,1],color=\"r\",fill=False)\r\n rect3 = plt.Rectangle([pre_left[0,5,5,2],pre_top[0,5,5,2]],box_wh[0,5,5,2,0],box_wh[0,5,5,2,1],color=\"r\",fill=False)\r\n\r\n ax.add_patch(rect1)\r\n ax.add_patch(rect2)\r\n ax.add_patch(rect3)\r\n\r\n plt.show()\r\n\r\n feat = np.random.normal(0,0.5,[4,13,13,75])\r\n anchors = [[142, 110],[192, 243],[459, 401]]\r\n get_anchors_and_decode(feat,anchors,20)\r\n\r\n\r\n'''\r\nYOLOによるオブジェクト検出のためのバウンディングボックスのデコーディングと調整を行う関数を定義します。\r\nyolo_correct_boxes: 予測されたバウンディングボックスの座標を実際の画像サイズに合わせて調整します。\r\nget_anchors_and_decode: 各特徴レイヤーの予測値を実際のバウンディングボックスの座標に変換します。\r\nDecodeBox: YOLOの出力から実際のバウンディングボックス、スコア、およびクラスを取得します。\r\nこれには、信頼度しきい値と非極大抑制を使用して、重要なバウンディングボックスのみを選択します。\r\nこれらの関数は、YOLOの出力を解釈し、検出されたオブジェクトの位置とクラスを正確に特定するために使用されます。\r\n'''\r\n","repo_name":"ultra-supara/automatic_train","sub_path":"utils/utils_bbox.py","file_name":"utils_bbox.py","file_ext":"py","file_size_in_byte":15568,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23320358550","text":"from flask import render_template, redirect, request\n\nfrom flask_app import app\n\nfrom flask_app.models.cookie import Cookie_order\n\n@app.route('/')\ndef index():\n return redirect('/cookies')\n\n@app.route('/cookies')\ndef cookie_summary():\n return render_template('cookies.html', cookies=Cookie_order.get_all())\n\n@app.route('/cookies/new')\ndef new():\n return render_template('new_cookie.html')\n\n@app.route('/cookies/create', methods = ['POST'])\ndef create_order():\n if Cookie_order.validate_order(request.form) == True:\n print(request.form)\n Cookie_order.save(request.form)\n return redirect('/cookies')\n else:\n print('not valid')\n return redirect('/cookies/new')\n\n@app.route('/cookies/edit/')\ndef edit_order(id):\n data = {\n \"id\":id\n }\n return render_template('edit_order.html', cookie=Cookie_order.get_one(data))\n\n@app.route('/cookies/update', methods = ['POST'])\ndef update():\n if Cookie_order.validate_order(request.form) == True:\n Cookie_order.update(request.form)\n return redirect('/')\n else:\n return redirect(f'/cookies/edit/{request.form[\"id\"]}')","repo_name":"tiffanyliang333/Coding-Dojo-Python-Full-Stack","sub_path":"flask_mysql/validation/cookie_orders_validation/flask_app/controllers/cookies.py","file_name":"cookies.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7055077091","text":"# http://adventofcode.com/2015/day/14\nfrom __future__ import print_function\nfrom collections import defaultdict\n\n\ndef calc_distance(reindeer, in_time):\n name, speed, speed_time, rest_time = reindeer\n cycles, rem = divmod(in_time, speed_time + rest_time)\n return (cycles * speed_time + min(speed_time, rem)) * speed, name\n\n\ndef calc_race(data, in_time):\n return [calc_distance(reindeer, in_time) for reindeer in data]\n\n\ndef award_points(data, in_time, tally):\n race = calc_race(data, in_time)\n winner_distance = max(race)[0]\n # check for multiple winners\n winners = filter(lambda r: r[0] == winner_distance, race)\n for _, winner in winners:\n tally[winner] += 1\n\n\ndef process(data):\n # part 1\n race = calc_race(data, 2503)\n winning = max(race)\n print(\"Part 1:\", winning[0])\n # part 2\n tally = defaultdict(int)\n for tick in range(1, 2503 + 1):\n award_points(data, tick, tally)\n winner = max(tally, key = lambda key: tally[key])\n print(\"Part 2:\", tally[winner])\n\n\ndef parse(line):\n line = line.strip().split()\n name, speed, speed_time, rest_time = line[0], int(line[3]), int(line[6]), int(line[13])\n return name, speed, speed_time, rest_time\n\n\ndef load_data(fileobj):\n data = [parse(line) for line in fileobj]\n return data\n\n\ndef main(file):\n print(file)\n with open(file) as f:\n process(load_data(f))\n\n\nif __name__ == \"__main__\":\n # main(\"test.txt\")\n main(\"input.txt\")\n","repo_name":"PetrPrazak/AdventOfCode","sub_path":"2015/14/aoc2015_14.py","file_name":"aoc2015_14.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39982484695","text":"import json\nfrom django.http import JsonResponse\nimport os\n# Create your views here.\nfrom django.shortcuts import render\nfrom django.http import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\ncurrent_app_dir = os.path.dirname(os.path.abspath(__file__ ))\ndbg=open('symptoms.txt','a')\napi_url=[]\n@csrf_exempt\ndef chatbot_view(request):\n if request.method == 'POST':\n user_message = request.POST.get('message')\n bot_response = process_user_message(user_message)\n return JsonResponse({'message': bot_response})\n return render(request, 'chatbot_app/chatbot.html')\n\ndef process_user_message(user_message):\n with open(os.path.join(current_app_dir, 'medicalterms.json'), 'r') as json_file:\n data = json.load(json_file)\n search_query = user_message\n for item in data:\n if search_query.lower() in item['name'].lower() or search_query.lower() in item['text'].lower():\n return (item['text'])\n return \"I don't know\"\n","repo_name":"Saurabhpal052/medinavi","sub_path":"MediNavi/chatbot/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5671360013","text":"# -*- coding: utf-8 -*-\n\nfrom os import path\nfrom scipy.misc import imread\nfrom wordcloud import WordCloud, ImageColorGenerator\n\nd = path.dirname(__file__)\n\nstopwordsHR = ['a', 'ako', 'ali', 'bi', 'bih', 'bila', 'bili', 'bilo', 'bio',\n'bismo', 'biste', 'biti', 'bumo', 'da', 'do', 'duž', 'ga', 'hoće', 'hoćemo',\n'hoćete', 'hoćeš', 'hoću', 'i', 'iako', 'ih', 'ili', 'iz', 'ja', 'je',\n'jedna', 'jedne', 'jedno', 'jer', 'jesam', 'jesi', 'jesmo', 'jest', 'jeste',\n'jesu', 'jim', 'joj', 'još', 'ju', 'kada', 'kako', 'kao', 'koja', 'koje',\n'koji', 'kojima', 'koju', 'kroz','li', 'me', 'mene', 'meni', 'mi', 'mimo',\n'moj', 'moja', 'moje', 'mu', 'na', 'nad', 'nakon', 'nam', 'nama', 'nas',\n'naš', 'naša', 'naše', 'našeg', 'ne', 'nego', 'neka', 'neki', 'nekog', 'neku',\n'nema', 'netko', 'neće', 'nećemo', 'nećete', 'nećeš', 'neću', 'nešto', 'ni',\n'nije', 'nikoga', 'nikoje', 'nikoju', 'nisam', 'nisi', 'nismo', 'niste',\n'nisu', 'njega', 'njegov', 'njegova', 'njegovo', 'njemu', 'njezin', 'njezina',\n'njezino', 'njih', 'njihov', 'njihova', 'njihovo', 'njim', 'njima', 'njoj',\n'nju', 'no', 'o', 'od', 'odmah', 'on', 'ona', 'oni', 'ono', 'ova', 'pa',\n'pak', 'po', 'pod', 'pored', 'prije', 's', 'sa', 'sam', 'samo', 'se', 'sebe',\n'sebi', 'si', 'smo', 'ste', 'su', 'sve', 'svi', 'svog', 'svoj', 'svoja',\n'svoje', 'svom', 'ta', 'tada', 'taj', 'tako', 'te', 'tebe', 'tebi', 'ti',\n'to', 'toj', 'tome', 'tu', 'tvoj', 'tvoja', 'tvoje', 'u', 'uz', 'vam', 'vama',\n'vas', 'vaš', 'vaša', 'vaše', 'već', 'vi', 'vrlo', 'za', 'zar', 'će', 'ćemo',\n'ćete', 'ćeš', 'ću', 'što', 'e', 'ima', 'ce', 'kad', 'sad', 'sto', 'ovo',\n'onda', 'ma', 'zato', 'ko', 'jo', 'tko', 'im', 'gdje', 'sta', 'šta', 'bez',\n'jos', 'kod', 'ovaj', 'treba', 'nek', 'vise', 'zasto', 'al', 'ono', 'dok', 'mogu',\n'ove', 'tamo', 'neko', 'jako', 'jel', 'zbog', 'nebi', 'http', 'ovi', 'ove', 'one',\n'on', 'https', 'kaj', 'svaka', 'niti', 'u', 'kakva', 'oko', 'ovu', 'niko', 'di',\n'onaj', 'nesto', 'com', 'neke', 'imam', 'ide', 'eto', 'imamo', 'isto', 'ide', 'tak',\n'preko', 'bit', 'baš', 'tom', 'tek', 'ipak', 'bar', 'www', 'netko', 'cu', ]\n\n# Sources - contains the names of sources to process\n# To be able to process a source we need two files:\n# [SOURCE_NAME]_comments.txt - the file used as a source for the wordcloud\n# [SOURCE_NAME].png - Image that the wordcloud will be shaped/colored into\nsourceNames = []\n\nfor sourceName in sourceNames:\n\t# Read the whole text.\n\ttext = open(path.join(d, sourceName + '_comments.txt')).read()\n\tcoloring = imread(path.join(d, sourceName + \".png\"))\n\n\t# Generate a word cloud image \n\twordcloud = WordCloud(\n\t\tmask=coloring,\n\t\tfont_path='/Users/igorrinkovec/Library/Fonts/LobsterTwo-Regular.otf',\n\t\trelative_scaling=.5,\n\t\tbackground_color=\"white\",\n\t\twidth=3000,\n\t\theight=3000,\n\t\tstopwords=stopwordsHR).generate(text)\n\timage_colors = ImageColorGenerator(coloring)\n\n\t# Display the generated image:\n\t# the matplotlib way:\n\timport matplotlib.pyplot as plt\n\tplt.imshow(wordcloud.recolor(color_func=image_colors))\n\tplt.axis(\"off\")\n\tfig = plt.gcf();\n\tfig.set_size_inches(30, 30)\n\tfig.savefig(sourceName + '_cloud.png', dpi=100)\n","repo_name":"TheWildHorse/FacebookCommentsWordcloud","sub_path":"wordcloud_generator/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"sh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29795758740","text":"## Imports\nimport random\n\nfrom Animal import animal\nfrom world.Water import water\nfrom world.Food import food\n\nclass empty_space:\n type = \"empty\"\n finished = False\n\n##########################\n## Sim helper functions ##\n##########################\n\ndef create_animals(number_of_animals, world_size):\n animals = []\n for i in range(number_of_animals):\n # Generate traits of animal\n endurance = random.randint(1, 10)\n strength = random.randint(1, 10)\n speed = random.randint(1, 10)\n reproductive_score = random.randint(1, 100)\n position = random.randint(1, world_size-1)\n\n # Create animal object and add to list of animals\n new_animal = animal(endurance, strength, speed, reproductive_score, position)\n animals.append(new_animal)\n\n return animals\n\ndef create_world(world_size):\n world = [None, ] * world_size\n amount_water_remaining = world_size // 10\n amount_food_remaining = world_size // 10\n\n while amount_water_remaining:\n sickness_chance = random.randint(1, 100)\n amount = random.randint(1, 3)\n new_water = water(sickness_chance, amount)\n while True:\n location = random.randint(0, world_size-1)\n if world[location] is None:\n world[location] = new_water\n break\n amount_water_remaining -= 1\n\n while amount_food_remaining:\n nutrition = random.randint(1, 5)\n amount = random.randint(1, 3)\n sickness_chance = random.randint(1, 100)\n\n while True:\n location = random.randint(0, world_size-1)\n if world[location] is None:\n new_food = food(nutrition, amount, sickness_chance)\n world[location] = new_food\n break\n amount_food_remaining -= 1\n\n for n, space in enumerate(world):\n if space is None:\n world[n] = empty_space()\n\n return world\n\ndef create_food(world, food_missing):\n world_size = len(world)\n while food_missing:\n nutrition = random.randint(1, 5)\n amount = random.randint(1, 3)\n sickness_chance = random.randint(1, 100)\n\n new_food = food(nutrition, amount, sickness_chance)\n while True:\n location = random.randint(0, world_size-1)\n if world[location].type == \"empty\":\n world[location] = new_food\n break\n food_missing -= 1\n return world\n\ndef create_water(world, water_missing):\n world_size = len(world)\n while water_missing:\n sickness_chance = random.randint(1, 100)\n amount = random.randint(1, 3)\n new_water = water(sickness_chance, amount)\n while True:\n location = random.randint(0, world_size-1)\n # print (f\"L: {location}\")\n if world[location].type == \"empty\":\n world[location] = new_water\n break\n water_missing -= 1\n return world\n\n## Sim object\nclass sim:\n def __init__(self):\n self.world_size = 100\n self.animals = create_animals(1, self.world_size) # List of animal objects\n self.world = create_world(self.world_size)\n self.food_missing = 0\n self.water_missing = 0\n\n ## Sim main loop function\n def run(self):\n cycles = 0\n animals_to_be_added = []\n while len(self.animals) > 0: # and cycles < 100\n print (f\"Cycle: {cycles}\")\n print (f\"📘📘📘Population: {len(self.animals)}\")\n #print ([animal.total_health for animal in self.animals])\n\n for animal_index, animal in enumerate(self.animals):\n animal.total_health = animal.hunger * 0.33 + animal.thirst * 0.33 + animal.fatigue * 0.33\n if animal.sickness:\n animal.total_health -= 5\n if animal.total_health <= 0:\n #print (f\"Animal died, Hunger: {animal.hunger} Thirst: {animal.thirst} Fatigue: {animal.fatigue} number_reproduced: {animal.number_reproduced}\")\n self.animals.pop(animal_index)\n continue\n\n reached_goal = animal.move(self.world)\n if reached_goal:\n if animal.goal_type == \"sleep\":\n animal.rest()\n elif animal.goal_type == \"food\":\n self.world = animal.eat(self.world)\n elif animal.goal_type == \"water\":\n self.world = animal.drink(self.world)\n #print (\"drink\")\n\n # Check for possible_mates\n mates_nearby = [[mate, index] for index, mate in enumerate(self.animals) if abs(mate.position - mate.position) < 2]\n possible_mates = [[mate, index] for mate, index in mates_nearby if mate.total_health + animal.reproductive_score > 150 and animal.total_health + mate.reproductive_score > 150]\n if possible_mates:\n nearest_mate = possible_mates.pop()\n while possible_mates:\n next_mate = possible_mates.pop()\n if abs(animal.position - next_mate[0].position) < abs(animal.position - nearest_mate[0].position):\n nearest_mate = next_mate\n\n mate, mate_index = nearest_mate\n new_animal = animal.reproduce(mate)\n\n mate.fatigue -= 10\n animal.fatigue -= 10\n self.animals[mate_index] = mate\n animals_to_be_added.append(new_animal)\n\n # Update list to reflect new animal state\n self.animals[animal_index] = animal\n\n\n # Update world to reflect resources that are gone\n for n, space in enumerate(self.world):\n if space.finished:\n self.world[n] = empty_space()\n if space.type == \"food\":\n self.food_missing += 1\n elif space.type == \"water\":\n self.water_missing += 1\n\n if self.food_missing:\n self.world = create_food(self.world, self.food_missing)\n\n if self.water_missing:\n self.world = create_water(self.world, self.water_missing)\n\n self.food_missing, self.water_missing = 0, 0\n\n self.animals.extend(animals_to_be_added)\n animals_to_be_added = []\n cycles += 1\n\n\n## Run the sim fucntion\nif __name__ == '__main__':\n s = sim()\n s.run()\n","repo_name":"NoahBlack012/ecosystem_v2","sub_path":"src/simulation/Sim.py","file_name":"Sim.py","file_ext":"py","file_size_in_byte":6611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16312241274","text":"import speech_recognition as sr \nfrom gtts import gTTS \nimport time\nimport playsound\nimport os\nimport pyttsx3 \nimport webbrowser\nimport requests\nfrom bs4 import BeautifulSoup\nimport time\nfrom googlesearch import search\nimport subprocess\nimport datetime\nimport random\nfrom pynput.keyboard import Key, Controller\nimport psutil\nimport wikipedia\nfrom deep_translator import GoogleTranslator\nimport threading\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\n\n\n##DİL\ndef speak_t(text):\n tts = gTTS(text=text, lang=\"tr\")\n filename = \"voice.mp3\"\n tts.save(filename)\n playsound.playsound(filename)\n\n\ndef speak(text):\n engine = pyttsx3.init('sapi5')\n engine.setProperty('rate', 160)\n engine.setProperty('voice', engine.getProperty('voices')[0].id)\n engine.say(text)\n engine.runAndWait()\n\n\ndef get_audio():\n r = sr.Recognizer()\n \n with sr.Microphone() as source:\n r.adjust_for_ambient_noise(source)\n audio = r.listen(source)\n said = \"\"\n\n try:\n said = r.recognize_google(audio,language=\"tr-TR\")\n print(said)\n except Exception as e:\n print(\"Exception: \" + str(e))\n return said.lower()\n\n\ndef get_audio_eng():\n r = sr.Recognizer()\n with sr.Microphone() as source:\n audio = r.listen(source)\n said = \"\"\n\n try:\n said = r.recognize_google(audio, language=\"en-US\")\n print(said)\n except Exception as e:\n print(\"Exception: \" + str(e))\n return said.lower()\n\n\ndef get_audio_withoutwriting():\n r = sr.Recognizer()\n with sr.Microphone() as source:\n audio = r.listen(source)\n said = \"\"\n try:\n said = r.recognize_google(audio,language=\"tr-TR\")\n except Exception as e:\n print(\"\")\n return said.lower()\n\n\ndef get_audio_withoutwriting_eng():\n r = sr.Recognizer()\n with sr.Microphone() as source:\n audio = r.listen(source)\n said = \"\"\n try:\n said = r.recognize_google(audio, language='en-US')\n except Exception as e:\n print(\"\")\n return said.lower()\n\n\ndef countdown(t):\n from Func import countdown\n countdown(t)\n\n\ndef alarm_tanıma(t):\n from Func import alarm_tanıma\n alarm_tanıma(t)\n\n\ndef film_bul(irating):\n from Func import film_bul\n film_bul(irating)\n\n\ndef film_seç():\n from Func import film_seç\n film_seç()\n\n\ndef bekleme(answer):\n from Func import bekleme\n bekleme(answer)\n\n\ndef googlesearch(answer):\n from Func import googlesearch\n googlesearch(answer)\n\n\ndef not_alma(text):\n from Func import not_alma\n not_alma(text)\n\n\ndef notları_oku():\n from Func import notları_oku\n\n\ndef notları_göster():\n from Func import notları_göster\n\n\ndef notları_yazdır():\n from Func import notları_yazdır\n\n\ndef notları_temizle():\n from Func import notları_temizle\n\n\ndef liste_aç():\n from Func import liste_aç\n\n\ndef listeye_yaz():\n from Func import listeye_yaz\n\n\ndef ses_kıs(ses):\n from Func import ses_kıs\n ses_kıs(ses)\n\n\ndef ses_aç(ses):\n from Func import ses_aç\n ses_aç(ses)\n\n\ndef hava_durumu():\n from Func import hava_durumu\n hava_durumu()\n\n\ndef translate():\n from Func import translate\n\nWAKE = [\"uyan\", \"kalk\", \"sana da\", \"alita\", \"harita\", \"alita kalk\", \"alita uyan\", \"burada mısın\", \"günaydın\",\n \"iyi günler\", \"iyi akşamlar\"]\nSHUT = [\"kapat kendini\", \"Kapat kendini\", \"mute\", \"mut\", \"kapan\", \"kendini kapat\", \"kapan\", \"uyu alita\",\n \"işim bitti alita\", \"bu kadardı alita\", \"bu kadar\"]\nWHATSAPP = [\"whatsapp\"]\nSEARCH_STR = [\"google'da arat\", \"search up google\", \"search at google\"]\nFILM_SEARCH_STR = [\"film bul\", \"bize film bul\", \"bize bir film bul\", \"film bulabilir musun\"]\nTAKE_NOTE = [\"not alır mısın\", \"şunu not alır mısın\", \"not al\", \"notlara şunu ekle\", \"notlara şunu ekler misin\",\"şunu yaz\", \"şunu notlara yaz\", \"notlarıma şunu ekler misin\"]\nNOT_YAZDIR_STR = [\"notlarımı ekrana yazdır\", \"notları yazdır\", \"notlarımı ekranda göster\"]\nDATE_STR = [\"bugünün tarihi ne\", \"tarihi söyle\", \"bugünün tarihini söyle\", \"tarih ne\"]\nGREETINGS_STR = [\"merhaba\", \"Merhaba\", \"hey alita\", \"Hey alita\"]\nCONDITION = [\"nasılsın\", \"nasılsınız alita\", \"Nasılsın alita\", \"nasılsın alita\"]\nNAME_STR = [\"adın ne\", \"Adın ne\", \"Adını söyle\", \"İsmin ne\", \"ismini söyle\", \"ismin ne\", \"İsmin ne senin\",\n \"Adın ne senin\", \"Adın ne senin\", \"ismini söyle bakalım\"]\nDCMUTE_STR = [\"discord'da modemi\", \"mutemi\", \"mutemi kapat\", \"Mute mi\", \"sustur\", \"mute mi\", \"mutele\"]\nSS_STR = [\"seal\", \"SS al\", \"eses al\", \"Eses al\"]\nALTIN_STR = [\"altın kaç tl\", \"altın ne kadar\", \"altın kaç olmuş\", \"altın kaç oldu\"]\nDOLAR_STR = [\"dolar kaç tl\", \"dolar ne kadar\", \"dolar kaç olmuş\", \"dolar kaç oldu\"]\nEURO_STR = [\"euro kaç oldu\", \"euro kaç tl\", \"euro ne kadar\", \"Euro ne kadar\", \"euro kaç olmuş\"]\nSLEEP_STR = [\"bilgisayarı uyut\", \"uyku moduna geç\"]\nCHESS_STR = [\"satranç oynayacağım\", \"satranç oynayalım\", \"satranç sitesini\", \"satranç\"]\nINTEGRALSITE_STR = [\"integral hesaplayıcı\", \"integral sitesini\", \"integral hesaplayıcı sını\",\n \"hesaplanacak integrallerin var\"]\nARASTIRMA_STR = [\"bunu araştır\", \"buna bak\", \"bu ne araştır\", \"araştırma yap\", \"araştır\"]\n\nCHATBOT =[\"konuşma botu\",\"konuşma botunu aç\",\"chatbot\",\"cat bot aç\",\"cat bot\",\"chat botunu aç\",\"chat botun aç\"]\nCHATBOT_KAPATMA=[\"konuşma botunu kapat\",\"kapat kendini\",\"botu durdur\",\"kendini durdur\",\"yeterli\",\"sus artık\",\"tamam sus\"]\n\ndef nn_chatbot(training=1):\n speak(\"Chatbot is loading...\")\n tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-large\")\n model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-large\").to(\"cuda\")\n \n step=0\n speak(\"All ready\")\n while True:\n text=get_audio()\n for words in CHATBOT_KAPATMA:\n if text==words:\n break\n if text!=\"\":\n text=GoogleTranslator(source=\"auto\", target=\"en\").translate(text)\n print(text)\n new_user_input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors='pt').to(\"cuda\")\n bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n\n chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).to(\"cuda\")\n\n speak(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))\n step=training\n else:\n print(\"\")\n\n\n\n\nkeyboard = Controller()\n\ncondition2 = 1 #İngilizce olması\ncondition3 = 0 #yazıp yazmaması\n\n# MAIN LOOP\n\n\nAWAKE = True\nspeak(\"Device connected...\")\nif 4 <= int(datetime.datetime.now().strftime(\"%H\")) <= 11:\n speak(\"Good Morning, sir\")\nelif 11 < int(datetime.datetime.now().strftime(\"%H\")) < 18:\n speak(\"Good Afternoon, sir\")\nelif int(datetime.datetime.now().strftime(\"%H\")) >= 18 or int(datetime.datetime.now().strftime(\"%H\")) < 4:\n speak(\"Good evening, sir\")\n\nwhile True:\n if not AWAKE:\n text = get_audio_withoutwriting()\n for words in WAKE:\n if text.count(words) > 0:\n AWAKE = True\n speak(\"On your command\")\n break\n\n while AWAKE:\n if condition3 == 1 and condition2 == 1:\n text = get_audio_eng()\n elif condition3 == 1 and condition2 == 0:\n text = get_audio_withoutwriting_eng()\n elif condition2 == 0 and condition3 == 0:\n text = get_audio_withoutwriting()\n elif condition2 == 1 and condition3 == 0:\n text = get_audio()\n\n if text.count(\"dili değiştir\") > 0 or text.count(\"change the language\") > 0:\n if condition3 == 0:\n condition3 = 1\n speak(\"It's English\")\n elif condition3 == 1:\n condition3 = 0\n speak(\"It's Turkish\")\n # -------------------------------------------------------------------\n if text.count(\"yazılı kalsın\") > 0 or text.count(\"yazılı olsun\") > 0 or text.count(\"write what i say\") > 0:\n speak(\"Are you sure?\")\n answer1 = get_audio()\n if answer1.count(\"evet\") > 0 or answer1.count(\"yes\") > 0 or answer1.count(\"he\") > 0:\n speak(\"Alright...\")\n condition2 = 1\n if text.count(\"dediklerimi yazma\") > 0 or text.count(\"dediklerim yazılı olmasın\") > 0 or text.count(\n \"don't write what i say\") > 0:\n speak(\"Are you sure?\")\n answer2 = get_audio()\n if answer1.count(\"evet\") > 0 or answer1.count(\"yes\") > 0 or answer1.count(\"he\") > 0:\n speak(\"Okey...\")\n condition2 = 0\n for words in CHATBOT:\n if text==words:\n nn_chatbot()\n if \"söylemek istediğin bir şey var mı\" in text:\n speak(\"Yes. Fuck you beykan...\")\n # --------------------------------------------------------------------\n for words1 in SHUT:\n if text.count(words1) > 0:\n speak(\"As you wish sir\")\n AWAKE = False\n if text.count(\"burada mısın\") > 0 or text.count(\"açık mısın\") > 0:\n speak(\"Yes,I am\")\n for i in GREETINGS_STR:\n if text.count(i) > 0:\n speak(\"Hi, how can I help you?\")\n for i in CONDITION:\n if text.count(i) > 0:\n liste = [\"Not bad\", \"Doing fine\", \"Cool\", \"So so\", \"Fine\"]\n condition = random.randint(0, 4)\n speak(liste[condition])\n speak(\"How can I help you?\")\n for i in NAME_STR:\n if text.count(i) > 0:\n liste1 = [\"My name is Alita\", \"Alita\", \"They call me Alita\", \"Master named me Alita\"]\n condition1 = random.randint(0, 3)\n speak(liste1[condition1])\n # \"\"\"***************ARAŞTIRMA************************\"\"\"\n if text.count(\"araştır\") > 0 or text.count(\"internetten bak\") > 0 or text.count(\"search for\"):\n if text.count(\"araştır\") > 0:\n text = text.replace(\"araştır \", \"\")\n print(text)\n if text.count(\"search\") > 0:\n text = text.replace(\"search for \", \"\")\n print(text)\n try:\n print(wikipedia.summary(text, sentences=2))\n speak(\"Do you want me to read it?\")\n read = get_audio()\n if read.count(\"evet\") > 0:\n speak(wikipedia.summary(text, sentences=1))\n except:\n try:\n wikipedia.set_lang('tr')\n print(wikipedia.summary(text, sentences=2))\n speak(\"I found it on Turkish resources.Do you want me to read it?\")\n read = get_audio()\n if read.count(\"evet\") > 0:\n speak_t(wikipedia.summary(text, sentences=1))\n os.remove(\"voice.mp3\")\n except:\n speak(\"I could not find any information...\")\n for words in SEARCH_STR:\n if text.count(words) > 0:\n if text.count(\"search at google\") > 0:\n text = text.replace(\"search at google \", \"\")\n if text.count(\"search up google\") > 0:\n text = text.replace(\"search up google \", \"\")\n if text.count(\"google'da arat\") > 0:\n text = text.replace(\"google'da arat \", \"\")\n googlesearch(text)\n for i in FILM_SEARCH_STR:\n if text.count(i) > 0:\n speak(\"What rating do you want?\")\n try:\n while True:\n a = get_audio()\n if a == \"vazgeçtim\":\n speak(\"I am not searching for a film\")\n break\n elif float(a) >= 10:\n speak(\"That is not a valid number for imdb\")\n elif float(a) < 8:\n speak(\"Really!\")\n elif 8 <= float(a) < 10:\n film_bul(float(a))\n speak(\"Here you are\")\n while True:\n speak(\"Did you make your choice?\")\n answer = get_audio()\n bekleme(answer)\n break\n film_seç()\n except:\n speak(\"There is a mistake\")\n # if text.count(\"haritada şunu arar mısın\")>0 or text.count(\"haritada arama yap\")>0 or text.count(\"haritada ara\")>0:\n # answer3=get_audio()\n # answer3.replace(\" \",\"+\")\n # webbrowser.open(f\"https://www.google.com/maps/{answer3}\")\n # \"\"\"*********************************************************************\"\"\"\n # \"\"\"****************************NOTLAR***********************************\"\"\"\n for i in TAKE_NOTE:\n if text.count(i) > 0:\n speak(\"What would you like me to write down?\")\n\n note = get_audio()\n not_alma(note)\n if text.count(\"notlarımı oku\") > 0:\n speak(\"Yes sir\")\n notları_oku()\n if text.count(\"notlarımı göster\") > 0:\n speak(\"Yes sir\")\n notları_göster()\n for i in NOT_YAZDIR_STR:\n if text.count(i) > 0:\n notları_yazdır()\n if text.count(\"notları temizle\") > 0:\n notları_temizle()\n speak(\"Sector clear\")\n # \"\"\"********************************************************************\"\"\"\n # \"\"\"***************************DATE İŞLERİ******************************\"\"\"\n if text.count(\"Bugün günlerden ne\") > 0 or text.count(\"günlerden ne\") > 0:\n day_talk = \"Today is \" + datetime.datetime.now().strftime(\"%A\")\n speak(day_talk)\n for i in DATE_STR:\n if text.count(i) > 0:\n date_talk = \"We are in \" + datetime.datetime.now().strftime(\"%x\")\n speak(date_talk)\n if text.count(\"Hangi aydayız\") or text.count(\"hangi aydayız\") > 0:\n month_talk = \"We are in \" + datetime.datetime.now().strftime(\"%B\")\n speak(month_talk)\n if text.count(\"hangi yıldayız\") > 0:\n year_talk = \"We are in \" + datetime.datetime.now().strftime(\"%Y\")\n speak(year_talk)\n if text.count(\"saat kaç\") > 0 or text.count(\"Saati söyle\") > 0:\n hour_talk = \"It is \" + datetime.datetime.now().strftime(\"%X\")\n speak(hour_talk)\n # \"\"\"**************************************************************************\"\"\"\n if text.count(\"sağırlaştır\") > 0 or text.count(\"sağırlaştır beni\") > 0:\n keyboard.press(Key.f10)\n keyboard.release(Key.f10)\n for i in SS_STR:\n if text.count(i) > 0:\n speak(\"Ss taken\")\n keyboard.press(Key.ctrl_l)\n keyboard.press(Key.f8)\n keyboard.release(Key.ctrl_l)\n keyboard.release(Key.f8)\n for i in DOLAR_STR:\n if text.count(i) > 0:\n url = \"https://kur.doviz.com/serbest-piyasa/amerikan-dolari\"\n response = requests.get(url)\n içerik = response.content\n soup = BeautifulSoup(içerik, \"html.parser\")\n piyasa = soup.find_all(\"span\", {\"class\": \"value\"})\n dolar = piyasa[1].text\n speak(\"Dolar is \" + dolar)\n print(dolar)\n for i in ALTIN_STR:\n if text.count(i) > 0:\n url = \"https://kur.doviz.com/serbest-piyasa/amerikan-dolari\"\n response = requests.get(url)\n içerik = response.content\n soup = BeautifulSoup(içerik, \"html.parser\")\n piyasa = soup.find_all(\"span\", {\"class\": \"value\"})\n altın = piyasa[0].text\n speak(\"Gold is \" + altın)\n print(altın)\n for i in EURO_STR:\n if text.count(i) > 0:\n url = \"https://kur.doviz.com/serbest-piyasa/amerikan-dolari\"\n response = requests.get(url)\n içerik = response.content\n soup = BeautifulSoup(içerik, \"html.parser\")\n piyasa = soup.find_all(\"span\", {\"class\": \"value\"})\n euro = piyasa[2].text\n speak(\"Euro is \" + euro)\n print(euro)\n for i in SLEEP_STR:\n if text.count(i) > 0:\n speak(\"Sleep mode activated\")\n os.system(\"rundll32.exe powrprof.dll,SetSuspendState Sleep \")\n if text.count(\"liste yarat\") > 0:\n liste_aç()\n if text.count(\"listeye yaz\") > 0:\n listeye_yaz()\n # **************************AÇMA**********************************************\n if text.count(\"aç\") > 0 and text.count(\"sesi\") == 0 and text.count(\"kaç\") == 0:\n speak(\"Yes sir\")\n for i in WHATSAPP:\n if text.count(i) > 0:\n webbrowser.open(\"https://web.whatsapp.com/\")\n if text.count(\"youtube'u\") > 0 or text.count(\"Youtube'u\") > 0:\n webbrowser.open(\"https://youtube.com/\")\n if text.count(\"netflix'i\") > 0 or text.count(\"netflix'i\") > 0:\n webbrowser.open(\"https://www.netflix.com/browse\")\n if text.count(\"classroom u\") > 0 or text.count(\"classroom\") > 0:\n webbrowser.open(\"https://classroom.google.com/h\")\n if text.count(\"ders sistemini\") > 0:\n webbrowser.open(\"https://lms.gazi.edu.tr/\")\n if text.count(\"sınav sistemini\") > 0:\n webbrowser.open(\"https://gazi-sinav-lms.almscloud.net/Home/Login\")\n if text.count(\"spotify\") > 0 or text.count(\"spotify'ı\") > 0:\n spotifyPath = \"C:\\\\Users\\\\Murat\\AppData\\\\Local\\\\Microsoft\\\\WindowsApps\\\\Spotify.exe\"\n os.startfile(spotifyPath)\n if text.count(\"valorant\") > 0 or text.count(\"valorant'ı \") > 0:\n valorantPath = \"C:\\\\Users\\Murat\\\\Desktop\\OYUN\\\\VALORANT\"\n os.startfile(valorantPath)\n if text.count(\"cs yi\") > 0 or text.count(\"counter aç\") > 0:\n csPath = \"C:\\\\Users\\\\Murat\\\\Desktop\\\\OYUN\\\\Counter-Strike Global Offensive\"\n os.startfile(csPath)\n if text.count(\"discord\") > 0 or text.count(\"discord'u\") > 0:\n dcPath = \"C:\\\\Users\\\\Murat\\\\Desktop\\\\PROGRAM\\\\Discord\"\n os.startfile(dcPath)\n if text.count(\"olasılık hocasının notlarını\") > 0 or text.count(\"olasılık notlarını\"):\n webbrowser.open(\"https://drive.google.com/drive/folders/1RvmriC5va3kZEhskdoAEhE7xdLT0xeqY\")\n if text.count(\"email\") > 0 or text.count(\"gmail'i\") > 0 or text.count(\"gmail\") > 0:\n webbrowser.open(\"https://mail.google.com/mail/u/0/#inbox\")\n if text.count(\"udemy\") > 0:\n webbrowser.open(\"https://www.udemy.com/\")\n if text.count(\"google'ı\") > 0 or text.count(\"google\") > 0:\n webbrowser.open(\"https://www.google.com/\")\n if text.count(\"çalışma alanımı\") > 0 or text.count(\"günlüğü\") > 0 or text.count(\"çalışma alanını\") > 0:\n webbrowser.open(\"https://app.clickup.com/4600131/v/b/li/42560592\")\n for phrases in CHESS_STR:\n if text.count(phrases) > 0:\n speak(\"Which site do you want?\")\n answer4 = get_audio()\n if answer4.count(\"farketmez\") > 0 or answer4.count(\"chess\") > 0:\n webbrowser.open(\"https://www.chess.com/home\")\n if answer4.count(\"lichess\") > 0:\n webbrowser.open(\"https://lichess.org/\")\n for i in INTEGRALSITE_STR:\n if text.count(i) > 0:\n webbrowser.open(\"https://www.symbolab.com/solver/integral-calculator\")\n for i in DCMUTE_STR:\n if text.count(i) > 0:\n keyboard.press(Key.f9)\n keyboard.release(Key.f9)\n # \"\"\"*********************************************************************************\"\"\"\n if text.count(\"yüzdelerini\") > 0 or text.count(\"yüzdeler\") > 0:\n speak(f\"memory %{psutil.virtual_memory()[2]} used\")\n print(f\"Memory %{psutil.virtual_memory()[2]} used...\")\n speak(f\"cpu %{psutil.cpu_percent(interval=1)} used\")\n print(f\"cpu %{psutil.cpu_percent(interval=1)} used...\")\n speak(\"disk usage is {}\".format(psutil.disk_usage(\"/\")[3]))\n print(\"Disk usage is {}...\".format(psutil.disk_usage(\"/\")[3]))\n if text.count(\"bunu bana hatırlat\") > 0 or text.count(\"bunu unutma\") > 0:\n with open(\"memory.txt\", \"r+\", encoding=\"utf-8\") as file:\n speak(\"What you want to remember?\")\n memory = get_audio()\n for i in memory:\n file.write(i)\n file.write(\"\\n\")\n if text.count(\"hatırlamam gereken\") > 0 or text.count(\"unutmamam gereken\") > 0:\n with open(\"Memory/memory.txt\", \"r\", encoding=\"utf-8\") as file:\n speak(file.read())\n if text.count(\"ses ayarları\") > 0 or text.count(\"sesi\") > 0 or text.count(\"müziği\") > 0:\n if text.count(\"kıs\") > 0 or text.count(\"azalt\") > 0:\n print(\"kıs\")\n ses_kıs(25)\n if text.count(\"arttır\") > 0 or text.count(\"çoğalt\") > 0:\n print(\"aç\")\n ses_aç(25)\n if text.count(\"kapat\") > 0:\n print(\"kapat\")\n ses_kıs(50)\n if text.count(\"fulle\") > 0 or text.count(\"aç\"):\n print(\"full\")\n ses_aç(50)\n if text.count(\"ekranı değiştir\") > 0 or text.count(\"ekran değiş\") > 0 or text.count(\"ekranı değiş\") > 0:\n keyboard.press(Key.alt)\n keyboard.press(Key.tab)\n keyboard.release(Key.alt)\n keyboard.release(Key.tab)\n if text.count(\"hava nasıl\") > 0 or text.count(\"hava durumu\") > 0:\n hava_durumu()\n if text.count(\"çeviri\") > 0:\n translate()\n if text.count(\"alarm\") > 0 and text.count(\"kur\") > 0:\n speak(\"Alarm set\")\n p1=threading.Thread(target=alarm_tanıma,args=[text])\n p1.start()\n\n\n if text.count(\"uyandır\")>0 and text.count(\"beni\"):\n speak(\"Alarm set\")\n p1 = threading.Thread(target=alarm_tanıma, args=[text])\n p1.start()\n p1.join()\n\n while True:\n text=get_audio_withoutwriting()\n speak(\"Time is up sir, wake up\")\n if text.count(\"tamam\")>0 or text.count(\"kapat\")>0 or text.count(\"sus\")>0:\n break\n\n\n if text.count(\"antrenman moduna geç\") > 0:\n go = \"\"\n while go.count(\"bitti\") == 0:\n speak(\"What do you want to do?\")\n go = get_audio_withoutwriting()\n if go.count(\"kardiyo\") > 0:\n speak(\"when you are ready\")\n while True:\n go = get_audio_withoutwriting()\n if go.count(\"başlat\") > 0 or go.count(\"go\") > 0:\n speak(\"Burpees for 1 minute\")\n speak(\"1, 2, 3, go\")\n countdown(60) # 60\n speak(\"move on to planck for 30 second\")\n time.sleep(2)\n countdown(30) # 30\n speak(\"move on to high knees for 30 second\")\n countdown(30) # 30\n speak(\"move on to planck for 30 second\")\n countdown(30) # 30\n speak(\"leg raise, 20 times\")\n go = get_audio_withoutwriting()\n if go.count(\"bitti\") > 0:\n speak(\"You have been amazing so far, sir\")\n break\n if text.count(\"sınav moduna geç\") > 0:\n speak(\"War mode Activated\")\n while True:\n yardım = get_audio_withoutwriting()\n if yardım.count(\"sınav bitti\") > 0:\n speak(\"I hope you have done well, sir\")\n break\n if yardım.count(\"olasılık hocasının notlarını\") > 0 or yardım.count(\"olasılık notlarını\"):\n webbrowser.open(\"https://drive.google.com/drive/folders/1RvmriC5va3kZEhskdoAEhE7xdLT0xeqY\")\n if yardım.count(\"çeviri\") > 0:\n translate()\n for i in INTEGRALSITE_STR:\n if text.count(i) > 0:\n webbrowser.open(\"https://www.symbolab.com/solver/integral-calculator\")\n for i in DCMUTE_STR:\n if text.count(i) > 0:\n keyboard.press(Key.f9)\n keyboard.release(Key.f9)\n if text.count(\"classroom u\") > 0 or text.count(\"classroom\") > 0:\n webbrowser.open(\"https://classroom.google.com/h\")\n if text.count(\"ders sistemini\") > 0:\n webbrowser.open(\"https://lms.gazi.edu.tr/\")\n if text.count(\"sınav sistemini\") > 0:\n webbrowser.open(\"https://gazi-sinav-lms.almscloud.net/Home/Login\")\n if text.count(\"spotify\") > 0 or text.count(\"spotify'ı\") > 0:\n spotifyPath = \"C:\\\\Users\\\\Murat\\AppData\\\\Local\\\\Microsoft\\\\WindowsApps\\\\Spotify.exe\"\n os.startfile(spotifyPath)\n for i in WHATSAPP:\n if text.count(i) > 0:\n webbrowser.open(\"https://web.whatsapp.com/\")\n if text.count(\"youtube'u\") > 0 or text.count(\"Youtube'u\") > 0:\n webbrowser.open(\"https://youtube.com/\")\n if text.count(\"saat kaç\") > 0 or text.count(\"Saati söyle\") > 0:\n hour_talk = \"It is \" + datetime.datetime.now().strftime(\"%X\")\n speak(hour_talk)\n if text.count(\"araştır\") > 0 or text.count(\"internetten bak\") > 0 or text.count(\"search for\"):\n if text.count(\"araştır\") > 0:\n text = text.replace(\"araştır \", \"\")\n print(text)\n if text.count(\"search\") > 0:\n text = text.replace(\"search for \", \"\")\n print(text)\n try:\n print(wikipedia.summary(text, sentences=2))\n speak(\"Do you want me to read it?\")\n read = get_audio()\n if read.count(\"evet\") > 0:\n speak(wikipedia.summary(text, sentences=1))\n except:\n try:\n wikipedia.set_lang('tr')\n print(wikipedia.summary(text, sentences=2))\n speak(\"I found it on Turkish resources.Do you want me to read it?\")\n read = get_audio()\n if read.count(\"evet\") > 0:\n speak_t(wikipedia.summary(text, sentences=1))\n os.remove(\"voice.mp3\")\n except:\n speak(\"I could not find any information...\")\n for words in SEARCH_STR:\n if text.count(words) > 0:\n if text.count(\"search at google\") > 0:\n text = text.replace(\"search at google \", \"\")\n if text.count(\"search up google\") > 0:\n text = text.replace(\"search up google \", \"\")\n if text.count(\"google'da arat\") > 0:\n text = text.replace(\"google'da arat \", \"\")\n googlesearch(text)\n \n \n","repo_name":"uraninjo/Alita-Asisstant-","sub_path":"ALITA.py","file_name":"ALITA.py","file_ext":"py","file_size_in_byte":28543,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"482823364","text":"import tensorflow as tf\nimport tensorflow_datasets as tfds\nfrom tensorflow import keras\nfrom tensorflow.keras.layers import Dense, Embedding, LSTM, Bidirectional\nfrom tensorflow.keras.preprocessing.text import Tokenizer\n\n# set GPU as device\ntf.config.list_physical_devices('GPU')\n\n# Prepare the data\nchatbot_data = tfds.builder(\"imdb_reviews\").as_dataset(split=\"train[:10%]\", as_supervised=True)\ntokenizer = Tokenizer()\ntokenizer.fit_on_texts(chatbot_data)\ndata = tokenizer.texts_to_sequences(chatbot_data)\n\n# Build the model\nmodel = keras.Sequential()\nmodel.add(Embedding(len(tokenizer.word_index)+1, 128))\nmodel.add(Bidirectional(LSTM(64)))\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dense(len(tokenizer.word_index)+1, activation='softmax'))\n\n# Compile and train the model\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(data, labels, epochs=10)\n","repo_name":"Werby213/pythonl","sub_path":"CV2PPG/CRR/my_projects/chatGPT/Neural_network/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70890356883","text":"from res import id as id, values as values, constants as constants\nfrom util import utils as utils\nimport handler.database as db\nimport pandas as pd\nimport numpy as np\nimport talib as ta\n\n\ndef check_double_top(price_action, indices_maxima, obv):\n\n isDoubleTop = False\n\n for i in range(0, len(indices_maxima) - 1):\n highVal = price_action[id.high].iloc[indices_maxima[i]]\n closeVal = price_action[id.close].iloc[indices_maxima[i]]\n nextHighVal = price_action[id.high].iloc[indices_maxima[i + 1]]\n minVal = price_action[id.low].iloc[indices_maxima[i]:indices_maxima[i + 1]].min()\n\n if nextHighVal >= closeVal and nextHighVal <= highVal and obv[indices_maxima[i]] >= obv[indices_maxima[ i +1]]:\n for j in range(indices_maxima[ i +1] + 1, len(price_action ) -1):\n if price_action[id.close].iloc[j] < minVal:\n isDoubleTop = True\n break\n return isDoubleTop\n\ndef check_double_bottom(price_action, indices_minima, obv):\n\n isDoubleBottom = False\n\n for i in range(0, len(indices_minima) - 1):\n lowVal = price_action[id.low].iloc[indices_minima[i]]\n closeVal = price_action[id.close].iloc[indices_minima[i]]\n nextLowVal = price_action[id.low].iloc[indices_minima[i + 1]]\n maxVal = price_action[id.high].iloc[indices_minima[i]:indices_minima[i + 1]].min()\n\n if nextLowVal <= closeVal and nextLowVal >= lowVal and obv[indices_minima[i]] >= obv[indices_minima[i + 1]]:\n for j in range(indices_minima[i + 1] + 1, len(price_action) - 1):\n if price_action[id.close].iloc[j] > maxVal:\n isDoubleBottom = True\n break\n return isDoubleBottom\n\ndef double_top_double_bottom(key, dataList, time_frame):\n\n price_action = pd.DataFrame(dataList, columns=[id.time, id.open, id.close, id.high, id.low, id.volume])\n if price_action.empty:\n return\n\n window_size = constants.strategy_params[id.window_size]\n # To determine up/down trend and the strength\n upTrend = utils.__uptrend(price_action.iloc[- 2 *window_size - 3:-3][id.close].values, window_size)\n downTrend = utils.__downtrend(price_action.iloc[- 2 *window_size - 3:-3][id.close].values, window_size)\n\n # ADX to determine the strength of the trend and OBV to get volume of trades placed\n adx = ta.ADX(price_action[id.high], price_action[id.low], price_action[id.close], window_size)\n obv = ta.OBV(price_action[id.close], price_action[id.volume])\n\n # Calculate the local maxima and minima in the window frame\n local_minima, local_maxima, indices_minima, indices_maxima = utils.__local_min_max(np.array(price_action[id.high]))\n\n notifier = {\n values.double_top: False,\n values.double_bottom: False\n }\n\n if upTrend and adx[len(adx ) -1] >= constants.strategy_params[id.trend_strength]:\n notifier[values.double_top] = check_double_top(price_action, indices_maxima, obv)\n if downTrend and adx[len(adx ) -1] >= constants.strategy_params[id.trend_strength]:\n notifier[values.double_bottom] = check_double_bottom(price_action, indices_minima, obv)\n\n if notifier[values.double_top]:\n try:\n #db.insert_strategy(key, time_frame, values.double_top, price_action.iloc[-1][id.time])\n return {\n id.name: id.double_top,\n id.key: key,\n id.price_action: dataList\n }\n except:\n print('Unable to add to database')\n return {}\n\n if notifier[values.double_bottom]:\n try:\n #db.insert_strategy(key, time_frame, values.double_bottom, price_action.iloc[-1][id.time])\n return {\n id.name: id.double_bottom,\n id.key: key,\n id.price_action: dataList\n }\n except:\n print('Unable to add to database')\n return {}","repo_name":"CryptoBullMarket/popo-socket","sub_path":"strategy/doubleTopDoubleBottom.py","file_name":"doubleTopDoubleBottom.py","file_ext":"py","file_size_in_byte":3932,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"45383571608","text":"from machine import ADC\nimport time\n\nclass POS:\n def __init__(self,baselines) -> None:\n self.baselines = baselines\n self.minsSet = False\n self.minV1 = 0\n self.minV2 = 0\n self.maxV1 = 0\n self.maxV2 = 0\n self.orbitCompleteTime = 7680000;\n\n def get(self):\n \n # updateVREF();\n vrefs = []\n vrefs.append(ADC(28).read_u16()) # ADC_2\n vrefs.append(ADC(27).read_u16()) # ADC_1\n vrefs.append(ADC(26).read_u16()) # ADC_0\n\n current1 = (self.baselines[0] - vrefs[0]) / 0.1;\n current2 = (self.baselines[1] - vrefs[1]) / 0.1;\n\n volt1 = current1 * vrefs[2];\n volt2 = current2 * vrefs[2];\n # using these two voltage measurements we will calculate the angle to the sun\n\n # posPercent is the percent between 180 and 0\n # volt1 in the front pannel volt2 is the back\n if self.minsSet:\n if (volt1 > volt2):\n posPercent = (volt1 - self.minV1) / (self.maxV1 - self.minV1);\n pos = 180 * posPercent;\n else:\n posPercent = (volt2 - self.minV2) / (self.maxV2 - self.minV2);\n pos = (180 * posPercent) * -1;\n\n # Send pos immediately\n # Estimated degrees to the sun\n\n # Add 180 to bring pos to a 360 then get percent\n posPercent = (pos + 180) / 360;\n\n # percent of orbit complete in time from peak light\n timePercent = (time.ticks_ms() / self.orbitCompleteTime) * 100;\n\n # Get the average of the estimated percents\n avgPercent = (posPercent + timePercent) / 2;\n\n avgDeg = 360 * avgPercent;\n # POSUPDATE+degs\n return avgDeg\n else:\n if (time.ticks_ms() <= self.orbitCompleteTime):\n testV1 = ADC(26).read_u16();\n testV2 = ADC(27).read_u16();\n\n # set maxV* to the max voltage we see\n if (testV1 > self.maxV1):\n self.maxV1 = testV1\n if (testV2 > self.maxV2):\n self.maxV2 = testV2\n # set maxV* to the min voltage we see\n if (testV1 < self.minV1):\n self.minV1 = testV1\n if (testV2 < self.minV2):\n self.minV2 = testV2\n else:\n self.minsSet = True\n return False\n","repo_name":"lakefox/CARD_SAT","sub_path":"code/lib/getpos.py","file_name":"getpos.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5723871342","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 3 11:26:10 2020\n\n@author: Zhaoliang\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random as rnd\nfrom scipy import linalg\nimport binary_HT_1d.Gaussian_dist as Gd\n\nclass Compare_pln2statis_hist(object):\n \n def __init__(self, mean_pln = None, Sigma_pln = None,name = 'plan'):\n if(mean_pln is None or Sigma_pln is None):\n raise ValueError(\"Set proper statistics.\")\n \n self.mean_Pln = mean_pln\n self.Sigma_Pln = Sigma_pln\n self.name = name\n \n def calculate_stat(self,data):\n mean_stat = np.mean(data)\n var_stat = np.var(data)\n return mean_stat,var_stat\n \n def numer_compare(self,data):\n mean_stat,var_stat = self.calculate_stat(data)\n mean_error = np.abs(mean_stat- self.mean_Pln)\n var_error = np.abs(var_stat - self.Sigma_Pln)\n return mean_error,var_error\n \n def visualization_compare(self,data):\n mean_stat,var_stat = self.calculate_stat(data)\n dim = len(data)\n bins = dim/10\n x_stat = np.linspace(np.min(data)-1,np.max(data)+1,bins)\n x_pln = np.linspace(np.floor(np.min(data)),np.ceil(np.max(data)),bins)\n \n plt.figure()\n plt.title(\"The error histogram\")\n plt.hist(data, bins=int(bins), normed=True, alpha=1, histtype='stepfilled',\n color='steelblue', edgecolor='none')\n plt.ylabel(\"Frequency\")\n plt.xlabel(\"Error in bins\")\n Gd.plotGaussian(x_stat,mean_stat,var_stat,'v-g','mean=$\\mu_{statis}$,var=$Sigma_{statis}$')\n Gd.plotGaussian(x_pln,self.mean_Pln,self.Sigma_Pln,'.-r','mean=$\\mu_{%s}$, var=$Sigma_{%s}$'%(self.name,self.name))\n plt.legend()\n plt.title(\"Statistical histogram vs. planning mean and variance\")\n\n \n#if __name__ == '__main__':","repo_name":"CREATE-knowledge-planning/Sensing_planning_framework","sub_path":"utility_functions/CompP2SHist.py","file_name":"CompP2SHist.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"20842314321","text":"from PyQt5 import Qt\n\n\nfrom .ui import UILoadable\n\n\nclass Options(object):\n def __init__(self):\n # variables and default values\n self.optlist = [\n \"LOCAL_maxUnbound\",\n \"SA_minaccratio\",\n \"SA_tol\",\n \"SA_stopT\",\n \"SA_maxiter\",\n \"SA_meltratio\",\n \"SA_direct\",\n \"BI_stab\",\n \"BI_length\",\n \"BI_report\",\n \"BI_savehist\",\n \"BI_histFile\",\n \"workDirectory\",\n \"manualFile\",\n \"seed\",\n \"warning_chi2_low\",\n \"warning_chi2_high\",\n \"autoWizardOnLoad\",\n ]\n self.dfltlist = [\n 0,\n 0.0,\n 0.0,\n 0.1,\n 0,\n 0.97,\n True,\n 5e3,\n 5e4,\n 5e2,\n False,\n \"\",\n str(Qt.QDir.currentPath()),\n str(Qt.QDir.currentPath()) + \"/html/User Manual.html\",\n 12345,\n 0.6,\n 1.4,\n True,\n ]\n self.reset()\n\n def reset(self):\n for opt, dflt in zip(self.optlist, self.dfltlist):\n setattr(self, opt, dflt)\n\n def _pprint(self):\n print(str(self))\n for opt, dflt in zip(self.optlist, self.dfltlist):\n v = getattr(self, opt)\n print(\"\\t%s, %s %r (default=%s %r)\" % (opt, type(v), v, type(dflt), dflt))\n\n\n@UILoadable\nclass OptionsDlg(Qt.QDialog):\n \"\"\"Dialog containing options for PAScual. The options are members of this object.\"\"\"\n\n def __init__(self, parent=None):\n super(OptionsDlg, self).__init__(parent)\n self.loadUi()\n # set validators\n self.BI_lengthLE.setValidator(Qt.QDoubleValidator(self))\n self.BI_stabLE.setValidator(Qt.QDoubleValidator(self))\n self.BI_reportLE.setValidator(Qt.QDoubleValidator(self))\n self.seedLE.setValidator(Qt.QIntValidator(self))\n # connections\n self.buttonBox.clicked.connect(self.onclicked)\n self.manualFilePB.clicked.connect(self.onChangeManualFile)\n self.workDirectoryPB.clicked.connect(self.onChangeWorkDirectory)\n self.BI_savehistPB.clicked.connect(self.onChangeHistoryFile)\n\n # set options\n self.reset()\n\n def onChangeWorkDirectory(self):\n filename = Qt.QFileDialog.getExistingDirectory(\n self,\n \"Select work directory (it must be user-writable)\",\n self.workDirectoryLE.text(),\n )\n if filename:\n self.workDirectoryLE.setText(filename)\n\n def onChangeManualFile(self):\n filename, _ = Qt.QFileDialog.getOpenFileName(\n self, \"Select User Manual File\", self.manualFileLE.text(), \"(*.html *.htm)\"\n )\n if filename:\n self.manualFileLE.setText(filename)\n\n def onChangeHistoryFile(self):\n filename, _ = Qt.QFileDialog.getSaveFileName(\n self,\n \"BI history File Selection\",\n self.BI_savehistLE.text(),\n \"ASCII (*.txt)\\nAll (*)\",\n Qt.QFileDialog.DontConfirmOverwrite | Qt.QFileDialog.DontUseNativeDialog,\n )\n if filename:\n self.BI_savehistLE.setText(filename)\n\n def onclicked(self, button):\n if self.buttonBox.buttonRole(button) == Qt.QDialogButtonBox.ResetRole:\n self.reset()\n\n def reset(self):\n self.setOptions(Options())\n\n def getOptions(self):\n \"\"\"returns an options object filled with values from the dialog\"\"\"\n options = Options()\n # LOCAL\n # Max number of unbound LMA runs between L-BFGS-B optimisations\n # (this only takes place if first LMA is outside bounds)\n options.LOCAL_maxUnbound = int(self.LOCAL_maxUnboundSB.value())\n # SA\n # If the acceptance ratio drps below this value, the SA stops.\n options.SA_minaccratio = float(self.SA_minaccratioSB.value())\n # Tolerance for stopping the SA\n options.SA_tol = float(self.SA_tolSB.value())\n # Stop temperature for SA (put to 0 to disable). (SA_stopT>1 is not recommended)\n options.SA_stopT = float(self.SA_stopTSB.value())\n # Max number of iterations in the SimAnn fit\n options.SA_maxiter = int(self.SA_maxiterSB.value())\n # The \"melting\" phase of the SA will stop when this acceptance ratio is reached\n options.SA_meltratio = float(self.SA_meltratioSB.value())\n # Whether to use the direct mode in NNRLA for SA (asymetrical transit prob)\n options.SA_direct = bool(self.SA_directCB.isChecked())\n # BI\n # This many steps (multiplied by the order of the searching space!) of BI\n # will be done and not considered for statistical purposes.\n # Put this to 0 to skip stabilisation.\n options.BI_stab = float(self.BI_stabLE.text())\n # This many steps (multiplied by the order of the searching space)\n # will be calculated by BI\n options.BI_length = float(self.BI_lengthLE.text())\n # A report will be shown every this steps during BI (put to -1 for no reports).\n # Be Careful: too much reports may slow down the calc.\n options.BI_report = float(self.BI_reportLE.text())\n # This controls wheter the fitpar history should be saved (=FileName)\n # or not (=False).\n options.BI_savehist = bool(self.BI_savehistCB.isChecked())\n # Caution!: this will increase the RAM requeriments approximately by\n # 11Bytes*BI_length*(3+2*NC)^2 , where NC is the number of components!\n options.BI_histFile = str(self.BI_savehistLE.text())\n # PATHS\n options.workDirectory = str(self.workDirectoryLE.text())\n options.manualFile = str(self.manualFileLE.text())\n # MISC\n options.seed = int(self.seedLE.text()) # Seed for pseudorandom generator\n options.warning_chi2_low = float(self.warning_chi2_lowSB.value())\n options.warning_chi2_high = float(self.warning_chi2_highSB.value())\n options.autoWizardOnLoad = bool(self.autoWizardOnLoadCB.isChecked())\n return options\n\n def setOptions(self, options):\n \"\"\"set dialog values from the passed options object\"\"\"\n # LOCAL\n self.LOCAL_maxUnboundSB.setValue(int(options.LOCAL_maxUnbound))\n # SA\n self.SA_minaccratioSB.setValue(float(options.SA_minaccratio))\n self.SA_tolSB.setValue(float(options.SA_tol))\n self.SA_stopTSB.setValue(float(options.SA_stopT))\n self.SA_maxiterSB.setValue(int(options.SA_maxiter))\n self.SA_meltratioSB.setValue(float(options.SA_meltratio))\n self.SA_directCB.setChecked(bool(options.SA_direct))\n # BI\n self.BI_stabLE.setText(\"%g\" % float(options.BI_stab))\n self.BI_lengthLE.setText(\"%g\" % float(options.BI_length))\n self.BI_reportLE.setText(\"%g\" % float(options.BI_report))\n self.BI_savehistCB.setChecked(bool(options.BI_savehist))\n self.BI_savehistLE.setText(str(options.BI_histFile))\n # PATHS\n self.workDirectoryLE.setText(str(options.workDirectory))\n self.manualFileLE.setText(str(options.manualFile))\n # MISC\n self.seedLE.setText(str(options.seed))\n self.warning_chi2_lowSB.setValue(float(options.warning_chi2_low))\n self.warning_chi2_highSB.setValue(float(options.warning_chi2_high))\n self.autoWizardOnLoadCB.setChecked(bool(options.autoWizardOnLoad))\n\n\nif __name__ == \"__main__\":\n import sys\n\n app = Qt.QApplication(sys.argv)\n form = OptionsDlg(None)\n form.show()\n sys.exit(app.exec())\n","repo_name":"cpascual/PAScual","sub_path":"PAScual/PASoptions.py","file_name":"PASoptions.py","file_ext":"py","file_size_in_byte":7597,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"9933974199","text":"import os\r\nimport sys\r\nimport io\r\nimport json\r\nfrom google.cloud import vision\r\nfrom PIL import Image as PIL_Image\r\nfrom PIL import ImageOps\r\nfrom PIL.ExifTags import TAGS\r\nfrom exif import Image as exif_Image\r\n\r\n# connect Google environment variable\r\n# USER will need to update this `credential_path with (.json) file with Google creds\r\ncredential_path = \"\"\r\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credential_path\r\n\r\nADDITION = \"./\"\r\n\r\n\r\ndef image_process_only(raw_url):\r\n processed_label_findings_dict = get_image_processing_dict(raw_url)\r\n\r\n results_JSON = json.dumps(processed_label_findings_dict)\r\n\r\n print(\"Begin printing processing results\\n\" + results_JSON + \"\\nFinished printing processing results\")\r\n\r\n return results_JSON\r\n\r\n\r\ndef image_process_with_save(raw_url):\r\n processed_label_findings_dict = get_image_processing_dict(raw_url)\r\n\r\n results_JSON = json.dumps(processed_label_findings_dict)\r\n\r\n image_add_METADATA(processed_label_findings_dict, raw_url)\r\n\r\n print(\"\\nNewly processed Metadata labels have been saved within the image.\")\r\n\r\n return results_JSON\r\n\r\n\r\ndef image_del_METADATA(raw_url):\r\n print(\"\\nBegin deleting Metadata.\")\r\n\r\n # load image, wrangle URL if needed\r\n del_METADATA_url = url_wrangler(raw_url, 1)\r\n original = PIL_Image.open(del_METADATA_url)\r\n save_image_path = del_METADATA_url\r\n\r\n # rotate image to correct orientation before removing EXIF data\r\n original = ImageOps.exif_transpose(original)\r\n\r\n # save image without tags (metadata is cleared)\r\n stripped = PIL_Image.new(original.mode, original.size)\r\n stripped.putdata(list(original.getdata()))\r\n original.close()\r\n stripped.save(save_image_path)\r\n stripped.close()\r\n\r\n print(\"\\nMetadata has been deleted from image.\")\r\n\r\n return None\r\n\r\n\r\ndef url_wrangler(raw_url, needed_url_type):\r\n \"\"\"Update and decide the proper url name convention to be used,\r\n basically do we/do not want './' in url string\r\n default :: 1 is assumed to be 'current_url_type'\r\n --> needed_url_type[arg] = 1 --- 'photos/nyc.JPG'\r\n --> needed_url_type[arg] = 2 --- './photos/nyc.JPG'\r\n \"\"\"\r\n checked_url, current_url_type = raw_url, 1\r\n\r\n # figure out the current labeling within the given raw_url\r\n if checked_url[0:2] == ADDITION:\r\n current_url_type = 2\r\n\r\n # decide if the raw_url is fit for the url_type_needed\r\n if current_url_type == needed_url_type:\r\n pass\r\n # update the raw_url (if needed) to the url_type_needed\r\n elif current_url_type < needed_url_type:\r\n temp = ADDITION + checked_url\r\n checked_url = temp\r\n else:\r\n # need to rid the first 2 bytes of the string\r\n temp = checked_url\r\n checked_url = temp[2:]\r\n\r\n return checked_url\r\n\r\n\r\ndef image_add_METADATA(label_dict, raw_url):\r\n print(\"\\nBegin adding and saving image Metadata.\")\r\n\r\n add_METADATA_url = url_wrangler(raw_url, 2)\r\n with open(f\"{add_METADATA_url}\", \"r+b\") as image_file:\r\n image_file = exif_Image(image_file)\r\n\r\n # update the labels within the metadata tags\r\n results_JSON_labels = json.dumps(label_dict[\"labels\"])\r\n image_file.image_description = results_JSON_labels\r\n\r\n # save image file with metadata updates\r\n with open(f\"{add_METADATA_url}\", \"wb\") as image_file_updated:\r\n image_file_updated.write(image_file.get_file())\r\n\r\n image_file_updated.close()\r\n\r\n print(\"\\nFinished adding and saving image Metadata.\")\r\n\r\n return None\r\n\r\n\r\ndef image_check_METADATA(raw_url):\r\n \"\"\"\r\n Mostly this function is implemented to be able to check/test/debug\r\n the metadata of a image after del or add\r\n \"\"\"\r\n print(\"\\nBegin checking current image metadata.\")\r\n\r\n check_METADATA_url = url_wrangler(raw_url, 1)\r\n file_name = os.path.abspath(check_METADATA_url)\r\n\r\n # for current/saved metadata extraction, using PIL's Image, not of Google API Vision\r\n check_image = PIL_Image.open(file_name)\r\n exifdata = check_image.getexif()\r\n\r\n exifdata_dict = {}\r\n updated_exifdata_dict = exifdata_extractor(exifdata, exifdata_dict)\r\n\r\n results_JSON = json.dumps(updated_exifdata_dict)\r\n\r\n print(\"\\nCurrent image metadata: \\n\" + results_JSON + \"\\nChecking current image metadata is complete.\\n\")\r\n\r\n check_image.close()\r\n\r\n return results_JSON\r\n\r\n\r\ndef get_image_processing_dict(raw_url):\r\n load_image_url = url_wrangler(raw_url, 1)\r\n file_name = os.path.abspath(load_image_url)\r\n try:\r\n with io.open(file_name, 'rb') as image_file:\r\n content = image_file.read()\r\n image = vision.Image(content=content)\r\n print(\"\\nBegin processing image:\\n\" + file_name)\r\n except:\r\n print('Error with loading photo')\r\n exit(1)\r\n\r\n processed_label_findings = {\"labels\": []}\r\n\r\n # create a Google API vision classed object\r\n client = vision.ImageAnnotatorClient()\r\n\r\n processed_label_findings = processing_for_labels(processed_label_findings, client, image)\r\n processed_label_findings = processing_for_logos(processed_label_findings, client, image)\r\n processed_label_findings = processing_for_landmark(processed_label_findings, client, image)\r\n processed_label_findings = processing_for_text(processed_label_findings, client, image)\r\n\r\n image_file.close()\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef processing_for_labels(processed_label_findings, client, image):\r\n label_response = client.label_detection(image=image)\r\n\r\n for label in label_response.label_annotations:\r\n processed_label_findings[\"labels\"].append(label.description)\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef processing_for_logos(processed_label_findings, client, image):\r\n logo_response = client.logo_detection(image=image)\r\n\r\n for logo in logo_response.logo_annotations:\r\n processed_label_findings[\"labels\"].append(logo.description)\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef processing_for_landmark(processed_label_findings, client, image):\r\n landmark_response = client.landmark_detection(image=image)\r\n\r\n for landmark in landmark_response.landmark_annotations:\r\n processed_label_findings[\"labels\"].append(landmark.description)\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef processing_for_text(processed_label_findings, client, image):\r\n text_response = client.text_detection(image=image)\r\n\r\n # counter needed as this would give a string with \\n listing all\r\n counter_to_skip_first = 0\r\n for text in text_response.text_annotations:\r\n if counter_to_skip_first != 0:\r\n processed_label_findings[\"labels\"].append(text.description)\r\n else:\r\n counter_to_skip_first += 1\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef exifdata_extractor(exifdata, exifdata_dict):\r\n for tag_id in exifdata:\r\n # get the tag name, instead of human unreadable tag id\r\n tag, data = TAGS.get(tag_id, tag_id), exifdata.get(tag_id)\r\n\r\n # ignore all byte-saved information, it is probably TIFF-type associated\r\n if isinstance(data, bytes):\r\n pass\r\n else:\r\n exifdata_dict[tag] = data\r\n return exifdata_dict\r\n\r\n\r\nif __name__ == '__main__':\r\n raw_url = sys.argv[1]\r\n","repo_name":"potatoe88888/CS361--microservice","sub_path":"imageMETADATA.py","file_name":"imageMETADATA.py","file_ext":"py","file_size_in_byte":7230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19978278254","text":"\"\"\"Logic for Celebrations, i.e. Feasts and commemorations of Saints.\"\"\"\n\nfrom datetime import date\n\n_PASCHA_LOOKUP_TABLE = {\n \"2015\": date(2015, 4, 12),\n \"2016\": date(2016, 5, 1),\n \"2017\": date(2017, 4, 16),\n \"2018\": date(2018, 4, 8),\n \"2019\": date(2019, 4, 28),\n \"2020\": date(2020, 4, 19),\n \"2021\": date(2021, 5, 2),\n \"2022\": date(2022, 4, 24),\n \"2023\": date(2023, 4, 16),\n \"2024\": date(2024, 5, 5),\n \"2025\": date(2025, 4, 20),\n}\n\n\nclass DateNotFound(Exception):\n \"\"\"Represents a date lookup that failed.\"\"\"\n\n\ndef get_date_of_pascha(datelike):\n \"\"\"Return the date of Pascha given a datetime.date-like object.\n\n Raises a DateNotFound exception if it cannot find the date.\n \"\"\"\n year_string = datelike.strftime(\"%Y\")\n try:\n return _PASCHA_LOOKUP_TABLE[year_string]\n except KeyError as key_error:\n msg = f\"Could not find the date of Pascha for {year_string}.\"\n raise DateNotFound(msg) from key_error\n","repo_name":"nicholaskachur/doxacal","sub_path":"doxacal/celebrations.py","file_name":"celebrations.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"29499342267","text":"from ASG import *\r\nfrom ATOM3Type import *\r\nfrom ATOM3List import *\r\nfrom ATOM3Constraint import *\r\nfrom ATOM3Attribute import *\r\nfrom copy import *\r\n\r\nfrom graph_ASG_ERmetaMetaModel import *\r\n\r\nclass ASG_ERmetaMetaModel(ASG, ATOM3Type):\r\n\r\n def __init__(self, parent=None, ASGroot = None):\r\n ASG.__init__(self, 'ERmetaMetaModel', ASGroot, [\"ERentity\", \"ERrelationship\", \"ASG_ERmetaMetaModel\"] )\r\n ATOM3Type.__init__(self)\r\n self.parent = parent\r\n self.name = ATOM3String('ERModel')\r\n self.attributes = ATOM3List([1,1,1, None], ATOM3Attribute, parent.types)\r\n self.constraints = ATOM3List([1,1,1, None], ATOM3Constraint)\r\n self.containerFrame = None\r\n\r\n self.graphClass_ = graph_ASG_ERmetaMetaModel\r\n\r\n self.keyword_ = self.name\r\n\r\n self.generatedAttributes = { 'name' : ('ATOM3String', ),\r\n 'attributes' : ('ATOM3List', 'ATOM3Attribute'),\r\n 'constraints': ('ATOM3List', 'ATOM3Constraint')}\r\n\r\n def show(self, parent, parentWindow):\r\n self.containerFrame = Frame(parent)\r\n Label(self.containerFrame, text=\"name\").grid(row=0, column=0, sticky=W)\r\n self.name.show (self.containerFrame, parentWindow).grid(row=0, column=1, sticky=W) # pack the widget to edit the name\r\n\r\n Label(self.containerFrame, text=\"atributes\").grid(row=1, column=0, sticky=W)\r\n self.attributes.show(self.containerFrame, parentWindow).grid(row=1, column=1, sticky=W) # attributes to generate\r\n\r\n Label(self.containerFrame, text=\"constraints\").grid(row=2, column=0, sticky=W)\r\n self.constraints.show(self.containerFrame, parentWindow).grid(row=2, column=1, sticky=W) # constraints to generate\r\n return self.containerFrame\r\n\r\n def open(self, parent, parentWindow):\r\n \"Method to open an ATOM3 instance to edit the entities (to be overwritten)\"\r\n from ATOM3\t import ATOM3\r\n a = ATOM3 (parentWindow , \"EntityRelationship\", 0, 1, self)\r\n return a\r\n\r\n def toString(self):\r\n return (self.name.toString(), self.attributes.toString(), self.constraints.toString())\r\n\r\n def clone(self):\r\n \"creates an exact copy of this object\"\r\n #cloneObject = ASG_UMLmetaMetaModel(self.parent)\r\n #cloneObject.listNodes = copy.copy(self.listNodes)\r\n #cloneObject.name = self.name.clone()\r\n #cloneObject.attributes = self.attributes.clone()\r\n #cloneObject.constraints = self.constraints.clone()\r\n #cloneObject.containerFrame= self.containerFrame\r\n #cloneObject.keyword_ = cloneObject.keyword_\r\n #return cloneObject\r\n return self\r\n\r\n def copy(self, other):\r\n \"copies each field of the other object into its own state\"\r\n ATOM3Type.copy(self, other)\t\t\t# call the ancestor (copies the parent field)\r\n self.name = other.name\r\n self.constraints = other.constraints\r\n self.attributes = other.attributes\r\n self.keyword_ = self.name\r\n self.containerFrame= other.containerFrame\r\n\r\n def destroy(self):\r\n ASGNode.GGcheckSetNone(self)\r\n self.destroyNodes() \r\n self.name.destroy()\r\n self.attributes.destroy()\r\n self.constraints.destroy()\r\n self.containerFrame = None\r\n\r\n def allClassesDistinct(self):\r\n for node1 in self.listNodes[\"ERentity\"]:\r\n for node2 in self.listNodes[\"ERentity\"]:\r\n if (node1 != node2) and (node1.keyword_.toString() == node2.keyword_.toString()):\r\n return (\"allClassesDistinct: classes have the same keyword: \", node1.keyword_.toString())\r\n return None\r\n\r\n def allRelationshipsDistinct(self):\r\n for node1 in self.listNodes[\"ERrelationship\"]:\r\n for node2 in self.listNodes[\"ERrelationship\"]:\r\n if (node1 != node2) and (node1.keyword_.toString() == node2.keyword_.toString()):\r\n return (\"allRelationshipsDistinct: Relationships have the same keyword: \", node1.keyword_.toString())\r\n return None\r\n\r\n def preCondition(self, actionID, * params):\r\n if actionID==ASG.SAVE:\r\n res = self.allClassesDistinct()\r\n if res: return res\r\n res = self.allRelationshipsDistinct()\r\n if res: return res\r\n return None\r\n\r\n def invalid(self):\r\n \"Decides if the attribute is valid, that is, if the initial value (if any) is valid\"\r\n import string\r\n vname = self.name.getValue()\r\n # check if we have a name\r\n if (not vname) or (vname == \"\"): # the name is mandatory\r\n return \"Entity name must be specified\"\r\n # now check that the name is valid (a variable name)\r\n if string.count(vname, \" \") > 0:\r\n return \"Invalid entity name, no white spaces allowed\"\r\n # check first character\r\n if (vname[0] >= '0') and (vname[0] <= '9'): # a number\r\n return \"Invalid variable name, first character must be a letter or '_'\"\r\n if vname[0] != '_' and (vname[0]<'A' or vname[0]>'z'):\r\n return \"Invalid entity name, first character must be a letter or '_'\"\r\n # now check for the rest of not allowed characters...\r\n for c in range(len(vname)-1):\r\n if vname[c+1] < 'A' or vname[c+1] > 'z': # not a letter\r\n if vname[c+1] < '0' or vname[c+1] > '9': # not a number\r\n if vname[c+1] != '_': # not underscore\r\n return \"Invalid model name, character '\"+vname[c+1]+\"' is not allowed\"\r\n return ASG.invalid(self)\r\n\r\n \r\n","repo_name":"AILab-FOI/LSMASOMM","sub_path":"atom3/Kernel/ERModels/ASG_ERmetaMetaModel.py","file_name":"ASG_ERmetaMetaModel.py","file_ext":"py","file_size_in_byte":5634,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"6004903782","text":"from tkinter import *\nfrom tkinter.messagebox import showinfo\n\n# criando crasse frame / creating a frame crass\nclass myGui(Frame):\n def __init__(self, parent=None): \n Frame.__init__(self, parent)\n botao = Button(self, text='aperte\\npress', command= self.reply)\n botao.pack()\n\n def reply(self):\n showinfo(title='popup', message='botao apertado\\nbutton pressed')\n\nif __name__ == '__main__':\n window = myGui()\n window.pack()\n window.mainloop()\n\n ","repo_name":"jonasht/programmingPython-book","sub_path":"1-chapter-aSneakPreview/5-step5-addingA-GUI/2-using-OOP-ForGUI/tkinter002.py","file_name":"tkinter002.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"1577908837","text":"import json\nfrom datetime import datetime, timedelta\n\nfrom experts_dw import db, pure_json\nfrom experts_etl import loggers\nfrom pureapi import client\n\n# defaults:\n\ntransaction_record_limit = 100\n# Named for the Pure API endpoint:\npure_api_record_type = 'changes'\n\n# functions:\n\ndef ensure_valid_startdate(cursor, api_version, startdate=None):\n if startdate is None:\n change_history_date = pure_json.max_change_history_inserted_date(cursor, api_version=api_version)\n change_date = pure_json.max_change_inserted_date(cursor, api_version=api_version)\n if change_history_date is None and change_date is not None:\n startdate = change_date\n if change_date is None and change_history_date is not None:\n startdate = change_history_date\n if change_date is not None and change_history_date is not None:\n startdate = max(change_date, change_history_date)\n if startdate is None:\n # Default to 1 day ago (yesterday):\n startdate = datetime.now() - timedelta(days=1)\n return startdate\n\ndef required_fields_exist(api_document):\n for field in ['uuid','familySystemName','version','changeType']:\n if field not in api_document:\n return False\n return True\n\n# entry point/public api:\n\ndef run(\n startdate=None,\n startdate_str=None, # yyyy-MM-dd or yyyy-MM-dd_HH-mm-ss format\n transaction_record_limit=transaction_record_limit,\n experts_etl_logger=None,\n api_version=None\n):\n if experts_etl_logger is None:\n experts_etl_logger = loggers.experts_etl_logger()\n experts_etl_logger.info('starting: extracting/loading raw json', extra={'pure_api_record_type': pure_api_record_type})\n\n client_config = client.Config() if api_version is None else client.Config(version=api_version)\n api_version = client_config.version\n\n with db.cx_oracle_connection() as connection:\n try:\n cursor = connection.cursor()\n family_system_names = pure_json.collection_family_system_names_for_api_version(\n cursor,\n api_version=api_version\n )\n\n if startdate_str is None:\n startdate_str = ensure_valid_startdate(cursor, api_version, startdate).isoformat()\n\n documents_to_insert = {}\n for api_document in client.get_all_changes_transformed(startdate_str, config=client_config):\n if not required_fields_exist(api_document):\n continue\n if api_document.familySystemName not in family_system_names:\n continue\n\n # We make this a dict with unique keys to avoid attempts to insert duplicates:\n documents_to_insert[f'{api_document.uuid}:{api_document.version}'] = {\n 'uuid': api_document.uuid,\n 'pure_version': api_document.version,\n 'change_type': api_document.changeType,\n 'family_system_name': api_document.familySystemName,\n 'inserted': datetime.now(),\n 'json_document': json.dumps(api_document),\n }\n\n if len(documents_to_insert) % transaction_record_limit == 0:\n pure_json.insert_change_documents(\n cursor,\n documents=list(documents_to_insert.values()),\n api_version=api_version\n )\n documents_to_insert = {}\n connection.commit()\n\n if documents_to_insert:\n pure_json.insert_change_documents(\n cursor,\n documents=list(documents_to_insert.values()),\n api_version=api_version\n )\n connection.commit()\n\n except Exception as e:\n connection.rollback()\n formatted_exception = loggers.format_exception(e)\n experts_etl_logger.error(\n f'exception encountered during extractin/loading raw json: {formatted_exception}',\n extra={'pure_api_record_type': pure_api_record_type}\n )\n\n experts_etl_logger.info('ending: extracting/loading raw json', extra={'pure_api_record_type': pure_api_record_type})\n","repo_name":"UMNLibraries/experts_etl","sub_path":"experts_etl/pure_to_edw/changes.py","file_name":"changes.py","file_ext":"py","file_size_in_byte":4276,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"43098387094","text":"import okhound\nimport pyaudio\n\n\nCHUNKSIZE = 1024\n\naudio = pyaudio.PyAudio()\nstream = audio.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=CHUNKSIZE)\n\nokhound.setThreshold(0.4)\n\nwhile True:\n\ttry:\n\t\taudio = stream.read(CHUNKSIZE)\n\texcept IOError:\n\t\tprint(\"Skipped frame\")\n\t\tcontinue\n\n\tphraseSpotted = okhound.processSamples(audio)\n\tif phraseSpotted: break\n\n\nprint(\"'Ok Hound' spotted! Sensitivity: {0}\".format(okhound.getThreshold()))\n","repo_name":"alexdong/okhound","sub_path":"example_pyaudio.py","file_name":"example_pyaudio.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15445388842","text":"import time\nfrom pathlib import Path\nimport dataGenerator as dg\nfrom regexChecker import RegexChecker as rc\nfrom toolBox import *\n\nif __name__==\"__main__\":\n data_folder = Path(\"data/\")\n file = data_folder / \"email_test.csv\"\n\n test_emails = dg.create_email()\n modified_emails = dg.add_text(test_emails)\n dg.write_to_csv_for_test(modified_emails, file)\n\n expected_emails = dg.email_quant\n print(\"There are \" + str(expected_emails) + \" email entities\")\n\n time.sleep(3)\n\n nlp = spacy.load('ja_ginza_nopn', disable=[\"tagger\", \"parser\", \"ner\", \"textcat\"])\n stop_file = \"C:\\\\Users\\\\Ko.In\\\\Desktop\\\\PiiExtractionData\\\\StopKey_pii.csv\"\n add_stop_words(read_to_list(stop_file, find_encoding(stop_file)), nlp)\n df = read_to_df(file, find_encoding(file))\n\n raw_msgs = list(df[0])\n\n new_msgs = pre_process(raw_msgs)\n\n email_regex = rc.email_regex()\n phone_regex = rc.phone_regex()\n\n result = []\n count = 0\n\n docs = nlp.pipe(new_msgs)\n\n for msg in docs:\n match = False\n row = {}\n\n email = re.findall(email_regex, str(msg))\n if len(email) > 0:\n print(\"Found email at \" + str(count) + \": \" + \",\".join([str(n) for n in email]))\n row[\"Email\"] = \"\".join([str(n) for n in email])\n match = True\n\n if match == True:\n result.append(row)\n count += 1\n\n output_df = pd.DataFrame(result)\n print(output_df)\n\n actual_emails = len(output_df.index)\n print(\"The actual detected emails entities are: \" + str(actual_emails))\n print(\"Accuracy of email extraction is: \" + str(actual_emails/expected_emails))\n","repo_name":"koasurion/PiiChecker3","sub_path":"emailAccuracy.py","file_name":"emailAccuracy.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74369892242","text":"import numpy as np\nimport tensorflow as tf\nimport math\nimport pickle\nimport operator\nimport gensim\nfrom gensim.models import word2vec\nimport logging\n\ndef readDataBible():\n '''\n Detects sentences based on the occurance of the full_stop_char_sans variable\n Inserts that between every 2 sentences as well\n As of now, removes occurances of ''\n '''\n filename = 'Bible/bible_translation.pickle'\n list_of_words = []\n words = {}\n word_count = 0\n full_stop_char_sans = 2404\n\n with open(filename, 'rb') as f:\n d = pickle.load(f)\n for spair in d:\n sans = spair[0]\n sans_words = []\n # sans.replace(ord(full_stop_char_sans),'')\n\n for w in sans.split(chr(full_stop_char_sans)):\n w1 = w.split(' ')\n sans_words.extend(w1)\n if len(w1) != 0 and w1[0] != '':\n sans_words.append(chr(full_stop_char_sans))\n\n for word in sans_words:\n '''\n Some preprocessing can be done here\n It will definitely help\n '''\n if word is '':\n continue\n if word not in words:\n words[word] = 1\n word_count += 1\n else:\n words[word] += 1\n list_of_words.append(word)\n\n return list_of_words\n\ndef main():\n '''\n list_of_words, words, word_count=readDataBible()\n\n list_of_words contains words in the order in which they appear. As it is\n word_count is the number of unique words in the vocabulary\n words is a dictionary. Key is word and value is the number of occurances of that word\n '''\n\n embedding_size = 64\n batch_size = 64\n window_size = 5\n\n print(readDataBible())\n input()\n logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n model = word2vec.Word2Vec(readDataBible(),sg=1,size=embedding_size,window=window_size,workers=4)\n\n print(model.wv.index2word)\n \n print(model.wv.similarity('मम','तत्'))\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Genius1237/NeuralMT","sub_path":"Code/word2vec_gensim.py","file_name":"word2vec_gensim.py","file_ext":"py","file_size_in_byte":2095,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"4368170680","text":"from typing import List, Dict, Optional\nfrom uuid import uuid4\n\n\nclass Annotation:\n def __init__(self, cls, selected_text, notes=None, raw=None):\n self.cls = cls\n self.selected_text = selected_text\n self.notes = notes\n self.raw = raw\n\n @classmethod\n def create_from_labeler(cls, project, obj):\n annotation_class = 'unknown'\n for fc in project.fragment_classes:\n if fc['id'] == obj['categoryId']:\n annotation_class = fc['name']\n\n return cls(annotation_class, obj['selectedText'], raw=obj)\n\n @classmethod\n def deserialize(cls, obj):\n return cls(obj['annotationCategory'], obj['selectedText'])\n\n def serialize(self, format='df'):\n if format == 'df':\n return {\n 'class': self.cls,\n 'selected_text': self.selected_text,\n 'notes': self.notes\n }\n elif format == 'api':\n return self.raw\n else:\n raise Exception('Unsupported serialization format.')\n\n\n\nclass Label:\n @classmethod\n def deserialize_from_task(cls, task, response):\n annotations = [Annotation.deserialize(a) for a in response['contents']['annotations']]\n return cls(\n task.document.id,\n task.document.external_id,\n response['contents'].get('categoryName'),\n response['contents'].get('categoryId'),\n annotations,\n id=response['id']\n )\n\n @classmethod\n def deserialize(cls, label):\n if label['annotations']:\n annotations = [Annotation.deserialize(a) for a in label['annotations']]\n else:\n annotations = None\n\n document_class = label.get('documentCategory')\n\n return cls(\n label['documentId'], # TODO-1: replace with id\n label['documentId'], # TODO-2: this is external id\n document_class,\n None, # document_class_id\n annotations=annotations\n # TODO-2: we need to pull id so that it isn't auto-generated\n )\n\n @classmethod\n def create_from_labeler(cls, project, document, document_class, annotations):\n class_id = document_class.get('id')\n class_name = document_class.get('name')\n\n if not annotations:\n annotations = []\n annotations = [Annotation.create_from_labeler(project, a) for a in annotations]\n\n return cls(document['id'], document['external_id'], class_name, class_id, annotations)\n\n def __init__(self, document_id, document_external_id, class_name, class_id, annotations, id=uuid4()):\n self.id: str = id\n self.document_id: str = document_id\n self.document_external_id: str = document_external_id\n self.cls: str = class_name\n self.cls_id: str = class_id\n\n if not annotations:\n annotations = []\n self.annotations: List[Annotation] = annotations # TODO-2: convert away from dict to Annotation class\n # TODO-3: creator information and other metadata, coming from API\n\n def serialize(self, project=None, format='api'):\n if len(self.annotations) > 0:\n serialized = {\n 'annotations': [a.serialize(format=format) for a in self.annotations]\n }\n else:\n serialized = { 'annotations': None }\n\n if format == 'api':\n serialized['document_id'] = self.document_id\n serialized['class'] = self.cls\n serialized['class_id'] = self.cls_id\n elif format == 'df':\n serialized['document_id'] = self.document_external_id\n serialized['label'] = self.cls\n else:\n raise Exception('Unsupported serialization format.')\n\n return serialized\n","repo_name":"Witch27/Python-tiptaptoe","sub_path":"venv/lib/python3.9/site-packages/markers/label.py","file_name":"label.py","file_ext":"py","file_size_in_byte":3777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11601007715","text":"# All Imports\n#-------------------------------------------------------------------------------\nimport pygame\n# from movement import *\nfrom pygame.locals import *\nimport sys\nfrom config import *\nfrom blocks import blocks\nfrom player import player as p\nfrom movement import collision_test, move\nfrom sprites import *\nfrom load import *\n\n\n#Clock Configuration\n#-------------------------------------------------------------------------------\nclock = pygame.time.Clock()\n\n#Pygame Initialization\n#-------------------------------------------------------------------------------\npygame.init()\npygame.display.set_caption(\"CloackMan\")\n\n#Window Configuration\n#-------------------------------------------------------------------------------\nWINDOW_SIZE = (WIN_WIDTH, WIN_HEIGHT)\n\n\n#Block/Background Configuration\n#-------------------------------------------------------------------------------\ngrass=blocks.transformsprites(\"blocks/tile001.png\")\ndirt=blocks.transformsprites(\"blocks/tile013.png\")\nscene = pygame.image.load(\"spritesheets/background/background.png\")\nscreen = pygame.display.set_mode(WINDOW_SIZE,0,32)\ndisplay = pygame.Surface((320, 240))\ntrue_display=pygame.transform.scale(display, WINDOW_SIZE)\nscreen.blit(true_display, (0,0))\n# **scaling the display to window size\ntrue_scene=pygame.transform.scale(scene, BACKGROUND)\ntrue_scroll = [0,0]\n\ntilemap = map.load_map('tilemap')\n\nrect = pygame.Rect(100,100,32,32)\np1 = p(rect)\n\nanimation_database = {}\n\nanimation_database['run'] = sprites.load_animation('player_animations/run',[7,7,7,7,7,7])\nanimation_database['idle'] = sprites.load_animation('player_animations/idle',[7,7,7,7])\nanimation_database['jump'] = sprites.load_animation('player_animations/jump',[4,4,4])\nanimation_database['jumpdown'] = sprites.load_animation('player_animations/jumpdown',[4,4,4])\n\n#Game Loop\nwhile True:\n screen.blit(true_scene, (0,-480))\n tile_rects = []\n\n# Below is the scrolling feaature within the game that allows the screen to essentially follow the p1.player.\n true_scroll[0] += (p1.player_rect.x-true_scroll[0]-222)/20\n true_scroll[1] += (p1.player_rect.y-true_scroll[1]-212)/20\n scroll = true_scroll.copy()\n scroll[0] = int(scroll[0])\n scroll[1] = int(scroll[1])\n y = 0\n# Populates the tiles with the correct images on to the screen in occordance with the tilemap.\n for row in tilemap:\n x = 0\n for tile in row:\n if tile == '2':\n screen.blit(grass, (x * TILE_SIZE - true_scroll[0], y * TILE_SIZE - true_scroll[1]))\n if tile == '1':\n screen.blit(dirt, (x * TILE_SIZE - true_scroll[0], y * TILE_SIZE - true_scroll[1]))\n if tile != '0':\n tile_rects.append(pygame.Rect(x * TILE_SIZE, y * TILE_SIZE, TILE_SIZE, TILE_SIZE))\n x += 1\n y += 1\n# This is the coordinate/movement detection for the player [0,1]=[x,y].\n p1.player_movement = [0,0]\n if p1.moving_right == True:\n p1.player_movement[0] += 4\n if p1.moving_left == True:\n p1.player_movement[0] -= 4\n p1.player_movement[1] += p1.vertical_momentum\n p1.vertical_momentum += 0.2\n if p1.vertical_momentum > 4:\n p1.vertical_momentum = 4\n\n# This is the key & direction detection for the sprite's frame.\n if p1.player_movement[0] == 0:\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'idle')\n if p1.player_movement[0] > 0 and collisions['bottom']==True:\n p1.player_flip = False\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'run')\n if p1.player_movement[0] < 0 and collisions['bottom']==True:\n p1.player_flip = True\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'run')\n if p1.player_movement[0] > 0:\n if p1.vertical_momentum < 0 :\n p1.player_flip = False\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'jump')\n if p1.player_movement[0] < 0:\n if p1.vertical_momentum < 0 :\n p1.player_flip = True\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'jump')\n p1.player_rect,collisions = move(p1.player_rect, p1.player_movement, tile_rects)\n\n if collisions['bottom']:\n p1.air_timer = 0\n p1.vertical_momentum = 0\n p1.jumping = False\n else:\n p1.air_timer += 1\n \n p1.player_frame += 1\n if p1.player_frame >= len(animation_database[p1.player_action]):\n p1.player_frame = 0\n p1.player_img_id = animation_database[p1.player_action][p1.player_frame]\n p1.player = animation_frames[p1.player_img_id]\n screen.blit(pygame.transform.flip(p1.player,p1.player_flip,False),(p1.player_rect.x-scroll[0],p1.player_rect.y-scroll[1]))\n\n for event in pygame.event.get():\n\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n\n if event.type == KEYDOWN:\n if event.key == K_RIGHT:\n p1.moving_right = True\n if event.key == K_LEFT:\n p1.moving_left = True\n if event.key == K_UP:\n if (p1.air_timer < 6):\n p1.jumping=True\n p1.falling=False\n p1.vertical_momentum = -6\n p1.player_movement[1] = -5.2\n if event.type == KEYUP:\n if event.key == K_RIGHT:\n p1.moving_right = False\n if event.key == K_LEFT:\n p1.moving_left = False\n if event.key == K_UP:\n p1.jumping = False\n\n\n pygame.display.update()\n clock.tick(60)\n","repo_name":"ojimba01/CloakmanV3","sub_path":"cloakmanv3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71285793361","text":"import sys\n#sys.path.append(\"../\")\nfrom PyQt5.QtWidgets import *\nfrom japrp.audio_backends.audio_backend_vlc import VlcBackend\nfrom japrp.audio_backends.audio_backend_pyqt5 import QtMediaPlayerWrapper\n\n_BACKEND = \"vlc\"\n\nclass Player(QWidget):\n\n def __init__(self, value_dict=None, parent=None):\n super().__init__(parent=parent)\n self.layout = QGridLayout()\n if value_dict is None:\n value_dict = {}\n self._station_name = value_dict.get(\"name\", \"NO NamE\")\n self._icon = value_dict.get(\"icon\", \"NO ICON\")\n self.url = value_dict.get(\"url\")\n self.station_name = QLabel(self._station_name)\n self.icon = QLabel(self._icon)\n self.playBtn = QPushButton(\"Play\")\n self.stopBtn = QPushButton(\"Stop\")\n self.playBtn.clicked.connect(self.start_playing)\n self.stopBtn.clicked.connect(self.stop_playing)\n if _BACKEND == \"vlc\":\n self.player = VlcBackend()\n else:\n self.player = QtMediaPlayerWrapper()\n\n self.layout.addWidget(self.station_name, 0, 0, 1, 3)\n self.layout.addWidget(self.icon, 1, 0, 1, 3)\n self.layout.addWidget(self.playBtn, 2, 0, 1, 1)\n self.layout.addWidget(self.stopBtn, 2, 1, 1, 1)\n self.setLayout(self.layout)\n\n def start_playing(self):\n #self.stream = subprocess.Popen([\"python\", \"-m\", \"streamer_script.py\"], stdout=sys.stdout)\n if self.url == \"\" or self.url is None:\n pass\n else:\n self.player.set_media(self.url, media_type='infer')\n if _BACKEND == \"vlc\":\n if self.player.media_player.is_playing():\n self.player.pause()\n else:\n self.player.play()\n else:\n self.player.play()\n\n def stop_playing(self):\n if self.player is not None:\n self.player.stop()\n\n\n def set_url(self, url):\n #URL = \"http://bob.hoerradar.de/radiobob-hartesaite-mp3-hq?sABC=6020sp0s%230%23r731s0685son37qn82q119rrn30n0ss5%23zrqvncynlre&=&amsparams=playerid:mediaplayer;skey:1612774415\" # BBC\n #URL = \"http://mp3-live.swr3.de/swr3_m.m3u\"\n self.url = url\n\n\n\n\n\n def closeEvent(self, event):\n #quit_msg = \"Are you sure you want to exit the program?\"\n if self.player is not None:\n self.player.stop()\n\n\n\nif __name__ == \"__main__\":\n URL = \"http://bob.hoerradar.de/radiobob-hartesaite-mp3-hq?sABC=6020sp0s%230%23r731s0685son37qn82q119rrn30n0ss5%23zrqvncynlre&=&amsparams=playerid:mediaplayer;skey:1612774415\" # BBC\n app = QApplication(sys.argv)\n player = Player()\n player.set_url(URL)\n player.show()\n sys.exit(app.exec())","repo_name":"Banjio/japrp","sub_path":"japrp/app_parts/player_qt.py","file_name":"player_qt.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70589737363","text":"from django.contrib.auth.models import User\nfrom django.test import TestCase\nfrom django.urls import reverse\n\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase, APIClient\n\nfrom professors.models import Professor\n\ndef get_or_none(Model, **conditions):\n try:\n model = Model.objects.get(**conditions)\n except Model.DoesNotExist:\n model = None\n\n return model\n\nADMIN_USER_DATA = {\n 'username': 'fake-admin-user',\n 'password': 'fake-admin-user'\n}\n\nUSER_DATA = {\n 'username': 'fake-user',\n 'password': 'fake-user'\n}\n\nPROFESSOR_1_DATA = {\n 'first_name': 'Gary',\n 'last_name': 'Gillespie',\n 'biography': 'Professor for CSE 12, CSE 15L, and CSE 110.'\n}\n\nPROFESSOR_2_DATA = {\n 'first_name': 'Rick',\n 'last_name': 'Ord',\n 'biography': 'Professor for CSE 11 and CSE 30.'\n}\n\nclass ProfessorAPITestCase(APITestCase):\n\n @classmethod\n def setUpTestData(cls):\n\n cls.client = APIClient()\n\n cls.admin_user = User(**ADMIN_USER_DATA)\n cls.admin_user.is_staff = True\n cls.admin_user.save()\n \n cls.user = User(**USER_DATA)\n cls.user.save()\n\n cls.professor_1 = Professor(**PROFESSOR_1_DATA)\n cls.professor_1.save()\n\n def test_post_professor_list_admin(self):\n\n self.client.force_authenticate(self.admin_user)\n\n response = self.client.post(reverse('professor_list'), PROFESSOR_2_DATA)\n professor = get_or_none(Professor, **PROFESSOR_2_DATA)\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertNotEqual(professor, None)\n self.client.logout()\n\n def test_post_professor_list_no_admin(self):\n\n self.client.force_authenticate(self.user)\n\n response = self.client.post(reverse('professor_list'), PROFESSOR_2_DATA)\n professor = get_or_none(Professor, **PROFESSOR_2_DATA)\n \n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n self.assertEqual(professor, None)\n self.client.logout()\n\n def test_get_professor_list_admin(self):\n\n self.client.force_authenticate(self.admin_user)\n\n response = self.client.get(reverse('professor_list'))\n\n self.assertEqual(len(response.data['results']), 1)\n\n def test_get_professore_list_no_admin(self):\n\n self.client.force_authenticate(self.user)\n\n response = self.client.get(reverse('professor_list'))\n \n self.assertEqual(len(response.data['results']), 1)","repo_name":"chtka/GradeTracker","sub_path":"professors/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1155492920","text":"\nprint (\"Aşağıdaki işlemlerden birini seçinizi.\\n1- Toplama \\n2- Çıkarma\")\n\nToplama = 1\nÇıkarma = 2\n\noutput = \"İlk sayı: {}, ikinci sayı: {}\"\nİşlem = int (input(\"Lütfen işlem seçiniz.\"))\n\nif İşlem == 1 :\n birinci_input = int(input(\"Lütfen ilk sayıyı giriniz.\"))\n ikinci_input = int(input(\"Lütfen ikinci sayıyı giriniz.\"))\n print(output.format(birinci_input, ikinci_input), \"için toplama işleminin sonucu\", (birinci_input + ikinci_input))\nelif İşlem == 2 :\n birinci_input = int(input(\"Lütfen ilk sayıyı giriniz.\"))\n ikinci_input = int(input(\"Lütfen ikinci sayıyı giriniz.\"))\n\n print(output.format(birinci_input, ikinci_input), \"için çıkarma işleminin sonucu\", (birinci_input - ikinci_input))\nelse:\n print (\"Yanlış seçim yaptınız.\")\n\n\n","repo_name":"busraylmz00/Homework1","sub_path":"Homework_1.py","file_name":"Homework_1.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35658229161","text":"import json\nimport asyncio\nimport pyppeteer\nimport pydantic\nimport re\nfrom typing import List, Optional, Union\nimport datetime\nimport yt_dlp\nfrom moviepy.editor import *\nimport uuid\nfrom enum import Enum\n\ndef milliseconds_to_time(milliseconds: int) -> datetime.time:\n \"\"\"Convert milliseconds to datetime.time\"\"\"\n\n return datetime.time(\n int(milliseconds / 1000) // 3600, \n (int(milliseconds / 1000) % 3600) // 60, \n int(milliseconds / 1000) % 60\n )\n\n\ndef seconds_to_time(seconds: Union[int, float]) -> datetime.time:\n \"\"\"Convert seconds to datetime.time\"\"\"\n\n if isinstance(seconds, float):\n seconds = int(round(seconds))\n\n return datetime.time(seconds // 3600, (seconds % 3600) // 60, seconds % 60)\n\n\ndef time_to_seconds(time: datetime.time) -> int:\n \"\"\"Convert datetime.time to milliseconds\"\"\"\n\n return (time.hour * 60 + time.minute) * 60 + time.second\n\n\ndef valid_filename(filename: str):\n \"\"\"Convert a string into a valid filename\"\"\"\n\n return re.sub(\n r'(?u)[^-\\w.]', '', \n str(filename).strip().replace(' ', '_')\n )\n\n\nclass Thumbnail(pydantic.BaseModel):\n \"\"\"Data model for Thumbnail data\"\"\"\n\n url: str\n width: int\n height: int\n\n\nclass Chapter(pydantic.BaseModel):\n \"\"\"Data model for Chapter data\"\"\"\n\n title: str\n start_time: datetime.time\n end_time: datetime.time\n thumbnails: List[Thumbnail]\n\n\nclass Heatmap(pydantic.BaseModel):\n \"\"\"Data model for Heatmap data\"\"\"\n\n chapter: Optional[Chapter]\n start_time: Optional[datetime.time]\n end_time: Optional[datetime.time]\n intensity_score_normalised: Optional[float]\n\n\nclass VideoHeatmap(pydantic.BaseModel):\n \"\"\"Data model for YouTube video Heatmap data\"\"\"\n\n url: str\n heatmaps: List[Heatmap]\n \n @property\n def chapters(self) -> List[Chapter]:\n \"\"\"Get video chapters from heatmaps\"\"\"\n\n return [heatmap.chapter for heatmap in self.heatmaps if heatmap.chapter]\n\n def most_intense_heatmaps(self, merge: bool=True, count: int=3) -> List[Heatmap]:\n \"\"\"Return the most intense heatmaps, optionally joining by chapter title\"\"\"\n\n sorted_heatmaps = list(sorted(self.heatmaps, key=lambda x: x.intensity_score_normalised, reverse=True))\n\n if not merge or sum(1 for x in sorted_heatmaps if x.chapter) < count:\n return sorted_heatmaps[:count]\n\n merged_heatmaps = {}\n for heatmap in sorted_heatmaps:\n\n # Skip if heatmap has no chapter\n if not heatmap.chapter:\n continue\n\n # Add heatmap if not already exist and so far less than count added\n if not merged_heatmaps.get(heatmap.chapter.title) and len(merged_heatmaps) <= count:\n merged_heatmaps[heatmap.chapter.title] = heatmap\n\n else:\n merged_heatmap = merged_heatmaps.get(heatmap.chapter.title)\n\n if not merged_heatmap:\n continue\n\n # Update heatmap times if different to existing\n if heatmap.start_time <= merged_heatmap.start_time:\n merged_heatmap.start_time = heatmap.start_time\n \n if merged_heatmap.end_time <= heatmap.end_time:\n merged_heatmap.end_time = heatmap.end_time\n\n return list(merged_heatmaps.values())\n\n\n def most_intense_chapters(self, merge: bool=True, count: int=3) -> List[Chapter]:\n \"\"\"Return the most intense chapters\"\"\"\n\n return [heatmap.chapter for heatmap in self.most_intense_heatmaps(merge, count) if heatmap.chapter]\n\n\nclass ClipAlign(str, Enum):\n \"\"\"Enumeration for clip allignment\"\"\"\n\n left = \"left\"\n center = \"center\"\n right = \"right\"\n\n\nclass Video:\n \"\"\"Class to manage, download, and clip video\"\"\"\n\n url: str\n ydl_info: dict\n\n def __init__(self, url: str) -> None:\n self.url = url\n self.ydl_info = {}\n\n\n def download(self):\n \"\"\"Download YouTube video using YT-Dlp\"\"\"\n\n ydl_opts = {\n \"outtmpl\": \"/tmp/videos/%(id)s.%(ext)s\",\n }\n\n with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n self.ydl_info = ydl.extract_info(self.url, download=True)\n\n\n def generate_clip(self, file_name: str, clip_file_name: str, start_time: datetime.time, end_time: datetime.time) -> None:\n \"\"\"Generate clip using start time and end time, returning file name\"\"\"\n \n clip = VideoFileClip(f\"tmp/videos/{file_name}\").subclip(\n time_to_seconds(start_time) if time_to_seconds(start_time) > 0 else 0, \n time_to_seconds(end_time) if time_to_seconds(end_time) < self.ydl_info[\"duration\"] else None\n )\n \n \"\"\"\n -vf: \n This option is used to apply video filtergraph to the input video.\n In this case, the filtergraph is a series of filter commands that will be applied to the input video.\n The filtergraph is split into 3 parts:\n 1. split [original][copy] : This filter command creates a copy of the input video and assigns it to the label \"original\" and \"copy\"\n 2. [copy] crop=ih*9/16:ih:iw/2-ow/2:0, scale=1080:1920, gblur=sigma=50[blurred] : This filter command applies crop, scale and gblur to the video labeled as \"copy\"\n 3. [original] scale=-1:1080, crop=1080:1080:iw/2-ow/2:ih/2-oh/2 [scaled]: This filter command applies scale and crop to the video labeled as \"original\"\n 4. [blurred][scaled] overlay=(main_w-overlay_w)/2:(main_h-overlay_h)/2 : This filter command overlays the blurred video on top of the scaled video\n\n -auto-alt-ref:\n This option is used to enable use of alternate reference frames in VP9. It is used to improve the efficiency of the video compression.\n 0 means it is disabled.\n\n -c:a:\n This option is used to set the codec for the audio stream.\n In this case, the audio stream is copied from the input file to the output file, without any changes.\n\n -c:v:\n This option is used to set the codec for the video stream.\n In this case, the codec for the video stream is set to h264.\n \"\"\"\n ffmpeg_params = [\n \"-vf\", \n \"split [original][copy]; [copy] crop=ih*9/16:ih:iw/2-ow/2:0, scale=1080:1920, gblur=sigma=50[blurred]; [original] scale=-1:1080, crop=1080:1080:iw/2-ow/2:ih/2-oh/2 [scaled]; [blurred][scaled] overlay=(main_w-overlay_w)/2:(main_h-overlay_h)/2\", \n \"-auto-alt-ref\", \"0\", \n \"-c:a\", \"copy\",\n \"-c:v\", \"h264\", \n ]\n\n try:\n clip.write_videofile(\n f\"tmp/clips/{clip_file_name}\",\n preset=\"ultrafast\",\n ffmpeg_params=ffmpeg_params,\n )\n except Exception as e:\n print(f\"Error: {str(e)}\")\n\n clip.close()\n\n\n def generate_chapter_clips(self, chapters: List[Chapter], align: Optional[ClipAlign]=ClipAlign.left, length: Optional[int]=None):\n \"\"\"Generate clips from chapters\"\"\"\n\n for chapter in chapters:\n\n start_time = None\n end_time = None\n\n if length:\n\n if align == ClipAlign.left:\n start_time = chapter.start_time\n end_time = seconds_to_time(time_to_seconds(chapter.start_time) + length)\n\n elif align == ClipAlign.right:\n start_time = seconds_to_time(time_to_seconds(chapter.start_time) - length)\n end_time = chapter.end_time\n\n else:\n middle = (time_to_seconds(chapter.start_time) + time_to_seconds(chapter.end_time)) // 2\n start_time = seconds_to_time(middle - (length/2))\n end_time = seconds_to_time(middle + (length/2))\n\n self.generate_clip(\n f\"{self.ydl_info['id']}.webm\", \n f\"{valid_filename(chapter.title)}.mp4\",\n start_time or chapter.start_time,\n end_time or chapter.end_time,\n )\n\n\n def generate_heatmap_clips(self, heatmaps: List[Heatmap], align: Optional[ClipAlign]=ClipAlign.left, length: Optional[int]=None):\n \"\"\"Generate clips from heatmaps\"\"\"\n\n for heatmap in heatmaps:\n\n start_time = None\n end_time = None\n\n if length:\n\n if align == ClipAlign.left:\n start_time = heatmap.start_time\n end_time = seconds_to_time(time_to_seconds(heatmap.start_time) + length)\n\n elif align == ClipAlign.right:\n start_time = seconds_to_time(time_to_seconds(heatmap.start_time) - length)\n end_time = heatmap.end_time\n\n else:\n middle = (time_to_seconds(heatmap.start_time) + time_to_seconds(heatmap.end_time)) // 2\n start_time = seconds_to_time(middle - (length/2))\n end_time = seconds_to_time(middle + (length/2))\n\n self.generate_clip(\n f\"{self.ydl_info['id']}.webm\", \n f\"{heatmap.chapter.title if heatmap.chapter else uuid.uuid4()}.mp4\",\n start_time or heatmap.start_time,\n end_time or heatmap.end_time,\n )\n\n\nasync def get_page_content(url: str) -> str:\n \"\"\"Get page HTML content from URL\"\"\"\n\n # Create browser\n browser = await pyppeteer.launch()\n page = await browser.newPage()\n\n # Open video page and get HTML content\n await page.goto(url)\n html_content = await page.content()\n\n # Close page and browser\n await browser.close()\n\n return html_content\n\n\ndef get_initial_data(html_content: str) -> dict:\n \"\"\"Get YouTube initial data from video URL\"\"\"\n\n # Get intitial video data\n yt_initial_data = re.search(\n r\"var ytInitialData = (.*?);<\",\n html_content,\n re.DOTALL,\n ).group(1)\n\n # Return YouTube intitial JSON data as dict\n return json.loads(yt_initial_data)\n\n\ndef get_marker_data(yt_initial_data: dict) -> dict:\n \"\"\"Get YouTube marker data from intitial data\"\"\"\n\n return yt_initial_data[\"playerOverlays\"][\"playerOverlayRenderer\"][\"decoratedPlayerBarRenderer\"][\"decoratedPlayerBarRenderer\"][\"playerBar\"][\"multiMarkersPlayerBarRenderer\"][\"markersMap\"]\n\n\ndef get_chapters(marker_data: dict, video: Video) -> List[Chapter]:\n \"\"\"Get YouTube chapters from initial data\"\"\"\n\n chapter_data = []\n\n # Check if chapter data exists in marker data\n for data in marker_data:\n if data[\"key\"] == \"DESCRIPTION_CHAPTERS\":\n chapter_data = [chapter[\"chapterRenderer\"] for chapter in data[\"value\"][\"chapters\"]]\n break\n\n chapters = []\n\n # Iterate over each chapter\n for i in range(0, len(chapter_data)):\n\n chapter = chapter_data[i]\n\n end_time = None\n if i+1 < len(chapter_data):\n end_time = milliseconds_to_time(chapter_data[i+1][\"timeRangeStartMillis\"])\n else:\n end_time = seconds_to_time(video.ydl_info[\"duration\"])\n\n chapters.append(\n Chapter(\n title=chapter[\"title\"][\"simpleText\"],\n start_time=milliseconds_to_time(chapter[\"timeRangeStartMillis\"]),\n end_time=end_time,\n thumbnails=[Thumbnail(**thumbnail) for thumbnail in chapter[\"thumbnail\"][\"thumbnails\"]],\n )\n )\n\n return chapters\n\n\ndef get_heatmaps(marker_data: dict, chapters: Optional[List[Chapter]]=[]) -> List[Heatmap]:\n \"\"\"Get YouTube heatmap data from marker data\"\"\"\n\n heatmap_data = []\n\n # Check if chapter data exists in intital data\n for data in marker_data:\n if data[\"key\"] == \"HEATSEEKER\":\n heatmap_data = [heatmap[\"heatMarkerRenderer\"] for heatmap in data[\"value\"][\"heatmap\"][\"heatmapRenderer\"][\"heatMarkers\"]]\n break\n\n heatmaps = []\n\n # Iterate over each heatmap\n for heatmap in heatmap_data:\n\n matching_chapter = None\n\n # Find a matching chapter timestamp if exists\n for chapter in chapters:\n\n if milliseconds_to_time(heatmap[\"timeRangeStartMillis\"]) >= chapter.start_time:\n matching_chapter = chapter\n \n heatmaps.append(\n Heatmap(\n chapter=matching_chapter,\n start_time=milliseconds_to_time(heatmap[\"timeRangeStartMillis\"]),\n end_time=milliseconds_to_time(heatmap[\"timeRangeStartMillis\"] + heatmap[\"markerDurationMillis\"]),\n intensity_score_normalised=heatmap[\"heatMarkerIntensityScoreNormalized\"]\n )\n )\n\n return heatmaps\n\n\ndef get_video_heatmap(video: Video) -> VideoHeatmap:\n \"\"\"Get YouTube video heatmap from URL\"\"\"\n\n event_loop = asyncio.get_event_loop()\n html_content = event_loop.run_until_complete(asyncio.gather(get_page_content(video.url)))[0]\n event_loop.close()\n\n initial_data = get_initial_data(html_content)\n marker_data = get_marker_data(initial_data)\n chapters = get_chapters(marker_data, video)\n heatmaps = get_heatmaps(marker_data, chapters)\n\n return VideoHeatmap(\n url=video.url,\n heatmaps=heatmaps,\n )\n","repo_name":"BadgerHobbs/Heatmap-Clips","sub_path":"heatmap_clips.py","file_name":"heatmap_clips.py","file_ext":"py","file_size_in_byte":13164,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"9483732839","text":"currentIndex = 0\r\nmiddleIndex = 12\r\n\r\nfor i in range(5):\r\n\tdata = input().split()\r\n\tfound = False\r\n\r\n\tfor index, value in enumerate(data):\r\n\t\tif value == '1':\r\n\t\t\tcurrentIndex += index\r\n\t\t\tfound = True\r\n\r\n\tif found:\r\n\t\tbreak\r\n\r\n\tcurrentIndex += 5\r\n\r\ndifferent = abs(currentIndex - middleIndex)\r\n\r\nrowMove = different // 5\r\ncolumnMove = different % 5\r\n\r\nif columnMove == 4:\r\n\tcolumnMove -= 2\r\n\r\nprint(rowMove + columnMove)\r\n","repo_name":"hhanhvu99/Personal-CS114.L21.KHCL","sub_path":"Practice/Matrix.py","file_name":"Matrix.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1514400706","text":"import interfaceTools as it\nimport deconvolution as dv\n\nentryIterations,entryWeight,dropdownImg, dropdownPSF = (None,None,None,None)\n\ndef deconvolutionEvent():\n\tglobal entryIterations, entryWeight, dropdownImg, dropdownPSF\n\topcDeconv = it.NewWindow('Deconvolution Options','300x180') #Objeto de la clase NewWindow\n\topcDeconv.createLabel('Image: ',20,20)\n\topcDeconv.createLabel('PSF: ',20,50)\n\topcDeconv.createLabel('Iterations: ',20,80)\n\topcDeconv.createLabel('Weight: ',20,110)\n\tentryIterations = opcDeconv.createEntry('50',110,80)\n\tentryWeight = opcDeconv.createEntry('20',110,110)\n\tdropdownImg = opcDeconv.createCombobox(110,20)\n\tdropdownPSF = opcDeconv.createCombobox(110,50)\n\topcDeconv.createButton('OK', deconvolutionKernel, 'bottom')\n\ndef deconvolutionKernel():\n\tglobal entryIterations, entryWeight, dropdownImg, dropdownPSF\n\tdv.deconvolutionMain(it.filesPath[dropdownImg.current()],it.filesPath[dropdownPSF.current()],entryIterations.get(),entryWeight.get())\n\n#Se crea la ventana principal del programa\nit.createWindowMain()\n#Se crea menu desplegable\nmenu = it.createMenu()\n#Se añaden las opciones del menu\nopc1 = it.createOption(menu)\nit.createCommand(opc1, \"Abrir\", it.openFile)\nit.createCommand(opc1, \"Guardar\", it.saveFile)\nit.createCommand(opc1, \"Salir\", it.mainWindow.quit)\nit.createCascade(menu, 'Archivo', opc1)\n\nopc2 = it.createOption(menu)\nit.createCommand(opc2, \"Deconvolution\", deconvolutionEvent)\n#it.createCommand(opc2, \"Guardar\", it.saveFile)\n#it.createCommand(opc2, \"Salir\", mainWindow.quit)\nit.createCascade(menu, 'Image', opc2)\n\nit.createStatusBar()\n#statusBar = it.createStatusBar()\n#statusBar['text']=dv.message\n\nit.mainWindow.mainloop()","repo_name":"CharlieBrianML/IFCSoftDeconv","sub_path":"IFCSoftDeconv.py","file_name":"IFCSoftDeconv.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8331780688","text":"from tqdm import trange\nimport numpy as np\nimport pandas as pd\n\nimport cvxpy as cp\n\nfrom tqdm import trange\n\n\ndef feature_engineer(return_predictions):\n \"\"\"\n param return_predictions: pandas DataFrame of return predictions\n\n Splits each row into positive and negative parts\n \"\"\"\n return_predictions_pos = return_predictions.copy()\n return_predictions_neg = return_predictions.copy()\n return_predictions_pos[return_predictions_pos < 0] = 0\n return_predictions_neg[return_predictions_neg > 0] = 0\n return pd.concat([return_predictions_pos, return_predictions_neg], axis=1)\n\n\ndef tune_alpha(returns, return_predictors, alphas, memory=1000):\n correlations = []\n rmses = []\n all_weights_list = []\n for alpha in alphas:\n combinator = ReturnCombinator()\n # combinator.build(250, 7, alpha)\n # returns_hats, all_weights = combinator.fit(returns.iloc[:,0], returns_predictions)\n\n returns_hats, all_weights = combinator.fit(\n pd.DataFrame(returns), return_predictors, memory, alpha\n )\n\n correlations.append(\n np.corrcoef(\n returns_hats.values.flatten(),\n returns.loc[returns_hats.index].values.flatten(),\n )[0, 1]\n )\n rmses.append(\n np.sqrt(\n np.mean(\n (\n returns_hats.values.flatten()\n - returns.loc[returns_hats.index].values.flatten()\n )\n ** 2\n )\n )\n )\n all_weights_list.append(all_weights)\n\n return alphas, correlations, rmses, all_weights_list\n\n\n# def get_corr(returns, alpha):\n# combinator = ReturnCombinator()\n# combinator.build(250, 7, alpha)\n\n# returns_predictions = ewma_predictors(returns, [1, 5, 10, 21, 63, 125, 250])\n\n\n# returns_hats, all_weights = combinator.fit(returns, returns_predictions)\n# return np.corrcoef(returns_hats.values.flatten(), returns.loc[returns_hats.index].values.flatten())[0,1]\n\n\ndef _combinator_problem(memory, K, alpha):\n \"\"\"\n param memory: number of days to look back\n param K: number of predictors\n param alpha: regularization parameter in combination problem\n \"\"\"\n\n weights = cp.Variable(K)\n return_param = cp.Parameter(memory)\n return_prediction_param = cp.Parameter((memory, K))\n\n # halflife = 250\n # beta = np.exp(np.log(0.5) / halflife)\n # decay_weights = np.array([beta ** i for i in range(memory)])\n # decay_weights = decay_weights / np.sum(decay_weights)\n # decay_weights = decay_weights[::-1]\n # decay_weights = np.ones_like(decay_weights)\n\n objective = cp.Minimize(\n cp.sum_squares(return_param - return_prediction_param @ weights)\n + alpha * cp.norm1(weights)\n )\n # constraints = [cp.sum(weights)==1]\n # constraints = [weights<=1, weights>=-1]\n\n # problem = cp.Problem(objective, constraints)\n\n problem = cp.Problem(objective)\n\n return problem, weights, return_param, return_prediction_param\n\n\n# def _combinator_problem2(memory, n, K, alpha):\n# \"\"\"\n# param memory: number of days to look back\n# param K: number of predictors\n# param alpha: regularization parameter in combination problem\n# \"\"\"\n\n# weights = cp.Variable((K,n))\n# returns_param = cp.Parameter((memory, n))\n# returns_predictions_param = [cp.Parameter((memory, K)) for _ in range(n)]\n\n# # halflife = 250\n# # beta = np.exp(np.log(0.5) / halflife)\n# # decay_weights = np.array([beta ** i for i in range(memory)])\n# # decay_weights = decay_weights / np.sum(decay_weights)\n# # decay_weights = decay_weights[::-1]\n# # decay_weights = np.ones_like(decay_weights)\n\n# objective = 0\n# for i in range(n):\n# objective += cp.sum_squares(\n# returns_param[:,i] - returns_predictions_param[i] @ weights[:,i]\n# )\n# objective += alpha * cp.norm1(weights)\n\n# problem = cp.Problem(cp.Minimize(objective))\n\n# return problem, weights, returns_param, returns_predictions_param\n\n\nclass ReturnCombinator:\n def __init__(self) -> None:\n pass\n\n def _get_weights(self, returns, returns_predictions, scale, alpha, weighted_hf=500):\n \"\"\"\n param returns: pandas DataFrame of observed returns\n param returns_predictions: pandas DataFrame of various return predictors\n param scale: pandas series of scales for weighted least squares\n param alpha: regularization parameter in combination problem\n param weighted_hf: halflife for weighted least squares\n\n return: pandas series of weights\n \"\"\"\n\n beta = np.exp(np.log(0.5) / weighted_hf)\n decay_weights = np.array([beta**i for i in range(len(returns_predictions))])\n decay_weights = decay_weights / np.sum(decay_weights) * len(returns_predictions)\n decay_weights = decay_weights[::-1]\n b = decay_weights.reshape(-1, 1)\n # b = np.ones_like(b)\n\n D = np.diag(scale.values)\n\n weights = np.linalg.solve(\n (returns_predictions.values.T * b.T) @ returns_predictions.values\n + alpha * D,\n (returns_predictions.values.T * b.T) @ returns.values,\n )\n\n return pd.Series(weights, index=returns_predictions.columns)\n\n # return np.linalg.inv((returns_predictions.T * b.T) @ returns_predictions + alpha * D) @ (returns_predictions.T * b.T) @ returns\n\n def _get_scales(self, returns_predictions, halflife=250):\n \"\"\"\n param returns_predictions: pandas DataFrame of various return predictors\n\n return: pandas series of scales\n \"\"\"\n squared_returns_predictions = [\n prediction**2 for prediction in returns_predictions\n ]\n\n variances = [\n squared_prediction.ewm(halflife).mean().shift(1)\n for squared_prediction in squared_returns_predictions\n ]\n\n # scales = [1 / variance for variance in variances]\n scales = [variance for variance in variances]\n\n return scales\n\n def fit(self, returns, returns_predictions, memory, alpha):\n \"\"\"\n param returns: pandas DataFrame of observed returns\n param return_predictions: list of pandas DataFrames of various\n return predictors\n \"\"\"\n\n n = len(returns_predictions)\n scales = self._get_scales(returns_predictions)\n\n times = returns_predictions[0].index\n for i in range(n):\n assert (times == returns_predictions[i].index).all()\n \"predictors must have same indices\"\n\n assert set(times).issubset(returns.index)\n \"returns must have all predictor indices\"\n\n # Create empty pandas dataframe to store weights\n all_weights = [\n np.nan * returns_predictions[i].iloc[memory:].copy() for i in range(n)\n ]\n\n # for t in trange(memory, len(times)):\n # for t in range(2, len(times)):\n for t in trange(memory, len(times)): # Note: scales are None for day one\n t_start = times[max(0, t - memory)]\n t_end = times[t - 1]\n\n returns_temp = returns.loc[t_start:t_end]\n\n t_next = times[t]\n for i, asset in enumerate(returns.columns):\n scale = scales[i].loc[t_next]\n all_weights[i].loc[t_next] = self._get_weights(\n returns_temp[asset],\n returns_predictions[i].loc[t_start:t_end],\n scale,\n alpha,\n )\n\n returns_hats = {}\n for i in range(n):\n returns_hat = returns_predictions[i].iloc[memory:] * all_weights[i]\n returns_hats[i] = returns_hat.sum(axis=1)\n\n returns_hats = pd.DataFrame(returns_hats)\n returns_hats.columns = returns.columns\n\n return returns_hats, all_weights\n\n\n# def get_r_hat(returns):\n# return_predictions = ewma_predictors(returns, [1, 5, 10, 21, 63, 125, 250])\n# returns_hat, all_weights = ewma_return_combination(returns.iloc[1:], return_predictions, alpha=0.0001, memory=250)\n\n# return returns_hat, all_weights\n\n\nclass ReturnCombinatorOld:\n def __init__(self) -> None:\n pass\n\n def build(self, memory, K, alpha):\n \"\"\"\n param memory: number of days to look back\n param n: number of assets\n param K: number of predictors\n param alpha: regularization parameter in combination problem\n \"\"\"\n self.K = K\n self.memory = memory\n\n (\n self.problem,\n self.weights,\n self.returns_param,\n self.returns_predictions_param,\n ) = _combinator_problem(memory, K, alpha)\n\n def fit(self, returns, returns_predictions):\n \"\"\"\n param returns: pandas DataFrame of observed returns\n param return_predictions: list of pandas DataFrames of various\n return predictors\n \"\"\"\n assert self.K is not None\n \"build the model first\"\n\n assert returns_predictions.shape[1] == self.K\n \"number of predictors must match built model\"\n\n times = returns_predictions.index\n\n assert set(times).issubset(returns.index)\n \"returns must have all predictor indices\"\n\n # Create empty pandas dataframe to store weights\n all_weights = np.nan * returns_predictions.iloc[self.memory :].copy()\n\n for t in trange(self.memory, len(times)):\n t_start = times[t - self.memory]\n t_end = times[t - 1]\n self.returns_param.value = returns.loc[t_start:t_end].values\n self.returns_predictions_param.value = returns_predictions.loc[\n t_start:t_end\n ].values\n self.problem.solve(ignore_dpp=True)\n\n t_next = times[t]\n all_weights.loc[t_next] = self.weights.value\n\n returns_hat = (returns_predictions.iloc[self.memory :] * all_weights).sum(\n axis=1\n )\n\n return returns_hat, all_weights\n\n # def fit(self, returns, returns_predictions):\n # \"\"\"\n # param returns: pandas DataFrame of observed returns\n # param return_predictions: list of pandas DataFrames of various\n # return predictors\n # \"\"\"\n # assert self.K is not None; \"build the model first\"\n\n # for i in range(self.n):\n # assert returns_predictions[i].shape[1] == self.K; \"number of predictors must match built model\"\n # assert returns.shape[1] == self.n; \"number of assets must match built model\"\n\n # times = returns_predictions[0].index\n # for i in range(self.n):\n # assert returns_predictions[i].index.equals(times); \"predictor indices must match\"\n\n # assert set(times).issubset(returns.index); \"returns must have all predictor indices\"\n\n # # Create empty pandas dataframe to store weights\n # all_weights = [np.nan * predictor.iloc[self.memory:].copy() for predictor in returns_predictions]\n\n # for t in trange(self.memory, len(times)):\n # t_start = times[t-self.memory]\n # t_end = times[t-1]\n # self.returns_param.value = returns.loc[t_start:t_end].values\n # for i in range(self.n):\n # self.returns_predictions_param[i].value = returns_predictions[i].loc[t_start:t_end].values\n # self.problem.solve(ignore_dpp=True)\n\n # t_next = times[t]\n # for i in range(self.n):\n # all_weights[i].loc[t_next] = self.weights.value[:,i]\n\n # returns_hats = {}\n # for i in range(self.n):\n # returns_hat = returns_predictions[i].iloc[self.memory:] * all_weights[i]\n # returns_hats[i] = returns_hat.sum(axis=1)\n\n # returns_hats = pd.DataFrame(returns_hats)\n\n # return returns_hats, all_weights\n\n\ndef ewma_return_combination(returns, return_predictions, alpha, memory):\n \"\"\"\n param returns: pandas Series of observed returns\n param return_predictions: pandas DataFrame of various return predictions\n param alpha: regularization parameter in combination problem\n param memory: number of days to look back in combination problem\n \"\"\"\n times = returns.index\n\n problem, weights, return_param, return_prediction_param = _combinator_problem(\n memory, return_predictions.shape[1], alpha\n )\n\n # Create empty pandas datafrema to store weights of shape (T, K)\n all_weights = np.nan * return_predictions.iloc[memory:].copy()\n\n for t in trange(memory, returns.shape[0]):\n t_start = times[t - memory]\n t_end = times[t - 1]\n return_param.value = returns.loc[t_start:t_end].values\n return_prediction_param.value = return_predictions.loc[t_start:t_end].values\n problem.solve(ignore_dpp=True)\n\n t_next = times[t]\n all_weights.loc[t_next] = weights.value.T\n\n returns_hat = ((all_weights * return_predictions).dropna()).sum(axis=1)\n\n return returns_hat, all_weights\n\n\ndef daily_predictors(returns, lags):\n \"\"\"\n param returns: pandas Series of observed returns\n param lags: list of lags to use in daily predictors\n \"\"\"\n\n return_predictions = {}\n\n for lag in lags:\n return_predictions[lag] = returns.shift(lag).dropna()\n return_predictions = pd.DataFrame(return_predictions, index=returns.index)\n\n return return_predictions\n\n\ndef ewma_predictors(returns, halflives):\n \"\"\"\n param returns: pandas Series of observed returns\n param halflives: list of halflives to use in EWMA\n \"\"\"\n\n return_predictions = {}\n\n for halflife in halflives:\n return_predictions[halflife] = (\n returns.ewm(halflife=halflife).mean().shift(1).dropna()\n )\n return_predictions = pd.DataFrame(return_predictions, index=returns.index[1:])\n\n return return_predictions\n\n\n# def combinator_problem(returns, K, alpha):\n# T, n = returns.shape\n\n# weights = cp.Variable(K)\n# return_prediction_params = {i: cp.Parameter((T, n)) for i in range(K)}\n\n# print(returns.shape)\n# print(cp.sum([weights[i] * return_prediction_params[i] for i in range(K)]).shape)\n\n# print(returns.shape)\n# print(return_prediction_params[0].shape)\n# print((returns - return_prediction_params[0]).shape)\n\n\n# objective = cp.Minimize(\n# cp.sum_squares(returns - cp.sum([weights[i] * return_prediction_params[i] for i in range(K)])) + alpha * cp.norm1(weights)\n# )\n# problem = cp.Problem(objective)\n\n# return problem, weights, return_prediction_params\n\n\n# def return_combination(returns, return_predictions, alpha):\n# \"\"\"\n# param returns: pandas DataFrame of observed returns\n# param return_predictions: dictionary of various return predictions\n# each prediction is a pandas DataFrame\n# param alpha: regularization parameter in combination problem\n\n# return: weight to put on each predictior\n# \"\"\"\n# K = len(return_predictions)\n\n# problem, weights, return_prediction_params = combinator_problem(returns, K, alpha)\n","repo_name":"cvxgrp/cov_pred_finance","sub_path":"experiments/utils/return_combination.py","file_name":"return_combination.py","file_ext":"py","file_size_in_byte":15070,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"}
+{"seq_id":"30118246707","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING\nfrom dm.skills.Hero._hero import HeroSkill\nfrom utilities import CooldownType\n\nif TYPE_CHECKING:\n from dm.core.contexts import AttackContext\n from dm.core.game.game import DMGame\n from dm.core.objects.unit import DMUnit\n################################################################################\n\n__all__ = (\"Venom\",)\n\n################################################################################\nclass Venom(HeroSkill):\n\n def __init__(self, state: DMGame, parent: DMUnit = None):\n\n super().__init__(\n state, parent,\n _id=\"SKL-362\",\n name=\"Venom\",\n description=(\n \"When attacking enemy, apply Poison as much as 8 % of target's \"\n \"current LIFE.\"\n ),\n rank=5,\n cooldown=CooldownType.Passive\n )\n\n################################################################################\n def on_attack(self, ctx: AttackContext) -> None:\n\n # If we're attacking\n if self.owner == ctx.source:\n # Apply Poison\n ctx.target.add_status(\"Poison\", 0.08 * ctx.target.life, self)\n\n################################################################################\n","repo_name":"AllegroVivo/DungeonDefense","sub_path":"dm/skills/Hero/Grand/Venom.py","file_name":"Venom.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3790840026","text":"MAX_CYCLE = 2 ** 17 - 1\n\nclass LFSR:\n def __init__(self, initial_state, feedback_states):\n self.initial_state = initial_state\n self.feedback_states = feedback_states\n self.state = None\n self.reset()\n\n def next(self):\n r = self.state[0]\n feedback_bit = sum(self.state[x] for x in self.feedback_states) % 2\n for i in range(len(self.state) - 1):\n self.state[i] = self.state[i + 1]\n self.state[len(self.state) - 1] = feedback_bit\n return r\n\n def take(self, n):\n return (self.next() for _ in range(n))\n\n def reset(self):\n self.state = self.initial_state[:]\n\n\ndef calc_cycle(lfsr):\n lfsr.reset()\n lfsr.next()\n c = 1\n while lfsr.state != lfsr.initial_state:\n lfsr.next()\n c += 1\n return c\n\n\ndef test(lfsr):\n print('first 30 bits: ', list(lfsr.take(30)))\n c = calc_cycle(lfsr)\n print('cycle length = %d' % c)\n print('precentage of max cycle: %d%%' % (float(c) / MAX_CYCLE * 100))\n\n\ndef main():\n print('max cycle length: %d' % MAX_CYCLE)\n initial_state = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n key1 = [0, 2, 3, 5]\n print('Test a')\n test(LFSR(initial_state, key1))\n key2 = [0, 8]\n print('Test b')\n test(LFSR(initial_state, key2))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"randomUserNameThatIsRandom/Crypto","sub_path":"hw2/ex1_q7.py","file_name":"ex1_q7.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11741116198","text":"from django.shortcuts import render, redirect\nfrom video.models import Video1\nfrom video.forms import VideoForm, AddForm\nfrom rest_framework import routers\n# from video.serializers import APItestSerializer\nfrom rest_framework import viewsets\nfrom django.contrib.auth.decorators import login_required\n\n# Create your views here.\n\n@login_required(login_url='login')\ndef index(request):\n if request.method == 'POST':\n video = VideoForm(request.POST)\n if video.is_valid():\n ag = video.cleaned_data['agegroup']\n sb = video.cleaned_data['subject']\n to = video.cleaned_data['topic']\n vi = video.cleaned_data['video']\n reg = video(agegroup = ag, subject = sb)\n reg.save()\n else:\n video = VideoForm()\n video=Video1.objects.all()\n return render (request, 'index.html', {\"video\":video})\n\n\n\ndef addvideoview(request):\n if request.method == 'POST':\n addd = AddForm(request.POST)\n if addd.is_valid():\n addd.save()\n return redirect('/home/')\n else:\n \n \n addd = AddForm() \n return render(request, 'addvideo.html', {'form':addd})\n \ndef deleteview(request):\n if request.method == \"POST\":\n data = request.POST \n id = data.get('id')\n \n \n \n\n\n\n# class APItestViewSet(viewsets.ModelViewSet):\n# serializer_class = APItestSerializer\n# queryset = APItest.objects.all()","repo_name":"ashish1700/API_Registration","sub_path":"video/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73997376721","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def mergeInBetween(self, list1: ListNode, a: int, b: int, list2: ListNode) -> ListNode:\n \n head = list1\n \n # Find (a-1)th node\n for _ in range(a-1):\n list1 = list1.next\n \n a_minus_1_node = list1\n \n # Now points to a\n list1 = list1.next\n \n # Attach list2 start\n a_minus_1_node.next = list2\n \n # Get to bth node\n for _ in range(b - a + 1):\n list1 = list1.next\n \n # Get end of list2\n while list2.next:\n list2 = list2.next\n \n # Attach end of list2 to list1\n list2.next = list1\n \n return head\n \n","repo_name":"KajalGada/leetcode-python","sub_path":"leetcode 1669 Merge In Between Linked Lists/sol1.py","file_name":"sol1.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1980300721","text":"# %%\nfrom pathlib import Path\nimport pandas as pd\nimport os\nos.getcwd()\n\ndata_path = Path(\"data\")\ninput_path = data_path / \"split\"\n\ndata_path = Path(\"data\")\n\npd.options.display.max_rows = 63\n\n# %%\ndf = pd.read_csv(data_path / \"labels_racism.csv\", sep=\"|\")\n\nwith (data_path /\"racist_data/racist_words_lemma.txt\").open(\"r\") as f:\n racist_words_lemma = f.read().splitlines()\n\n\ndff = pd.DataFrame([])\n\nfor i in racist_words_lemma:\n df_counts = pd.DataFrame({\"word\":[i],\n \"count\":sum(df['message'].apply(lambda x: x.count(i)))})\n df_counts.set_index('word')\n dff = dff.append(df_counts)\n\n\n\ndff.set_index('word')\n","repo_name":"david26694/datathon-racism","sub_path":"eda/count_words.py","file_name":"count_words.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41142280886","text":"import requests\nimport frappe\nfrom frappe import _\nimport json\nfrom frappe.utils.password import get_decrypted_password\nfrom frappe.utils import today\nfrom json.decoder import JSONDecodeError\nfrom frappe.utils.data import format_date\ndef execute(filters=None):\n\tcolumns = get_columns()\n\tdata=get_data(filters)\n\treturn columns, data\n\n\ndef get_columns():\n columns = [\n {\n \"label\": (\"Description\"),\n \"fieldname\": \"heading\",\n \"fieldtype\": \"Data\",\n \"width\": 250\n\t\t},\n {\n \"label\": (\"Value\"),\n \"fieldname\": \"amount\",\n \"fieldtype\": \"Data\",\n \"width\": 250\n\t\t},\n ]\n return columns\n\n\n\ndef get_data(filters={}):\n\t\tdata = []\n\t\tcondition = \"\"\n\t\tif filters.get(\"company\") != \"TEAM CYCLE WORLD PVT. LTD.\":\n\t\t\tcondition=f\"\"\" and branch = '{filters.get(\"branch\")}'\"\"\"\n\t\tno_of_amt = frappe.db.sql(f\"\"\"\n\t\tSELECT SUM(grand_total) as amt_si , COUNT(name) as no_of_si\n\t\tFROM `tabSales Invoice`\n\t\tWHERE docstatus != 2\n\t\t{condition}\n\t\tAND company = \"{filters.get(\"company\")}\"\n\t\tAND sales_type_link != \"Order\"\n\t\tAND DATE(posting_date) = DATE(\"{filters.get('posting_date')}\");\n\t\"\"\",as_dict = 1)\n\t\tif len(no_of_amt):\n\t\t\tdata.append({'heading':'Total Sales Amount','amount':no_of_amt[0]['amt_si'] or 0})\n\t\t\tdata.append({'heading':'Total No of Invoice','amount':int(no_of_amt[0]['no_of_si'] or 0)})\n\t\tbicycle = []\n\t\tparent_grps = ['BICYCLES']\n\t\tfor i in parent_grps:\n\t\t\tbicycle.extend(frappe.get_all('Item Group', filters={'parent_item_group': ['in', i]}, pluck='name'))\n\t\t\tparent_grps.extend(frappe.get_all('Item Group', filters={'parent_item_group': ['in', i], 'is_group': 1}, pluck='name'))\n\t\tbicycle += parent_grps\n\t\tif filters.get(\"company\") != \"TEAM CYCLE WORLD PVT. LTD.\":\n\t\t\tcondition=f\"\"\" and si.branch = '{filters.get(\"branch\")}'\"\"\"\n\t\tsi_cycle = frappe.db.sql(f\"\"\"\n\t\t\tSELECT SUM(sii.qty) as no_of_bi\n\t\t\tFROM `tabSales Invoice` si\n\t\t\tLEFT JOIN `tabSales Invoice Item` sii ON sii.parent = si.name\n\t\t\tWHERE si.docstatus != 2 \n\t\t\tAND DATE(si.posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\t\tAND si.company = \"{filters.get('company')}\"\n\t\t\t{condition}\n\t\t\tAND sii.item_group IN %(bicycle)s\n\t\t\"\"\", {'bicycle': bicycle}, as_dict=1)\n\t\tif len(si_cycle):\n\t\t\tdata.append({'heading':'Total No of Bicycle Sold','amount':int(si_cycle[0]['no_of_bi'] or 0) })\n\t\taccess = []\n\t\tparent_grps = ['ACCESSORIES']\n\t\tfor i in parent_grps:\n\t\t\taccess.extend(frappe.get_all('Item Group', filters={'parent_item_group':['in', i]}, pluck='name'))\n\t\t\t\n\t\t\tparent_grps.extend(frappe.get_all('Item Group', filters={'parent_item_group':['in', i], 'is_group':1}, pluck='name'))\n\t\taccess+=parent_grps\n\t\tsi_access = frappe.db.sql(f\"\"\"SELECT SUM(sii.qty) as no_of_ac\n\t\tFROM `tabSales Invoice` si\n\t\tLEFT JOIN `tabSales Invoice Item` sii ON sii.parent = si.name\n\t\tWHERE si.docstatus != 2 \n\t\t\t\tAND si.posting_date = DATE(\"{filters.get('posting_date')}\")\n\t\tAND si.company = \"{filters.get(\"company\")}\"\n\t\t{condition} AND\n\t\tsii.item_group IN %(access)s\n\t\"\"\",{\"access\": tuple(access)}, as_dict = 1)\n\t\tif len(si_access):\n\t\t\tdata.append({'heading':'Total No of Accessories Sold','amount':int(si_access[0]['no_of_ac'] or 0) })\n\t\tsum = 0.0\n\t\texchange_count = 0\n\t\tmop = frappe.db.sql(f\"\"\"\n\t\tSELECT sip.mode_of_payment, SUM(sip.amount) as mop\n\t\tFROM `tabSales Invoice` si\n\t\tLEFT JOIN `tabSales Invoice Payment` sip ON si.name = sip.parent\n\t\tWHERE \n\t\t si.docstatus != 2 {condition}\n\t\tAND si.company = \"{filters.get(\"company\")}\"\n\t\tAND DATE(si.posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\tGROUP BY sip.mode_of_payment\n\t\"\"\", as_dict = True)\n\t\tif(filters.get(\"company\") == \"TEAM CYCLE WORLD PVT. LTD.\"):\n\t\t\tpe = frappe.db.sql(f\"\"\"\n\t\t\tSELECT mode_of_payment, SUM(paid_amount) as mop\n\t\t\tFROM `tabPayment Entry`\n\t\t\tWHERE \n\t\t\tdocstatus != 2 AND payment_type = \"Receive\"\n\t\t\tAND company = \"{filters.get(\"company\")}\"\n\t\t\tAND DATE(posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\t\tGROUP BY mode_of_payment\n\t\t\t\"\"\", as_dict = True)\n\t\t\tmop+=pe\n\t\tfor i in mop:\n\t\t\tif i['mop']:\n\t\t\t\tdata.append({'heading': i['mode_of_payment'], 'amount': i['mop'] or 0})\n\t\t\t\tsum+=i['mop']\n\t\tif(sum):\n\t\t\tdata.append({'heading':'Total MOP Amount','amount':sum or 0})\n\n\t\tcash = frappe.get_all(\"Account\",filters={'company':filters.get(\"company\"),'account_type':'Cash'},pluck='name')\n\t\tif filters.get(\"company\") != \"TEAM CYCLE WORLD PVT. LTD.\":\n\t\t\tcondition=f\"\"\" and branch = '{filters.get(\"branch\")}'\"\"\"\n\t\tno_of_pi_amt = frappe.db.sql(f\"\"\"\n\t\tSELECT SUM(credit) as outward , SUM(debit) as inward\n\t\tFROM `tabGL Entry`\n\t\tWHERE is_cancelled = 0\n\t\t{condition}\n\t\tAND company = \"{filters.get(\"company\")}\"\n\t\tAND DATE(posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\tAND account IN {tuple(cash)}\n\t\"\"\", as_dict = 1)\n\t\tif len(no_of_pi_amt):\n\t\t\tdata.append({'heading':'Cash Outward','amount':no_of_pi_amt[0]['outward'] or 0})\n\t\t\tdata.append({'heading':'Cash Inward','amount':no_of_pi_amt[0]['inward'] or 0})\n\t\tadvance = frappe.db.sql(f\"\"\"\n\t\tSELECT SUM(credit) as adv\n\t\tFROM `tabGL Entry`\n\t\tWHERE is_cancelled = 0\n\t\t{condition}\n\t\tAND company = \"{filters.get(\"company\")}\"\n\t\tAND DATE(posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\tAND against IN %(cash)s AND\n\t\tagainst_voucher_type = \"Sales Order\"\n\t\"\"\", {\"cash\": cash}, as_dict = 1)\n\t\tif len(advance):\n\t\t\tdata.append({'heading':'Cash in Advance','amount':advance[0]['adv'] or 0})\n\t\tif filters.get(\"company\") != \"TEAM CYCLE WORLD PVT. LTD.\":\n\t\t\tcondition=f\"\"\" and si.branch = '{filters.get(\"branch\")}'\"\"\"\n\t\tmop_exe = frappe.db.sql(f\"\"\"\n\t\tSELECT COUNT(sip.name) as mop_ex\n\t\tFROM `tabSales Invoice` si\n\t\tLEFT JOIN `tabSales Invoice Payment` sip ON si.name = sip.parent\n\t\tWHERE \n\t\t si.docstatus != 2 {condition}\n\t\tAND si.company = \"{filters.get(\"company\")}\"\n\t\tAND DATE(si.posting_date) = DATE(\"{filters.get('posting_date')}\") AND\n\t\tsip.amount > 0 AND\n\t\tsip.mode_of_payment = \"Exchange\"\n\t\"\"\", as_dict = True)\n\t\tdata.append({'heading': 'Exchange cycle received today', 'amount':int(mop_exe[0]['mop_ex'] or 0) })\n\t\texe_sold = frappe.db.sql(f\"\"\"\n\t\tSELECT SUM(sii.qty) as no_of_si_exe\n\t\tFROM `tabSales Invoice` si\n\t\tLEFT JOIN `tabSales Invoice Item` sii ON sii.parent = si.name\n\t\tWHERE si.docstatus != 2 \n\t\tAND si.posting_date = DATE(\"{filters.get('posting_date')}\")\n\t\tAND si.company = \"{filters.get(\"company\")}\" \n\t\t{condition} AND\n\t\tsii.item_group = \"Exchange\"\n\t\"\"\", as_dict = 1)\n\t\tdata.append({'heading': 'Exchange cycle Sold today', 'amount':int(exe_sold[0]['no_of_si_exe'] or 0 ) })\n\t\tget_branch = frappe.get_value(\"Branch\",filters.get(\"branch\"),'sales_warehouse')\n\t\t\n\t\tbin = frappe.db.sql(f\"\"\"\n\t\tSELECT actual_qty as act_qty\n\t\tFROM `tabBin`\n\t\tWHERE \n\t\titem_code in (Select name from `tabItem` where item_group = \"Exchange\")\n\t\tAND warehouse = %(get_branch)s\n\t\"\"\", {\"get_branch\": get_branch}, as_dict = 1)\n\t\tif len(bin):\n\t\t\tdata.append({'heading':'Total Exchange Cycle Available','amount':int(bin[0]['act_qty'] or 0 ) })\n\t\t\n\n\t\treturn data \n\n\nimport frappe\nimport json\nfrom frappe.utils.password import get_decrypted_password\n\n\n@frappe.whitelist()\ndef message_loop():\n\tif not frappe.db.get_value(\"Company API Credentials\", {\"enable\":1, \"use_this_to_send_daily_summary\":1}, \"name\"):\n\t\treturn\n\tcompanies = frappe.db.get_all(\"Company\", fields=[\"name\"])\n\tfor company in companies:\n\t\tbranches = frappe.get_all(\"Branch\", filters={\"company\": company.name}, pluck=\"name\")\n\t\tif(len(branches) == 0):\n\t\t\tbranches = [None]\n\t\tfor branch in branches:\n\t\t\tmessage_txt = get_report_content({'company': company.name, 'branch': branch, 'posting_date': today()})\n\t\t\tif(branch):\n\t\t\t\tuser = frappe.get_all(\"User\",filters={\"company\":company.name,\"role_profile_name\":\"Branch Admin\"},pluck=\"email\")\n\t\t\t\tuser_permission = frappe.get_all(\"User Permission\",filters={\"user\":[\"in\",user],\"allow\":\"Branch\",\"for_value\":branch},pluck=\"user\")\n\t\t\t\tfor i in user_permission:\n\t\t\t\t\tmobile_no = frappe.get_value(\"User\",{\"company\":company.name,\"email\":i, \"enabled\":1},\"mobile_no\")\n\t\t\t\t\tif not mobile_no:\n\t\t\t\t\t\twhatsapp = frappe.new_doc(\"Whatsapp Log\")\n\t\t\t\t\t\twhatsapp.update({\n\t\t\t\t\t\t\t\"response\":f\"Mobile Number Not found for user {i}, Company:{company.name}, Branch:{branch}\", \n\t\t\t\t\t\t\t'status':'Not Sent',\n\t\t\t\t\t\t\t'branch':branch,\n\t\t\t\t\t\t\t'message':message_txt,\n\t\t\t\t\t\t\t'user':i,\n\t\t\t\t\t\t\t'company':company.name\n\t\t\t\t\t\t\t})\n\t\t\t\t\t\twhatsapp.save(ignore_permissions = True)\n\t\t\t\t\t\tfrappe.db.commit()\n\t\t\t\t\telse:\n\t\t\t\t\t\tmobile_no = \"\".join(mobile_no.split(\" \"))\n\t\t\t\t\t\tsend_message(company.name,i,message_txt,f\"91{mobile_no}\",branch)\n\t\t\telse:\n\t\t\t\tuser = frappe.get_all(\"User\",filters={\"company\":company.name,\"role_profile_name\":\"HO Admin Super Admin\", \"enabled\":1},pluck=\"email\")\n\t\t\t\tfor i in user:\n\t\t\t\t\tmobile_no = frappe.get_value(\"User\",{\"company\":company.name,\"email\":i},\"mobile_no\")\n\t\t\t\t\tif not mobile_no:\n\t\t\t\t\t\twhatsapp = frappe.new_doc(\"Whatsapp Log\")\n\t\t\t\t\t\twhatsapp.update({\n\t\t\t\t\t\t\t\"response\":f\"Mobile Number Not found for user {i}, Company:{company.name}, Branch:{branch}\", \n\t\t\t\t\t\t\t'status':'Not Sent',\n\t\t\t\t\t\t\t\"message\":message_txt,\n\t\t\t\t\t\t\t'user':i,\n\t\t\t\t\t\t\t'company':company.name\n\t\t\t\t\t\t\t})\n\t\t\t\t\t\twhatsapp.save(ignore_permissions = True)\n\t\t\t\t\t\tfrappe.db.commit()\n\t\t\t\t\telse:\n\t\t\t\t\t\tmobile_no = \"\".join(mobile_no.split(\" \"))\n\t\t\t\t\t\tsend_message(company.name,i,message_txt,f\"91{mobile_no}\")\n\ndef send_message(company,user,message_txt,mobile_no,branch=None):\n\tresponse=\"\"\n\turl_message = \"\"\n\ttry: \n\t\tnumber\t= mobile_no\n\t\tchild_name = frappe.db.get_value(\"Company API Credentials\", {\"enable\":1, \"use_this_to_send_daily_summary\":1}, \"name\")\n\t\tif not child_name:\n\t\t\treturn\n\t\tinstance_id = frappe.db.get_value(\"Company API Credentials\", child_name, \"instance_id\")\n\t\t\n\t\taccess = get_decrypted_password(\"Company API Credentials\",child_name, \"access_token\")\n\t\turl=frappe.db.get_value(\"Company API Credentials\", child_name, \"api_url\")\n\t\tenabled= frappe.db.get_single_value(\"API Credentials\", \"enable\") and frappe.db.get_value(\"Company API Credentials\", child_name, \"enable\")\n\t\tif enabled == 1:\n\t\t\turl_message = f\"{url}?number={number}&type=text&message={message_txt}&instance_id={instance_id}&access_token={access}\"\n\t\t\tpayload={}\n\t\t\theaders = {}\n\t\t\tresponse = requests.request(\"GET\", url_message, headers=headers, data=payload)\n\t\t\twhatsapp = frappe.new_doc(\"Whatsapp Log\")\n\t\t\ttry:\n\t\t\t\tresponse = response.json()\n\t\t\t\tif(response.get('status') == 'error'):\n\t\t\t\t\twhatsapp.update({'status':'Failed', \"response\":json.dumps(response, indent=4),})\n\t\t\t\telse:\n\t\t\t\t\twhatsapp.update({'status':'Success', \"response\":json.dumps(response, indent=4),})\n\t\t\texcept JSONDecodeError:\n\t\t\t\tfrappe.log_error(response.text)\n\t\t\t\twhatsapp.update({'status':'Failed', \"success_html\":response.text})\n\t\t\texcept Exception as e:\n\t\t\t\tfrappe.log_error(title=\"Whatsapp Error\", message=f\"\"\"Error: {company}\\n{user}\\n{mobile_no}\\n{branch}\\n{response.text}\\n{url_message}\\n{e}\"\"\")\n\t\t\t\tfrappe.db.commit()\n\t\t\t\n\t\t\twhatsapp.update({\"request\":url_message,\"message\":message_txt,\"number\":number, \"branch\":branch,\"company\":company,\"user\":user})\n\t\t\twhatsapp.save(ignore_permissions = True)\n\t\t\tfrappe.db.commit()\n\texcept Exception as e:\n\t\tfrappe.log_error(title=\"Whatsapp Error\", message=f\"\"\" {company}\\n{user}\\n{mobile_no}\\n{branch}\\n{response}\\n{url_message}\\n{e}\"\"\")\n\t\tfrappe.db.commit()\n\n\ndef get_report_content(filters):\n\treport = frappe.get_doc(\"Report\", \"Daily Summary\")\n\n\tfilters = frappe.parse_json(filters) if filters else {}\n\n\tcolumns, data = report.get_data(\n\t\tfilters=filters,\n\t)\n\treport_data = \"\"\n\tfor i in data:\n\t\treport_data += f\"%0A{i['heading']}%09-%09{i['amount']}\"\n\tbranch_txt = \"\"\n\tif filters.get(\"branch\"):\n\t\tbranch_txt = f\"*BRANCH* : {filters.get('branch')} %0A\"\n\ttext_msg = f\"\"\"\n*Daily Summary*%09%09%09%0A\n*COMPANY* : {filters.get(\"company\")}%0A\n{branch_txt}\n*DATE* : {format_date(filters.get(\"posting_date\"), \"dd-mm-yyyy\")}\n{report_data}\n\"\"\"\n\treturn text_msg","repo_name":"thirvusoft/Cycle-World-Franchise","sub_path":"cycle_world/cycle_world/report/daily_summary/daily_summary.py","file_name":"daily_summary.py","file_ext":"py","file_size_in_byte":11747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72437329360","text":"import sqlite3\r\n\r\nsquare = lambda n: n * n # creamos la funcion que enviaremos a la base de datos\r\n\r\n\r\nconn = sqlite3.connect(\"Northwind.db\") # instanciamos la base de datos\r\nconn.create_function(\r\n \"square\", 1, square\r\n) # creamos la funcion dentro de la base de datos(\"nombre de la funcion a crear\",cuantos parametros al usuario,la funcion)\r\n\r\ncursor = conn.cursor()\r\ncursor.execute(\r\n \"\"\"\r\n SELECT * FROM Products\r\n GROUP BY ProductID\r\n ORDER BY ProductID\r\n \"\"\"\r\n)\r\n\r\nresults = cursor.fetchall()\r\nprint(results)\r\n","repo_name":"zgamerHT/miexperiencia","sub_path":"UDF.py","file_name":"UDF.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24069329705","text":"import numpy as np\n\n\nclass Vehicle:\n def __init__(self, name, board, fuel=100):\n self.name = name\n self.position = self.get_pos(board)\n self.orientation = self.get_orientation()\n self.fuel = fuel\n\n def get_pos(self, board):\n position = []\n for i in range(6):\n for j in range(6):\n if board.grid[i][j] == self.name:\n position.append([i, j])\n return position\n\n def get_orientation(self):\n # check same row\n if self.position[0][0] == self.position[1][0]:\n return 'X'\n else:\n return 'Y'\n\n # need to check head from top of the car, leftmost element of the pos list\n # check fuel, orientation, if going out of board, and if next pos is free\n # remove 1 from each ROW\n def can_move_up(self, current_board):\n return self.fuel > 0 \\\n and self.orientation == 'Y' \\\n and self.position[0][0] - 1 >= 0 \\\n and current_board.grid[self.position[0][0] - 1][self.position[0][1]] == '.'\n\n # check the last element of the pos list\n def can_move_down(self, current_board):\n return self.fuel > 0 \\\n and self.orientation == 'Y' \\\n and self.position[-1][0] + 1 < 6 \\\n and current_board.grid[self.position[-1][0] + 1][self.position[0][1]] == '.'\n\n # check first element of the pos list (left most pos of the car)\n def can_move_left(self, current_board):\n return self.fuel > 0 \\\n and self.orientation == 'X' \\\n and self.position[0][1] - 1 >= 0 \\\n and current_board.grid[self.position[0][0]][self.position[0][1] - 1] == '.'\n\n # check LAST element of the pos list (right most pos of the car)\n def can_move_right(self, current_board):\n # need check col of last pos if its less than 6\n return self.fuel > 0 \\\n and self.orientation == 'X' \\\n and self.position[-1][1] + 1 < 6 \\\n and current_board.grid[self.position[0][0]][self.position[-1][1] + 1] == '.'\n\n def move(self, current_board, direction):\n # remove from grid\n for pos in self.position:\n current_board.grid[pos[0]][pos[1]] = '.'\n\n # new position\n if direction == 'right':\n for pos in self.position:\n pos[1] = pos[1] + 1\n elif direction == 'left':\n for pos in self.position:\n pos[1] = pos[1] - 1\n elif direction == 'up':\n for pos in self.position:\n pos[0] = pos[0] - 1\n elif direction == 'down':\n for pos in self.position:\n pos[0] = pos[0] + 1\n\n # fuel and grid update\n self.fuel -= 1\n current_board.change_fuel()\n\n def print_vehicle(self):\n print(self.name, end=' ')\n print(self.position, end=' ')\n print(self.fuel)\n","repo_name":"stevenvuong00/comp472Project2","sub_path":"src/vehicle.py","file_name":"vehicle.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43360317862","text":"import logging\n\nimport aiohttp\n\nLOG = logging.getLogger(\"obu\")\n\nclass teltonikaRUT9x:\n async def __aenter__(self):\n self.session = aiohttp.ClientSession()\n await self._get_session_id()\n return self\n\n async def __aexit__(self,exc_type,exc,tb):\n await self.session.close()\n\n def __init__(self,url,username=\"root\",password=\"\"):\n \"\"\"Makes an teltonikaRUT9x object to read JSON_RPC from the RUT9x mobile router.\n \n Parameters\n ----------\n url : string\n URL to router\n username : string\n username to log into the router, default: root\n password : string\n Password for username, default: \"\"\n \"\"\"\n\n self._url = url\n payload = { \n \"jsonrpc\": \"2.0\", \n \"id\": 1, \n \"method\": \"call\", \n \"params\": [ \n \"00000000000000000000000000000000\", \n \"session\", \n \"login\",\n {\"username\": \"\", \"password\": \"\", \"timeout\": 0 } \n ] \n }\n payload[\"params\"][3][\"username\"] = username\n payload[\"params\"][3][\"password\"] = password\n \n self.payload = payload\n \n async def _get_session_id(self):\n async with self.session.post(self._url,json=self.payload) as resp:\n LOG.info(\"Request JSON-RPC session ID from router\")\n resp = await resp.json()\n self._sessionId = resp[\"result\"][1][\"ubus_rpc_session\"]\n LOG.info(\"Session id retrieved\")\n \n async def readSignalStrength(self):\n \"\"\"\n Reads the RSRP, SINR and RSRQ from the RUT9x \n\n Returns\n -------\n dict: with RSRP, SINR and RSPQ.\n \"\"\"\n\n payload = {\n \"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"call\", \"params\":\n [ \n \"\",\n \"file\",\n \"exec\",\n {\n \"command\":\"gsmctl\",\n \"params\":[\"-WZM\" ]\n }\n ]\n }\n payload[\"params\"][0] = self._sessionId\n \n async with self.session.post(self._url,json=payload) as r:\n r = await r.json()\n \n stdout = r[\"result\"][1][\"stdout\"].split(\"\\n\")\n \n result = {\n \"RSRP\" : \"\",\n \"SINR\" : \"\",\n \"RSRQ\" : \"\"\n }\n i = 0\n for x in result:\n result[x] = stdout[i]\n i += 1\n #print(result)\n LOG.info(result)\n return result\n\n async def readGPS(self):\n \"\"\" Reads the latitude and longitude\n\n Returns\n -------\n dict:\n a dict with the latitude and longitude \n \"\"\"\n\n payload = {\n \"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"call\", \"params\":\n [ \n \"\",\n \"file\",\n \"exec\",\n {\n \"command\":\"gpsctl\",\n \"params\":[\"-ix\" ]\n }\n ]\n }\n payload[\"params\"][0] = self._sessionId\n #payload = json.dumps(payload)\n #print(payload)\n async with self.session.post(self._url,json=payload) as r:\n r = await r.json()\n #print(r.text)\n stdout = r[\"result\"][1][\"stdout\"].split(\"\\n\")\n #print(stdout)\n result = {\n \"Lat\" : \"\",\n \"Lon\" : \"\"\n }\n i = 0\n for x in result:\n result[x] = stdout[i]\n i += 1\n #print(result)\n LOG.info(result)\n return result\n ","repo_name":"svinz/msc_project_onboardunit","sub_path":"src/ReadJSONRPC.py","file_name":"ReadJSONRPC.py","file_ext":"py","file_size_in_byte":3641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34949562114","text":"import warnings\nfrom copy import deepcopy\n\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport umap\nfrom hdbscan import HDBSCAN\nfrom h5.constants import BARCODE, ID, SAMPLE\nfrom h5.data.assay import Assay as H5_Assay\nfrom plotly.subplots import make_subplots\nfrom scipy import stats\nfrom scipy.cluster import hierarchy\nfrom scipy.spatial.distance import pdist\nfrom scipy.stats import gaussian_kde\nfrom sklearn.cluster import DBSCAN, KMeans\nfrom sklearn.decomposition import PCA\nfrom sklearn.neighbors import NearestNeighbors\n\nfrom mosaic.constants import (\n COLORS,\n LABEL,\n ORG_BARCODE,\n PALETTE,\n PCA_LABEL,\n READS,\n SCALED_LABEL,\n UMAP_LABEL,\n)\nfrom mosaic.plotting import plt, require_seaborn, sns\nfrom mosaic.utils import clipped_values, get_indexes\n\n\nclass _Assay(H5_Assay):\n \"\"\"\n Abstract class for all assays\n\n Each of :class:`mosaic.dna.Dna`, :class:`mosaic.cnv.Cnv`,\n and :class:`mosaic.protein.Protein` are inherited from this base class,\n hence all of them have the following functionality:\n\n `_Assay` objects can be filtered using Python's slice notation. It requires two\n keys (barcodes, ids). Both could be a list of positions, a Boolean list,\n or a list of the respective values.\n\n Load the sample.\n\n >>> import mosaic.io as mio\n >>> sample = mio.load('/path/to/h5')\n\n Select the first 250 cells (these aren't necessarily the cells\n with the highest reads, they're arbitrary cells) and the dna\n variants obtained from filtering.\n\n >>> select_bars = sample.dna.barcodes()[: 250]\n >>> select_vars = sample.dna.filter_variants()\n\n Slice the dna assay for the selected cells and variants.\n\n >>> filtered_dna = sample.dna[select_bars, select_vars]\n\n Note: This example is for DNA, but the same can be done for\n any `_Assay` object.\n\n .. rubric:: Modifying data\n .. autosummary::\n\n add_layer\n add_row_attr\n add_col_attr\n add_metadata\n del_layer\n del_row_attr\n del_col_attr\n del_metadata\n\n .. rubric:: Selecting data\n .. autosummary::\n\n barcodes\n clustered_barcodes\n ids\n clustered_ids\n drop\n get_palette\n set_palette\n get_labels\n set_labels\n set_selected_labels\n rename_labels\n get_attribute\n\n .. rubric:: Tertiary analysis methods\n .. autosummary::\n\n scale_data\n run_pca\n run_umap\n run_lda\n cluster\n group_clusters\n\n .. rubric:: Visualizations\n .. autosummary::\n\n scatterplot\n heatmap\n violinplot\n ridgeplot\n stripplot\n read_distribution\n feature_scatter\n fishplot\n barplot\n\n .. rubric:: Statistics\n .. autosummary::\n\n feature_signature\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Calls h5.data.assay.__init__.\n \"\"\"\n super().__init__(*args, **kwargs)\n self.selected_bars = {}\n self.selected_ids = np.array([])\n self.title = \", \".join(self.metadata[SAMPLE].flatten())\n\n self._rename_barcodes()\n\n pal = None\n if PALETTE in self.metadata:\n pal = self.get_palette()\n\n labs = self.get_labels()\n self.set_labels(labs)\n\n if pal is not None and set(pal.keys()) == set(labs):\n self.set_palette(pal)\n\n # --------- Interactivity\n\n def __getitem__(self, key):\n \"\"\"\n Implemented for convenience when analyzing\n multiple samples in a Jupyter Notebook.\n\n Parameters\n ----------\n key : 2-tuple\n Both elements of the tuple must be an\n array of values, indices or bools. The first\n element is used to filter barcodes and the\n second element is used to filter ids.\n\n Returns\n -------\n :class:`mosaic.assay._Assay`\n A copy of itself with the relevant\n barcodes and ids selected.\n\n Raises\n ------\n KeyError\n When the tuple provided does not match the\n expected format.\n \"\"\"\n def _isstrlike(val):\n if (isinstance(val, list) and isinstance(val[0], str)) or (isinstance(val, np.ndarray) and isinstance(val[0], str)):\n return True\n return False\n\n if isinstance(key, tuple):\n assay = deepcopy(self)\n key_row, key_col = key\n\n if _isstrlike(key_row):\n key_row = get_indexes(assay.barcodes(), key_row)\n\n if _isstrlike(key_col):\n key_col = get_indexes(assay.ids(), key_col)\n\n if key_row is not None:\n assay.select_rows(key_row)\n\n if key_col is not None:\n assay.select_columns(key_col)\n else:\n raise KeyError(f\"Expected tuple, got {type(key)}\")\n\n return assay\n\n def _rename_barcodes(self):\n \"\"\"\n Appends the name of the sample to the barcode\n in case of multi sample files. This is required to\n avoid duplicate barcodes from colliding.\n\n In case two different samples have duplicate\n barcodes, then there are high chances of\n misidentification of the cell signature. Moreover,\n many plots and analyses assume that barcodes\n are unique. They fail in case of duplicate\n barcodes. This is the easiset workaround to tackle\n both the situations.\n\n The original barcodes are stored in the org_barcode\n row attribute.\n \"\"\"\n\n if self.metadata[SAMPLE].shape[0] > 1:\n msg = 'Multiple samples detected. Appending the sample name to the barcodes to avoid duplicate barcodes.'\n msg += f' The original barcodes are stored in the {ORG_BARCODE} row attribute'\n\n warnings.warn(msg)\n\n self.add_row_attr(ORG_BARCODE, self.barcodes())\n self.add_row_attr(BARCODE, self.barcodes() + '-' + self.row_attrs[SAMPLE])\n\n def drop(self, values, value_type=None):\n \"\"\"\n Drops the given values from the assay.\n\n Parameters\n ----------\n values : list-like\n The values that are to be dropped,\n which could be barcodes or ids.\n value_type : str\n Accepted values are `id`, `barcode`, and\n `None`. When `None`, the type is guessed\n based on the values.\n\n Returns\n -------\n :class:`mosaic.assay._Assay`\n A copy of itself with the relevant\n ids removed.\n\n Raises\n ------\n ValueError\n Raise when the type of the value (barcode or id)\n is not determinable, and value_type is expected.\n \"\"\"\n\n remaining_bars = None\n remaining_ids = None\n\n def check_complete(array):\n nonlocal values\n if not np.isin(values, array).all():\n raise ValueError('Not all the given values are present in the assay.')\n\n if value_type == 'id':\n check_complete(self.ids())\n remaining_ids = ~np.isin(self.ids(), values)\n elif value_type == 'barcode':\n check_complete(self.barcodes())\n remaining_bars = ~np.isin(self.barcodes(), values)\n elif value_type is None:\n if np.isin(values, self.ids()).any():\n check_complete(self.ids())\n remaining_ids = ~np.isin(self.ids(), values)\n elif np.isin(values, self.barcodes()).any():\n check_complete(self.barcodes())\n remaining_bars = ~np.isin(self.barcodes(), values)\n else:\n raise ValueError('Not all the given values are present in the assay.')\n else:\n raise ValueError('value_type must be `id`, `barcode`, or `None`.')\n\n return self[remaining_bars, remaining_ids]\n\n def del_layer(self, key):\n \"\"\"\n Delete a layer from the assay layers.\n\n Parameters\n ----------\n key : str\n The name of the layer.\n \"\"\"\n if key in self.layers:\n del self.layers[key]\n\n def del_row_attr(self, key):\n \"\"\"\n Delete an attribute from the row_attrs.\n\n Parameters\n ----------\n key : str\n The name of the row attribute.\n \"\"\"\n if key in self.row_attrs:\n del self.row_attrs[key]\n\n def del_col_attr(self, key):\n \"\"\"\n Delete an attribute from the col_attrs.\n\n Parameters\n ----------\n key : str\n The name of the column attribute.\n \"\"\"\n if key in self.col_attrs:\n del self.col_attrs[key]\n\n def del_metadata(self, key):\n \"\"\"\n Delete an attribute from the metadata.\n\n Parameters\n ----------\n key : str\n The name of the metadata.\n \"\"\"\n if key in self.metadata:\n del self.metadata[key]\n\n def get_attribute(self, attribute, features=None, constraint=None):\n \"\"\"\n Retrieve any attribute in the assay\n\n Returns an np.array which could either be\n a row attribute, column attribute, layer\n or the passed attribute itself.\n\n Parameters\n ----------\n attribute : str / np.ndarray\n The attribute to be searched for in the assay.\n\n features : str\n In case the attribute is a layer or col, then\n the subset of features to select\n\n constraint : str\n One of the following is accepted.\n 1. None\n No contraint.\n 2. 'row'\n The first dimension must be equal\n to the number of cells.\n 3. 'col'\n The second dimension must be equal\n to the number of ids or the number of\n features given in the input.\n 4. 'row+col'\n The dimension must be exactly\n (number of cells, number of ids).\n The layers have this shape.\n\n Returns\n -------\n pd.DataFrame\n The array of the attribute with the given name\n found in the assay layer, row attributes,\n or col attributes in that order. If a constraint\n is provided, the array is reshaped appropriately\n if possible. In case `constraint` is provided then\n the columns and index are named based on the\n barcodes, ids, or a range depending on where\n the given attribute is found.\n\n Raises\n ------\n ValueError\n When the attribute is not found or\n when the constraint is not satisfied.\n TypeError\n When the attribute is neither a str\n not an np.ndarray\n\n Notes\n -----\n In case the constraint can reshape the array\n into the expected shape then no error will be raised.\n Eg. An assay with 100 barcodes and 10 ids has a shape\n (100, 10). When the attribute 'barcode' is fetched\n constrained by 'col' it will not raise an error,\n but rather return a 10x10 dataframe of barcodes.\n\n >>> assay.shape\n (100, 10)\n >>> attr = assay.get_attribute('barcode', constrain='col')\n >>> attr.shape\n (10, 10)\n\n Possible expected behavior\n >>> assay.shape\n (100, 11)\n >>> attr = assay.get_attribute('barcode', constrain='col')\n ValueError - 'The given attribute does not have the expected\n shape nor could be reshaped appropriately.'\n \"\"\"\n\n # Find where the attribute is\n if isinstance(attribute, str):\n title = attribute\n\n if attribute in self.row_attrs and attribute in self.layers:\n warnings.warn(f'{attribute} found in the row attributes and the layers both.'\n f' Defaulting to the {attribute} in the layers.')\n\n if attribute in self.col_attrs and attribute in self.layers:\n warnings.warn(f'{attribute} found in the col attributes and the layers both.'\n f' Defaulting to the {attribute} in the layers.')\n\n if attribute in self.col_attrs and attribute in self.row_attrs:\n warnings.warn(f'{attribute} found in the col attributes and the row attributes both.'\n f' Defaulting to the {attribute} in the row attributes.')\n\n if attribute in self.layers:\n attribute = self.layers[attribute]\n if features is None:\n features = self.ids()\n elif np.isin(features, self.ids()).all():\n inds = [np.where(self.ids() == f)[0][0] for f in features]\n attribute = attribute[:, inds]\n else:\n raise ValueError('Not all features are found in the arrays ids')\n elif attribute in self.row_attrs:\n attribute = self.row_attrs[attribute]\n elif attribute in self.col_attrs:\n attribute = self.col_attrs[attribute]\n if features is None:\n features = self.ids()\n elif np.isin(features, self.ids()).all():\n inds = [np.where(self.ids() == f)[0][0] for f in features]\n attribute = attribute[inds, :]\n else:\n raise ValueError('Not all features are found in the arrays ids')\n else:\n raise ValueError(f'{attribute} not found in either the row attributes, col attributes or the layers')\n elif isinstance(attribute, np.ndarray):\n title = '-'\n else:\n raise TypeError(\"'attribute' must be of type str or np.ndarray\")\n\n # Constraint the attribute\n attribute = attribute.copy()\n err_msg = 'The given attribute does not have the expected shape nor could be reshaped appropriately.'\n if constraint == 'row':\n try:\n attribute = attribute.reshape((self.shape[0], -1))\n except ValueError:\n raise ValueError(f'{err_msg} Given {attribute.shape}, expected ({self.shape[0]}, *).')\n elif constraint == 'col':\n try:\n if features is None:\n attribute = attribute.reshape((-1, self.shape[1]))\n else:\n attribute = attribute.reshape((-1, len(features)))\n except ValueError:\n raise ValueError(f'{err_msg} Given {attribute.shape}, expected (*, {self.shape[1]}).')\n elif constraint == 'row+col':\n try:\n if features is None:\n attribute = attribute.reshape(self.shape)\n else:\n attribute = attribute.reshape((self.shape[0], len(features)))\n except ValueError:\n raise ValueError(f'{err_msg} Given {attribute.shape}, expected {self.shape}.')\n elif constraint is not None:\n raise TypeError(f'Constraint must be one of None, \"row\", \"col\", \"row+col\". Given {constraint}')\n\n # Mark the indices and columns appropriately\n attribute = pd.DataFrame(attribute)\n if constraint is not None:\n if features is None:\n attribute.columns = (np.arange(attribute.shape[1]) + 1).astype(str)\n elif len(features) == attribute.shape[1]:\n attribute.columns = features\n else:\n raise ValueError(f\"Expected {attribute.shape[1]} features, given {len(features)}\")\n\n if 'row' in constraint:\n attribute.index = self.barcodes()\n\n attribute.index.name = title\n\n return attribute\n\n def barcodes(self, label=None):\n \"\"\"\n Get the list of barcodes.\n\n Parameters\n ----------\n label : list\n The list of labels whose barcodes are to be retrieved.\n If `None`, then all barcodes are returned.\n\n Returns\n -------\n numpy.ndarray\n An array of barcode ids.\n \"\"\"\n\n bars = np.array(self.row_attrs[BARCODE])\n\n if isinstance(label, str):\n label = np.array([label])\n\n if label is None:\n return bars\n else:\n return bars[get_indexes(self.get_labels(), label)]\n\n def clustered_barcodes(self, orderby=None, splitby=LABEL, override=False):\n \"\"\"\n Hierarchical clustering of barcodes.\n\n If the layer is not provided, the barcodes are\n returned according to the labels.\n\n If a layer is provided, then further hierarchical\n clustering is performed based on the values\n in this layer for each label.\n\n Parameters\n ----------\n orderby : str / np.ndarray\n An attribute with at least one dimension equal\n to the number of barcodes. Uses :meth:`_Assay.get_attribute`\n to retrieve the values constrained by `row`.\n splitby : bool\n Whether to order the barcodes based on the given\n labels or not. Only applicable when labels `orderby`\n is `None`. Uses :meth:`_Assay.get_attribute` to\n retrieve the values constrained by `row`. The shape\n must be equal to `(#cells)`\n override : bool\n Continue clustering even if the number of `id`\n is greater than 1,000.\n\n Returns\n -------\n numpy.ndarray\n Ordered barcodes\n\n Raises\n ------\n ValueError\n When the shape of orderby is not appropriate\n\n Exception\n When the number of `id` in the assay is greater\n than 1,000. This can take a long time\n to process, hence denied.\n\n Warning\n If the number of `id` is greater than 1,000 and\n override is passed.\n \"\"\"\n\n if splitby is None:\n splitby = np.array(['-'] * self.shape[0])\n else:\n splitby = self.get_attribute(splitby, constraint='row').values.flatten()\n\n if len(splitby) != self.shape[0]:\n raise ValueError('Only 1-D values for `splitby` are permitted.')\n\n if orderby is None:\n return self.barcodes()[splitby.argsort()]\n else:\n df = self.get_attribute(orderby, constraint='row')\n\n if df.shape[1] > 10 ** 3:\n message = f\"Clustering using {df.shape[1]} features. This might take a long time.\"\n if override:\n warnings.warn(message)\n else:\n raise Exception(f\"{message} Pass 'override' to continue clustering.\")\n\n df.loc[:, LABEL] = splitby\n\n # Cluster at the level of labels\n if len(set(splitby)) > 1:\n df_avg = df.groupby(LABEL).mean()\n\n Z = hierarchy.linkage(df_avg, metric='cityblock', method='average')\n order = hierarchy.leaves_list(Z)[::-1]\n label_order = df_avg.index[order]\n\n df.loc[:, LABEL] = pd.Categorical(splitby, label_order)\n\n df = df.sort_values(by=LABEL)\n\n # Cluster barcodes within each label\n leaf_order = []\n cells_done = 0\n for lab, df_lab in df.groupby(LABEL):\n df_lab = df_lab.drop(LABEL, axis=1).copy()\n if (df_lab.shape[0] == 1):\n order = np.array([0])\n else:\n Z = hierarchy.linkage(df_lab, metric='cityblock', method='average')\n order = hierarchy.leaves_list(Z)[::-1]\n leaf_order.extend(order + cells_done)\n cells_done += len(order)\n\n return df.index[leaf_order].values\n\n def ids(self):\n \"\"\"\n Get the list of ids.\n\n Returns\n -------\n np.ndarray\n The list of ids in the column attribute 'id'.\n \"\"\"\n return self.col_attrs[ID]\n\n def clustered_ids(self, orderby, features=None, override=False):\n \"\"\"\n Hierarchical clustering of ids.\n\n Parameters\n ----------\n orderby : str / np.ndarray\n An attribute with at least one dimension equal\n to the number of ids in the assay. Uses\n :meth:`_Assay.get_attribute` to retrieve the value\n constrained by `col`\n features : list-like\n The subset of ids to use and return, in case orderby\n is found in the layers, otherwise it is the name\n of the ids to be given to the orderby attribute.\n override : bool\n Continue clustering even if the number of `id`\n is greater than 1,000.\n\n Returns\n -------\n numpy.ndarray\n ordered ids\n\n Raises\n ------\n ValueError\n When the labels are not set.\n\n Exception\n When number of `id` in the assay is greater\n than 1,000. This can take a long time\n to process, hence denied.\n\n Warning\n If the number of `id` is greater than 1,000 and\n override is passed.\n \"\"\"\n\n orderby = self.get_attribute(orderby, constraint='col', features=features)\n features = orderby.columns\n orderby = orderby.values\n\n if orderby.shape[1] > 10 ** 3:\n message = f\"Clustering using {orderby.shape[1]} ids. This might take a long time.\"\n if override:\n warnings.warn(message)\n else:\n raise Exception(f\"{message} Pass 'override' to continue clustering.\")\n\n df = pd.DataFrame(orderby, columns=features)\n\n # No need to reorder if fewer than 2 features\n if df.T.shape[0] < 2:\n return df.columns\n\n Z = hierarchy.linkage(df.T, metric='cityblock', method='average')\n order = hierarchy.leaves_list(Z)[::-1]\n\n return df.columns[order]\n\n def get_palette(self):\n \"\"\"\n Get the label color palette.\n\n Returns\n -------\n dictionary\n The keys are the names of the labels\n and the values are the hex color codes.\n \"\"\"\n if PALETTE not in self.metadata:\n self.set_palette()\n\n return {v[0]: v[1] for v in self.metadata[PALETTE]}\n\n def set_palette(self, palette=None):\n \"\"\"\n Set the label color palette.\n\n Modifies the `palette` metadata of the assay.\n\n Parameters\n ----------\n palette : dictionary\n The color values for each label.\n A subset of labels is not accepted.\n\n Raises\n ------\n TypeError\n When the provided palette\n is not a dictionary.\n ValueError\n When one of the labels is not\n present in the given palette.\n \"\"\"\n\n unique_labels = np.unique(self.get_labels())\n pal = []\n\n if palette is None:\n for i in range(len(unique_labels)):\n pal.append([unique_labels[i], COLORS[i % len(COLORS)]])\n else:\n if not isinstance(palette, dict):\n raise TypeError('The palette should be of type dictionary.')\n\n for lab in unique_labels:\n if lab not in palette:\n raise ValueError(f'The color value {lab} is missing from the given palette - {palette}.')\n pal.append([lab, palette[lab]])\n\n self.add_metadata(PALETTE, np.array(pal))\n\n def get_labels(self, barcodes=None):\n \"\"\"\n Get the labels corresponding to the barcodes.\n\n Parameters\n ----------\n barcodes : list-like / None\n The set of barcodes for which the\n labels are to be returned. The order\n of the barcodes is maintained. The\n LABEL row attribute is returned when\n `barcodes` is `None`\n\n Returns\n -------\n np.array\n The list of labels.\n \"\"\"\n if LABEL not in self.row_attrs:\n return np.array(['-'] * self.shape[0])\n\n all_labs = self.row_attrs[LABEL].copy()\n if barcodes is None:\n return all_labs\n\n all_bars = self.barcodes()\n labels = []\n for bar in barcodes:\n labels.append(all_labs[np.where(all_bars == bar)[0][0]])\n\n labels = np.array(labels)\n\n return labels\n\n def set_labels(self, clusters, others_label='others'):\n \"\"\"\n Set the labels for the barcodes.\n\n Modifies the `id` row attribute of the assay.\n\n Parameters\n ----------\n clusters : list or dictionary\n If a list, then the order of the barcodes\n should be considered because the labels\n are set in the same order as the\n order of barcodes.\n others_label : str\n In case not all barcodes are labeled,\n then this is the label of the remaining\n barcodes.\n \"\"\"\n\n if isinstance(clusters, np.ndarray) or isinstance(clusters, list):\n labels = np.array(clusters)\n else:\n barcodes = self.barcodes()\n labels = np.array([others_label] * len(barcodes), dtype=object)\n for cluster_name in clusters:\n bars = clusters[cluster_name]\n if not np.isin(bars, self.barcodes()).all():\n raise ValueError(f'One of the barcodes in {cluster_name} was not found in the assay.')\n indexes = get_indexes(barcodes, bars)\n labels[indexes] = cluster_name\n\n self.add_row_attr(LABEL, labels.astype(str))\n self.set_palette()\n\n def set_selected_labels(self):\n \"\"\"\n Custom clustering from scatter plots.\n\n Setting the labels of the barcodes based on\n the clusters selected on the scatter plot.\n\n Modifies the `id` row attribute of the assay.\n \"\"\"\n\n i = 1\n labs = {}\n pal = {}\n for k in self.selected_bars:\n if self.selected_bars[k].size > 0:\n labs[str(i)] = self.selected_bars[k]\n pal[str(i)] = k\n i += 1\n\n self.set_labels(labs)\n self.set_palette(pal)\n\n def rename_labels(self, label_map):\n \"\"\"\n Change the name of the labels.\n\n Parameters\n ----------\n label_map : dictionary\n The key is the existing name of the label, and\n the value is the new name of the label.\n \"\"\"\n\n palette = self.get_palette()\n labels = self.get_labels().copy()\n\n for k in label_map:\n labels = np.where(labels == k, label_map[k], labels)\n palette[label_map[k]] = palette[k]\n del palette[k]\n\n self.add_row_attr(LABEL, labels)\n self.set_palette(palette)\n\n # --------- Tertiary analysis methods\n\n def cluster(self, attribute, method='dbscan', **kwargs):\n \"\"\"\n Identify clusters of cells.\n\n Modifies the `label` row attribute of the assay.\n\n Parameters\n ----------\n attribute : str / np.array\n The attribute to use as coordinates for clustering.\n Uses :meth:`_Assay.get_attribute` to retrieve the values\n constrained by `row`.\n method : str\n dbscan, hdbscan, kmeans, or graph-community (Louvain\n on a shared nearest neighbor graph).\n kwargs : dictionary\n Passed to the appropriate clustering method.\n\n Available parameters for the methods are:\n 1. dbscan : eps (float)\n A measure of how close the points are.\n 2. hdbscan : min_cluster_size (int)\n The number of cells in a cluster.\n 3. kmeans : n_clusters (int)\n The number of clusters to be generated.\n 4. graph-community : k (int)\n The number of nearest neighbors to consider\n in the shared nearest neighbor graph generation.\n\n Raises\n ------\n ValueError\n When the row attribute is not available in the assay.\n Exception\n When an unsupported clustering method is provided.\n \"\"\"\n\n supported_methods = {\n 'dbscan': DBSCAN,\n 'hdbscan': HDBSCAN,\n 'kmeans': KMeans,\n 'graph-community': self._community_clustering\n }\n\n if method in supported_methods:\n data = self.get_attribute(attribute, constraint='row').values\n if method == 'graph-community':\n self._community_clustering(data, **kwargs)\n else:\n clusterer = supported_methods[method](**kwargs).fit\n output = clusterer(data)\n labels, idx, cnt = np.unique(output.labels_, return_inverse=True, return_counts=True)\n\n # Order by counts and maintain -1 for outliers\n order = cnt.argsort()[::-1]\n clones = labels[order] != -1\n labels[order[clones]] = np.arange(clones.sum()) + 1\n labels[order[~clones]] = -1\n labels = labels[idx]\n\n self.set_labels(labels)\n else:\n raise Exception(f'{method} not supported. Try {supported_methods.keys()}.')\n\n def _community_clustering(self, data, k=20, show_plot=False, **kwargs):\n \"\"\"\n Louvain clustering\n\n Modifies the `id` row attribute of the assay.\n\n This is a graph-based clustering approach where\n the graph structure uses the Jaccard similarity coefficient as\n the edge weights, which is followed by a community\n cluster detection method (Louvain).\n\n Currently, the highest k value is used to set labels.\n The user expected is expected to try various k values.\n\n The plot for k vs number of clusters is provided to\n the user to interpret the accuracy of the k selected.\n\n Parameters\n ----------\n data : numpy.ndarray\n The array to use to create an SNN graph.\n k : int\n The number of nearest neighbors to consider\n when creating the SNN graph.\n show_plot : bool\n If true, then a plot for various k-values is\n shown to ascertain whether the provided k-value\n is sufficient.\n kwargs : dict\n Passed to the NearestNeighbors algorithm.\n\n Raises\n ------\n ValueError\n When the igraph package is not installed\n \"\"\"\n\n try:\n import igraph\n except ImportError as err:\n raise ValueError(\"The igraph library is required for community clustering.\") from err\n\n # Find the nearest neighbors\n print(\"Creating the Shared Nearest Neighbors graph.\")\n knn = NearestNeighbors(n_neighbors=k, algorithm='ball_tree', **kwargs).fit(data)\n dist, nbrs = knn.kneighbors(data)\n\n # Set the edges\n v1 = 0\n edges = []\n for v1_nbrs in nbrs:\n for v2 in v1_nbrs:\n edges.append((v1, v2))\n v1 += 1\n\n edges = np.array(edges)\n\n # Set the weight using Jaccard similarity\n # Optimize this step further, if possible\n print('-' * 50)\n i = 0\n weights = []\n similarities = {}\n nbrs = [set(t) for t in nbrs]\n for e in edges:\n if (i % (edges.shape[0] // 50) == 0):\n print('#', flush=True, end='')\n\n i += 1\n if (e[0], e[1]) not in similarities:\n intersection = len(nbrs[e[0]].intersection(nbrs[e[1]]))\n common = 2 * k - intersection\n similarity = intersection / common\n similarities[(e[1], e[0])] = similarity\n else:\n similarity = similarities[(e[0], e[1])]\n\n weights.append(similarity)\n\n # Construct the graphs and identify clusters for various values of k less than the given value\n print(\"\\nIdentifying clusters using Louvain community detection.\")\n edges = np.array(edges).reshape(len(nbrs), k, 2)\n weights = np.array(weights).reshape(len(nbrs), k, 1)\n sizes = np.linspace(10, k, 5) if show_plot else [k]\n clusters_found = []\n for size in sizes:\n size = int(size)\n sub_edges = np.array(edges)[:, :size, :]\n sub_edges = sub_edges.reshape(\n sub_edges.shape[0] * sub_edges.shape[1], sub_edges.shape[2])\n sub_weights = np.array(weights)[:, :size]\n sub_weights = sub_weights.reshape(\n sub_weights.shape[0] * sub_weights.shape[1])\n\n g = igraph.Graph()\n g.add_vertices(len(self.barcodes()))\n g.add_edges(sub_edges)\n g.es['weight'] = sub_weights\n\n vc = g.community_multilevel(weights='weight')\n vc_labels = np.array(vc.as_cover().membership).flatten()\n\n clusters_found.append((size, len(set(vc_labels))))\n\n print(f'\\nNumber of clusters found: {clusters_found[-1][1]}.')\n print(f'Modularity: {vc.modularity:.3f}')\n\n if show_plot:\n x = np.array(clusters_found)[:, 0]\n y = np.array(clusters_found)[:, 1]\n plt.figure(figsize=(10, 10))\n plt.scatter(x, y)\n plt.ylabel('Number of clusters found')\n plt.xlabel('Number of nearest neighbors used')\n\n labels, idx, cnt = np.unique(vc_labels, return_inverse=True, return_counts=True)\n labels[cnt.argsort()[::-1]] = np.arange(len(labels)) + 1\n labels = labels[idx]\n self.set_labels(labels)\n\n def group_clusters(self, attribute):\n \"\"\"\n Relabel clusters based on expression.\n\n Takes an assay object and reorders the\n cluster labels so that the clusters are\n grouped hierarchically. Ordering uses\n the median cluster value in the layer\n denoted by the input parameter \"layer.\"\n\n For example, after implementation, all protein\n clusters with high CD3 might be numbered next\n to each other.\n\n Parameters\n ----------\n layer : str / np.array\n The name of the attribute from the assay object used\n to hierarchically group the clusters. Uses\n :meth:`_Assay.get_attribute` to retrieve the values\n constrained by `row`\n\n \"\"\"\n\n labels = self.get_labels()\n\n u, lbl = np.unique(labels, return_inverse=True)\n\n nc = len(np.unique(lbl))\n\n if nc == 1:\n return\n\n data = self.get_attribute(attribute, constraint='row').values\n z = np.zeros((nc, len(self.ids())))\n for jj in range(nc):\n z[jj, :] = np.median(data[lbl == jj, :], axis=0)\n\n D = pdist(z, 'cosine')\n tree = hierarchy.linkage(D)\n s = hierarchy.leaves_list(hierarchy.optimal_leaf_ordering(tree, D))\n\n s = np.argsort(s) + 1\n\n self.set_labels(s[lbl])\n\n def scale_data(self, layer, output_label=SCALED_LABEL):\n \"\"\"\n Z-score normalization\n\n Ensures that all `id` have a mean\n of 0 and a standard deviation of 1.\n\n Adds `scaled_counts` to the layers.\n\n Parameters\n ----------\n layer : str / np.array\n The data to scale. Uses :meth:`_Assay.get_attribute`\n to retrieve the value constrained by `row+col`.\n output_label : str, default SCALED_LABEL\n The name of the output layer.\n \"\"\"\n\n data = self.get_attribute(layer, constraint='row+col').values\n data = data - data.mean(axis=0)\n data = data / data.std(axis=0)\n self.add_layer(output_label, data)\n\n def run_pca(self, attribute, components, output_label=PCA_LABEL, show_plot=False, **kwargs):\n \"\"\"\n Principal component analysis\n\n Adds `output_label` to the row attributes.\n\n Parameters\n ----------\n attribute : str\n The attribute to be used for PCA. Uses\n :meth:`_Assay.get_attribute` to retrieve\n the values constrained by `row`.\n components : int\n The number of PCA components to reduce to.\n output_label : str, default PCA_LABEL\n The name of the row attribute to store\n the data in.\n show_plot : bool\n Show the plot for the explained variance\n by the PCAs.\n kwargs : dict\n Passed to the PCA.\n\n Raises\n ------\n ValueError\n When both layer and attribute are provided.\n Only one is permitted at a time.\n \"\"\"\n\n data = self.get_attribute(attribute, constraint='row').values\n components = np.minimum(components, data.shape[1])\n\n pca = PCA(n_components=components, **kwargs)\n pca.fit(data.T)\n pca_data = pca.components_.T\n self.add_row_attr(output_label, pca_data)\n\n if show_plot:\n plt.figure(figsize=(10, 10))\n plt.scatter(np.arange(components), pca.explained_variance_ratio_)\n plt.xlabel('Principal component')\n plt.ylabel('Explained variance ratio')\n plt.title(self.title)\n\n def run_umap(self, attribute, output_label=UMAP_LABEL, **kwargs):\n \"\"\"\n Perform UMAP on the given data.\n\n Adds `output_label` to the row attributes.\n\n Parameters\n ----------\n attribute : str\n The attribute to be used for UMAP. Uses\n :met:`_Assay.get_attribute` to retrieve the values\n constrained by `row`.\n output_label : str, default UMAP_LABEL\n The name of the row attribute where the UMAP output is.\n kwargs : dictionary\n Passed to UMAP.\n\n Raises\n ------\n ValueError\n When both layer and attribute are provided.\n Only one is permitted at a time.\n \"\"\"\n\n data = self.get_attribute(attribute, constraint='row').values\n\n fit = umap.UMAP(**kwargs)\n u = fit.fit_transform(data)\n\n # Sets UMAP x/y range ~[-10,10]\n u = stats.zscore(u) * 5\n\n self.add_row_attr(output_label, u)\n\n def run_lda(self, layer, attribute, output_label, cycles=1):\n \"\"\"\n Perform LDA on a row attribute using a layer to type clusters.\n\n The row attribute is dimensionally reduced using UMAP.\n\n Adds `output_label` to the row attributes.\n\n Parameters\n ----------\n layer : str / np.array\n The layer used for cluster typing. Uses\n :meth:`_Assay.get_attribute` to retrieve\n the value constrained by `row+col`.\n attribute : str / np.array\n The row attribute to be used for UMAP. Uses\n :meth:`_Assay.get_attribute` to retrieve\n the value constrained by `row`.\n output_label : str\n The name of the row attribute where the UMAP output is.\n cycles : int\n The number of rounds of LDA to perform.\n kwargs : dictionary\n Passed to UMAP.\n \"\"\"\n\n cnt = 0\n\n data = self.get_attribute(layer, constraint='row+col').values\n attribute = self.get_attribute(layer, constraint='row').values\n self.add_row_attr(output_label, attribute)\n\n for jj in range(cycles):\n\n u = self.row_attrs[output_label]\n nc = np.minimum(30, int(data.shape[0] / 20))\n\n kmeans = KMeans(n_clusters=nc, random_state=1).fit(u)\n lbl = kmeans.labels_\n\n clust_profile = np.zeros((nc, data.shape[1], 2))\n\n for ii in range(nc):\n clust_profile[ii, :, 0] = np.median(data[lbl == ii, :], axis=0)\n clust_profile[ii, :, 1] = np.std(data[lbl == ii, :], axis=0)\n\n clust_project = np.zeros((data.shape[0], nc))\n for ii in range(nc):\n coord = np.absolute(\n (data - clust_profile[ii, :, 0][None, :]) / (clust_profile[ii, :, 1][None, :] + 0.0001))\n clust_project[:, ii] = 1 / (np.sum(np.minimum(coord, 10), axis=1) + .1)\n\n X = clust_project\n\n self.add_row_attr(output_label, X)\n\n self.run_umap(attribute=output_label,\n output_label=output_label,\n n_neighbors=50,\n metric='cosine',\n min_dist=0,\n spread=1,\n repulsion_strength=3,\n negative_sample_rate=10,\n random_state=42)\n\n cnt = cnt + 1\n print(f'Round {cnt} complete.')\n\n # --------- Plotting\n\n def scatterplot(self, attribute, colorby=None, features=None, title=''):\n \"\"\"\n Scatter plot for all barcodes.\n\n Requires a 2-D row attribute.\n\n If `colorby` is None, then this plot can be used\n to create custom clusters using the plotly lasso tool.\n\n Not selecting any cell deselects all clusters, and double\n clicking refocuses all points.\n\n Parameters\n ----------\n attribute : str / np.array\n The row attribute to use as coordinates,\n This row attribute must be 2D. Uses :meth:`_Assay.get_attribute`\n to retrieve the values constrained by `row`.\n The shape must be `(#cells, 2)`.\n colorby : str / np.array\n The values used to color the scatterplot.\n Uses :meth:`_Assay.get_attribute` to retrieve\n the values constrained by `row`.\n The shape must be `(#cells, *)` if 2D, otherwise\n the shape must be `(#cells)`.\n 1. None\n The scatterplot is not colored and\n highlighting using the lasso tool\n can be used to select subpopulations.\n 2. Any 1D attribute\n In case 'label' is provided then the\n stored paltte is used. If the values\n are strings, then a discrete color map\n is assumed. For numerical values a\n continuous color scale is used.\n 3. Any 2D attribute\n One dimension must match the number of\n cells in the assay.\n 4. 'density'\n Colors the plot based on the density of\n the nearby points.\n features : list-like\n In the case that `colorby` is a layer then\n features sets the subset of features to\n color the plot by.\n\n In the case that `colorby` is a 2D array\n features sets the title of each scatterplot.\n In this case, the length of `features`\n must match the number of columns in the\n `colorby` 2D array.\n title : str\n The title given to the plot after\n the name of the assay.\n\n Returns\n -------\n fig : plotly.graph_objs.FigureWidget\n\n Raises\n ------\n ValueError\n Raised when the attribute is not present in the assay\n or when length of `features` does not match the second\n dimension of `colorby`.\n \"\"\"\n\n if features is not None and colorby is None:\n raise ValueError(\"'colorby' must be provided when features are given.\")\n\n # Get data\n if isinstance(attribute, str):\n xlabel, ylabel = f'{attribute}-1', f'{attribute}-2'\n else:\n xlabel, ylabel = 'x', 'y'\n\n data = self.get_attribute(attribute, constraint='row+col', features=[xlabel, ylabel])\n data.loc[:, 'hovertext'] = 'label: ' + self.get_labels().astype(object) + '
' + self.barcodes().astype(object)\n\n # Determine source of colorby\n colorby_type = colorby if isinstance(colorby, str) else '----'\n if colorby_type == 'density':\n x = data.iloc[:, 0]\n y = data.iloc[:, 1]\n xy = np.vstack([x, y])\n colorby = gaussian_kde(xy)(xy)\n idx = colorby.argsort()\n data, colorby = data.iloc[idx, :], colorby[idx][:, None]\n elif colorby is not None:\n feature_values = self.get_attribute(colorby, constraint='row', features=features)\n colorby = feature_values.values\n features = feature_values.columns.values\n\n # Plot\n layout = dict(xaxis=dict(zeroline=False, title_text=xlabel),\n yaxis=dict(zeroline=False, title_text=ylabel),\n title=f'{self.title}{title}',\n legend_tracegroupgap=0,\n dragmode='pan',\n hovermode='closest',\n template='simple_white',\n coloraxis_colorbar_title=colorby_type)\n\n if colorby is None:\n p = px.scatter(data, x=xlabel, y=ylabel, hover_data=['hovertext'])\n p.data[0].marker.color = np.array([COLORS[0]] * self.shape[0])\n fw = go.FigureWidget(data=p.data, layout=layout)\n fw.update_layout(dragmode='lasso', width=600, height=550)\n fw.update_traces(marker_size=4, hovertemplate='%{customdata}
')[-1] for i in d.customdata.flatten()])\n d.marker.color = np.array([d.marker.color] * len(d.x))\n else:\n fw.update_layout(dragmode='zoom', width=600, height=550)\n\n else:\n val_1, val_99 = clipped_values(colorby.flatten())\n\n datas = pd.DataFrame()\n for feature in features:\n d = data.copy()\n d.loc[:, 'value'] = feature_values.loc[:, feature].values\n d.loc[:, 'name'] = feature\n datas = pd.concat([datas, d])\n\n nplots = len(features)\n nrows = round(nplots ** 0.5)\n ncols = nplots // nrows + min(1, nplots % nrows)\n\n p = px.scatter(datas, x=xlabel, y=ylabel, color='value', hover_data=['hovertext'],\n facet_row_spacing=max(0.01, 0.2 / nrows), facet_col_spacing=max(0.01, 0.2 / ncols),\n facet_col='name', facet_col_wrap=ncols, color_discrete_sequence=COLORS,\n width=max(600, 350 * ncols), height=max(350 * nrows, 550 / ncols))\n p.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n\n fw = go.Figure(p)\n\n if not isinstance(val_1, str):\n layout.update(coloraxis=dict(cauto=False,\n cmax=val_99,\n cmin=val_1,\n colorbar=dict(thickness=25,\n len=1 / nrows,\n yanchor=\"top\",\n y=1.035,\n x=1.05,\n ticks=\"outside\")))\n fw.update_layout(layout)\n fw.update_traces(marker_size=4, hovertemplate='%{customdata}
%{x}%{customdata} ',\n showlegend=False,\n showscale=False)\n fig.add_trace(vals, row=1, col=2)\n\n # Add dividers\n if ordered:\n for i in range(len(cnt_ordered_labs) - 1):\n start = cnt_ordered_labs[:i + 1].sum()\n fig.add_hline(start - 0.5, line_color='rgba(255,255,255,1)')\n else:\n tickvals = []\n ticktexts = []\n\n # Update layout\n val_1, val_99 = clipped_values(data.values.flatten())\n fig.update_layout(coloraxis_colorscale='magma',\n legend_tracegroupgap=0,\n width=800,\n height=800,\n title_text=f'{self.title}{title}',\n template='plotly_white',\n coloraxis=dict(cmax=val_99,\n cmin=val_1,\n cauto=False,\n colorbar=dict(thickness=15,\n len=0.3,\n yanchor=\"bottom\",\n y=0,\n ticks=\"outside\",\n title=data.index.name)),\n yaxis=dict(type='category',\n tickvals=tickvals,\n ticktext=ticktexts,\n tickformat='{^text}',\n range=[0, data.shape[0]]),\n yaxis2=dict(type='category',\n range=[0, data.shape[0]]),\n xaxis=dict(ticktext=data.columns, # Added for heatmap selection\n showticklabels=False),\n xaxis2=dict(tickangle=-90,\n tickvals=np.arange(data.shape[1]),\n ticktext=data.columns,\n tickmode='array'))\n\n ann_text = f'{data.shape[0]} cells, {data.shape[1]} features'\n if convolve != 0 and ordered:\n ann_text = ann_text + '
Values smoothed using a moving average.'\n\n ann = go.layout.Annotation(text=ann_text,\n xanchor='left',\n yanchor='bottom',\n align='left',\n showarrow=False,\n xref='x2',\n yref='y2',\n x=-0.5,\n y=data.shape[0],\n font_size=10)\n fig.add_annotation(ann)\n\n self.selected_ids = np.array([])\n self.__heatmap = go.FigureWidget(fig)\n self.__heatmap.data[1].on_click(self._heatmap_selection)\n\n return self.__heatmap\n\n def violinplot(self, attribute, splitby=None, features=None, title=''):\n \"\"\"\n Violin plot for all barcodes.\n\n Parameters\n ----------\n attribute : str / np.array\n The attribute used to plot the values on the violinplot.\n Uses :meth:`_Assay.get_attribute` to retrieve the values.\n constrained by 'row'.\n splitby : str / np.array\n The values used to split and color the violinplot.\n Uses :meth:`_Assay.get_attribute` to retrieve\n the values. The shape must be `(#cells)`.\n 1. None\n The violinplot is not colored.\n 2. Any 1D attribute\n In case 'label' is provided then the\n stored paltte is used. A discrete color map\n is used for all other cases.\n features : list-like\n In the case that `attribute` is a layer then\n features sets the subset of features to plot.\n Otherwise it sets the name of each feature shown.\n title : str\n The title given to the plot after\n the name of the assay.\n\n Returns\n -------\n fig : plotly.graph_objects.Figure\n \"\"\"\n\n data = self.get_attribute(attribute, constraint='row', features=features)\n if features is None:\n features = data.columns.values\n\n layout = dict(width=min(1200, data.shape[1] * 200),\n height=550,\n xaxis=dict(zeroline=False),\n yaxis=dict(zeroline=False),\n title=f'{self.title}{title}',\n dragmode='zoom',\n hovermode='closest',\n template='simple_white')\n\n if splitby is None:\n data = data.melt(var_name=ID)\n fig = go.Figure(layout=layout)\n\n for i, df_i in data.groupby(ID):\n fig.add_trace(go.Violin(x=df_i[ID],\n y=df_i['value'],\n scalemode='width',\n scalegroup=i,\n box_visible=True,\n box_width=0.05,\n spanmode='hard',\n points=False,\n line_color=COLORS[0],\n showlegend=False))\n else:\n splitby = self.get_attribute(splitby, constraint='row')\n if splitby.shape[1] != 1:\n raise ValueError('Only one dimensional `splitby` values are permitted.')\n\n data.loc[:, LABEL] = splitby.values\n data = data.melt(id_vars=LABEL, var_name=ID)\n\n un_labs = np.unique(splitby)\n line_colors = {}\n for i in range(len(un_labs)):\n line_colors[un_labs[i]] = self.get_palette()[un_labs[i]] if splitby.index.name == LABEL else COLORS[i]\n\n fig = go.Figure(layout=layout)\n for grp, df in data.groupby([LABEL, ID]):\n lab, i = grp\n showlegend = features[0] == str(i)\n fig.add_trace(go.Violin(x=df[ID],\n y=df['value'],\n scalemode='width',\n scalegroup=str(i) + str(lab),\n box_visible=True,\n box_width=0.02,\n meanline_visible=True,\n spanmode='hard',\n points=False,\n legendgroup=str(lab),\n name=str(lab),\n line_color=line_colors[lab],\n showlegend=showlegend))\n\n fig.update_layout(violinmode='group',\n width=min(1200, data.shape[1] * len(set(self.get_labels())) * 100))\n\n return fig\n\n def ridgeplot(self, attribute, splitby=None, features=None, title=''):\n \"\"\"\n Ridge plot for all barcodes.\n\n Parameters\n ----------\n attribute : str / np.array\n The attribute used to plot the values on the ridgeplot.\n Uses :meth:`_Assay.get_attribute` to retrieve the values.\n constrained by 'row'.\n splitby : str / np.array\n The values used to split and color the ridgeplot.\n Uses :meth:`_Assay.get_attribute` to retrieve\n the values. The shape must be `(#cells)`.\n 1. None\n The ridgeplot is not colored.\n 2. Any 1D attribute\n In case 'label' is provided then the\n stored paltte is used. A discrete color map\n is used for all other cases.\n features : list-like\n In the case that `attribute` is a layer then\n features sets the subset of features to plot.\n Otherwise it sets the name of each feature shown.\n title : str\n The title given to the plot after\n the name of the assay.\n\n Returns\n -------\n fig: plotly.graph_objects.Figure\n \"\"\"\n\n data = self.get_attribute(attribute, constraint='row', features=features)\n features = data.columns.values\n name = f'{data.index.name}'\n\n layout = dict(xaxis=dict(zeroline=False, showgrid=False),\n title=f'{self.title} {name} {title}',\n legend_tracegroupgap=0,\n dragmode='lasso',\n hovermode='closest',\n template='simple_white')\n\n if splitby is None:\n data = data.melt(var_name=ID)\n fig = go.Figure(layout=layout)\n\n for i, df_i in data.groupby(ID):\n fig.add_trace(go.Violin(y=df_i[ID],\n x=df_i['value'],\n scalemode='width',\n scalegroup=i,\n box_visible=True,\n box_width=0.05,\n spanmode='soft',\n orientation='h',\n side='positive',\n width=3,\n points=False,\n line_color=COLORS[0],\n line_width=1,\n showlegend=False))\n\n fig.update_layout(xaxis_showgrid=False,\n xaxis_zeroline=False,\n xaxis_title=name,\n yaxis_categoryarray=features,\n width=800,\n height=max(600, len(features) * 50),)\n else:\n nplots = data.shape[1]\n nrows = round(nplots ** 0.5)\n ncols = nplots // nrows + min(1, nplots % nrows)\n\n splitby = self.get_attribute(splitby, constraint='row')\n if splitby.shape[1] != 1:\n raise ValueError('Only one dimensional `splitby` values are permitted.')\n\n data.loc[:, LABEL] = splitby.values\n data = data.melt(id_vars=LABEL, var_name=ID)\n un_labs = np.unique(data[LABEL])\n\n line_colors = {}\n for i in range(len(un_labs)):\n line_colors[un_labs[i]] = self.get_palette()[un_labs[i]] if splitby.index.name == LABEL else COLORS[i]\n\n fig = make_subplots(rows=nrows,\n cols=ncols,\n subplot_titles=features,\n horizontal_spacing=0.1 / ncols,\n vertical_spacing=0.2 / nrows)\n\n for grp, df in data.groupby([LABEL, ID]):\n lab, i = grp\n showlegend = features[0] == i\n row_num = np.where(features == i)[0][0] // ncols + 1\n col_num = np.where(features == i)[0][0] % ncols + 1\n\n fig.add_trace(go.Violin(y=df[LABEL],\n x=df['value'],\n scalemode='width',\n spanmode='soft',\n legendgroup=str(lab),\n name=str(lab),\n line_color=line_colors[lab],\n orientation='h',\n side='positive',\n width=3,\n points=False,\n scalegroup=i,\n showlegend=showlegend,\n line_width=1),\n row=row_num, col=col_num)\n\n fig.update_layout(layout,\n width=max(500, 300 * ncols),\n height=max(500, max(300 * nrows, 30 * len(un_labs) * nrows)))\n fig.update_yaxes(tickvals=un_labs,\n ticktext=[''] * len(un_labs),\n showgrid=True,\n type='category')\n\n return fig\n\n def stripplot(self, attribute, colorby=None, features=None, title=''):\n \"\"\"\n Strip plot for all barcodes.\n\n Generates a strip plot for the given ids (x-axis), and\n shows the layer (y-axis) and color_layer (color) for all barcodes.\n Additional lateral jitter is added to help visualize the distribution.\n\n Parameters\n ----------\n attribute : str / np.array\n The attribute used to plot the values on the ridgeplot.\n Uses :meth:`_Assay.get_attribute` to retrieve the values.\n constrained by 'row'.\n colorby : str / np.array\n The values used to color the stripplot.\n Uses :meth:`_Assay.get_attribute` to retrieve\n the values constrained by 'row'. It must be the\n same shape as attribute.\n features : list-like\n In the case that `attribute` is a layer then\n features sets the subset of features to plot.\n Otherwise it sets the name of each feature shown.\n title : str\n The title given to the plot after\n the name of the assay.\n\n Returns\n -------\n fig: plotly.graph_objects.Figure\n \"\"\"\n\n attribute = self.get_attribute(attribute, constraint='row', features=features)\n\n if colorby is None:\n colorby = pd.DataFrame(np.full_like(attribute, COLORS[0], dtype='%{{x:.2f}}% {data.legendgroup} '\n else:\n data.hovertemplate = f'%{{x}}{data.legendgroup} '\n\n return fig\n\n def _brush(self, trace, points, state):\n \"\"\"\n Scatter plot custom clustering.\n\n The selection of the points on a scatter plot\n created using plotly.\n\n This method is passed to the on_selection\n method of the FigureWidget object in plotly.\n It is not meant to be used as-is.\n \"\"\"\n inds = np.array(points.point_inds)\n\n # Reset values from previous selection\n if points.trace_index == 0:\n # Remove colors which are empty\n dels = []\n for k in self.selected_bars:\n if self.selected_bars[k].size == 0:\n dels.append(k)\n\n for d in dels:\n del self.selected_bars[d]\n\n # Add new color if not there\n # Store new color as a variable to use on other traces\n for c in COLORS:\n if c not in self.selected_bars.keys():\n self.selected_bars[c] = np.array([])\n self.__brush_color = c\n break\n\n if inds.size:\n bars = np.array([i.split('
')[-1] for i in trace.customdata.flatten()])[inds]\n # Remove bars from the color in which they are present\n # Add bars to the new color\n for k in self.selected_bars:\n if k != self.__brush_color:\n shift = np.isin(self.selected_bars[k], bars)\n self.selected_bars[self.__brush_color] = np.append(self.selected_bars[self.__brush_color], self.selected_bars[k][shift])\n self.selected_bars[k] = self.selected_bars[k][~shift]\n\n # Change the color of the bars which are moved\n new_colors = trace.marker.color.copy()\n new_colors[inds] = self.__brush_color\n trace.marker.color = new_colors\n\n def _heatmap_selection(self, trace, points, selector):\n \"\"\"\n Heatmap id selection.\n\n The selection of the ids on the heatmap\n created using plotly.\n\n This method is passed to the on_click\n method of the FigureWidget object in\n plotly. It is not meant to be used as-is.\n \"\"\"\n\n def switch_color(text):\n style_open = \"\"\n style_close = \"\"\n if style_open in text:\n t = text.replace(style_open, '').replace(style_close, '')\n else:\n t = style_open + str(text) + style_close\n\n return t\n\n if len(points.xs) != 0:\n click_point = points.xs[0]\n click_pos = np.where(trace.x == click_point)[0][0]\n ticks = self.__heatmap.layout.xaxis.ticktext.copy()\n hovertext = trace.x.copy()\n\n # Add tick to selected ids\n old_id = ticks[click_pos]\n new_id = switch_color(old_id)\n ticks[click_pos] = new_id\n if len(self.selected_ids) > 0 and new_id in self.selected_ids:\n self.selected_ids = np.delete(self.selected_ids, np.where(self.selected_ids == new_id))\n else:\n self.selected_ids = np.append(self.selected_ids, old_id)\n\n # Update color of tick text\n if set(self.__heatmap.layout.xaxis2.ticktext) == set(self.__heatmap.layout.xaxis.ticktext):\n self.__heatmap.update_layout(xaxis2_ticktext=ticks)\n self.__heatmap.update_layout(xaxis_ticktext=ticks)\n\n # Update color of hover text\n old_hovertext = hovertext[click_pos]\n new_hovertext = switch_color(old_hovertext)\n hovertext[click_pos] = new_hovertext\n\n trace.x = hovertext\n\n # --------- Statistics\n\n def feature_signature(self, layer, exclude_missing=False):\n \"\"\"\n T-Test on given data.\n\n Generate feature signatures for each cluster/feature pair across all barcodes\n using the supplied assay and layer. For each cluster/feature, returns median feature,\n standard deviation of feature, p-value, and the t-statistic of the feature in the cluster\n compared to all other clusters.\n\n Parameters\n ----------\n layer : string\n Layer name to be evaluated.\n exclude_missing : bool\n Whether missing dna data is considered.\n\n Returns\n -------\n med : dataframe\n The median value of the layer for each cluster/feature pair.\n stdev : dataframe\n The standard deviation of the layer for each cluster/feature pair.\n pval : dataframe\n The feature p-value of for each cluster vs other clusters.\n tstat : dataframe\n The value of the t-statistic corresponding to the pval.\n \"\"\"\n\n dat = deepcopy(self.layers[layer])\n features = self.ids()\n lbl = self.get_labels()\n u, uind = np.unique(lbl, return_inverse=True)\n nc = len(u)\n nf = len(features)\n\n med = pd.DataFrame(data=np.zeros((nf, nc)), index=features, columns=u)\n stdev = pd.DataFrame(data=np.zeros((nf, nc)), index=features, columns=u)\n pval = pd.DataFrame(data=np.zeros((nf, nc)), index=features, columns=u)\n tstat = pd.DataFrame(data=np.zeros((nf, nc)), index=features, columns=u)\n\n for i in range(nc):\n for j in range(nf):\n ind = (uind == i)\n nind = np.invert(ind)\n if (exclude_missing is True) & (self.name == 'dna'):\n ind = ind & (self.layers['NGT'][:, j] < 3)\n nind = nind & (self.layers['NGT'][:, j] < 3)\n\n if np.sum(ind) == 0:\n med.iloc[j, i] = np.nan\n pval.iloc[j, i] = 1\n else:\n med.iloc[j, i] = np.median(dat[ind, j], axis=0)\n stdev.iloc[j, i] = np.std(dat[ind, j], axis=0)\n if np.sum(ind) > 1:\n tstat.iloc[j, i], pval.iloc[j, i] = stats.ttest_ind(dat[ind, j], dat[nind, j], axis=0)\n else:\n pval.iloc[j, i] = 1\n\n return med, stdev, pval, tstat\n","repo_name":"haochenz96/mosaic","sub_path":"src/mosaic/assay.py","file_name":"assay.py","file_ext":"py","file_size_in_byte":88615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1528638798","text":"# By Neerja Thakkar for \"Balancing sensitivity and specificity in\n# distinguishing TCR groups by CDR sequence similarity\"\n# See README for license information\n\n# command line arguments:\n# for beta:\n# python2 structural_cluster_histograms.py results/stcr_results/ STCR_global_beta_SW \"datasets/stcr_data/B3-10_11_12_13-A,datasets/stcr_data/B3-10_11-A,datasets/stcr_data/B3-12_A,datasets/stcr_data/B3-12-B,datasets/stcr_data/B3-13_14-A,datasets/stcr_data/B3-14-A\"\n# for alpha:\n# python2 structural_cluster_histograms.py results/stcr_results/ STCR_global_alpha_SW \"datasets/stcr_data/A3-9-A,datasets/stcr_data/A3-10-A,datasets/stcr_data/A3-13-B,datasets/stcr_data/A3-13-A,datasets/stcr_data/A3-10_11_12-A\"\n\n\nfrom matplotlib import pyplot as plt\nimport random\n\n\nfrom sw_scoring import getDistanceSW\nfrom parse_sequences import txttoseqlist\nimport seaborn as sns\nimport sys\n\n\n# inputs (from command line): list of cluster files, directory, title\nif len(sys.argv) < 3:\n print(\"need the following arguments: output directory, file title, cluster files\")\n sys.exit()\nelse:\n output_directory = sys.argv[1]\n title = sys.argv[2]\n cluster_file_list = sys.argv[3].split(',')\n\n\n# return a list with the min scores of cdr1 compared to cdrs2, cdr_list1 = cdr_list2\ndef calculate_min_distances_within(cdr_list1, cdr_list2):\n scores = []\n # for each cdr in cdrs1, find the min score\n for cdr1 in cdr_list1:\n min = 1000000\n for cdr2 in cdr_list2:\n if cdr1 != cdr2:\n score = getDistanceSW(cdr1, cdr2, length_dep=True, gap_penalty=-10)\n if score < min:\n min = score\n scores.append(min)\n return scores\n\n# return a list with the min scores of cdr1 compared to cdrs2, cdr_list1 != cdr_list2\n# same method as above except that the cdr in cdr list 2 must not be in cdr list 1\ndef calculate_min_distances_between(cdr_list1, cdr_list2):\n scores = []\n # for each cdr in cdrs1, find the min score\n for cdr1 in cdr_list1:\n min = 100000\n for cdr2 in cdr_list2:\n if cdr1 != cdr2 and cdr2 not in cdr_list1:\n score = getDistanceSW(cdr1, cdr2, length_dep=True, gap_penalty=-10)\n if score < min:\n min = score\n scores.append(min)\n return scores\n\n\n# generate a PDF and a CDF of the data\ndef generate_plots(self_scores, non_self_scores, output_directory, title):\n sns.set(font_scale=2) # crazy big\n\n # density plot - PDF\n figure = sns.kdeplot(self_scores, shade=True)\n figure = sns.kdeplot(non_self_scores, shade=True)\n figure.set(ylim=(0, 5))\n fig = figure.get_figure()\n fig.show()\n fig.savefig(output_directory + title + \"_kde_updated.pdf\")\n\n plt.figure() # new figure\n figure = sns.kdeplot(self_scores, shade=True, cumulative = True)\n figure = sns.kdeplot(non_self_scores, shade=True, cumulative = True)\n figure.set(ylim=(0, 1))\n fig = figure.get_figure()\n fig.savefig(output_directory + title + \"_kde_cumulative_updated.pdf\")\n\nprint(\"calculating self-scores...\")\n# calculate self-scores\nall_self_scores = []\nnum_self_scores = 0\nfor file in cluster_file_list:\n cdrs = txttoseqlist(file)\n cdrs = set(cdrs)\n cdrs = list(cdrs)\n scores = calculate_min_distances_within(cdrs, cdrs)\n all_self_scores += scores\n num_self_scores += len(scores)\nprint(all_self_scores)\n\nprint(\"calculating non-self scores...\")\n# calculate non-self scores\nall_non_self_scores = []\nnum_non_self_scores = 0\nfor file1 in cluster_file_list:\n for file2 in cluster_file_list:\n if file1 != file2:\n cdrs1 = list(set(txttoseqlist(file1)))\n cdrs2 = list(set(txttoseqlist(file2)))\n scores = calculate_min_distances_between(cdrs1, cdrs2)\n all_non_self_scores += scores\n num_non_self_scores += len(scores)\n\nfor i in range(len(all_non_self_scores)):\n if all_non_self_scores[i] == 1.0:\n all_non_self_scores[i] = random.uniform(0.9, 1.0)\n\nprint(all_non_self_scores)\n\n\ngenerate_plots(all_self_scores, all_non_self_scores, output_directory, title)\n\n","repo_name":"neerjathakkar/Distinguishing-TCR-Groups","sub_path":"structural_cluster_histograms.py","file_name":"structural_cluster_histograms.py","file_ext":"py","file_size_in_byte":4106,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"70098998481","text":"from collections import defaultdict\nfrom enum import Enum\nfrom queue import Queue\nfrom market import Order\n\n\n\nclass OrderBook:\n def __init__(self) -> None:\n self.bids = defaultdict(Queue)\n self.offers = defaultdict(Queue)\n\n @property\n def highest_bid(self):\n if self.bids:\n return max(price for price in self.bids.keys() if self.bids[price].empty() is not True)\n else:\n return None\n\n @property\n def lowest_offer(self):\n if self.offers:\n return min(price for price in self.offers.keys() if self.offers[price].empty() is not True)\n else:\n return float('inf')\n\n def order(self, order:Order):\n if order.type == Type.BID:\n self.bids[order.price].put(order)\n elif order.type == Type.OFFER:\n self.offers[order.price].put(order)\n\n def match(self, order:Order):\n if order.type == Type.BID:\n if order.price >= self.lowest_offer:\n matched_bid = self.bids[order.price].get()\n matched_offer = self.offers[self.lowest_offer].get()\n print('MATCH BID {0} = OFFER {1}'.format(matched_bid, matched_offer))\n return matched_bid, matched_offer\n else:\n print(\"No Match\")\n return None, None\n if order.type == Type.OFFER:\n if not self.bids[order.price].empty():\n matched_bid = self.bids[order.price].get()\n matched_offer = self.offers[order.price].get()\n print('MATCH BID {0} = OFFER {1}'.format(matched_bid, matched_offer))\n\n else:\n print(\"No Match\")\n\n def match_all(self):\n executed_bids = []\n executed_offers = []\n bids = self.bids\n for _, orders in bids.items():\n for order in list(orders.queue):\n bids,offers = self.match(order)\n if bids != None: executed_bids.append(bids)\n if offers != None: executed_offers.append(offers)\n \n return executed_bids, executed_offers\n\n def show(self):\n print('---BID---')\n for price,orders in self.bids.items():\n for order in list(orders.queue):\n print(order)\n print('--OFFER--')\n for price,orders in self.offers.items():\n for order in list(orders.queue):\n print(order)\n\nif __name__ == \"__main__\":\n book = OrderBook()\n\n orders = [\n Order(1,5,Type.OFFER),\n Order(2,5,Type.OFFER),\n Order(3,6,Type.OFFER),\n Order(4,6,Type.OFFER),\n Order(4,7,Type.OFFER),\n Order(5,4,Type.BID),\n Order(6,4,Type.BID),\n Order(7,4,Type.BID),\n Order(8,5,Type.BID),\n Order(9,6,Type.BID),\n Order(9,7,Type.BID),\n Order(10,4,Type.OFFER),\n ]\n\n for order in orders:\n book.order(order)\n\n bids, offers = book.match_all()\n print(bids)\n print(offers)\n print('{0} : {1}'.format(book.highest_bid, book.lowest_offer))\n book.show()\n","repo_name":"jc-doyle/market-model","sub_path":"orderbook.py","file_name":"orderbook.py","file_ext":"py","file_size_in_byte":3058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5975017884","text":"\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\" A photosphere class. \"\"\"\n\nfrom __future__ import division, absolute_import, print_function\n\n__author__ = \"Andy Casey \"\n\nimport logging\nfrom textwrap import dedent\n\nimport astropy.io\nimport astropy.table\n\n# Create logger.\nlogger = logging.getLogger(__name__)\n\nclass Photosphere(astropy.table.Table):\n \"\"\"\n A model photosphere object.\n \"\"\"\n pass\n\n\n# MOOG writer and identifier.\ndef _moog_writer(photosphere, filename, **kwargs):\n \"\"\"\n Writes an :class:`photospheres.photosphere` to file in a MOOG-friendly\n format.\n\n :param photosphere:\n The photosphere.\n\n :path filename:\n The filename to write the photosphere to.\n \"\"\"\n\n def _get_xi():\n xi = photosphere.meta[\"stellar_parameters\"].get(\"microturbulence\", 0.0)\n if 0 >= xi:\n logger.warn(\"Invalid microturbulence value: {:.3f} km/s\".format(xi))\n return xi\n\n if photosphere.meta[\"kind\"] == \"marcs\":\n\n xi = _get_xi()\n\n output = dedent(\"\"\"\n WEBMARCS\n MARCS (2011) TEFF/LOGG/[M/H]/XI {1:.0f}/{2:.3f}/{3:.3f}/{4:.3f}\n NTAU {0:.0f}\n 5000.0\n \"\"\".format(len(photosphere),\n photosphere.meta[\"stellar_parameters\"][\"effective_temperature\"],\n photosphere.meta[\"stellar_parameters\"][\"surface_gravity\"],\n photosphere.meta[\"stellar_parameters\"][\"metallicity\"],\n xi)).lstrip()\n\n for i, line in enumerate(photosphere):\n output += \" {0:>3.0f} {0:>3.0f} {1:10.3e} {0:>3.0f} {2:10.3e} \"\\\n \"{3:10.3e} {4:10.3e}\\n\".format(i + 1, line[\"lgTau5\"], line[\"T\"],\n line[\"Pe\"], line[\"Pg\"])\n\n output += \" {0:.3f}\\n\".format(xi)\n output += \"NATOMS 0 {0:.3f}\\n\".format(\n photosphere.meta[\"stellar_parameters\"][\"metallicity\"])\n output += \"NMOL 0\\n\"\n\n\n elif photosphere.meta[\"kind\"] == \"castelli/kurucz\":\n\n xi = _get_xi()\n \n output = dedent(\"\"\"\n KURUCZ\n CASTELLI/KURUCZ (2004) {1:.0f}/{2:.3f}/{3:.3f}/{4:.3f}/{5:.3f}\n NTAU {0:.0f}\n \"\"\".format(len(photosphere),\n photosphere.meta[\"stellar_parameters\"][\"effective_temperature\"],\n photosphere.meta[\"stellar_parameters\"][\"surface_gravity\"],\n photosphere.meta[\"stellar_parameters\"][\"metallicity\"],\n photosphere.meta[\"stellar_parameters\"][\"alpha_enhancement\"],\n xi)).lstrip()\n\n for line in photosphere:\n output += \" {0:.8e} {1:10.3e}{2:10.3e}{3:10.3e}{4:10.3e}\\n\".format(\n line[\"RHOX\"], line[\"T\"], line[\"P\"], line[\"XNE\"], line[\"ABROSS\"])\n \n output += \" {0:.3f}\\n\".format(xi)\n output += \"NATOMS 0 {0:.3f}\\n\".format(\n photosphere.meta[\"stellar_parameters\"][\"metallicity\"])\n output += \"NMOL 0\\n\"\n # MOOG11 fails to read if you don't add an extra line\n output += \"\\n\"\n\n else:\n raise ValueError(\"photosphere kind '{}' cannot be written to a MOOG-\"\\\n \"compatible format\".format(photosphere.meta[\"kind\"]))\n\n with open(filename, \"w\") as fp:\n fp.write(output)\n\n return None\n\n\ndef _moog_identifier(*args, **kwargs):\n return isinstance(args[0], basestring) and args[0].lower().endswith(\".moog\")\n\n# Register the MOOG writer and identifier\nastropy.io.registry.register_writer(\"moog\", Photosphere, _moog_writer)\nastropy.io.registry.register_identifier(\"moog\", Photosphere, _moog_identifier)\n","repo_name":"andycasey/smhr","sub_path":"smh/photospheres/photosphere.py","file_name":"photosphere.py","file_ext":"py","file_size_in_byte":3635,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"}
+{"seq_id":"29650856444","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Aug 9 00:39:08 2020\n\n@author: Anobhama\n\"\"\"\n#(Q27)WAP to check whether a number is even or odd.\nnum= int(input(\"Enter the number : \"))\nif num % 2 == 0:\n print(\"{} is a even number\".format(num))\nelse:\n print(\"{} is a odd number\".format(num))","repo_name":"anobhama/Intern-Pie-Infocomm-Pvt-Lmt","sub_path":"Python Basics/q27_OddEven.py","file_name":"q27_OddEven.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36011972140","text":"import pandas as pd\nimport os\n\ndataset = input('DATASET: ')\nfolder_path = os.path.join(dataset, 'images')\nsampled_path = os.path.join(folder_path, 'sampled')\n\nif not os.path.exists(sampled_path):\n os.makedirs(sampled_path)\n\nfor i in ['1', '2', '3']:\n df = pd.read_csv(os.path.join(folder_path, f'{i}.csv'))\n\n sampled_df = df.sample(n=8500, random_state=1)\n\n sampled_df.to_csv(os.path.join(sampled_path, f'{i}.csv'), index=False)\n","repo_name":"sky9154/Fall_Detection","sub_path":"Train/tools/data_sampled.py","file_name":"data_sampled.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"17909622524","text":"import copy\n\nvisited = set()\nd_dict = {\n \"U\": [0, -1],\n \"D\": [0, 1],\n \"L\": [-1, 0],\n \"R\": [1, 0]\n}\n\ndef string_to_int(string_or_int):\n try:\n return int(string_or_int)\n except ValueError:\n return string_or_int\n\ndef prepare_input(file):\n return [list(map(string_to_int, list(line.split()))) for line in file]\n\ndef is_neighbour(h_x, h_y, t_x, t_y):\n for d in [[0, 0], [1, 0], [1, 0], [-1, 0], [0, 1], [0, -1], [1, 1], [1, -1], [-1, 1], [-1, -1]]: # all directions\n i, j = t_x + d[0], t_y + d[1]\n if i == h_x and j == h_y:\n return True\n return False\n\ndef is_neighbour_manhattan(h_x, h_y, t_x, t_y):\n for d in [[0, 0], [1, 0], [1, 0], [-1, 0], [0, 1], [0, -1]]: # direction: right, left, down, up\n i, j = t_x + d[0], t_y + d[1]\n if i == h_x and j == h_y:\n return True\n return False\n\ndef move_tail(h_x, h_y, t_x, t_y):\n for d in [[1, 0], [-1, 0], [0, 1], [0, -1], [1, 1], [1, -1], [-1, 1], [-1, -1]]: # all directions\n i, j = t_x + d[0], t_y + d[1]\n if is_neighbour_manhattan(h_x, h_y, i, j):\n return i, j\n return move_tail_all_dir(h_x, h_y, t_x, t_y)\n\ndef move_tail_all_dir(h_x, h_y, t_x, t_y):\n for d in [[1, 0], [-1, 0], [0, 1], [0, -1], [1, 1], [1, -1], [-1, 1], [-1, -1]]: # all directions\n i, j = t_x + d[0], t_y + d[1]\n if is_neighbour(h_x, h_y, i, j):\n return i, j\n\ndef part_one(_input):\n global visited\n h_x = 0\n h_y = 0\n t_x = 0\n t_y = 0\n visited.add(tuple((t_x, t_y)))\n for direction, steps in _input:\n for _ in range(steps):\n for d in [d_dict[direction]]:\n h_x, h_y = h_x + d[0], h_y + d[1]\n if is_neighbour(h_x, h_y, t_x, t_y):\n continue\n else:\n t_x, t_y = move_tail(h_x, h_y, t_x, t_y)\n if tuple((t_x, t_y)) not in visited:\n visited.add(tuple((t_x, t_y)))\n return len(visited)\n\ndef part_two(_input):\n global visited\n h_x = 0\n h_y = 0\n t_x = [0, 0, 0, 0, 0, 0, 0, 0, 0]\n t_y = [0, 0, 0, 0, 0, 0, 0, 0, 0]\n visited = set()\n visited.add(tuple((0, 0)))\n for direction, steps in _input:\n for _ in range(steps):\n for d in [d_dict[direction]]:\n h_x, h_y = h_x + d[0], h_y + d[1]\n curr_x = h_x\n curr_y = h_y\n for curr_tail in range(len(t_x)):\n if is_neighbour(curr_x, curr_y, t_x[curr_tail], t_y[curr_tail]):\n pass\n else:\n new_x, new_y = move_tail(curr_x, curr_y, t_x[curr_tail], t_y[curr_tail])\n t_x[curr_tail] = new_x\n t_y[curr_tail] = new_y\n if curr_tail == 8 and tuple((t_x[curr_tail], t_y[curr_tail])) not in visited:\n visited.add(tuple((t_x[curr_tail], t_y[curr_tail])))\n curr_x, curr_y = t_x[curr_tail], t_y[curr_tail]\n return len(visited)\n\nif __name__ == '__main__':\n with open('input.txt', 'r') as f:\n _input = prepare_input(f)\n\n _input_copy = copy.deepcopy(_input)\n answer1 = part_one(_input_copy)\n print(\"Part one:\")\n print(\"Answer: {}\".format(answer1))\n print()\n answer2 = part_two(_input)\n print(\"Part two:\")\n print(\"Answer: {}\".format(answer2))\n","repo_name":"jernejjanez/advent-of-code","sub_path":"2022/day-09/day09.py","file_name":"day09.py","file_ext":"py","file_size_in_byte":3424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34952203601","text":"import threading\nimport logging\nimport time\n\ndef daemon():\n logging.debug(\"Starting\")\n time.sleep(0.2)\n logging.debug(\"Exiting\")\n\ndef non_daemon():\n logging.debug(\"Starting\")\n logging.debug(\"Exiting\")\n\nlogging.basicConfig(\n level=logging.DEBUG,\n format='[%(levelname)s] %(threadName)-10s %(message)s',\n)\n\n# d = threading.Thread(target=daemon, name=\"Daemon\")\nd = threading.Thread(target=daemon, name=\"Daemon\", daemon=True)\nnd = threading.Thread(target=non_daemon, name=\"Non Daemon\")\n\nd.start()\nnd.start()\n\nd.join()\nnd.join()\n","repo_name":"Kaushiksekar/SDE_Prepration","sub_path":"Python_Specifics/Threading/check_exiting.py","file_name":"check_exiting.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36200920357","text":"import sys\n\nfrom sdbpyTestLib import stdOptions, checkOptions\nfrom sqltest import sqltest\n# from dbapitest import dbapitest\n\ndef main (options, args):\n checkOptions (options)\n ok = 1\n #for function in [sqltest, dbapitest]:\n for function in [sqltest]:\n if not function (options, args):\n ok = None\n if not ok:\n sys.exit (3)\n\nif __name__ == '__main__':\n import optlib\n optlib.optMain2 (main, stdOptions ())\n\n","repo_name":"GunterMueller/MaxDB_GPL_Releases","sub_path":"7.6.00.37/V76_00_37/MaxDB_ORG/sys/src/SAPDB/Scripting/Test/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"38677471552","text":"#!/usr/bin/python\n#\\file filter2d_test2_test.py\n#\\brief Test of filter2d_test2.\n#\\author Akihiko Yamaguchi, info@akihikoy.net\n#\\version 0.1\n#\\date Sep.26, 2023\nimport filter2d_test2\nimport cv2\nimport copy\nimport numpy as np\n\nif __name__=='__main__':\n cap= cv2.VideoCapture(0)\n #cap.set(cv2.CAP_PROP_FRAME_WIDTH, 320)\n #cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)\n filter2d_test2.MakeKernel(10,30)\n\n while(True):\n ret,frame= cap.read()\n if frame is None: continue\n\n filtered= np.empty_like(frame)\n filtered= filter2d_test2.ApplyFilter(frame)\n\n cv2.imshow('camera',frame)\n cv2.imshow('filtered',filtered)\n if cv2.waitKey(1)&0xFF==ord('q'):\n break\n\n cap.release()\n cv2.destroyAllWindows()\n","repo_name":"akihikoy/ay_test","sub_path":"opencv/cpp_py_bind/filter2d_test2_test.py","file_name":"filter2d_test2_test.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"31782681442","text":"import re\nfrom os import path\nfrom ..interpreter import *\nfrom ..SIMode import SIMode\n\nclass PythonInterpreter(Interpreter):\n\n def run(self):\n keys = {\n \"module\": \"[^\\s]+\",\n \"variable\": \"[^\\s]+\"\n }\n\n self.settings = {\n \"extensions\": [\".py\"],\n \"remove_extensions\": [\".py\"],\n \"extra_extensions\": [],\n \"modules_folder\": None\n }\n\n self.handlers = [\n Handler(\n name=\"import_all\",\n matchers=[\n \"from {variable} import \\*\",\n \"{module}\\.\\*\"\n ],\n keys=keys\n ),\n Handler(\n name=\"import_from\",\n matchers=[\n \"from {module} import {variable}\",\n \"{variable}\\:\\:{module}\"\n ],\n keys=keys\n ),\n Handler(\n name=\"import\",\n matchers=[\n \"import {module}\"\n ],\n keys=keys\n )\n ]\n\n self.defaultHandler = \"import_from\"\n\n def parseModuleKey(self, value):\n for ext in self.getSetting('remove_extensions'):\n if value.endswith(ext):\n value = value[0:-len(ext)]\n break\n return value.replace(\"/\", \".\")[1:] if \"/\" in value else value\n\n def parseVariableKey(self, value):\n return re.sub(r\"!|@|\\*\", \"\", value)\n\n\n def onInterprete(self, interpreted, mode=SIMode.REPLACE_MODE):\n statements = interpreted.statements\n\n if not len(statements.keys()):\n statements['module'] = interpreted.simport.expression\n\n if \"variable\" not in statements:\n statements[\"variable\"] = statements[\"module\"]\n\n return super().onInterprete(interpreted)\n\n def onSearchResultChosen(self, interpreted, option_key, value, mode=SIMode.REPLACE_MODE):\n if option_key != 'modules':\n value = path.dirname(value) if value.endswith('/__init__.py') else value\n\n interpreted.statements['module'] = self.parseModuleKey(value)\n\n def stringifyStatements(self, statements, handler_name=None, insert_type=Interpreted.IT_INSERT):\n if handler_name == \"import\":\n return \"import {0}\".format(statements['module'])\n\n return \"from {0} import {1}\".format(statements['module'], statements['variable'])\n\n def buildRegexForFiles(self, filename):\n return r\"({0}|{0}\\/__init__)({1})$\".format(filename, \"|\".join(self.getSetting('extensions')))\n","repo_name":"vinpac/sublime-simple-import","sub_path":"lib/interpreters/PythonInterpreter.py","file_name":"PythonInterpreter.py","file_ext":"py","file_size_in_byte":2241,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"3"}
+{"seq_id":"37547927099","text":"heure = int(input(\"Entrez le nombres d'heures : \"))\nsHoraire = int(input(\"Entrez le salaire horaire : \"))\n\nif heure<=160:\n print(f'salaire = {heure*sHoraire}')\nelif heure>160 and heure<=200:\n heurev = heure - 160\n print(f\"salaire = {heure*sHoraire + heurev*(sHoraire*1.25)} \")\nelse:\n heurev = heure - 200\n print(f\"salaire = {160*sHoraire + 40*(sHoraire*1.25) + heurev*(sHoraire*1.50)}\")","repo_name":"jameschm/tp_python","sub_path":"TP5/tp5exo5.py","file_name":"tp5exo5.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29029366987","text":"import json\nimport random\nfrom unittest.mock import Mock, patch, DEFAULT, MagicMock\n\nimport pretend\nimport pytest\nfrom waffle.testutils import override_switch\n\nfrom django.conf import settings\nfrom django.core import mail\nfrom django.core.management import call_command\nfrom django.utils import timezone\n\nfrom plan.oebs.constants import OEBS_DEVIATIONS_REASONS\nfrom plan.api.exceptions import IntegrationError\nfrom plan.common.person import Person\nfrom plan.denormalization.tasks import check_model_denormalized_fields\nfrom plan.idm.exceptions import IDMError\nfrom plan.services import tasks, models\nfrom plan.services.state import SERVICE_STATE\nfrom plan.unistat.tasks import service_level_broken\nfrom plan.duty.models import Schedule\n\nfrom common import factories\nfrom utils import MockIdmResponse, Response\n\npytestmark = pytest.mark.django_db\n\n\n@pytest.fixture\ndef data(metaservices):\n metaservice, meta_other = metaservices\n submeta_other = factories.ServiceFactory(parent=meta_other)\n service = factories.ServiceFactory(parent=meta_other)\n\n # пользователи с которыми изначально \"все хорошо\"\n person = factories.StaffFactory()\n factories.ServiceMemberFactory(\n staff=person,\n service=service,\n )\n\n superuser = factories.UserFactory(is_superuser=True)\n moderator = factories.StaffFactory(user=superuser)\n check_model_denormalized_fields('services.Service')\n for obj in (service, metaservice, meta_other, submeta_other):\n obj.refresh_from_db()\n\n fixture = pretend.stub(\n service=service,\n metaservice=metaservice,\n meta_other=meta_other,\n submeta_other=submeta_other,\n moderator=moderator,\n )\n return fixture\n\n\n@pytest.fixture\ndef yql_query_oebs_mock(data):\n class FakeYQLRequest:\n def __init__(self):\n self.status = 'COMPLETED'\n self.run = MagicMock()\n table_mock = MagicMock()\n table_mock.rows = (\n (\n data.service.id, 30, 'LEAF_CODE',\n 'GROUP_CODE', 'some', 'some_en',\n 'true', 'false', 'false', 'true'\n ),\n )\n self.get_results = MagicMock(return_value=[table_mock])\n\n with patch('yql.api.v1.client.YqlClient.query', return_value=FakeYQLRequest()) as _mock:\n yield _mock\n\n\n@patch('plan.services.tasks.request_service_head')\n@patch('plan.api.idm.actions.add_service')\ndef test_register_service(add_service, request_service_head, data):\n request = factories.ServiceCreateRequestFactory(\n service=data.service,\n state=models.ServiceCreateRequest.REQUESTED\n )\n\n tasks.register_service(request.id)\n\n assert add_service.called\n assert request_service_head.apply_async.called\n\n request.refresh_from_db()\n assert request.state == models.ServiceCreateRequest.PROCESSING_HEAD\n\n\n@patch('plan.services.tasks.finalize_service_creation')\ndef test_register_service_no_service_owner(finalize_service_creation, data):\n \"\"\"\n Если после создания сервиса owner исчез (например, запросы подвисли, а на следующий день owner уволился)\n из-за этого не должен останавливаться процесс создания сервиса.\n \"\"\"\n\n request = factories.ServiceCreateRequestFactory(\n service=data.service,\n state=models.ServiceCreateRequest.PROCESSING_HEAD\n )\n\n data.service.owner = None\n data.service.save()\n\n tasks.request_service_head(request.id)\n\n request.refresh_from_db()\n assert request.state == models.ServiceCreateRequest.PROCESSING_ABC\n assert finalize_service_creation.apply_async.called\n\n\n@patch('plan.api.idm.actions.add_service')\ndef test_register_service_saves_request_state_on_exception(add_service, data):\n request = factories.ServiceCreateRequestFactory(\n service=data.service,\n state=models.ServiceCreateRequest.REQUESTED\n )\n\n add_service.side_effect = Exception\n\n with pytest.raises(Exception):\n tasks.register_service(request.id)\n\n assert add_service.called\n\n request.refresh_from_db()\n assert request.state == models.ServiceCreateRequest.PROCESSING_IDM\n\n\n@patch('plan.idm.nodes.get_role_node')\n@patch('plan.idm.nodes.get_service_roles_node')\n@patch('plan.idm.nodes.get_service_node')\n@patch('plan.api.idm.actions.add_service')\ndef test_register_service_continues_on_integration_error(\n add_service, get_service_node, get_service_roles_node, get_role_node, owner_role, data):\n request = factories.ServiceCreateRequestFactory(\n service=data.service,\n state=models.ServiceCreateRequest.REQUESTED\n )\n\n add_service.side_effect = IntegrationError\n\n service_node = Mock()\n service_node.exists.return_value = True\n get_service_node.return_value = service_node\n\n roles_node = Mock()\n roles_node.exists.return_value = False\n get_service_roles_node.return_value = roles_node\n\n owner_role_node = Mock()\n owner_role_node.exists.return_value = False\n get_role_node.return_value = owner_role_node\n\n tasks.register_service(request.id)\n assert add_service.called\n assert roles_node.register.called\n assert owner_role_node.register.called\n\n request.refresh_from_db()\n assert request.state == models.ServiceCreateRequest.PROCESSING_HEAD\n\n\n@patch('plan.api.idm.actions.add_service_head')\ndef test_request_service_head_on_service_creation(add_service_head, data):\n request = factories.ServiceCreateRequestFactory(\n service=data.service,\n state=models.ServiceCreateRequest.PROCESSING_HEAD\n )\n\n tasks.request_service_head(request.id)\n\n assert add_service_head.called\n\n\ndef test_request_move_on_service_creation(data):\n request = factories.ServiceCreateRequestFactory(\n service=data.service,\n move_to=data.submeta_other,\n state=models.ServiceCreateRequest.PROCESSING_ABC\n )\n\n tasks.finalize_service_creation(request.id)\n\n request.refresh_from_db()\n assert request.state == models.ServiceCreateRequest.COMPLETED\n\n move_request = data.service.move_requests.active().get()\n assert move_request.destination == data.submeta_other\n\n\n@pytest.mark.parametrize('service_state', SERVICE_STATE.ALL_STATES)\n@patch('plan.services.tasks.notify_staff')\ndef test_update_services_members(notify_staff, data, service_state):\n service = data.service\n service.state = service_state\n service.save(update_fields=('state',))\n assert data.service.members.filter(from_department__isnull=False).count() == 0\n\n tasks.update_service_department_members(data.service.id)\n\n assert not notify_staff.delay.called\n\n department = factories.DepartmentFactory()\n person = factories.StaffFactory(department=department)\n service_department = factories.ServiceMemberDepartmentFactory(\n service=data.service,\n department=department,\n service_id=data.service.id,\n )\n assert service_department.members_count == 0\n\n tasks.update_service_department_members(data.service.id)\n\n service_department.refresh_from_db()\n\n if service_state == SERVICE_STATE.DELETED:\n assert service_department.members_count == 0\n\n assert data.service.members.filter(from_department__isnull=False).count() == 0\n assert not data.service.members.filter(from_department__isnull=False).exists()\n\n assert not notify_staff.delay.called\n else:\n assert service_department.members_count == 1\n\n assert data.service.members.filter(from_department__isnull=False).count() == 1\n assert data.service.members.filter(from_department__isnull=False).get().staff == person\n\n assert notify_staff.delay.called\n\n\ndef test_update_services_members_call_count(data):\n service = data.service\n\n for _ in range(2):\n department = factories.DepartmentFactory()\n factories.StaffFactory(department=department)\n factories.ServiceMemberDepartmentFactory(service=service, department=department)\n\n with patch('plan.services.tasks.notify_staff') as notify_staff:\n tasks.update_service_department_members(service.id)\n assert notify_staff.delay.call_count == 1\n\n\ndef test_move_service(data, move_services_subtasks, mailoutbox, owner_role):\n name = 'Новые щенята & ^ # кусь 2 '\n typograf_name = 'Новые щенята & ^ # кусь 2 <script>console.log(1)</script>'\n head_membership = factories.ServiceMemberFactory(service=data.service, role=owner_role)\n move_request = factories.ServiceMoveRequestFactory(service=data.service, destination=data.metaservice)\n move_request._approve_transition(Person(data.moderator))\n move_request.save()\n\n data.service.name = name\n data.service.save()\n\n tasks.move_service(move_request.id)\n\n data.service.refresh_from_db()\n move_request.refresh_from_db()\n assert data.service.parent == data.metaservice\n assert move_request.state == 'completed'\n\n assert move_services_subtasks.idm_call.called\n assert move_services_subtasks.rerequest_roles.apply_async.called\n assert move_services_subtasks.notify_staff.apply_async.called\n\n assert len(mailoutbox) == 1\n\n expected_service = (\n 'Ваш сервис {name}'\n .format(service=data.service, abc_url=settings.ABC_URL, name=typograf_name)\n )\n expected_parent = (\n 'Новый родительский сервис: {destination.name}'\n .format(abc_url=settings.ABC_URL, destination=data.metaservice)\n )\n\n assert expected_service in mailoutbox[0].body\n assert expected_parent in mailoutbox[0].body\n assert 'был успешно перенесен' in mailoutbox[0].body\n assert name in mailoutbox[0].subject\n assert mailoutbox[0].to == [head_membership.staff.email]\n\n\ndef test_moving_service_clears_readonly(data, move_services_subtasks):\n MOVING = 'moving'\n\n parent = factories.ServiceFactory(\n parent=data.metaservice,\n readonly_state=MOVING,\n readonly_start_time=timezone.now()\n )\n child = factories.ServiceFactory(\n parent=parent,\n readonly_state=MOVING,\n readonly_start_time=timezone.now()\n )\n grandchild = factories.ServiceFactory(\n parent=child,\n readonly_state=MOVING,\n readonly_start_time=timezone.now()\n )\n check_model_denormalized_fields('services.Service')\n\n move_request = factories.ServiceMoveRequestFactory(service=parent, state='approved')\n\n tasks.move_service(move_request.id)\n\n for service in (parent, child, grandchild):\n service.refresh_from_db()\n assert service.readonly_state is None\n\n\ndef test_moving_flips_is_exportable(data, move_services_subtasks):\n move_request = factories.ServiceMoveRequestFactory(\n service=data.service, destination=data.metaservice, state='approved')\n tasks.move_service(move_request.id)\n data.service.refresh_from_db()\n assert data.service.is_exportable\n\n move_request = factories.ServiceMoveRequestFactory(\n service=data.service, destination=data.meta_other, state='approved')\n tasks.move_service(move_request.id)\n data.service.refresh_from_db()\n assert not data.service.is_exportable\n\n move_request = factories.ServiceMoveRequestFactory(\n service=data.service, destination=data.submeta_other, state='approved')\n tasks.move_service(move_request.id)\n data.service.refresh_from_db()\n assert not data.service.is_exportable\n\n\ndef test_moving_flips_is_exportable_on_entire_family(data, move_services_subtasks):\n parent = factories.ServiceFactory(parent=data.meta_other)\n child = factories.ServiceFactory(parent=parent)\n grandchild = factories.ServiceFactory(parent=child)\n check_model_denormalized_fields('services.Service')\n\n family = (parent, child, grandchild)\n\n # there\n move_request = factories.ServiceMoveRequestFactory(\n service=parent, destination=data.metaservice, state='approved')\n tasks.move_service(move_request.id)\n\n for service in family:\n service.refresh_from_db()\n assert service.is_exportable is True\n\n # and back again\n move_request = factories.ServiceMoveRequestFactory(\n service=parent, destination=data.meta_other, state='approved')\n tasks.move_service(move_request.id)\n\n for service in family:\n service.refresh_from_db()\n assert service.is_exportable is False\n\n\n@patch('plan.services.tasks.move_service_abc_side')\n@patch('plan.api.idm.actions.move_service')\n@patch('plan.api.idm.actions.assert_service_node_exists', Mock())\ndef test_move_service_retry(move_service, move_service_abc_side, data):\n move_request = factories.ServiceMoveRequestFactory(service=data.service, destination=data.metaservice)\n move_request._approve_transition(Person(data.moderator))\n move_request.save()\n\n move_service.side_effect = (IDMError, IDMError, IDMError, IDMError, IDMError, DEFAULT)\n\n tasks.move_service.delay(move_request.id)\n\n assert move_service.called\n assert move_service_abc_side.apply_async.called\n\n\n@patch('plan.services.tasks.move_service_abc_side')\n@patch('plan.api.idm.actions.move_service')\ndef test_move_service_retry_fail(move_service, move_service_abc_side, data):\n move_request = factories.ServiceMoveRequestFactory(service=data.service, destination=data.metaservice)\n move_request._approve_transition(Person(data.moderator))\n move_request.save()\n\n move_service.side_effect = (IDMError, IDMError, IDMError, IDMError, IDMError, IDMError, DEFAULT)\n\n tasks.move_service.delay(move_request.id)\n\n assert move_service.called\n assert not move_service_abc_side.delay.called\n\n\n@patch('plan.services.tasks.move_service_abc_side')\n@patch('plan.api.idm.actions.assert_service_node_exists')\n@pytest.mark.parametrize(\"move_service_abc_side_called, idm_exceptions\", [\n (True, (IDMError, IDMError, IDMError, IDMError, IDMError, DEFAULT)),\n (False, (IDMError, IDMError, IDMError, IDMError, IDMError, IDMError, DEFAULT)),\n])\ndef test_verify_and_move_service_retry(assert_service_node_exists, move_service_abc_side, data,\n move_service_abc_side_called, idm_exceptions):\n move_request = factories.ServiceMoveRequestFactory(service=data.service, destination=data.metaservice)\n move_request._approve_transition(Person(data.moderator))\n move_request.process_oebs()\n move_request.process_d()\n move_request.process_idm()\n move_request.save()\n\n assert_service_node_exists.side_effect = idm_exceptions\n\n tasks.verify_and_move_service.delay(move_request.id)\n assert assert_service_node_exists.call_count == settings.ABC_IDM_MAX_RETRY + 1\n assert move_service_abc_side.apply_async.called is move_service_abc_side_called\n\n\n@patch('plan.api.idm.actions.delete_service')\ndef test_delete_service(delete_service, service_with_owner):\n service = service_with_owner\n service2 = factories.ServiceFactory()\n\n move_request = factories.ServiceMoveRequestFactory(\n service=service,\n destination=service2,\n state=models.ServiceMoveRequest.REQUESTED,\n )\n\n models.ServiceDeleteRequest.request(service, Person(service.owner))\n\n assert delete_service.call_count == 0\n\n service.refresh_from_db()\n assert service.state == SERVICE_STATE.IN_DEVELOP\n\n move_request.refresh_from_db()\n assert move_request.state == models.ServiceMoveRequest.REQUESTED\n\n\n@patch('plan.api.idm.actions.delete_service')\ndef test_delete_service_with_schedule(delete_service, service_with_owner):\n service = service_with_owner\n schedule = factories.ScheduleFactory(service=service)\n with patch('plan.services.tasks.drop_requests'):\n with patch('plan.services.models.get_unclosable_services') as get_unclosable_services:\n get_unclosable_services.return_value = []\n models.ServiceDeleteRequest.request(service, Person(service.owner))\n\n assert delete_service.call_count == 1\n\n service.refresh_from_db()\n assert service.state == SERVICE_STATE.DELETED\n\n schedule.refresh_from_db()\n\n assert schedule.status == Schedule.DELETED\n\n\ndef test_delete_service_with_moving_descendants(service_with_owner):\n service = service_with_owner\n with patch('plan.services.models.Service.has_moving_descendants', return_value=True) as mock:\n models.ServiceDeleteRequest.request(service, Person(service.owner))\n assert mock.call_count == 6 # 1 + 5 (max_retries)\n service.refresh_from_db()\n assert service.state == SERVICE_STATE.IN_DEVELOP\n\n\n@patch('plan.common.utils.startrek.get_lsr_amount')\n@patch('plan.common.utils.startrek.get_filter_amount')\ndef test_update_kpi(get_filter_amount, get_lsr_amount, service):\n get_filter_amount.return_value = 1\n get_lsr_amount.return_value = 2\n\n ct_bugs = factories.ContactTypeFactory(code=settings.STARTREK_BUGS_CONTACT)\n factories.ServiceContactFactory(\n service=service,\n type=ct_bugs,\n )\n\n tasks.update_services_kpi()\n\n service.refresh_from_db()\n assert service.kpi_bugs_count == 1\n assert service.kpi_release_count is None\n assert service.kpi_lsr_count == 2\n\n\n@patch('plan.common.utils.startrek.get_lsr_amount')\n@patch('plan.common.utils.startrek.get_filter_amount')\ndef test_update_kpi_double(get_filter_amount, get_lsr_amount, service):\n get_filter_amount.return_value = 3\n get_lsr_amount.return_value = 2\n\n ct_bugs = factories.ContactTypeFactory(code=settings.STARTREK_BUGS_CONTACT)\n factories.ServiceContactFactory(service=service, type=ct_bugs)\n factories.ServiceContactFactory(service=service, type=ct_bugs)\n\n tasks.update_services_kpi()\n\n service.refresh_from_db()\n assert service.kpi_bugs_count == 6\n\n\n@patch('plan.common.utils.startrek.get_lsr_amount')\ndef test_skip_kpi(get_lsr_amount, service):\n get_lsr_amount.return_value = None\n\n def bad_save(self, *args, **kwargs):\n raise ValueError()\n\n original_save = models.Service.save\n models.Service.save = bad_save\n\n tasks.update_services_kpi()\n\n models.Service.save = original_save\n\n\ndef test_service_ancestors():\n metaservice = factories.ServiceFactory()\n parent = factories.ServiceFactory(parent=metaservice)\n service = factories.ServiceFactory(parent=parent)\n factories.ServiceFactory(parent=parent)\n\n tasks.update_service_fields(parent.id)\n\n metaservice.refresh_from_db()\n assert metaservice.children_count == 1\n assert metaservice.descendants_count == 3\n\n parent.refresh_from_db()\n assert parent.children_count == 2\n assert parent.descendants_count == 2\n assert [rec['id'] for rec in parent.ancestors] == [metaservice.id]\n\n service.refresh_from_db()\n assert [rec['id'] for rec in service.ancestors] == [metaservice.id, parent.id]\n\n\ndef test_service_ancestors_tag_serialization():\n metaservice = factories.ServiceFactory()\n parent = factories.ServiceFactory(parent=metaservice)\n service = factories.ServiceFactory(parent=parent)\n\n meta_tag = factories.ServiceTagFactory()\n metaservice.tags.add(meta_tag)\n\n parent_tag = factories.ServiceTagFactory()\n parent.tags.add(parent_tag)\n\n tasks.update_service_fields(parent.id)\n\n service.refresh_from_db()\n\n ancestors = {ancestor['id']: ancestor for ancestor in service.ancestors}\n\n for ancestor in (metaservice, parent):\n assert len(ancestors[ancestor.id]['tags']) == 1\n\n tag = ancestors[ancestor.id]['tags'][0]\n assert set(tag.keys()) == {'id', 'name', 'color', 'slug', 'description'}\n\n\ndef test_notification_move_request():\n person = factories.StaffFactory()\n person2 = factories.StaffFactory()\n service = factories.ServiceFactory()\n service2 = factories.ServiceFactory(owner=person2)\n\n request = factories.ServiceMoveRequestFactory(\n requester=person,\n service=service,\n destination=service2,\n approver_outgoing=person2,\n )\n\n tasks.notify_move_request(request.id)\n\n assert len(mail.outbox) == 1\n\n email = mail.outbox[0]\n assert email.subject == 'Запрос на перенос сервиса %s' % service.name\n assert 'руководителем которого вы являетесь' in email.body\n assert person.first_name in email.body\n\n\n@patch('plan.api.idm.actions.move_service')\n@patch('plan.api.idm.actions.assert_service_node_exists')\n@patch('plan.api.idm.actions.rerequest_requested', Mock())\ndef test_move_to_root(assert_service_node_exists, move_service,\n mailoutbox, data, owner_role):\n name = 'Новые щенята & ^ # кусь 2 '\n typograf_name = 'Новые щенята & ^ # кусь 2 <script>console.log(1)</script>'\n data.service.name = name\n data.service.save()\n tasks.move_to_root(data.service, Person(data.moderator))\n\n data.service.refresh_from_db()\n assert data.service.parent is None\n\n move_service.assert_called_once_with(data.service, None)\n assert_service_node_exists.assert_called_once_with(data.service, None)\n assert len(mailoutbox) == 1\n\n expected_service = (\n 'Дочерний сервис {name} вашего сервиса '\n '{parent_service.name}'\n\n ).format(service=data.service, abc_url=settings.ABC_URL, name=typograf_name, parent_service=data.meta_other)\n\n assert expected_service in mailoutbox[0].body\n assert name in mailoutbox[0].subject\n assert 'был успешно перенесен' in mailoutbox[0].body\n assert 'Сервис стал корневым
' in mailoutbox[0].body\n assert mailoutbox[0].to == [data.meta_other.owner.email]\n\n\n@patch('plan.services.tasks.drop_requests')\n@patch('plan.services.tasks.notify_staff')\ndef test_close_service(notify_staff, drop_requests, data):\n tasks.close_service_admin(data.service.id)\n notify_staff.apply_async.assert_called_once_with(\n args=[data.service.id],\n countdown=settings.ABC_DEFAULT_COUNTDOWN\n )\n drop_requests.apply_async.assert_called_once_with(\n args=[data.service.id],\n countdown=settings.ABC_DEFAULT_COUNTDOWN\n )\n\n\ndef test_update_idm_roles_count(patch_tvm):\n service = factories.ServiceFactory()\n mock_response = MockIdmResponse({\n 'meta': {\n 'total_count': 2,\n 'next': None\n },\n 'objects': ['some test objects'],\n })\n\n def mocked_session_send(*args, **kwargs):\n return Response(200, mock_response.response)\n\n with patch('requests.Session.send') as patched:\n patched.side_effect = mocked_session_send\n tasks.update_service_idm_roles_count(service.id)\n\n service.refresh_from_db()\n assert service.idm_roles_count == 2\n\n\ndef test_update_puncher_rules_count_from_api():\n service = factories.ServiceFactory()\n\n def mocked_session_send(*args, **kwargs):\n response = {\n 'count': 5,\n 'rules': []\n }\n\n return Response(200, json.dumps(response))\n\n with patch('requests.Session.send') as patched:\n patched.side_effect = mocked_session_send\n tasks.update_service_puncher_rules_count_from_api([service.id])\n\n service.refresh_from_db()\n assert service.puncher_rules_count == 5\n\n\ndef test_sync_owners(owner_role):\n user = factories.StaffFactory()\n another_user = factories.StaffFactory()\n\n service_with_no_owner = factories.ServiceFactory(slug='no_owner')\n factories.ServiceMemberFactory(\n service=service_with_no_owner,\n role=owner_role,\n staff=user,\n )\n service_with_no_owner.owner = None\n service_with_no_owner.save()\n\n service_with_two_heads = factories.ServiceFactory(slug='two_heads')\n to_delete = factories.ServiceMemberFactory(service=service_with_two_heads, role=owner_role, staff=user)\n factories.ServiceMemberFactory(service=service_with_two_heads, role=owner_role, staff=service_with_two_heads.owner)\n assert models.ServiceMember.objects.filter(service=service_with_two_heads, role=owner_role).count() == 2\n\n service_with_different_head_and_owner = factories.ServiceFactory(slug='different_head_and_owner')\n factories.ServiceMemberFactory(service=service_with_different_head_and_owner, role=owner_role, staff=user)\n service_with_different_head_and_owner.owner = another_user\n service_with_different_head_and_owner.save()\n\n with patch('plan.api.idm.actions.deprive_role') as deprive_role:\n tasks.sync_owners()\n\n deprive_role.assert_called_once_with(\n to_delete, comment='Неактуальный руководитель'\n )\n\n service_with_no_owner.refresh_from_db()\n service_with_no_owner.fetch_owner()\n assert service_with_no_owner.owner is not None\n\n service_with_different_head_and_owner.refresh_from_db()\n service_with_different_head_and_owner.fetch_owner()\n head_sm = models.ServiceMember.objects.get(service=service_with_different_head_and_owner, role=owner_role)\n head = head_sm.staff\n assert service_with_different_head_and_owner.owner == head\n\n\ndef test_sync_owners_two_heads(owner_role):\n \"\"\"\n Роль старого руководителя по какой-то причине создана позже, чем нового.\n Остаться должна роль, указанная в service.owner\n \"\"\"\n\n user = factories.StaffFactory()\n service_with_two_heads = factories.ServiceFactory(slug='two_heads')\n factories.ServiceMemberFactory(\n service=service_with_two_heads,\n role=owner_role,\n staff=service_with_two_heads.owner\n )\n to_delete = factories.ServiceMemberFactory(service=service_with_two_heads, role=owner_role, staff=user)\n\n assert models.ServiceMember.objects.filter(service=service_with_two_heads, role=owner_role).count() == 2\n\n with patch('plan.api.idm.actions.deprive_role') as deprive_role:\n tasks.sync_owners()\n\n deprive_role.assert_called_once_with(\n to_delete, comment='Неактуальный руководитель'\n )\n\n\ndef test_notify_services_owners(mailoutbox):\n top_owner = factories.StaffFactory()\n child_owner = factories.StaffFactory()\n top_service = factories.ServiceFactory(owner=top_owner)\n factories.ServiceFactory(parent=top_service, owner=child_owner)\n factories.ServiceFactory(owner=top_owner)\n\n tasks.notify_services_owners()\n assert len(mailoutbox) == 2\n\n\ndef test_cleanup_service_requests():\n source = factories.ServiceFactory()\n move_request_a = factories.ServiceMoveRequestFactory(\n service=source,\n updated_at=timezone.now(),\n state=models.ServiceMoveRequest.REQUESTED,\n )\n move_request_b = factories.ServiceMoveRequestFactory(\n service=source,\n updated_at=timezone.now() + timezone.timedelta(seconds=1),\n state=models.ServiceMoveRequest.REQUESTED,\n )\n other_request = factories.ServiceMoveRequestFactory(\n state=models.ServiceMoveRequest.REQUESTED,\n )\n tasks.cleanup_service_requests()\n\n for request in [move_request_a, move_request_b, other_request]:\n request.refresh_from_db()\n\n assert move_request_a.state == models.ServiceMoveRequest.REJECTED\n assert move_request_b.state == models.ServiceMoveRequest.REQUESTED\n assert other_request.state == models.ServiceMoveRequest.REQUESTED\n\n\ndef test_find_memberships_in_staff():\n factories.ServiceFactory(slug='meta_other')\n service = factories.ServiceFactory()\n member1 = factories.ServiceMemberFactory(service=service)\n member2 = factories.ServiceMemberFactory(service=service)\n member3 = factories.ServiceMemberFactory(service=service)\n\n with patch('plan.staff.tasks.MembershipStaffImporter.get_objects') as get_objects:\n get_objects.return_value = [\n {\n 'id': member1.id,\n 'person': {'login': member1.staff.login},\n 'group': {'url': f'svc_{member1.service.slug}'},\n },\n {\n 'id': member1.id,\n 'person': {'login': member1.staff.login},\n 'group': {'url': f'svc_{member1.service.slug}_{member1.role.scope.slug}'},\n },\n {\n 'id': member3.id,\n 'person': {'login': member3.staff.login},\n 'group': {'url': f'svc_{member3.service.slug}'},\n },\n {\n 'id': member3.id,\n 'person': {'login': member3.staff.login},\n 'group': {'url': f'role_svc_{member3.service.slug}_{member3.role.scope.slug}'},\n },\n ]\n tasks.find_memberships_in_staff()\n\n for member, found in [\n (member1, True),\n (member2, False),\n (member3, True),\n ]:\n member.refresh_from_db()\n assert (member.found_in_staff_at is not None) == found\n\n\n@pytest.mark.parametrize('ok', [True, False])\ndef test_find_interactive_services(robot, ok, patch_tvm, responsible_role):\n service = factories.ServiceFactory()\n moves = []\n for i in range(10):\n moves.append(factories.ServiceMoveRequestFactory(service=service, completed_at=timezone.now()))\n with patch('plan.idm.manager.Manager._run_request') as run_request:\n response = Mock()\n response.ok = ok\n response.text = 'x'\n response.json.return_value = {}\n run_request.return_value = response\n tasks.find_interactive_services()\n\n for i in range(10):\n moves[i].refresh_from_db()\n if ok:\n assert moves[i].interactive_at is not None\n else:\n assert moves[i].interactive_at is None\n\n\ndef test_finish_all_service_move_requests():\n service_a = factories.ServiceFactory(parent=None, slug='A')\n service_b = factories.ServiceFactory(parent=None, slug='B')\n service_c = factories.ServiceFactory(parent=None, slug='C')\n requests = [\n factories.ServiceMoveRequestFactory(\n service=source,\n destination=destination,\n state=models.ServiceMoveRequest.PROCESSING_ABC,\n # чтобы письмо не собиралось\n from_creation=True,\n )\n for source, destination in [(service_b, service_a), (service_c, service_b)]\n ]\n tasks.finish_all_service_move_requests()\n for request in requests:\n request.refresh_from_db()\n assert request.state == models.ServiceMoveRequest.COMPLETED\n service_b.refresh_from_db()\n assert service_b.parent == service_a\n service_c.refresh_from_db()\n assert service_c.parent == service_b\n\n\n@pytest.mark.parametrize('async_mode', [False])\ndef test_move_service_abc_side(async_mode):\n \"\"\"Меняется родитель перемещаемого сервиса и обновляются уровни всех его потомков.\"\"\"\n service_a = factories.ServiceFactory(parent=None, slug='A')\n service_b = factories.ServiceFactory(parent=None, slug='B')\n factories.ServiceFactory(parent=service_b, slug='C')\n request = factories.ServiceMoveRequestFactory(\n service=service_b,\n destination=service_a,\n state=models.ServiceMoveRequest.PROCESSING_ABC,\n # чтобы письмо не собиралось\n from_creation=True,\n )\n with override_switch('async_move_service_abc_side', active=async_mode):\n tasks.move_service_abc_side(request.id)\n request.refresh_from_db()\n assert request.state == models.ServiceMoveRequest.COMPLETED\n service_b.refresh_from_db()\n assert service_b.parent == service_a\n with patch('plan.unistat.tasks.fix_service_closuretree_levels') as fix_service_closuretree_levels_mock:\n assert service_level_broken() == 0\n fix_service_closuretree_levels_mock.apply_async.assert_not_called()\n\n\ndef test_move_service_abc_side_to_root(service):\n \"\"\"Сервис перемещается на верхний уровень, уровни всех его потомков обновляются.\"\"\"\n factories.ServiceFactory(parent=service, slug='C')\n request = factories.ServiceMoveRequestFactory(\n service=service,\n destination=None,\n state=models.ServiceMoveRequest.PROCESSING_ABC,\n # чтобы письмо не собиралось\n from_creation=True,\n )\n tasks.move_service_abc_side(request.id)\n request.refresh_from_db()\n assert request.state == models.ServiceMoveRequest.COMPLETED\n service.refresh_from_db()\n assert service.parent is None\n with patch('plan.unistat.tasks.fix_service_closuretree_levels') as fix_service_closuretree_levels_mock:\n assert service_level_broken() == 0\n fix_service_closuretree_levels_mock.apply_async.assert_not_called()\n\n\ndef test_deep_disable_membership_inheritance():\n root = factories.ServiceFactory()\n factories.ServiceFactory(parent=root, membership_inheritance=True)\n node = factories.ServiceFactory(parent=root, membership_inheritance=True)\n factories.ServiceFactory(parent=node, membership_inheritance=True)\n factories.ServiceFactory(parent=node, membership_inheritance=False)\n services = (root, *root.get_descendants())\n\n assert any(s.membership_inheritance is True for s in services)\n call_command('deep_disable_membership_inheritance', service_id=root.id)\n for s in services:\n s.refresh_from_db()\n assert all(s.membership_inheritance is False for s in services)\n\n\ndef test_sync_department_members_state():\n active_state = models.ServiceMember.states.ACTIVE\n deprved_state = models.ServiceMember.states.DEPRIVED\n service = factories.ServiceFactory()\n department_deprived = factories.ServiceMemberDepartmentFactory(\n state=deprved_state,\n service=service,\n )\n member_should_be_deprived = factories.ServiceMemberFactory(\n from_department=department_deprived,\n state=active_state,\n service=service,\n )\n member_out_of_department = factories.ServiceMemberFactory(\n state=active_state,\n service=service,\n )\n department_active = factories.ServiceMemberDepartmentFactory(\n state=active_state,\n service=service,\n )\n member_should_stay_active = factories.ServiceMemberFactory(\n from_department=department_active,\n state=active_state,\n service=service,\n )\n\n tasks.sync_department_members_state()\n\n member_out_of_department.refresh_from_db()\n member_should_stay_active.refresh_from_db()\n\n assert member_should_stay_active.state == active_state\n assert member_out_of_department.state == active_state\n\n deprived_member = models.ServiceMember.all_states.get(pk=member_should_be_deprived.pk)\n assert deprived_member.state == deprved_state\n\n\n@pytest.mark.parametrize('tag_slug', settings.REVIEW_REQUIRED_TAG_SLUGS)\n@pytest.mark.parametrize('review_required', [True, False, None])\n@pytest.mark.parametrize('service_review_required', [True, False])\ndef test_update_service_review_policy(review_required_tags, review_required, service_review_required, tag_slug):\n service = factories.ServiceFactory()\n tag = models.ServiceTag.objects.get(slug=tag_slug)\n if service_review_required:\n service.tags.add(tag)\n\n with patch('plan.idm.nodes.Node.update') as node_update_mock:\n tasks.update_service_review_policy(service_slug=service.slug, review_required=review_required)\n\n node_update_mock.assert_has_calls([\n {'review_required': review_required is None and service_review_required or review_required}\n for _ in service.role_set.all()\n ])\n\n\n@pytest.mark.parametrize('tag_slug', settings.REVIEW_REQUIRED_TAG_SLUGS)\ndef test_check_service_review_policy_enabled(review_required_tags, tag_slug):\n fake_manager = object()\n tag = models.ServiceTag.objects.get(slug=tag_slug)\n\n class FakeFetchDataMethod:\n def __init__(self, results: list = None):\n self._results = results or []\n self.call_count = 0\n\n def __call__(self, *args, **kwargs):\n result = self._results[self.call_count]\n self.call_count += 1\n return result\n\n service = factories.ServiceFactory()\n\n sox_service = factories.ServiceFactory()\n sox_service.tags.add(tag)\n custom_roles = [\n factories.RoleFactory(service=service),\n factories.RoleFactory(service=sox_service),\n ]\n default_roles_count = models.Role.objects.filter(service=None).count()\n\n fake_fetch_responses = [\n {'review_required': bool(random.randint(0, 1))}\n for _ in range(len([role for role in custom_roles if role.service.review_required]) + default_roles_count)\n ]\n\n with patch('plan.api.idm.actions.idm_manager', return_value=fake_manager), \\\n patch(\n 'plan.idm.nodes.Node.fetch_data',\n new=FakeFetchDataMethod(fake_fetch_responses),\n ) as node_fetch_data_mock, \\\n patch('plan.idm.nodes.Node.update') as update_node_mock:\n tasks.check_service_review_policy_enabled()\n\n assert node_fetch_data_mock.call_count == \\\n len(fake_fetch_responses)\n update_node_mock.assert_has_calls([\n ((), {'manager': fake_manager, 'review_required': True})\n for fake_response in fake_fetch_responses if not fake_response['review_required']\n ])\n\n\n@pytest.mark.parametrize('diff_source', ['name', 'name_en', 'use_for_hr', 'leaf_code', 'parent'])\ndef test_sync_oebs_structure(data, yql_query_oebs_mock, diff_source):\n \"\"\"\n Проверяем синхронизацию данных с oebs выгрузкой в YT\n У одного сервиса сменили родителя, другой из выгрузки пропал\n Соответственно такие же изменения должны произойти у нас\n \"\"\"\n\n oebs_type = factories.ResourceTypeFactory(code=settings.OEBS_PRODUCT_RESOURCE_TYPE_CODE)\n factories.ServiceResourceFactory(\n service=data.service,\n resource=factories.ResourceFactory(type=oebs_type),\n type_id=oebs_type.id,\n state='granted',\n attributes={\n 'parent_oebs_id': 'GROUP_CODE',\n 'leaf_oebs_id': 'FAKE_ONE' if diff_source == 'leaf_code' else 'LEAF_CODE',\n },\n )\n\n data.service.name = 'some'\n data.service.name_en = 'some_en'\n data.service.use_for_group_only = True\n data.service.use_for_hr = False\n data.service.use_for_procurement = False\n data.service.use_for_revenue = True\n data.service.oebs_parent_id = 42\n\n if diff_source == 'name_en':\n deviation_reason = OEBS_DEVIATIONS_REASONS.NAME\n data.service.name_en = 'hello'\n elif diff_source == 'name':\n deviation_reason = OEBS_DEVIATIONS_REASONS.NAME\n data.service.name = 'hello'\n elif diff_source == 'use_for_hr':\n deviation_reason = OEBS_DEVIATIONS_REASONS.FLAG\n data.service.use_for_hr = True\n elif diff_source == 'leaf_code':\n deviation_reason = OEBS_DEVIATIONS_REASONS.RESOURCE\n else:\n deviation_reason = OEBS_DEVIATIONS_REASONS.PARENT\n\n data.metaservice.oebs_parent_id = 50\n data.metaservice.oebs_data = {\n 'smth': 'else',\n 'deviation_reason': 'flag'\n }\n data.service.save()\n data.metaservice.save()\n\n tasks.sync_oebs_structure()\n yql_query_oebs_mock.assert_called_once()\n\n data.service.refresh_from_db()\n data.metaservice.refresh_from_db()\n\n assert data.service.oebs_parent_id == 30\n assert data.service.oebs_data == {\n 'leaf_oebs_id': 'LEAF_CODE', 'name': 'some',\n 'name_en': 'some_en', 'oebs_parent_id': 30,\n 'parent_oebs_id': 'GROUP_CODE',\n 'use_for_group_only': True, 'use_for_hr': False,\n 'use_for_procurement': False,\n 'use_for_revenue': True,\n 'deviation_reason': deviation_reason,\n }\n\n assert data.metaservice.oebs_parent_id is None\n assert data.metaservice.oebs_data == {}\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/unit/services/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":40232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8390255993","text":"from flask import Flask, render_template, request\nfrom parcer import parcing_base_horoscope, parcing_personal_horoscope\nfrom scripts import random_img, zodiac_sign, name_zodiac, create_home\nfrom scripts.birthday import write_and_read_birthday\nfrom scripts.constructor import constructor_horoscope\nimport create_db\n\napp = Flask(__name__)\n\n\n@app.route('/home')\ndef home():\n zodiac_numb = 12\n images = create_home.template_home(zodiac_numb)\n\n return render_template('home.html', data=images)\n\n\n@app.route('/zodiac_sign//')\ndef gemini_data(sign, date):\n zodiac_name = name_zodiac.get_name_zodiac(sign)\n image = random_img.random_image_horoscope_base(sign)\n\n if date == 'today' or date == 'tomorrow':\n text = parcing_base_horoscope.get_horoscope_by_day_and_tomorrow(sign, date)\n\n if text is None:\n text = constructor_horoscope.create_horoscope(date)\n\n return render_template('zodiacs.html', texts=text, len_texts=len(text), name=zodiac_name,\n sign=sign)\n else:\n texts = parcing_base_horoscope.get_horoscope_by_week_and_month(sign, date)\n\n if texts is None:\n texts = constructor_horoscope.create_horoscope(date)\n\n return render_template('zodiacs.html', texts=texts, len_texts=len(texts), file=image,\n name=zodiac_name, sign=sign)\n\n\n@app.route('/home_filter/', methods=['POST', 'GET'])\ndef get_data_form(date):\n if request.method == 'POST':\n name = request.form.get('text')\n birthday = request.form.get('date')\n\n sign = zodiac_sign.get_sign_from_form(birthday)\n image_1, image_2 = random_img.random_image_horoscope_personal(sign)\n\n if date == 'today' or 'tomorrow':\n texts = parcing_personal_horoscope.get_personal_horoscope_by_day_and_tomorrow(sign, date)\n\n else:\n texts = parcing_personal_horoscope.get_personal_horoscope_by_week_and_month(sign, date)\n\n if texts is None:\n texts = constructor_horoscope.create_horoscope(date)\n\n write_and_read_birthday.write_birthday(name, birthday, sign)\n\n return render_template('personal_horoscope.html', name=name, texts=texts, len_texts=len(texts),\n file_1=image_1, file_2=image_2)\n else:\n name, birthday, sign = write_and_read_birthday.read_birthday()\n image_1, image_2 = random_img.random_image_horoscope_personal(sign)\n\n if date == 'today' or date == 'tomorrow':\n texts = parcing_personal_horoscope.get_personal_horoscope_by_day_and_tomorrow(sign, date)\n else:\n texts = parcing_personal_horoscope.get_personal_horoscope_by_week_and_month(sign, date)\n\n if texts is None:\n texts = constructor_horoscope.create_horoscope(date)\n\n return render_template('personal_horoscope.html', name=name, texts=texts, len_texts=len(texts),\n file_1=image_1, file_2=image_2)\n\n\n@app.route('/news')\ndef news():\n result = create_db.main_db()\n return render_template('news.html', result=result)\n\n\nif __name__ == \"__main__\":\n app.run(port=8080, debug=True)\n","repo_name":"IlyaCherevachenko/Web-site-Horoscope-","sub_path":"horoscope.py","file_name":"horoscope.py","file_ext":"py","file_size_in_byte":3171,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31394915413","text":"import json\nimport re\n\nimport pymorphy2\nfrom razdel import tokenize, sentenize\n\nmorph = pymorphy2.MorphAnalyzer()\nfrom natasha import (\n Segmenter,\n NewsEmbedding,\n NewsNERTagger,\n\n Doc\n)\nfrom collections import Counter\n\n\n# Текст\nwith open('ТЗ - короткое.txt', 'r', encoding='utf-8-sig') as file:\n text = file.read()\n# print(text)\n\n# Выделение ссылок, адресов, NE,\nurl_extract_pattern = \"https?:\\\\/\\\\/(?:www\\\\.)?[-a-zA-Z0-9@:%._\\\\+~#=]{1,256}\\\\.[a-zA-Z0-9()]{1,6}\\\\b(?:[-a-zA-Z0-9()@:%_\\\\+.~#?&\\\\/=]*)\"\ntext = re.sub(url_extract_pattern, '', text)\n\ndoc = Doc(text)\nsegmenter = Segmenter()\ndoc.segment(segmenter)\nemb = NewsEmbedding()\nner_tagger = NewsNERTagger(emb)\n\ndoc.tag_ner(ner_tagger)\nner_dict = {}\nfor i in doc.spans:\n ner_dict[i.text] = i.type\n\n# Токенизация текста\n\ntext_newline = text.split('\\n')\nsents = []\nfor part in text_newline:\n sents.append(list(sentenize(part))[0].text)\n\nsents = list(sentenize(text))\n# print(sents)\nfor sent in sents:\n tokens = list(tokenize(text))\n# print(tokens)\n\n# POS-taggingл\npos_list = []\nfor i in tokens:\n pos_list.append([i.text, str(morph.parse(i.text)[0].tag)[:4]])\nprint(pos_list)\n\nfor pair in pos_list:\n for word, tag in ner_dict.items():\n if pair[0] == word:\n pair[1] = tag\n\nwith open('POS_tagging.json', 'w', encoding='utf-8-sig') as file:\n json.dump(pos_list, file, indent=4, ensure_ascii=False)\n\n\n# выводим пос-тэггинг по парам\nwith open(\"POS_tagging.json\", \"r\", encoding='utf-8-sig') as read_file:\n data = json.load(read_file)\n\nex_tags = ['PNCT']\ntags = []\nfor i in range(len(data)-1):\n if data[i][1] in ex_tags or data[i+1][1] in ex_tags:\n pass\n else:\n # print(data[i], data[i+1])\n new_str = str(data[i][1] + ' ' + data[i+1][1])\n tags.append(new_str)\nc = Counter(tags)\n\n","repo_name":"tertiadecima/SRS-Russian-CFG","sub_path":"POS-tagging.py","file_name":"POS-tagging.py","file_ext":"py","file_size_in_byte":1891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43563098129","text":"# Create the new DataFrame: tracks\ntracks = billboard[['year', 'artist', 'track','time']]\n\n# Print info of tracks\nprint(tracks.info())\n\n# Drop the duplicates: tracks_no_duplicates\ntracks_no_duplicates = tracks.drop_duplicates()\n\n# Print info of tracks\nprint(tracks_no_duplicates.info())\n#-----------------------------\n# fill missing values \n# Calculate the mean of the Ozone column: oz_mean\noz_mean = airquality.Ozone.mean()\n\n# Replace all the missing values in the Ozone column with the mean\nairquality['Ozone'] = airquality.fillna(oz_mean)\n\n# Print the info of airquality\nprint(airquality.info())\n\n#---- assert -------------------\n\n# Assert that there are no missing values\nassert pd.notnull(ebola).all().all()\n\n# Assert that all values are >= 0\nassert (ebola >= 0).all().all()\n","repo_name":"JitenKumar/Data-Science-Practice-With-Python","sub_path":"Cleaning Data in Python/duplicates.py","file_name":"duplicates.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"8311051544","text":"import random\n\ndef random_gift(category):\n books = ['Чистый код', 'Совершенный код', 'Паттерны ООП']\n gadgets = ['Умные часы', 'Умные Весы', 'Робот пылесос']\n games = ['Village', 'Halo Infinite', 'Far Cry 6']\n\n if category =='игры':\n return random.sample(games, 1)[0]\n elif category == 'книги':\n return random.sample(books, 1)[0]\n elif category == 'гаджеты':\n return random.sample(gadgets, 1)[0]\n else:\n return 'Нет подарков'\n\nprint(random_gift('игры'))\nprint(random_gift('книги'))\nprint(random_gift('гаджеты'))\n","repo_name":"W83w/Course-2-Django","sub_path":"Г1 5.1.5Подаркипотипу/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6366555376","text":"import copy\nfrom typing import TypeVar\n\nfrom torch.nn import Module, Parameter\n\nT = TypeVar(\"T\")\n\n\ndef module_only_deepcopy(obj: T, memo=None) -> T:\n \"\"\"Recursively copy but only modules, not the weights.\n In the return value, all weights are still shared with those in `obj`.\"\"\"\n\n memo = {}\n\n def recursive_scan_parameters(obj_):\n # Don't recurse down primitive types\n if isinstance(obj_, (int, float, bool, complex, str, bytes)):\n return\n # Additionally share all buffers of Module. For example, this accounts for\n # running_{mean|var} in BatchNorm.\n if isinstance(obj_, Module):\n buffers = obj_.__dict__.get(\"_buffers\")\n for buffer in buffers.values():\n memo[id(buffer)] = buffer\n # Share all parameters.\n if isinstance(obj_, Parameter):\n memo[id(obj_)] = obj_\n # Walk down all other types.\n elif isinstance(obj_, dict):\n for k in obj_.keys():\n recursive_scan_parameters(k)\n for v in obj_.values():\n recursive_scan_parameters(v)\n elif isinstance(obj_, (list, tuple)):\n for x in obj_:\n recursive_scan_parameters(x)\n elif hasattr(obj_, \"__dict__\"):\n for x in obj_.__dict__.values():\n recursive_scan_parameters(x)\n\n # Populate `memo`, and then deepcopy with `memo` so that things in memo are not copied.\n recursive_scan_parameters(obj)\n # noinspection PyArgumentList\n copied = copy.deepcopy(obj, memo)\n return copied\n","repo_name":"vzyrianov/hpvm-autograd","sub_path":"hpvm/projects/predtuner/predtuner/approxes/_copy.py","file_name":"_copy.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33203256990","text":"import maya.cmds as cmds\r\nimport maya.mel as mel\r\nfrom sys import exit\r\n\r\n\r\n#VARIABLES FOR USER TO ADJUST\r\nconstraintType = \"parent\" # \"orient\" for rotation, \"point\" for translation, \"parent\" for both\r\nbakeInterval = 1 # You can choose to bake on 1s, 2s, 3s, whatever interval you set\r\nsmartBake = False # Choose between True/False, on whether to use the Smart Bake option\r\nsmartBakeIntensity = 5 #This is the intensity of the smart bake. Bigger number means less keys but less accurate animation\r\n\r\n\r\n#FUNCTIONS\r\ndef assistMessage(message, time, toExit):\r\n #POPS UP A MESSAGE ON THE USER'S SCREEN TO INFORM THEM OF SOMETHING\r\n cmds.inViewMessage(amg=message, pos='midCenter', fade=True, fst=time, ck=True)\r\n if toExit == True:\r\n exit()\r\n \r\n \r\ndef hideAttributes(type, *controls):\r\n#HIDES UNNECESSARY ATTRIBUTES ON THE CONTROLS \r\n for item in controls:\r\n for attr in [\".\" + type + \"X\", \".\" + type + \"Y\", \".\" + type + \"Z\"]:\r\n cmds.setAttr(item + attr, k=False, cb=False)\r\n\r\n\r\ndef setConstraint(constraintType, parent, child, translateCurves, rotateCurves):\r\n #CONSTRAINT FUNCTION\r\n if constraintType == \"parent\":\r\n constraint = cmds.parentConstraint(parent, child, skipTranslate = translateCurves, skipRotate = rotateCurves)[0]\r\n elif constraintType == \"orient\":\r\n constraint = cmds.orientConstraint(parent, child, skip = rotateCurves)[0]\r\n elif constraintType == \"point\":\r\n constraint = cmds.pointConstraint(parent, child, skip = translateCurves)[0]\r\n \r\n return constraint\r\n\r\n\r\ndef getConstraintAttribute(constraintType):\r\n #SETS A KEY ON THE START AND END OF THE TIMELINE, SO THAT WE ENSURE THERE'S A BLEND NODE ALL THE TIME. IF THERE'S NO KEY BEFORE ADDING THE SETUP, THE SCRIPT WON'T APPLY A SWITCH ON THE BLEND NODE\r\n if constraintType == \"orient\":\r\n tempAttribute = \"rotate\"\r\n elif constraintType == \"point\":\r\n tempAttribute = \"translate\"\r\n elif constraintType == \"parent\":\r\n tempAttribute = [\"translate\", \"rotate\"]\r\n return tempAttribute\r\n \r\ndef getLockedCurves(obj, attribute):\r\n #GETS A LIST OF THE LOCKED CURVES FOR EITHER TRANSLATE, ROTATE OR SCALE, SO THE CONSTRAINTS KNOW WHICH CURVES TO AVOID\r\n curves = {\".{0}X\".format(attribute):\"x\", \".{0}Y\".format(attribute):\"y\", \".{0}Z\".format(attribute):\"z\"}\r\n for curve in curves.copy():\r\n if cmds.getAttr(obj+curve, l=True) == False:\r\n curves.pop(curve)\r\n curves = list(curves.values())\r\n return curves\r\n \r\ndef setup(obj, tempControl, constraintType, timelineStart, timelineEnd, translateCurves, rotateCurves):\r\n #ADDS A LOCATORS ONTO EVERY SELECTION AND SWITCHES IT TO WORLD SPACE BY REVERSING THE CONSTRAINTS\r\n cmds.currentTime(timelineStart)\r\n cmds.matchTransform(tempControl, obj)\r\n original_RO = cmds.getAttr(obj + \".rotateOrder\") #STORES THE ROTATION ORDER OF THE CURRENT CONTROL, TO BE ASSIGNED TO THE TEMP CONTROLS\r\n cmds.setAttr(tempControl + \".rotateOrder\", original_RO)\r\n matchScale(obj, tempControl)\r\n setConstraint(constraintType, obj, tempControl, translateCurves, rotateCurves)\r\n cmds.select(tempControl)\r\n cmds.bakeResults(t=(timelineStart, timelineEnd), pok=True, simulation=False, sr = [smartBake,smartBakeIntensity], sampleBy=bakeInterval)\r\n if smartBake == True:\r\n cmds.keyTangent(tempControl, e=True, itt=\"auto\", ott=\"auto\", t=(timelineStart, timelineEnd))\r\n cmds.filterCurve(tempControl + \".translate\", tempControl + \".rotate\")\r\n cmds.delete(cmds.listRelatives(type=\"constraint\"))\r\n\r\n\r\ndef createControl(name):\r\n #CREATES THE SHAPE OF THE CONTROL\r\n tempControl = cmds.curve(n=name, degree=1, point=[[-1, 0, -0], [1, 0, 0], [0,0,0], [0,0,-1], [0, 0, 1], [0,0,0], [0,-1,0], [0,1,0]])\r\n\r\n cmds.setAttr(tempControl + \".lineWidth\", 3)\r\n cmds.setAttr(tempControl + \".overrideEnabled\", 1)\r\n cmds.setAttr(tempControl + \".overrideColor\", 6)\r\n return tempControl\r\n \r\ndef matchScale(parent, children, scale=True):\r\n #SCALE UP AN OBJECT TO ANOTHER ONE'S BOUNDING BOX SCALE, INCASE IT'S BEEN FREEZE-TRANSFORMED. THIS WAY THE USER DOESN'T HAVE TO MANUALLY ADJUST THE SIZE\r\n children = cmds.ls(children, flatten=True)\r\n parentShapeNode = cmds.listRelatives(parent, shapes=True)[0]\r\n \r\n xMin, yMin, zMin, xMax, yMax, zMax = cmds.exactWorldBoundingBox(parentShapeNode)\r\n parentDistanceX, parentDistanceY, parentDistanceZ = [xMax-xMin, yMax-yMin, zMax-zMin]\r\n \r\n #result=[]\r\n for child in children:\r\n xMin, yMin, zMin, xMax, yMax, zMax = cmds.exactWorldBoundingBox(child)\r\n childDistanceX, childDistanceY, childDistanceZ = [xMax-xMin, yMax-yMin, zMax-zMin]\r\n \r\n #WE QUERY THE ORIGINAL SCALE OF THE LOCATOR \r\n originalX, originalY, originalZ = cmds.xform(child, q=True, s=True, r=True)\r\n \r\n \r\n divisionX, divisionY, divisionZ = [parentDistanceX/childDistanceX, parentDistanceY/childDistanceY, parentDistanceZ/childDistanceZ]\r\n \r\n #WE GET THE FINAL SCALE HERE, WE TAKE THE LONGEST NUMBER AND APPLY THAT TO ALL SCALE AXIS\r\n largestAxis = max([originalX*divisionX, originalY*divisionY, originalZ*divisionZ]) * 2\r\n newScale = [largestAxis, largestAxis, largestAxis]\r\n \r\n #this part is for return the information outside the function\r\n #[result.append(i) for i in newScale]\r\n\r\n if scale:\r\n cmds.xform(child, scale=newScale)\r\n \r\n\r\ndef add(a,b):\r\n result = a + b\r\n return result\r\ndef subtract(a,b):\r\n result = a - b\r\n return result \r\n \r\n\r\n\r\ndef divideInfluence(curve, timelineStart, timelineEnd, operator, value):\r\n #USUALLY APPLIED ON AN EMPTY SPACE WHERE THE RANGE ISN'T TOUCHING ANY PRE-EXISTING RANGES, SO IT HANDLES BOTH SIDES - THE START AND END\r\n cmds.setKeyframe(curve, t=(timelineStart, timelineEnd), value=value) \r\n cmds.setKeyframe(curve, t=(timelineStart-1, timelineEnd+1), value=operator(value,1)) \r\n\r\ndef adjustInfluence(curve, frame, offset, operator, value):\r\n #USUALLY USED WHEN THE RANGE WE'RE APPLYING IS RIGHT NEXT TO THE END OR START OF AN EXISTING RANGE, SO WE MERGE ONE PART, AND ONLY ADD KEYS ON THE OTHER PART. \r\n cmds.setKeyframe(curve, t=(frame), value=value) \r\n cmds.setKeyframe(curve, t=(frame + offset), value=operator(value,1)) \r\n\r\n\r\n\r\ndef applyInfluenceSwitch(curve, selectionShapeNode, timelineStart, timelineEnd, operator, value, storedFrameValue):\r\n #FUNCTION THAT HANDLES APPLYING A SWITCH ON THE CONSTRAINT INFLUENCE, AS WELL AS THE VISIBILITY \r\n \r\n keyframes = cmds.keyframe(curve, q=True)\r\n storedFrames = []\r\n\r\n #WE CHECK TO SEE WHAT EXISTING KEYFRAMES HAVE THE VALUE OF 0 OR 1 (DEPENDING ON IF YOU'VE CHOSEN THE VISIBILITY OR BLEND INDEX CURVES), AND STORE THEM IN A LIST SO THAT WE CAN PAIR THEM UP IN A DICTIONARY\r\n if keyframes != None:\r\n for keyframe in keyframes:\r\n if cmds.keyframe(curve, q=True, t=(keyframe, keyframe), eval=True)[0] == storedFrameValue:\r\n storedFrames.append(keyframe)\r\n \r\n pairedFrames = {storedFrames[frame]: storedFrames[frame + 1] for frame in range(0, len(storedFrames), 2)}\r\n timelineRange = []\r\n \r\n #IF THE TIMELINE RANGE IS IN-BETWEEN 1 OR 2 PAIRS, WE STORE THE START OR THE END OF THE TIMELINE RANGE IN A LIST FOR LATER COMPARISON \r\n for start,end in pairedFrames.items(): \r\n if start - 1 <= timelineStart <= end + 1:\r\n timelineRange.append(timelineStart)\r\n \r\n if start - 1 <= timelineEnd <= end + 1: \r\n timelineRange.append(timelineEnd)\r\n \r\n \r\n for start,end in pairedFrames.items():\r\n #IF OUR TIMELINE RANGE OVERSHADOWS A GIVEN PAIRING OR IS IN-BETWEEN IT, THE SCRIPT WILL ABORT AND THE LOCATOR WON'T BE ADDED\r\n if timelineStart <= int(start) and int(end) <= timelineEnd or int(start) <= timelineStart and timelineEnd <= int(end) or timelineStart < int(end) and int(start) < timelineEnd :\r\n assistMessage(\"Error: This locator overlaps with another locator on the timeline. \", 5000, True)\r\n \r\n confirmation = []\r\n #IF TIMELINE RANGE IS 0, THAT MEANS THAT OUR RANGE IS NOT INTERSECING WITH ANY EXISTING PAIRINGS AND CAN BE APPLIED NORMALLY\r\n if len(timelineRange) == 0:\r\n confirmation = 1\r\n divideInfluence(curve, timelineStart, timelineEnd, operator, value)\r\n \r\n #IF WE'RE TRYING TO FIT A TIMELINE RANGE INBETWEEN 2 OTHER RANGES, THIS CODE GETS EXECUTED \r\n \r\n elif len(timelineRange) == 2:\r\n cmds.cutKey(curve, t=(timelineStart, timelineStart - 1))\r\n cmds.cutKey(curve, t=(timelineEnd, timelineEnd + 1))\r\n confirmation = 1\r\n else:\r\n #IF TIMELINE RANGE IS 1, WE CHECK TO SEE IF OUR TIMELINESTART AND TIMELINEEND ARE TOUCHING THE VALUE STORED IN TIMELINERANGE. IF IT IS TOUCHING, WE MERGE THE POINT THEY TOUCH AND EXTEND UP TOWARDS THE OTHER END\r\n for start,end in pairedFrames.items():\r\n if timelineStart == int(end) + 1:\r\n confirmation = 1\r\n cmds.cutKey(curve, t=(timelineStart - 1, timelineEnd))\r\n adjustInfluence(curve, timelineEnd, 1, operator, value )\r\n elif int(start) - 1 == timelineEnd :\r\n confirmation = 1\r\n cmds.cutKey(curve, t=(timelineStart, timelineEnd + 1))\r\n adjustInfluence(curve, timelineStart, -1, operator, value )\r\n \r\n #IF TIMELINE RANGE IS 1 OR 2, BUT IT'S OVERLAPPING RATHER THAN TOUCHING ON ONE OF THE ENDS, WELL THEN WE PROCLAIM THAT THE USER CAN'T APPLY THE SETUP\r\n if confirmation != 1 and selectionShapeNode == curve:\r\n assistMessage(\"Error: This locator overlaps with another locator on the timeline. \", 5000, True)\r\n\r\n\r\n\r\n\r\naTimeSlider = mel.eval('$tmpVar=$gPlayBackSlider')\r\ntimeRange = cmds.timeControl(aTimeSlider, q=True, rangeArray=True)\r\n\r\nif 2 < (timeRange[1] - timeRange[0]):\r\n specificTimelineMode = True\r\n timelineStart = timeRange[0]\r\n timelineEnd = timeRange[1]\r\nelse:\r\n specificTimelineMode = False\r\n #QUERIES THE START AND END FRAME OF THE CURRENT TIMELINE\r\n timelineStart = cmds.playbackOptions(min=True, q=True)\r\n timelineEnd = cmds.playbackOptions(max=True, q=True)\r\n\r\n#PREVENTS THE USER FROM APPLYING THE SETUP IN A NEGATIVE RANGE TIMELINE\r\nif timelineStart < 0 or timelineEnd <0:\r\n assistMessage(\"Error: You can't apply a locator setup on a negative time-range \", 5000, True)\r\n\r\nselection = cmds.ls(sl=True)\r\n\r\n\r\n\r\n#ADVISES THE USER TO SELECT SOMETHING BEFORE RUNNING THIS SCRIPT\r\nif len(selection) == 0:\r\n assistMessage(\"You need to select at least 1 object to turn into world space\", 4000, True)\r\nelse: \r\n for obj in selection:\r\n #PREVENTS THE USER FROM APPLYING A LOCATOR SETUP ON TOP OF AN EXISTING LOCATOR\r\n if \"Petar3D\" in obj:\r\n assistMessage(\"Error: You can't stack locator setups\", 4000, True)\r\n \r\n #CHECKS TO SEE IF THERE'S ALREADY AN ORIENT OR A POINT CONSTRAINT, SO THAT IT DOESN'T TRY TO APPLY A PARENT CONSTRAINT, AND VICE VERSA\r\n if cmds.listConnections(obj, c=True) != None:\r\n for item in cmds.ls(\"*_Petar3D_worldSpaceLocator*\", tr=True):\r\n if obj + \"_Petar3D_worldSpaceLocator_point\" in item and constraintType == \"parent\" or obj + \"_Petar3D_worldSpaceLocator_orient\" in item and constraintType == \"parent\":\r\n assistMessage(\"Error: You already have an orient/point point constraint applied, you can't apply a parent constraint \", 5000, True)\r\n if obj + \"_Petar3D_worldSpaceLocator_parent\" in item and constraintType == \"orient\" or obj + \"_Petar3D_worldSpaceLocator_parent\" in item and constraintType == \"point\":\r\n assistMessage(\"Error: You already have a parent cosntraint applied, you can't applied an orient/point constraint \", 5000, True)\r\n\r\n\r\n #CHECKS TO SEE IF THERE'S ALREADY A NIS-TYPE OF LOCATOR SETUO, AND IF SO IT DOESN'T LET THE USER APPLY AN IFS-TYPE LOCATOR, AND VICE VERSA\r\n for item in cmds.ls(obj + \"_Petar3D*\", tr=True):\r\n if \"NIS\" in item and specificTimelineMode == True:\r\n assistMessage(\"Error: You already have a locator with overall influence over this selection. You can't mix overall with partial influence locators\", 4000, True)\r\n elif \"IFS\" in item and specificTimelineMode == False:\r\n assistMessage(\"Error: You already have a locator with partial influence over this selection. You can't mix partial with overall influence locators\", 4000, True)\r\n \r\n #GETS A LIST OF ALL THE LOCKED CURVES SO THAT THE CONSTRAINTS KNOW WHICH CURVES TO SKIP OVER\r\n translateCurves = getLockedCurves(obj, \"translate\") \r\n rotateCurves = getLockedCurves(obj, \"rotate\")\r\n \r\n #WE CHECK TO SEE IF ALL ROTATE AND TRANSLATE CHANNELS ARE LOCKED. IF THEY ARE, THERE'S NO POINT IN APPLYING THIS SCRIPT\r\n if len(translateCurves + rotateCurves) != 6: \r\n \r\n #SWITCHES THE VISIBILITY ON THE ORIGINAL SELECTION,\r\n selectionShapeNode = cmds.listRelatives(obj, shapes=True, children=True)[0] \r\n if specificTimelineMode:\r\n applyInfluenceSwitch(selectionShapeNode + \".v\", selectionShapeNode + \".v\", timelineStart, timelineEnd, add, 0, 0)\r\n else:\r\n cmds.setAttr(selectionShapeNode + \".v\", 0)\r\n \r\n #CREATES THE TEMP LOCATOR \r\n if specificTimelineMode:\r\n tempControl = createControl(obj + \"_Petar3D_worldSpaceLocator_{0}_IFS_{1}_{2}\".format(constraintType, int(timelineStart), int(timelineEnd))) \r\n else:\r\n tempControl = createControl(obj + \"_Petar3D_worldSpaceLocator_{0}_NIS\".format(constraintType)) \r\n \r\n #POSITIONS THE LOCATOR TO THE ORIGINAL SELECTION AND BAKES IT DOWN\r\n setup(obj, tempControl, constraintType, timelineStart, timelineEnd, translateCurves, rotateCurves) \r\n locatorShapeNode = cmds.listRelatives(tempControl, shapes=True, children=True)[0] \r\n \r\n hideAttributes(\"scale\", tempControl)\r\n cmds.lockNode(tempControl, l=True)\r\n \r\n #VISIBILITY SWITCH FOR LOCATOR\r\n if specificTimelineMode:\r\n applyInfluenceSwitch(locatorShapeNode + \".v\", selectionShapeNode + \".v\", timelineStart, timelineEnd, subtract, 1, 0)\r\n \r\n \r\n #CHECKS WHICH ATTRIBUTE TO PLACE THE INITIAL KEYS ON\r\n tempAttribute = getConstraintAttribute(constraintType)\r\n \r\n #CHECKS TO SEE IF THE ORIGINAL CONTROL HAS ANY KEYS ON ITS CURVES ALREADY, AND IF NOT IT PLACES THEM TO ACTIVATE THE BLEND INDEX\r\n if cmds.keyframe(selection, at =tempAttribute, q=True) == None:\r\n cmds.setKeyframe(obj, t=(timelineStart, timelineEnd), at=tempAttribute) \r\n else:\r\n cmds.setKeyframe(obj, t=(timelineStart, timelineEnd), at=tempAttribute, pcs=True, i=True) \r\n \r\n \r\n #LOCATOR CONSTRAINT SECTION \r\n constraint = setConstraint(constraintType, tempControl, obj, translateCurves, rotateCurves)\r\n #IF THE CONSTRAINT TYPE IS ORIENT, WE APPLY A REVERSE POINT CONSTRAINT\r\n if constraintType == \"orient\":\r\n pointConstraint = setConstraint(\"point\", obj, tempControl, translateCurves, rotateCurves) \r\n \r\n #IF THE RIG IS REFERENCED, WE STORE THE NAME OF THE TEMP LOCATOR WITHOUT THE NAMESPACE, BECAUSE THE COSNTRAINT WE'LL INFLUENCE DON'T HAVE THE NAMESPACE INSIDE\r\n if cmds.referenceQuery(obj, isNodeReferenced=True) or \":\" in obj: \r\n tempControl = tempControl.split(\":\")[1]\r\n \r\n #WE'RE TRYING TO FIND THE INDEX AT THE END OF THE CONSTRAINT'S WEIGHT ATTRIBUTE. BECAUSE THERE COULD BE MANY CONSTRAINTS APPLIED ON THE SAME OBJECT, WE CAN'T ALWAYS KNOW WHAT THAT NUMBER WILL BE\r\n for item in cmds.listConnections(constraint, c=True): \r\n if \"{0}.{1}W\".format(constraint, tempControl) in item:\r\n constraintIndex = item[-1:]\r\n \r\n #THE CONSTRAINT HAVE A NUMBER AT THE END, WE STORE THIS NUMBER IN A VARIABLE SO WE KNOW WHICH NUMBER TO ATTACHA WHEN WE INFLUENCE THE BLEND NODE\r\n if specificTimelineMode:\r\n applyInfluenceSwitch(\"{0}.{1}W{2}\".format(constraint, tempControl, constraintIndex), selectionShapeNode + \".v\", timelineStart, timelineEnd, subtract, 1, 0) \r\n blendIndex = constraint[-1:] \r\n \r\n #BLEND NODE SWITCH SECTION \r\n if specificTimelineMode:\r\n applyInfluenceSwitch(\"{0}.blend{1}{2}\".format(obj, constraintType.capitalize(), blendIndex), selectionShapeNode + \".v\", timelineStart, timelineEnd, subtract, 1, 1)\r\n \r\n #GIVES THIS ERROR IF ALL 6 CURVES ARE LOCKED AND THERE'S NO POINT IN APPLYING THE SCRIPT\r\n else:\r\n assistMessage(\"Error: All translate and rotate curves on this selection are locked - {0} \".format(obj), 4000, False)\r\n ","repo_name":"PetarPehchevski3D/World_Space_Conversion_Maya_Python_Script","sub_path":"World_Space_Conversion - ApplyLocator.py","file_name":"World_Space_Conversion - ApplyLocator.py","file_ext":"py","file_size_in_byte":17455,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"11864194462","text":"\"\"\"empty message\n\nRevision ID: 5cef6dbead0a\nRevises: 1beffd33a2f2\nCreate Date: 2023-05-16 01:57:32.993552\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '5cef6dbead0a'\ndown_revision = '1beffd33a2f2'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('collaborator',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('user_id', sa.Integer(), nullable=False),\n sa.Column('project_id', sa.Integer(), nullable=False),\n sa.ForeignKeyConstraint(['project_id'], ['project.id'], ),\n sa.ForeignKeyConstraint(['user_id'], ['user.id'], ),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('collaborator')\n # ### end Alembic commands ###\n","repo_name":"olaracode/wave-api","sub_path":"migrations/versions/5cef6dbead0a_.py","file_name":"5cef6dbead0a_.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34721127221","text":"\"\"\"\nPython et la POO\n\"\"\"\nfrom enum import Enum\nfrom abc import ABC, abstractmethod\n\nclass TypeEvaluation(Enum):\n CONTROLE = 1\n EXAMEN = 2\n\nclass Module:\n \"\"\"\n Permet de mémoriser les informations sur un module\n \"\"\"\n def __init__(self, id=None, nom_module=None, volume_horaire=None, coefficient=None):\n \"\"\"Initialiseur de la classe Module \"\"\"\n self.id = id\n self.nom_module = nom_module\n self.volume_horaire = volume_horaire\n self.coefficient = coefficient\n \n def __str__(self):\n return f\"Id: {self.id}\\nNom: {self.nom_module} \\\n \\nVolume horaire: {self.volume_horaire} \\\n \\nCoefficient: {self.coefficient}\"","repo_name":"Eduildo/Base-python","sub_path":"gestion_etudiant/Modules/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"fr","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"42656578893","text":"from datetime import datetime\nfrom typing import Optional, cast\n\nimport torch\nfrom rich import inspect, print\nfrom transformers import logging as t_logging\n\nimport predict\nimport preprocess\nimport tune\nfrom params import PARAMS\nimport models\nimport logging\n\n\nimport typer\n\napp = typer.Typer()\n\n\n@app.command(help=\"Train model on the `train` dataset.\")\ndef train(\n pretrain: bool = typer.Option(False, help=\"Whether to include pretrain step.\"),\n save_model: Optional[str] = typer.Option(\n None,\n \"--save\",\n help=\"Whether to save the trained model and its name (dist/model.().(timestamp).pt).\",\n ),\n context: int = typer.Option(\n 9, help=\"How many utterances are included in the question as context.\"\n ),\n freeze_layers: int = typer.Option(\n 9, help=\"How many BERT layers are freezed during (pre)training.\"\n ),\n data_size: int = typer.Option(1500, help=\"Size of training data.\"),\n learning_rate: float = typer.Option(\n 5e-5, \"--lr\", help=\"Learning rate of training step.\"\n ),\n batch_size: int = typer.Option(16, help=\"Batch size of training step.\"),\n):\n PARAMS.BATCH_SIZE = batch_size\n PARAMS.CONTEXT_LENGTH = context\n PARAMS.DATA_SIZE = data_size\n PARAMS.FREEZE_LAYERS = freeze_layers\n PARAMS.LEARNING_RATE = learning_rate\n\n timestamp = datetime.now().strftime(\"%m%dT%H%M%S\")\n\n # Load pretrained BERT from Huggingface Hub\n tokenizer, bert = tune.get_pretrained()\n model = models.ModelMultipleChoice(cast(models.BertModel, bert))\n\n # A pretrain step on a next sentence prediction task\n if pretrain:\n dataloader_pretrain = preprocess.get_nsp_dataloader(tokenizer=tokenizer)\n model = tune.pretrain_on_nsp(\n model, dataloader=dataloader_pretrain, device=torch.device(0)\n )\n\n dataloader_train = preprocess.get_dataloader(\"train\", tokenizer=tokenizer)\n dataloader_valid = preprocess.get_dataloader(\"valid\", tokenizer=tokenizer)\n\n # Train (Finetune)\n model = tune.train(\n model=model,\n dataloader=dataloader_train,\n device=torch.device(0),\n dataloader_eval=dataloader_valid,\n )\n\n timestamp_to = datetime.now().strftime(\"%m%dT%H%M%S\")\n\n if save_model is not None:\n torch.save(model, f\"models/model.{save_model}.{timestamp}.{timestamp_to}.pt\")\n\n\n@app.command(help=\"Evaluate model with `valid` dataset.\")\ndef eval(\n name: str,\n valid_size: Optional[int] = typer.Option(\n None,\n help=\"Sampling amount of validation on samples. Leave `None` for no sampling.\",\n ),\n):\n print(f\"[blue bold]eval: [underline]{name}\")\n PARAMS.VALID_SIZE = valid_size\n\n tokenizer, model = tune.get_pretrained(name)\n dataloader = preprocess.get_dataloader(\"valid\", tokenizer=tokenizer)\n\n # Evaluate the model\n model = tune.eval(model, dataloader=dataloader, device=torch.device(0))\n\n\n@app.command(help=\"Predict labels on the `test` dataset.\")\ndef test(name: str):\n print(f\"[green bold]test: [underline]{name}\")\n tokenizer, model = tune.get_pretrained(name)\n dataloader = preprocess.get_test_dataloader(tokenizer=tokenizer)\n\n with open(\"data/test_label.json\", \"w\") as buf:\n predict.predict(\n model, buffer=buf, dataloader=dataloader, device=torch.device(0)\n )\n\n\nif __name__ == \"__main__\":\n # Suppress `huggingface` warnings\n t_logging.set_verbosity_error()\n logging.getLogger(\"transformers.tokenization_utils\").setLevel(logging.ERROR)\n\n # CLI entrance\n app()\n","repo_name":"Yixuan-Wang/datamine-xiangsheng","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44181286943","text":"from dataclasses import dataclass\nimport sys\nimport os\ncurrent_dir = os.path.dirname(__file__)\nsys.path.append(current_dir)\n\nfrom custom_classifier import _CustomClassifier\n# from SBMLKinetics.common.simple_sbml import SimpleSBML\n# from SBMLKinetics.common.reaction import Reaction\nfrom typing import List, Dict, Optional\nfrom SBMLKinetics.common.simple_sbml import SimpleSBML\nimport util\nfrom results import Results\n\nimport antimony\nimport libsbml\nimport os\nimport re\nimport sympy as sp\n\nfrom typing import List, Any\n\nZERO = \"ZERO\"\nUNDR1 = \"UNDR1\"\nUNDR2 = \"UNDR2\"\nUNDR3 = \"UNDR3\"\nUNDR_A1 = \"UNDR-A1\"\nUNDR_A2 = \"UNDR-A2\"\nUNDR_A3 = \"UNDR-A3\"\nBIDR11 = \"BIDR11\"\nBIDR12 = \"BIDR12\"\nBIDR21 = \"BIDR21\"\nBIDR22 = \"BIDR22\"\nBIDR_A11 = \"BIDR-A11\"\nBIDR_A12 = \"BIDR-A12\"\nBIDR_A21 = \"BIDR-A21\"\nBIDR_A22 = \"BIDR-A22\"\nMM = \"MM\"\nMM_CAT = \"MMcat\"\nAMM = \"AMM\"\nIMM = \"IMM\"\nRMM = \"RMM\"\nRMM_CAT = \"RMMcat\"\nHILL = \"Hill\"\n\nNON_MM_KEYS = [\n ZERO, UNDR1, UNDR2, UNDR3, UNDR_A1, UNDR_A2, UNDR_A3,\n BIDR11, BIDR12, BIDR21, BIDR22, BIDR_A11, BIDR_A12, BIDR_A21, BIDR_A22\n]\n\nMM_KEYS = [MM, MM_CAT, AMM, IMM, RMM, RMM_CAT]\n\nUNDR_KEYS = [UNDR1, UNDR2, UNDR3]\n\nUNDR_A_KEYS = [UNDR_A1, UNDR_A2, UNDR_A3]\n\nBIDR_ALL_KEYS = [BIDR11, BIDR12, BIDR21, BIDR22,\n BIDR_A11, BIDR_A12, BIDR_A21, BIDR_A22]\n\nMM_CAT_KEYS = [MM_CAT, AMM, IMM, RMM_CAT]\n\nUNDR_SBOS = [41, 43, 44, 45, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 140, 141, 142, 143, 144, 145, 146, 163, 166, 333, 560, 561, 562, 563, 564, 430, 270, 458,\n 275, 273, 379, 440, 443, 451, 454, 456, 260, 271, 378, 387, 262, 265, 276, 441, 267, 274, 444, 452, 453, 455, 457, 386, 388, 442, 277, 445, 446, 447, 448, 266, 449, 450]\n\nUNDR_A_SBOS = [41, 43, 44, 45, 47, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,\n 140, 141, 142, 143, 144, 145, 146, 163, 166, 333, 560, 561, 562, 563, 564]\n\nBI_SBOS = [42, 69, 78, 88, 109, 646, 70, 71, 74, 79, 80, 81, 84, 89, 99, 110, 120, 130, 72, 73, 75, 76, 77, 82, 83, 85, 86, 87, 90, 91, 92, 95, 100, 101, 102, 105, 111, 112,\n 113, 116, 121, 122, 123, 126, 131, 132, 133, 136, 93, 94, 96, 97, 98, 103, 104, 106, 107, 108, 114, 115, 117, 118, 119, 124, 125, 127, 128, 129, 134, 135, 137, 138, 139]\n\nMM_SBOS = [28, 29, 30, 31, 199]\n\nMM_CAT_SBOS = [28, 29, 30, 31, 199, 430, 270, 458, 275, 273, 379, 440, 443, 451, 454, 456, 260, 271, 378, 387,\n 262, 265, 276, 441, 267, 274, 444, 452, 453, 455, 457, 386, 388, 442, 277, 445, 446, 447, 448, 266, 449, 450]\n\nHILL_SBOS = [192, 195, 198]\n\n\n@dataclass\nclass ReactionData:\n reaction_id: str\n kinetics: str\n kinetics_sim: str\n reactant_list: List[str]\n product_list: List[str]\n species_in_kinetic_law: List[str]\n parameters_in_kinetic_law: List[str]\n ids_list: List[str]\n sorted_species: List[str]\n boundary_species: List[str]\n parameters_in_kinetic_law_only: List[str]\n compartment_in_kinetic_law: List[str]\n is_reversible: bool\n sbo_term: int\n codes: List[int]\n\n\nclass Analyzer:\n \"\"\"\n The Analyzer class analyzes SBML models to check if the rate laws are following \n some rules. It uses sympy for processing mathematical symbol expressions, libsbmlpython \n for processing SBML models, and SBMLKinetics for analyzing SBML models and classifying \n rate laws. The class can return errors and warnings based on the specified checks.\n \"\"\"\n\n def __init__(self, model_str: str, rate_law_classifications_path: str = None):\n \"\"\"\n Initializes the Analyzer class.\n\n Args:\n model_str (str): Path to the model file, or the string representation of model.\n rate_law_classifications_path (str): Path to the rate law classification file.\n customized rate law classification.\n\n Examples:\n import Analyzer from ratesb_python.common.analyzer\n analyzer = Analyzer(\"path/to/biomodel.xml\", \"path/to/rate_laws.json\")\n analyzer.check_all()\n results = analyzer.results\n print(str(results))\n str(results)\n \"\"\"\n splitted_path = os.path.splitext(model_str)\n ext = splitted_path[1]\n xml = ''\n if ext == '.ant' or ext == '.txt':\n ant = util.get_model_str(model_str, False)\n load_int = antimony.loadAntimonyString(ant)\n if load_int > 0:\n xml = antimony.getSBMLString()\n else:\n raise ValueError(\"Invalid Antimony model.\")\n elif ext == '.xml':\n xml = util.get_model_str(model_str, True)\n else:\n raise ValueError(\n \"Invalid file format, accepting .xml, .ant, and .txt\")\n reader = libsbml.SBMLReader()\n document = reader.readSBMLFromString(xml)\n util.checkSBMLDocument(document)\n self.model = document.getModel()\n self.simple = SimpleSBML(self.model)\n self.custom_classifier = None\n self.default_classifications = {}\n self.custom_classifications = {}\n self.results = Results()\n self.default_classifier = _CustomClassifier(os.path.join(\n os.path.dirname(__file__), \"default_classifier.json\"))\n\n if rate_law_classifications_path:\n self.custom_classifier = _CustomClassifier(\n rate_law_classifications_path)\n if len(self.custom_classifier.warning_message) > 0:\n print(self.custom_classifier.warning_message)\n\n def check(self, code: Optional[int] = None):\n \"\"\"\n Performs a check based on the provided error or warning code. If no code is provided, \n all checks are performed.\n\n Args:\n code (Optional[int]): Code of the check to perform. If None, all checks are performed.\n\n Updates:\n The results of the check(s) to self.results.\n \"\"\"\n if code is None:\n self.check_except([])\n else:\n self.checks([code])\n\n def check_except(self, excluded_codes: Optional[List[int]] = []):\n \"\"\"\n Performs all checks except the ones corresponding to the provided list of error or warning codes.\n\n Args:\n excluded_codes (Optional[List[int]]): List of codes of the checks to exclude. If None, all checks are performed.\n\n Updates:\n The results of the check(s) to self.results.\n \"\"\"\n all_checks = []\n for i in range(1, 3):\n all_checks.append(i)\n for i in range(1001, 1007):\n all_checks.append(i)\n for i in range(1010, 1011):\n all_checks.append(i)\n for i in range(1020, 1023):\n all_checks.append(i)\n for i in range(1030, 1038):\n all_checks.append(i)\n for i in range(1040, 1045):\n all_checks.append(i)\n self.checks(list(set(all_checks) - set(excluded_codes)))\n\n def check_all(self):\n \"\"\"\n Performs all checks.\n\n Updates:\n The results of the check_all to self.results.\n \"\"\"\n self.check_except([])\n\n def checks(self, codes):\n \"\"\"\n Performs multiple checks based on the provided list of error or warning codes. If no codes are provided, \n all checks are performed.\n\n Args:\n codes (List[int]): List of codes of the checks to perform.\n\n Updates:\n The results of the checks to self.results.\n \"\"\"\n \n self.default_classifications = {}\n self.custom_classifications = {}\n self.results.clear_results()\n\n for reaction in self.simple.reactions:\n reaction.kinetic_law.formula = reaction.kinetic_law.formula.replace(\n '^', '**')\n ids_list = list(dict.fromkeys(reaction.kinetic_law.symbols))\n\n libsbml_kinetics = reaction.kinetic_law.libsbml_kinetics\n sbo_term = -1\n if libsbml_kinetics:\n sbo_term = libsbml_kinetics.getSBOTerm()\n\n reaction_id = reaction.getId()\n sorted_species = self._get_sorted_species(reaction.reaction)\n species_list, parameter_list, compartment_list, kinetics, kinetics_sim = self._preprocess_reactions(\n reaction)\n reactant_list, product_list = self._extract_kinetics_details(\n reaction)\n species_in_kinetic_law, parameters_in_kinetic_law_only, compartment_in_kinetic_law, others_in_kinetic_law = self._identify_parameters_in_kinetics(\n ids_list, species_list, parameter_list, compartment_list)\n boundary_species = self._get_boundary_species(\n reactant_list, product_list)\n\n data = ReactionData(\n reaction_id=reaction_id,\n kinetics=kinetics,\n kinetics_sim=kinetics_sim,\n reactant_list=reactant_list,\n product_list=product_list,\n species_in_kinetic_law=species_in_kinetic_law,\n parameters_in_kinetic_law=parameters_in_kinetic_law_only + others_in_kinetic_law,\n ids_list=ids_list,\n sorted_species=sorted_species,\n boundary_species=boundary_species,\n parameters_in_kinetic_law_only=parameters_in_kinetic_law_only,\n compartment_in_kinetic_law=compartment_in_kinetic_law,\n is_reversible=reaction.reaction.getReversible(),\n sbo_term=sbo_term,\n codes=codes\n )\n\n self._set_kinetics_type(**data.__dict__)\n if 1 in codes:\n self._check_empty_kinetics(**data.__dict__)\n if 2 in codes:\n self._check_floating_species(**data.__dict__)\n if 1001 in codes:\n self._check_pure_number(**data.__dict__)\n if 1002 in codes:\n self._check_unrecognized_rate_law(**data.__dict__)\n if 1003 in codes:\n self._check_flux_increasing_with_reactant(**data.__dict__)\n if 1004 in codes:\n self._check_flux_decreasing_with_product(**data.__dict__)\n if 1005 in codes:\n self._check_boundary_floating_species(**data.__dict__)\n if 1006 in codes:\n self._check_constant_parameters(**data.__dict__)\n if 1010 in codes:\n self._check_irreversibility(**data.__dict__)\n if 1020 in codes or 1021 in codes or 1022 in codes:\n self._check_naming_conventions(**data.__dict__)\n if any(isinstance(num, int) and 1030 <= num <= 1037 for num in codes):\n self._check_formatting_conventions(**data.__dict__)\n if any(isinstance(num, int) and 1040 <= num <= 1044 for num in codes):\n self._check_sboterm_annotations(**data.__dict__)\n\n # self._check_empty_kinetics(**kwargs)\n # self._check_boundary_floating_species(**kwargs)\n\n def _get_sorted_species(self, reaction):\n sorted_species_reference = [reaction.getReactant(n) for n in range(\n reaction.getNumReactants())] + [reaction.getProduct(n) for n in range(reaction.getNumProducts())]\n sorted_species = []\n for species_reference in sorted_species_reference:\n sorted_species.append(species_reference.getSpecies())\n return sorted_species\n\n def _preprocess_reactions(self, reaction):\n # Extract and process necessary data from the reaction\n species_num = self.model.getNumSpecies()\n parameter_num = self.model.getNumParameters()\n compartment_num = self.model.getNumCompartments()\n\n species_list = [self.model.getSpecies(\n i).getId() for i in range(species_num)]\n parameter_list = [self.model.getParameter(\n i).getId() for i in range(parameter_num)]\n compartment_list = [self.model.getCompartment(\n i).getId() for i in range(compartment_num)]\n\n kinetics = reaction.kinetic_law.expanded_formula\n\n try:\n kinetics_sim = str(sp.simplify(kinetics))\n except:\n kinetics_sim = kinetics\n\n return species_list, parameter_list, compartment_list, kinetics, kinetics_sim\n\n def _extract_kinetics_details(self, reaction):\n reactant_list = [r.getSpecies() for r in reaction.reactants]\n product_list = [p.getSpecies() for p in reaction.products]\n return reactant_list, product_list\n\n def _identify_parameters_in_kinetics(self, ids_list, species_list, parameter_list, compartment_list):\n species_in_kinetic_law = []\n parameters_in_kinetic_law_only = []\n compartment_in_kinetic_law = []\n others_in_kinetic_law = []\n\n for id in ids_list:\n if id in species_list:\n species_in_kinetic_law.append(id)\n elif id in parameter_list:\n parameters_in_kinetic_law_only.append(id)\n elif id in compartment_list:\n compartment_in_kinetic_law.append(id)\n others_in_kinetic_law.append(id)\n else:\n others_in_kinetic_law.append(id)\n\n return species_in_kinetic_law, parameters_in_kinetic_law_only, compartment_in_kinetic_law, others_in_kinetic_law\n\n def _get_boundary_species(self, reactant_list, product_list):\n boundary_species = [reactant for reactant in reactant_list if self.model.getSpecies(\n reactant).getBoundaryCondition()]\n boundary_species += [product for product in product_list if self.model.getSpecies(\n product).getBoundaryCondition()]\n return boundary_species\n\n def _set_kinetics_type(self, **kwargs):\n reaction_id = kwargs[\"reaction_id\"]\n self.default_classifications[reaction_id] = self.default_classifier.custom_classify(\n is_default=True, **kwargs)\n if self.custom_classifier:\n self.custom_classifications[reaction_id] = self.custom_classifier.custom_classify(\n **kwargs)\n\n def _check_empty_kinetics(self, **kwargs):\n \"\"\"\n Checks if the given reaction has an empty kinetic law.\n Code: 1\n\n Args:\n reaction_id (str): The reaction's id'.\n kinetics (str): The kinetic law\n\n Adds:\n An error message to results specifying that no rate law was entered for the reaction.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n kinetics = kwargs[\"kinetics\"]\n if len(kinetics.replace(' ', '')) == 0:\n self.results.add_message(\n reaction_id, 1, f\"No rate law entered.\", False)\n\n def _check_floating_species(self, **kwargs):\n \"\"\"\n Checks if all reactants in the rate law of the given reaction are defined as species, excluding boundary species.\n Code: 2\n\n Args:\n reaction_id (str): The reaction's id'.\n species_in_kinetic_law (list): A list of species present in the rate law.\n reactant_list (list): A list of reactants for the reaction.\n boundary_species (list): A list of boundary species in the model.\n\n Adds:\n An error message to results specifying the missing reactants that are expected in the rate law.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n species_in_kinetic_law = kwargs[\"species_in_kinetic_law\"]\n reactant_list = kwargs[\"reactant_list\"]\n boundary_species = kwargs[\"boundary_species\"]\n\n floating_species = []\n for reactant in reactant_list:\n if reactant not in species_in_kinetic_law:\n if reactant not in boundary_species:\n floating_species.append(reactant)\n if len(floating_species) > 0:\n floating_species = \",\".join(floating_species)\n self.results.add_message(\n reaction_id, 2, f\"Expecting reactants in rate law: {floating_species}\", False)\n\n def _check_pure_number(self, **kwargs):\n \"\"\"\n Checks if the rate law contains only number\n Code: 1001\n\n Args:\n reaction_id (str): The reaction's id'.\n kinetics_sim: string-simplified kinetics\n\n Adds:\n A warning message to results specifying that the rate law contains only numbers.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n kinetics_sim = kwargs[\"kinetics_sim\"]\n try:\n float(kinetics_sim)\n self.results.add_message(\n reaction_id, 1001, \"Rate law contains only number.\")\n except:\n return\n\n def _check_unrecognized_rate_law(self, **kwargs):\n \"\"\"\n Checks if the rate law from the standard and custom list (if given) is recognized\n Code: 1002\n\n Args:\n reaction_id (str): The reaction's id'.\n\n Adds:\n A warning message to results specifying that the rate law is unrecognized.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n if len(self.custom_classifications) > 0:\n if not (any(self.default_classifications[reaction_id].values()) or any(self.custom_classifications[reaction_id].values())):\n self.results.add_message(\n reaction_id, 1002, \"Unrecognized rate law from the standard list and the custom list.\")\n else:\n if not any(self.default_classifications[reaction_id].values()):\n self.results.add_message(\n reaction_id, 1002, \"Unrecognized rate law from the standard list.\")\n\n def _check_flux_increasing_with_reactant(self, **kwargs):\n \"\"\"\n Checks if the flux is increasing as the reactant increases.\n Code: 1003\n\n Args:\n reaction_id (str): The reaction's id'.\n reactant_list (str): List of reactants in reaction\n kinetics (str): the kinetic law\n\n Adds:\n A warning message to results specifying if the flux is not increasing as reactant increases.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n reactant_list = kwargs[\"reactant_list\"]\n kinetics = kwargs[\"kinetics\"]\n\n if not util.check_symbols_derivative(sp.sympify(kinetics), reactant_list):\n self.results.add_message(\n reaction_id, 1003, \"Flux is not increasing as reactant increases.\")\n\n def _check_flux_decreasing_with_product(self, **kwargs):\n \"\"\"\n Checks if the flux is increasing as the product decreases.\n Code: 1004\n\n Args:\n reaction_id (str): The reaction's id'.\n is_reversible (bool): the reaction's reversibility\n product_list (list): List of products in reaction\n kinetics (str): the kinetic law\n\n Adds:\n A warning message to results specifying if the flux is not decreasing as product increases.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n is_reversible = kwargs[\"is_reversible\"]\n product_list = kwargs[\"product_list\"]\n kinetics = kwargs[\"kinetics\"]\n\n if is_reversible:\n if not util.check_symbols_derivative(sp.sympify(kinetics), product_list, False):\n self.results.add_message(\n reaction_id, 1004, \"Flux is not decreasing as product increases.\")\n\n def _check_boundary_floating_species(self, **kwargs):\n \"\"\"\n Checks if any reactant in the rate law of the given reaction is a boundary species.\n Code: 1005\n\n Args:\n reaction_id (str): The reaction's id'.\n species_in_kinetic_law: A list of species present in the rate law.\n reactant_list: A list of reactants for the reaction.\n boundary_species: A list of boundary species in the model.\n\n Adds:\n A warning message to results specifying the boundary species reactants found in the rate law.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n species_in_kinetic_law = kwargs[\"species_in_kinetic_law\"]\n reactant_list = kwargs[\"reactant_list\"]\n boundary_species = kwargs[\"boundary_species\"]\n\n boundary_floating_species = []\n for reactant in reactant_list:\n if reactant not in species_in_kinetic_law:\n if reactant in boundary_species:\n boundary_floating_species.append(reactant)\n if len(boundary_floating_species) > 0:\n boundary_floating_species = \",\".join(boundary_floating_species)\n self.results.add_message(\n reaction_id, 1005, f\"Expecting boundary species reactant in rate law: {boundary_floating_species}\")\n\n def _check_constant_parameters(self, **kwargs):\n \"\"\"\n Checks if the parameters in the rate law are constants.\n Code: 1006\n TODO remove libsbml dependency\n\n Args:\n reaction_id (str): The reaction's id'.\n parameters_in_kinetic_law_only (list): the parameters in kinetic law\n\n Adds:\n A warning message to results specifying if the parameters are not constants.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n parameters_in_kinetic_law_only = kwargs[\"parameters_in_kinetic_law_only\"]\n non_constant_params = []\n for param in parameters_in_kinetic_law_only:\n libsbml_param = self.model.getParameter(param)\n if not libsbml_param.getConstant():\n non_constant_params.append(param)\n if len(non_constant_params) > 0:\n non_constant_params = \",\".join(non_constant_params)\n self.results.add_message(\n reaction_id, 1006, f\"Expecting these parameters to be constants: {non_constant_params}\")\n\n def _check_irreversibility(self, **kwargs):\n \"\"\"\n Checks if an irreversible reaction's kinetic law contains products.\n Code: 1010\n\n Args:\n reaction_id (str): The reaction's id'.\n is_reversible (bool): the reaction's reversibility\n species_in_kinetic_law: A list of species present in the rate law.\n product_list: A list of products for the reaction.\n\n Returns:\n An error message specifying the products found in the rate law of an irreversible reaction.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n is_reversible = kwargs[\"is_reversible\"]\n species_in_kinetic_law = kwargs[\"species_in_kinetic_law\"]\n product_list = kwargs[\"product_list\"]\n\n inconsistent_products = []\n if not is_reversible:\n for product in product_list:\n if product in species_in_kinetic_law:\n inconsistent_products.append(product)\n if len(inconsistent_products) > 0:\n inconsistent_products = \",\".join(inconsistent_products)\n self.results.add_message(\n reaction_id, 1010, f\"Irreversible reaction kinetic law contains products: {inconsistent_products}\")\n\n def _check_naming_conventions(self, **kwargs):\n \"\"\"\n Checks if certain parameters in the rate law follow the recommended naming convention (starting with 'k', 'K', or 'V').\n Code: 1020, 1021, 1022\n\n Args:\n reaction_id (str): The reaction's id'.\n\n Adds:\n A warning message to results specifying that certain parameters in the rate law are not following the recommended naming convention.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n parameters_in_kinetic_law = kwargs[\"parameters_in_kinetic_law\"]\n kinetics_sim = kwargs[\"kinetics_sim\"]\n ids_list = kwargs[\"ids_list\"]\n codes = kwargs[\"codes\"]\n\n naming_convention_warnings = {'k': [], 'K': [], 'V': []}\n if any(self.default_classifications[reaction_id][key] for key in NON_MM_KEYS):\n naming_convention_warnings['k'] = self._check_symbols_start_with(\n 'k', parameters_in_kinetic_law)\n elif self.default_classifications[reaction_id]['MM']:\n eq = self._numerator_denominator(kinetics_sim, ids_list)\n eq0 = [param for param in parameters_in_kinetic_law if param in eq[0]]\n eq1 = [param for param in parameters_in_kinetic_law if param in eq[1]]\n naming_convention_warnings['V'] = self._check_symbols_start_with(\n 'V', eq0)\n naming_convention_warnings['K'] = self._check_symbols_start_with(\n 'K', eq1)\n elif self.default_classifications[reaction_id]['MMcat']:\n eq = self._numerator_denominator(kinetics_sim, ids_list)\n eq0 = [param for param in parameters_in_kinetic_law if param in eq[0]]\n eq1 = [param for param in parameters_in_kinetic_law if param in eq[1]]\n naming_convention_warnings['K'] = self._check_symbols_start_with('K', eq0)\n naming_convention_warnings['K'] = self._check_symbols_start_with('K', eq1)\n elif self.default_classifications[reaction_id]['Hill']:\n eq = self._numerator_denominator(kinetics_sim, ids_list)\n eq0 = [param for param in parameters_in_kinetic_law if param in eq[0]]\n eq1 = [param for param in parameters_in_kinetic_law if param in eq[1]]\n naming_convention_warnings['K'] = self._check_symbols_start_with(\n 'K', eq1)\n\n if 1020 in codes and len(naming_convention_warnings['k']) > 0:\n naming_convention_warnings_k = \",\".join(\n naming_convention_warnings['k'])\n self.results.add_message(\n reaction_id, 1020, f\"We recommend that these parameters start with 'k': {naming_convention_warnings_k}\")\n if 1021 in codes and len(naming_convention_warnings['K']) > 0:\n naming_convention_warnings_K = \",\".join(\n naming_convention_warnings['K'])\n self.results.add_message(\n reaction_id, 1021, f\"We recommend that these parameters start with 'K': {naming_convention_warnings_K}\")\n if 1022 in codes and len(naming_convention_warnings['V']) > 0:\n naming_convention_warnings_V = \",\".join(\n naming_convention_warnings['V'])\n self.results.add_message(\n reaction_id, 1022, f\"We recommend that these parameters start with 'V': {naming_convention_warnings_V}\")\n\n def _check_symbols_start_with(self, start, symbols):\n ret = []\n for symbol in symbols:\n if not symbol.startswith(start):\n ret.append(symbol)\n return ret\n\n def _numerator_denominator(self, kinetics_sim, ids_list):\n \"\"\"\n Get the numerator and denominator of a \"fraction\" function.\n\n Parameters\n ---- \n kinetics_sim: string-simplified kinetics\n ids_list: list-id list including all the ids in kinetics, reactants and products\n\n Returns\n -------\n Type - the numerator and the denominator of the fraction\n \"\"\"\n\n strange_func = 0\n pre_symbols = ''\n for i in range(len(ids_list)):\n pre_symbols += ids_list[i]\n pre_symbols += ' '\n pre_symbols = pre_symbols[:-1] # remove the space at the end\n pre_symbols_comma = pre_symbols.replace(\" \", \",\")\n stmt = \"%s = sp.symbols('%s')\" % (pre_symbols_comma, pre_symbols)\n try: # sometimes there is \"invalid syntax error\"\n exec(stmt, globals())\n except:\n strange_func = 1\n\n try: # check if there is strange func (i.e. delay) in kinetic law;\n # sometimes ids_list is not enough for all the ids in kinetics\n eq_stat = \"kinetics_eq = \" + kinetics_sim\n exec(eq_stat, globals())\n except:\n strange_func = 1\n\n eq = ['', '']\n\n if strange_func == 0:\n try:\n numerator = str(kinetics_eq.as_numer_denom()[0])\n denominator = str(kinetics_eq.as_numer_denom()[1])\n eq[0] = numerator\n eq[1] = denominator\n except:\n pass\n\n return eq\n\n def _numerator_denominator_order_remained(self, kinetics, ids_list):\n # Split the fraction at the '/' character\n split_fraction = kinetics.split('/')\n\n # Check if there's a numerator and a denominator\n if len(split_fraction) != 2:\n return ['', '']\n\n # Return the numerator and the denominator as a tuple\n return split_fraction[0].strip(), split_fraction[1].strip()\n\n def _find_positions_in_rate_law(self, element_list, rate_law):\n largest_position = -1\n smallest_position = sys.maxsize\n prev_position = -1\n increasing_flag = True\n\n for element in element_list:\n pattern = re.compile(rf\"\\b{re.escape(element)}\\b\")\n match = pattern.search(rate_law)\n if match:\n index = match.start()\n largest_position = max(index, largest_position)\n smallest_position = min(index, smallest_position)\n if index < prev_position:\n increasing_flag = False\n prev_position = index\n return largest_position, smallest_position, increasing_flag\n\n def _check_product_of_terms_format(self, kinetics, compartment_in_kinetic_law, parameters_in_kinetic_law_only, sorted_species):\n comp_stats = self._find_positions_in_rate_law(\n compartment_in_kinetic_law, kinetics)\n param_stats = self._find_positions_in_rate_law(\n parameters_in_kinetic_law_only, kinetics)\n spec_stats = self._find_positions_in_rate_law(sorted_species, kinetics)\n is_formatted = (comp_stats[0] < param_stats[1] or param_stats[1] == sys.maxsize) and \\\n (param_stats[0] < spec_stats[1] or spec_stats[1] == sys.maxsize) and \\\n (comp_stats[0] < spec_stats[1]\n or spec_stats[1] == sys.maxsize)\n increasing_flag = comp_stats[2] and param_stats[2] and spec_stats[2]\n if is_formatted:\n return 0 if increasing_flag else 1\n return 2\n\n def _check_expression_format(self, kinetics, compartment_in_kinetic_law, parameters_in_kinetic_law_only, sorted_species):\n compartment_in_kinetic_law.sort()\n parameters_in_kinetic_law_only.sort()\n product_of_terms = re.split(r\"[+-]\", kinetics)\n ret = 0\n for term in product_of_terms:\n format_status = self._check_product_of_terms_format(\n term, compartment_in_kinetic_law, parameters_in_kinetic_law_only, sorted_species)\n if format_status > ret:\n ret = format_status\n return ret\n\n def _check_formatting_conventions(self, **kwargs):\n reaction_id = kwargs[\"reaction_id\"]\n compartment_in_kinetic_law = kwargs[\"compartment_in_kinetic_law\"]\n kinetics = kwargs[\"kinetics\"]\n ids_list = kwargs[\"ids_list\"]\n parameters_in_kinetic_law_only = kwargs[\"parameters_in_kinetic_law_only\"]\n codes = kwargs[\"codes\"]\n sorted_species = kwargs[\"sorted_species\"]\n\n flag = 0\n assert len(self.default_classifications[reaction_id]) > 0\n if any(self.default_classifications[reaction_id][key] for key in MM_KEYS):\n eq = self._numerator_denominator_order_remained(\n kinetics, ids_list) # _numerator_denominator not provided\n flag = self._check_expression_format(\n eq[0], compartment_in_kinetic_law, parameters_in_kinetic_law_only, sorted_species)\n flag += 3 * self._check_expression_format(\n eq[1], compartment_in_kinetic_law, parameters_in_kinetic_law_only, sorted_species)\n else:\n flag = self._check_expression_format(\n kinetics, compartment_in_kinetic_law, parameters_in_kinetic_law_only, sorted_species)\n if flag == 1 and 1030 in codes:\n self.results.add_message(\n reaction_id, 1030, f\"Elements of the same type are not ordered alphabetically\")\n if flag == 2 and 1031 in codes:\n self.results.add_message(\n reaction_id, 1031, f\"Formatting convention not followed (compartment before parameters before species)\")\n # TODO: currently the default classification does not classify these as MM, so these checks are not performed\n if flag == 3 and 1032 in codes:\n self.results.add_message(\n reaction_id, 1032, f\"Denominator not in alphabetical order\")\n if flag == 4 and 1033 in codes:\n self.results.add_message(\n reaction_id, 1033, f\"Numerator and denominator not in alphabetical order\")\n if flag == 5 and 1034 in codes:\n self.results.add_message(\n reaction_id, 1034, f\"Numerator convention not followed and denominator not in alphabetical order\")\n if flag == 6 and 1035 in codes:\n self.results.add_message(\n reaction_id, 1035, f\"Denominator convention not followed\")\n if flag == 7 and 1036 in codes:\n self.results.add_message(\n reaction_id, 1036, f\"Numerator not in alphabetical order and denominator convention not followed\")\n if flag == 8 and 1037 in codes:\n self.results.add_message(\n reaction_id, 1037, f\"Numerator and denominator convention not followed\")\n\n def _check_sboterm_annotations(self, **kwargs):\n \"\"\"\n Checks if the SBOTerm annotations for the rate law follow recommended SBO terms.\n Code: 1040, 1041, 1042, 1043, 1044\n\n Args:\n reaction_id (str): The reaction's id'.\n sbo_term (int): the sbo_term annotation for the kinetic law, -1 if not annotated\n\n Adds:\n A warning message to results specifying that the annotations for the rate law do not follow recommended SBO terms.\n \"\"\"\n reaction_id = kwargs[\"reaction_id\"]\n sbo_term = kwargs[\"sbo_term\"]\n codes = kwargs[\"codes\"]\n assert len(self.default_classifications) > 0\n flag = 0\n if sbo_term < 0:\n flag = 0\n elif any(self.default_classifications[reaction_id][key] for key in UNDR_KEYS):\n if sbo_term not in UNDR_SBOS:\n flag = 1\n elif any(self.default_classifications[reaction_id][key] for key in UNDR_A_KEYS):\n if sbo_term not in UNDR_A_SBOS:\n flag = 2\n elif any(self.default_classifications[reaction_id][key] for key in BIDR_ALL_KEYS):\n if sbo_term not in BI_SBOS:\n flag = 3\n elif self.default_classifications[reaction_id]['MM']:\n if sbo_term not in MM_SBOS:\n flag = 4\n elif any(self.default_classifications[reaction_id][key] for key in MM_CAT_KEYS):\n if sbo_term not in MM_CAT_SBOS:\n flag = 5\n elif self.default_classifications[reaction_id]['Hill']:\n if sbo_term not in HILL_SBOS:\n flag = 6\n if flag == 1 and 1040 in codes:\n self.results.add_message(\n reaction_id, 1040, f\"Uni-directional mass action annotation not following recommended SBO terms, we recommend annotations to be subclasses of: SBO_0000430, SBO_0000041\")\n elif flag == 2 and 1041 in codes:\n self.results.add_message(\n reaction_id, 1041, f\"Uni-Directional Mass Action with an Activator annotation not following recommended SBO terms, we recommend annotations to be subclasses of: SBO_0000041\")\n elif flag == 3 and 1042 in codes:\n self.results.add_message(\n reaction_id, 1042, f\"Bi-directional mass action (with an Activator) annotation not following recommended SBO terms, we recommend annotations to be subclasses of: SBO_0000042\")\n elif flag == 4 and 1043 in codes:\n self.results.add_message(\n reaction_id, 1043, f\"Michaelis-Menten kinetics without an explicit enzyme annotation not following recommended SBO terms, we recommend annotations to be subclasses of: SBO_0000028\")\n elif flag == 5 and 1044 in codes:\n self.results.add_message(\n reaction_id, 1044, f\"Michaelis-Menten kinetics with an explicit enzyme annotation not following recommended SBO terms, we recommend annotations to be subclasses of: SBO_0000028, SBO_0000430\")\n","repo_name":"sys-bio/ratesb_python","sub_path":"ratesb_python/common/analyzer.py","file_name":"analyzer.py","file_ext":"py","file_size_in_byte":36307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28395218401","text":"'''----------------------------------------------------------------------------\\\n| ||\\\\ //|| /|¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯\\ |\n| || \\\\ // || (o_ / | SUPPLEMENTARY FILE | |\n| || \\\\// || //\\/ | ---- | |\n| || \\/ || V_/_ | SETTINGS COMMANDS | |\n| || || |‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗‗/ |\n\\----------------------------------------------------------------------------'''\n\nimport discord\nfrom discord.ext import commands\nfrom datetime import datetime\n\nfrom foundationBotDatabaseStuff import update_setting\n\n# import logger\nfrom foundationBotLogger import *\nlogger = Logger()\n\nclass SettingsCommands(commands.Cog):\n def __init__(self, bot, conn, settings):\n self.bot = bot\n self.conn = conn\n self.settings = settings\n \n @commands.group()\n @commands.has_guild_permissions(administrator=True)\n async def set(self, ctx):\n if ctx.invoked_subcommand is None:\n await ctx.send('You need to provide a setting to set!')\n \n @set.command(description='Displays the Current Settings')\n async def check(self, ctx):\n embed = discord.Embed(\n title = 'Current Settings',\n colour = discord.Colour.dark_green()\n )\n settingsList = ('botActive\\n'\n 'guildID\\n'\n 'excludedRoles\\n'\n 'pinsChannel\\n'\n 'pinsRoles\\n'\n 'leaderboardEmoji\\n'\n 'leaderboardInterval\\n'\n 'leaderboardChannel\\n'\n 'screenshotChannel\\n'\n 'screenshotPostTime\\n'\n 'messagesPostTime\\n'\n 'moddingChannel\\n'\n 'rankingChannel\\n'\n 'rankingMessage\\n\\n'\n 'labourEmoji\\n'\n 'clergyEmoji\\n'\n 'kingdomEmoji\\n'\n 'defaultRole\\n'\n 'labourRole1\\n'\n 'labourRole2\\n'\n 'labourRole3\\n'\n 'labourRole4\\n'\n 'clergyRole1\\n'\n 'clergyRole2\\n'\n 'clergyRole3\\n'\n 'clergyRole4\\n'\n 'kingdomRole1\\n'\n 'kingdomRole2\\n'\n 'kingdomRole3\\n'\n 'kingdomRole4\\n'\n 'highestRole\\n'\n 'expLevel1\\n'\n 'expLevel2\\n'\n 'expLevel3\\n'\n 'expLevel4\\n'\n 'expLevelH\\n'\n )\n \n if self.settings.guildID != None:\n guildIDstr = ctx.bot.get_guild(self.settings.guildID).name\n else:\n guildIDstr = 'Not Set'\n if self.settings.excludedRoles:\n excludedRolesstr = ''\n for i,role in enumerate(self.settings.excludedRoles):\n if i == 0:\n excludedRolesstr += ctx.guild.get_role(role).mention\n else:\n excludedRolesstr += ', ' + ctx.guild.get_role(role).mention\n else:\n excludedRolesstr = 'Not Set'\n if self.settings.pinsChannel != None:\n pinsChannelstr = self.bot.get_channel(self.settings.pinsChannel).mention\n else:\n pinsChannelstr = 'Not Set'\n if self.settings.pinsRoles:\n pinsRolesstr = ''\n for i,role in enumerate(self.settings.pinsRoles):\n if i == 0:\n pinsRolesstr += ctx.guild.get_role(role).mention\n else:\n pinsRolesstr += ', ' + ctx.guild.get_role(role).mention\n else:\n pinsRolesstr = 'Not Set'\n if self.settings.leaderboardEmoji != None:\n leaderboardEmojistr = str(self.bot.get_emoji(self.settings.leaderboardEmoji))\n else:\n leaderboardEmojistr = 'Not Set'\n if self.settings.leaderboardInterval != None:\n leaderboardIntervalstr = str(self.settings.leaderboardInterval)\n else:\n leaderboardIntervalstr = 'Not Set'\n if self.settings.leaderboardChannel != None:\n leaderboardChannelstr = self.bot.get_channel(self.settings.leaderboardChannel).mention\n else:\n leaderboardChannelstr = 'Not Set'\n if self.settings.screenshotChannel != None:\n screenshotChannelstr = self.bot.get_channel(self.settings.screenshotChannel).mention\n else:\n screenshotChannelstr = 'Not Set'\n if self.settings.screenshotPostTime != None:\n date = datetime.fromtimestamp(self.settings.screenshotPostTime)\n screenshotPostTimestr = str(date.strftime('%d/%m/%Y %H:%M'))\n else:\n screenshotPostTimestr = 'Not Set'\n if self.settings.messagesPostTime != None:\n date = datetime.fromtimestamp(self.settings.messagesPostTime)\n messagesPostTimestr = str(date.strftime('%d/%m/%Y %H:%M'))\n else:\n messagesPostTimestr = 'Not Set'\n if self.settings.moddingChannel != None:\n moddingChannelstr = self.bot.get_channel(self.settings.moddingChannel).mention\n else:\n moddingChannelstr = 'Not Set'\n if self.settings.rankingChannel != None:\n rankingChannelstr = self.bot.get_channel(self.settings.rankingChannel).mention\n else:\n rankingChannelstr = 'Not Set'\n if self.settings.rankingMessage != None:\n rankingMessage = await self.bot.get_channel(self.settings.rankingChannel).fetch_message(self.settings.rankingMessage)\n rankingMessagestr = rankingMessage.jump_url\n else:\n rankingMessagestr = 'Not Set\\n'\n if self.settings.labourEmoji != None:\n labourEmojistr = str(self.bot.get_emoji(self.settings.labourEmoji))\n else:\n labourEmojistr = 'Not Set'\n if self.settings.clergyEmoji != None:\n clergyEmojistr = str(self.bot.get_emoji(self.settings.clergyEmoji))\n else:\n clergyEmojistr = 'Not Set'\n if self.settings.kingdomEmoji != None:\n kingdomEmojistr = str(self.bot.get_emoji(self.settings.kingdomEmoji))\n else:\n kingdomEmojistr = 'Not Set'\n if self.settings.defaultRole != None:\n defaultRolestr = ctx.guild.get_role(self.settings.defaultRole).mention\n else:\n defaultRolestr = 'Not Set'\n if self.settings.labourRole1 != None:\n labourRole1str = ctx.guild.get_role(self.settings.labourRole1).mention\n else:\n labourRole1str = 'Not Set'\n if self.settings.labourRole2 != None:\n labourRole2str = ctx.guild.get_role(self.settings.labourRole2).mention\n else:\n labourRole2str = 'Not Set'\n if self.settings.labourRole3 != None:\n labourRole3str = ctx.guild.get_role(self.settings.labourRole3).mention\n else:\n labourRole3str = 'Not Set'\n if self.settings.labourRole4 != None:\n labourRole4str = ctx.guild.get_role(self.settings.labourRole4).mention\n else:\n labourRole4str = 'Not Set'\n if self.settings.clergyRole1 != None:\n clergyRole1str = ctx.guild.get_role(self.settings.clergyRole1).mention\n else:\n clergyRole1str = 'Not Set'\n if self.settings.clergyRole2 != None:\n clergyRole2str = ctx.guild.get_role(self.settings.clergyRole2).mention\n else:\n clergyRole2str = 'Not Set'\n if self.settings.clergyRole3 != None:\n clergyRole3str = ctx.guild.get_role(self.settings.clergyRole3).mention\n else:\n clergyRole3str = 'Not Set'\n if self.settings.clergyRole4 != None:\n clergyRole4str = ctx.guild.get_role(self.settings.clergyRole4).mention\n else:\n clergyRole4str = 'Not Set'\n if self.settings.kingdomRole1 != None:\n kingdomRole1str = ctx.guild.get_role(self.settings.kingdomRole1).mention\n else:\n kingdomRole1str = 'Not Set'\n if self.settings.kingdomRole2 != None:\n kingdomRole2str = ctx.guild.get_role(self.settings.kingdomRole2).mention\n else:\n kingdomRole2str = 'Not Set'\n if self.settings.kingdomRole3 != None:\n kingdomRole3str = ctx.guild.get_role(self.settings.kingdomRole3).mention\n else:\n kingdomRole3str = 'Not Set'\n if self.settings.kingdomRole4 != None:\n kingdomRole4str = ctx.guild.get_role(self.settings.kingdomRole4).mention\n else:\n kingdomRole4str = 'Not Set'\n if self.settings.highestRole != None:\n highestRolestr = ctx.guild.get_role(self.settings.highestRole).mention\n else:\n highestRolestr = 'Not Set'\n if self.settings.expLevel1 != None:\n expLevel1str = str(self.settings.expLevel1)\n else:\n expLevel1str = 'Not Set'\n if self.settings.expLevel2 != None:\n expLevel2str = str(self.settings.expLevel2)\n else:\n expLevel2str = 'Not Set'\n if self.settings.expLevel3 != None:\n expLevel3str = str(self.settings.expLevel3)\n else:\n expLevel3str = 'Not Set'\n if self.settings.expLevel4 != None:\n expLevel4str = str(self.settings.expLevel4)\n else:\n expLevel4str = 'Not Set'\n if self.settings.expLevelH != None:\n expLevelHstr = str(self.settings.expLevelH)\n else:\n expLevelHstr = 'Not Set'\n \n values = (str(self.settings.botActive) + '\\n' +\n guildIDstr + '\\n' +\n excludedRolesstr + '\\n' +\n pinsChannelstr + '\\n' +\n pinsRolesstr + '\\n' +\n leaderboardEmojistr + '\\n' +\n leaderboardIntervalstr + '\\n' +\n leaderboardChannelstr + '\\n' +\n screenshotChannelstr + '\\n' +\n screenshotPostTimestr + '\\n' +\n messagesPostTimestr + '\\n' +\n moddingChannelstr + '\\n' +\n rankingChannelstr + '\\n' +\n rankingMessagestr + '\\n' +\n labourEmojistr + '\\n' +\n clergyEmojistr + '\\n' +\n kingdomEmojistr + '\\n' +\n defaultRolestr + '\\n' +\n labourRole1str + '\\n' +\n labourRole2str + '\\n' +\n labourRole3str + '\\n' +\n labourRole4str + '\\n' +\n clergyRole1str + '\\n' +\n clergyRole2str + '\\n' +\n clergyRole3str + '\\n' +\n clergyRole4str + '\\n' +\n kingdomRole1str + '\\n' +\n kingdomRole2str + '\\n' +\n kingdomRole3str + '\\n' +\n kingdomRole4str + '\\n' +\n highestRolestr + '\\n' +\n expLevel1str + '\\n' +\n expLevel2str + '\\n' +\n expLevel3str + '\\n' +\n expLevel4str + '\\n' +\n expLevelHstr + '\\n'\n )\n embed.add_field(name='Setting' , value=settingsList, inline=True)\n embed.add_field(name='Value', value=values, inline=True)\n await ctx.send(embed=embed)\n \n @set.command(description='Sets the Bot Status')\n async def botActive(self, ctx, status: str):\n status = status.lower()\n if status == 'on' or status == 'true' or status == 'yes':\n update_setting(self.conn, ('True','botActive'))\n self.settings.botActive = True\n await ctx.send('botActive set to: `True`')\n elif status == 'off' or status == 'false' or status == 'no':\n update_setting(self.conn, ('False','botActive'))\n self.settings.botActive = False\n await ctx.send('botActive set to: `False`')\n \n @set.command(description='Sets the Guild ID')\n async def guildID(self, ctx):\n update_setting(self.conn, (str(ctx.guild.id),'guildID'))\n self.settings.guildID = ctx.guild.id\n await ctx.send('guildID set to: ' + ctx.bot.get_guild(self.settings.guildID).name)\n \n @set.command(description='Sets the Roles to exclude from the Leaderboards')\n async def excludedRoles(self, ctx, *, roles: str):\n roles = roles.split(',')\n rolesList = [0]*len(roles)\n rolesmsg = ''\n savestring = ''\n for i,role in enumerate(roles):\n try:\n role = int(role)\n if ctx.guild.get_role(role) != None:\n rolesList[i] = role\n if i == 0:\n rolesmsg += ctx.guild.get_role(role).mention\n savestring += str(role)\n else:\n rolesmsg += ', ' + ctx.guild.get_role(role).mention\n savestring += ',' + str(role)\n else:\n await ctx.send('At least one of the provided role IDs is invalid!')\n return\n except ValueError as e:\n await ctx.send('At least one of the provided role IDs is invalid!')\n return\n update_setting(self.conn, (savestring,'excludedRoles'))\n self.settings.excludedRoles = rolesList\n await ctx.send('excludedRoles set to: ' + rolesmsg)\n \n @set.command(description='Sets the Channel for the Pinning System')\n async def pinsChannel(self, ctx, channel: str):\n try:\n if self.bot.get_channel(int(channel)) != None:\n update_setting(self.conn, (channel,'pinsChannel'))\n self.settings.pinsChannel = int(channel)\n await ctx.send('pinsChannel set to: ' + self.bot.get_channel(int(channel)).mention)\n else:\n await ctx.send('No channel with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The channel ID should only consist of an integer number!')\n \n @set.command(description='Sets the Roles to activate the Pinning System')\n async def pinsRoles(self, ctx, *, roles: str):\n roles = roles.split(',')\n rolesList = [0]*len(roles)\n rolesmsg = ''\n savestring = ''\n for i,role in enumerate(roles):\n try:\n role = int(role)\n if ctx.guild.get_role(role) != None:\n rolesList[i] = role\n if i == 0:\n rolesmsg += ctx.guild.get_role(role).mention\n savestring += str(role)\n else:\n rolesmsg += ', ' + ctx.guild.get_role(role).mention\n savestring += ',' + str(role)\n else:\n await ctx.send('At least one of the provided role IDs is invalid!')\n return\n except ValueError as e:\n await ctx.send('At least one of the provided role IDs is invalid!')\n return\n update_setting(self.conn, (savestring,'pinsRoles'))\n self.settings.pinsRoles = rolesList\n await ctx.send('pinsRoles set to: ' + rolesmsg)\n \n @set.command(description='Sets the Emoji to activate the Screenshot Leaderboard')\n async def leaderboardEmoji(self, ctx, emoji: str):\n try:\n if self.bot.get_emoji(int(emoji)) != None:\n update_setting(self.conn, (emoji,'leaderboardEmoji'))\n self.settings.leaderboardEmoji = int(emoji)\n await ctx.send('leaderboardEmoji set to: ' + str(self.bot.get_emoji(int(emoji))))\n else:\n await ctx.send('No emoji with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The emoji ID should only consist of an integer number!')\n \n @set.command(description='Sets the Emoji to activate the Screenshot Leaderboard')\n async def leaderboardInterval(self, ctx, days: str):\n try:\n self.settings.leaderboardInterval = int(days)\n update_setting(self.conn, (days,'leaderboardInterval'))\n await ctx.send('leaderboardInterval set to: ' + days + ' days')\n except ValueError as e:\n await ctx.send('Invalid Number of Days! The number of days should only consist of an integer number!')\n \n @set.command(description='Sets the Channel to post the Leaderboards')\n async def leaderboardChannel(self, ctx, channel: str):\n try:\n if self.bot.get_channel(int(channel)) != None:\n update_setting(self.conn, (channel,'leaderboardChannel'))\n self.settings.leaderboardChannel = int(channel)\n await ctx.send('leaderboardChannel set to: ' + self.bot.get_channel(int(channel)).mention)\n else:\n await ctx.send('No channel with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The channel ID should only consist of an integer number!')\n \n @set.command(description='Sets the Channel for the Screenshot Leaderboard')\n async def screenshotChannel(self, ctx, channel: str):\n try:\n if self.bot.get_channel(int(channel)) != None:\n update_setting(self.conn, (channel,'screenshotChannel'))\n self.settings.screenshotChannel = int(channel)\n await ctx.send('screenshotChannel set to: ' + self.bot.get_channel(int(channel)).mention)\n else:\n await ctx.send('No channel with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The channel ID should only consist of an integer number!')\n \n @set.command(description='Sets the Time for the next Screenshot Leaderboard Post')\n async def screenshotPostTime(self, ctx, *, dateTime: str):\n try:\n date = datetime.strptime(dateTime + ' UTC+0000', '%d/%m/%Y %H:%M %Z%z')\n timeStamp = date.timestamp()\n update_setting(self.conn, (timeStamp,'screenshotPostTime'))\n self.settings.screenshotPostTime = int(timeStamp)\n await ctx.send('screenshotPostTime set to: ' + dateTime + ' UTC. The screenshot leaderboard will be posted according to the leaderboardInterval starting then.')\n except ValueError as e:\n await ctx.send('Invalid dateTime! The dateTime format should be input as: DD/MM/YYYY HH:mm!')\n \n @set.command(description='Sets the Time for the next Messages Leaderboard Post')\n async def messagesPostTime(self, ctx, *, dateTime: str):\n try:\n date = datetime.strptime(dateTime + ' UTC+0000', '%d/%m/%Y %H:%M %Z%z')\n timeStamp = date.timestamp()\n update_setting(self.conn, (timeStamp,'messagesPostTime'))\n self.settings.messagesPostTime = int(timeStamp)\n await ctx.send('messagesPostTime set to: ' + dateTime + ' UTC. The messages leaderboard will be posted according to the leaderboardInterval starting then.')\n except ValueError as e:\n await ctx.send('Invalid dateTime! The dateTime format should be input as: DD/MM/YYYY HH:mm!')\n \n @set.command(description='Sets the Channel for the Modding Help Command')\n async def moddingChannel(self, ctx, channel: str):\n try:\n if self.bot.get_channel(int(channel)) != None:\n update_setting(self.conn, (channel,'moddingChannel'))\n self.settings.moddingChannel = int(channel)\n await ctx.send('moddingChannel set to: ' + self.bot.get_channel(int(channel)).mention)\n else:\n await ctx.send('No channel with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The channel ID should only consist of an integer number!')\n \n @set.command(description='Initialises Ranking Emojis on the Ranking Message')\n async def ranking(self, ctx):\n if self.settings.rankingChannel != None \\\n and self.settings.rankingMessage != None \\\n and self.settings.labourEmoji != None \\\n and self.settings.clergyEmoji != None \\\n and self.settings.kingdomEmoji != None:\n channel = self.bot.get_channel(self.settings.rankingChannel)\n message = await channel.fetch_message(self.settings.rankingMessage)\n await message.add_reaction(discord.utils.get(channel.guild.emojis, id=self.settings.labourEmoji))\n await message.add_reaction(discord.utils.get(channel.guild.emojis, id=self.settings.clergyEmoji))\n await message.add_reaction(discord.utils.get(channel.guild.emojis, id=self.settings.kingdomEmoji))\n await message.add_reaction('♂')\n await message.add_reaction('♀')\n else:\n await ctx.send('At least one of the Required Parameters for the Ranking System has not been set, consult `/set check`!')\n \n @set.command(description='Sets the Channel for the Ranking Message')\n async def rankingChannel(self, ctx, channel: str):\n try:\n if self.bot.get_channel(int(channel)) != None:\n update_setting(self.conn, (channel,'rankingChannel'))\n self.settings.rankingChannel = int(channel)\n await ctx.send('rankingChannel set to: ' + self.bot.get_channel(int(channel)).mention)\n if self.settings.rankingMessage != None:\n update_setting(self.conn, (None,'rankingMessage'))\n self.settings.rankingMessage = None\n await ctx.send('The Ranking Channel was changed and thus the Ranking Message has been reset!\\nUse `/set rankingMessage MessageID` to set it again!')\n else:\n await ctx.send('No channel with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The channel ID should only consist of an integer number!')\n \n @set.command(description='Sets the Message for the Ranking Reactions')\n async def rankingMessage(self, ctx, message: str):\n try:\n actualMessage = await self.bot.get_channel(self.settings.rankingChannel).fetch_message(int(message))\n self.settings.rankingMessage = int(message)\n update_setting(self.conn, (message,'rankingMessage'))\n await ctx.send('rankingMessage set to: ' + actualMessage.jump_url)\n except ValueError as e:\n await ctx.send('Invalid ID! The message ID should only consist of an integer number!')\n \n @rankingMessage.error\n async def rankingMessage_error(self, ctx, error):\n if isinstance(error, commands.CommandInvokeError):\n if isinstance(error.original, discord.errors.NotFound):\n await ctx.send('No message with the provided ID was found! Make sure you have set the correct Channel with `/set rankingChannel`!')\n \n @set.command(description='Sets the Emoji for the Labour Ranking Tree')\n async def labourEmoji(self, ctx, emoji: str):\n try:\n if self.bot.get_emoji(int(emoji)) != None:\n update_setting(self.conn, (emoji,'labourEmoji'))\n self.settings.labourEmoji = int(emoji)\n await ctx.send('labourEmoji set to: ' + str(self.bot.get_emoji(int(emoji))))\n else:\n await ctx.send('No emoji with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The emoji ID should only consist of an integer number!')\n \n @set.command(description='Sets the Emoji for the Clergy Ranking Tree')\n async def clergyEmoji(self, ctx, emoji: str):\n try:\n if self.bot.get_emoji(int(emoji)) != None:\n update_setting(self.conn, (emoji,'clergyEmoji'))\n self.settings.clergyEmoji = int(emoji)\n await ctx.send('clergyEmoji set to: ' + str(self.bot.get_emoji(int(emoji))))\n else:\n await ctx.send('No emoji with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The emoji ID should only consist of an integer number!')\n \n @set.command(description='Sets the Emoji for the Kingdom Ranking Tree')\n async def kingdomEmoji(self, ctx, emoji: str):\n try:\n if self.bot.get_emoji(int(emoji)) != None:\n update_setting(self.conn, (emoji,'kingdomEmoji'))\n self.settings.kingdomEmoji = int(emoji)\n await ctx.send('kingdomEmoji set to: ' + str(self.bot.get_emoji(int(emoji))))\n else:\n await ctx.send('No emoji with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The emoji ID should only consist of an integer number!')\n \n @set.command(description='Sets the starting Role for the Ranking System')\n async def defaultRole(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'defaultRole'))\n self.settings.defaultRole = int(role)\n await ctx.send('defaultRole set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 1 Role for the Labour Ranking Tree')\n async def labourRole1(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'labourRole1'))\n self.settings.labourRole1 = int(role)\n await ctx.send('labourRole1 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 2 Role for the Labour Ranking Tree')\n async def labourRole2(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'labourRole2'))\n self.settings.labourRole2 = int(role)\n await ctx.send('labourRole2 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 3 Role for the Labour Ranking Tree')\n async def labourRole3(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'labourRole3'))\n self.settings.labourRole3 = int(role)\n await ctx.send('labourRole3 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 4 Role for the Labour Ranking Tree')\n async def labourRole4(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'labourRole4'))\n self.settings.labourRole4 = int(role)\n await ctx.send('labourRole4 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 1 Role for the Clergy Ranking Tree')\n async def clergyRole1(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'clergyRole1'))\n self.settings.clergyRole1 = int(role)\n await ctx.send('clergyRole1 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 2 Role for the Clergy Ranking Tree')\n async def clergyRole2(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'clergyRole2'))\n self.settings.clergyRole2 = int(role)\n await ctx.send('clergyRole2 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 3 Role for the Clergy Ranking Tree')\n async def clergyRole3(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'clergyRole3'))\n self.settings.clergyRole3 = int(role)\n await ctx.send('clergyRole3 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 4 Role for the Clergy Ranking Tree')\n async def clergyRole4(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'clergyRole4'))\n self.settings.clergyRole4 = int(role)\n await ctx.send('clergyRole4 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 1 Role for the Kingdom Ranking Tree')\n async def kingdomRole1(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'kingdomRole1'))\n self.settings.kingdomRole1 = int(role)\n await ctx.send('kingdomRole1 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 2 Role for the Kingdom Ranking Tree')\n async def kingdomRole2(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'kingdomRole2'))\n self.settings.kingdomRole2 = int(role)\n await ctx.send('kingdomRole2 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 3 Role for the Kingdom Ranking Tree')\n async def kingdomRole3(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'kingdomRole3'))\n self.settings.kingdomRole3 = int(role)\n await ctx.send('kingdomRole3 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the level 4 Role for the Kingdom Ranking Tree')\n async def kingdomRole4(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'kingdomRole4'))\n self.settings.kingdomRole4 = int(role)\n await ctx.send('kingdomRole4 set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the Highest Role all Ranking Trees lead to')\n async def highestRole(self, ctx, role: str):\n try:\n if ctx.guild.get_role(int(role)) != None:\n update_setting(self.conn, (role,'highestRole'))\n self.settings.highestRole = int(role)\n await ctx.send('highestRole set to: ' + ctx.guild.get_role(int(role)).mention)\n else:\n await ctx.send('No role with the provided ID was found!')\n except ValueError as e:\n await ctx.send('Invalid ID! The role ID should only consist of an integer number!')\n \n @set.command(description='Sets the Experience Required for Level 1')\n async def expLevel1(self, ctx, exp: str):\n try:\n self.settings.expLevel1 = int(exp)\n update_setting(self.conn, (exp,'expLevel1'))\n await ctx.send('expLevel1 set to: ' + exp + ' messages')\n except ValueError as e:\n await ctx.send('Invalid Amount of Experience! The experience amount should only consist of an integer number!')\n \n @set.command(description='Sets the Experience Required for Level 2')\n async def expLevel2(self, ctx, exp: str):\n try:\n self.settings.expLevel2 = int(exp)\n update_setting(self.conn, (exp,'expLevel2'))\n await ctx.send('expLevel2 set to: ' + exp + ' messages')\n except ValueError as e:\n await ctx.send('Invalid Amount of Experience! The experience amount should only consist of an integer number!')\n \n @set.command(description='Sets the Experience Required for Level 3')\n async def expLevel3(self, ctx, exp: str):\n try:\n self.settings.expLevel3 = int(exp)\n update_setting(self.conn, (exp,'expLevel3'))\n await ctx.send('expLevel3 set to: ' + exp + ' messages')\n except ValueError as e:\n await ctx.send('Invalid Amount of Experience! The experience amount should only consist of an integer number!')\n \n @set.command(description='Sets the Experience Required for Level 4')\n async def expLevel4(self, ctx, exp: str):\n try:\n self.settings.expLevel4 = int(exp)\n update_setting(self.conn, (exp,'expLevel4'))\n await ctx.send('expLevel4 set to: ' + exp + ' messages')\n except ValueError as e:\n await ctx.send('Invalid Amount of Experience! The experience amount should only consist of an integer number!')\n \n @set.command(description='Sets the Experience Required for the Highest Level')\n async def expLevelH(self, ctx, exp: str):\n try:\n self.settings.expLevelH = int(exp)\n update_setting(self.conn, (exp,'expLevelH'))\n await ctx.send('expLevelH set to: ' + exp + ' messages')\n except ValueError as e:\n await ctx.send('Invalid Amount of Experience! The experience amount should only consist of an integer number!')\n \n @set.command(aliases=['commands'], description='Displays this help message')\n async def help(self, ctx):\n helpMessage1 = '```Available Settings Commands\\n\\n'\n helpMessage2 = '```'\n settingsCog = self.bot.get_cog('SettingsCommands')\n maxLength = 0\n for command in settingsCog.walk_commands():\n length = 0\n if len(command.aliases) > 0:\n for alias in command.aliases:\n length += len(alias) + 7\n length += len(command.name)\n else:\n length = len(command.name)\n if length > maxLength:\n maxLength = length\n \n settingsCommands = []\n for command in settingsCog.walk_commands():\n settingsCommands.append(command)\n settingsCommands.pop(0)\n settingsCommands.sort(key=lambda command: command.name)\n \n for i,command in enumerate(settingsCommands):\n if i < len(settingsCommands)/2:\n spaces = ''\n length = 0\n if len(command.aliases) > 0:\n aliasStr = ''\n for alias in command.aliases:\n length += len(alias) + 7\n aliasStr += ', /set ' + alias\n length += len(command.name)\n for _ in range(length,maxLength + 4):\n spaces += ' '\n helpMessage1 += '/set ' + command.name + aliasStr + spaces + command.description + '\\n'\n else:\n length = len(command.name)\n for _ in range(length,maxLength + 4):\n spaces += ' '\n helpMessage1 += '/set ' + command.name + spaces + command.description + '\\n'\n else:\n spaces = ''\n length = 0\n if len(command.aliases) > 0:\n aliasStr = ''\n for alias in command.aliases:\n length += len(alias) + 7\n aliasStr += ', /set ' + alias\n length += len(command.name)\n for _ in range(length,maxLength + 4):\n spaces += ' '\n helpMessage2 += '/set ' + command.name + aliasStr + spaces + command.description + '\\n'\n else:\n length = len(command.name)\n for _ in range(length,maxLength + 4):\n spaces += ' '\n helpMessage2 += '/set ' + command.name + spaces + command.description + '\\n'\n \n helpMessage1 += '```'\n helpMessage2 += '```'\n await ctx.send(helpMessage1)\n await ctx.send(helpMessage2)\n","repo_name":"Minotorious/FoundationBot","sub_path":"foundationBotSettingsCommands.py","file_name":"foundationBotSettingsCommands.py","file_ext":"py","file_size_in_byte":39397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30513488332","text":"_base_ = ['slowfast_r50_8xb8-8x8x1-256e_kinetics400-rgb.py']\n\nmodel = dict(backbone=dict(slow_pathway=dict(lateral_norm=True)))\n\nparam_scheduler = [\n dict(\n type='LinearLR',\n start_factor=0.1,\n by_epoch=True,\n begin=0,\n end=34,\n convert_to_iter_based=True),\n dict(\n type='MultiStepLR',\n begin=0,\n end=256,\n by_epoch=True,\n milestones=[94, 154, 196],\n gamma=0.1)\n]\n","repo_name":"open-mmlab/mmaction2","sub_path":"configs/recognition/slowfast/slowfast_r50_8xb8-8x8x1-steplr-256e_kinetics400-rgb.py","file_name":"slowfast_r50_8xb8-8x8x1-steplr-256e_kinetics400-rgb.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","stars":3560,"dataset":"github-code","pt":"3"}
+{"seq_id":"4281660193","text":"class InvalidInputException(Exception):\n def __str__(self):\n return \"Invalid Input given.\"\n\n\ndef numShortestPaths(g, start, end):\n \"\"\"graph, start node, end node -> int\n Purpose: find the number of shortest paths between two nodes in a graph\n Raises: raise InvalidInputException if an input is None, or \n if start or end are not in g\"\"\"\n shortestPath = []\n found = False\n queue = []\n decorate = {}\n\n # add start node to queue\n queue.append(start)\n # iterate on queue and decorate neighbors that have not been decorated\n while len(queue) != 0 and not found:\n vertex = queue.pop(0)\n # get to last node, and trace back to first node\n if vertex is end:\n found = True\n #count paths\n while vertex is not start:\n shortestPath.insert(0, vertex)\n vertex = decorate[vertex]\n shortestPath.insert(0, start)\n\n for edge in g.incidentEdges(vertex):\n nextVertex = g.opposite(vertex, edge)\n if nextVertex not in decorate:\n decorate[nextVertex] = [vertex]\n queue.append(nextVertex)\n else:\n decorate[nextVertex].append(vertex)\n\n # or else return empty list\n return shortestPath\n","repo_name":"AT1924/Homework","sub_path":"hw9/numShortestPaths.py","file_name":"numShortestPaths.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36524834017","text":"import numpy as np\nfrom sympy import *\nfrom sympy import Symbol, Derivative, solve\nfrom scipy.interpolate import InterpolatedUnivariateSpline\n\n\ndef inver(s):\n return (-(1250*s - 81*((10000*s)/81 + 4)**(1/2) + 162)/(1250*s))**(1/2)/2 + 1/2\n\ndef f(s):\n return 0.1296*(s**2/(s**2+(1-s)**2))\n\ndef df(s):\n return 0.1296*(-2*(s-1)*s/(2*s**2-2*s+1)**2);\n\ndef BL_exact(S0,x, fi, A, Swr, Sor, ftime):\n\n SL = 1 - Sor;\n SR = 0 + Swr;\n inf_point=0.5;\n St = 0.7071;\n\n tstep = 0.2;\n S = S0;\n t = ftime;\n for i in range(len(x)-1):\n if SRpos:\n S[i]=S[i+1];\n\n\n elif inf_point<=SR and SR(x[i]/t):\n S[i]=inver(x[i]/t)\n elif ss<(x[i]/t):\n S[i]=S[i+1]\n\n return S\n","repo_name":"DudaGalindo/compositional_adm","sub_path":"case_plots/BL_exact_Gustavo.py","file_name":"BL_exact_Gustavo.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"857978922","text":"from django.shortcuts import render\nfrom .models import Order, Product, Client\nfrom datetime import date\n\n\ndef get_orders(request):\n orders = Order.objects.all()\n context = {\"orders\": orders}\n return render(request, \"hw3/orders.html\", context)\n\n\ndef get_order(request, order_id):\n order = Order.objects.get(pk=order_id)\n products = Product.objects.filter(order=order)\n # products = Product.objects.select_related()\n context = {\n 'order': order,\n 'products': products,\n }\n return render(request, \"hw3/order.html\", context)\n\n\ndef get_order_gt(request, delta):\n order_list = []\n orders = Order.objects.all()\n corrent_date = date.today()\n\n for order in orders:\n res_delta = (corrent_date - order.date_order).days\n if res_delta > delta:\n order_list.append(order)\n context = {\n 'delta': delta,\n 'orders': order_list,\n }\n return render(request, \"hw3/delta.html\", context)\n\n\ndef get_order_sort(request):\n order_list_sort = {}\n orders = Order.objects.all()\n corrent_date = date.today()\n\n for order in orders:\n res_delta = (corrent_date - order.date_order).days\n order_list_sort.setdefault(order, res_delta)\n print(order_list_sort)\n context = {\n 'orders': order_list_sort,\n }\n return render(request, \"hw3/delta_sort.html\", context)\n","repo_name":"ArhipovVladimir/Django","sub_path":"Seminar1/hw3/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29265691551","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\na = np.array([[8,9,20], [10,31,22]])\nb = np.array([[1,2,3], [4,5,6]])\nprint(a-b)\n\n\n# In[5]:\n\n\ns=np.array([ [1,2,3], [4,5,6] ])\n\nprint(s.shape)\n\n\n# In[6]:\n\n\nimport numpy as np\nb = np.array([[1,2], [3,4]])\nprint(b.transpose())\n\n\n# In[7]:\n\n\n4\nimport numpy as np\nb = np.array([[1,2], [3,4]])\nprint(np.sum (b, axis = 1))\n\n\n# In[10]:\n\n\n1\ns = 'Sheher mein'\na = 'aeiouAEIOU'\nfor i in range(len(s)):\n\n if(s.index(s[i])%2 == 0): \n print(i)\n\n if(s[i] in a):\n\n s = s.replace(s[i], '_')\n\nprint(s)\n\n\n# In[11]:\n\n\n123\ns = 'The Joy of Computing'\nprint(s[3:12])\n\n\n# In[15]:\n\n\n1\n2\na = ['', 'h', 'e', 'l','','l']\nprint('.'.join(a))\n\n\n# In[17]:\n\n\ns = 'Hello Everyone' \nprint(s.lower())\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"Chirayu2727/NPTEL--THE-JOY-OF-COMPUTING-OF-PYTHON","sub_path":"_ Assignment 10 .py","file_name":"_ Assignment 10 .py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18614418661","text":"import asyncio\n\nfrom aiohttp.web import Application\nfrom aiohttp_json_rpc import JsonRpc\n\nimport logging\n\nfrom .utils import TYPE_CHECKING, NewType\n\nif TYPE_CHECKING:\n from .bot import Red\n\nlog = logging.getLogger('red.rpc')\nJsonSerializable = NewType('JsonSerializable', dict)\n\n_rpc = JsonRpc(logger=log)\n\n_rpc_server = None # type: asyncio.AbstractServer\n\n\nasync def initialize(bot: \"Red\"):\n global _rpc_server\n\n app = Application(loop=bot.loop)\n app.router.add_route('*', '/rpc', _rpc)\n\n handler = app.make_handler()\n\n _rpc_server = await bot.loop.create_server(handler, '127.0.0.1', 6133)\n\n log.debug('Created RPC _rpc_server listener.')\n\n\ndef add_topic(topic_name: str):\n \"\"\"\n Adds a topic for clients to listen to.\n\n Parameters\n ----------\n topic_name\n \"\"\"\n _rpc.add_topics(topic_name)\n\n\ndef notify(topic_name: str, data: JsonSerializable):\n \"\"\"\n Publishes a notification for the given topic name to all listening clients.\n\n data MUST be json serializable.\n\n note::\n\n This method will fail silently.\n\n Parameters\n ----------\n topic_name\n data\n \"\"\"\n _rpc.notify(topic_name, data)\n\n\ndef add_method(prefix, method):\n \"\"\"\n Makes a method available to RPC clients. The name given to clients will be as\n follows::\n\n \"{}__{}\".format(prefix, method.__name__)\n\n note::\n\n This method will fail silently.\n\n Parameters\n ----------\n prefix\n method\n MUST BE A COROUTINE OR OBJECT.\n \"\"\"\n _rpc.add_methods(\n ('', method),\n prefix=prefix\n )\n\n\ndef clean_up():\n if _rpc_server is not None:\n _rpc_server.close()\n","repo_name":"ATeddyBear/Niddbot","sub_path":"redbot/core/rpc.py","file_name":"rpc.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"36055676500","text":"import cv2\nimport urllib\n\nIMAGES_URL = {\n 'lena.png': 'https://i.imgur.com/ncE3dty.png',\n 'ednaldo.jpg': 'https://i.imgur.com/JnJD9FB.jpg',\n 'batman.jpg': 'https://i.imgur.com/8jKnbg5.jpg',\n 'calculista.jpg': 'https://i.imgur.com/MQNty8H.jpg',\n 'vacilao.jpg': 'https://i.imgur.com/cgp0aY9.jpg',\n 'harold.jpg': 'https://i.imgur.com/C8YrIjB.jpg',\n 'tool.jpg': 'https://i.imgur.com/bgyZxWt.jpg'\n}\n\n# Load Face classifier\nfrontal_face_classifier = cv2.CascadeClassifier(\n cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n\n# Load Eye classifier\neye_classifier = cv2.CascadeClassifier(\n cv2.data.haarcascades + 'haarcascade_eye.xml')\n\n# Load Smile classifier\nsmile_classifier = cv2.CascadeClassifier(\n cv2.data.haarcascades + 'haarcascade_smile.xml')\n\n# Load Upper Body classifier\nupperbody_classifier = cv2.CascadeClassifier(\n cv2.data.haarcascades + 'haarcascade_upperbody.xml')\n\n# Load Profile Face classifier\nprofile_face_classifier = cv2.CascadeClassifier(\n cv2.data.haarcascades + 'haarcascade_profileface.xml')\n\n# Load Hand classifier\nurllib.request.urlretrieve('https://raw.githubusercontent.com/Aravindlivewire/Opencv/master/haarcascade/aGest.xml', filename='xml/aGest.xml')\nhand_classifier = cv2.CascadeClassifier('xml/aGest.xml')\n\n# Create classifiers dictionary\nDEFAULT_CLASSIFIERS = {\n 'frontal_face':\n { \n 'c_name' : 'Frontal face',\n 'classifier' : frontal_face_classifier,\n 'minNeighbors': 5,\n 'color' : (255,0,0),\n 'sub_search' : True,\n 'is_sub' : False,\n 'sub_class' : ['eye','smile']\n },\n 'profile_face':\n {\n 'c_name' : 'Profile face',\n 'classifier' : profile_face_classifier,\n 'minNeighbors': 5,\n 'color' : (255,0,255),\n 'sub_search' : True,\n 'is_sub' : False,\n 'sub_class' : ['eye','smile']\n },\n 'hand':\n {\n 'c_name' : 'Hand',\n 'classifier' : hand_classifier,\n 'minNeighbors': 3,\n 'color' : (255,255,0),\n 'sub_search' : False,\n 'is_sub' : False,\n 'sub_class' : None\n },\n 'eye':\n {\n 'c_name' : 'Eye',\n 'classifier' : eye_classifier,\n 'minNeighbors': 8,\n 'color' : (0,0,255),\n 'sub_search' : False,\n 'is_sub' : True,\n 'sub_class' : None\n },\n 'smile':\n {\n 'c_name' : 'Smile',\n 'classifier' : smile_classifier,\n 'minNeighbors': 10,\n 'color' : (0,255,0),\n 'sub_search' : False,\n 'is_sub' : True,\n 'sub_class' : None\n },\n}","repo_name":"baiochi/Unstructured-Data-Analysis","sub_path":"src/defines.py","file_name":"defines.py","file_ext":"py","file_size_in_byte":2702,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"8515300482","text":"from pydub import AudioSegment\nfrom pydub.silence import split_on_silence\nimport os\nimport requests\nimport json\nimport unidecode\nimport base64\n\ndef split_audio(filename):\n sound = AudioSegment.from_file(filename+'_adjusted.wav')\n\n audio_chunks = split_on_silence(sound, \n # must be silent for at least half a second\n min_silence_len=1000,\n\n # consider it silent if quieter than -16 dBFS\n silence_thresh=-32\n )\n\n if not os.path.isdir(filename+'_splitted'):\n os.mkdir(filename+'_splitted')\n\n print(\"Number of chunks splitteds: \", len(audio_chunks))\n\n base64chunks = []\n for i, chunk in enumerate(audio_chunks):\n out_file = filename+\"_splitted/chunk(2)-{0}.wav\".format(i)\n print(\"exporting\", out_file)\n chunk.export(out_file, format=\"ogg\")\n\n f1 = open(out_file, 'rb')\n encoded_f1 = base64.b64encode(f1.read())\n base64chunks.append(str(encoded_f1,'ascii', 'ignore'))\n \n return base64chunks\n\nserverurl = '104.131.48.82'\nlocalurl = '127.0.0.1:8080'\n\ndata_query_auth = '{\"username\": \"desenvolvimento@tagchat.com\", \"password\": \"Devs@tagchat!@#\"}'\nmy_headers_auth = {'Content-Type': 'application/json'}\nresponse = requests.post('http://'+localurl+'/auth', data=data_query_auth, headers=my_headers_auth)\n\naccess_token = 'JWT ' + str(response.json()['access_token'])\nprint(access_token)\n\nbase64chunks = split_audio('../Audios/audio3')\nfinal_response = \"\"\n\nmy_headers = {'Authorization': access_token}\nfor base64chunk in base64chunks:\n data_query = {\"base64audio\": base64chunk}\n response = requests.post('http://'+serverurl+'/audio-to-text', data=json.dumps(data_query), headers=my_headers)\n print(response.json())\n print(type(response))\n print(response)\n final_response += \" \" + response.json()\n\nprint(final_response)","repo_name":"DCandelero/Audio-Management","sub_path":"src/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15639590627","text":"import xmlrunner\nimport unittest\nimport logging\nimport configparser\nimport xlsxwriter as xl\nimport xlrd\nfrom itertools import zip_longest\nimport sys\nimport os\n\n\nlogger = logging.getLogger()\nlogger.level = logging.ERROR\nstream_handler = logging.StreamHandler(sys.stdout)\nlogger.addHandler(stream_handler)\n\n\nclass piHiValidation(unittest.TestCase):\n\n def setUp(self):\n \"\"\"code will parse the configuration to get the path and HSS and TRUST\"\"\"\n config = configparser.ConfigParser()\n config.read('config.ini')\n global path\n global path_HI\n global HSS_id\n global TRUST_NAME\n global wb\n global file\n file = open(\"logfile.txt\", \"w\")\n wb = xlrd.open_workbook('piHI_Mapping.xlsx')\n path = config['DEV']['PATH']\n path_HI = config['DEV']['PATH_HI']\n HSS_id = config['DEV']['HSS_ID']\n TRUST_NAME = config['DEV']['TRUST_NAME']\n\n def add_log(self, text):\n file.write(text + '\\n')\n\n def test_piHiComparison(self):\n \"\"\"PI CDE text file comparison with HI for needed HSS and TRUST\"\"\"\n self.PI_CDE_files = [x for x in os.listdir(path) if x.__contains__(HSS_id + '_' + TRUST_NAME)]\n \"\"\"pick up the excel files from HI downloaded from Vertica\"\"\"\n self.HI_CDE_files = [x for x in os.listdir(path_HI) if x.startswith('EDWUK')]\n \"\"\"convert the CDE PI files before comparison\"\"\"\n self.pi_file_convert(self.PI_CDE_files)\n \"\"\"pick all the converted excel files for PI\"\"\"\n self.PI_CDE_excel_list = [x for x in os.listdir(path) if x.__contains__('.xlsx')]\n \"\"\"all the files are ready for data validation\"\"\"\n piHiValidation.prepare(self, self.PI_CDE_excel_list, self.HI_CDE_files)\n\n \"\"\"check the files for appropriate CDE type and rename before comparison\"\"\"\n def rename(self, text_file):\n ws = wb.sheet_by_index(0)\n for row_act in range(1, ws.nrows):\n if ws.row_values(row_act)[0] in text_file:\n renamed_txt = text_file.replace(text_file, 'PI_' + ws.row_values(row_act)[1])\n piHiValidation.conversion(self, renamed_txt, text_file)\n\n \"\"\"convert the CDE PI text file to CDE excel from PI\"\"\"\n def conversion(self, text, text_file):\n workbook = xl.Workbook(path + text + \".xlsx\")\n worksheet = workbook.add_worksheet(text)\n with open(path + text_file, \"r\", errors='ignore') as file: # this is to ignore the encoding on the file\n for r, row in enumerate(file.readlines()):\n for c, value in enumerate(row.split('|')):\n worksheet.write(r, c, value)\n # os.remove(path + text_file) # remove the text CDE PI files after conversion\n workbook.close()\n\n \"\"\"check the number of CDE PI files before conversion\"\"\"\n def pi_file_convert(self, files):\n if len(files) > 1:\n for filename in files:\n piHiValidation.rename(self, filename)\n elif len(files) == 0:\n self.assertNotEqual(files, [], msg='no PI CDE files or with expected naming convention')\n else:\n piHiValidation.rename(self, files[0])\n\n \"\"\"making sure the PI and HI excel are ready for comparison after removal of unnecessary columns\"\"\"\n def prepare(self, pi_list, hi_list):\n for count in range(0, max(len(pi_list), len(hi_list))):\n pi_excel = xlrd.open_workbook(path+pi_list[count])\n hi_excel = xlrd.open_workbook(path_HI+hi_list[count])\n # to remove the unwanted columns\n # new_hi = pd.ExcelFile(path_HI+hi_list[count]).parse(sheet_name=0).drop(['nhs_trust_nbr', 'health_system_id', 'health_system_source_id', 'cde_trigger_date', 'update_dt_tm'], axis=1)\n # new_hi.to_excel(path_HI+hi_list[count], index=False)\n print('Comparing- ' + hi_list[count])\n piHiValidation.add_log(self, '\\n' + 'Comparing- ' + hi_list[count])\n piHiValidation.compare(self, pi_excel, hi_excel)\n\n \"\"\"compare the excel line by line for data validation\"\"\"\n def compare(self, pi_excel, hi_excel):\n self.fail = 0\n sheet1 = pi_excel.sheet_by_index(0)\n sheet2 = hi_excel.sheet_by_index(0)\n for rownum in range(max(sheet1.nrows, sheet2.nrows)):\n if rownum < sheet2.nrows:\n try:\n pi_rb1 = sheet1.row_values(rownum)\n hi_rb2 = sheet2.row_values(rownum)\n for colnum, (c1, c2) in enumerate(zip_longest(pi_rb1, hi_rb2)):\n if c1 != c2:\n self.fail = 1\n self.assertNotEqual(c1, c2)\n print(\"=======================================================================================\")\n piHiValidation.add_log(self, \"=======================================================================================\")\n print(\"ISSUE in ROW {} COL {} - {} IN PI IS NOT SAME AS {} IN ABOVE MENTIONED HI \".format(rownum + 1, colnum + 1, c1, c2))\n piHiValidation.add_log(self, 'ISSUE in ROW {} COL {} - \"{}\" IN PI IS NOT SAME AS \"{}\" IN ABOVE MENTIONED HI '.format(rownum + 1, colnum + 1, c1, c2))\n else:\n self.assertEqual(c1, c2)\n except IndexError:\n print(\"Total Rows in PI - {} and total rows in HI - {} aren't same\".format(sheet1.nrows, sheet2.nrows))\n piHiValidation.add_log(self, \"Total Rows in PI - {} and total rows in HI - {} aren't same\".format(sheet1.nrows, sheet2.nrows))\n else:\n print(\"Row {} missing in HI excel file\".format(rownum + 1))\n piHiValidation.add_log(self, \"Row {} missing in HI excel file\".format(rownum + 1))\n if self.fail == 0:\n print(\"=======================================================================================\")\n piHiValidation.add_log(self, \"=======================================================================================\")\n print(\"Comparison was successful\\n\")\n piHiValidation.add_log(self, \"Comparison was successful\\n\")\n if self.fail == 1:\n print(\"Comparison was unsuccessful with above issue(s), refer \" + file.name + \" for more details\\n\")\n piHiValidation.add_log(self, \"Comparison was unsuccessful with above issue(s)\\n\")\n\n\nif __name__ == \"__main__\":\n unittest.main(testRunner=xmlrunner.XMLTestRunner(output=\"./xml_logs\"))\n\n\n","repo_name":"vivekrkini/CernerUK","sub_path":"HelloWorld/Comparison/piHIComparison.py","file_name":"piHIComparison.py","file_ext":"py","file_size_in_byte":6576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23183418721","text":"# 2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder.\n\n# What is the smallest positive number that is evenly divisible by all of the numbers from 1 to 20?\n\n# 错误的思路，这样12位数以下还可以，20位太慢了\n# def smallest_multiple(n):\n# num = 1\n# flag = 0\n# while flag!=n:\n# num+=1\n# for i in range(1,n+1):\n# # print(\"i=\",i )\n# if num % i != 0:\n# flag = 0\n \n# else:\n# flag+=1\n# # print(\"@:\",num)\n\n# print(\"#:\",num)\n\ndef biggest_common(a,b):\n result = 1\n small = 1\n if a < b:\n small = a\n else:\n small = b\n\n for i in range(1,small+1):\n if a % i == 0 and b % i ==0:\n result = i\n print('bigggestcommon=',result)\n return result\n\ndef smallest_multiple(a,b):\n c = biggest_common(a, b)\n a1 = a//c\n b1 = b//c\n print(\"a=\",a,\"b=\",b,\"c=\",c,a1,b1)\n return c*a1*b1\n\ndef find_smallest_multiple(n):\n result = 1\n for i in range(1, n+1):\n result = smallest_multiple(i,result)\n print(result)\n\nif __name__ == \"__main__\":\n find_smallest_multiple(20)\n","repo_name":"greatabel/puzzle_I_cracked","sub_path":"3ProjectEuler/i1_25/i05smallest_multiple.py","file_name":"i05smallest_multiple.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"31763251646","text":"import json\n\nimport requests\nfrom sqlalchemy.orm import Session\n\nfrom core.config import settings\nfrom sql_app.crud import get_label\nfrom sql_app.models import Label\nfrom sql_app.models import Task\n\nurl = \"https://api.trello.com/1\"\n\nheaders = {\"Accept\": \"application/json\"}\n\ndefault_query = {\n \"idList\": settings.TRELLO_TO_DO_LIST_ID,\n \"key\": settings.TRELLO_API_KEY,\n \"token\": settings.TRELLO_API_TOKEN,\n}\n\n\nasync def validate_fields(type: str, title: str, description: str, category: str):\n errors = {}\n if type not in [x[0] for x in Task.TYPES]:\n errors[\"type\"] = f\"'{type}' is an invalid type for tasks.\"\n if not category and type == Task.TASK:\n errors[\"category\"] = f\"The type '{type}' required caregory field.\"\n if category:\n if type != Task.TASK:\n errors[\"category\"] = f\"The type '{type}' don't support caregory field.\"\n else:\n if category not in [x[0] for x in Task.CATEGORIES]:\n errors[\"category\"] = f\"'{category}' is an invalid category.\"\n if title and type == Task.BUG:\n errors[\"title\"] = f\"The type '{type}' don't support title field.\"\n if not title and type != Task.BUG:\n errors[\"title\"] = f\"The type '{type}' required title field.\"\n if description and type == Task.TASK:\n errors[\"description\"] = f\"The type '{type}' don't support description field.\"\n if not description and type != Task.TASK:\n errors[\"description\"] = f\"The type '{type}' required description field.\"\n return errors\n\n\nasync def get_id_label(label: str, db: Session):\n label_id = None\n label_obj = get_label(db=db, label=label)\n if not label_obj:\n label_id = await create_label(label=label, db=db)\n else:\n label_id = label_obj.trello_id\n return label_id\n\n\nasync def create_label(label: str, db: Session):\n label_obj = Label(name=label)\n query = default_query.copy()\n query[\"idBoard\"] = settings.TRELLO_BOARD_ID\n query[\"name\"] = label\n response = requests.request(\"POST\", f\"{url}/labels\", headers=headers, params=query)\n response_data = json.loads(response.text)\n label_id = response_data[\"id\"]\n label_obj.trello_id = label_id\n db.add(label_obj)\n db.commit()\n db.refresh(label_obj)\n return label_id\n\n\nasync def sync_with_trello(task: Task, db: Session):\n query = default_query.copy()\n if task.title:\n query[\"name\"] = str(task.title)\n if task.description:\n query[\"desc\"] = task.description\n if task.category:\n query[\"idLabels\"] = await get_id_label(label=task.category, db=db)\n if task.type == Task.BUG:\n query[\"idLabels\"] = await get_id_label(label=\"bug\", db=db)\n requests.request(\"POST\", f\"{url}/cards\", headers=headers, params=query)\n","repo_name":"emmanuel-santos1/space-x","sub_path":"task/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25009592308","text":"import pytest\n\nfrom .task2 import sort_array\n\n\n@pytest.mark.parametrize(\n \"arr, expected\",\n [\n ([3, 7, 8, 9], [8, 3, 9, 7]),\n ([9, 7, 8, 3], [8, 3, 9, 7]),\n ([], []),\n (\"abc\", \"Function accepting only list\"),\n ],\n)\ndef test_sort_array(arr, expected):\n assert sort_array(arr) == expected\n","repo_name":"Dm1triiSmirnov/tasks","sub_path":"task2/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29525034452","text":"# 練習題decorator\n\ndef revise_divide(func):\n def inner(n, d):\n if d == 0:\n print(\"除數不可為0\")\n else:\n m = func(n, d)\n print(m)\n return inner\n\ndef divide(n, d):\n return n/d\n\nn = input(\"請輸入被除數: \")\nd = input(\"請輸入除數: \")\nn = int(n)\nd = int(d)\ndivide = revise_divide(divide)\ndivide(n, d)","repo_name":"PaulSurgeon/decorator","sub_path":"div.py","file_name":"div.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70682633363","text":"import os\nfrom datetime import datetime # 导入时间函数\n\nif not os.path.exists('./6_7'): # 创建文件夹\n os.mkdir('./6_7')\nfp = open('暂时.txt','a',encoding = 'utf-8') # 创建文件 ，类型.txt.(a,w均可文件不存在时创建新的文件，且把写入的字符串加入到文件末尾)\n\na = datetime.now() # 获取当前时间,类型位\n# print(a) # 打印现在的时间\n# print(type(a))\ntime_now = a.strftime(\"%A, %d. %B %Y %I:%M%p\") # 时间格式化，类型为\n# print(time_now)\n# print(type(time_now))\n# print(a)\n# time_now = str(a)\nfp.writelines(time_now+'\\n') # 写入时间 weitelines 我用'\\n'符加入进去还是一列","repo_name":"jihaikang/2021-","sub_path":"新建文件夹.py","file_name":"新建文件夹.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32677203284","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\n\nimport six\n\nfrom tensorflow_docs.api_generator import tf_inspect\n\n\ndef maybe_singleton(py_object):\n \"\"\"Returns `True` if `py_object` might be a singleton value .\n\n Many immutable values in python act like singletons: small ints, some strings,\n Bools, None, the empty tuple.\n\n We can't rely on looking these up by their `id()` to find their name or\n duplicates.\n\n This function checks if the object is one of those maybe singleton values.\n\n Args:\n py_object: the object to check.\n\n Returns:\n A bool, True if the object might be a singleton.\n \"\"\"\n # isinstance accepts nested tuples of types.\n is_immutable_type = isinstance(\n py_object, (six.integer_types, six.string_types, six.binary_type,\n six.text_type, float, complex, bool, type(None)))\n\n # Check if the object is the empty tuple.\n return is_immutable_type or py_object is () # pylint: disable=literal-comparison\n\n\nclass ApiTreeNode(object):\n \"\"\"Represents a single API end-point.\n\n Attributes:\n path: A tuple of strings containing the path to the object from the root\n like `('tf', 'losses', 'hinge')`\n obj: The python object.\n children: A dictionary from short name to `ApiTreeNode`, including the\n children nodes.\n parent: The parent node.\n short_name: The last path component\n full_name: All path components joined with \".\"\n \"\"\"\n\n def __init__(self, path, obj, parent):\n self.path = path\n self.obj = obj\n self.children = {}\n self.parent = parent\n\n @property\n def short_name(self):\n return self.path[-1]\n\n @property\n def full_name(self):\n return '.'.join(self.path)\n\n\nclass ApiTree(object):\n \"\"\"Represents all api end-points as a tree.\n\n Items must be inserted in order, from root to leaf.\n\n Attributes:\n index: A dict, mapping from path tuples to `ApiTreeNode`.\n aliases: A dict, mapping from object ids to a list of all `ApiTreeNode` that\n refer to the object.\n root: The root `ApiTreeNode`\n \"\"\"\n\n def __init__(self):\n root = ApiTreeNode((), None, None)\n self.index = {(): root}\n self.aliases = collections.defaultdict(list)\n self.root = root\n\n def __contains__(self, path):\n \"\"\"Returns `True` if path exists in the tree.\n\n Args:\n path: A tuple of strings, the api path to the object.\n\n Returns:\n True if `path` exists in the tree.\n \"\"\"\n return path in self.index\n\n def __getitem__(self, path):\n \"\"\"Fetch an item from the tree.\n\n Args:\n path: A tuple of strings, the api path to the object.\n\n Returns:\n An `ApiTreeNode`.\n\n Raises:\n KeyError: If no node can be found at that path.\n \"\"\"\n return self.index[path]\n\n def __setitem__(self, path, obj):\n \"\"\"Add an object to the tree.\n\n Args:\n path: A tuple of strings.\n obj: The python object.\n \"\"\"\n parent_path = path[:-1]\n parent = self.index[parent_path]\n\n node = ApiTreeNode(path=path, obj=obj, parent=parent)\n\n self.index[path] = node\n if not maybe_singleton(obj):\n # We cannot use the duplicate mechanism for some constants, since e.g.,\n # id(c1) == id(c2) with c1=1, c2=1. This isn't problematic since constants\n # have no usable docstring and won't be documented automatically.\n self.aliases[id(obj)].append(node)\n parent.children[node.short_name] = node\n\n\nclass DocGeneratorVisitor(object):\n \"\"\"A visitor that generates docs for a python object when __call__ed.\"\"\"\n\n def __init__(self):\n \"\"\"Make a visitor.\n\n This visitor expects to be called on each node in the api. It is passed the\n path to an object, the object, and the filtered list of the object's\n children. (see the `__call__` method for details.\n\n This object accumulates the various data-structures necessary to build the\n docs, including (see the property definitions for details.):\n\n In the decsription below \"master name\" is the object's preferred fully\n qualified name.\n\n Params:\n index: A mapping from master names to python python objects.\n tree: A mapping from master names to a list if attribute names.\n reverse_index: Mapping from python object ids to master names.\n Note that this doesn't work for python numbers, strings or tuples.\n duplicate_of: A mapping from a fully qualified names to the object's\n master name. The master names are not included as keys.\n duplicates: A mapping from master names to lists of other fully qualified\n names for the object.\n \"\"\"\n self._index = {}\n self._tree = {}\n self._reverse_index = None\n self._duplicates = None\n self._duplicate_of = None\n\n self._api_tree = ApiTree()\n\n @property\n def index(self):\n \"\"\"A map from fully qualified names to objects to be documented.\n\n The index is filled when the visitor is passed to `traverse`.\n\n Returns:\n The index filled by traversal.\n \"\"\"\n return self._index\n\n @property\n def tree(self):\n \"\"\"A map from fully qualified names to all its child names for traversal.\n\n The full name to member names map is filled when the visitor is passed to\n `traverse`.\n\n Returns:\n The full name to member name map filled by traversal.\n \"\"\"\n return self._tree\n\n @property\n def reverse_index(self):\n \"\"\"A map from `id(object)` to the preferred fully qualified name.\n\n This map only contains non-primitive objects (no numbers or strings) present\n in `index` (for primitive objects, `id()` doesn't quite do the right thing).\n\n It is computed when it, `duplicate_of`, or `duplicates` are first accessed.\n\n Returns:\n The `id(object)` to full name map.\n \"\"\"\n self._maybe_find_duplicates()\n return self._reverse_index\n\n @property\n def duplicate_of(self):\n \"\"\"A map from duplicate full names to a preferred fully qualified name.\n\n This map only contains names that are not themself a preferred name.\n\n It is computed when it, `reverse_index`, or `duplicates` are first accessed.\n\n Returns:\n The map from duplicate name to preferred name.\n \"\"\"\n self._maybe_find_duplicates()\n return self._duplicate_of\n\n @property\n def duplicates(self):\n \"\"\"A map from preferred full names to a list of all names for this symbol.\n\n This function returns a map from preferred (master) name for a symbol to a\n lexicographically sorted list of all aliases for that name (incl. the master\n name). Symbols without duplicate names do not appear in this map.\n\n It is computed when it, `reverse_index`, or `duplicate_of` are first\n accessed.\n\n Returns:\n The map from master name to list of all duplicate names.\n \"\"\"\n self._maybe_find_duplicates()\n return self._duplicates\n\n def __call__(self, parent_path, parent, children):\n \"\"\"Visitor interface, see `tensorflow/tools/common:traverse` for details.\n\n This method is called for each symbol found in a traversal using\n `tensorflow/tools/common:traverse`. It should not be called directly in\n user code.\n\n Args:\n parent_path: A tuple of strings. The fully qualified path to a symbol\n found during traversal.\n parent: The Python object referenced by `parent_name`.\n children: A list of `(name, py_object)` pairs enumerating, in alphabetical\n order, the children (as determined by `tf_inspect.getmembers`) of\n `parent`. `name` is the local name of `py_object` in `parent`.\n\n Returns:\n The list of children, with any __metaclass__ removed.\n\n Raises:\n RuntimeError: If this visitor is called with a `parent` that is not a\n class or module.\n \"\"\"\n parent_name = '.'.join(parent_path)\n self._index[parent_name] = parent\n self._tree[parent_name] = []\n if parent_path not in self._api_tree:\n self._api_tree[parent_path] = parent\n\n if not (tf_inspect.ismodule(parent) or tf_inspect.isclass(parent)):\n raise RuntimeError('Unexpected type in visitor -- %s: %r' % (parent_name,\n parent))\n\n for (name, child) in children:\n self._api_tree[parent_path + (name,)] = child\n\n full_name = '.'.join([parent_name, name]) if parent_name else name\n self._index[full_name] = child\n self._tree[parent_name].append(name)\n\n return children\n\n def _score_name(self, name):\n \"\"\"Return a tuple of scores indicating how to sort for the best name.\n\n This function is meant to be used as the `key` to the `sorted` function.\n\n This returns a score tuple composed of the following scores:\n defining_class: Prefers method names pointing into the defining class,\n over a subclass (`ParentClass.method` over `Subclass.method`, if it\n referrs to the same method implementation).\n experimental: Prefers names that are not in \"contrib\" or \"experimental\".\n keras: Prefers keras names to non-keras names.\n module_length: Prefers submodules (tf.sub.thing) over the root namespace\n (tf.thing) over deeply nested paths (tf.a.b.c.thing)\n name: Fallback, sorts lexicographically on the full_name.\n\n Args:\n name: the full name to score, for example `tf.estimator.Estimator`\n\n Returns:\n A tuple of scores. When sorted the preferred name will have the lowest\n value.\n \"\"\"\n parts = name.split('.')\n short_name = parts[-1]\n if len(parts) == 1:\n return (-99, -99, -99, -99, short_name)\n\n container = self._index['.'.join(parts[:-1])]\n\n defining_class_score = 1\n if tf_inspect.isclass(container):\n if short_name in container.__dict__:\n # prefer the defining class\n defining_class_score = -1\n\n experimental_score = -1\n if 'contrib' in parts or any('experimental' in part for part in parts):\n experimental_score = 1\n\n keras_score = 1\n if 'keras' in parts:\n keras_score = -1\n\n while parts:\n container = self._index['.'.join(parts)]\n if tf_inspect.ismodule(container):\n break\n parts.pop()\n\n module_length = len(parts)\n\n if len(parts) == 2:\n # `tf.submodule.thing` is better than `tf.thing`\n module_length_score = -1\n else:\n # shorter is better\n module_length_score = module_length\n\n return (defining_class_score, experimental_score, keras_score,\n module_length_score, name)\n\n def _maybe_find_duplicates(self):\n \"\"\"Compute data structures containing information about duplicates.\n\n Find duplicates in `index` and decide on one to be the \"master\" name.\n\n Computes a reverse_index mapping each object id to its master name.\n\n Also computes a map `duplicate_of` from aliases to their master name (the\n master name itself has no entry in this map), and a map `duplicates` from\n master names to a lexicographically sorted list of all aliases for that name\n (incl. the master name).\n\n All these are computed and set as fields if they haven't already.\n \"\"\"\n if self._reverse_index is not None:\n return\n\n # Maps the id of a symbol to its fully qualified name. For symbols that have\n # several aliases, this map contains the first one found.\n # We use id(py_object) to get a hashable value for py_object. Note all\n # objects in _index are in memory at the same time so this is safe.\n reverse_index = {}\n\n # Decide on master names, rewire duplicates and make a duplicate_of map\n # mapping all non-master duplicates to the master name. The master symbol\n # does not have an entry in this map.\n duplicate_of = {}\n\n # Duplicates maps the main symbols to the set of all duplicates of that\n # symbol (incl. itself).\n duplicates = {}\n\n for path, node in six.iteritems(self._api_tree.index):\n if not path:\n continue\n full_name = node.full_name\n py_object = node.obj\n object_id = id(py_object)\n if full_name in duplicates:\n continue\n\n aliases = self._api_tree.aliases[object_id]\n if not aliases:\n aliases = [node]\n\n names = [alias.full_name for alias in aliases]\n\n names = sorted(names)\n # Choose the master name with a lexical sort on the tuples returned by\n # by _score_name.\n master_name = min(names, key=self._score_name)\n\n if names:\n duplicates[master_name] = list(names)\n\n names.remove(master_name)\n for name in names:\n duplicate_of[name] = master_name\n\n # Set the reverse index to the canonical name.\n if not maybe_singleton(py_object):\n reverse_index[object_id] = master_name\n\n self._duplicate_of = duplicate_of\n self._duplicates = duplicates\n self._reverse_index = reverse_index\n","repo_name":"DpD-Nightmare/Li-Ion-Battery-SOC-estimation-Using-Back-propogation-algorithm","sub_path":"docs-master/tools/tensorflow_docs/api_generator/doc_generator_visitor.py","file_name":"doc_generator_visitor.py","file_ext":"py","file_size_in_byte":12697,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"10402658353","text":"\"\"\"Classes for computing sensitivity metrics.\"\"\"\n\nfrom typing import Literal, Optional\n\nfrom cyclops.evaluate.metrics.metric import Metric\nfrom cyclops.evaluate.metrics.precision_recall import (\n BinaryRecall,\n MulticlassRecall,\n MultilabelRecall,\n)\n\n\nclass BinarySensitivity(BinaryRecall, registry_key=\"binary_sensitivity\"):\n \"\"\"Computes sensitivity score for binary classification.\n\n Parameters\n ----------\n pos_label : int, default=1\n Label of the positive class.\n threshold : float, default=0.5\n Threshold for deciding the positive class.\n zero_division : Literal[\"warn\", 0, 1], default=\"warn\"\n Value to return when there is a zero division. If set to \"warn\", this\n acts as 0, but warnings are also raised.\n\n Examples\n --------\n >>> from cyclops.evaluation.metrics import BinarySensitivity\n >>> target = [0, 1, 0, 1]\n >>> preds = [0, 1, 1, 0]\n >>> metric = Sensitivity()\n >>> metric(target, preds)\n 0.5\n >>> metric.reset_state()\n >>> target = [[0, 1, 0, 1], [0, 0, 1, 1]]\n >>> preds = [[0.1, 0.9, 0.8, 0.2], [0.2, 0.3, 0.6, 0.1]]\n >>> for t, p in zip(target, preds):\n ... metric.update_state(t, p)\n >>> metric.compute()\n 0.5\n\n \"\"\"\n\n def __init__(\n self,\n pos_label: int = 1,\n threshold: float = 0.5,\n zero_division: Literal[\"warn\", 0, 1] = \"warn\",\n ) -> None:\n \"\"\"Initialize the metric.\"\"\"\n super().__init__(\n pos_label=pos_label,\n threshold=threshold,\n zero_division=zero_division,\n )\n\n\nclass MulticlassSensitivity(MulticlassRecall, registry_key=\"multiclass_sensitivity\"):\n \"\"\"Compute the sensitivity score for multiclass classification tasks.\n\n Parameters\n ----------\n num_classes : int\n Number of classes in the dataset.\n top_k : int, optional\n If given, and predictions are probabilities/logits, the sensitivity will\n be computed only for the top k classes. Otherwise, ``top_k`` will be\n set to 1.\n average : Literal[\"micro\", \"macro\", \"weighted\", None], default=None\n If ``None``, return the sensitivity score for each class. Otherwise,\n use one of the following options to compute the average score:\n\n - ``micro``: Calculate metric globally from the total count of true\n positives and false negatives.\n - ``macro``: Calculate metric for each class, and find their\n unweighted mean. This does not take label imbalance into account.\n - ``weighted``: Calculate metric for each class, and find their\n average weighted by the support (the number of true instances\n for each class). This alters \"macro\" to account for class\n imbalance.\n zero_division : Literal[\"warn\", 0, 1], default=\"warn\"\n Value to return when there is a zero division. If set to \"warn\", this\n acts as 0, but warnings are also raised.\n\n Examples\n --------\n >>> from cyclops.evaluation.metrics import MulticlassSensitivity\n >>> target = [0, 1, 2, 0]\n >>> preds = [2, 0, 2, 1]\n >>> metric = MulticlassSensitivity(num_classes=3)\n >>> metric(target, preds)\n array([0., 0., 1.])\n >>> metric.reset_state()\n >>> target = [[0, 1, 2, 0], [2, 1, 2, 0]]\n >>> preds = [\n ... [[0.1, 0.6, 0.3],\n ... [0.05, 0.1, 0.85],\n ... [0.2, 0.7, 0.1],\n ... [0.9, 0.05, 0.05]],\n ... [[0.1, 0.6, 0.3],\n ... [0.05, 0.1, 0.85],\n ... [0.2, 0.7, 0.1],\n ... [0.9, 0.05, 0.05]]\n ... ]\n >>> for t, p in zip(target, preds):\n ... metric.update_state(t, p)\n >>> metric.compute()\n array([0.66666667, 0. , 0. ])\n\n \"\"\"\n\n def __init__(\n self,\n num_classes: int,\n top_k: Optional[int] = None,\n average: Literal[\"micro\", \"macro\", \"weighted\", None] = None,\n zero_division: Literal[\"warn\", 0, 1] = \"warn\",\n ) -> None:\n \"\"\"Initialize the metric.\"\"\"\n super().__init__(\n num_classes=num_classes,\n top_k=top_k,\n average=average,\n zero_division=zero_division,\n )\n\n\nclass MultilabelSensitivity(MultilabelRecall, registry_key=\"multilabel_sensitivity\"):\n \"\"\"Compute the sensitivity score for multilabel classification tasks.\n\n Parameters\n ----------\n num_labels : int\n Number of labels in the dataset.\n threshold : float, default=0.5\n Threshold for deciding the positive class.\n average : Literal[\"micro\", \"macro\", \"weighted\", None], default=None\n If ``None``, return the score for each class. Otherwise,\n use one of the following options to compute the average score:\n\n - ``micro``: Calculate metric globally from the total count of true\n positives and false negatives.\n - ``macro``: Calculate metric for each label, and find their\n unweighted mean. This does not take label imbalance into account.\n - ``weighted``: Calculate metric for each label, and find their\n average weighted by the support (the number of true instances\n for each label). This alters \"macro\" to account for label\n imbalance.\n zero_division : Literal[\"warn\", 0, 1], default=\"warn\"\n Value to return when there is a zero division. If set to \"warn\", this\n acts as 0, but warnings are also raised.\n\n Examples\n --------\n >>> from cyclops.evaluation.metrics import MultilabelSensitivity\n >>> target = [[0, 1, 0, 1], [0, 0, 1, 1]]\n >>> preds = [[0.1, 0.9, 0.8, 0.2], [0.2, 0.3, 0.6, 0.1]]\n >>> metric = MultilabelSensitivity(num_labels=4)\n >>> metric(target, preds)\n array([0., 1., 1. , 0. ])\n >>> metric.reset_state()\n >>> target = [[[0, 1, 0, 1], [0, 0, 1, 1]], [[0, 1, 0, 1], [0, 0, 1, 1]]]\n >>> preds = [[[0.1, 0.9, 0.8, 0.2], [0.2, 0.3, 0.6, 0.1]],\n ... [[0.1, 0.9, 0.8, 0.2], [0.2, 0.3, 0.6, 0.1]]]\n >>> for t, p in zip(target, preds):\n ... metric.update_state(t, p)\n >>> metric.compute()\n array([0., 1., 1., 0.])\n\n \"\"\"\n\n def __init__(\n self,\n num_labels: int,\n threshold: float = 0.5,\n top_k: Optional[int] = None,\n average: Literal[\"micro\", \"macro\", \"weighted\", None] = None,\n zero_division: Literal[\"warn\", 0, 1] = \"warn\",\n ) -> None:\n \"\"\"Initialize the metric.\"\"\"\n super().__init__(\n num_labels=num_labels,\n threshold=threshold,\n top_k=top_k,\n average=average,\n zero_division=zero_division,\n )\n\n\nclass Sensitivity(Metric, registry_key=\"sensitivity\", force_register=True):\n \"\"\"Compute the sensitivity score for different types of classification tasks.\n\n This metric can be used for binary, multiclass, and multilabel classification\n tasks. It creates the appropriate class based on the ``task`` parameter.\n\n Parameters\n ----------\n task : Literal[\"binary\", \"multiclass\", \"multilabel\"]\n Type of classification task.\n pos_label : int, default=1\n Label to consider as positive for binary classification tasks.\n num_classes : int, default=None\n Number of classes for the task. Required if ``task`` is ``\"multiclass\"``.\n threshold : float, default=0.5\n Threshold for deciding the positive class. Only used if ``task`` is\n ``\"binary\"`` or ``\"multilabel\"``.\n top_k : int, optional\n If given, and predictions are probabilities/logits, the precision will\n be computed only for the top k classes. Otherwise, ``top_k`` will be\n set to 1. Only used if ``task`` is ``\"multiclass\"`` or ``\"multilabel\"``.\n num_labels : int, default=None\n Number of labels for the task. Required if ``task`` is ``\"multilabel\"``.\n average : Literal[\"micro\", \"macro\", \"weighted\", None], default=None\n If ``None``, return the sensitivity score for each label/class. Otherwise,\n use one of the following options to compute the average score:\n\n - ``micro``: Calculate metrics globally by counting the total true\n positives and false negatives.\n - ``macro``: Calculate metrics for each class/label, and find their\n unweighted mean. This does not take label imbalance into account.\n - ``weighted``: Calculate metrics for each label/class, and find\n their average weighted by support (the number of true instances\n for each label/class). This alters ``macro`` to account for\n label/class imbalance.\n zero_division : Literal[\"warn\", 0, 1], default=\"warn\"\n Value to return when there is a zero division. If set to \"warn\", this\n acts as 0, but warnings are also raised.\n\n Examples\n --------\n >>> # (binary)\n >>> from cyclops.evaluation.metrics import Sensitivity\n >>> target = [0, 1, 0, 1]\n >>> preds = [0, 1, 1, 1]\n >>> metric = Sensitivity(task=\"binary\")\n >>> metric.update_state(target, preds)\n >>> metric.compute()\n 1.\n >>> metric.reset_state()\n >>> target = [[0, 1, 0, 1], [0, 0, 1, 1]]\n >>> preds = [[0.1, 0.9, 0.8, 0.2], [0.2, 0.3, 0.6, 0.1]]\n >>> for t, p in zip(target, preds):\n ... metric.update_state(t, p)\n >>> metric.compute()\n 0.5\n\n >>> # (multiclass)\n >>> from cyclops.evaluation.metrics import Sensitivity\n >>> target = [0, 1, 2, 0]\n >>> preds = [0, 2, 1, 0]\n >>> metric = Sensitivity(task=\"multiclass\", num_classes=3)\n >>> metric(target, preds)\n array([1. , 0. , 0.])\n >>> metric.reset_state()\n >>> target = [[0, 1, 2, 0], [2, 1, 2, 0]]\n >>> preds = [\n ... [[0.1, 0.6, 0.3],\n ... [0.05, 0.1, 0.85],\n ... [0.2, 0.7, 0.1],\n ... [0.9, 0.05, 0.05]],\n ... [[0.1, 0.6, 0.3],\n ... [0.05, 0.1, 0.85],\n ... [0.2, 0.7, 0.1],\n ... [0.9, 0.05, 0.05]]\n ... ]\n >>> for t, p in zip(target, preds):\n ... metric.update_state(t, p)\n >>> metric.compute()\n array([0.66666667, 0. , 0. ])\n\n >>> # (multilabel)\n >>> from cyclops.evaluation.metrics import Sensitivity\n >>> target = [[0, 1], [1, 1]]\n >>> preds = [[0.1, 0.9], [0.2, 0.8]]\n >>> metric = Sensitivity(task=\"multilabel\", num_labels=2)\n >>> metric(target, preds)\n array([0., 1.])\n >>> metric.reset_state()\n >>> target = [[[0, 1], [1, 1]], [[1, 1], [1, 0]]]\n >>> preds = [\n ... [[0.1, 0.7], [0.2, 0.8]],\n ... [[0.5, 0.9], [0.3, 0.4]]\n ... ]\n >>> for t, p in zip(target, preds):\n ... metric.update_state(t, p)\n >>> metric.compute()\n array([0.33333333, 1. ])\n\n \"\"\"\n\n def __new__( # type: ignore # mypy expects a subclass of Sensitivity\n cls,\n task: Literal[\"binary\", \"multiclass\", \"multilabel\"],\n pos_label: int = 1,\n num_classes: Optional[int] = None,\n threshold: float = 0.5,\n top_k: Optional[int] = None,\n num_labels: Optional[int] = None,\n average: Literal[\"micro\", \"macro\", \"weighted\", None] = None,\n zero_division: Literal[\"warn\", 0, 1] = \"warn\",\n ) -> Metric:\n \"\"\"Create a task-specific metric for computing the sensitivity score.\"\"\"\n if task == \"binary\":\n return BinarySensitivity(\n threshold=threshold,\n pos_label=pos_label,\n zero_division=zero_division,\n )\n if task == \"multiclass\":\n assert (\n isinstance(num_classes, int) and num_classes > 0\n ), \"Number of classes must be specified for multiclass classification.\"\n return MulticlassSensitivity(\n num_classes=num_classes,\n top_k=top_k,\n average=average,\n zero_division=zero_division,\n )\n if task == \"multilabel\":\n assert (\n isinstance(num_labels, int) and num_labels > 0\n ), \"Number of labels must be specified for multilabel classification.\"\n return MultilabelSensitivity(\n num_labels=num_labels,\n threshold=threshold,\n top_k=top_k,\n average=average,\n zero_division=zero_division,\n )\n raise ValueError(\n f\"Task '{task}' not supported, expected 'binary', 'multiclass' or \"\n f\"'multilabel'.\",\n )\n","repo_name":"VectorInstitute/cyclops","sub_path":"cyclops/evaluate/metrics/sensitivity.py","file_name":"sensitivity.py","file_ext":"py","file_size_in_byte":12381,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"3"}
+{"seq_id":"27210017761","text":"from django.shortcuts import render, get_object_or_404\nfrom django.urls import reverse\nfrom django.http.response import HttpResponseRedirect\nfrom . import models\n\n# Create your views here.\ndef index_view(request): \n todo_list = models.ToDoItem.objects.all()\n context = {\n 'todo_list_context': todo_list\n }\n return render(request, 'todo_app/index.html', context)\n\ndef save_todo_item(request):\n todo_dict = request.POST\n todo_string = todo_dict['todo_name']\n priority_id = int(request.POST['priority'])\n priority_object = get_object_or_404(models.Priority, pk=priority_id)\n todo_object = models.ToDoItem.objects.create(text=todo_string, priority=priority_object)\n print(priority_object, todo_object)\n return HttpResponseRedirect(reverse('todo_app:index_name'))\n ","repo_name":"PdxCodeGuild/class_HB2","sub_path":"code/zach/Django/lab01-todo/todo_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":803,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"22909331846","text":"import torch\nimport pandas as pd\nimport torch.nn as nn\nimport numpy as np\n\n\n\"\"\"# Data Preparation\"\"\"\n\ndef get_df_vindr(root,pathology,cross_eval=False):\n \n train_df=pd.read_csv(root+'/image_labels_train.csv')\n valid_df=pd.read_csv(root+'/image_labels_test.csv')\n \n train_df=train_df.loc[(train_df[pathology]==1)|((train_df[pathology]==0) & (train_df['No finding']==1))]\n valid_df=valid_df.loc[(valid_df[pathology]==1)|((valid_df[pathology]==0) & (valid_df['No finding']==1))]\n train_df=train_df.drop_duplicates(subset='image_id')\n valid_df=valid_df.drop_duplicates(subset='image_id')\n #Handle the partitioning better for the sake of evaluating in the entire dataset\n test=['test' for x in range(len(valid_df))]\n valid_df=valid_df.assign(partition=test)\n train=['train' for x in range(len(train_df))]\n train_df=train_df.assign(partition=train)\n eval_df=pd.concat([train_df,valid_df],ignore_index=True)\n \n if cross_eval:\n return eval_df\n else:\n return train_df,valid_df","repo_name":"dafisilva/attention-contrastive-learning-icmla","sub_path":"utilities/vindr_utils.py","file_name":"vindr_utils.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24982668175","text":"import pandas as pd\r\n\r\ndef getinput(onefile, dlimiter, colname, formula):\r\n if not dlimiter: \r\n df = pd.DataFrame(onefile)\r\n univariate(df, colname, formula)\r\n else:\r\n df = pd.DataFrame(onefile,sep=dlimiter)\r\n univariate(df, colname, formula)\r\n \r\ndef univariate(df, colname, formula):\r\n \r\n #Formula application\r\n Salary = colname[1]\r\n output = eval(formula)\r\n \r\n #Replace missing value by mean\r\n updateddataframe = df.fillna(output)\r\n\r\ndef main():\r\n dlimiter = \";\"\r\n filepath = \"Salary_Data.csv\"\r\n #Mean calculation or anything\r\n formula = \"Salary*2\"\r\n onefile = pd.read_csv(filepath)\r\n columnname = \"Salary, Yearsofexperience\"\r\n colname = columnname.split(',')\r\n getinput(onefile, dlimiter, colname, formula)\r\n \r\nif __name__ == \"__main__\":\r\n main()","repo_name":"Lav2891/python","sub_path":"taskthreefn.py","file_name":"taskthreefn.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"524313924","text":"from __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom typing import Optional, Sequence\n\nimport tensorflow as tf\n\nfrom trieste.types import TensorType\n\n\n@dataclass(frozen=True)\nclass Dataset:\n \"\"\"\n Container for the query points and corresponding observations from an\n :class:`~trieste.observer.Observer`.\n \"\"\"\n\n query_points: TensorType\n \"\"\" The points at which the :class:`~trieste.observer.Observer` was queried. \"\"\"\n\n observations: TensorType\n \"\"\" The observed output of the :class:`~trieste.observer.Observer` for each query point. \"\"\"\n\n def __post_init__(self) -> None:\n \"\"\"\n :raise ValueError (or InvalidArgumentError): If ``query_points`` or ``observations`` have \\\n rank less than two, or they have unequal shape in any but their last dimension.\n \"\"\"\n tf.debugging.assert_rank_at_least(self.query_points, 2)\n tf.debugging.assert_rank_at_least(self.observations, 2)\n\n if 0 in (self.query_points.shape[-1], self.observations.shape[-1]):\n raise ValueError(\n f\"query_points and observations cannot have dimension 0, got shapes\"\n f\" {self.query_points.shape} and {self.observations.shape}.\"\n )\n\n if (\n self.query_points.shape[:-1] != self.observations.shape[:-1]\n # can't check dynamic shapes, so trust that they're ok (if not, they'll fail later)\n and None not in self.query_points.shape[:-1]\n ):\n raise ValueError(\n f\"Leading shapes of query_points and observations must match. Got shapes\"\n f\" {self.query_points.shape}, {self.observations.shape}.\"\n )\n\n def __add__(self, rhs: Dataset) -> Dataset:\n r\"\"\"\n Return the :class:`Dataset` whose query points are the result of concatenating the\n `query_points` in each :class:`Dataset` along the zeroth axis, and the same for the\n `observations`. For example:\n\n >>> d1 = Dataset(\n ... tf.constant([[0.1, 0.2], [0.3, 0.4]]),\n ... tf.constant([[0.5, 0.6], [0.7, 0.8]])\n ... )\n >>> d2 = Dataset(tf.constant([[0.9, 1.0]]), tf.constant([[1.1, 1.2]]))\n >>> (d1 + d2).query_points\n \n >>> (d1 + d2).observations\n \n\n :param rhs: A :class:`Dataset` with the same shapes as this one, except in the zeroth\n dimension, which can have any size.\n :return: The result of concatenating the :class:`Dataset`\\ s.\n :raise InvalidArgumentError: If the shapes of the `query_points` in each :class:`Dataset`\n differ in any but the zeroth dimension. The same applies for `observations`.\n \"\"\"\n return Dataset(\n tf.concat([self.query_points, rhs.query_points], axis=0),\n tf.concat([self.observations, rhs.observations], axis=0),\n )\n\n def __len__(self) -> tf.Tensor:\n \"\"\"\n :return: The number of query points, or equivalently the number of observations.\n \"\"\"\n return tf.shape(self.observations)[0]\n\n def __deepcopy__(self, memo: dict[int, object]) -> Dataset:\n return self\n\n def astuple(self) -> tuple[TensorType, TensorType]:\n \"\"\"\n **Note:** Unlike the standard library function `dataclasses.astuple`, this method does\n **not** deepcopy the attributes.\n\n :return: A 2-tuple of the :attr:`query_points` and :attr:`observations`.\n \"\"\"\n return self.query_points, self.observations\n\n\ndef check_and_extract_fidelity_query_points(\n query_points: TensorType, max_fidelity: Optional[int] = None\n) -> tuple[TensorType, TensorType]:\n \"\"\"Check whether the final column of a tensor is close enough to ints\n to be reasonably considered to represent fidelities.\n\n The final input column of multi-fidelity data should be a reference to\n the fidelity of the query point. We cannot have mixed type tensors, but\n we can check that thhe final column values are suitably close to integers.\n\n :param query_points: Data to check final column of.\n :raise: ValueError: If there are not enough columns to be multifidelity data\n :raise InvalidArgumentError: If any value in the final column is far from an integer\n :return: Query points without fidelity column\n and the fidelities of each of the query points\n \"\"\"\n # Check we have sufficient columns\n if query_points.shape[-1] < 2:\n raise ValueError(\n \"Query points do not have enough columns to be multifidelity,\"\n f\" need at least 2, got {query_points.shape[1]}\"\n )\n input_points = query_points[..., :-1]\n fidelity_col = query_points[..., -1:]\n # Check fidelity column values are close to ints\n tf.debugging.assert_equal(\n tf.round(fidelity_col),\n fidelity_col,\n message=\"Fidelity column should be float(int), but got a float that\"\n \" was not close to an int\",\n )\n # Check fidelity column values are non-negative\n\n tf.debugging.assert_non_negative(fidelity_col, message=\"Fidelity must be non-negative\")\n if max_fidelity is not None:\n max_input_fid = tf.reduce_max(fidelity_col)\n max_fidelity_float = tf.cast(max_fidelity, dtype=query_points.dtype)\n tf.debugging.assert_less_equal(\n max_input_fid,\n max_fidelity_float,\n message=(\n f\"Model only supports fidelities up to {max_fidelity},\"\n f\" but {max_input_fid} was passed\"\n ),\n )\n\n return input_points, fidelity_col\n\n\ndef split_dataset_by_fidelity(dataset: Dataset, num_fidelities: int) -> Sequence[Dataset]:\n \"\"\"Split dataset into individual datasets without fidelity information\n\n :param dataset: Dataset for which to split fidelities\n :param num_fidlities: Number of fidelities in the problem (not just dataset)\n :return: Ordered list of datasets with lowest fidelity at index 0 and highest at -1\n \"\"\"\n if num_fidelities < 1:\n raise ValueError(f\"Data must have 1 or more fidelities, got {num_fidelities}\")\n datasets = [get_dataset_for_fidelity(dataset, fidelity) for fidelity in range(num_fidelities)]\n return datasets\n\n\ndef get_dataset_for_fidelity(dataset: Dataset, fidelity: int) -> Dataset:\n \"\"\"Get a dataset with only the specified fidelity of data in\n\n :param dataset: The dataset from which to extract the single fidelity data\n :param fidelity: The fidelity to extract the data for\n :return: Dataset with a single fidelity and no fidelity column\n \"\"\"\n\n input_points, fidelity_col = check_and_extract_fidelity_query_points(\n dataset.query_points\n ) # [..., D], [..., 1]\n mask = fidelity_col == fidelity # [..., ]\n inds = tf.where(mask)[..., 0] # [..., ]\n inputs_for_fidelity = tf.gather(input_points, inds, axis=0) # [..., D]\n observations_for_fidelity = tf.gather(dataset.observations, inds, axis=0) # [..., 1]\n return Dataset(query_points=inputs_for_fidelity, observations=observations_for_fidelity)\n\n\ndef add_fidelity_column(query_points: TensorType, fidelity: int) -> TensorType:\n \"\"\"Add fidelity column to query_points without fidelity data\n\n :param query_points: query points without fidelity to add fidelity column to\n :param fidelity: fidelity to populate fidelity column with\n :return: TensorType of query points with fidelity column added\n \"\"\"\n fidelity_col = tf.ones((tf.shape(query_points)[-2], 1), dtype=query_points.dtype) * fidelity\n query_points_for_fidelity = tf.concat([query_points, fidelity_col], axis=-1)\n return query_points_for_fidelity\n","repo_name":"secondmind-labs/trieste","sub_path":"trieste/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":7924,"program_lang":"python","lang":"en","doc_type":"code","stars":190,"dataset":"github-code","pt":"3"}
+{"seq_id":"73981713362","text":"import os, fnx, jax, re\r\nimport haiku as hk\r\nfrom functools import partial\r\nimport jax.numpy as jnp\r\nimport numpy as np\r\n\r\ncolor_mean = np.asarray([0.485, 0.456, 0.406])\r\ncolor_std = np.asarray([0.229, 0.224, 0.225])\r\ndef preprocess(color):\r\n color = color / 255.0\r\n color = (color - color_mean) / color_std\r\n return color\r\n\r\nconfig = fnx.intercept.chain(\r\n fnx.intercept.replace(fnx.norm, fnx.layernorm),\r\n fnx.intercept.replace(fnx.act, jax.nn.gelu),\r\n fnx.config.pytorch,\r\n)\r\n\r\nclass Builder:\r\n def __init__(self, variant, patch_size, pos_embed_shape, url):\r\n self.encoder = vars(fnx.vit)[f\"vit_{variant}\"]\r\n self.patch_size = patch_size\r\n self.pos_embed_shape = pos_embed_shape\r\n self.url = url\r\n self.preprocess = preprocess\r\n\r\n @fnx.module(name=\"model\", is_unbound_method=True)\r\n def __call__(self, x):\r\n def model_fn(x):\r\n def replace(next_interceptor, func, args, kwargs, context):\r\n # 1. They don't use layerscale\r\n # 2. They don't have normalization in patch embedding\r\n if context.fullname.endswith(\"scale\") or context.fullname.endswith(\"patch_embed/norm\") or context.fullname.endswith(\"encode/norm\"):\r\n return args[0]\r\n else:\r\n return next_interceptor(func, args, kwargs)\r\n\r\n with config:\r\n with fnx.intercept.custom(replace):\r\n x = self.encoder(x, pos_embed_shape=self.pos_embed_shape, patch_size=self.patch_size, prefix_tokens=1)\r\n with fnx.scope(\"classifier\"):\r\n x = jnp.mean(x[:, 1:], axis=1)\r\n x = fnx.norm(x)\r\n x = fnx.linear(x, channels=1000, bias=True, name=\"logits\")\r\n x = jax.nn.softmax(x, axis=-1)\r\n return x\r\n\r\n file = fnx.pretrained.weights.download(self.url)\r\n weights = fnx.pretrained.weights.load_pytorch(file)\r\n\r\n # 3. They add an additional positional embedding onto class token, we add positional embedding only on image tokens\r\n pos_embed = weights[\"pos_embed\"]\r\n cls_token = weights[\"cls_token\"]\r\n weights[\"cls_token\"] = cls_token + pos_embed[:, :1, :]\r\n weights[\"pos_embed\"] = pos_embed[:, 1:, :].reshape((1, self.pos_embed_shape[0], self.pos_embed_shape[1], pos_embed.shape[-1]))\r\n\r\n model_fn = fnx.pretrained.weights.init(model_fn, weights, hints=[(\"attn/norm\", \"norm1\")])\r\n\r\n return model_fn(x)\r\n\r\nvit_large_patch14_196_eva_in22k_ft_in22k_in1k = Builder(\"large\", 14, (14, 14), \"https://huggingface.co/timm/eva_large_patch14_196.in22k_ft_in22k_in1k/blob/main/pytorch_model.bin\")\r\nvit_large_patch14_336_eva_in22k_ft_in22k_in1k = Builder(\"large\", 14, (24, 24), \"https://huggingface.co/timm/eva_large_patch14_336.in22k_ft_in22k_in1k/blob/main/pytorch_model.bin\")\r\n\r\nvit_large_patch14_196_eva_in22k_ft_in1k = Builder(\"large\", 14, (14, 14), \"https://huggingface.co/timm/eva_large_patch14_196.in22k_ft_in1k/blob/main/pytorch_model.bin\")\r\nvit_large_patch14_336_eva_in22k_ft_in1k = Builder(\"large\", 14, (24, 24), \"https://huggingface.co/timm/eva_large_patch14_336.in22k_ft_in1k/blob/main/pytorch_model.bin\")\r\n","repo_name":"fferflo/fnx","sub_path":"fnx/pretrained/timm/vit.py","file_name":"vit.py","file_ext":"py","file_size_in_byte":3225,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31296628193","text":"from __future__ import division, absolute_import\n\nfrom zope.interface.verify import verifyObject\n\ntry:\n from twisted.conch.ssh import channel\n from twisted.conch.ssh.address import SSHTransportAddress\n from twisted.conch.ssh.transport import SSHServerTransport\n from twisted.conch.ssh.service import SSHService\n from twisted.internet import interfaces\n from twisted.internet.address import IPv4Address\n from twisted.test.proto_helpers import StringTransport\n skipTest = None\nexcept ImportError:\n skipTest = 'Conch SSH not supported.'\n SSHService = object\nfrom twisted.trial import unittest\nfrom twisted.python.compat import intToBytes\n\n\nclass MockConnection(SSHService):\n \"\"\"\n A mock for twisted.conch.ssh.connection.SSHConnection. Record the data\n that channels send, and when they try to close the connection.\n\n @ivar data: a L{dict} mapping channel id #s to lists of data sent by that\n channel.\n @ivar extData: a L{dict} mapping channel id #s to lists of 2-tuples\n (extended data type, data) sent by that channel.\n @ivar closes: a L{dict} mapping channel id #s to True if that channel sent\n a close message.\n \"\"\"\n\n def __init__(self):\n self.data = {}\n self.extData = {}\n self.closes = {}\n\n\n def logPrefix(self):\n \"\"\"\n Return our logging prefix.\n \"\"\"\n return \"MockConnection\"\n\n\n def sendData(self, channel, data):\n \"\"\"\n Record the sent data.\n \"\"\"\n self.data.setdefault(channel, []).append(data)\n\n\n def sendExtendedData(self, channel, type, data):\n \"\"\"\n Record the sent extended data.\n \"\"\"\n self.extData.setdefault(channel, []).append((type, data))\n\n\n def sendClose(self, channel):\n \"\"\"\n Record that the channel sent a close message.\n \"\"\"\n self.closes[channel] = True\n\n\n\ndef connectSSHTransport(service, hostAddress=None, peerAddress=None):\n \"\"\"\n Connect a SSHTransport which is already connected to a remote peer to\n the channel under test.\n\n @param service: Service used over the connected transport.\n @type service: L{SSHService}\n\n @param hostAddress: Local address of the connected transport.\n @type hostAddress: L{interfaces.IAddress}\n\n @param peerAddress: Remote address of the connected transport.\n @type peerAddress: L{interfaces.IAddress}\n \"\"\"\n transport = SSHServerTransport()\n transport.makeConnection(StringTransport(\n hostAddress=hostAddress, peerAddress=peerAddress))\n transport.setService(service)\n\n\n\nclass ChannelTests(unittest.TestCase):\n \"\"\"\n Tests for L{SSHChannel}.\n \"\"\"\n\n skip = skipTest\n\n def setUp(self):\n \"\"\"\n Initialize the channel. remoteMaxPacket is 10 so that data is able\n to be sent (the default of 0 means no data is sent because no packets\n are made).\n \"\"\"\n self.conn = MockConnection()\n self.channel = channel.SSHChannel(conn=self.conn,\n remoteMaxPacket=10)\n self.channel.name = b'channel'\n\n\n def test_interface(self):\n \"\"\"\n L{SSHChannel} instances provide L{interfaces.ITransport}.\n \"\"\"\n self.assertTrue(verifyObject(interfaces.ITransport, self.channel))\n\n\n def test_init(self):\n \"\"\"\n Test that SSHChannel initializes correctly. localWindowSize defaults\n to 131072 (2**17) and localMaxPacket to 32768 (2**15) as reasonable\n defaults (what OpenSSH uses for those variables).\n\n The values in the second set of assertions are meaningless; they serve\n only to verify that the instance variables are assigned in the correct\n order.\n \"\"\"\n c = channel.SSHChannel(conn=self.conn)\n self.assertEqual(c.localWindowSize, 131072)\n self.assertEqual(c.localWindowLeft, 131072)\n self.assertEqual(c.localMaxPacket, 32768)\n self.assertEqual(c.remoteWindowLeft, 0)\n self.assertEqual(c.remoteMaxPacket, 0)\n self.assertEqual(c.conn, self.conn)\n self.assertIsNone(c.data)\n self.assertIsNone(c.avatar)\n\n c2 = channel.SSHChannel(1, 2, 3, 4, 5, 6, 7)\n self.assertEqual(c2.localWindowSize, 1)\n self.assertEqual(c2.localWindowLeft, 1)\n self.assertEqual(c2.localMaxPacket, 2)\n self.assertEqual(c2.remoteWindowLeft, 3)\n self.assertEqual(c2.remoteMaxPacket, 4)\n self.assertEqual(c2.conn, 5)\n self.assertEqual(c2.data, 6)\n self.assertEqual(c2.avatar, 7)\n\n\n def test_str(self):\n \"\"\"\n Test that str(SSHChannel) works gives the channel name and local and\n remote windows at a glance..\n \"\"\"\n self.assertEqual(\n str(self.channel), '')\n self.assertEqual(\n str(channel.SSHChannel(localWindow=1)),\n '')\n\n\n def test_bytes(self):\n \"\"\"\n Test that bytes(SSHChannel) works, gives the channel name and\n local and remote windows at a glance..\n\n \"\"\"\n self.assertEqual(\n self.channel.__bytes__(),\n b'')\n self.assertEqual(\n channel.SSHChannel(localWindow=1).__bytes__(),\n b'')\n\n\n def test_logPrefix(self):\n \"\"\"\n Test that SSHChannel.logPrefix gives the name of the channel, the\n local channel ID and the underlying connection.\n \"\"\"\n self.assertEqual(self.channel.logPrefix(), 'SSHChannel channel '\n '(unknown) on MockConnection')\n\n\n def test_addWindowBytes(self):\n \"\"\"\n Test that addWindowBytes adds bytes to the window and resumes writing\n if it was paused.\n \"\"\"\n cb = [False]\n def stubStartWriting():\n cb[0] = True\n self.channel.startWriting = stubStartWriting\n self.channel.write(b'test')\n self.channel.writeExtended(1, b'test')\n self.channel.addWindowBytes(50)\n self.assertEqual(self.channel.remoteWindowLeft, 50 - 4 - 4)\n self.assertTrue(self.channel.areWriting)\n self.assertTrue(cb[0])\n self.assertEqual(self.channel.buf, b'')\n self.assertEqual(self.conn.data[self.channel], [b'test'])\n self.assertEqual(self.channel.extBuf, [])\n self.assertEqual(self.conn.extData[self.channel], [(1, b'test')])\n\n cb[0] = False\n self.channel.addWindowBytes(20)\n self.assertFalse(cb[0])\n\n self.channel.write(b'a'*80)\n self.channel.loseConnection()\n self.channel.addWindowBytes(20)\n self.assertFalse(cb[0])\n\n\n def test_requestReceived(self):\n \"\"\"\n Test that requestReceived handles requests by dispatching them to\n request_* methods.\n \"\"\"\n self.channel.request_test_method = lambda data: data == b''\n self.assertTrue(self.channel.requestReceived(b'test-method', b''))\n self.assertFalse(self.channel.requestReceived(b'test-method', b'a'))\n self.assertFalse(self.channel.requestReceived(b'bad-method', b''))\n\n\n def test_closeReceieved(self):\n \"\"\"\n Test that the default closeReceieved closes the connection.\n \"\"\"\n self.assertFalse(self.channel.closing)\n self.channel.closeReceived()\n self.assertTrue(self.channel.closing)\n\n\n def test_write(self):\n \"\"\"\n Test that write handles data correctly. Send data up to the size\n of the remote window, splitting the data into packets of length\n remoteMaxPacket.\n \"\"\"\n cb = [False]\n def stubStopWriting():\n cb[0] = True\n # no window to start with\n self.channel.stopWriting = stubStopWriting\n self.channel.write(b'd')\n self.channel.write(b'a')\n self.assertFalse(self.channel.areWriting)\n self.assertTrue(cb[0])\n # regular write\n self.channel.addWindowBytes(20)\n self.channel.write(b'ta')\n data = self.conn.data[self.channel]\n self.assertEqual(data, [b'da', b'ta'])\n self.assertEqual(self.channel.remoteWindowLeft, 16)\n # larger than max packet\n self.channel.write(b'12345678901')\n self.assertEqual(data, [b'da', b'ta', b'1234567890', b'1'])\n self.assertEqual(self.channel.remoteWindowLeft, 5)\n # running out of window\n cb[0] = False\n self.channel.write(b'123456')\n self.assertFalse(self.channel.areWriting)\n self.assertTrue(cb[0])\n self.assertEqual(data, [b'da', b'ta', b'1234567890', b'1', b'12345'])\n self.assertEqual(self.channel.buf, b'6')\n self.assertEqual(self.channel.remoteWindowLeft, 0)\n\n\n def test_writeExtended(self):\n \"\"\"\n Test that writeExtended handles data correctly. Send extended data\n up to the size of the window, splitting the extended data into packets\n of length remoteMaxPacket.\n \"\"\"\n cb = [False]\n def stubStopWriting():\n cb[0] = True\n # no window to start with\n self.channel.stopWriting = stubStopWriting\n self.channel.writeExtended(1, b'd')\n self.channel.writeExtended(1, b'a')\n self.channel.writeExtended(2, b't')\n self.assertFalse(self.channel.areWriting)\n self.assertTrue(cb[0])\n # regular write\n self.channel.addWindowBytes(20)\n self.channel.writeExtended(2, b'a')\n data = self.conn.extData[self.channel]\n self.assertEqual(data, [(1, b'da'), (2, b't'), (2, b'a')])\n self.assertEqual(self.channel.remoteWindowLeft, 16)\n # larger than max packet\n self.channel.writeExtended(3, b'12345678901')\n self.assertEqual(data, [(1, b'da'), (2, b't'), (2, b'a'),\n (3, b'1234567890'), (3, b'1')])\n self.assertEqual(self.channel.remoteWindowLeft, 5)\n # running out of window\n cb[0] = False\n self.channel.writeExtended(4, b'123456')\n self.assertFalse(self.channel.areWriting)\n self.assertTrue(cb[0])\n self.assertEqual(data, [(1, b'da'), (2, b't'), (2, b'a'),\n (3, b'1234567890'), (3, b'1'), (4, b'12345')])\n self.assertEqual(self.channel.extBuf, [[4, b'6']])\n self.assertEqual(self.channel.remoteWindowLeft, 0)\n\n\n def test_writeSequence(self):\n \"\"\"\n Test that writeSequence is equivalent to write(''.join(sequece)).\n \"\"\"\n self.channel.addWindowBytes(20)\n self.channel.writeSequence(map(intToBytes, range(10)))\n self.assertEqual(self.conn.data[self.channel], [b'0123456789'])\n\n\n def test_loseConnection(self):\n \"\"\"\n Tesyt that loseConnection() doesn't close the channel until all\n the data is sent.\n \"\"\"\n self.channel.write(b'data')\n self.channel.writeExtended(1, b'datadata')\n self.channel.loseConnection()\n self.assertIsNone(self.conn.closes.get(self.channel))\n self.channel.addWindowBytes(4) # send regular data\n self.assertIsNone(self.conn.closes.get(self.channel))\n self.channel.addWindowBytes(8) # send extended data\n self.assertTrue(self.conn.closes.get(self.channel))\n\n\n def test_getPeer(self):\n \"\"\"\n L{SSHChannel.getPeer} returns the same object as the underlying\n transport's C{getPeer} method returns.\n \"\"\"\n peer = IPv4Address('TCP', '192.168.0.1', 54321)\n connectSSHTransport(service=self.channel.conn, peerAddress=peer)\n\n self.assertEqual(SSHTransportAddress(peer), self.channel.getPeer())\n\n\n def test_getHost(self):\n \"\"\"\n L{SSHChannel.getHost} returns the same object as the underlying\n transport's C{getHost} method returns.\n \"\"\"\n host = IPv4Address('TCP', '127.0.0.1', 12345)\n connectSSHTransport(service=self.channel.conn, hostAddress=host)\n\n self.assertEqual(SSHTransportAddress(host), self.channel.getHost())\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/twisted/conch/test/test_channel.py","file_name":"test_channel.py","file_ext":"py","file_size_in_byte":12000,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"}
+{"seq_id":"37360646769","text":"import luigi\nfrom time import time\nimport random \nimport numpy as np\nimport pandas as pd \nimport matplotlib.pyplot as plt\nimport os\n\nfrom sklearn.cluster import KMeans\nfrom sklearn.neighbors import NearestNeighbors\n\ndef data_check(df):\n df.info()\n df.describe()\n df.isnull().sum() #count number of null values in each column \n df.fillna(0) #fix null values (e.g. Replace with 0, mean, etc)\n\ndef moviesDataIngestion():\n movies = pd.read_csv('ml-latest/movies.csv', header=0)\n movies.to_csv('movies.csv', index=False)\n return movies\n\ndef moviesDataIngestionCheck(movies):\n data_check(movies)\n movies.to_csv('moviesChecked.csv', index=False)\n return movies\n\ndef ratingsDataIngestion():\n ratings = pd.read_csv('ml-latest/ratings.csv', header=0)\n ratings.to_csv('ratings.csv', index=False)\n return ratings\n\ndef ratingsDataIngestionCheck(ratings):\n data_check(ratings)\n ratings.to_csv('ratingsChecked.csv', index=False)\n return ratings\n\ndef linksDataIngestion():\n links = pd.read_csv('ml-latest/links.csv', header=0)\n links.to_csv('links.csv', index=False)\n return links\n\ndef linksDataIngestionCheck(links):\n data_check(links)\n links.to_csv('linksChecked.csv', index=False)\n return links\n\ndef tagsDataIngestion():\n tags = pd.read_csv('ml-latest/tags.csv', header=0)\n tags.to_csv('tags.csv', index=False)\n return tags\n\ndef tagsDataIngestionCheck(tags):\n data_check(tags)\n tags.to_csv('tagsChecked.csv', index=False)\n return tags\n\ndef genome_tagsDataIngestion():\n genome_tags = pd.read_csv('ml-latest/genome-tags.csv', header=0)\n genome_tags.to_csv('genome_tags.csv', index=False)\n return genome_tags\n\ndef genome_tagsDataIngestionCheck(genome_tags):\n data_check(genome_tags)\n genome_tags.to_csv('genome_tagsChecked.csv', index=False)\n return genome_tags\n\ndef genome_scoresDataIngestion():\n genome_scores = pd.read_csv('ml-latest/genome-scores.csv', header=0)\n genome_scores.to_csv('genome_scores.csv', index=False)\n return genome_scores\n\ndef genome_scoresDataIngestionCheck(genome_scores):\n data_check(genome_scores)\n genome_scores.to_csv('genome_scoresChecked.csv', index=False)\n return genome_scores\n\ndef DataPreProcessing1(movies, genome_score, genome_tags): \n\n data = pd.merge(movies, genome_scores, how='inner', on='movieId')\n data = pd.merge(data, genome_tags, how='inner', on='tagId')\n data = data.sort_values(['movieId','tagId'])\n \n #data = data[:1]\n \n data.to_csv('preProcessedData1.csv', index=False)\n \n return data\n\ndef DataPreProcessing1Check(data1):\n \n #data quality check: each movieId should have relevance scores for all the tags\n sample = random.sample(list(data1.movieId.unique()),100)\n temp = [list(data1.loc[data1.movieId==i].tagId) for i in sample]\n compare = [i==[j for j in range(1,len(data1.tagId.unique())+1)] for i in temp]\n \n if False in compare: \n raise Exception(\"Data quality check failed: Not all movieId's have relevance scores for all the tags\")\n \n #check dimension of df\n if len(data1) != (len(data1.movieId.unique())*len(data1.tagId.unique())): \n raise Exception(\"Data quality check failed: Incorrect dataframe dimension\")\n\n data1.to_csv('preProcessedData1Checked.csv', index=False)\n \n return data1 \n\ndef DataPreProcessing2a(data1checked): \n \n relevance = np.asarray(data1checked.relevance)\n data1a = np.reshape(relevance,(len(data1checked.movieId.unique()),len(data1checked.tagId.unique())))\n \n #X = X[1:]\n\n pd.DataFrame(data1a).to_csv('preProcessedData2a.csv', index=False)\n\n return data1a \n\ndef DataPreProcessing2aCheck(data1Checked, data2a):\n\n #data quality check: mean score for each movie should equal to the ones calculated pre-transformation\n if max(abs(np.array(list(data1Checked.groupby(data1Checked.movieId).relevance.mean())) - np.array(list(data2a.mean(axis=1))))) > 0.001:\n raise Exception(\"Data quality check failed: Mean score for each movie not equal to the ones pre-transformation\")\n \n #check dimension of df\n if data2a.shape != (len(data1Checked.movieId.unique()), len(data1Checked.tagId.unique())):\n raise Exception(\"Data quality check failed: Incorrect dataframe dimension\")\n\n pd.DataFrame(data2a).to_csv('DataPreProcessing2aChecked.csv', index=False)\n \n return data2a\n\ndef DataPreProcessing2b(data1Checked): \n \n data2b = data1Checked.sort_values(by=['tagId','movieId'])[:len(data1Checked.movieId.unique())] \n \n #data2 = data2[1:]\n \n data2b.to_csv('preProcessedData2b.csv', index=False)\n \n return data2b\n\ndef DataPreProcessing2bCheck(data1Checked, data2b):\n\n #check dimension of df\n if len(data2b) != len(data1Checked.movieId.unique()):\n raise Exception(\"Data quality check failed: Incorrect number of rows (movies)\")\n\n data2b.to_csv('DataPreProcessing2bChecked.csv', index=False)\n \n return data2b \n\ndef TrainKMeans(movies, data2aChecked, data2bChecked):\n\n n_clusters = 10 # number of clusters\n n = 20 # number of movies to show in each cluster \n \n kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(data2aChecked)\n \n labels = pd.DataFrame(kmeans.labels_)\n labels['movieId'] = labels.index\n labels.reset_index(level=0, inplace=False)\n labels.columns = ['cluster', 'movieId']\n labels2 = pd.merge(labels, movies, how='left', on='movieId')\n labels2.title.fillna(labels2.movieId, inplace=True)\n labels2.genres.fillna(labels2.movieId, inplace=True)\n \n clusters = labels2.groupby('cluster').groups\n clusters2 = dict()\n \n for k in clusters: \n clusters2[k] = clusters[k][:n]\n \n clusters2 = pd.DataFrame(clusters2)\n clusters2.index += 1\n \n for i in clusters2.columns: \n clusters2[i] = clusters2[i].map(data2bChecked.title)\n \n clusters2.columns = ['cluster '+str(i) for i in range(0,n_clusters)]\n \n print(clusters2)\n \n pd.DataFrame(clusters2).to_csv('clusters.csv', index=False)\n\ndef TrainNearestNeighbors(data2aChecked, data2bChecked):\n\n n = 20 # number of movies to show in each cluster \n\n neighbors = NearestNeighbors(n_neighbors=n+1, algorithm='ball_tree').fit(data2aChecked)\n distances, indices = neighbors.kneighbors(data2aChecked)\n indices = np.delete(indices,0,1)\n distances = np.delete(distances,0,1)\n\n indices2 = pd.DataFrame(np.transpose(indices))\n indices2.index += 1\n \n for i in indices2.columns: \n indices2[i] = indices2[i].map(data2bChecked.title)\n \n indices2.columns = [data2bChecked.title]\n \n most_similar_movies = indices2\n \n print(most_similar_movies)\n \n pd.DataFrame(most_similar_movies).to_csv('most_similar_movies.csv', index=False)\n\nif __name__ == '__main__':\n \n #Data ingestion and check \n movies = moviesDataIngestion()\n moviesChecked = moviesDataIngestionCheck(movies) \n ratings = ratingsDataIngestion()\n ratingsChecked = ratingsDataIngestionCheck(ratings)\n links = linksDataIngestion()\n linksChecked = linksDataIngestionCheck(links)\n tags = tagsDataIngestion()\n tagsChecked = tagsDataIngestionCheck(tags)\n genome_tags = genome_tagsDataIngestion()\n genome_tagsChecked = genome_tagsDataIngestionCheck(genome_tags)\n genome_scores = genome_scoresDataIngestion()\n genome_scoresChecked = genome_scoresDataIngestionCheck(genome_scores)\n \n #Data processing \n data1 = DataPreProcessing1(moviesChecked, genome_scoresChecked, genome_tagsChecked)\n data1Checked = DataPreProcessing1Check(data1)\n data2a = DataPreProcessing2a(data1Checked)\n data2b = DataPreProcessing2b(data1Checked) \n data2aChecked = DataPreProcessing2aCheck(data1Checked, data2a)\n data2bChecked = DataPreProcessing2bCheck(data1Checked, data2b)\n \n #Modeling \n TrainKMeans(moviesChecked, data2aChecked, data2bChecked)\n \n TrainNearestNeighbors(data2aChecked, data2bChecked)\n \n \n \n","repo_name":"user2017/luigi_movielens","sub_path":"movielens_modular.py","file_name":"movielens_modular.py","file_ext":"py","file_size_in_byte":7996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2352179611","text":"# imports\n\nfrom splinter import Browser\nfrom bs4 import BeautifulSoup as bs\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport datetime as dt\n\n# scrape all function\ndef scrape_all():\n \n executable_path = {'executable_path': ChromeDriverManager().install()}\n browser = Browser('chrome', **executable_path, headless=False)\n\n \n \n \n \n news_title, news_paragraph = scrape_news(browser)\n\n mars_data = {\n 'news_title': news_title,\n 'news_paragraph': news_paragraph,\n 'featured_image': scrape_feature_img(browser),\n 'facts': scrape_facts(browser),\n 'hemispheres': scrape_hemispheres(browser),\n 'last_updated': dt.datetime.now()\n }\n\n browser.quit()\n\n return mars_data\n\n\n\n\n# scrape mars news page\ndef scrape_news(browser):\n url = 'https://redplanetscience.com/'\n browser.visit(url)\n\n\n\n browser.is_element_present_by_css('div.list_text', wait_time=1)\n html = browser.html\n news_soup = bs(html, 'html.parser')\n\n slide_elem = news_soup.select_one('div.list_text')\n\n news_title = slide_elem.find('div', class_='content_title').get_text()\n\n news_p = slide_elem.find('div', class_='article_teaser_body').get_text()\n\n return news_title, news_p\n\n# scrape featured image page\ndef scrape_feature_img(browser):\n url = 'https://spaceimages-mars.com'\n browser.visit(url)\n full_image_link = browser.find_by_tag('button')\n full_image_link[1].click()\n html = browser.html\n img_soup = bs(html, 'html.parser')\n img_url_rel = img_soup.find('img', class_='fancybox-image').get('src')\n img_url = f'https://spaceimages-mars.com/{img_url_rel}'\n return img_url\n\n# scrape facts page\ndef scrape_facts(browser):\n url = 'https://galaxyfacts-mars.com/'\n browser.visit(url)\n html = browser.html\n fact_soup = bs(html, 'html.parser')\n facts_loc = fact_soup.find('div', class_='diagram mt-4')\n fact_table = facts_loc.find('table')\n facts = ''\n facts += str(fact_table)\n return facts\n\n# scrape hemispheres page\ndef scrape_hemispheres(browser):\n url = 'https://marshemispheres.com/'\n browser.visit(url)\n # create a list to hold the images and title\n hemisphere_image_urls = []\n for i in range(4):\n hemisphereInfo = {}\n browser.find_by_css('a.product-item img')[i].click()\n sample = browser.links.find_by_text('Sample').first\n hemisphereInfo['img_url'] = sample['href']\n hemisphereInfo['title'] =browser.find_by_css('h2.title').text\n hemisphere_image_urls.append(hemisphereInfo)\n browser.back()\n return hemisphere_image_urls\n\n# stop the webdriver\n \n\n# set up as a flask app\n\nif __name__ == \"__main__\":\n print(scrape_all())","repo_name":"danskan/web-scraping-challenge","sub_path":"scrape_mars.py","file_name":"scrape_mars.py","file_ext":"py","file_size_in_byte":2722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27173038262","text":"'''\nCreated on Aug 5, 2013\n\n@author: justin\n'''\nimport wx, wx.lib.customtreectrl, wx.gizmos\ntry:\n import treemixin \nexcept ImportError:\n from wx.lib.mixins import treemixin\n\noverview = treemixin.__doc__\n\n\nclass TreeListCtrl(treemixin.VirtualTree, treemixin.DragAndDrop, \n treemixin.ExpansionState, wx.gizmos.TreeListCtrl):\n\n '''holds the domain objects that are shown in the different\n tree controls. Each domain object is simply a two-tuple consisting of\n a label and a list of child tuples, i.e. ([list of labels], [list of child tuples]). \n ''' \n \n def __init__(self, *args, **kwargs):\n self.items = []\n self.itemCounter = 0\n kwargs['style'] = wx.TR_DEFAULT_STYLE | wx.TR_FULL_ROW_HIGHLIGHT\n super(TreeListCtrl, self).__init__(*args, **kwargs)\n \n self.AddColumn('Column 0')\n self.AddColumn('Column 1')\n self.AddColumn('Column 2')\n\n self.AddChild((), [\"value1\", \"value2\", \"value3\"])\n self.AddChild((), [\"value8\", \"value2\", \"value3\"])\n self.AddChild((1,), [\"value7\", \"value5\", \"value6\"])\n self.AddChild((1,), [\"value4\", \"value5\", \"value6\"])\n self.RefreshItems()\n \n #Helper functions\n def GetItem(self, indices):\n text, children = 'Hidden root', self.items\n for index in indices:\n text, children = children[index]\n return text, children\n\n def GetText(self, indices, column):\n return self.GetItem(indices)[0][column]\n\n def GetChildren(self, indices):\n return self.GetItem(indices)[1]\n\n def GetChildrenCount(self, indices):\n return len(self.GetChildren(indices))\n\n def AddChild(self, indicies, labels):\n \"\"\"\n Adds a child to the tree\n \"\"\"\n self.GetChildren(indicies).append((labels, []))\n \n def RemoveChild(self, indicies):\n \"\"\"\n Removes a child from the tree\n \"\"\"\n pass\n\n #For moving items around\n def MoveItem(self, itemToMoveIndex, newParentIndex):\n itemToMove = self.GetItem(itemToMoveIndex)\n newParentChildren = self.GetChildren(newParentIndex)\n newParentChildren.append(itemToMove)\n oldParentChildren = self.GetChildren(itemToMoveIndex[:-1])\n oldParentChildren.remove(itemToMove)\n #overloading for virtual tree\n def OnGetItemText(self, indices, column=0):\n return self.GetText(indices, column)\n\n def OnGetChildrenCount(self, indices):\n return self.GetChildrenCount(indices)\n\n #Overloading for drag and drop\n def OnDrop(self, dropTarget, dragItem):\n dropIndex = self.GetIndexOfItem(dropTarget)\n #dropText = self.GetText(dropIndex,0)\n dragIndex = self.GetIndexOfItem(dragItem)\n #dragText = self.GetText(dragIndex)\n\n self.MoveItem(dragIndex, dropIndex)\n self.RefreshItems()\n\n\n# class VirtualTreeListCtrl(DemoTreeMixin, wx.gizmos.TreeListCtrl):\n# def __init__(self, *args, **kwargs):\n# kwargs['style'] = wx.TR_DEFAULT_STYLE | wx.TR_FULL_ROW_HIGHLIGHT\n# super(VirtualTreeListCtrl, self).__init__(*args, **kwargs)\n# self.AddColumn('Column 0')\n# self.AddColumn('Column 1')\n# self.AddColumn('Column 2')\n# \n# self.model.AddChild((), [\"value1\", \"value2\", \"value3\"])\n# self.model.AddChild((), [\"value1\", \"value2\", \"value3\"])\n# self.model.AddChild((1,), [\"value4\", \"value5\", \"value6\"])\n# self.model.AddChild((1,), [\"value4\", \"value5\", \"value6\"])\n# self.RefreshItems()\n# # def OnGetItemText(self, indices, column=0):\n# # # Return a different label depending on column.\n# # return '%s, column %d'%\\\n# # (super(VirtualTreeListCtrl, self).OnGetItemText(indices), column)\n# \n# def GetIndicesOfSelectedItems(self):\n# \n# if self.GetSelections():\n# return [self.GetIndexOfItem(item) for item in self.GetSelections()]\n# else:\n# return [()]\n\nclass TreeModel(object):\n ''' TreeModel holds the domain objects that are shown in the different\n tree controls. Each domain object is simply a two-tuple consisting of\n a label and a list of child tuples, i.e. (label, [list of child tuples]). \n '''\n def __init__(self, *args, **kwargs):\n self.items = []\n self.itemCounter = 0\n super(TreeModel, self).__init__(*args, **kwargs)\n\n def GetItem(self, indices):\n text, children = 'Hidden root', self.items\n for index in indices:\n text, children = children[index]\n return text, children\n\n def GetText(self, indices):\n return self.GetItem(indices)[0]\n\n def GetChildren(self, indices):\n return self.GetItem(indices)[1]\n\n def GetChildrenCount(self, indices):\n return len(self.GetChildren(indices))\n\n def SetChildrenCount(self, indices, count):\n children = self.GetChildren(indices)\n while len(children) > count:\n children.pop()\n while len(children) < count:\n children.append(('item %d'%self.itemCounter, []))\n self.itemCounter += 1\n\n def MoveItem(self, itemToMoveIndex, newParentIndex):\n itemToMove = self.GetItem(itemToMoveIndex)\n newParentChildren = self.GetChildren(newParentIndex)\n newParentChildren.append(itemToMove)\n oldParentChildren = self.GetChildren(itemToMoveIndex[:-1])\n oldParentChildren.remove(itemToMove)\n\n\nclass DemoTreeMixin(treemixin.VirtualTree, treemixin.DragAndDrop, \n treemixin.ExpansionState):\n def __init__(self, *args, **kwargs):\n self.model = kwargs.pop('treemodel')\n self.log = kwargs.pop('log')\n super(DemoTreeMixin, self).__init__(*args, **kwargs)\n self.CreateImageList()\n\n def CreateImageList(self):\n size = (16, 16)\n self.imageList = wx.ImageList(*size)\n for art in wx.ART_FOLDER, wx.ART_FILE_OPEN, wx.ART_NORMAL_FILE:\n self.imageList.Add(wx.ArtProvider.GetBitmap(art, wx.ART_OTHER, \n size))\n self.AssignImageList(self.imageList)\n\n def OnGetItemText(self, indices):\n return self.model.GetText(indices)\n\n def OnGetChildrenCount(self, indices):\n return self.model.GetChildrenCount(indices)\n\n def OnGetItemFont(self, indices):\n # Show how to change the item font. Here we use a small font for\n # items that have children and the default font otherwise.\n if self.model.GetChildrenCount(indices) > 0:\n return wx.SMALL_FONT\n else:\n return super(DemoTreeMixin, self).OnGetItemFont(indices)\n\n def OnGetItemTextColour(self, indices):\n # Show how to change the item text colour. In this case second level\n # items are coloured red and third level items are blue. All other\n # items have the default text colour.\n if len(indices) % 2 == 0:\n return wx.RED\n elif len(indices) % 3 == 0:\n return wx.BLUE\n else:\n return super(DemoTreeMixin, self).OnGetItemTextColour(indices)\n\n def OnGetItemBackgroundColour(self, indices):\n # Show how to change the item background colour. In this case the\n # background colour of each third item is green.\n if indices[-1] == 2:\n return wx.GREEN\n else: \n return super(DemoTreeMixin, \n self).OnGetItemBackgroundColour(indices)\n\n def OnGetItemImage(self, indices, which):\n # Return the right icon depending on whether the item has children.\n if which in [wx.TreeItemIcon_Normal, wx.TreeItemIcon_Selected]:\n if self.model.GetChildrenCount(indices):\n return 0\n else:\n return 2\n else:\n return 1\n\n def OnDrop(self, dropTarget, dragItem):\n dropIndex = self.GetIndexOfItem(dropTarget)\n dropText = self.model.GetText(dropIndex)\n dragIndex = self.GetIndexOfItem(dragItem)\n dragText = self.model.GetText(dragIndex)\n self.log.write('drop %s %s on %s %s'%(dragText, dragIndex,\n dropText, dropIndex))\n self.model.MoveItem(dragIndex, dropIndex)\n self.GetParent().RefreshItems()\n\n\nclass VirtualTreeListCtrl(DemoTreeMixin, wx.gizmos.TreeListCtrl):\n def __init__(self, *args, **kwargs):\n kwargs['style'] = wx.TR_DEFAULT_STYLE | wx.TR_FULL_ROW_HIGHLIGHT\n super(VirtualTreeListCtrl, self).__init__(*args, **kwargs)\n self.AddColumn('Column 0')\n self.AddColumn('Column 1')\n\n def OnGetItemText(self, indices, column=0):\n # Return a different label depending on column.\n return '%s, column %d'%\\\n (super(VirtualTreeListCtrl, self).OnGetItemText(indices), column)\n\n def GetIndicesOfSelectedItems(self):\n\n if self.GetSelections():\n return [self.GetIndexOfItem(item) for item in self.GetSelections()]\n else:\n return [()]\n\n def RefreshItems(self):\n\n self.RefreshItems()\n self.UnselectAll()\n \n\nclass TreeNotebook(wx.Notebook):\n def __init__(self, *args, **kwargs):\n treemodel = kwargs.pop('treemodel')\n log = kwargs.pop('log')\n super(TreeNotebook, self).__init__(*args, **kwargs)\n self.trees = []\n for class_, title in [(VirtualTreeListCtrl, 'TreeListCtrl')]:\n tree = class_(self, treemodel=treemodel, log=log)\n self.trees.append(tree)\n self.AddPage(tree, title)\n self.Bind(wx.EVT_NOTEBOOK_PAGE_CHANGED, self.OnPageChanged)\n\n def OnPageChanged(self, event):\n oldTree = self.GetPage(event.OldSelection)\n newTree = self.GetPage(event.Selection)\n newTree.RefreshItems()\n newTree.SetExpansionState(oldTree.GetExpansionState())\n event.Skip()\n\n def GetIndicesOfSelectedItems(self):\n tree = self.trees[self.GetSelection()]\n if tree.GetSelections():\n return [tree.GetIndexOfItem(item) for item in tree.GetSelections()]\n else:\n return [()]\n\n def RefreshItems(self):\n tree = self.trees[self.GetSelection()]\n tree.RefreshItems()\n tree.UnselectAll()\n\n\nclass TestPanel(wx.Panel):\n def __init__(self, parent, log):\n self.log = log\n super(TestPanel, self).__init__(parent)\n self.treemodel = TreeModel()\n self.CreateControls()\n self.LayoutControls()\n\n def CreateControls(self):\n self.notebook = TreeNotebook(self, treemodel=self.treemodel, \n log=self.log)\n self.label = wx.StaticText(self, label='Number of children: ')\n self.childrenCountCtrl = wx.SpinCtrl(self, value='0', max=10000)\n self.button = wx.Button(self, label='Update children')\n self.button.Bind(wx.EVT_BUTTON, self.OnEnter)\n\n def LayoutControls(self):\n hSizer = wx.BoxSizer(wx.HORIZONTAL)\n options = dict(flag=wx.ALIGN_CENTER_VERTICAL|wx.ALL, border=2)\n hSizer.Add(self.label, **options)\n hSizer.Add(self.childrenCountCtrl, 2, **options)\n hSizer.Add(self.button, **options)\n sizer = wx.BoxSizer(wx.VERTICAL)\n sizer.Add(self.notebook, 1, wx.EXPAND)\n sizer.Add(hSizer, 0, wx.EXPAND)\n self.SetSizer(sizer)\n\n def OnEnter(self, event):\n indicesList = self.notebook.GetIndicesOfSelectedItems()\n newChildrenCount = self.childrenCountCtrl.GetValue()\n for indices in indicesList:\n text = self.treemodel.GetText(indices)\n oldChildrenCount = self.treemodel.GetChildrenCount(indices)\n self.log.write('%s %s now has %d children (was %d)'%(text, indices,\n newChildrenCount, oldChildrenCount))\n self.treemodel.SetChildrenCount(indices, newChildrenCount)\n self.notebook.RefreshItems()\n\n\ndef runTest(frame, nb, log):\n win = TestPanel(nb, log)\n return win\n\n\nif __name__ == '__main__':\n import sys, os, run\n run.main(['', os.path.basename(sys.argv[0])] + sys.argv[1:])\n\n","repo_name":"Paladiamors/ProjectManager","sub_path":"src/UI/treeListCtrl.py","file_name":"treeListCtrl.py","file_ext":"py","file_size_in_byte":12039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39634506304","text":"class Solution:\n def climbStairs(self, n: int) -> int:\n hm = dict()\n def aux(n):\n if n == 0:\n return 1\n if n < 0:\n return 0\n res = (aux(n-1) if n-1 not in hm else hm[n-1]) + (aux(n-2) if n-2 not in hm else hm[n-2])\n hm[n] = res\n return res\n return aux(n)","repo_name":"pyl/leetcode","sub_path":"70-climbing-stairs/70-climbing-stairs.py","file_name":"70-climbing-stairs.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6528754314","text":"\n# coding: utf-8\n\n# In[3]:\n\nimport numpy as np\nimport sys\nimport argparse\nfrom seq2seqLSTM import translate\nfrom seq2seqLSTM import learning\nfrom seq2seqLSTM import save_model\n\n\n# In[1]:\n\nparser = argparse.ArgumentParser(description='Enlish to French translator.')\nsubparsers = parser.add_subparsers()\nstranslate = subparsers.add_parser('translate', aliases=['t'])\nstranslate.add_argument('text', help='English text file')\nstranslate.set_defaults(which='translate')\n\nlearn = subparsers.add_parser('learn', aliases=['l'])\nlearn.add_argument('X', help='learning input (English)')\nlearn.add_argument('Y', help='learning output (French)')\nlearn.add_argument('-vs', '--vocab_size', help='vocabulary size (default=0 if vocab.txt should stay unchanged)', type=int, default=0)\nlearn.add_argument('-lcn', '--layer_cell_number', help='number of cells in a layer', type=int, default=1000)\nlearn.set_defaults(which='learn')\n\nprint(\"Reading input\")\nargs=vars(parser.parse_args())\n\nif args['which'] == 'translate':\n translate(args['text'])\nelse:\n print(\"Learning - train & test\")\n model=learning(args['X'], args['Y'], args['vocab_size'], args['layer_cell_number'])\n print(\"Saving model\")\n save_model(model)\n\n","repo_name":"nikola-sp/projekatNIZ","sub_path":"seq2seqLSTM_start.py","file_name":"seq2seqLSTM_start.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18124349012","text":"#investigate scrapy\n#async efficiency\n# https://sempioneer.com/python-for-seo/asynchronous-web-scraping-python/#How_Is_Asychronous_Web_Scraping_Different_To_Using_Python_Requests\n# import aiohttp\n# import asyncio\nimport requests\nimport lxml\nimport chardet\nimport sys\nimport datetime\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup\nimport firebase_admin\nfrom firebase_admin import credentials\nfrom firebase_admin import firestore\nfrom forex_python.converter import CurrencyRates\n\n# Iterate through all pages of search results\ndef iterateSeach():\n page = requests.get(baseURL)\n soup = BeautifulSoup(page.content, 'lxml')\n lastPage = int(soup.find('ul',class_=\"paging-middle centertext\").find_all('li')[-1].text.strip())\n print(lastPage)\n print(\"-Iterating through search pages...\")\n thePage = lastPage\n for i in range(0,thePage,1):\n print(\"*SCRAPING PAGE \" + str(i))\n URL = \"https://www.pinkbike.com/buysell/list/?region=3&page=\" + str(i) + \"&category=2\"\n scrapeSearchPage(URL)\n\n# Iterates through all pages of search results - Starting From End\ndef iterateSeachBtoF():\n page = requests.get(baseURL)\n soup = BeautifulSoup(page.content, 'lxml')\n lastPage = int(soup.find('ul',class_=\"paging-middle centertext\").find_all('li')[-1].text.strip())\n print(lastPage)\n print(\"-Iterating through search pages BtoF...\")\n thePage = lastPage\n for i in range(thePage,0,-1):\n print(\"*SCRAPING PAGE \" + str(i))\n URL = \"https://www.pinkbike.com/buysell/list/?region=3&page=\" + str(i) + \"&category=2\"\n scrapeSearchPage(URL)\n\n#Iterates through all bikes in a result page \ndef scrapeSearchPage(URL):\n page = requests_session.get(URL)\n soup = BeautifulSoup(page.content, 'html.parser')\n bikeElements = soup.find_all('div', class_=\"bsitem\")\n # lastPage = int(soup.find('ul',class_=\"paging-middle centertext\").find_all('li')[-1].text.strip())\n for bikeElement in bikeElements:\n scrapeElement(bikeElement)\n \n#Scrapes a single bike element, its details, and adds it to the database\ndef scrapeElement(bikeElement):\n global theCount\n if theCount == 20000:\n sys.exit(\"Firebase Limit Reached: 20000 Writes\")\n pbID = bikeElement.get('id').replace(\"csid\",\"\")\n print(\" ID: \" + pbID)\n bikeURL = \"https://www.pinkbike.com/buysell/\" + pbID\n\n bikeData = bikeElement.find_all('td')\n bikeSpecs = bikeData[1].find_all(\"div\")\n #check for all necessary information\n if len(bikeSpecs) < 7:\n print(\"Missing Specs Data\")\n return\n title = bikeData[1].a.string\n condition = bikeSpecs[1].text.split(\":\",1)[1].strip()\n frameSize = bikeSpecs[2].text.split(\":\",1)[1].strip()\n wheelSize = bikeSpecs[3].text.split(\":\",1)[1].replace('\"',\"\").strip()\n material = bikeSpecs[4].text.split(\":\",1)[1].strip()\n frontTravel = bikeSpecs[5].text.split(\":\",1)[1].strip()\n rearTravel = bikeSpecs[6].text.split(\":\",1)[1].strip()\n\n bikeInfo = bikeData[1].find_all(\"tr\")\n location = bikeInfo[0].text.strip()\n # seller = bikeInfo[1].text.split(\":\")[1].split(\"|\")[0].replace(\"Outside+\", \"\").strip()\n seller = bikeInfo[1].a.text.replace(\"Outside+\", \"\").strip()\n price = int(bikeInfo[2].text.replace(\"$\",\"\").split(\" \")[0].strip())\n #if price is in CAD, rough conversion to USD\n if((bikeInfo[2].text.replace(\"$\",\"\").split(\" \")[1].strip()) == \"CAD\"):\n price = price * exchangeRate\n price = int(price)\n\n #get listing data from Bike's page by pbID\n bikePage = requests_session.get(bikeURL)\n bikeSoup = BeautifulSoup(bikePage.content, 'lxml') \n bikeDetails = bikeSoup.find_all(\"div\",class_=\"buysell-details-column\")[1].get_text(strip=True, separator='\\n').splitlines()\n postDate = datetime.strptime((bikeDetails[1].split(\" \")[0].replace(\"-\",\" \")), '%b %d %Y')\n repostDate = datetime.strptime((bikeDetails[3].split(\" \")[0].replace(\"-\",\" \")), '%b %d %Y')\n if(bikeDetails[5] == \"since\"):\n forSale = bikeDetails[6]\n viewCount = int(bikeDetails[8].replace(\",\", \"\"))\n watchCount = int(bikeDetails[10].replace(\",\", \"\"))\n else:\n forSale = bikeDetails[5]\n viewCount = int(bikeDetails[7].replace(\",\", \"\"))\n watchCount = int(bikeDetails[9].replace(\",\", \"\"))\n\n \n #publish data as json to fireStore\n doc_ref = db.collection(u'trailEnduro').document(pbID)\n doc_ref.set({\n u'title': title,\n u'location': location,\n u'seller': seller,\n u'price_usd': price,\n u'spec': {\n u'condition': condition,\n u'frame_size': frameSize,\n u'wheel_size': wheelSize,\n u'material': material,\n u'front_travel': frontTravel,\n u'rear_travel': rearTravel\n },\n u'listing': {\n u'post_date': postDate,\n u'repost_date': repostDate,\n u'for_sale': forSale,\n u'view_count': viewCount,\n u'watch_count': watchCount,\n },\n })\n theCount += 1\n # print(\" \" + theCount)\n\n# def scrapeListing():\n# print(\"Scraping Listing\")\n\n# def publishData():\n# print(\"Publishing Data\")\n\ndef assessTitle(title):\n print(\"Assessing Title\")\n\n# Get CAD to USD conversion rate\ndef getExchange():\n global exchangeRate\n exchangeRate = CurrencyRates().get_rate('CAD', 'USD')\n exchangeRate = round(exchangeRate, 2)\n\nif __name__ == '__main__':\n cred = credentials.Certificate('/Users/seangaffney/Documents/Code/pb-analytics-616-firebase-adminsdk-5rw79-c5f78deb10.json')\n firebase_admin.initialize_app(cred)\n db = firestore.client()\n\n #URL = 'https://www.pinkbike.com/buysell/list/?lat=37.6806&lng=-122.4073&distance=101&category=2' #local Trail\n baseURL = 'https://www.pinkbike.com/buysell/list/?region=3&category=2' #All Trail\n theCount = 0\n requests_session = requests.Session()\n getExchange()\n\n iterateSeachBtoF()\n # scrapeSearchPage(baseURL)","repo_name":"seangaff/pb-analytics","sub_path":"Scraper/pbScraper.py","file_name":"pbScraper.py","file_ext":"py","file_size_in_byte":5984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27741360011","text":"import re\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.options import Options\nfrom bs4 import BeautifulSoup\nimport sqlite3 # We sometimes need to directly query the database\nimport random\nimport requests # Check for 404s and other undesireables\n\nfrom tomodb import Database # local\n\ndriver = webdriver.Firefox() # You can change this to `webdriver.Chrome`, but results may vary\n\ndef get_curseforge_id(url: str, driver: webdriver = None):\n \"\"\"Scrapes the provided CurseForge page for the project identifier.\"\"\"\n driver.get(url)\n is_404 = \"\"\n try:\n is_404 = driver.find_element(By.CSS_SELECTOR, \".error-page__message--not-found\")\n except:\n pass\n if not is_404:\n tag = driver.find_element(By.CSS_SELECTOR, \"div.mb-3:nth-child(2) > div:nth-child(1) > span:nth-child(2)\") # Complicated CSS selector to extract the project ID from the HTML; this could change in the future\n return tag.get_attribute(\"innerHTML\"), True\n else:\n print(\"Bad URL: \" + url)\n return None, False\n\n\ndb = Database(\"urls.sqlite3\")\n\ndef store_project_ids(driver: webdriver = None):\n conn = sqlite3.connect(\"./urls.sqlite3\")\n c = conn.cursor()\n c.execute(\"SELECT title, curseforge_url FROM mods WHERE curseforge_project_id IS NULL\")\n rows = c.fetchall()\n \n for row in rows:\n title, url = row\n id, is_live = get_curseforge_id(url, driver=driver)\n if is_live:\n try:\n db.update({\"title\": title, \"curseforge_url\": url, \"curseforge_project_id\": id})\n time.sleep(random.randint(2, 4))\n except sqlite3.IntegrityError:\n print(f\"IntegrityError: {{'title': title, 'curseforge_url': url, 'curseforge_project_id': id}}\")\n else:\n pass\n \n conn.commit()\n\n conn.close()\n\nstore_project_ids(driver=driver)\n\n# We're all done here folks\ndriver.quit()\ndb.close()\n","repo_name":"tomodachi94/wikidata-things","sub_path":"scripts/scrape-curseforge/curseforge_urls_to_project_ids.py","file_name":"curseforge_urls_to_project_ids.py","file_ext":"py","file_size_in_byte":1983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10303827846","text":"''' 10989번 수 정렬하기 3 '''\nimport sys\ninput = sys.stdin.readline\nN = int(input().rstrip())\n\ncount = [0] * 10001\nfor _ in range(N):\n temp = int(input().rstrip())\n count[temp] += 1 # Increase the value in the index.\n\n\nfor i in range(len(count)): # Check the sort information recorded on the list.\n for j in range(count[i]):\n print(i) # Print each index as many times as it appears.","repo_name":"kingbj940429/Coding_Test_Solution","sub_path":"beak_joon/b_10989.py","file_name":"b_10989.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38050655895","text":"from django.shortcuts import render\nfrom .models import Product\nfrom .forms import ProductForm\n# Create your views here.\n\ndef product_create_view1(request):\n\tform = ProductForm() #request.POST or None\n\terror = []\n\tprint(request.POST)\n\tif request.method == 'POST':\n\t\ttitle = request.POST.get('title')\n\t\tif not title:\n\t\t\terror.append(\"title reuqired\")\n\t\tform = ProductForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tobj = form.save()\n\t\t\tform = ProductForm()\n\t\telse:\n\t\t\tprint(\"error\")\t\n\tcontext = {\n\t'form': form\n\t}\n\t# context = {\n\t# \t\"title\": obj.title,\n\t# \t\"description\" : obj.description\n\t# }\n\treturn render(request,\"products/product_create.html\",context)\n\n\n\n\n# def product_detail_view(request):\n# \tobj=Product.objects.get(id=1)\n# \t# context = {\n# \t# \t\"title\": obj.title,\n# \t# \t\"description\" : obj.description\n# \t# }\n# \tcontext = {\n# \t\t'object' : obj\n# \t}\n# \treturn render(request,\"products/product_detail.html\",context)","repo_name":"AbdulrahmanSerhan/html_login_form","sub_path":"products/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36193840442","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.http import JsonResponse\nfrom libs.promiseLib import PromiseLib, CategoryLib\nfrom libs.statusLib import APILib\n\nfrom helpers import Collection\n\nimport json\n\n# Create your views here.\ndef index(request):\n promiseLib = PromiseLib()\n topPromises = promiseLib.get_top_promises()\n response = {'topPromises':topPromises,'status':{'name':'paul'}}\n return render(request,'pinarayi_meter/index.html',response)\n\ndef promise(request, uuid):\n reqObj = Collection()\n reqObj.uuid = uuid\n\n promiseLib =PromiseLib()\n promiseDetails = promiseLib.get_promise(reqObj)\n response = {'promiseDetails':promiseDetails}\n return render(request,'pinarayi_meter/promise.html',response)\n\ndef statusApi(request):\n reqObj = Collection()\n apiLib = APILib()\n response = apiLib.getPromiseStatus(reqObj)\n return JsonResponse(response)\n\ndef categoryApi(request):\n reqObj = Collection()\n reqObj.categoryList = request.GET.getlist('categoryList')\n reqObj.type = request.GET.dict()['type']\n categoryLib = CategoryLib()\n response = categoryLib.get_promise_by_category(reqObj)\n return JsonResponse(response)\n\ndef thirtFivePoint(request):\n reqObj = Collection()\n reqObj.category = 'Thirty Five Point Programme'\n promiseLib = PromiseLib()\n response = {}\n response['promises'] = promiseLib.get_promise_by_category(reqObj)\n return render(request, 'pinarayi_meter/thirty_five.html',response)\n\ndef category(request,category_name):\n reqObj = Collection()\n response = {}\n reqObj.category_name = category_name\n promiseLib = PromiseLib()\n response = promiseLib.get_promise_by_category_list(reqObj)\n \n return render(request, 'pinarayi_meter/category.html',response)\n\n","repo_name":"ashwineaso/pinarayi-meter","sub_path":"pinarayiMeter/pinarayi_meter/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1803,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"8846436789","text":"from random import randint\n\nnames_string = input('Input the name of guests seperating them by a comma: ')\nnames = names_string.split( ', ' )\n\ndef banker_roulette(a):\n i = randint( 0, len( a ) -1 )\n payee = a[i]\n return f'{payee} is going to buy the meal today!'\n\n\nif __name__ == '__main__':\n result = banker_roulette(names)\n print(result)","repo_name":"reubendevries/100-days-of-code-python","sub_path":"day-04/banker_roulette.py","file_name":"banker_roulette.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14941360853","text":"#define a function that take list of words as a argument and this function will return the reverse of every element in that list\n#exapmle: ['abc','tuv','xyz']\n#output: ['cba','vut','zyx']\n\nList = ['abc', 'tuv', 'xyz']\nprint(f\"List is: {List}\")\n\ndef reverse_List(list):\n element = []\n for i in list:\n element.append(i[::-1])\n return element\nprint(f\"reverse of each element in list: {reverse_List(List)}\")","repo_name":"BeenashPervaiz/Command_Line_Task","sub_path":"list_Ex3.py","file_name":"list_Ex3.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1578993074","text":"import sys\nimport cgitb\n\nfrom PyQt5.QtWidgets import QWidget, QApplication, QTreeWidgetItem, QCheckBox, QSpinBox, QDateTimeEdit, QComboBox\nfrom PyQt5.QtCore import pyqtSignal, QDateTime\nfrom ui_py.ui_setting import Ui_widget_setting\n\n\nclass Setting(QWidget, Ui_widget_setting):\n signal_core_number = pyqtSignal(int)\n signal_suspend_status = pyqtSignal(bool)\n signal_waiting_min = pyqtSignal(int)\n signal_schedule_status = pyqtSignal(bool)\n signal_cancel_plan = pyqtSignal(str)\n signal_change_language = pyqtSignal(str)\n\n def __init__(self, suspend, cores, schedule_status, waiting_min, language):\n super().__init__()\n self.setupUi(self)\n # ----------init variable------------\n self.suspend = suspend\n self.cores = cores\n self.schedule_status = schedule_status\n self.waiting_min = waiting_min\n self.language = language\n self.language_list = ['English', 'Chinese']\n # ----------init widget--------------\n self.label_suspend = QTreeWidgetItem()\n self.checkbox_suspend = QCheckBox()\n self.label_cores = QTreeWidgetItem()\n self.edit_cores = QSpinBox()\n self.label_plan = QTreeWidgetItem()\n self.edit_plan_datetime = QDateTimeEdit()\n self.label_about = QTreeWidgetItem()\n self.label_language = QTreeWidgetItem()\n self.combobox_lauguage = QComboBox()\n # -----------init function----------------\n self.ui_set()\n self.btn()\n self.init_data_show()\n self.self_translate(language)\n self.show()\n\n def btn(self):\n self.tree_setting.itemClicked.connect(self.effect_expand)\n self.tree_setting.itemChanged.connect(self.enable_schedule)\n self.combobox_lauguage.activated.connect(self.choose_language)\n self.checkbox_suspend.stateChanged.connect(self.change_suspend_status)\n\n def ui_set(self):\n # style\n self.setStyleSheet(\"QSpinBox{border:1.5px solid #778899;border-radius:4px; padding:2px 2px}\"\n \"QDateTimeEdit{border:1.5px solid #778899;border-radius:4px; padding:2px 2px}\")\n self.tree_setting.setColumnWidth(0, 180)\n self.tree_setting.expandItem(self.tree_setting.topLevelItem(0))\n self.tree_setting.topLevelItem(1).setCheckState(1, 0)\n self.edit_cores.setMaximumSize(50, 25)\n self.edit_cores.setContentsMargins(0, 4, 0, 0)\n self.edit_cores.setMinimum(1)\n self.edit_cores.setMaximum(132)\n self.label_plan.setDisabled(True)\n self.edit_plan_datetime.setMaximumSize(135, 28)\n self.edit_plan_datetime.setDisabled(True)\n self.combobox_lauguage.setMaximumSize(135, 28)\n self.combobox_lauguage.addItems(self.language_list)\n self.combobox_lauguage.setCurrentText(self.language)\n\n # function\n self.add_tree_item(0, self.label_suspend, \"暂停队列\", self.checkbox_suspend)\n self.add_tree_item(0, self.label_cores, \"使用核数\", self.edit_cores)\n self.add_tree_item(1, self.label_plan, \"计划启动于\", self.edit_plan_datetime)\n self.add_tree_item(2, self.label_language, \"语言选择\", self.combobox_lauguage)\n\n def add_tree_item(self, top_level_index, label, label_name, input_edit):\n label.setText(0, label_name)\n self.tree_setting.topLevelItem(top_level_index).addChild(label)\n self.tree_setting.setItemWidget(label, 1, input_edit)\n\n def effect_expand(self, item, column):\n index = self.tree_setting.indexOfTopLevelItem(item)\n if index >= 0:\n if item.isExpanded():\n item.setExpanded(False)\n else:\n item.setExpanded(True)\n\n def enable_schedule(self, item, column):\n index = self.tree_setting.indexOfTopLevelItem(item)\n if index == 1:\n check_state = item.checkState(column)\n self.schedule_status = bool(check_state)\n self.label_plan.setDisabled(2 - check_state)\n self.edit_plan_datetime.setEnabled(check_state)\n item.setExpanded(2 - check_state)\n self.reset_date_edit()\n self.suspend = self.checkbox_suspend.checkState()\n self.checkbox_suspend.setCheckState(self.suspend + check_state)\n self.checkbox_suspend.setDisabled(check_state)\n\n def init_data_show(self):\n self.edit_cores.setValue(self.cores)\n self.checkbox_suspend.setCheckState(self.suspend * 2)\n if self.schedule_status:\n self.tree_setting.topLevelItem(1).setCheckState(1, 2)\n self.label_plan.setDisabled(False)\n self.edit_plan_datetime.setEnabled(True)\n self.tree_setting.topLevelItem(1).setExpanded(True)\n waiting_seconds = self.waiting_min * 60\n self.edit_plan_datetime.setDisplayFormat(\"yyyy/MM/dd HH:mm\")\n self.edit_plan_datetime.setDateTime(QDateTime.currentDateTime().addSecs(waiting_seconds))\n else:\n self.reset_date_edit()\n\n def reset_date_edit(self):\n self.edit_plan_datetime.setDisplayFormat(\"yyyy/MM/dd HH:mm\")\n self.edit_plan_datetime.setDateTime(QDateTime.currentDateTime())\n self.edit_plan_datetime.setMinimumDateTime(QDateTime.currentDateTime())\n self.edit_plan_datetime.setMaximumDateTime(QDateTime.currentDateTime().addDays(5))\n self.edit_plan_datetime.setCalendarPopup(True)\n\n def change_suspend_status(self, status):\n self.suspend = bool(status)\n\n def plan_start(self):\n curr_time = QDateTime.currentSecsSinceEpoch()\n schedule_time = self.edit_plan_datetime.dateTime().toSecsSinceEpoch()\n self.waiting_min = int(round((schedule_time - curr_time) / 60, 0))\n print('waiting min', self.waiting_min)\n self.signal_waiting_min.emit(self.waiting_min)\n\n def choose_language(self):\n language = self.combobox_lauguage.currentText()\n self.signal_change_language.emit(language)\n self.self_translate(language)\n\n def self_translate(self, language):\n if language == 'English':\n self.label_suspend.setText(0, 'Suspend next')\n self.label_cores.setText(0, 'Threads number')\n self.label_plan.setText(0, 'Scheduled in')\n self.label_language.setText(0, 'Language')\n else:\n self.label_suspend.setText(0, \"暂停队列\")\n self.label_cores.setText(0, \"使用核数\")\n self.label_plan.setText(0, \"计划启动于\")\n self.label_language.setText(0, \"语言选择\")\n self.retranslateUi(self)\n\n def closeEvent(self, event):\n self.cores = self.edit_cores.value()\n suspend_status = self.checkbox_suspend.checkState()\n self.signal_core_number.emit(self.cores)\n self.signal_schedule_status.emit(self.schedule_status)\n if self.schedule_status:\n self.plan_start()\n else:\n self.signal_cancel_plan.emit(' ')\n self.signal_suspend_status.emit(bool(suspend_status))\n self.close()\n\n\nif __name__ == '__main__':\n cgitb.enable(format='text')\n app = QApplication(sys.argv)\n suspend = True\n cores = 24\n schedule_status = True\n waiting_min = 60\n language = 'English'\n myWin = Setting(suspend, cores, schedule_status, waiting_min, language)\n app.installEventFilter(myWin)\n sys.exit(app.exec_())\n\n\n","repo_name":"qweaxdzsc/fluent_add_on","sub_path":"fluent_queue/func/func_setting.py","file_name":"func_setting.py","file_ext":"py","file_size_in_byte":7369,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"40203123878","text":"# import datetime\n# from dateutil.relativedelta import relativedelta\nimport re\n\nfrom django.core.exceptions import ValidationError\nfrom django.forms import DateInput, ModelForm\nimport django_filters\n\nfrom students.models import Student\n\n\nclass StudentBaseForm(ModelForm):\n class Meta:\n model = Student\n fields = [\n 'first_name',\n 'last_name',\n 'age',\n 'birthdate',\n 'email',\n 'phone_number',\n 'enroll_date',\n 'graduate_date',\n 'graduate_date2',\n 'group',\n ]\n # fields = '__all__'\n widgets = {\n 'birthdate': DateInput(attrs={'type': 'date'}),\n 'enroll_date': DateInput(attrs={'type': 'date'}),\n 'graduate_date': DateInput(attrs={'type': 'date'}),\n 'graduate_date2': DateInput(attrs={'type': 'date'}),\n }\n\n\n @staticmethod\n def normalize_name(value):\n return value.lower().capitalize()\n\n\n def clean_first_name(self):\n first_name = self.cleaned_data['first_name']\n result = self.normalize_name(first_name)\n return result\n\n\n def clean_last_name(self):\n last_name = self.cleaned_data['last_name']\n result = self.normalize_name(last_name)\n return result\n\n\n def clean_phone_number(self):\n if self.cleaned_data['phone_number']:\n return re.sub('[^+0-9]','',self.cleaned_data['phone_number'])\n else: \n return self.cleaned_data['phone_number']\n # result = re.sub('[^+0-9]','',self.cleaned_data['phone_number'])\n # if Student.objects.filter(phone_number=result).exclude(id=self.instance.id).exists():\n # raise ValidationError('The phone number already exists. Please try another one.')\n # return result\n\n\n # def clean_birthdate(self):\n # birthdate = self.cleaned_data['birthdate']\n # age = datetime.datetime.now().year - birthdate.year\n # if age < 18:\n # raise ValidationError('Age should be greater than 18 y.o.')\n #\n # return birthdate\n\n\n def clean(self):\n enroll_date = self.cleaned_data['enroll_date']\n graduate_date = self.cleaned_data['graduate_date']\n if enroll_date > graduate_date:\n raise ValidationError('Enroll date coudnt be greater than graduate date!')\n\n\nclass StudentCreateForm(StudentBaseForm):\n pass\n\n\nclass StudentUpdateForm(StudentBaseForm):\n class Meta(StudentBaseForm.Meta):\n exclude = ['age']\n # class Meta(StudentBaseForm.Meta):\n # fields = [\n # 'first_name',\n # 'last_name',\n # 'age',\n # 'birthdate',\n # 'email',\n # 'phone_number',\n # 'enroll_date',\n # 'graduate_date',\n # 'graduate_date2',\n # 'group',\n # ]\n\n\nclass StudentsFilter(django_filters.FilterSet):\n class Meta:\n model = Student\n fields = {\n 'age': ['gt', 'lt',],\n 'first_name': ['exact', 'icontains'],\n 'last_name': ['exact', 'startswith'],\n 'group': ['exact'],\n }\n","repo_name":"RenKus96/test_django","sub_path":"students/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32696773031","text":"import math, os\nimport numpy as np\nfrom collections import deque\n\n\nimport pkg_resources\nimport rclpy\nfrom rclpy.node import Node\nfrom std_msgs.msg import String\n\nfrom tqdm import tqdm\nfrom scipy import interpolate\nimport matplotlib.pyplot as plt\n# from pykdtree.kdtree import KDTree\nfrom scipy.spatial import cKDTree as KDTree\n\ntry:\n #### for debug ######\n from HDMap import HDmap\n from pcc.main import PCC_algorithm\n from coord_tranform import Coord_Trans\nexcept:\n ##### for release #####\n from trajectory_planner.pcc.main import PCC_algorithm\n from trajectory_planner.HDMap import HDmap\n from trajectory_planner.coord_tranform import Coord_Trans\n\nfrom ad_map_server_py.ad_map_server import CoordTrans\nfrom ament_index_python.packages import get_package_share_directory\npackage_share_directory = get_package_share_directory('trajectory_planner')\n\nclass OfflinePCC(Node):\n # add some contraint here\n def __init__(self, start_point, end_point):\n super().__init__('offline_pcc')\n self._map = HDmap()\n\n self.pcc_file_dir = os.path.join(package_share_directory, \"offline_pcc.txt\")\n self.global_plan_dir = os.path.join(package_share_directory, \"global_routing.txt\")\n\n ## trucksim coord start and end\n self.coord_trans = CoordTrans(-225.151904, -6337.384475)\n self.start_point = self.coord_trans.trucksim_to_map(start_point[0], start_point[1]) +[start_point[2]]\n self.end_point = self.coord_trans.trucksim_to_map(end_point[0], end_point[1]) + [end_point[2]]\n ## map coord \n\n self._global_plan = self._map.GlobalPlanner_waypoints(self.start_point, self.end_point)\n self._speed_profile = deque(maxlen=1000000000000000000)\n self._x = deque(maxlen=1000000000000000000)\n self.speed_planner = PCC_algorithm()\n self.grad_x = None\n self.grad = None\n\n ## reference/target speed for pcc\n self.target_speed = 16.67\n \n def pcc_peak_adjust(self, pcc_x, pcc_v, grad_x, grad):\n def find_peak(array_like):\n peak_indexs_up = []\n peak_indexs_down = []\n peak_indexs = []\n direction_signal = 0\n for i in range(len(array_like)-1):\n #down peak\n if array_like[i]array_like[i-1]:\n if direction_signal != -1:\n peak_indexs_up.append(i)\n peak_indexs.append(i)\n direction_signal = -1\n else:\n pass\n return peak_indexs_up, peak_indexs_down, peak_indexs\n \n pcc_v_peaks_up, pcc_v_peaks_down, pcc_v_peaks = find_peak(pcc_v)\n grad_peaks_up, grad_peaks_down, grad_peaks = find_peak(grad)\n\n def find_nearest_pcc_peak(peaks_list, peak):\n dist = 10000\n candidiate_idx = 0\n for i in range(len(peaks_list)):\n if dist>(peaks_list[i]-peak)**2:\n dist = (peaks_list[i]-peak)**2\n candidiate_idx = i\n return candidiate_idx\n\n previous_pcc_peak_idx = 0\n for i in range(0, len(grad_peaks)):\n ## find nearest pcc peak comparing to grad peak\n choosen_pcc_peaks = pcc_v_peaks_up\n for itm in grad_peaks_up:\n if grad_peaks[i] == itm:\n choosen_pcc_peaks = pcc_v_peaks_down\n break\n this_pcc_peak_idx = find_nearest_pcc_peak(choosen_pcc_peaks, grad_peaks[i])\n for j in range(len(pcc_v_peaks)):\n if pcc_v_peaks[j] == choosen_pcc_peaks[this_pcc_peak_idx]:\n this_pcc_peak_idx = j\n break\n leng_old = pcc_x[pcc_v_peaks[this_pcc_peak_idx]]-pcc_x[pcc_v_peaks[previous_pcc_peak_idx]]\n leng_new = grad_x[grad_peaks[i]]-pcc_x[pcc_v_peaks[previous_pcc_peak_idx]]\n this_peak_offset = grad_x[grad_peaks[i]] - pcc_x[pcc_v_peaks[this_pcc_peak_idx]]\n pcc_x[pcc_v_peaks[this_pcc_peak_idx]] = grad_x[grad_peaks[i]]\n for idx in range(pcc_v_peaks[previous_pcc_peak_idx]+1, pcc_v_peaks[this_pcc_peak_idx]):\n pcc_x[idx] = leng_new*(pcc_x[idx]-pcc_x[pcc_v_peaks[previous_pcc_peak_idx]])/leng_old + pcc_x[pcc_v_peaks[previous_pcc_peak_idx]]\n for idxp in range(pcc_v_peaks[this_pcc_peak_idx]+1, len(pcc_x)):\n pcc_x[idxp] = pcc_x[idxp]+this_peak_offset\n previous_pcc_peak_idx = this_pcc_peak_idx\n return pcc_x, pcc_v\n\n \n def compute_speed_profile(self):\n def compute_distance(loc1, loc2):\n return math.sqrt((loc1[0]-loc2[0])**2+(loc1[1]-loc2[1])**2)\n grad_x, grad = self._map.query_gradient(self._global_plan)\n self.grad_x = grad_x\n self.grad = grad\n step_size = 10000\n grad_x_step = np.array(grad_x[::step_size])\n grad_step = grad[::step_size]\n pcc_v, pcc_x = self.speed_planner.pcc_speed_planner(grad_x_step.tolist(), grad_step, self.target_speed)\n pcc_x, pcc_v = self.pcc_peak_adjust(pcc_x, pcc_v, grad_x_step, grad_step)\n for v, x in zip(pcc_v, pcc_x):\n self._speed_profile.append(v)\n self._x.append(x)\n\n def write_planning_result(self):\n with open(self.pcc_file_dir, \"w\") as f:\n for i in range(len(self._speed_profile)):\n write_line = str(self._x[i])+\" \"+str(self._speed_profile[i])+\"\\n\"\n f.write(write_line)\n\n def write_global_plan(self):\n with open(self.global_plan_dir, \"w\") as f:\n for i in range(len(self._global_plan)):\n write_line = str(self._global_plan[i][0])+\" \"+str(self._global_plan[i][1])+\" \"+str(self._global_plan[i][2])+\" \"+str(self.grad_x[i])+\" \"+str(self.grad[i])+\"\\n\"\n f.write(write_line)\n\nclass PCCReader(object):\n def __init__(self, pcc_file_dir = None, global_plan_dir = None):\n\n if pcc_file_dir == None:\n self.pcc_file_dir = os.path.join(package_share_directory, \"offline_pcc.txt\")\n else:\n self.pcc_file_dir = pcc_file_dir\n\n if global_plan_dir == None:\n self.global_plan_dir = os.path.join(package_share_directory, \"global_routing.txt\")\n else:\n self.global_plan_dir = global_plan_dir\n\n self.f = open(self.pcc_file_dir, \"r\")\n self.gp_f = open(self.global_plan_dir)\n self.pcc_x = []\n self.pcc_v = []\n self.global_plan=None\n self.x = []\n self.y = []\n self.z = []\n self.grad = []\n self.xp = []\n self.xy_xp = None\n self.tree = None\n self.xy_numpy = []\n self._process_file()\n\n \n ## no use if there is offline planned routing, use read global plan instead\n def set_global_plan(self, global_plan, xp):\n self.global_plan = global_plan\n self.xp = xp\n x = []\n y = []\n step = 1000\n for waypoint in self.global_plan:\n x.append(waypoint[0])\n y.append(waypoint[1])\n self.xy_xp = interpolate.interp2d(x[0:-1:step], y[0:-1:step], self.xp[0:-1:step])\n \n def read_global_plan(self):\n return self.global_plan\n\n def _process_file(self):\n ## offline pcc speed profile\n for line in self.f.readlines():\n processed_line = line.split( )\n self.pcc_x.append(float(processed_line[0]))\n self.pcc_v.append(float(processed_line[1]))\n self.pcc_v_f = interpolate.interp1d(self.pcc_x, self.pcc_v, kind='linear', fill_value='extrapolate')\n\n ## offline routing\n step = 100\n for line in self.gp_f.readlines():\n processed_line = line.split( )\n self.x.append(float(processed_line[0]))\n self.y.append(float(processed_line[1]))\n self.z.append(float(processed_line[2]))\n self.xp.append(float(processed_line[3]))\n self.xy_numpy.append([float(processed_line[0]),float(processed_line[1])])\n self.xy_numpy = np.array(self.xy_numpy[::step])\n self.xp = np.array(self.xp[::step])\n self.tree = KDTree(self.xy_numpy)\n\n\n def qurey_x(self, x, y):\n dist, ind = self.tree.query([x,y], k=1)\n return self.xp[ind]\n\n def qurey_v(self, x):\n return self.pcc_v_f(x)\n\n def plot_road_grad(self):\n x_node = self.xp\n h_node = self.z\n dx_elem = np.array([x_node[i+1] - x_node[i] for i in range(0, len(x_node)-1, 1000)])\n dh_elem = np.array([h_node[i+1] - h_node[i] for i in range(0, len(h_node)-1, 1000)])\n Ge = dh_elem/dx_elem\n x = np.array([x_node[i] for i in range(0, len(x_node)-1, 1000)])\n plt.plot(x, Ge)\n plt.show()\n\nclass PCCReader2(Node):\n def __init__(self, pcc_file_dir = None):\n super().__init__('pcc_reader')\n if pcc_file_dir == None:\n self.pcc_file_dir = os.path.join(package_share_directory, \"new_pcc_result.txt\")\n else:\n self.pcc_file_dir = pcc_file_dir\n\n self.f = open(self.pcc_file_dir, \"r\")\n self.coord_trans = CoordTrans()\n self.pcc_v = []\n self.x = []\n self.y = []\n self.z = []\n self.xy_numpy = []\n self.tree = None\n \n self.v_scaling = 16.67/16.67\n \n self._process_file()\n\n def utm_to_enu(self, xyz):\n if len(xyz) == 3:\n return [xyz[0]-572196.963588907, xyz[1]-4026790.1365981903,xyz[2]]\n if len(xyz) == 2:\n return [xyz[0]-572196.963588907, xyz[1]-4026790.1365981903]\n \n\n def _process_file(self):\n ## offline pcc speed profile\n for line in self.f.readlines():\n processed_line = line.split( )\n wx, wy, wz = float(processed_line[0]),float(processed_line[1]),float(processed_line[2])\n [x, y, z] = self.utm_to_enu([wx, wy, wz])\n self.x.append(float(x))\n self.y.append(float(y))\n self.z.append(float(z))\n self.xy_numpy.append([x,y])\n self.pcc_v.append(float(processed_line[3]))\n self.xy_numpy = np.array(self.xy_numpy)\n self.tree = KDTree(self.xy_numpy)\n \n \n def qurey_v(self, x, y):\n dist, ind = self.tree.query([x,y], k=1)\n if self.pcc_v[ind] < 10:\n v = 10\n else:\n v = self.pcc_v[ind]\n return v*self.v_scaling\n\n def plot_pcc_result(self):\n ax = plt.gca()\n distance_list = []\n time_tick = []\n t = np.array([i for i in range(len(self.x))])\n ax.plot(t, np.array(self.z)/7)\n ax.plot(t, self.pcc_v)\n for i in range(len(self.x)-1):\n distance_list.append(math.sqrt((self.x[i]-self.x[i+1])**2+(self.y[i]-self.y[i+1])**2+(self.z[i]-self.z[i+1])**2))\n time_tick.append(distance_list[i]/(self.pcc_v[i]+1e-100))\n self.pcc_v = np.array(self.pcc_v)\n \n plt.show()\n \n def test(self, x_list, y_list, z_list):\n ax = plt.gca()\n t = np.array([i for i in range(len(x_list[::1000]))])\n ax.plot(t, (np.array(z_list[::1000])-300)/5)\n v = []\n for x, y in zip(x_list[::1000], y_list[::1000]):\n v.append(self.qurey_v(x, y))\n ax.plot(t, v)\n plt.show()\n\n\n################## main entrance ######################\ndef main(args=None):\n rclpy.init(args=args)\n # offline_pcc_node = OfflinePCC([-229.323,-6344.25,297.901], [686.306,6672.5,408.462])\n offline_pcc_node = OfflinePCC([-170.51309600000002, 102.1044750000001, 295.313], [927.53599-2.114085999999986, 12948.4684-3.2139649999999165, 408.727])\n offline_pcc_node.compute_speed_profile()\n offline_pcc_node.write_planning_result()\n offline_pcc_node.write_global_plan()\n offline_pcc_node.destroy_node()\n rclpy.shutdown()\n\ndef test_pcc_reader(args=None):\n rclpy.init(args=args)\n pcc_reader = PCCReader2()\n pcc_reader.plot_pcc_result()\n pcc_reader_previous = PCCReader()\n pcc_reader.destroy_node()\n rclpy.shutdown()\n\n\nif __name__ == \"__main__\":\n # main()\n test_pcc_reader()","repo_name":"Forrest-Z/IITS","sub_path":"planning/trajectory_planner/trajectory_planner/offline_pcc.py","file_name":"offline_pcc.py","file_ext":"py","file_size_in_byte":12348,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"11808584093","text":"\"\"\"This script is used to get and install non-commercial Houdini licenses.\"\"\"\n\n# Future\nfrom __future__ import annotations\n\n# Standard Library\nimport argparse\nimport socket\nimport subprocess\nfrom typing import List\n\n# SideFX\nimport sidefx\n\nACCESS_TOKEN_URL = \"https://www.sidefx.com/oauth2/application_token\"\nENDPOINT_URL = \"https://www.sidefx.com/api/\"\nPRODUCTS_TO_INSTALL = \"HOUDINI-NC;RENDER-NC\"\n\n\ndef build_parser() -> argparse.ArgumentParser:\n \"\"\"Build an argument parser to get the command line data.\n\n Returns:\n An argument parser to get the required input information.\n\n \"\"\"\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"client_id\")\n\n parser.add_argument(\"client_secret\")\n\n return parser\n\n\ndef get_keys_to_install(\n client_id: str,\n client_secret_key: str,\n server_name: str,\n server_code: str,\n) -> List[str]:\n \"\"\"Get a list of non-commercial license keys to install.\n\n Args:\n client_id: The SideFX client id.\n client_secret_key: The SideFX client secret.\n server_name: The local server name.\n server_code: The local server code.\n\n Returns:\n The list of license keys to install.\n \"\"\"\n service = sidefx.service(\n access_token_url=ACCESS_TOKEN_URL,\n client_id=client_id,\n client_secret_key=client_secret_key,\n endpoint_url=ENDPOINT_URL,\n )\n\n license_strings = service.license.get_non_commercial_license(\n server_name=server_name,\n server_code=server_code,\n products=PRODUCTS_TO_INSTALL,\n )\n return license_strings[\"license_keys\"]\n\n\ndef get_server_code() -> str:\n \"\"\"Get the server code for the locally running server.\n\n Returns:\n The sesinetd server code.\n \"\"\"\n result = subprocess.check_output([\"sesictrl\", \"print-server\"])\n server_code = result.decode().split(\"\\n\")[1].split()[2]\n\n return server_code\n\n\ndef install_licenses(license_keys: List[str]) -> None:\n \"\"\"Install a list of license keys.\n\n Args:\n license_keys: The licenses keys to install.\n \"\"\"\n for license_key in license_keys:\n subprocess.call([\"sesictrl\", \"install\", license_key])\n\n\ndef main() -> None:\n \"\"\"The main execution function.\"\"\"\n # Parse the args from the action execution.\n parser = build_parser()\n parsed_args = parser.parse_args()\n\n server_code = get_server_code()\n server_name = socket.getfqdn()\n\n license_keys = get_keys_to_install(\n parsed_args.client_id,\n parsed_args.client_secret,\n server_name,\n server_code,\n )\n\n install_licenses(license_keys)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"captainhammy/install-houdini-apprentice-license-action","sub_path":"install_apprentice_keys.py","file_name":"install_apprentice_keys.py","file_ext":"py","file_size_in_byte":2643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26457640992","text":"import blessed \nterm = blessed.Terminal()\nfrom random import randint\nimport socket\nimport time\n\n\ndef create_server_socket(local_port, verbose):\n \"\"\"Creates a server socket.\n \n Parameters\n ----------\n local_port: port to listen to (int)\n verbose: True if verbose (bool)\n \n Returns\n -------\n socket_in: server socket (socket.socket)\n \n \"\"\"\n \n socket_in = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n socket_in.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # deal with a socket in TIME_WAIT state\n\n if verbose:\n print(' binding on local port %d to accept a remote connection' % local_port)\n \n try:\n socket_in.bind(('', local_port))\n except:\n raise IOError('local port %d already in use by your group or the referee' % local_port)\n socket_in.listen(1)\n \n if verbose:\n print(' done -> can now accept a remote connection on local port %d\\n' % local_port)\n \n return socket_in\n\n\ndef create_client_socket(remote_IP, remote_port, verbose):\n \"\"\"Creates a client socket.\n \n Parameters\n ----------\n remote_IP: IP address to send to (int)\n remote_port: port to send to (int)\n verbose: True if verbose (bool)\n \n Returns\n -------\n socket_out: client socket (socket.socket)\n \n \"\"\"\n\n socket_out = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n socket_out.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) # deal with a socket in TIME_WAIT state\n \n connected = False\n msg_shown = False\n \n while not connected:\n try:\n if verbose and not msg_shown:\n print(' connecting on %s:%d to send orders' % (remote_IP, remote_port))\n \n socket_out.connect((remote_IP, remote_port))\n connected = True\n \n if verbose:\n print(' done -> can now send orders to %s:%d\\n' % (remote_IP, remote_port))\n except:\n if verbose and not msg_shown:\n print(' connection failed -> will try again every 100 msec...')\n \n time.sleep(.1)\n msg_shown = True\n \n return socket_out\n \n \ndef wait_for_connection(socket_in, verbose):\n \"\"\"Waits for a connection on a server socket.\n \n Parameters\n ----------\n socket_in: server socket (socket.socket)\n verbose: True if verbose (bool)\n \n Returns\n -------\n socket_in: accepted connection (socket.socket)\n \n \"\"\"\n \n if verbose:\n print(' waiting for a remote connection to receive orders')\n \n socket_in, remote_address = socket_in.accept()\n \n if verbose:\n print(' done -> can now receive remote orders from %s:%d\\n' % remote_address)\n \n return socket_in \n\n\ndef create_connection(your_group, other_group=0, other_IP='127.0.0.1', verbose=False):\n \"\"\"Creates a connection with a referee or another group.\n \n Parameters\n ----------\n your_group: id of your group (int)\n other_group: id of the other group, if there is no referee (int, optional)\n other_IP: IP address where the referee or the other group is (str, optional)\n verbose: True only if connection progress must be displayed (bool, optional)\n \n Returns\n -------\n connection: socket(s) to receive/send orders (dict of socket.socket)\n \n Raises\n ------\n IOError: if your group fails to create a connection\n \n Notes\n -----\n Creating a connection can take a few seconds (it must be initialised on both sides).\n \n If there is a referee, leave other_group=0, otherwise other_IP is the id of the other group.\n \n If the referee or the other group is on the same computer than you, leave other_IP='127.0.0.1',\n otherwise other_IP is the IP address of the computer where the referee or the other group is.\n \n The returned connection can be used directly with other functions in this module.\n \n \"\"\"\n \n # init verbose display\n if verbose:\n print('\\n[--- starts connection -----------------------------------------------------\\n')\n \n # check whether there is a referee\n if other_group == 0:\n if verbose:\n print('** group %d connecting to referee on %s **\\n' % (your_group, other_IP))\n \n # create one socket (client only)\n socket_out = create_client_socket(other_IP, 42000+your_group, verbose)\n \n connection = {'in':socket_out, 'out':socket_out}\n \n if verbose:\n print('** group %d successfully connected to referee on %s **\\n' % (your_group, other_IP))\n else:\n if verbose:\n print('** group %d connecting to group %d on %s **\\n' % (your_group, other_group, other_IP))\n\n # create two sockets (server and client)\n socket_in = create_server_socket(42000+your_group, verbose)\n socket_out = create_client_socket(other_IP, 42000+other_group, verbose)\n \n socket_in = wait_for_connection(socket_in, verbose)\n \n connection = {'in':socket_in, 'out':socket_out}\n\n if verbose:\n print('** group %d successfully connected to group %d on %s **\\n' % (your_group, other_group, other_IP))\n \n # end verbose display\n if verbose:\n print('----------------------------------------------------- connection started ---]\\n')\n\n return connection\n \n \ndef bind_referee(group_1, group_2, verbose=False):\n \"\"\"Put a referee between two groups.\n \n Parameters\n ----------\n group_1: id of the first group (int)\n group_2: id of the second group (int)\n verbose: True only if connection progress must be displayed (bool, optional)\n \n Returns\n -------\n connections: sockets to receive/send orders from both players (dict)\n \n Raises\n ------\n IOError: if the referee fails to create a connection\n \n Notes\n -----\n Putting the referee in place can take a few seconds (it must be connect to both groups).\n \n connections contains two connections (dict of socket.socket) which can be used directly\n with other functions in this module. connection of first (second) player has key 1 (2).\n \n \"\"\"\n \n # init verbose display\n if verbose:\n print('\\n[--- starts connection -----------------------------------------------------\\n')\n\n # create a server socket (first group)\n if verbose:\n print('** referee connecting to first group %d **\\n' % group_1) \n\n socket_in_1 = create_server_socket(42000+group_1, verbose)\n socket_in_1 = wait_for_connection(socket_in_1, verbose)\n\n if verbose:\n print('** referee succcessfully connected to first group %d **\\n' % group_1) \n \n # create a server socket (second group)\n if verbose:\n print('** referee connecting to second group %d **\\n' % group_2) \n\n socket_in_2 = create_server_socket(42000+group_2, verbose)\n socket_in_2 = wait_for_connection(socket_in_2, verbose)\n\n if verbose:\n print('** referee succcessfully connected to second group %d **\\n' % group_2) \n \n # end verbose display\n if verbose:\n print('----------------------------------------------------- connection started ---]\\n')\n\n return {1:{'in':socket_in_1, 'out':socket_in_1},\n 2:{'in':socket_in_2, 'out':socket_in_2}}\n\n\ndef close_connection(connection):\n \"\"\"Closes a connection with a referee or another group.\n \n Parameters\n ----------\n connection: socket(s) to receive/send orders (dict of socket.socket)\n \n \"\"\"\n \n # get sockets\n socket_in = connection['in']\n socket_out = connection['out']\n \n # shutdown sockets\n socket_in.shutdown(socket.SHUT_RDWR) \n socket_out.shutdown(socket.SHUT_RDWR)\n \n # close sockets\n socket_in.close()\n socket_out.close()\n \n \ndef notify_remote_orders(connection, orders):\n \"\"\"Notifies orders to a remote player.\n \n Parameters\n ----------\n connection: sockets to receive/send orders (dict of socket.socket)\n orders: orders to notify (str)\n \n Raises\n ------\n IOError: if remote player cannot be reached\n \n \"\"\"\n\n # deal with null orders (empty string)\n if orders == '':\n orders = 'null'\n \n # send orders\n try:\n connection['out'].sendall(orders.encode())\n except:\n raise IOError('remote player cannot be reached')\n\n\ndef get_remote_orders(connection):\n \"\"\"Returns orders from a remote player.\n\n Parameters\n ----------\n connection: sockets to receive/send orders (dict of socket.socket)\n \n Returns\n ----------\n player_orders: orders given by remote player (str)\n\n Raises\n ------\n IOError: if remote player cannot be reached\n \n \"\"\"\n \n # receive orders \n try:\n orders = connection['in'].recv(65536).decode()\n except:\n raise IOError('remote player cannot be reached')\n \n # deal with null orders\n if orders == 'null':\n orders = ''\n \n return orders\n\ndef read_file(file):\n \"\"\" \n Read the file and turn it into a data structure \n Parameters\n -----------\n file : file of the game (file)\n\n Returns\n -------\n food: food on the map (dict)\n player_1: wolves of the player 1 (dict)\n player_2: wolves of the player 2 (dict)\n width: width of the map (int)\n height: height of the map (int)\n\n Versions\n --------\n specification : Aurélie Genot (v.1 17/02/2022)\n specification: Eline Mota (v.2 24/02/2022)\n Implementation: Eline Mote (v.1 24/02/2022)\n \"\"\"\n f = open(file, 'r')\n i = 0\n player_1 = {}\n player_2 = {}\n height = 0\n width = 0\n food = {}\n for lines in f.readlines():\n i+=1\n if i == 2:\n liste = lines.split(' ')\n height = int(liste[0])\n width = int(liste[1])\n\n elif i >= 4 and i <= 12:\n liste = lines.split(' ')\n coordonate = (int(liste[1]), int(liste[2]))\n y = liste[3] #enlève le retour à la ligne\n y = y[0:-1]\n player_1[coordonate] = {'type': y, 'life': 100, 'pacifie': 'non'}\n \n elif i > 12 and i <= 21:\n liste = lines.split(' ')\n coordonate = (int(liste[1]), int(liste[2]))\n y = liste[3]\n y = y[0:-1]\n player_2[coordonate] = {'type': y, 'life': 100, 'pacifie': 'non'}\n \n elif i > 22:\n liste = lines.split(' ')\n coordonate = (int(liste[0]), int(liste[1]))\n food[coordonate] = {'type': liste[2], 'life': int(liste[3])}\n\n return width, height, player_1, player_2, food \n\ndef trans_coord(x, y):\n \"\"\"Adapt and return the coordonates of an element for the map\n Parameters:\n -----------\n x: coordonate x (int)\n y: coordonate y (int)\n \n Return:\n -------\n x: coordonate x (int)\n y: coordonate y (int)\n Version:\n --------\n specification: Eline Mota (v.1 1/03/2022)\n implementation: Eline Mota (v.1 1/03/2022) \"\"\"\n\n\n x = (x-1)*3 + 1\n y = y-1\n return x, y\n\ndef can_eat (player_A, orders) :\n \"\"\"\n Check if a wolf can eat \n Parametres :\n ------------\n player_A: wolves of the player eating (dict)\n orders: orders of player_A (str)\n Returns :\n ---------\n maybe : The fact that the wolf can eat or not (bool)\n Versions :\n ----------\n specification : Aline Boulanger (v.1 10/03/2022)\n implementation : Louise Delpierre (v.1 10/03/2022)\n \n \"\"\"\n order = turn_list(orders)\n \n for elements in order :\n if \"<\" in elements :\n coords = elements.split (\":<\") \n i = 0\n for coord in coords : \n i += 1\n coord = coord.split (\"-\")\n if i == 1 :\n x_A = int(coord[0])\n y_A = int(coord[1])\n coords_wolfs = (x_A, y_A)\n elif i == 2 : \n x_food = int(coord[0])\n y_food = int(coord[1]) \n coords_food = (x_food, y_food) \n\n distance = count_cases(coords_food, coords_wolfs)\n distance_x = distance[0]\n distance_y = distance[1]\n if distance_x <= 1 and distance_y <= 1 :\n return True\n else : \n return False\ndef check_life(player_A, case):\n \"\"\"Check if a wolf has zero life\n\n Parameters:\n ----------\n player_A: wolves of the player playing (dict)\n case: place of the wolf we want to check (tuple)\n\n Return:\n -------\n Result: True if the wolf has zero life, False otherwise (bool)\n\n Versions:\n ---------\n specification: Eline Mota (v.1 19/02/2022)\n implementation: Aline Boulanger (v.1 03/03/2022)\n\n \"\"\"\n for wolves in player_A:\n if wolves == case:\n if player_A[wolves]['life'] == 0:\n return True\n else:\n None\n else:\n None\n return False\ndef can_pacify(player_A, x_A, y_A):\n \"\"\"Check if a wolf has enough energy to pacify and if he is an omega.\n Parameters:\n -----------\n player_A: wolves of the player playing (dict)\n x_A : the absisse of the wolf's position (int)\n y_A : the ordonate of the wolf's position (int)\n Return:\n -------\n Result: True if it can, False otherwise (bool)\n Versions:\n ---------\n specification: Aline Boulanger (v.1 19/02/2022)\n specification : Aline Boulanger (v.2 03/03/2022)\n implementation : Aline Boulanger (v.1 03/03/2022)\n \"\"\"\n #vérifie d'abord si le loup en x_A, y_A fait bien partie des loups du joueur_A\n for key in player_A:\n if key != (x_A, y_A):\n return False\n #Vérifie si le loup du joueur_A est bien un omega et si il a au moins 40 de vie\n if player_A[x_A, y_A][\"type\"] == \"omega\" :\n if player_A[x_A, y_A][\"life\"] >= 40 :\n return True\n else :\n return False\n else :\n return False\n\n\n#utiliser cette fonction après avoir adapter les coordonnées.\ndef clear_pos(x,y):\n \"\"\"Clear a position after moving a wolf\n Parameters:\n -----------\n x: coordonate x of the position (int)\n y: coordonate y of the position (int)\n \n Versions:\n ---------\n specification: Eline Mota (v.1 01/03/2022)\n implementation: Eline Mota (v.1 01/03/2022)\n \"\"\"\n if (x+y) % 2 ==0:\n print(term.move_xy(x, y) + term.on_snow2 + ' ')\n else:\n print(term.move_xy(x,y) + term.on_lavenderblush3 + ' ')\n\n\n\n\ndef color (team):\n \"\"\"\n Return the color of each team\n \n Parameters:\n -----------\n player_A: wolf of a player (dict)\n \n Return:\n -------\n term.stateblue1 : color of team 1 (var)\n term.purple4 : color of team 2 (var)\n \n Version:\n ---------\n specification: Eline Mota (v.1 03/03/2022)\n implementation: Eline Mota (v.1 03/03/2022)\"\"\"\n \n if team == 1:\n return term.slateblue1\n elif team == 2:\n return term.purple4\n else:\n return ''\n\ndef type_wolf(player_A, x, y):\n \"\"\"Return the type of a wolf\n \n Parameters:\n ------------\n player_A: wolves of a player (dict)\n x: coordonate x of a wolf (int)\n y: coordonate y of a wolf (int)\n \n Return:\n -------\n type: type of the wolf (A, O or L)\n\n Versions:\n ---------\n specification: Eline Mota (03/03/2022)\n implementation: Eline Mota (03/03/2022)\n \"\"\"\n type = player_A[(x,y)]['type']\n if type == 'alpha':\n return 'A'\n elif type == 'omega':\n return 'O'\n elif type == 'normal':\n return 'L'\n else:\n None\n\n\ndef update_life(player_A, number, x, y):\n \"\"\"Print on the map the life of the wolf\n\n Parameters:\n ------------\n player_A: wolves of a player (dict)\n number: number of the team playing (int)\n x: coordonate x of a wolf (int)\n y: coordonate y of a wolf (int)\n\n Versions:\n ----------\n specificaion: Eline Mota (v.1 03/03/2022)\n specification: Eline Mota (v.2 18/03/2022)\n implementation: Eline Mota (v.1 03/03/2022)\n \"\"\"\n colore = color(number) #la couleur de la team\n type = type_wolf(player_A, x, y) #le type du loup\n life = player_A[(x,y)]['life']\n pacifie = player_A[(x,y)]['pacifie']\n x, y = trans_coord(x,y) #adapte les coordonnées avec le plateau\n if life > 60 and life <= 100:\n print(term.move_xy(x,y) + colore + term.on_darkolivegreen3 + type)\n elif life <= 60 and life > 30:\n print(term.move_xy(x,y) + colore + term.on_yellow + type)\n elif life <= 30 and life > 0:\n print(term.move_xy(x,y) + colore + term.on_orangered + type)\n elif life == 0:\n print(term.move_xy(x, y) + colore + term.on_orangered + 'H')\n else:\n None\n if pacifie == 'oui':\n print(term.move_xy(x, y) + colore + 'P')\n else:\n None\n print(term.move_xy(20,20))\n\n\ndef turn_list(order):\n \"\"\"Turn an order into a list\n Parameters:\n -----------\n order: the order that a player has given (list)\n Return:\n -------\n order: the order turned into a list (list)\n Version:\n --------\n specification: Aurélie Genot (v.1 19/02/2022)\n implementation: Aurélie Genot (v.1 24/02/2022) \n \"\"\"\n order = order.split(\" \")\n return order\ndef check_case(case, dictionnary_checked):\n \"\"\"Check if there is an element of the dictionnary on a case \n \n Parameters:\n -----------\n case: a case of the map (tuple)\n dictionnary_checked: dictionnary in which we check the information(dict)\n\n Return:\n -------\n Result: True if there is a item on the case, False otherwise (bool)\n\n Versions:\n ---------\n specification: Louise Delpierre (v.1 19/02/2022)\n specification: Aurélie Genot (v.2 03/03/2022)\n implementation: Aurélie Genot (v.1 03/03/2022)\n \"\"\"\n for key in dictionnary_checked :\n if case == key :\n return True \n return False\n\n\ndef can_use(order, x, y):\n \"\"\"Check if a wolf can play, if it hasn't already been used\n\n Parameters:\n ------------\n order: order for the wolves (str)\n x: coordonate x of the wolf (str)\n y: coordonate y of the wolf (str)\n \n Return:\n -------\n result: True if he can play, False otherwise \n\n Versions:\n ---------\n specification: Eline Mota (v.1 11/03/2022)\n implementation: Eline Mota (v.1 11/03/2022)\n \"\"\"\n i = 0\n if ':' in order:\n order = turn_list(order)\n for element in order: \n element = element.split(':')\n element = element[0]\n element = element.split('-')\n if int(element[0]) == x and int(element[1]) == y:\n i += 1\n if i >= 2:\n return False\n elif i < 1:\n return True\n else:\n return False\n\ndef count_cases(e1, e2):\n \"\"\"\n Count the cases that separes two elements\n \n Parameters:\n ------------\n e1: position of the first element (tuple)\n e2: position of the second element (tuple) \n \n Returns:\n --------\n distance: the difference of the two positions (with the X and the Y) (tuple)\n \n Versions:\n ----------\n specification: Louise Delpierre (v.1 19/02/2022)\n specification: Aurélie Genot (v.2 07/03/2022)\n implementation : Aline Boulanger (v.1 3/03/2022)\n \"\"\"\n x_1 = e1[0]\n y_1 = e1[1]\n x_2 = e2[0]\n y_2 = e2[1]\n\n x = x_2 - x_1\n distance_x = abs(x)\n y = y_2 - y_1\n distance_y = abs(y)\n distance = (distance_x, distance_y)\n return distance\n \n\ndef type_food(type):\n \"\"\"Identify the type of the food\n\n Parameters:\n -----------\n type: type of the food (str)\n\n Return:\n -------\n types: type of the food (str)\n\n Version:\n --------\n specification: Louise Delpierre (v.1 03/03/2022)\n implementation: Eline Mota (v.1 03/03/2022)\n \"\"\"\n\n\n if type == 'berries':\n types = 'b'\n elif type == 'apples':\n types = 'a'\n elif type == 'mice':\n types = 'm'\n elif type == 'rabbits':\n types = 'r'\n elif type == 'deers':\n types = 'd'\n return types\n\n\ndef update_food(food):\n \"\"\"Print the food on the map\n\n Parameters:\n -----------\n food: food on the map (dict)\n\n Versions:\n ---------\n specification: Eline Mota (v.1 03/03/2022)\n implementation: Eline Mota (v.1 03/03/2022)\n \"\"\"\n for coord in food:\n type = type_food(food[coord]['type'])\n x = coord[0]\n y = coord[1]\n x, y = trans_coord(x,y)\n life = food[coord]['life']\n if life > 60:\n print(term.move_xy(x, y) + term.black + term.on_darkolivegreen3 + type)\n elif life <= 60 and life > 30:\n print(term.move_xy(x, y) + term.black + term.on_yellow + type)\n elif life <= 30 and life > 0:\n print(term.move_xy(x, y) + term.black + term.on_orangered + type)\n elif life == 0:\n print(term.move_xy(x,y) + term.black + term.on_orangered + '0')\n else:\n raise ValueError('you cannot have a negative life')\n\ndef create_map(width, height, player_1, player_2, food): \n \"\"\" \n Create the map of the board \n Parameters\n ----------\n width: width of the map (int)\n height: height of the map (int)\n player_1: wolves of the player 1 (dict)\n player_2: wolves of the player 2 (dict)\n food : food on the map (dict)\n Versions\n --------\n specification : Louise Delpierre (v1 17/02/2022)\n implementation: Eline Mota (v.1 28/02/2022)\n implementation: Eline Mota (v.2 10/03/2022)\n \"\"\"\n print(term.home + term.clear)\n #crée le plateau\n for x in range(0, width * 3, 3):\n for y in range(0, height, 1):\n if (x+y)%2 ==0:\n print(term.move_xy(x, y) + term.on_lavenderblush3 + ' ')\n else:\n print(term.move_xy(x, y) + term.on_snow2 + ' ')\n #positionne les loups des deux joueurs et la nourriture\n for key in player_1:\n x = key[0]\n y = key[1]\n update_life(player_1, 1, x, y)\n for key in player_2:\n x = key[0]\n y = key[1]\n update_life(player_2, 2, x, y)\n\n for key in food:\n x = key[0]\n y = key[1]\n update_food(food)\n\ndef bonus(player_A, case):\n '''\n Get a bonus to the wolves of the player who is playing \n Parameters\n ----------\n player_A : wolves of the player who plays (dict) # peut-être juste utilisé player\n case : the case of the wolf who wants to have a bonus (tuple) \n Returns\n --------\n power_up_wolves: wolves with their bonus (dict)\n Versions\n --------\n specification : Aurélie Genot (v.1 17/02/2022) \n implementation : Louise Delpierre (v.1 9/03/2022)\n '''\n bonusL = 0\n bonusA = 0\n\n for key in player_A :\n distance = count_cases (case, key)\n distance_x = distance[0]\n distance_y = distance[1]\n if distance_x + distance_y != 0 :#Empêche le bonus de s'activer sur la case du loup même\n if player_A [key]['type'] == 'normal': \n if distance_x <= 2 and distance_y <= 2: \n bonusL += 10\n if player_A [key]['type'] == 'alpha': \n if distance_x <= 4 and distance_y <= 4 :\n bonusA += 30\n bonusTotal = bonusL + bonusA \n\n return bonusTotal\n\ndef attack(player_A, player_B, orders, number, i): \n '''\n Attacks the wolves of the other player \n\n Parameters\n ----------\n player_A : wolves of the player who attacks (dict)\n player_B : wolves of the player who is attacked (dict)\n orders : orders of the player who attacks (str)\n number: number of the team attacked (int)\n i:\n\n Returns\n --------\n player_1: wolves of the player 1 (dict)\n player_2: wolves of the player 2 (dict)\n\n Versions\n --------\n specification : Eline Mota (v.1 17/02/2022)\n implementation: Louise Delpierre, Aline Boulanger (v.1 24/02/2022)\n implementation: Eline Mota (v.2, 03/03/2022)\n '''\n bonuss = 0\n order = turn_list(orders) \n\n for elements in order:\n \n if '*' in elements:\n coords = elements.split (\":*\")\n \n i = 0\n for coord in coords:\n i += 1\n\n coord = coord.split (\"-\")\n if i == 1:\n x_A = int(coord[0])\n y_A = int(coord[1])\n elif i == 2 :\n x_B = int(coord[0])\n y_B = int(coord[1])\n #vérifie une série de choses avant que le loup puisse attaquer afin d'éviter les erreurs \n if check_life(player_A, (x_A, y_A)) == False and check_case((x_B, y_B), player_B) == True and can_use(orders, x_A, y_A) == True:\n if player_A[(x_A, y_A)]['pacifie'] == 'non':\n vie = player_A[(x_A,y_A)][\"life\"]\n bonuss = bonus(player_A, (x_A, y_A))\n vie += bonuss\n attaque = vie/10\n #boucle pour éviter de tomber dans les négatifs\n while player_B [(x_B,y_B)][\"life\"] > 0 and attaque > 0:\n player_B [(x_B,y_B)][\"life\"] -= 1 \n attaque -= 1 \n update_life(player_B, number, x_B, y_B )\n i = 0\n else:\n i += 1\n\n return player_A, player_B, i\n\ndef eat(food, player_A, orders, team):\n \"\"\"\n Add energy point to a wolf depending on what he eats\n \n Parameters:\n -----------\n food: food placed on the map (dict)\n player_A: wolves of the player eating (dict)\n orders: orders of player_A (str)\n team: team of the player eating (int)\n Return:\n -------\n player_A: wolves of the player who has eaten (dict)\n food : food placed on the map (dict)\n Versions:\n ---------\n specifiaction: Aline Boulanger (v.1 19/02/2022)\n specification: Eline Mota (v.2 18/03/2022)\n implementation : Aline Boulanger (v.1 02/03/2022)\n \"\"\"\n \n order = turn_list(orders)\n\n for elements in order :\n if \"<\" in elements :\n coords = elements.split (\":<\")\n i = 0\n for coord in coords : \n i += 1\n coord = coord.split (\"-\")\n if i == 1 :\n x_A = int(coord[0])\n y_A = int(coord[1])\n elif i == 2 : \n x_food = int(coord[0])\n y_food = int(coord[1]) \n coords_food = (x_food, y_food)\n for key in food :\n if coords_food == key and can_use(orders, x_A, y_A) and can_eat(player_A, orders) and check_case((x_A, y_A),player_A) == True:\n #boucle pour éviter de dépasser 100 ou pour éviter que la vie de la nourriture soit négative\n while player_A[(x_A, y_A)][\"life\"] < 100 and food[x_food, y_food][\"life\"] > 0 :\n player_A[(x_A, y_A)][\"life\"] += 1\n food[x_food, y_food][\"life\"] -= 1\n update_food(food)\n update_life(player_A, team, x_A, y_A)\n else:\n None\n\n return food, player_A \n\n \ndef in_map(case, width, height): \n \"\"\"To check if the case where the wolf wants to go is well in the map\n Parameters:\n -----------\n case: the case who has to be checked (tuple)\n width: width of the map (int)\n height: height of the map (int)\n Return:\n -------\n Result: True if the case is in the map, False otherwise (bool)\n Versions:\n ----------\n specification: Aurélie Genot (v.1 19/02/2022)\n specification: Aurélie Genot (v.2 03/03/2022)\n specification: Eline Mota (v.3 18/03/2022)\n implementation: Aurélie Genot (v.1 03/03/2022)\n \"\"\"\n \n x = case[0]\n y = case[1]\n\n if x >= 1 and x<= width and y>=1 and y<= height: \n return True\n else:\n return False\n\n\ndef move(player_A, player_B, orders, width, height, team, food):\n \"\"\"\n Move a wolf on the board\n Parameters:\n -----------\n player_A: wolves of the player who is playing (dict)\n player_B: wolves of the other player (dict)\n orders : order of the player_A (str)\n width: width of the map (int)\n height: height of the map (int)\n team: team of the player moving (int)\n food: food on the map (dict)\n Returns:\n ---------\n player_A: wolves of the player who is playing after moving (dict)\n Versions:\n --------\n specification: Eline Mota (v.1 19/02/2022)\n specification: Eline Mota (v.2 18/03/2022)\n implementation : Aurélie Genot (v.1 03/03/2022)\n implementation: Aurélie Genot (v.2 07/03/2022)\n implementation: Eline Mota (v.3 18/03/2022)\n \"\"\"\n\n order = turn_list(orders) \n for elements in order:\n \n\n if '@' in elements: # Pour chaque @ dans l'ordre\n coords = elements.split (\":@\") #Met en liste les éléments entourant @ \n i = 0\n for coord in coords:\n i += 1\n\n coord = coord.split (\"-\") # Pour récupérer les coordonnées X et Y \n if i == 1:\n actual_x = int(coord[0])\n actual_y = int(coord[1])\n elif i == 2 :\n future_x = int(coord[0])\n future_y = int(coord[1])\n \n actual_pos = (actual_x, actual_y)\n future_pos = (future_x, future_y)\n\n # 1ère étape: Regarder si déplacement possible (ckeck_case, can_use et in_map) \n\n if check_case(actual_pos, player_A) == True and check_case(future_pos, player_A) == False and check_case(future_pos, player_B) == False and in_map(future_pos, width, height) == True and can_use(orders, actual_x, actual_y) == True:\n \n\n # 2ème étape: vérifie que c'est un déplacement d'une case maximumum (avec count_case)\n distance = count_cases(actual_pos, future_pos) \n distance_x = distance[0]\n distance_y = distance[1] \n if distance_x <=1 and distance_y <=1: \n \n #3ème étape: faire le déplacement \n case_deleted = player_A.pop(actual_pos) # Supprimer l'emplacement actuel\n value_case_deleted = case_deleted\n player_A[(future_pos)] = value_case_deleted # Créer le nouvel emplacement\n #change sur le plateau\n x, y = trans_coord(actual_x, actual_y)\n clear_pos(x, y)\n if team == 1:\n update_food(food)\n update_life(player_A, 1, future_x, future_y)\n elif team == 2:\n update_food(food)\n update_life(player_A, 2, future_x, future_y)\n else:\n None\n\n\n\n\n return player_A\n\n\ndef pacification(player_A, player_B, orders):\n \"\"\" \n Pacification of the wolves of the other player \n Parameters\n ----------\n player_A : the player who wants to pacify (dict)\n player_B : the player who is pacify (dict) \n orders : orders of the player who attacks (str)\n Returns\n --------\n player_A : the player with the type modification (dict)\n player_B : the player with the type modification (dict) \n Versions\n --------\n specification : Aline Boulanger (v.1 17/02/2022) \n implementation : Louise Delpierre (v.1 5/03/2022)\n \"\"\"\n order = turn_list(orders) \n for elements in order : \n if 'pacify' in elements: # recherche pacify dans order \n coords = elements.split (\":pacify\")\n i = 0\n for coord in coords:\n i += 1\n coord = coord.split (\"-\")\n if i == 1:\n x_A = int(coord[0])\n y_A = int(coord[1])\n coords_omega = (x_A, y_A)\n \n if can_pacify(player_A, x_A, y_A) == True and can_use(orders, x_A, y_A): # vérifie si player_A est un omega \n for key in player_A:\n distance = count_cases (key,coords_omega)\n distance_x = distance[0]\n distance_y = distance[1]\n if distance_x <= 6 and distance_y <= 6 and key != coords_omega: # la distance pour pacifier \n player_A[key]['pacifie'] = 'oui'\n else:\n None\n for key in player_B:\n distance = count_cases (key,coords_omega)\n distance_x = distance[0]\n distance_y = distance[1]\n if distance_x <= 6 and distance_y <= 6 and key != coords_omega: # la distance pour pacifier \n player_B[key]['pacifie']= 'oui'\n\n else :\n None \n player_A[coords_omega]['life'] -= 40\n else : \n None \n return player_A, player_B\n\ndef choose_random_move(wolf): \n \"\"\" \n To give a ramdom position for a move possible on the map\n \n Parameters\n -----------\n wolf : the wolf who will move (tuple)\n Returns \n --------\n coords_chosen: the coords chosen randomly (str)\n \n Versions\n ---------\n specification: Aurélie Genot (v.1 10/03/2022)\n implementation: Auréie Genot (v.1 10/03/2022)\n \"\"\"\n actual_x = wolf[0]\n actual_y = wolf[1]\n\n #Choisit un déplacement possible\n x_chosen= str(randint(actual_x-1,actual_x+1)) \n y_chosen= str(randint(actual_y-1,actual_y+1))\n\n coords_chosen = x_chosen + \"-\" + y_chosen\n\n return coords_chosen \n\ndef IA_game(player_A, player_B, food):\n \"\"\"\n Return a random order\n\n Parameters\n ----------\n player_A : wolves of the IA (dict)\n player_B : wolves of the other player (dict)\n food: food of the game (dict)\n\n Returns\n -------\n orders: orders of the IA (str)\n\n Versions:\n ---------\n specification: Aurélie Genot (v.1 10/03/2022)\n implementation: Aurélie Genot (v.1 10/03/2022)\n implementation : Aurélie Genot (v.2 15/03/2022)\n implemantation : Eline Mota (v.3 21/03/2022)\n \"\"\"\n orders = ''\n #se nourrir\n for wolves in player_A:\n for foods in food:\n \n distance = count_cases(wolves, foods)\n distance_x = distance[0]\n distance_y = distance[1]\n #vérifie une série de chose pour que l'action de se nourrir ait un minimum de sens\n if distance_x <= 1 and distance_y <= 1 and player_A[wolves]['life'] < 100 and food[foods]['life'] > 0 : \n eat_x = str(wolves[0])\n eat_y = str(wolves[1])\n eaten_x = str(foods[0])\n eaten_y = str(foods[1])\n action_2 = ':<'\n\n orders = orders + eat_x + '-' + eat_y + action_2 + eaten_x + '-' + eaten_y + ' '\n\n #l'attaque\n for wolves in player_A: \n for wolvess in player_B:\n\n distance = count_cases(wolves, wolvess)\n distance_x = distance[0]\n distance_y = distance[1]\n #vérifie une série de chose pour que l'attaque ait un minimum de sens\n if distance_x <= 1 and distance_y <= 1 and player_A[wolves]['life'] > 0: \n attacking_x = str(wolves[0])\n attacking_y = str(wolves[1])\n attacked_x = str(wolvess[0])\n attacked_y = str(wolvess[1])\n orders = orders + attacking_x + \"-\" + attacking_y + \":*\" + attacked_x + \"-\" + attacked_y + ' '\n #voir commentaire ci-dessous pour comprendre\n i = 0\n for wolves in player_A:\n x = wolves[0]\n y = wolves[1]\n order = str(x) + '-' + str(y)\n if order not in orders:\n i += 1\n q = 0\n for wolves in player_A:\n q += 1\n x = wolves[0]\n y = wolves[1]\n order = str(x) + '-' + str(y)\n if order not in orders:\n\n x = str(wolves[0])\n y = str(wolves[1])\n coords_chosen = choose_random_move(wolves)\n #on vérifie ça pour que le dernier ordre donné ne se temrine pas par un espace (pour éviter les problèmes)\n if q < i:\n orders = orders + x + '-' + y + ':@' + coords_chosen + ' '\n else:\n orders = orders + x + '-' + y + ':@' + coords_chosen\n \n\n return orders\n\n\ndef play_game(map_path, group_1, type_1, group_2, type_2):\n \"\"\"Play a game.\n \n Parameters\n ----------\n map_path: path of map file (str)\n group_1: group of player 1 (int)\n type_1: type of player 1 (str)\n group_2: group of player 2 (int)\n type_2: type of player 2 (str)\n \n Notes\n -----\n Player type is either 'human', 'AI' or 'remote'.\n \n If there is an external referee, set group id to 0 for remote player.\n \n \"\"\"\n #lit le fichier\n width, height, player_1, player_2, food = read_file(map_path)\n if type_1 == 'remote':\n connection = create_connection(group_2, group_1)\n elif type_2 == 'remote':\n connection = create_connection(group_1, group_2)\n\n #identifie l'alpha dans chaque team \n for key in player_1:\n if player_1[key]['type'] == 'alpha':\n alpha1 = key\n else:\n None\n for key in player_2:\n if player_2[key]['type'] == 'alpha':\n alpha2 = key\n else:\n None\n\n #crée la map\n create_map(width, height, player_1, player_2, food)\n\n i = 0\n #continue à jouer tant que les deux aplhas ont plus de 0 HP et que moins de deux cents tours ont été joués\n while player_1[alpha1]['life'] > 0 and player_2[alpha2]['life'] > 0 and i <= 200:\n\n\n #évite d'avoir les ordres sur le plateau\n print(term.move_xy(25,25))\n\n #définit le type de \"personne qui va jouer\"\n\n if type_1 == 'human':\n orders1 = input('choose an order')\n elif type_1 == 'AI':\n orders1 = IA_game(player_1, player_2, food)\n elif type_1 == 'remote':\n orders1 = get_remote_orders(connection)\n if type_2 == 'human':\n orders2 = input('choose an order')\n elif type_2 == 'AI': \n orders2 = IA_game(player_2, player_1, food)\n elif type_2 == 'remote':\n orders2 = get_remote_orders(connection)\n \n if '*' not in orders1 and '*' not in orders2:\n i += 1\n\n\n \n #phase 1: pacification\n player_1, player_2 = pacification(player_1, player_2, orders1)\n player_2, player_1 = pacification(player_2, player_1, orders2)\n #phase 2: se nourrir\n food, player_1 = eat(food, player_1, orders1, 1)\n food, player_2 = eat(food, player_2, orders2, 2)\n \n #phase 3: se déplacer\n player_1 = move(player_1, player_2, orders1, width, height, 1, food)\n player_2 = move(player_2, player_1, orders2, width, height, 2, food)\n\n\n player_2b = player_2 #ne donne pas un désavantage pour attaquer à l'équipe deux \n #phase 4 et 5: attaque et bonus\n player_1, player_2b, i = attack(player_1, player_2b, orders1, 2, i)\n player_2, player_1, i = attack(player_2, player_1, orders2, 1, i)\n #effectue les changements de l'attaque au joueur deux\n player_2 = player_2b\n\n \n\n #rénitialise le pacifie et effectue les changements après l'attaque\n for wolves in player_1:\n x = wolves[0]\n y = wolves[1]\n player_1[wolves]['pacifie'] = 'non'\n update_life(player_1, 1, x, y )\n for wolves in player_2:\n x = wolves[0]\n y = wolves[1]\n player_2[wolves]['pacifie'] = 'non'\n update_life(player_2, 2, x, y )\n #update la position de l'alpha pour la boucle\n for wolves in player_1:\n if player_1[wolves]['type'] == 'alpha':\n alpha1 = wolves\n for wolves in player_2:\n if player_2[wolves]['type'] == 'alpha':\n alpha2 = wolves\n \n\n #Annonce le gagnant ou pas....\n if player_1[alpha1]['life'] > 0 and i < 200:\n print('PLAYER 1 HAS WON')\n elif player_2[alpha2]['life'] > 0 and i < 200:\n print('PLAYER 2 HAS WON')\n elif player_2[alpha2]['life'] == 0 and player_1[alpha1]['life'] == 0:\n print('EQUALITY')\n else:\n tot_live1 = 0\n tot_live2 = 0\n for wolves in player_1:\n tot_live1 += player_1[wolves]['life']\n for wolves in player_2:\n tot_live2 += player_2[wolves]['life']\n \n if tot_live1 > tot_live2:\n print('Player 1 has won')\n elif tot_live2 > tot_live1:\n print('Player 2 has won')\n else:\n print('EQUALITY')\n\n if type_1 == 'remote' or type_2 == 'remote':\n close_connection(connection)\n\n\nplay_game('testfichier.txt', 1, 'AI', 2, 'AI')\n","repo_name":"Mozinou2013/INFOB132-Programmation-Project","sub_path":"THEGAME.py","file_name":"THEGAME.py","file_ext":"py","file_size_in_byte":41162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41719729735","text":"from django.contrib import admin\nfrom django.urls import path, include\nfrom rest_framework_simplejwt.views import TokenVerifyView\n\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('api-auth', include('rest_framework.urls')),\n path('login/', include('user.api.loginURL')),\n path('users/', include('user.api.urls')),\n path('verify/', TokenVerifyView.as_view()),\n]","repo_name":"PrasaDesk/DjangoRepo","sub_path":"Auth_CS/auth_ms/auth_ms/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18331376951","text":"import sys\nimport math\nfrom collections import deque\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\n\n\n# Nの素因数分解を辞書で返す(単体)\ndef prime_fact(n):\n root = int(n**0.5) + 1\n prime_dict = {}\n for i in range(2, root):\n cnt = 0\n while n % i == 0:\n cnt += 1\n n = n // i\n if cnt:\n prime_dict[i] = cnt\n if n != 1:\n prime_dict[n] = 1\n return prime_dict\n\n\ndef divisors(x):\n res = set()\n for i in range(1, int(x**0.5)+2):\n if x % i == 0:\n res.add(i)\n res.add(x//i)\n return res\n\n\ndef main():\n N = NI()\n AB = [NLI() for _ in range(N)]\n\n AG = divisors(AB[0][0])\n BG = divisors(AB[0][1])\n\n ans = 1\n\n for ag in AG:\n for bg in BG:\n ok = True\n for a, b in AB:\n if (a % ag or b % bg) and (a % bg or b % ag):\n ok = False\n break\n if ok:\n ans = max(ans, ag * bg // math.gcd(ag, bg))\n\n print(ans)\n\n\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"ARC/ARC124/ARC124C.py","file_name":"ARC124C.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16233394758","text":"\"\"\"Functional tests for the data_io integrals module.\"\"\"\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\n\nfrom rimseval.data_io import integrals\n\n\ndef test_export(crdproc_int):\n \"\"\"Export of integrals with default filename.\"\"\"\n integrals.export(crdproc_int)\n fname = Path(crdproc_int.fname)\n fname = fname.with_name(fname.stem + \"_int\").with_suffix(\".csv\").absolute()\n assert fname.is_file()\n\n\ndef test_export_fname(crdproc_int, tmpdir):\n \"\"\"Export of integrals with given filename.\"\"\"\n fname = Path(tmpdir.strpath).joinpath(\"test\")\n integrals.export(crdproc_int, fname)\n assert fname.with_suffix(\".csv\").is_file()\n\n\ndef test_export_valueerror(crdproc_int):\n \"\"\"Export of integrals with CRDProcessor class with no integrals.\"\"\"\n crdproc_int.integrals = None\n with pytest.raises(ValueError):\n integrals.export(crdproc_int)\n\n\ndef test_load(crdproc_int, tmpdir):\n \"\"\"Assure that export and load end up with the same data.\"\"\"\n fname = Path(tmpdir.strpath).joinpath(\"test.csv\")\n integrals.export(crdproc_int, fname)\n name_ld, timestamp_ld, peaks_ld, int_ld = integrals.load(fname)\n\n assert name_ld == crdproc_int.name\n assert timestamp_ld == crdproc_int.timestamp\n assert peaks_ld == crdproc_int.def_integrals[0]\n np.testing.assert_allclose(int_ld, crdproc_int.integrals)\n","repo_name":"RIMS-Code/RIMSEval","sub_path":"tests/func/data_io/test_integrals.py","file_name":"test_integrals.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15712169592","text":"\"\"\"\ntextgame.parser\n=====================\n\nThis module's main class is :class:`textgame.parser.Parser`. The parser can take\nuser input, call a function that's associated to the input and return to the user a\nmessage describing what happened.\n\nUse ``actionmap`` and ``legal_verbs`` to define how verbs should be mapped to functions, eg:\n\n.. code-block:: python\n\n parser.actionmap.update({\n \"scream\": player.scream\n })\n parser.legal_verbs.update({\n \"scream\": \"scream\",\n \"shout\": \"scream\"\n })\n\nYou can use ``legal_nouns`` to define synonyms for nouns.\n\nA parser is the only thing needed to during the main loop of a game:\n\n.. code-block:: python\n\n parser = textgame.parser.Parser(player)\n while player.status[\"alive\"]:\n response = parser.understand( input(\"> \") )\n print(response)\n\n\nThis module also provides :class:`textgame.parser.EnterYesNoLoop`. If a function\ncalled by the parser returns an ``EnterYesNoLoop`` instead of a string, the parser falls\ninto a mode where it only allows 'yes' and 'no' as an answer. An object of type\n``EnterYesNoLoop`` also provides strings/functions to print/call for each case.\n\nExample: a player method that saves the user from drinking poison\n\n.. code-block:: python\n\n @action_method\n def drink(self, noun):\n if noun == \"poison\":\n\n def actually_do_it():\n self.status[\"alive\"] = False\n return \"You drink the poison and die.\"\n\n return textgame.parser.EnterYesNoLoop(\n question = \"Do you really want to drink poison?\",\n yes = actually_do_it,\n no = \"You stay alive\")\n else:\n # ...\n\n\"\"\"\n\nfrom collections import namedtuple\nimport pickle\nimport os\nimport logging\nlogger = logging.getLogger(\"textgame.parser\")\nlogger.addHandler(logging.NullHandler())\n\nfrom textgame.globals import INFO\n\n\nclass EnterYesNoLoop:\n \"\"\"\n :param question: a yes/no question\n :type question: string\n :param yes: string to return or a function with signature ``f() -> str`` or ``f() -> EnterYesNoLoop`` that should get called if player answeres 'yes' to the question\n :param no: same as yes\n \"\"\"\n\n def __init__(self, question, yes, no):\n self.question = question\n self._yes = yes\n self._no = no\n\n def yes(self):\n \"\"\"\n if yes is callable, return its result, else return it\n \"\"\"\n if callable(self._yes):\n return self._yes()\n return self._yes\n\n def no(self):\n \"\"\"\n if no is callable, return its result, else return it\n \"\"\"\n if callable(self._no):\n return self._no()\n return self._no\n\n\nclass Parser:\n \"\"\"\n :param player: :class:`textgame.player.Player` object\n \"\"\"\n\n def __init__(self, player):\n\n self.player = player\n\n self.in_yesno = False # are we inside a yes/no conversation?\n # yesno_backup must be a function that takes a bool and returns\n # user output. it will be executed if yes/no conversation ends with yes\n self.yesno_backup = None\n\n self.legal_verbs = {\n \"\": \"continue\", # dont do anything on empty input\n \"attack\": \"attack\",\n \"back\": \"back\",\n \"close\": \"close\",\n \"d\": \"down\",\n \"down\": \"down\",\n \"drop\": \"drop\",\n \"e\": \"east\",\n \"east\": \"east\",\n \"enter\": \"go\",\n \"go\": \"go\",\n \"grab\": \"take\",\n \"hear\": \"listen\",\n \"hint\": \"hint\",\n \"inventory\": \"inventory\",\n \"kill\": \"attack\",\n \"listen\": \"listen\",\n \"lock\": \"close\",\n \"look\": \"look\",\n \"n\": \"north\",\n \"north\": \"north\",\n \"open\": \"open\",\n \"s\": \"south\",\n \"score\": \"score\",\n \"south\": \"south\",\n \"take\": \"take\",\n \"u\": \"up\",\n \"up\": \"up\",\n \"w\": \"west\",\n \"walk\": \"go\",\n \"west\": \"west\",\n \"save\": \"save\",\n \"load\": \"load\",\n }\n\n # this may be used to define synonyms\n self.legal_nouns = {\n \"d\": \"down\",\n \"e\": \"east\",\n \"n\": \"north\",\n \"s\": \"south\",\n \"u\": \"up\",\n \"w\": \"west\",\n }\n\n # the lambdas are there because the values in this dict must be\n # callable with exactly one argument\n self.actionmap = {\n \"attack\": player.attack,\n \"back\": lambda x: player.go(\"back\"),\n \"continue\": lambda x: \"\",\n \"down\": lambda x: player.go(\"down\"),\n \"drop\": player.drop,\n \"east\": lambda x: player.go(\"east\"),\n \"go\": player.go,\n \"hint\": player.ask_hint,\n \"inventory\": player.list_inventory,\n \"listen\": player.listen,\n \"look\": player.look,\n \"close\": player.close,\n \"north\": lambda x: player.go(\"north\"),\n \"open\": player.open,\n \"score\": player.show_score,\n \"south\": lambda x: player.go(\"south\"),\n \"take\": player.take,\n \"up\": lambda x: player.go(\"up\"),\n \"west\": lambda x: player.go(\"west\"),\n \"save\": lambda session=\"\": self.save_game(session=session),\n \"load\": lambda session=\"\": self.load_game(session=session),\n }\n\n self.check()\n\n\n def save_game(self, path=\"\", session=\"\"):\n \"\"\"\n dump self.player as textgame_session.pickle\n \"\"\"\n if session:\n filename = os.path.join(path, \"textgame_{}.pickle\".format(session))\n else:\n filename = os.path.join(path, \"textgame.pickle\")\n logger.info(\"saving game to {}\".format(filename))\n with open(filename, \"wb\") as f:\n pickle.dump(self.player, f, pickle.HIGHEST_PROTOCOL)\n return INFO.SAVED\n\n\n def load_game(self, path=\"\", session=\"\"):\n \"\"\"\n load textgame_session.pickle (player object) and reinitialize parser with it\n \"\"\"\n if session:\n filename = os.path.join(path, \"textgame_{}.pickle\".format(session))\n else:\n filename = os.path.join(path, \"textgame.pickle\")\n try:\n with open(filename, \"rb\") as f:\n logger.info(\"reinitializing parser with loaded player object\")\n self.__init__(pickle.load(f))\n except FileNotFoundError:\n return \"There's no game with the name '{}'.\".format(session)\n return INFO.LOADED\n\n\n def delete_game(self, path=\"\", session=\"\"):\n \"\"\"\n delete path/textgame_session.pickle if present\n \"\"\"\n if session:\n filename = os.path.join(path, \"textgame_{}.pickle\".format(session))\n else:\n filename = os.path.join(path, \"textgame.pickle\")\n if os.path.isfile(filename):\n os.remove(filename)\n\n\n def lookup_verb(self, verb):\n return self.legal_verbs.get(verb)\n\n\n def lookup_noun(self, noun):\n return self.legal_nouns.get(noun)\n\n\n def check(self):\n \"\"\"\n check if every verb in self.legal_verbs has a function mapped to.\n if not, the game will crash on the input of this verb\n\n logs the error\n \"\"\"\n for verb in set(self.legal_verbs.values()):\n if verb not in self.actionmap:\n logger.error(\"{} is a legal verb but has no definition\"\n \"in actionmap\".format(verb))\n\n\n def check_result(self, result):\n \"\"\"\n checks if result is EnterYesNoLoop or str, if it's EnterYesNoLoop,\n return the question and fall back to yes/no mode\n \"\"\"\n if type(result) is str:\n return result\n else:\n # assume that result is of type enteryesnoloop\n self.in_yesno = True\n self.yesno_backup = result\n return result.question\n\n\n def do(self, verb, noun):\n \"\"\"\n call function associated with verb with noun as argument\n \"\"\"\n return self.actionmap[verb](noun)\n\n\n def _split_input(self, input):\n \"\"\"\n take input and return verb and noun\n \"\"\"\n args = input.split()\n if len(args) > 2:\n # this gets catched in Parser.understand\n raise ValueError()\n elif len(args) == 2:\n verb, noun = args\n elif len(args) == 1:\n verb = args[0]\n noun = \"\"\n else:\n verb = \"\"\n noun = \"\"\n return verb, noun\n\n\n def understand(self, input):\n \"\"\"\n based on the input, perform player method and return its output\n the return value is what can be printed to the user\n \"\"\"\n try:\n verb, noun = self._split_input(input)\n except ValueError:\n return INFO.TOO_MANY_ARGUMENTS\n\n # if a yes/no conversation is going on, only allow yes/no as answers\n if self.in_yesno:\n if verb != \"yes\" and verb != \"no\":\n return INFO.YES_NO\n elif verb == \"yes\":\n self.in_yesno = False\n # return the yes case\n result = self.yesno_backup.yes()\n return self.check_result(result)\n else:\n self.in_yesno = False\n # return the no case\n result = self.yesno_backup.no()\n return self.check_result(result)\n\n commandverb = self.lookup_verb(verb)\n commandnoun = self.lookup_noun(noun)\n # if noun is illegal, reset to it's original value and feed it to\n # the actionmethods. More creative output if erronous input :)\n if not commandnoun:\n commandnoun = noun\n logger.debug(\"I understood: verb={} noun={}\".format(repr(commandverb), repr(commandnoun)))\n\n # illegal nouns are okay but illegal verbs are not\n if not commandverb:\n return INFO.NOT_UNDERSTOOD\n\n # perform the associated method\n result = self.do(commandverb, commandnoun)\n\n return self.check_result(result)\n","repo_name":"davekch/textgame","sub_path":"textgame/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":10128,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"10046018151","text":"import abc\nfrom piecewise.lcs.hyperparams import get_hyperparam\n\n\nclass IFitnessUpdateStrategy(metaclass=abc.ABCMeta):\n @abc.abstractmethod\n def __call__(self, action_set):\n raise NotImplementedError\n\n\nclass XCSAccuracyFitnessUpdate(IFitnessUpdateStrategy):\n _MAX_ACCURACY = 1.0\n\n def __call__(self, action_set):\n \"\"\"UPDATE FITNESS function from 'An Algorithmic Description of XCS'\n (Butz and Wilson, 2002).\n \"\"\"\n accuracy_vec, accuracy_sum = \\\n self._calc_accuracy_vec_and_accuracy_sum(action_set)\n self._update_fitness_values(action_set, accuracy_vec, accuracy_sum)\n\n def _calc_accuracy_vec_and_accuracy_sum(self, action_set):\n accuracy_sum = 0\n accuracy_vec = []\n for classifier in action_set:\n is_below_error_threshold = classifier.error < \\\n get_hyperparam(\"epsilon_nought\")\n if is_below_error_threshold:\n accuracy = self._MAX_ACCURACY\n else:\n accuracy = self._calc_accuracy(classifier)\n accuracy_vec.append(accuracy)\n accuracy_sum += accuracy * classifier.numerosity\n return accuracy_vec, accuracy_sum\n\n def _calc_accuracy(self, classifier):\n return get_hyperparam(\"alpha\") * \\\n (classifier.error / get_hyperparam(\"epsilon_nought\"))\\\n ** (-1 * get_hyperparam(\"nu\"))\n\n def _update_fitness_values(self, action_set, accuracy_vec, accuracy_sum):\n for (classifier, accuracy) in zip(action_set, accuracy_vec):\n adjustment = \\\n ((accuracy*classifier.numerosity/accuracy_sum) -\n classifier.fitness)\n classifier.fitness += get_hyperparam(\"beta\") * adjustment\n\n\nclass NullFitnessUpdate(IFitnessUpdateStrategy):\n def __call__(self, action_set):\n pass\n","repo_name":"jtbish/piecewise","sub_path":"piecewise/lcs/component/fitness_update.py","file_name":"fitness_update.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"43179372230","text":"T = int(input())\n\ntest = {'ZRO': 0, 'ONE': 1, 'TWO': 2, 'THR': 3, 'FOR': 4, 'FIV':5,\n 'SIX': 6, 'SVN': 7, 'EGT': 8, 'NIN': 9}\ntext_n = {0: 'ZRO', 1: 'ONE', 2: 'TWO', 3: 'THR', 4: 'FOR', 5: 'FIV',\n 6: 'SIX', 7: 'SVN', 8: 'EGT', 9: 'NIN'}\nfor TC in range(1,T+1):\n\n tc = list(input().split())\n tc_len = int(tc[1])\n li = list(input().split())\n li_n = [0] * tc_len\n for i in range(tc_len):\n li_n[i] = test[li[i]]\n li_n.sort()\n print(f'#{TC}')\n for i in li_n:\n print(text_n[i], end=' ')\n print()","repo_name":"sondongmin0419/study","sub_path":"python/s_1221.py","file_name":"s_1221.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39258557442","text":"import json\r\nimport spotipy\r\nimport webbrowser\r\nfrom spotipy.oauth2 import SpotifyOAuth\r\n\r\nusername = 'hsj7sxkq9hszo93uqei13o884'\r\nclientID = '04857dcfb612420baf59d1ec15b1b402'\r\nclientSecret = '24af9fea164b43e8b8bc64b377d6e73c'\r\nredirect_Uri = 'http://google.com/callback/'\r\n\r\nsp = spotipy.Spotify(client_credentials_manager=SpotifyOAuth(client_id=clientID,client_secret=clientSecret,redirect_uri=redirect_Uri, scope='user-read-playback-state,user-modify-playback-state'))\r\n\r\noauth_object = spotipy.SpotifyOAuth(clientID, clientSecret, redirect_Uri)\r\ntoken_dict = oauth_object.get_access_token()\r\ntoken = token_dict['access_token']\r\nspotifyObject = spotipy.Spotify(auth=token)\r\nuser_name = spotifyObject.current_user()\r\n \r\n# To print the response in readable format.\r\nprint(json.dumps(user_name, sort_keys=True, indent=4))\r\n\r\nwhile True:\r\n print(\"Welcome to the project, \" + user_name['display_name'])\r\n print(\"0 - Exit the console\")\r\n print(\"1 - Search for a Song\")\r\n user_input = int(input(\"Enter Your Choice: \"))\r\n if user_input == 1:\r\n search_song = input(\"Enter the song name: \")\r\n results = spotifyObject.search(search_song, 1, 0, \"track\")\r\n songs_dict = results['tracks']\r\n song_items = songs_dict['items']\r\n song = song_items[0]['external_urls']['spotify']\r\n webbrowser.open(song)\r\n # sp.start_playback(uris=['spotify:track:0HUTL8i4y4MiGCPId7M7wb'])\r\n print(song)\r\n print('Song has opened in your browser.')\r\n elif user_input == 0:\r\n print(\"Good Bye, Have a great day!\")\r\n break\r\n else:\r\n print(\"Please enter valid user-input.\")","repo_name":"raj9426/practice-mini-codes","sub_path":"mini project/spotify.py","file_name":"spotify.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5121085377","text":"import logging\nfrom loguru import logger\nfrom config import DEBUG, SYSTEM\n\n\ndef init_log():\n\n if SYSTEM == 'Windows':\n rotation = None\n elif SYSTEM == 'Linux':\n rotation = '1 day'\n else:\n rotation = None\n\n _format = '{time:YY-MM-DD HH:mm:ss.SSS} - {process.name} - {thread.name} - {function} - {line} - {level} - {message}'\n logger.remove()\n handlers = [\n {\n # 'sink': 'log/error-{time:YYMMDD}.log',\n 'sink': 'log/error.log',\n # 'sink': write,\n 'format': _format,\n 'level': 'ERROR',\n 'rotation': rotation,\n 'enqueue': True,\n 'encoding': 'utf-8',\n 'backtrace': True\n },\n {\n 'sink': 'log/log.log',\n 'format': _format,\n 'level': 'INFO',\n 'rotation': rotation,\n 'enqueue': True,\n 'encoding': 'utf-8',\n 'backtrace': True\n }\n ]\n\n if DEBUG:\n handlers.append({\n 'sink': 'log/debug.log',\n 'format': _format,\n 'level': 'DEBUG',\n 'rotation': rotation,\n 'enqueue': True,\n 'encoding': 'utf-8',\n 'backtrace': True\n })\n\n handlers.append({\n 'sink': logging.StreamHandler(),\n 'format': _format,\n 'level': 'DEBUG',\n 'enqueue': True,\n 'backtrace': True\n })\n\n logger.configure(handlers=handlers)\n\n return logger\n","repo_name":"shadowmimosa/mzitu","sub_path":"common/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39188682000","text":"\nimport os\n\nfrom zope import interface\nfrom zope.dottedname.resolve import resolve\n\nfrom ctrl.core.constants import RUN_FOREVER\nfrom ctrl.core.interfaces import ISubcommand\n\nfrom .pubsub import ZMQPublisher\nfrom .rpc import ZMQRPCClient\n\n\nclass ZMQUpCommand(object):\n\n @property\n def apps(self):\n return [\n (k[8:], v.split(' ')[0], v.split(' ')[1:])\n for k, v\n in os.environ.items()\n if k.startswith('ZMQ_APP')]\n\n @property\n def router(self):\n if 'ZMQ_ROUTER' in os.environ:\n router = os.environ['ZMQ_ROUTER'].split(' ')\n return router[0], router[1:]\n\n async def handle(self, *args, loop=None):\n if self.router:\n await resolve(self.router[0])().route(*self.router[1])\n for k, app, args in self.apps:\n handler = resolve(app)(k, args[0], loop=loop)\n response = await handler.run(*args[1:])\n if response is not None:\n print(response)\n return RUN_FOREVER\n\n\n@interface.implementer(ISubcommand)\nclass ZMQSubcommand(object):\n\n def __init__(self, context):\n self.context = context\n\n async def handle(self, command, *args, loop=None):\n return await getattr(self, 'handle_%s' % command)(*args, loop=loop)\n\n async def handle_rpc(self, server_addr, command, *args, loop=None):\n client = ZMQRPCClient('rpc', server_addr)\n return await client.handle(command, *args)\n\n async def handle_publish(self, server_addr, command, *args, loop=None):\n client = ZMQPublisher(loop, server_addr)\n return await client.handle(command, *args)\n\n async def handle_up(self, *args, loop=None):\n return await ZMQUpCommand().handle(*args)\n","repo_name":"phlax/ctrl.zmq","sub_path":"ctrl/zmq/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33235719914","text":"def table_rename_column(self, old_colname: str, new_colname: str, tablename: str) -> None:\n \"\"\"\n - Change a column name for a table in SQL\n\n Arguments:\n old_colname (str): name of the original column\n new_colname (str): desired name of the column\n tablename (str): name of the table to update\n\n Returns:\n None: but updates the column name in-place\n \"\"\"\n\n query = f\"ALTER TABLE {tablename} RENAME {old_colname} TO {new_colname};\"\n self.execute(query)\n\n\ndef table_add_uid_column(self, tablename: str, uid_col: str = \"uid\") -> None:\n \"\"\"\n - Add a unique ID column to a table as a SERIAL PRIMARY KEY type\n - If the `uid_col` already exists, it's dropped before being recreated\n\n Arguments:\n tablename (str): name of the table to add a primary key column to\n uid_col (str): name of the new column, defaults to 'uid'\n\n Returns:\n None: but adds a primary key column to the table\n \"\"\"\n\n query = f\"\"\"\n ALTER TABLE {tablename}\n DROP COLUMN IF EXISTS {uid_col};\n\n ALTER TABLE {tablename}\n ADD {uid_col} serial PRIMARY KEY;\n \"\"\"\n self.execute(query)\n","repo_name":"aaronfraint/pg-data-etl","sub_path":"pg_data_etl/database/actions/query/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40021602682","text":"from flask import Flask,render_template,redirect,url_for,request,flash\nfrom wtforms import StringField,TextAreaField,Form\nfrom flask_mail import Mail,Message\n\n\n\n\nclass ContactForm(Form):\n email=StringField(\"Mail adresiniz\")\n detay=TextAreaField(\"Detaylı bilgi\")\n\n\n\napp=Flask(__name__)\napp.config['MAIL_SERVER']='smtp.gmail.com'\napp.config['MAIL_PORT'] = 465\napp.config['MAIL_USERNAME'] = 'reklamvereneleman@gmail.com'\napp.config['MAIL_PASSWORD'] = '147852963Dark'\napp.config['MAIL_USE_TLS'] = False\napp.config['MAIL_USE_SSL'] = True\napp.config['MAIL_DEFAULT_SENDER'] = \"reklamvereneleman@gmail.com\"\napp.config['SECRET_KEY'] = \"denemedenemedımdımdım\"\n\nmail = Mail(app)\n\n\n\n\n@app.route(\"/\",methods=[\"GET\",\"POST\"])\ndef index():\n form=ContactForm(request.form)\n if request.method==\"POST\":\n try:\n \n \n email=form.email.data\n detay=form.detay.data\n \n msg=Message(\"DouSoftware Mesajı\",\n sender=\"reklamvereneleman@gmail.com\",\n recipients=[\"gozluklubukalemun@gmail.com\"])\n\n msg.body=email+\" \"+\"tarafından gelen mesaj\\n\"+\" \"+detay\n mail.send(msg)\n flash(\"Gönderme Başarılı\",\"succes\")\n return redirect(url_for(\"index\",form=form))\n except Exception as e:\n return(str(e))\n \n else:\n return render_template(\"index.html\",form=form)\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__==\"__main__\":\n app.run(debug=True)","repo_name":"sonic03/dousoftwareflask","sub_path":"dou.py","file_name":"dou.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35669247242","text":"import aws_cdk as cdk\nfrom aws_cdk import Stack\nfrom aws_cdk import aws_iam as iam\nfrom aws_cdk import aws_lambda as _lambda\nfrom aws_cdk import aws_logs as logs\nfrom aws_cdk import aws_s3 as s3\nfrom aws_cdk import aws_sns as sns\nfrom aws_cdk import aws_sqs as sqs\nfrom aws_cdk import aws_ssm as ssm\nfrom aws_cdk.aws_lambda_event_sources import SqsEventSource, SnsEventSource\nfrom constructs import Construct\n\nPROJECK_NAME = \"CollectYouTubeChatApp\"\nDESCRIPTION = \"Collect YouTube Chat Messages\"\n\n\ndef build_resource_name(resource_name: str, service_name: str) -> str:\n return f\"{resource_name}_{service_name}_cdk\"\n\n\nclass CollectYouTubeChatAppStack(Stack):\n\n def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:\n super().__init__(scope, construct_id, **kwargs)\n\n role = iam.Role(\n self, build_resource_name(\"rol\", \"collect_youtube_chat_role\"),\n assumed_by=iam.ServicePrincipal(\"lambda.amazonaws.com\"),\n managed_policies=[\n iam.ManagedPolicy.from_aws_managed_policy_name(\n \"service-role/AWSLambdaBasicExecutionRole\")\n ],\n role_name=build_resource_name(\n \"rol\", \"collect_youtube_chat_role\"),\n description=DESCRIPTION\n )\n cdk.Tags.of(role).add(\"service_name\", build_resource_name(\n \"rol\", \"collect_youtube_chat_role\"))\n\n layer = _lambda.LayerVersion(\n self, build_resource_name(\"lyr\", \"request\"),\n code=_lambda.Code.from_asset(\"layer\"),\n layer_version_name=build_resource_name(\"lyr\", \"request\"),\n compatible_runtimes=[_lambda.Runtime.PYTHON_3_9],\n description=\"Python lib: request\",\n )\n\n fn = _lambda.Function(\n self, build_resource_name(\"lmd\", \"collect_youtube_chat_service\"),\n code=_lambda.Code.from_asset(\"lambda\"),\n handler=\"lambda_function.handler\",\n runtime=_lambda.Runtime.PYTHON_3_9,\n function_name=build_resource_name(\n \"lmd\", \"collect_youtube_chat_service\"),\n environment={\n \"LOG_LEVEL\": \"INFO\",\n },\n description=DESCRIPTION,\n timeout=cdk.Duration.seconds(300),\n memory_size=256,\n role=role,\n log_retention=logs.RetentionDays.THREE_MONTHS,\n layers=[layer]\n )\n cdk.Tags.of(role).add(\"service_name\", build_resource_name(\n \"lmd\", \"collect_youtube_chat_service\"))\n\n bucket = s3.Bucket(\n self, \"s3s-collect-youtube-chat-bucket-cdk\",\n bucket_name=\"s3s-collect-youtube-chat-bucket-cdk\",\n )\n bucket.grant_put(role)\n cdk.Tags.of(role).add(\"service_name\",\n \"s3s-collect-youtube-chat-bucket-cdk\")\n fn.add_environment(\n key=\"BUCKET_NAME\",\n value=bucket.bucket_name\n )\n\n queue = sqs.Queue(\n self, build_resource_name(\"sqs\", \"collect_youtube_chat_queue\"),\n queue_name=build_resource_name(\n \"sqs\", \"collect_youtube_chat_queue\"),\n visibility_timeout=cdk.Duration.seconds(500)\n )\n queue.grant_send_messages(role)\n cdk.Tags.of(role).add(\"service_name\", build_resource_name(\n \"sqs\", \"collect_youtube_chat_queue\"))\n fn.add_environment(\n key=\"QUE_URL\",\n value=queue.queue_url\n )\n sqs_event = SqsEventSource(queue)\n fn.add_event_source(sqs_event)\n\n topic_collect = sns.Topic(\n self, build_resource_name(\"sns\", \"collect_youtube_chat_topic\"),\n topic_name=build_resource_name(\n \"sns\", \"collect_youtube_chat_topic\"),\n )\n topic_collect.grant_publish(role)\n cdk.Tags.of(role).add(\"service_name\", build_resource_name(\n \"sns\", \"collect_youtube_chat_topic\"))\n fn.add_environment(\n key=\"TOPIC_ARN\",\n value=topic_collect.topic_arn\n )\n\n collect_youtube_api_key = ssm.StringParameter.from_secure_string_parameter_attributes(\n self, \"collect_youtube_api_key\",\n version=1,\n parameter_name=\"collect_youtube_api_key\",\n )\n collect_youtube_api_key.grant_read(role)\n fn.add_environment(\n key=\"YOUTUBE_API_KEY\",\n value=collect_youtube_api_key.parameter_name,\n )\n\n topic_schedule = sns.Topic.from_topic_arn(\n self, \"sns_post_twitter_service_cdk\",\n topic_arn=f\"arn:aws:sns:{cdk.Stack.of(self).region}:{cdk.Stack.of(self).account}:sns_post_twitter_service_cdk\"\n )\n sns_event = SnsEventSource(topic_schedule)\n fn.add_event_source(sns_event)\n\n for resource in [role, fn, bucket, queue, topic_collect]:\n cdk.Tags.of(resource).add(\"project\", PROJECK_NAME)\n cdk.Tags.of(resource).add(\"creater\", \"cdk\")\n","repo_name":"tsuji-tomonori/CollectYouTubeChatApp","sub_path":"collect_you_tube_chat_app/collect_you_tube_chat_app_stack.py","file_name":"collect_you_tube_chat_app_stack.py","file_ext":"py","file_size_in_byte":4947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73769746962","text":"import sys\r\n\r\ncnt = 0\r\n\r\nn, k = map(int, sys.stdin.readline().split())\r\n\r\nfor i in range(1, n+1):\r\n if n % i ==0:\r\n cnt += 1\r\n if cnt == k:\r\n print(i)\r\n exit()\r\n\r\nprint(0)","repo_name":"gouyeonch/Algorithm","sub_path":"백준/Bronze/2501. 약수 구하기/약수 구하기.py","file_name":"약수 구하기.py","file_ext":"py","file_size_in_byte":198,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"2136533232","text":"from torch import nn\n\nclass LeNetTiny(nn.Module):\n def __init__(self):\n super(LeNetTiny, self).__init__()\n self.layer1 = nn.Sequential( # (batch_size,1,28,28)\n nn.Conv2d(in_channels=1,out_channels=6,kernel_size=5,stride=1,padding=0), # (batch_size,6,24,24)\n nn.MaxPool2d(kernel_size=2) # (batch_size,6,12,12)\n )\n self.layer2 = nn.Sequential(\n nn.Conv2d(in_channels=6,out_channels=16,kernel_size=5,stride=1,padding=0), # (batch_size,16,8,8)\n nn.MaxPool2d(kernel_size=2) # (batch_size,16,4,4)\n )\n self.fc = nn.Sequential(\n nn.Linear(16*4*4,120),\n nn.Linear(120,84),\n nn.Linear(84,10)\n )\n\n def forward(self,input):\n input = self.layer1(input)\n input = self.layer2(input)\n input = input.view(input.size(0),-1) # 展平数据 size(0)指第0维度 此句表示将输入形状转化为(1,n) 4*16*5*5\n # input = F.softmax(input,dim=0) 这里的dim=0指以0维度为单位进行softmax操作，此时1维度和为1\n output = self.fc(input)\n return output\n","repo_name":"Highler23/DL-learning","sub_path":"project/target-identification/digital_identification/modules/lenet_tiny.py","file_name":"lenet_tiny.py","file_ext":"py","file_size_in_byte":1127,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29137475397","text":"import furl\nimport pytest\nfrom async_asgi_testclient import TestClient\nfrom starlette import status\nfrom tortoise.contrib.test import TestCase\n\nfrom app.core.config import settings\nfrom app.schemas.call_center import CallCenterInCreateSchema, CallCenterInUpdateSchema\nfrom tests.helpers.random_data import random_lower_string\n\npytestmark = pytest.mark.asyncio\n\n_prefix = furl.furl(settings.API_V1_STR) / \"call-centers/\"\n\n\n@pytest.mark.usefixtures(\"create_superuser\", \"async_client_class\", \"superuser_token_headers_class\")\nclass TestCallCenters(TestCase):\n async_client: TestClient\n\n async def test_read_all_empty(self):\n expected = []\n res = await self.async_client.get(_prefix.url, headers=self.superuser_token_headers)\n assert status.HTTP_200_OK == res.status_code\n assert expected == res.json()\n\n async def test_read_all_some_and_batch_delete(self):\n call_center_in = CallCenterInCreateSchema(\n name=random_lower_string(), bunker_name=random_lower_string(), region_id=1\n )\n call_center_in2 = CallCenterInCreateSchema(\n name=random_lower_string(), bunker_name=random_lower_string(), region_id=1\n )\n await self.async_client.post(\n _prefix.url, headers=self.superuser_token_headers, json=call_center_in.dict(exclude_unset=True)\n )\n await self.async_client.post(\n _prefix.url, headers=self.superuser_token_headers, json=call_center_in2.dict(exclude_unset=True)\n )\n\n call_centers = await self.async_client.get(_prefix.url, headers=self.superuser_token_headers)\n\n assert status.HTTP_200_OK == call_centers.status_code\n ids = []\n for cc in call_centers.json():\n ids.append(cc[\"id\"])\n assert len(call_centers.json()) == 2\n path = _prefix / \"batch\"\n path.args = {\"ids\": ids}\n await self.async_client.delete(path.url, headers=self.superuser_token_headers)\n\n async def test_delete(self):\n call_center_in = CallCenterInCreateSchema(\n name=random_lower_string(), bunker_name=random_lower_string(), region_id=1\n )\n await self.async_client.post(\n _prefix.url, headers=self.superuser_token_headers, json=call_center_in.dict(exclude_unset=True)\n )\n call_center = await self.async_client.get(_prefix.url, headers=self.superuser_token_headers)\n url = _prefix / f\"{call_center.json()[0]['id']}\"\n response = await self.async_client.delete(url.url, headers=self.superuser_token_headers)\n assert response.status_code == status.HTTP_204_NO_CONTENT\n\n async def test_update(self):\n call_center_in = CallCenterInCreateSchema(\n name=random_lower_string(), bunker_name=random_lower_string(), region_id=1\n )\n call_center_in2 = CallCenterInUpdateSchema(name=random_lower_string())\n call_center = await self.async_client.post(\n _prefix.url, headers=self.superuser_token_headers, data=call_center_in.json()\n )\n path = _prefix / f\"{call_center.json()['id']}\"\n resp = await self.async_client.patch(\n path.url, json=call_center_in2.dict(exclude_unset=True), headers=self.superuser_token_headers\n )\n resp_json = resp.json()\n assert resp_json[\"name\"] == call_center_in2.name\n assert resp_json[\"region_id\"] == call_center_in.region_id\n await self.async_client.delete(path.url, headers=self.superuser_token_headers)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"classfields/tests/api/v1/test_call_centers.py","file_name":"test_call_centers.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7378240166","text":"from textobjects import templates, exceptions, regex\nfrom textobjects.textobject import StructuredText\nfrom typing import Iterable, Mapping\nfrom copy import deepcopy\n\ndef create(name, template, post=None, construct=None, scope={}):\n cls = templates.parse(template, name)\n if not post:\n post = lambda o: None\n\n init = cls.__init__\n def __init__(self, *args, scope=scope, **kwargs):\n init(self, *args, **kwargs)\n post(self)\n cls.__init__ = __init__\n __match = cls.__match__\n\n @classmethod\n def __match__(cls, *args, **kwargs):\n obj = __match(*args, scope=scope, **kwargs)\n post(obj)\n return obj\n cls.__match__ = __match__\n\n __findall = cls.__findall__\n\n @classmethod\n def __findall__(cls, *args, **kwargs):\n lst = __findall(*args, **kwargs)\n for obj in lst:\n post(obj)\n return lst\n cls.__findall__ = __findall__\n\n\n __search = cls.__search__\n\n @classmethod\n def __search__(cls, *args, **kwargs):\n obj = __search(*args, **kwargs)\n post(obj)\n return obj\n cls.__search__ = __search__\n\n if construct:\n new = cls.__new__\n def __new__(cls, *args, **kwargs):\n newargs = construct(*args, **kwargs)\n if isinstance(newargs, Mapping):\n return new(cls, **newargs)\n if isinstance(newargs, Iterable) and not isinstance(newargs, str):\n return new(cls, *newargs)\n return new(cls, newargs)\n cls.__new__ = __new__\n return cls\n\ndef re(name, template):\n \"\"\"create a textobject class based on the template\n\n .. note::\n\n Only regular placeholders (no wildcards) are supported at this time\n\n \"\"\"\n return regex.parse(name, template)\n\ndef match(Type: StructuredText, text: str, enclosing=None):\n return Type.__match__(text, enclosing)\n\ndef search(Type: StructuredText, text, enclosing=None):\n return Type.__search__(text, enclosing)\n\ndef findall(Type: StructuredText, text):\n return Type.__findall__(text)\n\ndef matchlines(Type: StructuredText, text: str) -> Iterable[StructuredText]:\n lines = text.split('\\n')\n results = []\n for line in lines:\n try:\n m = Type.__match__(line, text)\n results.append(m)\n except:\n pass\n return results\n\ndef searchlines(Type: StructuredText, text: str) -> Iterable[StructuredText]:\n lines = text.split('\\n')\n results = []\n for line in lines:\n try:\n results.append(search(Type, line, text))\n except:\n pass\n return results\n","repo_name":"BlakeASmith/textobjects","sub_path":"lib.py","file_name":"lib.py","file_ext":"py","file_size_in_byte":2604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23189570594","text":"import math\n\n# 01. Faça um programa que receba um numero e mostre qual deles é o maior.\nnum1 = int(input('01. Valor1: '))\nnum2 = int(input('Valor2: '))\nprint(max(num1, num2))\n\n# 02. Receba um valor do usuario, caso seja positivo calcule a raiz quadrada dele, caso contrario informe que o valor\n# é invalido.\nnum = int(input('02. Digite um valor:'))\nif num > 0:\n msg = math.sqrt(num)\nelse:\n msg = 'Valor invalido.'\n\nprint(msg)\n\n# 03. Leia um numero real, se ele for positivo imprima a raiz quadrada, se for negativo imprima o valor ao quadrado.\nnum = float(input('03. Digite um valor:'))\nif num > 0:\n msg = math.sqrt(num)\nelse:\n msg = num.__pow__(2)\n\nprint(msg)\n\n# 04. Leia um valor e caso ele seja positivo apresente o valor dele ao quadrado e sua raiz quadrada.\nnum = int(input('04. Digite um valor:'))\nif num > 0:\n raiz = math.sqrt(num)\n exp = num.__pow__(2)\n print(f'Raiz quadrada: {raiz} \\n Elevado ao quadrado: {exp}')\n\n# 05. Receba um numero inteiro e verifique se ele é par ou impar.\nnum = int(input('05. Digite um valor:'))\nif num % 2 == 0:\n msg = \"É par.\"\nelse:\n msg = \"É impar.\"\n\nprint(msg)\n\n# 06. Receba 2 numeros inteiros, mostre qual é o maior e a diferença entre eles.\nnum1 = int(input('06. Valor1: '))\nnum2 = int(input('Valor2: '))\nmaior = max(num1, num2)\ndif = maior - min(num1, num2)\nprint(f'Maior: {maior}\\nDiferença: {dif}')\n\n# 07. Receba dois valores e mostre o maior, caso sejam iguais imprima 'Valores iguais.'\nnum1 = int(input('07. Valor1: '))\nnum2 = int(input('Valor2: '))\nif num1 == num2:\n print('Valores iguais.')\nelse:\n print(f'{max(num1, num2)}')\n\n# 08. Leia 2 notas entre 0.0 e 10.0 e informe a media delas, caso alguma não esteja nesse escopo, informe.\nnota1 = int(input('08. nota1: '))\n\nif 0.0 <= nota1 <= 10.0:\n nota2 = int(input('nota2: '))\n if 0.0 <= nota2 <= 10.0:\n msg = (nota1 + nota2) / 2\n else:\n msg = \"Notas invalidas, favor verificar.\"\nelse:\n msg = \"Notas invalidas, favor verificar.\"\n\nprint(msg)\n\n# 09. Leia o salario de um trabalhador e o valor do emprestimo solicitado, caso seja menor que 20% do salaraio,\n# imprima \"Emprestimo concedido\", caso contraio, imprima \"Emprestimo não concedido\".\nsalario = float(input('09. Valor do salario: '))\nemp = float(input('Emprestimo solicitado: '))\nif emp < salario * 0.2:\n msg = \"Emprestimo concedido\"\nelse:\n msg = \"Emprestimo não concedido\"\n\nprint(msg)\n\n# 10. Receba a altura e o sexo de uma pessoa e mostre o seu peso ideal.\naltura = float(input('Digite sua altura: '))\nsexo = input('Digite o sexo: ')\nif 'M' or 'm' in sexo:\n pIdeal = (72.7 * altura) - 58\nelse:\n pIdeal = (62.1 * altura) - 44.7\n\nprint(f'Seu peso ideal é {pIdeal}')\n","repo_name":"Furtado145/GeekUniversity_-_Python_Exercicios","sub_path":"Secao05/Exec 01-10.py","file_name":"Exec 01-10.py","file_ext":"py","file_size_in_byte":2701,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13810756634","text":"import cmath\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\nfrom decimal import Decimal\r\n\r\n# Функция форматирования вывода\r\ndef format_float(f):\r\n d5 = Decimal(str(f));\r\n return d5.quantize(Decimal(1)) if d5 == d5.to_integral() else d5.normalize()\r\n\r\nprint('Введите коэффициенты квадратного уравнения:')\r\n\r\na = int(input('a = '))\r\nb = int(input('b = '))\r\nc = int(input('c = '))\r\n\r\n# Подсчет дискриминанта\r\nd = b ** 2 - 4 * a * c\r\n\r\nif a == 0 and b == 0:\r\n print('Решений нет')\r\nelif d == 0:\r\n print('Дискриминант = 0, следовательно, имеем два одинаковых корня:')\r\n x1 = -b / (2 * a)\r\n\r\n print('x=', format_float(x1))\r\n\r\n # Построение графика\r\n y1 = a * x1 * x1 + b * x1 + c\r\n\r\n x = np.linspace((-10 + x1), (x1 + 10), 50)\r\n y = a * x * x + b * x + c\r\n\r\n plt.plot(x, y)\r\n plt.plot(x1, y1, 'ro')\r\n plt.title(\"График квадратного уравнения\") # заголовок\r\n plt.xlabel(\"x\") # ось абсцисс\r\n plt.ylabel(\"y\") # ось ординат\r\n plt.grid()\r\n plt.show()\r\nelif d > 0:\r\n print('Дискриминант > 0, следовательно, имеем два действительных корня:')\r\n x1 = round((-b + d ** (0.5)) / (2 * a), 1)\r\n x2 = round((-b - d ** (0.5)) / (2 * a), 1)\r\n print('x_1 =', format_float(x1), ', x_2 =', format_float(x2))\r\n\r\n # Построение графика\r\n y1 = a * x1 * x1 + b * x1 + c\r\n y2 = a * x2 * x2 + b * x2 + c\r\n\r\n max1 = max([x1, x2])\r\n min1 = min([x1, x2])\r\n\r\n x = np.linspace((-10 + min1), (10 + max1), 50)\r\n y = a * x * x + b * x + c\r\n\r\n plt.plot(x, y)\r\n plt.plot([x1, x2], [y1, y2], 'ro')\r\n plt.title(\"График квадратного уравнения\") # заголовок\r\n plt.xlabel(\"x\") # ось абсцисс\r\n plt.ylabel(\"y\") # ось ординат\r\n plt.grid()\r\n plt.show()\r\nelse:\r\n print('Дискриминант < 0, следовательно, имеем два комплексных корня:')\r\n d1 = round(-b / (2 * a), 1)\r\n m1 = round((abs(d) ** (0.5)) / (2 * a), 1)\r\n d2 = round(-b / (2 * a), 1)\r\n m2 = round((abs(d) ** (0.5)) / (2 * a), 1)\r\n print(f'x_1 = {format_float(d1)} + {format_float(m1)}i,',\r\n f' x_2 = {format_float(d2)} - {format_float(m2)}i')\r\n # print('x_1 = ', (-b + cmath.sqrt(d)) / (2 * a), ', x_2 = ', (-b - cmath.sqrt(d)) / (2 * a))\r\n\r\n # Построение графика\r\n plt.plot([d1, d2], [m1, -m2], 'ro')\r\n plt.title(\"График комплексных решений\") # заголовок\r\n plt.xlabel(\"Rez(x)\") # ось абсцисс\r\n plt.ylabel(\"Im(y)\") # ось ординат\r\n plt.grid()\r\n plt.show()\r\n","repo_name":"Ahmetova/Project0","sub_path":"pet_project_00/quadratic.py","file_name":"quadratic.py","file_ext":"py","file_size_in_byte":2890,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71569499283","text":"from threading import Thread\nimport asyncio\nimport sys\nimport nest_asyncio\nimport time\n\nnest_asyncio.apply()\n\n\n# Setup the asyncio event loop for subprocess management\nALOOP = asyncio.new_event_loop()\nALOOP_FOREVER = Thread(target=ALOOP.run_forever)\nALOOP_FOREVER.start()\n\n\nasync def test(index):\n \"\"\"\n Launch an asyncio subprocess.\n\n :param AigisPlugin plugin: the plugin\n \"\"\"\n print(\"Hello World \" + index)\n _ext_proc = await asyncio.create_subprocess_exec(\n *[\n sys.executable,\n \"-c\", \"import time;time.sleep(2);print('Sub done')\"\n ]\n )\n await waitforsub(_ext_proc)\n print(\"Checkem\")\n\nasync def waitforsub(index):\n print(\"Waiting for sub\")\n await index.wait()\n print(\"Done waiting for sub\")\n\n\nasync def normalwait():\n print(\"Sleeping...\")\n time.sleep(2)\n print(\"Woke\")\n\nasyncio.run_coroutine_threadsafe(normalwait(), ALOOP)\n\nasyncio.run_coroutine_threadsafe(test('A'), ALOOP)\n\n\nprint(\"Done\")","repo_name":"Zaltu/simple-examples","sub_path":"AsyncProcessWait/eternaloop.py","file_name":"eternaloop.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30869310771","text":"## Sort sentence pseudo-alphabetically\r\n## 6 kyu\r\n## https://www.codewars.com/kata/52dffa05467ee54b93000712\r\n\r\n\r\ndef pseudo_sort(st):\r\n st = ''.join(char for char in st if char not in '!@#$%^&*(),.?:;\"')\r\n words = st.split()\r\n upper = sorted([word.strip() for word in words if word[0] == word[0].upper()], reverse=True)\r\n lower = sorted([word.strip() for word in words if word[0] == word[0].lower()])\r\n sentence = lower + upper\r\n return ' '.join(sentence)\r\n \r\n","repo_name":"stereoabuse/codewars","sub_path":"problems/sort_sentence_pseudoalphabetically.py","file_name":"sort_sentence_pseudoalphabetically.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37039920678","text":"import streamlit as st\nimport numpy as np\nimport time\nimport sys\nimport itertools\n\n# Functions for helping us calculating alignments\n\nmatch = 3\nmismatch = -1\ngap = -2\n\n# A scoring function for two letters.\ndef match_score(letterA,letterB): # Score an individual alignment position\n if letterA == '-' and letterB == '-':\n return 0 # Irrelevant position\n elif letterA == '-' or letterB == '-':\n return gap # Gap penalty\n elif letterA == letterB:\n return match # Match\n else:\n return mismatch # Mismatch\n\n# A function that give a global alignmen scor of two gapped sequences.\ndef scoreSequences(a_seq,b_seq): # Score a alignment\n score, score_seq = 0, \"\"\n for a, b in zip(a_seq, b_seq):\n s = match_score(a, b)\n score += s\n score_seq += f\"{s:3}\"\n return score, score_seq\n\n\n# Utility function that creates a gapped sequence from a sequence and a list of insertions.\ndef getSeq(seq, inserts, alignment_length):\n out = \"\"\n for i,s in zip(inserts,seq):\n out += '-'*i\n out += s\n out += '-'*(alignment_length-len(out))\n return out\n\n\n\n\n# Streamlit code setting up a webform\nst.set_page_config(layout=\"wide\", page_icon=\"🎓\", page_title=\"Naive Alignment Calculator\")\nst.title(\"🎓 Naive Alignment Calculator\")\n\nst.write(\n \"This shows you a very naive way to calculate an optimal alignment\"\n)\n\nleft, mid, right = st.columns(3)\n\nleft.write(\"Fill in the two sequences you want to align:\")\nmid.write(\"Calculations:\")\nright.write(\"Optimal Alignment:\")\n\nform = left.form(\"template_form\")\na_seq = form.text_input(\"sequence A\",\"CGA\")\nb_seq = form.text_input(\"sequence B\",\"ACG\")\nmatch = form.number_input(\"match score\", value = match, max_value = 9, min_value = -9)\nmismatch = form.number_input(\"mismatch score\", value = mismatch, max_value = 9, min_value = -9)\ngap = form.number_input(\"gap penalty\", value = gap, max_value = 9, min_value = -9)\nsubmit = form.form_submit_button(\"Calculate!\")\n\nalignment_length = max(len(a_seq),len(b_seq))**2\n\n\na_box, b_box, score_seq_box, score_box = mid.empty(), mid.empty(), mid.empty(), mid.empty()\nm_a_box, m_b_box, m_score_seq_box, m_score_box = right.empty(), right.empty(), right.empty(), right.empty()\n\n\nif submit:\n # execute an excaustive alignment algorithm\n m = - sys.maxsize\n # Loop over all possible insertions in sequence a\n for a_inserts in itertools.product(list(range(len(a_seq))), repeat = len(a_seq)):\n a = getSeq(a_seq, a_inserts, alignment_length)\n a_box.text(' '.join(list(a)))\n # Loop over all possible insertions in sequence b\n for b_inserts in itertools.product(list(range(len(b_seq))),repeat = len(b_seq)):\n b = getSeq(b_seq,b_inserts, alignment_length)\n # Update status text.\n b_box.text(' '.join(list(b)))\n # Calculate the score of the alignment\n score, score_seq = scoreSequences(a, b)\n # Output the score to the webform\n score_box.text(f\"{score: >3}\")\n score_seq_box.text(score_seq)\n\n # Update the best alignment if we found a better one.\n if score > m:\n m_a_box.text(' '.join(list(a)))\n m_b_box.text(' '.join(list(b)))\n m_score_seq_box.text(score_seq)\n m_score_box.text(f\"{score}\")\n m = score\n # Slow us down a bit.\n time.sleep(.5) \n st.balloons()\n","repo_name":"kth-gt/cb2442","sub_path":"lect/align/code/naive.py","file_name":"naive.py","file_ext":"py","file_size_in_byte":3464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43031946708","text":"\"\"\"\n给你一个包含n个整数的数组nums，判断 nums 中是否存在三个元素a,b,c，使得 a + b + c = 0 ？\n请你找出所有和为 0 且不重复的三元组。\n注意：答案中不可以包含重复的三元组。\n\n输入：nums = [-1,0,1,2,-1,-4]\n输出：[[-1,-1,2],[-1,0,1]]\n输入：nums = []\n输出：[]\n输入：nums = [0]\n输出：[]\n\n执行1： 解答错误 ([0,0,0] -> []) 错误考虑边界条件（>0的情况才应该排除，而不是>=0)\n执行2： 解答错误 ([-2,0,0,2,2] -> [[-2,0,2],[-2,0,2]]) 解决执行1的错误时没有举一反三想到左右指针所指元素也可能出现完全重复的情况\n\"\"\"\n\nfrom typing import Set\n\n\nclass Solution:\n def threeSum(self, nums: list) -> list:\n # 边界维护\n if len(nums) < 3:\n return []\n\n # 数组中不存在负数/0，不可能存在三元组\n SortedList = sorted(nums)\n if SortedList[0] > 0:\n return []\n\n # 从最小数开始使用左右指针查找\n Result = []\n ListLength = len(SortedList)\n a_idx = 0\n while ((a_idx < ListLength - 2) and SortedList[a_idx] <= 0):\n a = SortedList[a_idx]\n left_node = a_idx + 1\n right_node = ListLength - 1\n # print(\"start finding: a: {0}|{1}, leftnode: {2}, rightnode:{3}\".format(a_idx, a,left_node,right_node))\n\n # 左右指针\n while (left_node < right_node):\n b = SortedList[left_node]\n c = SortedList[right_node]\n CurrentSum = a + b + c\n # print(\"iter: a - ({0},{1}), b - ({2},{3}), c - ({4},{5})\".format(a_idx,a,left_node,b,right_node,c))\n if (CurrentSum == 0):\n # 添加结果\n Result.append([a,b,c])\n # print(\"find one: {0}|{1}|{2}\".format(a_idx, left_node, right_node))\n # 移动指针，跳过相同大小元素\n while (left_node < ListLength) and (SortedList[left_node] == b):\n left_node += 1\n while (right_node >= 0) and (SortedList[right_node] == c):\n right_node -= 1\n continue\n \n # 和比0小，移动左指针\n elif (CurrentSum < 0):\n left_node += 1\n \n # 和比0大，移动右指针\n else:\n right_node -= 1\n \n # 更新初始数所在位置\n while (a_idx + 1 < ListLength) and (SortedList[a_idx] == a):\n a_idx += 1\n # print(\"after update: a - {0}|{1}\".format(a_idx, SortedList[a_idx]))\n\n return Result\n\nsolution = Solution()\nnums = [-1]\nprint(solution.threeSum(nums))","repo_name":"AlAaraaf/leetcodelog","sub_path":"normal/leetcode15.py","file_name":"leetcode15.py","file_ext":"py","file_size_in_byte":2817,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27549700206","text":"class Person:\r\n def __init__(self, first_name, email):\r\n self.first_name = first_name\r\n self._email = email\r\n\r\n\r\ntk = Person('TK', 'tk@mail.com')\r\n# print(tk._email) # tk@mail.com\r\n# [pylint] W0212:Access to a protected member _email of a client class\r\n\r\n\"\"\"\r\nWe can access and update it. Non-public variables are just a convention and should be treated as a non-public part of the API.\r\n\"\"\"\r\n","repo_name":"loggar/py","sub_path":"py-core/class-object/class.8.private_var_constructor.py","file_name":"class.8.private_var_constructor.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2697909335","text":"import pybullet as p\nimport pybullet_data\n\n\nclass Plane:\n \"\"\"\n Class defining the Plane in the simulation environment.\n \"\"\"\n\n def __init__(self, client):\n \"\"\"\n Initializes the Plane object.\n\n Parameters:\n - client: the id of the physics client.\n \"\"\"\n\n # Set the search path to include PyBullet data\n p.setAdditionalSearchPath(pybullet_data.getDataPath())\n\n # Load the URDF model of the plane\n p.loadURDF('plane.urdf',\n basePosition=[0, 0, 0],\n physicsClientId=client)\n","repo_name":"jcob-sikorski/aicar","sub_path":"Driving/driving/resources/plane.py","file_name":"plane.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30271592848","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[8]:\n\n\nfrom pathlib import Path\nimport pandas as pd\nimport json\n\n\n# In[3]:\n\n\njsons = list(Path(\"./\").rglob(\"*.json\"))\n\n\n# In[4]:\n\n\njsons\n\n\n# structure that I want is, \n# 1 file per row, with a column that is named folder\n# \n# \n\n# In[29]:\n\n\ndef aggregator(jfile,collect):\n print(jfile)\n try:\n jdict= json.loads(open(f\"{jfile}\").read())\n for row in jdict:\n row[\"FolderName\"] = jfile.stem\n collect.extend(jdict)\n except Exception as e:\n print(e, jfile)\n \n \n\n\n# In[30]:\n\n\nres =[]\nfor j in jsons:\n aggregator(j,res)\n \n\n\n# In[31]:\n\n\ndf = pd.DataFrame(res)\n\ndf.to_csv(\"drive_files_analyzed.csv\")\n \n\n","repo_name":"DevinBayly/google_drive_size_profiling","sub_path":"drive-json-consolidator.py","file_name":"drive-json-consolidator.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23388770549","text":"import cv2 as cv\n\ndef face_detect_demo(img):\n #将图片灰度化\n gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)\n # 加载人脸特征数据\n face_detect = cv.CascadeClassifier(\n 'F:\\\\OpenCV\\\\opencv\\\\sources\\\\data\\\\haarcascades\\\\haarcascade_frontalface_default.xml')\n # 对人脸进行检测 返回值是人脸的左上角矩形区域的坐标以及宽度和高度\n faces = face_detect.detectMultiScale(gray,scaleFactor=1.01,minNeighbors=3,maxSize=(33,33),minSize=(28,28))#后三个参数控制人脸检测的效果\n for x,y,w,h in faces:\n cv.rectangle(img,(x,y),(x+w,y+h),color=(0,255,0),thickness=2)\n cv.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=2)\n #显示图片\n cv.imshow(\"img.jpg\",img)\n\n\n#加载图片\nimg = cv.imread(\"face2.jpg\")\n#调用人脸检测方法\nface_detect_demo(img)\ncv.waitKey(0)\ncv.destroyAllWindows()","repo_name":"YingnanHan/Python-400-Series","sub_path":"Python 400 集/Chapter25 人脸识别/06.检测多张人脸.py","file_name":"06.检测多张人脸.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"15764810208","text":"from useragent import random_user_agent\nfrom lxml import etree, html\nimport feedparser\n\n\ndef read_rss(url: str, headers=None) -> list:\n items = list()\n headers = headers if headers else {\n 'User-Agent': random_user_agent(),\n 'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.7',\n 'Accept': 'text/html,application/rss+xml,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n 'Accept-Encoding': 'gzip, deflate',\n 'Accept-Language': 'ru-RU,ru;q=0.9,en-US;q=0.8,en;q=0.7',\n }\n feed = feedparser.parse(url, request_headers=headers)\n if feed:\n for item in feed['items']:\n link = item['guid']\n title = item['title']\n items.append({'url': link, 'title': title})\n else:\n print('Nothing found in feed', url)\n return\n return items\n\n\ndef extract_post_data(content: str) -> dict:\n tree = html.fragment_fromstring(content)\n post_text = tree.xpath(\"//div[@id='post-content-body']\")\n post_text = etree.tostring(post_text[0], pretty_print=True, encoding='utf-8').decode('utf-8')\n post_author = tree.xpath(\"//header/*/span[contains(@class, 'user-info__nickname') \"\n \"and contains(@class, 'user-info__nickname_small')]/text()\")[0]\n post_author_url = tree.xpath(\"//a[contains(@class, 'post__user-info') and contains(@class, 'user-info')]/@href\")[0]\n post_tags = tree.xpath(\"//ul[contains(@class, 'js-post-tags')]/*/a[contains(@class, 'inline-list__item-link') \"\n \"and contains(@class, 'post__tag')]/text()\")\n post_posted_at = tree.xpath(\"//span[contains(@class, 'post__time')]/@data-time_published\")[0]\n return {\n 'post_text': post_text,\n 'post_author': post_author,\n 'post_author_url': post_author_url,\n 'post_tags': post_tags,\n 'post_posted_at': post_posted_at,\n }\n\n\nif __name__ == '__main__':\n pass\n","repo_name":"dorokhin/web_scraper","sub_path":"adapters/habr.py","file_name":"habr.py","file_ext":"py","file_size_in_byte":1913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70336491922","text":"from train import BASEWIDTH\nfrom util import * \nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nfrom models.unet import *\n\n\nLEARNING_RATE = 1e-6\nDEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nBATCH_SIZE = 1\nNUM_EPOCHS = 35\nNUM_WORKERS = 1\nIMAGE_HEIGHT = 368 \nIMAGE_WIDTH = 640 \nPIN_MEMORY = True\nLOAD_MODEL = True\nTRAIN_IMG_DIR = '/y/ayhassen/epick_object_crop/Images/crop_fit/train' \nTRAIN_MASK_DIR = '/y/ayhassen/epick_object_crop/Masks/crop_fit/train'\nVAL_IMG_DIR = '/y/ayhassen/epick_object_crop/Images/crop_fit/val' \nVAL_MASK_DIR = '/y/ayhassen/epick_object_crop/Masks/crop_fit/val' \n\n# TRAIN_IMG_DIR = '/y/ayhassen/epick_dataset/Images/train'\n# TRAIN_MASK_DIR = '/y/ayhassen/epick_dataset/Masks/train/objects'\n# VAL_IMG_DIR = '/y/ayhassen/epick_dataset/Images/val'\n# VAL_MASK_DIR = '/y/ayhassen/epick_dataset/Masks/val/objects'\n\n# TRAIN_MASK_DIR = \"/y/relh/epickitchens/Annotations/train\"\n# VAL_MASK_DIR = \"/y/relh/epickitchens/Annotations/val\"\n# TRAIN_IMG_DIR = \"/y/ayhassen/epick_dataset/Images/train\"\n# VAL_IMG_DIR = \"/y/ayhassen/epick_dataset/Images/val\"\n\n\ntrain_transform = A.Compose(\n [\n A.Resize(height=640, width=640),\n A.Normalize(\n mean=[0.0, 0.0, 0.0],\n std=[1.0, 1.0, 1.0],\n max_pixel_value=255.0,\n ),\n ToTensorV2(),\n ],\n) \n\nval_transforms = A.Compose(\n [\n A.Resize(height=640, width=640),\n A.Normalize(\n mean=[0.0, 0.0, 0.0],\n std=[1.0, 1.0, 1.0],\n max_pixel_value=255.0,\n ),\n ToTensorV2(),\n ],\n)\n\ntrain_loader, val_loader = get_loaders(\n TRAIN_IMG_DIR,\n TRAIN_MASK_DIR,\n VAL_IMG_DIR,\n VAL_MASK_DIR,\n BATCH_SIZE,\n train_transform,\n val_transforms,\n NUM_WORKERS,\n PIN_MEMORY,\n )\n\nmodel = Unet().to(DEVICE)\n\nif LOAD_MODEL:\n load_checkpoint(torch.load(\"./dilated_conv.pth\"), model)\n\n# check_accuracy(val_loader, model, device=DEVICE) \nsave_predictions_as_imgs(\n val_loader, model, folder=\"dilated_conv/\", device=DEVICE\n ) ","repo_name":"ashhass/HOS","sub_path":"random_stuff/dilated_convolution/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41219357626","text":"\"\"\"\nGiven an array of meeting time intervals intervals where intervals[i] = [starti, endi], return the minimum number of conference rooms required.\n\nExample 1:\nInput: intervals = [[0,30],[5,10],[15,20]]\nOutput: 2\n\"\"\"\nimport heapq\n\n\nclass Solution(object):\n def minMeetingRooms(self, intervals):\n \"\"\"\n :type intervals: List[List[int]]\n :rtype: int\n \"\"\"\n # sort intervals in ascending order by start-i\n intervals.sort(key=lambda x: x[0])\n \n # use a heap to record the meeting rooms are currently using\n room_heap = []\n heapq.heappush(room_heap, intervals[0][1])\n\n # traverse the intervals\n for i in range(1, len(intervals)):\n cur_interval = intervals[i]\n\n # if there is no overlap between cur_interval and last meeting room time\n if room_heap[0] <= cur_interval[0]:\n heapq.heappop(room_heap)\n heapq.heappush(room_heap, cur_interval[1])\n\n return len(room_heap)","repo_name":"BrianQJN/Daily-Leecode-Prac","sub_path":"Intervals/253_Meeting_RoomsII.py","file_name":"253_Meeting_RoomsII.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24790298296","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nfrom PIL import Image\r\n\r\nusername = input(\"请输入用户名： \")\r\npassword = input(\"请输入登陆密码： \")\r\n\r\nimgurl = \"https://www.zhihu.com/captcha.gif?type=login\"\r\nurl = \"http://www.zhihu.com/login/{0}\".format(\"phone_num\")\r\nhome = \"https://www.zhihu.com\"\r\nheaders = {\r\n \"Host\":\"www.zhihu.com\",\r\n \"User-Agent\":\"Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0\",\r\n \"Accept\":\"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\r\n \"Accept-Language\":\"zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3\",\r\n \"Accept-Encoding\":\"gzip, deflate, br\",\r\n \"Referer\":\"https://www.zhihu.com/\",\r\n \"Connection\":\"keep-alive\",\r\n \"Upgrade-Insecure-Requests\":\"1\"\r\n}\r\ns = requests.session()\r\nhtml = s.get(home,headers = headers).text\r\nbsj = BeautifulSoup(html,'lxml')\r\nxsrf = bsj.find(\"input\",{\"name\":\"_xsrf\"})['value']\r\n\r\nimg = s.get(imgurl,headers = headers).content\r\n\r\nwith open('captcha.gif','wb+') as f:\r\n f.write(img)\r\n f.close()\r\ncimg = Image.open('captcha.gif')\r\ncimg.show()\r\ncaptcha = input(\"请输入验证码： \")\r\n\r\npost_data = {\r\n \"_xsrf\":xsrf,\r\n \"password\":password,\r\n \"remember_me\":\"true\",\r\n \"phone_num\":username,\r\n \"captcha\":captcha\r\n}\r\nwer = s.post(url, data=post_data, headers=headers)\r\nif wer.json()[\"r\"] == 0:\r\n print(\"登录成功\")\r\nelse:\r\n print(\"登录失败\")\r\n print(\"错误信息 --->\", wer.json()[\"msg\"])\r\nhomeurl = \"https://www.zhihu.com/\"\r\nhomepage = s.get(homeurl,headers = headers)\r\nprint(homepage.text)\r\n\r\n\r\n\r\n","repo_name":"gaokaigithub/myspider","sub_path":"fen.py","file_name":"fen.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"33495638540","text":"# -*- coding: UTF-8 -*-\nimport sys\nimport os\nimport numpy as np\nimport time\n\ndef log_enable():\n\t#logging setting\n\timport logging\n\tif not os.path.exists('./log'):\n\t\tos.makedirs('./log')\n\n\tfile_name = os.path.basename(sys.argv[0])\n\ttimestr = time.strftime(\"%Y%m%d_%H%M%S\")\n\n\tlog_file = \"./log/\"+file_name+\"_\"+timestr+\".log\"\n\tlog_level = logging.DEBUG\n\tlogger = logging.getLogger(file_name)\n\thandler = logging.FileHandler(log_file, mode='w')\n\tformatter = logging.Formatter(\"[%(levelname)s][%(funcName)s]\\\n\t[%(asctime)s]%(message)s\")\n\thandler.setFormatter(formatter)\n\tlogger.addHandler(handler)\n\tlogger.setLevel(log_level)\n\n\treturn logger\n\ndef solve(Q, w2v_model, neighbor=1, logger=False):\n\tif logger:\n\t\t_logger = log_enable()\n\n\t## word2vec\n\tquestion = Q['question']\n\tchoices = Q['choices'].copy()\n\twordvec = []\n\tblank_index = question.index('')\n\n\tif len(question) is 0 or len(choices) is 0:\n\t\treturn None\n\n\t# convert question to vector\n\t#for i in range(blank_index-neighbor, blank_index+neighbor+1):\n\tfor i in range(len(question)):\n\t\tif question[i]=='':\n\t\t\tcontinue\n\t\tif question[i].decode('utf8') in w2v_model:\n\t\t\twordvec.append(w2v_model[question[i].decode('utf8')])\n\n\t# convert choices to vector\n\tfor key, value in choices.iteritems():\n\t\tif value.decode('utf8') in w2v_model:\n\t\t\tchoices[key] = w2v_model[value.decode('utf8')]\n\t\telse:\n\t\t\tchoices[key] = np.zeros(128)\n\n\tif logger:\n\t\t_logger.debug('wordvec')\n\t\t_logger.debug(wordvec)\n\t\t_logger.debug('choices')\n\t\t_logger.debug(choices)\n\n\t## calculate mean vector\n\t_mean = np.asarray(np.mean(wordvec, axis=0))\n\tif logger:\n\t\t_logger.debug('wordvec maen')\t\t\n\t\t_logger.debug(_mean)\n\n\t## find who is the closest one to mean\n\tdis = []\t\n\tfor key, value in choices.iteritems():\n\t\tchoices[key] = (np.linalg.norm(value-_mean))\n\n\t_sum = sum(list(choices.values()))\n\tfor key, value in choices.iteritems():\n\t\tchoices[key] = 1-value/_sum\n\t\t\n\t#ans = min(choices, key=choices.get)\n\n\t# calculate softmax\n\t_max = choices[max(choices, key=choices.get)]\n\t_sum = np.exp([x-_max for x in list(choices.values())]).sum()\n\t#_sum = sum(np.exp(choices.itervalues()))\n\tfor key, value in choices.iteritems():\n\t\tchoices[key] = np.exp(value - _max)/_sum\n\n\tif logger:\n\t\t_logger.debug('choices')\n\t\t_logger.debug(choices)\n\n\treturn choices","repo_name":"BlakePan/pixnethackathon2016_AI_Cloze","sub_path":"mymodel/mymodel_mk1.py","file_name":"mymodel_mk1.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2287501248","text":"#https://www.acmicpc.net/problem/9095\n\nimport sys\n\nT = int(sys.stdin.readline().strip())\n\narr = [-1 for _ in range(11)]\nresults = []\n\narr[0] = 0\narr[1] = 1\narr[2] = 2\narr[3] = 4\n\ndef dpSum(num):\n global arr\n if arr[num] == -1:\n arr[num] = dpSum(num-1) + dpSum(num-2) + dpSum(num-3)\n return arr[num]\n else:\n return arr[num]\n\n\nfor _ in range(T):\n results.append(dpSum(int(sys.stdin.readline().strip())))\n\nfor result in results:\n print(result)","repo_name":"Myunwoo/algorithm_study","sub_path":"solved.ac_3/baek9095.py","file_name":"baek9095.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11215912692","text":"import sys\nimport getopt\n\nsys.path.append('/opt/trex/current/automation/trex_control_plane/stl/examples')\nsys.path.append('./v2.48/automation/automation/trex_control_plane/interactive/trex/examples')\nsys.path.append('./v2.48/automation/trex_control_plane/interactive/trex/examples/stl')\nsys.path.append('./v2.48/automation/trex_control_plane/stf/trex_stf_lib/')\nsys.path.append('./v2.49/automation/automation/trex_control_plane/interactive/trex/examples')\nsys.path.append('./v2.49/automation/trex_control_plane/interactive/trex/examples/stl')\nsys.path.append('./v2.49/automation/trex_control_plane/stf/trex_stf_lib/')\nsys.path.append('./v2.59/automation/trex_control_plane/interactive/trex/examples')\nsys.path.append('./v2.59/automation/trex_control_plane/interactive/trex/examples/stl')\nsys.path.append('./v2.59/automation/trex_control_plane/stf/trex_stf_lib/')\nsys.path.append(\"./v2.71/automation/trex_control_plane/interactive/trex/examples/\")\nsys.path.append(\"./v2.71/automation/trex_control_plane/interactive/trex/examples/stl\")\nsys.path.append(\"./v2.71/automation/trex_control_plane/stf/trex_stf_lib/\")\nsys.path.append(\"./v2.82/automation/trex_control_plane/interactive/trex/examples/\")\nsys.path.append(\"./v2.82/automation/trex_control_plane/interactive/trex/examples/stl\")\nsys.path.append(\"./v2.82/automation/trex_control_plane/stf/trex_stf_lib/\")\n\n\nimport trex_client\nimport trex_status\nimport stl_path\nfrom trex_stl_lib.api import *\nimport json\nimport argparse\n\nclass TrexTest(object):\n def __init__(self, trex_host,pkt_size=64,duration=10,max_try=10,vlan_flag=False,dst_mac=None):\n self.trex_host = trex_host\n self.pkt_size = pkt_size - 4\n self.duration = duration\n self.max_try = max_try \n self.vlan_flag = vlan_flag\n self.dst_mac = dst_mac\n pass\n \n def create_stl_client(self):\n self.client = None\n self.client = STLClient(server=self.trex_host)\n return self.client\n \n\n def build_test_stream_basic(self):\n l2 = Ether()\n if self.vlan_flag == True:\n self.base_pkt = l2/Dot1Q(vlan=3)/IP(src=\"192.168.100.10\",dst=\"192.168.100.20\")/UDP(dport=12, sport=1025)\n else:\n self.base_pkt = l2/IP(src=\"192.168.100.10\", dst=\"192.168.100.20\")/UDP(dport=12, sport=1025)\n\n self.pad = max(0, self.pkt_size - len(self.base_pkt)) * 'x'\n\n return STLStream(isg=10.0,\n packet=STLPktBuilder(pkt=self.base_pkt/self.pad),\n #flow_stats=STLFlowStats(pg_id=1),\n flow_stats=None,\n mode=STLTXCont(percentage=100)\n )\n\n def build_test_stream(self):\n self.all_stream = []\n temp_stream = self.build_test_stream_basic()\n self.all_stream.append(temp_stream)\n self.streams = STLProfile(streams=self.all_stream).get_streams()\n return self.streams\n\n def test_stream_create(self,src_mac,dst_mac):\n l2 = Ether(dst=dst_mac,src=src_mac)\n pad = max(0, self.pkt_size - len(l2)) * 'x'\n return STLStream(isg=10.0,\n packet=STLPktBuilder(pkt=l2 / pad),\n #flow_stats=STLFlowStats(pg_id=1),\n flow_stats=None,\n mode=STLTXCont(percentage=100)\n )\n \n def create_stream_for_pvp(self,dst_mac):\n l2 = Ether(dst=dst_mac)/IP(src=\"192.168.100.10\", dst=\"192.168.100.20\")/UDP(dport=12, sport=1025)\n pad = max(0, self.pkt_size - len(l2)) * 'x'\n return STLStream(isg=10.0,\n packet=STLPktBuilder(pkt=l2 / pad),\n #flow_stats=STLFlowStats(pg_id=1),\n flow_stats=None,\n mode=STLTXCont(percentage=100)\n )\n\n\n def test_conn_ok(self):\n if self.client:\n all_ports = self.client.get_all_ports()\n self.client.reset(all_ports)\n self.port_stream_map = {}\n self.dst_mac_list = str(self.dst_mac).split(\" \")\n all_stream = []\n\n all_stream.append(self.create_stream_for_pvp(self.dst_mac_list[0]))\n all_stream.append(self.create_stream_for_pvp(self.dst_mac_list[1]))\n\n print(self.client.get_port_attr(0))\n print(self.client.get_port_attr(1))\n for port in all_ports:\n for stream in all_stream:\n self.client.reset(all_ports)\n # self.client.set_port_attr(ports=all_ports, promiscuous=False)\n self.client.set_port_attr(ports=all_ports, promiscuous=True)\n self.client.acquire(ports=all_ports, force=True)\n self.client.add_streams(stream, ports=port)\n print(\"start test conn test with 1pps duration 10s \")\n #print(stream)\n #print(\"port:{} \".format(port))\n #stream.to_pkt_dump()\n self.client.start(ports=port,mult=\"1pps\", duration=10)\n self.client.wait_on_traffic(ports=all_ports)\n ret_stat=self.client.get_stats(ports = all_ports)\n # from pprint import pprint\n # pprint(ret_stat)\n \"\"\"\n 'total': {'ibytes': 680,\n 'ierrors': 0,\n 'ipackets': 10,\n 'obytes': 680,\n 'oerrors': 0,\n 'opackets': 10,\n 'rx_bps': 538.2561645507812,\n 'rx_bps_L1': 696.5668067932129,\n 'rx_pps': 0.9894415140151978,\n 'rx_util': 6.965668067932129e-06,\n 'tx_bps': 538.7817132472992,\n 'tx_bps_L1': 697.2469528019428,\n 'tx_pps': 0.9904077472165227,\n 'tx_util': 6.9724695280194286e-06}}\n \"\"\"\n if ret_stat[\"total\"][\"ipackets\"] >= ret_stat[\"total\"][\"opackets\"] and ret_stat[\"total\"][\"ipackets\"] > 0:\n print(\"***********************************************************************\")\n print(\"Port info {}\".format(port))\n print(self.client.get_port_attr(port))\n print(ret_stat[\"total\"])\n print(\"Below Stream Info\")\n stream.to_pkt_dump()\n print(\"***********************************************************************\")\n self.port_stream_map[port] = stream\n # import pdb\n # pdb.set_trace()\n if len(self.port_stream_map) > 0 :\n return True\n else:\n return False\n else:\n return False\n\n def test_one_cycle(self,speed_percent=\"100%\",duration=10):\n all_ports = self.client.get_all_ports()\n self.client.reset(all_ports)\n self.client.set_port_attr(ports=all_ports, promiscuous=True)\n self.client.acquire(ports=all_ports,force=True)\n #self.client.add_streams(self.port_stream_map[0],ports=all_ports)\n for key in self.port_stream_map.keys():\n self.client.add_streams(self.port_stream_map[key],ports=key)\n #self.client.add_streams(self.streams,ports=all_ports)\n self.client.clear_stats()\n print(\"start {} throughput begin\".format(speed_percent))\n self.client.start(ports=all_ports, mult=speed_percent, duration=duration)\n self.client.wait_on_traffic(ports=all_ports)\n return self.client.get_stats(ports=all_ports)\n\n def start_test(self):\n self.client.connect()\n self.test_conn_ok()\n print(\"Begin performance now\")\n #import time\n #time.sleep(10000)\n max_value = 100\n min_value = 0\n cur_value = 100\n for i in range(self.max_try):\n print(\"Current try {} cycle \".format(i))\n stat = self.test_one_cycle(str(cur_value)+\"%\")\n \"\"\"\n {\n \"global\": {\n \"cpu_util\": 20.869110107421875,\n \"rx_cpu_util\": 0.0,\n \"bw_per_core\": 21.410884857177734,\n \"tx_bps\": 15638914048.0,\n \"tx_pps\": 27150892.0,\n \"rx_bps\": 13119664128.0,\n \"rx_pps\": 22777192.0,\n \"rx_drop_bps\": 2519249920.0,\n \"queue_full\": 0\n },\n \"1\": {\n \"opackets\": 135869430,\n \"ipackets\": 113993959,\n \"obytes\": 9782598960,\n \"ibytes\": 8207565048,\n \"oerrors\": 0,\n \"ierrors\": 0,\n \"tx_bps\": 7819472384.0,\n \"tx_pps\": 13575472.0,\n \"tx_bps_L1\": 9991547904.0,\n \"tx_util\": 99.91547904,\n \"rx_bps\": 6560308736.0,\n \"rx_pps\": 11389424.0,\n \"rx_bps_L1\": 8382616576.0,\n \"rx_util\": 83.82616576000001\n },\n \"0\": {\n \"opackets\": 135869430,\n \"ipackets\": 113970768,\n \"obytes\": 9782598960,\n \"ibytes\": 8205895296,\n \"oerrors\": 0,\n \"ierrors\": 0,\n \"tx_bps\": 7819441664.0,\n \"tx_pps\": 13575420.0,\n \"tx_bps_L1\": 9991508864.0,\n \"tx_util\": 99.91508864000001,\n \"rx_bps\": 6559354880.0,\n \"rx_pps\": 11387768.0,\n \"rx_bps_L1\": 8381397760.0,\n \"rx_util\": 83.8139776\n },\n \"total\": {\n \"opackets\": 271738860,\n \"ipackets\": 227964727,\n \"obytes\": 19565197920,\n \"ibytes\": 16413460344,\n \"oerrors\": 0,\n \"ierrors\": 0,\n \"tx_bps\": 15638914048.0,\n \"tx_pps\": 27150892.0,\n \"tx_bps_L1\": 19983056768.0,\n \"tx_util\": 199.83056768,\n \"rx_bps\": 13119663616.0,\n \"rx_pps\": 22777192.0,\n \"rx_bps_L1\": 16764014336.0,\n \"rx_util\": 167.64014336000002\n },\n \"flow_stats\": {},\n \"latency\": {}\n }\n \"\"\"\n print(json.dumps(stat, indent=4, sort_keys=False))\n if stat[\"total\"][\"opackets\"] > stat[\"total\"][\"ipackets\"]:\n max_value = cur_value\n cur_value = (min_value + max_value)/(2 * 1.0)\n else:\n self.last_value = cur_value\n min_value = cur_value\n cur_value = (min_value + max_value)/(2 * 1.0)\n last_result = json.dumps(stat[\"total\"], indent=4, sort_keys=False)\n self.last_value = cur_value\n self.last_result = last_result\n print(last_result)\n \n self.client.disconnect()\n return self.last_result\n\n def report_test_result(self):\n print(\"x\"*100)\n \n if self.last_result:\n print(self.last_result)\n else:\n print(\"Trex throughput performance failed \")\n print(\"x\"*100)\n\n def start_trex_server(self):\n trex = trex_client.CTRexClient(self.trex_host,trex_args=\"--no-ofed-check\")\n #trex = trex_client.CTRexClient(self.trex_host)\n trex.force_kill(confirm=False)\n time.sleep(3)\n print(\"Before Running, TRex status is: {}\".format(trex.is_running()))\n print(\"Before Running, TRex status is: {}\".format(trex.get_running_status()))\n\n self.trex_config = trex.get_trex_config()\n import yaml\n t_config_obj = yaml.load(self.trex_config)\n \"\"\"\n - version: 2\n interfaces: ['05:00.0', '05:00.1']\n #interfaces: ['enp5s0f0', 'enp5s0f1']\n port_info:\n - dest_mac: 90:e2:ba:29:bf:15 # MAC OF LOOPBACK TO IT'S DUAL INTERFACE\n src_mac: 90:e2:ba:29:bf:14\n - dest_mac: 90:e2:ba:29:bf:14 # MAC OF LOOPBACK TO IT'S DUAL INTERFACE\n src_mac: 90:e2:ba:29:bf:15\n\n platform:\n master_thread_id: 0\n latency_thread_id: 1\n dual_if:\n - socket: 0\n threads: [2,4,6,8,10,12,14]\n \"\"\"\n core_num = len(t_config_obj[0]['platform']['dual_if'][0]['threads'])\n #t_config_obj[\"platform\"][\"dual_if\"][\"threads\"].len()\n trex.trex_args = \"-c {} {}\".format(core_num,\"--no-ofed-check\")\n trex.start_stateless()\n #trex.get_trex_config()\n print(\"After Starting, TRex status is: {},{}\".format(trex.is_running(), trex.get_running_status()))\n # import time\n # time.sleep(5)\n # print(\"Is TRex running? {},{}\".format(trex.is_running(), trex.get_running_status()))\n self.trex = trex \n self.trex_config = trex.get_trex_config()\n return self.trex\n\n def start_all_test(self):\n self.start_trex_server()\n #import time\n #time.sleep(60)\n self.create_stl_client()\n self.build_test_stream()\n self.start_test()\n self.report_test_result()\n\nif __name__ == \"__main__\":\n\n\n parser = argparse.ArgumentParser(description='Test ovs dpdk bonding trex')\n parser.add_argument('-c', '--connect', type=str,help='trex server ip ', required=True)\n parser.add_argument('-m', '--maxcycle', type=int,help='get no loss packets cycle times', required=False)\n parser.add_argument('-t', '--time', type=int,help='one time test duration', required=False)\n parser.add_argument('-s', '--pkt_size', type=int,help='init packet size', required=False)\n parser.add_argument('-v', '--vlan', type=bool,help='enable vlan or else', required=False)\n parser.add_argument('-d', '--dst_mac', type=str,help='packet dest mac', required=False)\n args = parser.parse_args()\n print(args)\n if args.connect:\n trex_obj = TrexTest(args.connect,\n pkt_size=args.pkt_size,\n duration=args.time,\n max_try=args.maxcycle,\n vlan_flag=args.vlan,\n dst_mac=args.dst_mac)\n trex_obj.start_all_test()\n else:\n parser.print_help()\n import sys\n sys.exit(1)\n\n\n\n","repo_name":"ctrautma/RHEL_NIC_QUALIFICATION","sub_path":"throughput-test/trex_sport.py","file_name":"trex_sport.py","file_ext":"py","file_size_in_byte":14418,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"}
+{"seq_id":"71920546962","text":"import pygame\nfrom math import sqrt\n\ndef dist(p1, p2):\n x = p1[0] - p2[0]\n y = p1[1] - p2[1]\n return sqrt(x * x + y * y)\n\ndef get_abc(p1, p2):\n a = 1\n if p1[1] - p2[1] != 0:\n b = -(p1[0] - p2[0]) / (p1[1] - p2[1])\n else:\n a = 0\n b = 1\n c = -(p1[0] + b * p1[1])\n return a, b, c\n\ndef true_coll(p1, p2, o, r):\n a = dist(p1, p2)\n b = dist(p1, o)\n c = dist(p2, o)\n p = (a + b + c) / 2\n s = sqrt(p * (p - a) * (p - b) * (p - c))\n h = s / a * 2\n if h <= r:\n a, b, c = get_abc(p1, p2)\n d = o[0]\n e = o[1]\n aa = b ** 2 + 1\n bb = 2 * (b * d + b * c - e)\n cc = (c + d) ** 2 + e ** 2 - r ** 2\n dd = bb ** 2 - 4 * aa * cc\n print(c, d, e)\n dd = sqrt(dd)\n y1 = (-bb + dd) / (2 * aa)\n y2 = (-bb - dd) / (2 * aa)\n yy1 = y1 >= min(p1[1], p2[1]) and y1 <= max(p1[1], p2[1])\n yy2 = y2 >= min(p1[1], p2[1]) and y2 <= max(p1[1], p2[1])\n return yy1 or yy2\n return False\n\ndef coll(p1, p2, o, r):\n a = dist(p1, p2)\n b = dist(p1, o)\n c = dist(p2, o)\n p = (a + b + c) / 2\n s = sqrt(p * (p - a) * (p - b) * (p - c))\n h = s / a * 2\n if h <= r:\n if not p2[0]-p1[0] == 0:\n a = (p2[1]-p1[1])/(p2[0]-p1[0])\n else:\n a=9999999999999999\n b = p1[1]-a*p1[0]\n aa = a ** 2 + 1\n bb = 2 * (o[0] - (b - o[1]) * a)\n cc = (b - o[1]) ** 2 - r ** 2 + o[0] ** 2\n d = bb ** 2 - 4 * aa * cc\n d = sqrt(d)\n x1 = (bb + d) / (2 * aa)\n x2 = (bb - d) / (2 * aa)\n xx1 = x1 > min(p1[0], p2[0]) and x1 < max(p1[0], p2[0])\n xx2 = x2 > min(p1[0], p2[0]) and x2 < max(p1[0], p2[0])\n y1 = a * x1 + b\n y2 = a * x2 + b\n return xx1 or xx2\n return False\n\nppos = [100, 100]\n\npv = [0, 0]\n\nr = 10\n\nline = [[200, 200], [300, 200]]\n\nscreen = pygame.display.set_mode((400, 400))\n\ndone = False\n\npoints = False\n\nwhile not done:\n for e in pygame.event.get():\n if e.type == pygame.QUIT:\n done = True\n if e.type == pygame.KEYDOWN:\n if e.scancode == 17:\n pv[1] = -0.1\n if e.scancode == 30:\n pv[0] = -0.1\n if e.scancode == 31:\n pv[1] = 0.1\n if e.scancode == 32:\n pv[0] = 0.1\n if e.type == pygame.KEYUP:\n if e.scancode == 17 and pv[1] < 0:\n pv[1] = 0\n if e.scancode == 30 and pv[0] < 0:\n pv[0] = 0\n if e.scancode == 31 and pv[1] > 0:\n pv[1] = 0\n if e.scancode == 32 and pv[0] > 0:\n pv[0] = 0\n screen.fill((0, 0, 0))\n ppos[0] += pv[0]\n points = true_coll(line[0], line[1], ppos, r)\n if points:\n ppos[0] -= pv[0]\n ppos[1] += pv[1]\n points = true_coll(line[0], line[1], ppos, r)\n if points:\n ppos[1] -= pv[1]\n\n pygame.draw.circle(screen, (255, 255, 255), [int(ppos[0]), int(ppos[1])], r)\n\n pygame.draw.line(screen, (255, 255, 255), line[0], line[1])\n\n #pygame.draw.circle(screen, (255, 255, 255), (200, 250), 3)\n #if points:\n # if points[0]:\n # pygame.draw.circle(screen, (0, 255, 0), [int(points[2][0]), int(points[2][1])], 3)\n # if points[1]:\n # pygame.draw.circle(screen, (0, 255, 0), [int(points[3][0]), int(points[3][1])], 3)\n\n pygame.display.update()\npygame.quit()\n","repo_name":"ArturLukianov/yandex-games","sub_path":"FallingDagger/collision.py","file_name":"collision.py","file_ext":"py","file_size_in_byte":3430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26216856314","text":"#!/usr/bin/python\n# coding: utf-8\n\n\ndef test(n):\n \"\"\"Sums consecutive numbers from 0 to `n`.\n\n Parameters\n ----------\n n : int\n maximum integer\n \"\"\"\n\n s = 0\n for i in range(0, n):\n s += i\n return s\n","repo_name":"kgryte/micro-benchmarks-numeric-computing","sub_path":"benchmarks/python/tests/sum.py","file_name":"sum.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"40134004406","text":"import textwrap\nfrom utils import format\n\nimport string\nvalid_id = string.ascii_lowercase + string.ascii_uppercase + string.digits + \"_\"\ndef name_id(name, upper=False):\n if not upper:\n name = name.replace(\" \", \"\")\n else:\n name = name.upper().replace(\" \", \"_\")\n name = name.replace(\"-\", \"_\")\n name = \"\".join([char for char in name if char in valid_id])\n while name[0] in string.digits:\n name = name[1:]\n name = name[0].upper() + name[1:]\n return name\n\ndef Card(*, name, type, target, color, rarity, cost, const=None, flags=None, desc, upgrade_desc=None, **kwargs):\n if cost == \"x\":\n cost = -1\n if const is None:\n const = {}\n if flags is None:\n flags = {}\n\n if target == \"all_enemies\":\n flags[\"isMultiDamage\"] = \"true\"\n\n code = dict(use=None, upgrade=None, methods=\"\")\n arg = kwargs.pop(\"code\", None)\n if arg: code[\"use\"] = arg\n arg = kwargs.pop(\"upgrade_code\", None)\n if arg: code[\"upgrade\"] = textwrap.dedent(\"\"\"\n if (!this.upgraded) {{\n {}\n }}\n \"\"\").strip().format(textwrap.indent(textwrap.dedent(arg).strip(), \" \" * 4).lstrip())\n arg = kwargs.pop(\"upgrade_code_FULL\", None)\n if arg: code[\"upgrade\"] = arg\n arg = kwargs.pop(\"methods\", None)\n if arg: code[\"methods\"] = arg\n if not code[\"use\"] and type == \"power\":\n stacks = 1 if not \"MAGIC_NUMBER\" in const else \"magicNumber\"\n code[\"use\"] = format(\"\"\"\n AbstractDungeon.actionManager.addToBottom(\n new ApplyPowerAction(p, p, new {{ name_id(name) }}Power(p, {{ stacks }}), {{ stacks }})\n );\n \"\"\")\n code = {name: textwrap.dedent(impl).strip() for name, impl in code.items()}\n if kwargs:\n raise SyntaxError(kwargs)\n\n return dict(\n name = name,\n type = type,\n target = target,\n color = color,\n rarity = rarity,\n cost = cost,\n const = const,\n flags = flags,\n desc = desc,\n upgrade_desc = upgrade_desc,\n code = code,\n )\n\ndef Power(*, id=None, name, desc, init=None, code, icon=None, **kwargs):\n if id is None:\n id = name_id(name)\n id = id + \"Power\"\n if init:\n init = textwrap.dedent(init).strip()\n desc_code = None\n arg = kwargs.pop(\"desc_expr\", None)\n if arg: desc_code = \"description = {};\".format(arg)\n arg = kwargs.pop(\"desc_CODE\", None)\n if arg: desc_code = textwrap.dedent(arg).strip()\n code = textwrap.dedent(code).strip()\n if kwargs:\n raise SyntaxError(kwargs)\n return dict(\n id = id,\n name = name,\n desc = desc,\n desc_code = desc_code,\n init = init,\n code = code,\n icon = icon,\n )\n\ndef Action(*, id, args=None, flags=None, **kwargs):\n if args is not None:\n vars = [line.split() for line in args.strip().splitlines()]\n else:\n vars = []\n if flags is None:\n flags = {}\n code = None\n arg = kwargs.pop(\"code\", None)\n if arg: code = textwrap.dedent(\"\"\"\n @Override\n public void update() {{\n {}\n }}\n \"\"\").strip().format(textwrap.indent(textwrap.dedent(arg).strip(), \" \" * 4).lstrip())\n arg = kwargs.pop(\"code_FULL\", None)\n if arg: code = textwrap.dedent(arg).strip()\n if kwargs:\n raise SyntaxError(kwargs)\n\n return dict(\n id = id,\n vars = vars,\n flags = flags,\n code = code,\n )\n\ndef Java(*, path, base=None, code):\n path = path.split(\".\")\n code = textwrap.dedent(code).strip()\n return dict(\n package = path[:-1],\n name = path[-1],\n base = base,\n code = code,\n )\n","repo_name":"MrCoft/EngiMod","sub_path":"spire.py","file_name":"spire.py","file_ext":"py","file_size_in_byte":3706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17921883544","text":"#coding:utf8\n\n#导入spark的相关包\nfrom pyspark import SparkConf,SparkContext\n\nif __name__ == '__main__':\n\n # 初始化执行环境，构建SparkContext对象\n conf = SparkConf().setAppName(\"test_create1\").setMaster(\"local[*]\")\n sc = SparkContext(conf=conf)\n\n #演示通过并行化集合的方式去创建RDD，本地集合->分布式对象的转化\n rdd1 = sc.parallelize([1, 2, 3, 4, 5, 6, 7, 8, 9])\n # 查看默认分区\n print(\"默认分区数：\",rdd1.getNumPartitions())\n\n rdd2 = sc.parallelize([1, 2, 3],3)\n print(\"分区数：\",rdd2.getNumPartitions())\n\n # collect方法，是将RDD中每个分区的数据，都发送到Driver中，形成一个Python LIst对象进行输出的\n print(rdd1.collect())\n\n\n\n\n\n\n","repo_name":"Jermyn-code/python_code","sub_path":"Spark/RDD-教师示例/01_RDD_create_parallelize.py","file_name":"01_RDD_create_parallelize.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23343721040","text":"import os\nfrom collections import Callable\n\nfrom dataclasses import dataclass\nfrom os import walk\nfrom typing import List\n\nfrom matplotlib import pyplot as plt\nfrom reamber.osu import OsuMap\nfrom keras.layers import Dense, Dropout, GRU\nfrom tqdm import tqdm\n\nfrom consts import CONSTS\nfrom input import Preprocessing\nfrom osrparse.mania import ManiaHitError\n\nimport numpy as np\nfrom tensorflow import keras\n\n\n\n@dataclass\nclass LSTMModel:\n regressor: keras.Model\n key: int\n threshold: int = CONSTS.NEIGHBOUR_THRESHOLD\n window:int = CONSTS.WINDOW\n epochs:int = CONSTS.EPOCHS\n batch_size:int = CONSTS.BATCH_SIZE\n verbose: int = 1\n overwrite_data: bool = False\n aggregated: bool = False\n aggregation_method: Callable = np.median\n\n @staticmethod\n def load_model(key: int, aggregated: bool = False, aggregation_method=CONSTS.AGG_METHOD):\n return LSTMModel(keras.models.load_model(LSTMModel.model_path(key, aggregated)), key,\n aggregated=aggregated, aggregation_method=aggregation_method)\n\n def save_model(self):\n self.regressor.save(LSTMModel.model_path(self.key, self.aggregated))\n\n @staticmethod\n def create_model(key, lstm_cells: int = 15, aggregated: bool = False, aggregation_method=CONSTS.AGG_METHOD):\n \"\"\" Creates a model by its key and aggregation. \"\"\"\n regressor = keras.Sequential()\n regressor.add(GRU(units=lstm_cells,\n return_sequences=True,\n input_shape=(None, LSTMModel._features(key))))\n regressor.add(Dense(units=30))\n regressor.add(Dense(units=20))\n if aggregated:\n regressor.add(Dense(units=1, activation=\"sigmoid\"))\n else:\n regressor.add(Dense(units=LSTMModel._output(key), activation=\"sigmoid\"))\n\n regressor.compile(optimizer='adam',\n loss='mean_squared_error')\n return LSTMModel(regressor, key, aggregated=aggregated, aggregation_method=aggregation_method)\n\n @staticmethod\n def _features(key):\n # Keys ^ 2 * Moments *\n return key ** 2 * CONSTS.FEATURE_PER_COMBO\n\n @staticmethod\n def _output(key):\n return key * 2\n\n @property\n def features(self):\n return self._features(self.key)\n\n def train_from_rsc(self, retrain=False, lim=None):\n trainables = self.get_trainables(retrain, lim)\n self.train(trainables)\n\n def get_trainables(self, retrain=False, lim=None) -> list:\n _, dirs, filenames = next(walk(f'{CONSTS.RSC_PATH}'))\n trainables = []\n if lim: dirs = np.asarray(dirs)[np.random.choice(len(dirs), lim, replace=False)]\n for map_dir in dirs:\n if self.aggregated:\n data_dir = f\"{CONSTS.RSC_PATH}/{map_dir}/{CONSTS.DATA_NAME}{CONSTS.AGG}\"\n data_path = f\"{data_dir}/{CONSTS.DATA_NAME}{CONSTS.AGG}.npy\"\n else:\n data_dir = f\"{CONSTS.RSC_PATH}/{map_dir}/{CONSTS.DATA_NAME}\"\n data_path = f\"{data_dir}/{CONSTS.DATA_NAME}.npy\"\n\n if not os.path.exists(data_path) or self.overwrite_data:\n # This means it's new data\n # There may be cases where the dir is generated but there's no data\n if not os.path.exists(data_dir):\n os.makedirs(data_dir)\n print(f\"Generating Data and Training on {map_dir}\")\n data = Preprocessing(self.threshold, self.window,\n aggregated=self.aggregated,\n aggregation_method=self.aggregation_method).features_from_rsc(map_dir)\n np.save(data_path, data)\n trainables.append(data)\n elif retrain:\n # This means it's processed data but we retrain\n data = np.load(f\"{data_path}\")\n print(f\"Training on {map_dir}\")\n trainables.append(data)\n\n if len(trainables) == 0:\n print(\"No new Data to train on.\")\n\n return trainables\n\n def train(self, data:List[np.ndarray]):\n \"\"\" Trains the model using the data generated. \"\"\"\n if not isinstance(data, list):\n data = [data]\n\n for ar in tqdm(data, desc=\"Training ... \"):\n if self.aggregated:\n assert ar.shape[-1] == self.features + 1,\\\n f\"Aggregated method is invalid. {self.aggregation_method},\" \\\n f\"{ar.shape[-1]} mismatched {self.features + 1}\"\n\n self.regressor.fit(x=ar[..., :self.features],\n y=ar[..., self.features:],\n epochs=self.epochs,\n batch_size=self.batch_size,\n verbose=self.verbose)\n\n def evaluate(self, data:np.ndarray):\n return self.regressor.evaluate(data[..., :self.features],\n data[..., self.features:])\n\n def predict(self, data:np.ndarray):\n return self.regressor.predict(data[..., :self.features])\n\n def predict_agg_and_plot(self, data:np.ndarray, map_path:str):\n\n m = ManiaHitError.parse_map(OsuMap.readFile(map_path))\n pred = self.predict(data[..., :self.features])\n\n # Flattened Map\n a = [i for k in m[:1] for j in k for i in j]\n\n # Predicted\n plt.subplot(3,1,1)\n if self.aggregated:\n plt.plot(pred.squeeze())\n else:\n plt.plot(self.aggregation_method(pred.squeeze(), axis=1))\n\n # Expected\n plt.subplot(3,1,2)\n plt.plot(self.aggregation_method(data.squeeze()[:,self.features:], axis=1))\n\n # Density\n plt.subplot(3,1,3)\n plt.hist(a, bins=300)\n plt.show()\n\n def predict_and_plot(self, data:np.ndarray, map_path:str):\n m = ManiaHitError.parse_map(OsuMap.readFile(map_path))\n pred = self.predict(data[..., :self.features])\n\n # Flattened Map\n a = [i for k in m[:1] for j in k for i in j]\n\n # Predicted\n plt.subplot(3,1,1)\n plt.plot(pred.squeeze(), c='r',alpha=0.3)\n\n # Expected\n plt.subplot(3,1,2)\n plt.plot(data.squeeze()[:,self.features:], c='blue', alpha=0.3)\n\n # Density\n plt.subplot(3,1,3)\n plt.hist(a, bins=100)\n\n plt.show()\n\n @staticmethod\n def model_path(key, aggregated):\n return CONSTS.MODEL_PATH + f'/estimator{key}{CONSTS.AGG}' if aggregated else \\\n CONSTS.MODEL_PATH + f'/estimator{key}'\n","repo_name":"Eve-ning/ARXScore","sub_path":"model/lstm_model.py","file_name":"lstm_model.py","file_ext":"py","file_size_in_byte":6538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8361744598","text":"from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType\nimport copy as _copy\n\n\nclass Delta(_BaseTraceHierarchyType):\n\n # class properties\n # --------------------\n _parent_path_str = \"indicator\"\n _path_str = \"indicator.delta\"\n _valid_props = {\n \"decreasing\",\n \"font\",\n \"increasing\",\n \"position\",\n \"prefix\",\n \"reference\",\n \"relative\",\n \"suffix\",\n \"valueformat\",\n }\n\n # decreasing\n # ----------\n @property\n def decreasing(self):\n \"\"\"\n The 'decreasing' property is an instance of Decreasing\n that may be specified as:\n - An instance of :class:`plotly.graph_objs.indicator.delta.Decreasing`\n - A dict of string/value properties that will be passed\n to the Decreasing constructor\n\n Supported dict properties:\n\n color\n Sets the color for increasing value.\n symbol\n Sets the symbol to display for increasing value\n\n Returns\n -------\n plotly.graph_objs.indicator.delta.Decreasing\n \"\"\"\n return self[\"decreasing\"]\n\n @decreasing.setter\n def decreasing(self, val):\n self[\"decreasing\"] = val\n\n # font\n # ----\n @property\n def font(self):\n \"\"\"\n Set the font used to display the delta\n\n The 'font' property is an instance of Font\n that may be specified as:\n - An instance of :class:`plotly.graph_objs.indicator.delta.Font`\n - A dict of string/value properties that will be passed\n to the Font constructor\n\n Supported dict properties:\n\n color\n\n family\n HTML font family - the typeface that will be\n applied by the web browser. The web browser\n will only be able to apply a font if it is\n available on the system which it operates.\n Provide multiple font families, separated by\n commas, to indicate the preference in which to\n apply fonts if they aren't available on the\n system. The Chart Studio Cloud (at\n https://chart-studio.plotly.com or on-premise)\n generates images on a server, where only a\n select number of fonts are installed and\n supported. These include \"Arial\", \"Balto\",\n \"Courier New\", \"Droid Sans\",, \"Droid Serif\",\n \"Droid Sans Mono\", \"Gravitas One\", \"Old\n Standard TT\", \"Open Sans\", \"Overpass\", \"PT Sans\n Narrow\", \"Raleway\", \"Times New Roman\".\n size\n\n Returns\n -------\n plotly.graph_objs.indicator.delta.Font\n \"\"\"\n return self[\"font\"]\n\n @font.setter\n def font(self, val):\n self[\"font\"] = val\n\n # increasing\n # ----------\n @property\n def increasing(self):\n \"\"\"\n The 'increasing' property is an instance of Increasing\n that may be specified as:\n - An instance of :class:`plotly.graph_objs.indicator.delta.Increasing`\n - A dict of string/value properties that will be passed\n to the Increasing constructor\n\n Supported dict properties:\n\n color\n Sets the color for increasing value.\n symbol\n Sets the symbol to display for increasing value\n\n Returns\n -------\n plotly.graph_objs.indicator.delta.Increasing\n \"\"\"\n return self[\"increasing\"]\n\n @increasing.setter\n def increasing(self, val):\n self[\"increasing\"] = val\n\n # position\n # --------\n @property\n def position(self):\n \"\"\"\n Sets the position of delta with respect to the number.\n\n The 'position' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n ['top', 'bottom', 'left', 'right']\n\n Returns\n -------\n Any\n \"\"\"\n return self[\"position\"]\n\n @position.setter\n def position(self, val):\n self[\"position\"] = val\n\n # prefix\n # ------\n @property\n def prefix(self):\n \"\"\"\n Sets a prefix appearing before the delta.\n\n The 'prefix' property is a string and must be specified as:\n - A string\n - A number that will be converted to a string\n\n Returns\n -------\n str\n \"\"\"\n return self[\"prefix\"]\n\n @prefix.setter\n def prefix(self, val):\n self[\"prefix\"] = val\n\n # reference\n # ---------\n @property\n def reference(self):\n \"\"\"\n Sets the reference value to compute the delta. By default, it\n is set to the current value.\n\n The 'reference' property is a number and may be specified as:\n - An int or float\n\n Returns\n -------\n int|float\n \"\"\"\n return self[\"reference\"]\n\n @reference.setter\n def reference(self, val):\n self[\"reference\"] = val\n\n # relative\n # --------\n @property\n def relative(self):\n \"\"\"\n Show relative change\n\n The 'relative' property must be specified as a bool\n (either True, or False)\n\n Returns\n -------\n bool\n \"\"\"\n return self[\"relative\"]\n\n @relative.setter\n def relative(self, val):\n self[\"relative\"] = val\n\n # suffix\n # ------\n @property\n def suffix(self):\n \"\"\"\n Sets a suffix appearing next to the delta.\n\n The 'suffix' property is a string and must be specified as:\n - A string\n - A number that will be converted to a string\n\n Returns\n -------\n str\n \"\"\"\n return self[\"suffix\"]\n\n @suffix.setter\n def suffix(self, val):\n self[\"suffix\"] = val\n\n # valueformat\n # -----------\n @property\n def valueformat(self):\n \"\"\"\n Sets the value formatting rule using d3 formatting mini-\n languages which are very similar to those in Python. For\n numbers, see:\n https://github.com/d3/d3-format/tree/v1.4.5#d3-format.\n\n The 'valueformat' property is a string and must be specified as:\n - A string\n - A number that will be converted to a string\n\n Returns\n -------\n str\n \"\"\"\n return self[\"valueformat\"]\n\n @valueformat.setter\n def valueformat(self, val):\n self[\"valueformat\"] = val\n\n # Self properties description\n # ---------------------------\n @property\n def _prop_descriptions(self):\n return \"\"\"\\\n decreasing\n :class:`plotly.graph_objects.indicator.delta.Decreasing\n ` instance or dict with compatible properties\n font\n Set the font used to display the delta\n increasing\n :class:`plotly.graph_objects.indicator.delta.Increasing\n ` instance or dict with compatible properties\n position\n Sets the position of delta with respect to the number.\n prefix\n Sets a prefix appearing before the delta.\n reference\n Sets the reference value to compute the delta. By\n default, it is set to the current value.\n relative\n Show relative change\n suffix\n Sets a suffix appearing next to the delta.\n valueformat\n Sets the value formatting rule using d3 formatting\n mini-languages which are very similar to those in\n Python. For numbers, see:\n https://github.com/d3/d3-format/tree/v1.4.5#d3-format.\n \"\"\"\n\n def __init__(\n self,\n arg=None,\n decreasing=None,\n font=None,\n increasing=None,\n position=None,\n prefix=None,\n reference=None,\n relative=None,\n suffix=None,\n valueformat=None,\n **kwargs,\n ):\n \"\"\"\n Construct a new Delta object\n\n Parameters\n ----------\n arg\n dict of properties compatible with this constructor or\n an instance of\n :class:`plotly.graph_objs.indicator.Delta`\n decreasing\n :class:`plotly.graph_objects.indicator.delta.Decreasing\n ` instance or dict with compatible properties\n font\n Set the font used to display the delta\n increasing\n :class:`plotly.graph_objects.indicator.delta.Increasing\n ` instance or dict with compatible properties\n position\n Sets the position of delta with respect to the number.\n prefix\n Sets a prefix appearing before the delta.\n reference\n Sets the reference value to compute the delta. By\n default, it is set to the current value.\n relative\n Show relative change\n suffix\n Sets a suffix appearing next to the delta.\n valueformat\n Sets the value formatting rule using d3 formatting\n mini-languages which are very similar to those in\n Python. For numbers, see:\n https://github.com/d3/d3-format/tree/v1.4.5#d3-format.\n\n Returns\n -------\n Delta\n \"\"\"\n super(Delta, self).__init__(\"delta\")\n\n if \"_parent\" in kwargs:\n self._parent = kwargs[\"_parent\"]\n return\n\n # Validate arg\n # ------------\n if arg is None:\n arg = {}\n elif isinstance(arg, self.__class__):\n arg = arg.to_plotly_json()\n elif isinstance(arg, dict):\n arg = _copy.copy(arg)\n else:\n raise ValueError(\n \"\"\"\\\nThe first argument to the plotly.graph_objs.indicator.Delta\nconstructor must be a dict or\nan instance of :class:`plotly.graph_objs.indicator.Delta`\"\"\"\n )\n\n # Handle skip_invalid\n # -------------------\n self._skip_invalid = kwargs.pop(\"skip_invalid\", False)\n self._validate = kwargs.pop(\"_validate\", True)\n\n # Populate data dict with properties\n # ----------------------------------\n _v = arg.pop(\"decreasing\", None)\n _v = decreasing if decreasing is not None else _v\n if _v is not None:\n self[\"decreasing\"] = _v\n _v = arg.pop(\"font\", None)\n _v = font if font is not None else _v\n if _v is not None:\n self[\"font\"] = _v\n _v = arg.pop(\"increasing\", None)\n _v = increasing if increasing is not None else _v\n if _v is not None:\n self[\"increasing\"] = _v\n _v = arg.pop(\"position\", None)\n _v = position if position is not None else _v\n if _v is not None:\n self[\"position\"] = _v\n _v = arg.pop(\"prefix\", None)\n _v = prefix if prefix is not None else _v\n if _v is not None:\n self[\"prefix\"] = _v\n _v = arg.pop(\"reference\", None)\n _v = reference if reference is not None else _v\n if _v is not None:\n self[\"reference\"] = _v\n _v = arg.pop(\"relative\", None)\n _v = relative if relative is not None else _v\n if _v is not None:\n self[\"relative\"] = _v\n _v = arg.pop(\"suffix\", None)\n _v = suffix if suffix is not None else _v\n if _v is not None:\n self[\"suffix\"] = _v\n _v = arg.pop(\"valueformat\", None)\n _v = valueformat if valueformat is not None else _v\n if _v is not None:\n self[\"valueformat\"] = _v\n\n # Process unknown kwargs\n # ----------------------\n self._process_kwargs(**dict(arg, **kwargs))\n\n # Reset skip_invalid\n # ------------------\n self._skip_invalid = False\n","repo_name":"plotly/plotly.py","sub_path":"packages/python/plotly/plotly/graph_objs/indicator/_delta.py","file_name":"_delta.py","file_ext":"py","file_size_in_byte":11866,"program_lang":"python","lang":"en","doc_type":"code","stars":14438,"dataset":"github-code","pt":"3"}
+{"seq_id":"10275797958","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\n\nfrom typing import Dict, List # noqa: F401\nfrom datetime import date, datetime # noqa: F401\n\nfrom swagger_server import util\nfrom swagger_server.models.base_model_ import Model\n\n\nclass Item(Model):\n \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n Do not edit the class manually.\n \"\"\"\n\n def __init__(\n self,\n name: str = None,\n size: List[str] = None,\n calories: int = None,\n price: List[float] = None,\n ): # noqa: E501\n \"\"\"Item - a model defined in Swagger\n\n :param name: The name of this Item. # noqa: E501\n :type name: str\n :param size: The size of this Item. # noqa: E501\n :type size: List[str]\n :param calories: The calories of this Item. # noqa: E501\n :type calories: int\n :param price: The price of this Item. # noqa: E501\n :type price: List[float]\n \"\"\"\n self.swagger_types = {\n \"name\": str,\n \"size\": List[str],\n \"calories\": int,\n \"price\": List[float],\n }\n\n self.attribute_map = {\n \"name\": \"name\",\n \"size\": \"size\",\n \"calories\": \"calories\",\n \"price\": \"price\",\n }\n self._name = name\n self._size = size\n self._calories = calories\n self._price = price\n\n @classmethod\n def from_dict(cls, dikt) -> \"Item\":\n \"\"\"Returns the dict as a model\n\n :param dikt: A dict.\n :type: dict\n :return: The item of this Item. # noqa: E501\n :rtype: Item\n \"\"\"\n return util.deserialize_model(dikt, cls)\n\n @property\n def name(self) -> str:\n \"\"\"Gets the name of this Item.\n\n the name of the drink # noqa: E501\n\n :return: The name of this Item.\n :rtype: str\n \"\"\"\n return self._name\n\n @name.setter\n def name(self, name: str):\n \"\"\"Sets the name of this Item.\n\n the name of the drink # noqa: E501\n\n :param name: The name of this Item.\n :type name: str\n \"\"\"\n\n self._name = name\n\n @property\n def size(self) -> List[str]:\n \"\"\"Gets the size of this Item.\n\n The available sizes of a beverage # noqa: E501\n\n :return: The size of this Item.\n :rtype: List[str]\n \"\"\"\n return self._size\n\n @size.setter\n def size(self, size: List[str]):\n \"\"\"Sets the size of this Item.\n\n The available sizes of a beverage # noqa: E501\n\n :param size: The size of this Item.\n :type size: List[str]\n \"\"\"\n\n self._size = size\n\n @property\n def calories(self) -> int:\n \"\"\"Gets the calories of this Item.\n\n The total calories of a drink # noqa: E501\n\n :return: The calories of this Item.\n :rtype: int\n \"\"\"\n return self._calories\n\n @calories.setter\n def calories(self, calories: int):\n \"\"\"Sets the calories of this Item.\n\n The total calories of a drink # noqa: E501\n\n :param calories: The calories of this Item.\n :type calories: int\n \"\"\"\n\n self._calories = calories\n\n @property\n def price(self) -> List[float]:\n \"\"\"Gets the price of this Item.\n\n A list of prices associated with sizes # noqa: E501\n\n :return: The price of this Item.\n :rtype: List[float]\n \"\"\"\n return self._price\n\n @price.setter\n def price(self, price: List[float]):\n \"\"\"Sets the price of this Item.\n\n A list of prices associated with sizes # noqa: E501\n\n :param price: The price of this Item.\n :type price: List[float]\n \"\"\"\n\n self._price = price\n","repo_name":"dgisolfi/PythonPackaging","sub_path":"api/swagger_server/models/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":3755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71652126481","text":"import numpy as np\nimport math\nfrom typing import Any, Tuple, List, Optional, Set, Callable\n\nfrom bpy.types import Area, Object\nfrom mathutils import Matrix, Quaternion, Vector\n\nfrom .version import BVersion\nfrom .kt_logging import KTLogger\nfrom .fake_context import get_fake_context\nfrom .bpy_common import (bpy_current_frame,\n bpy_render_frame,\n bpy_render_aspect,\n evaluated_mesh,\n bpy_background_mode)\nfrom .animation import get_safe_evaluated_fcurve\n\n\n_log = KTLogger(__name__)\n\n\ndef nearest_point(x: float, y: float, points: List[Tuple[float, float]],\n dist: float=4000000.0) -> Tuple[int, float]: # dist squared\n dist2 = dist\n nearest = -1\n for i, p in enumerate(points):\n d2 = (x - p[0]) ** 2 + (y - p[1]) ** 2\n if d2 < dist2:\n dist2 = d2\n nearest = i\n return nearest, dist2\n\n\ndef xy_to_xz_rotation_matrix_3x3() -> Any:\n return np.array([[1., 0., 0.],\n [0., 0., 1.],\n [0., -1., 0.]], dtype=np.float32)\n\n\ndef xz_to_xy_rotation_matrix_3x3() -> Any:\n return np.array([[1., 0., 0.],\n [0., 0., -1.],\n [0., 1., 0.]], dtype=np.float32)\n\n\ndef xy_to_xz_rotation_matrix_4x4() -> Any:\n return np.array([[1., 0., 0., 0.],\n [0., 0., 1., 0.],\n [0., -1., 0., 0.],\n [0., 0., 0., 1.]], dtype=np.float32)\n\n\ndef xz_to_xy_rotation_matrix_4x4() -> Any:\n return np.array([[1., 0., 0., 0.],\n [0., 0., -1., 0.],\n [0., 1., 0., 0.],\n [0., 0., 0., 1.]], dtype=np.float32)\n\n\ndef update_head_mesh_geom(obj: Object, geom: Any) -> None:\n mesh = obj.data\n assert(len(geom) == len(mesh.vertices))\n npbuffer = geom @ xy_to_xz_rotation_matrix_3x3()\n mesh.vertices.foreach_set('co', npbuffer.ravel())\n if mesh.shape_keys:\n mesh.shape_keys.key_blocks[0].data.foreach_set('co', npbuffer.ravel())\n mesh.update()\n\n\ndef update_head_mesh_neutral(fb: Any, head: Any) -> None:\n geom = fb.applied_args_vertices()\n update_head_mesh_geom(head.headobj, geom)\n\n\ndef update_head_mesh_expressions(fb: Any, head: Any, keyframe: int) -> None:\n geom = fb.applied_args_model_vertices_at(keyframe)\n update_head_mesh_geom(head.headobj, geom)\n\n\ndef update_head_mesh_non_neutral(fb: Any, head: Any) -> None:\n if head.should_use_emotions():\n kid = head.get_expression_view_keyframe()\n if kid == 0: # Neutral selected\n pass\n elif fb.is_key_at(kid):\n update_head_mesh_expressions(fb, head, kid)\n return\n else:\n _log.error(f'NO KEYFRAME: {kid} in {fb.keyframes()}')\n update_head_mesh_neutral(fb, head)\n\n\ndef projection_matrix(w: float, h: float, fl: float, sw: float,\n near: float, far: float, scale: float = 1.0,\n shift_x: float = 0.0, shift_y: float = 0.0) -> Any:\n z_diff = near - far\n fl_to_sw = fl / sw\n return np.array(\n [[scale * w * fl_to_sw, 0, 0, 0],\n [0, scale * w * fl_to_sw, 0, 0],\n [w * (2 * shift_x - 0.5), h * (2 * shift_y - 0.5), (near + far) / z_diff, -1],\n [0, 0, 2 * near * far / z_diff, 0]]\n ).transpose()\n\n\ndef _compensate_view_scale(w: float, h: float, inverse=False) -> float:\n if w == 0 or h == 0:\n return 1.0\n if w >= h:\n return 1.0\n if inverse:\n return w / h\n else:\n return h / w\n\n\ndef custom_projection_matrix(w: float, h: float, fl: float, sw: float,\n near: float, far: float,\n shift_x: float = 0, shift_y: float = 0) -> Any:\n return projection_matrix(w, h, fl, sw, near, far,\n _compensate_view_scale(w, h), shift_x, shift_y)\n\n\ndef focal_by_projection_matrix_mm(pm: Any, sw: float) -> float:\n return -0.5 * pm[0][0] * sw / pm[0][2]\n\n\ndef focal_by_projection_matrix_px(pm: Any) -> float:\n return pm[0][0]\n\n\ndef focal_mm_to_px(fl_mm: float, image_width: float, image_height: float,\n sensor_width: float=36.0) -> float:\n sc = _compensate_view_scale(image_width, image_height)\n return sc * fl_mm * image_width / sensor_width\n\n\ndef focal_px_to_mm(fl_px: float, image_width: float, image_height: float,\n sensor_width: float=36.0) -> float:\n sc = _compensate_view_scale(image_width, image_height, inverse=True)\n return sc * fl_px * sensor_width / image_width\n\n\ndef camera_sensor_width(camobj: Any) -> float:\n if not camobj or not camobj.data:\n return 36.0\n return camobj.data.sensor_width\n\n\ndef camera_focal_length(camobj: Any) -> float:\n if not camobj or not camobj.data:\n return 50.0\n return camobj.data.lens\n\n\ndef image_space_to_frame(x: float, y: float, shift_x: float=0.0,\n shift_y: float=0.0) -> Tuple[float, float]:\n \"\"\" Image centered Relative coords to Frame pixels \"\"\"\n w, h = bpy_render_frame()\n return (x + shift_x + 0.5) * w, (y + shift_y) * w + 0.5 * h\n\n\ndef frame_to_image_space(frame_x: float, frame_y: float,\n frame_w: float, frame_h: float,\n shift_x: float=0.0,\n shift_y: float=0.0) -> Tuple[float, float]:\n return (frame_x / frame_w - 0.5 - shift_x,\n (frame_y - 0.5 * frame_h) / frame_w - shift_y)\n\n\ndef image_space_to_region(x: float, y: float, x1: float, y1: float,\n x2: float, y2: float, shift_x: float = 0.0,\n shift_y: float = 0.0) -> Tuple[float, float]:\n \"\"\" Relative coords to Region (screen) space \"\"\"\n w = (x2 - x1)\n h = (y2 - y1)\n sc = w\n return x1 + (x + 0.5 + 2 * shift_x) * sc, \\\n (y1 + y2) * 0.5 + y * sc + 2 * shift_y * h\n\n\ndef get_image_space_coord(px: float, py: float, area: Area,\n shift_x: float = 0.0,\n shift_y: float = 0.0) -> Tuple[float, float]:\n x1, y1, x2, y2 = get_camera_border(area)\n x, y = region_to_image_space(px, py, x1, y1, x2, y2, shift_x, shift_y)\n return x, y\n\n\ndef region_to_image_space(x: float, y: float, x1: float, y1: float,\n x2: float, y2: float, shift_x: float = 0.0,\n shift_y: float = 0.0) -> Tuple[float, float]:\n w = (x2 - x1) if x2 != x1 else 1.0\n h = (y2 - y1) if y2 != y1 else 1.0\n sc = w\n asp = h / w if w != 0 else 1.0\n return (x - (x1 + x2) * 0.5) / sc - 2 * shift_x,\\\n (y - (y1 + y2) * 0.5) / sc - 2 * asp * shift_y\n\n\ndef pin_to_xyz_from_mesh(\n pin: Any, obj: Object) -> Optional[Tuple[float, float, float]]:\n \"\"\" Surface point from barycentric to XYZ using passed mesh\"\"\"\n sp = pin.surface_point\n gp = sp.geo_point_idxs\n bar = sp.barycentric_coordinates\n vv = obj.data.vertices\n verts_count = len(vv)\n if len(gp) < 3 or gp[0] >= verts_count or \\\n gp[1] >= verts_count or gp[2] >= verts_count:\n return None\n p = vv[gp[0]].co * bar[0] + vv[gp[1]].co * bar[1] + vv[gp[2]].co * bar[2]\n return p\n\n\ndef pin_to_xyz_from_geo_mesh(pin: Any, geo_mesh: Any) -> Tuple[float, float, float]:\n \"\"\" Surface point from barycentric to XYZ using geo_mesh\"\"\"\n sp = pin.surface_point\n gp = sp.geo_point_idxs\n bar = sp.barycentric_coordinates\n v1 = geo_mesh.point(gp[0])\n v2 = geo_mesh.point(gp[1])\n v3 = geo_mesh.point(gp[2])\n p = v1 * bar[0] + v2 * bar[1] + v3 * bar[2]\n return p\n\n\ndef pin_to_normal_from_geo_mesh(pin: Any, geo_mesh: Any) -> Vector:\n sp = pin.surface_point\n gp = sp.geo_point_idxs\n v1 = geo_mesh.point(gp[0])\n v2 = geo_mesh.point(gp[1])\n v3 = geo_mesh.point(gp[2])\n return Vector(np.cross(v2 - v1, v3 - v2)).normalized()\n\n\ndef calc_model_mat(model_mat: Any, head_mat: Any) -> Optional[Any]:\n \"\"\" Convert model matrix to camera matrix \"\"\"\n rot_mat = xy_to_xz_rotation_matrix_4x4()\n\n try:\n nm = np.array(model_mat @ rot_mat) @ np.linalg.inv(head_mat)\n im = np.linalg.inv(nm)\n return im.transpose()\n except Exception:\n return None\n\n\ndef get_area_region_3d(area: Optional[Area]) -> Optional[Any]:\n if not area or not area.spaces or not area.spaces.active:\n return None\n return area.spaces.active.region_3d\n\n\ndef get_area_region(area: Area) -> Optional[Any]:\n return area.regions[-1]\n\n\ndef blender_zoom_formula(factor: float) -> float:\n if factor < 0:\n factor = 0\n return (math.sqrt(factor) - math.sqrt(0.5)) * 100.0\n\n\ndef blender_zoom_scale_factor(z: float) -> float:\n return (z * 0.01 + math.sqrt(0.5)) ** 2\n\n\ndef get_camera_border(area: Area) -> Tuple[float, float, float, float]:\n if bpy_background_mode():\n context = get_fake_context()\n area = context.area\n\n region = get_area_region(area)\n assert region.type == 'WINDOW'\n reg_w, reg_h = region.width, region.height\n\n rv3d = get_area_region_3d(area)\n z = rv3d.view_camera_zoom\n f = blender_zoom_scale_factor(z)\n\n region_aspect = reg_w / reg_h\n render_aspect = bpy_render_aspect()\n\n offset = (rv3d.view_camera_offset[0] * reg_w * 2 * f,\n rv3d.view_camera_offset[1] * reg_h * 2 * f)\n\n # This works when Camera Sensor Mode is Auto\n if region_aspect >= 1.0:\n if render_aspect >= 1.0:\n # Horizontal image in horizontal View\n kx = f\n ky = f * region_aspect / render_aspect # (ry / rx) * (w / h)\n else:\n kx = f * render_aspect # (rx / ry)\n ky = f * region_aspect # (w / h)\n\n else:\n if render_aspect < 1.0:\n # Vertical image in vertical View\n kx = f * render_aspect / region_aspect # (rx / ry) * (h / w)\n ky = f\n else:\n kx = f / region_aspect # (h / w)\n ky = f / render_aspect # (ry / rx)\n\n x1 = reg_w * 0.5 - kx * reg_w * 0.5 - offset[0]\n x2 = reg_w * 0.5 + kx * reg_w * 0.5 - offset[0]\n y1 = reg_h * 0.5 - ky * reg_h * 0.5 - offset[1]\n y2 = reg_h * 0.5 + ky * reg_h * 0.5 - offset[1]\n return x1, y1, x2, y2\n\n\ndef calc_camera_zoom_and_offset(\n area: Area, region_x1: float, region_y1: float,\n width: float) -> Tuple[float, Tuple[float, float]]:\n region = get_area_region(area)\n reg_w, reg_h = region.width, region.height\n reg_asp = reg_w / reg_h\n render_asp = bpy_render_aspect()\n kx = width / reg_w\n if reg_asp >= 1.0:\n if render_asp >= 1.0:\n f = kx\n ky = f * reg_asp / render_asp\n else:\n f = kx / render_asp\n ky = f * reg_asp\n else:\n if render_asp < 1.0:\n f = kx * reg_asp / render_asp\n ky = f\n else:\n f = kx * reg_asp\n ky = f / render_asp\n z = blender_zoom_formula(f)\n offset_x = reg_w * 0.5 * (1.0 - kx) - region_x1\n offset_y = reg_h * 0.5 * (1.0 - ky) - region_y1\n offset = (offset_x / (reg_w * 2 * f),\n offset_y / (reg_h * 2 * f))\n return z, offset\n\n\ndef point_is_in_area(area: Area, x: float, y: float, *,\n bottom_limit: float = 0, left_limit: float = 0) -> bool:\n if bpy_background_mode():\n context = get_fake_context()\n area = context.area\n return (left_limit <= x <= area.width) and (bottom_limit <= y <= area.height)\n\n\ndef point_is_in_service_region(area: Area, x: float, y: float) -> bool:\n \"\"\" No guarantee that point is in area!\n Only check if point is in service region \"\"\"\n if bpy_background_mode():\n context = get_fake_context()\n area = context.area\n\n x0 = area.x\n y0 = area.y\n for r in area.regions:\n if r.type != 'WINDOW':\n if (r.x <= x + x0 <= r.x + r.width) and (\n r.y <= y + y0 <= r.y + r.height):\n return True\n return False\n\n\ndef get_pixel_relative_size(area: Area) -> float:\n \"\"\" One Pixel size in relative coords via current zoom \"\"\"\n if bpy_background_mode():\n context = get_fake_context()\n area = context.area\n\n w = area.width if area.width > 0 else 1.0\n space = area.spaces.active\n rv3d = space.region_3d\n f = blender_zoom_scale_factor(rv3d.view_camera_zoom)\n ps = 1.0 / (w * f)\n return ps\n\n\ndef get_mesh_verts(mesh: Any) -> Any:\n verts = np.empty((len(mesh.vertices), 3), dtype=np.float32)\n mesh.vertices.foreach_get(\n 'co', np.reshape(verts, len(mesh.vertices) * 3))\n return verts\n\n\ndef get_obj_verts(obj: Any) -> Any:\n return get_mesh_verts(obj.data)\n\n\ndef to_homogeneous(verts: Any) -> Any:\n vv = np.ones((len(verts), 4), dtype=np.float32)\n vv[:, :-1] = verts\n return vv\n\n\ndef multiply_verts_on_matrix_4x4(verts: Any, mat: Any) -> Any:\n vv = to_homogeneous(verts) @ mat\n return vv[:, :3]\n\n\ndef get_scale_vec_3_from_matrix_world(obj_matrix_world: Any) -> Any:\n return np.array(obj_matrix_world.to_scale(), dtype=np.float32)\n\n\ndef get_scale_vec_4_from_matrix_world(obj_matrix_world: Any) -> Any:\n return np.array(obj_matrix_world.to_scale().to_4d(), dtype=np.float32)\n\n\ndef get_scale_matrix_4x4_from_matrix_world(obj_matrix_world: Any) -> Any:\n scale_vec = get_scale_vec_4_from_matrix_world(obj_matrix_world)\n return np.diag(scale_vec)\n\n\ndef get_world_matrix_without_scale(obj_matrix_world: Any) -> Any:\n scale_vec = get_scale_vec_4_from_matrix_world(obj_matrix_world)\n scminv = np.diag(1.0 / scale_vec)\n return scminv @ np.array(obj_matrix_world, dtype=np.float32).transpose()\n\n\ndef get_scale_matrix_3x3_from_matrix_world(obj_matrix_world: Any) -> Any:\n scale_vec = get_scale_vec_3_from_matrix_world(obj_matrix_world)\n return np.diag(scale_vec)\n\n\ndef compensate_view_scale() -> float:\n image_width, image_height = bpy_render_frame()\n if image_width >= image_height:\n return 1.0\n return image_width / image_height\n\n\ndef ScaleMatrix(rank: int, sc: Tuple) -> Matrix:\n scm = Matrix.Identity(rank)\n scm[0][0], scm[1][1], scm[2][2] = sc[:]\n return scm\n\n\ndef InvScaleMatrix(rank: int, sc: Tuple) -> Matrix:\n scm = Matrix.Identity(rank)\n try:\n scm[0][0], scm[1][1], scm[2][2] = 1.0 / sc[0], 1.0 / sc[1], 1.0 / sc[2]\n except ZeroDivisionError:\n pass\n return scm\n\n\ndef UniformScaleMatrix(rank: int, sc: float) -> Matrix:\n return ScaleMatrix(rank, (sc, sc, sc))\n\n\ndef RotationMatrix(r: Quaternion) -> Matrix:\n r.normalized().to_matrix().to_4x4()\n\n\ndef LocRotScale_old(t: Tuple[float, float, float], r: Quaternion,\n sc: Tuple[float, float, float]) -> Matrix:\n scm = ScaleMatrix(4, sc)\n return Matrix.Translation(t) @ r.normalized().to_matrix().to_4x4() @ scm\n\n\nLocRotScale: Callable = Matrix.LocRotScale \\\n if BVersion.LocRotScale_exist else LocRotScale_old\n\n\ndef LocRotWithoutScale(mat: Matrix) -> Matrix:\n t, r, s = mat.decompose()\n return LocRotScale(t, r, (1, 1, 1))\n\n\ndef calc_bpy_camera_mat_relative_to_model(geom_matrix_world: Matrix,\n camera_matrix_world: Matrix,\n gt_model_mat: Any) -> Matrix:\n rot_mat2 = xz_to_xy_rotation_matrix_4x4()\n geom_scale_vec = get_scale_vec_4_from_matrix_world(geom_matrix_world)\n geom_scale_inv = np.diag(1.0 / geom_scale_vec)\n sc = camera_matrix_world.to_scale()\n try:\n mat = np.array(geom_matrix_world) @ geom_scale_inv \\\n @ rot_mat2 @ np.linalg.inv(gt_model_mat)\n t, r, _ = Matrix(mat).decompose()\n new_mat = LocRotScale(t, r, sc)\n except Exception:\n new_mat = Matrix.Identity(4)\n return new_mat\n\n\ndef calc_bpy_model_mat_relative_to_camera(geom_matrix_world: Matrix,\n camera_matrix_world: Matrix,\n gt_model_mat: Any) -> Matrix:\n rot_mat = xy_to_xz_rotation_matrix_4x4()\n t, r, _ = camera_matrix_world.decompose()\n camera_mat = LocRotScale(t, r, (1, 1, 1))\n scale_mat = get_scale_matrix_4x4_from_matrix_world(geom_matrix_world)\n np_mw = np.array(camera_mat) @ gt_model_mat @ rot_mat @ scale_mat\n return Matrix(np_mw)\n\n\ndef camera_projection(camobj: Object, frame: Optional[int]=None,\n image_width: Optional[int]=None,\n image_height: Optional[int]=None) -> Any:\n cam_data = camobj.data\n near = cam_data.clip_start\n far = cam_data.clip_end\n if image_width is None or image_height is None:\n image_width, image_height = bpy_render_frame()\n if frame is None:\n frame =bpy_current_frame()\n lens = get_safe_evaluated_fcurve(cam_data, frame, 'lens')\n proj_mat = custom_projection_matrix(image_width, image_height, lens,\n cam_data.sensor_width, near, far,\n cam_data.shift_x, cam_data.shift_y)\n return proj_mat\n\n\ndef get_triangulation_indices(mesh: Any, calculate: bool = True) -> Any:\n if calculate:\n mesh.calc_loop_triangles()\n indices = np.empty((len(mesh.loop_triangles), 3), dtype=np.int32)\n mesh.loop_triangles.foreach_get(\n 'vertices', np.reshape(indices, len(mesh.loop_triangles) * 3))\n return indices\n\n\ndef get_polygons_in_vertex_group(obj: Object,\n vertex_group_name: str,\n inverted=False) -> Set[int]:\n vertex_group_index = obj.vertex_groups.find(vertex_group_name)\n if vertex_group_index < 0:\n return set()\n\n mesh = evaluated_mesh(obj)\n verts_in_group = set([v.index for v in mesh.vertices\n if vertex_group_index in\n [g.group for g in v.groups]])\n\n polys_in_group = set()\n\n if not inverted:\n for polygon in mesh.polygons:\n if verts_in_group.issuperset(polygon.vertices[:]):\n polys_in_group.add(polygon.index)\n else:\n for polygon in mesh.polygons:\n if not verts_in_group.issuperset(polygon.vertices[:]):\n polys_in_group.add(polygon.index)\n\n return polys_in_group\n\n\ndef get_triangles_in_vertex_group(obj: Object,\n vertex_group_name: str,\n inverted=False) -> List:\n if vertex_group_name == '':\n return []\n\n polys_in_group = get_polygons_in_vertex_group(obj, vertex_group_name,\n inverted)\n if len(polys_in_group) == 0:\n return []\n\n mesh = evaluated_mesh(obj)\n mesh.calc_loop_triangles()\n return [tris.vertices[:] for tris in mesh.loop_triangles\n if tris.polygon_index in polys_in_group]\n\n\ndef distance_between_objects(obj1: Object, obj2: Object) -> float:\n ar1 = np.asarray(obj1.matrix_world)\n ar2 = np.asarray(obj2.matrix_world)\n return np.linalg.norm(ar1[:, 3] - ar2[:, 3], axis=0)\n\n\ndef change_near_and_far_clip_planes(camobj: Object, geomobj: Object,\n *, step: float = 1.05,\n prev_clip_start: float,\n prev_clip_end: float,\n minimal_clip_start: float = 1e-5) -> bool:\n if not camobj or not geomobj:\n return False\n dist = distance_between_objects(camobj, geomobj)\n\n changed_flag = False\n clip_end = camobj.data.clip_end\n clip_start = camobj.data.clip_start\n new_far_dist = dist * step\n\n if clip_end < dist:\n _log.output(f'OBJECT IS BEYOND THE CAMERA FAR CLIP PLANE:\\n '\n f'DIST: {dist} OLD CLIP_END: {clip_end}')\n camobj.data.clip_end = new_far_dist\n changed_flag = True\n elif clip_end > prev_clip_end > new_far_dist:\n _log.output(f'REVERT THE CAMERA FAR CLIP PLANE:\\n '\n f'DIST: {dist} OLD CLIP_END: {clip_end}\\n'\n f'REVERT: {prev_clip_end}')\n camobj.data.clip_end = prev_clip_end\n changed_flag = True\n\n # Magic formula for near clip distance calculation to prevent Z-fighting\n safe_clip_start = new_far_dist * new_far_dist / 65536\n if safe_clip_start > clip_start:\n camobj.data.clip_start = safe_clip_start\n changed_flag = True\n clip_start = camobj.data.clip_start\n\n new_clip_start = max(dist * 0.5, minimal_clip_start)\n too_close_limit = dist * 0.75\n if clip_start > too_close_limit:\n _log.output(f'OBJECT IS TOO CLOSE TO THE CAMERA NEAR CLIP PLANE:\\n '\n f'DIST: {dist} OLD CLIP_START: {clip_start}')\n camobj.data.clip_start = new_clip_start \\\n if new_clip_start < prev_clip_start else prev_clip_start\n changed_flag = True\n clip_start = camobj.data.clip_start\n\n if clip_start > prev_clip_start >= safe_clip_start:\n _log.output(f'REVERT THE CAMERA NEAR CLIP PLANE:\\n '\n f'DIST: {dist} OLD CLIP_START: {clip_start}\\n'\n f'REVERT: {prev_clip_start}')\n camobj.data.clip_start = prev_clip_start\n changed_flag = True\n\n return changed_flag\n\n\ndef make_indices_for_wide_edges(numb: int) -> Tuple[Any, Any]:\n arr = np.tile(np.arange(0, numb).reshape((-1, 2)), (1, 3))\n return arr.reshape((-1, 3)).ravel(), \\\n np.flip(arr, 1).reshape((-1, 3)).ravel()\n\n\ndef bound_box_center(obj: Object) -> Vector:\n if BVersion.bound_box_has_foreach_get:\n verts = np.empty((8, 3), dtype=np.float32)\n obj.bound_box.foreach_get(verts)\n else:\n verts = np.array([obj.bound_box[i] for i in range(8)], dtype=np.float32)\n return Vector(np.mean(verts, axis=0))\n","repo_name":"KeenTools/keentools-blender","sub_path":"keentools/utils/coords.py","file_name":"coords.py","file_ext":"py","file_size_in_byte":21655,"program_lang":"python","lang":"en","doc_type":"code","stars":105,"dataset":"github-code","pt":"3"}
+{"seq_id":"11710436751","text":"from django.contrib import admin\nfrom django.contrib.auth.admin import UserAdmin as BaseUserAdmin\nfrom django.contrib.auth.forms import UsernameField, UserCreationForm, UserChangeForm\nfrom django.forms import EmailField\nfrom django.utils.translation import gettext_lazy as _\nfrom rest_framework_simplejwt.token_blacklist.admin import OutstandingTokenAdmin as BaseOutstandingTokenAdmin\nfrom rest_framework_simplejwt.token_blacklist.models import OutstandingToken\n\nfrom users.models import User\n\n\nclass CustomUsernameField(UsernameField):\n \"\"\"UsernameField but without built-in value normalization if value is None\"\"\"\n\n def to_python(self, value):\n # do not perform any normalization if username is None or empty\n if not value:\n return\n # else normalize username\n return super().to_python(value)\n\n\nclass UserCreateForm(UserCreationForm):\n \"\"\"Custom User Creation Form in Admin interface\"\"\"\n\n class Meta:\n model = User\n fields = (\n \"email\",\n \"first_name\",\n \"last_name\",\n \"birth_date\",\n \"type\",\n )\n field_classes = {'username': CustomUsernameField, 'email': EmailField}\n\n\nclass UserModifyForm(UserChangeForm):\n \"\"\"Custom User Change Form in Admin interface\"\"\"\n\n class Meta:\n model = User\n fields = '__all__'\n field_classes = {'username': CustomUsernameField}\n\n\nclass UserAdmin(BaseUserAdmin):\n \"\"\"Custom User Admin interface\"\"\"\n\n list_display = ('username', 'email', 'first_name', 'last_name', 'is_staff', 'type')\n list_filter = ('type', 'is_staff', 'is_superuser', 'is_active')\n fieldsets = (\n (_('User details'), {'fields': ('email', 'username', 'password')}),\n (_('Personal info'), {'fields': ('first_name', 'last_name', 'birth_date', 'type')}),\n (_('Important dates'), {'fields': ('last_login', 'date_joined')}),\n (_('Permissions'), {\n 'fields': ('is_active', 'is_staff', 'is_superuser', 'groups', 'user_permissions'),\n }),\n )\n add_fieldsets = (\n (None, {\n 'classes': ('wide',),\n 'fields': (\n 'email', 'first_name', 'last_name', 'birth_date', 'type', 'password1', 'password2'\n ),\n }),\n )\n form = UserModifyForm\n add_form = UserCreationForm\n\n\nclass OutstandingTokenAdmin(BaseOutstandingTokenAdmin):\n \"\"\"Overrides simple_jwt's token admin, so that deleting users is possible.\"\"\"\n\n def has_delete_permission(self, *args, **kwargs) -> bool:\n return True\n\n\nadmin.site.register(User, UserAdmin)\n\n# unregister model and later register it with a new admin\nadmin.site.unregister(OutstandingToken)\nadmin.site.register(OutstandingToken, OutstandingTokenAdmin)\n","repo_name":"BowellSolutions/bowell-backend","sub_path":"users/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2758,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"30136936820","text":"from graphviz import Digraph\nimport torch\nfrom torch.autograd import Variable, Function\n\ndef iter_graph(root, callback):\n queue = [root]\n seen = set()\n while queue:\n fn = queue.pop()\n if fn in seen:\n continue\n seen.add(fn)\n for next_fn, _ in fn.next_functions:\n if next_fn is not None:\n queue.append(next_fn)\n callback(fn)\n\ndef register_hooks(var):\n fn_dict = {}\n def hook_cb(fn):\n def register_grad(grad_input, grad_output):\n fn_dict[fn] = grad_input\n fn.register_hook(register_grad)\n iter_graph(var.grad_fn, hook_cb)\n\n def is_bad_grad(grad_output):\n grad_output = grad_output.data\n return grad_output.ne(grad_output).any() or grad_output.gt(1e6).any()\n\n def make_dot():\n node_attr = dict(style='filled',\n shape='box',\n align='left',\n fontsize='12',\n ranksep='0.1',\n height='0.2')\n dot = Digraph(node_attr=node_attr, graph_attr=dict(size=\"12,12\"))\n\n def size_to_str(size):\n return '('+(', ').join(map(str, size))+')'\n\n def build_graph(fn):\n if hasattr(fn, 'variable'): # if GradAccumulator\n u = fn.variable\n node_name = 'Variable\\n ' + size_to_str(u.size())\n dot.node(str(id(u)), node_name, fillcolor='lightblue')\n else:\n assert fn in fn_dict, fn\n fillcolor = 'white'\n if any(is_bad_grad(gi) for gi in fn_dict[fn]):\n fillcolor = 'red'\n dot.node(str(id(fn)), str(type(fn).__name__), fillcolor=fillcolor)\n for next_fn, _ in fn.next_functions:\n if next_fn is not None:\n next_id = id(getattr(next_fn, 'variable', next_fn))\n dot.edge(str(next_id), str(id(fn)))\n iter_graph(var.grad_fn, build_graph)\n\n return dot\n\n return make_dot\n\nif __name__ == '__main__':\n x = Variable(torch.randn(10, 10), requires_grad=True)\n y = Variable(torch.randn(10, 10), requires_grad=True)\n\n z = x / (y * 0)\n z = z.sum() * 2\n get_dot = register_hooks(z)\n z.backward()\n dot = get_dot()\n dot.save('tmp.dot')\n","repo_name":"juefeix/pnn.pytorch","sub_path":"bad_grad_viz.py","file_name":"bad_grad_viz.py","file_ext":"py","file_size_in_byte":2321,"program_lang":"python","lang":"en","doc_type":"code","stars":138,"dataset":"github-code","pt":"3"}
+{"seq_id":"17835615167","text":"from django.shortcuts import render, redirect, get_object_or_404\n\n# LOGIN\nfrom django.contrib.auth.decorators import login_required\n\n# IMPORT[wishes]\nfrom .models import Wish\nfrom .forms import WishForm\n\n# IMPORT json\nimport os\nimport json\n\n# Create your views here.\ndef homePage(request):\n \"\"\"\n Project home page\n \"\"\"\n return render(request, 'page/home.html')\n\ndef rules(request):\n return render(request, 'page/rules.html')\n\n# LOGIN [LOGIN REQUEST]\n@login_required\ndef wishList(request):\n \"\"\"\n Reading the database\n ├── Args:\n └── request : Important Django requirement\n\n ├── Return:\n ├── wish_list : All database values sorted in order of creation\n \"\"\"\n\n wish_list = Wish.objects.all().order_by('-created_at')\n return render(request, 'page/list.html', {'wishes':wish_list})\n\n@login_required\ndef createWish(request):\n \"\"\"\n Create a new wish.\n ├── Args:\n └── request : Important Django requirement\n\n ├── Return:\n ├── wish_list : All database values sorted in order of creation\n └── form : create form\n \"\"\"\n\n if request.method == 'POST':\n form = WishForm(request.POST)\n if form.is_valid():\n wish = form.save(commit=False)\n wish.done = 'adn'\n wish.user = request.user\n wish.save()\n\n return redirect('/list/')\n\n else:\n form = WishForm()\n return render(request, 'page/create.html', {'form':form})\n\n@login_required\ndef updateWish(request, wish_id):\n \"\"\"\n Update selected wish\n ├── Args:\n ├── request : Important Django requirement\n └── wish_id : Wish's ID\n\n ├── Return:\n ├── wish : Get selected wish data\n └── form : update form\n \"\"\"\n wish = get_object_or_404(Wish, pk=wish_id)\n form = WishForm(instance=wish)\n\n if request.method == 'POST':\n form = WishForm(request.POST, instance=wish)\n\n if form.is_valid():\n wish.save()\n return redirect('/list/')\n\n else:\n content = {'form':form, 'wish':wish}\n return render(request, 'page/edit.html', content)\n\n@login_required\ndef deleteWish(request, wish_id):\n \"\"\"\n Delete selected wish\n ├── Args:\n ├── request : Important Django requirement\n └── wish_id : Wish's ID\n\n ├── Return:\n ├── wish : Get selected wish data\n \"\"\"\n wish = get_object_or_404(Wish, pk=wish_id)\n wish.delete()\n return redirect('/list/')\n\n@login_required\ndef statusWish(request, wish_id):\n \"\"\"\n Change wish status\n ├── Args:\n ├── request : Important Django requirement\n └── wish_id : Wish's ID\n\n ├── Return:\n ├── wish : Get selected wish data\n \"\"\"\n \n wish = get_object_or_404(Wish, pk=wish_id)\n\n if (wish.done == 'adn'):\n wish.done = 'rld'\n\n else:\n wish.done = 'adn'\n\n wish.save()\n return redirect('/list/')\n\n@login_required\ndef dashboard(request):\n wishAndamento = Wish.objects.filter(done='adn', user=request.user).count()\n wishRealizados = Wish.objects.filter(done='rld', user=request.user).count()\n wishContent = {'wish_adn': wishAndamento, 'wish_rld': wishRealizados}\n \n return render(request, 'page/dashboard.html', wishContent)","repo_name":"Baku-Stark/pythonEXE","sub_path":"Python Challenger/No 009 - WishList App Using Django/wish/wishes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8708332262","text":"from pullupclub.models import Student, PullUpSession\n\ndef run():\n for i in range(5):\n stu = Student()\n stu.name = \"TestStudent\" + str(i)\n stu.nickname = \"wetwater%d\" % i\n stu.slug = stu.name\n stu.save()\n\n pus = PullUpSession(student=stu)\n pus.count = i\n pus.save()","repo_name":"andrewhylee/proj_2_pullUpClub","sub_path":"scripts/script1.py","file_name":"script1.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39744807222","text":"from django.urls import path,include\n\nfrom . import views\nfrom api import views as APIviews\n\n\nurlpatterns = [\n path('test', views.TestTemplate.as_view(), name='TestTemplate'),\n path('logout', views.LogoutView.as_view(), name='LogoutView'),\n path('login', views.LoginTemplate.as_view(), name='LoginTemplate'),\n path('api/budget', APIviews.MockAPIView, name=\"MockAPIView\"),\n path('api-auth/', include('rest_framework.urls', namespace='rest_framework')),\n]","repo_name":"MachosV/fin_assignment","sub_path":"mysite/finloup/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9338488896","text":"from __future__ import annotations\n\nfrom typing import Any\n\nimport cv2\nimport numpy as np\n\nfrom .image_model import ImageModel\nfrom .types import ListValue, NumericalValue, StringValue\nfrom .utils import AnomalyResult\n\n\nclass AnomalyDetection(ImageModel):\n __model__ = \"AnomalyDetection\"\n\n def __init__(self, inference_adapter, configuration=dict(), preload=False):\n super().__init__(inference_adapter, configuration, preload)\n self._check_io_number(1, 1)\n self.normalization_scale: float\n self.image_threshold: float\n self.pixel_threshold: float\n self.task: str\n self.labels: list[str]\n\n def postprocess(self, outputs: dict[str, np.ndarray], meta: dict[str, Any]):\n \"\"\"Post-processes the outputs and returns the results.\n\n Args:\n outputs (Dict[str, np.ndarray]): Raw model outputs\n meta (Dict[str, Any]): Meta data containing the original image shape\n\n Returns:\n AnomalyResult: Results\n \"\"\"\n anomaly_map: np.ndarray | None = None\n pred_label: str | None = None\n pred_mask: np.ndarray | None = None\n pred_boxes: np.ndarray | None = None\n predictions = outputs[list(self.outputs)[0]]\n\n if len(predictions.shape) == 1:\n pred_score = predictions\n else:\n anomaly_map = predictions.squeeze()\n pred_score = anomaly_map.reshape(-1).max()\n\n pred_label = (\n self.labels[1] if pred_score > self.image_threshold else self.labels[0]\n )\n\n assert anomaly_map is not None\n pred_mask = (anomaly_map >= self.pixel_threshold).astype(np.uint8)\n anomaly_map = self._normalize(anomaly_map, self.pixel_threshold)\n pred_mask = cv2.resize(\n pred_mask, (meta[\"original_shape\"][1], meta[\"original_shape\"][0])\n )\n\n # normalize\n pred_score = self._normalize(pred_score, self.image_threshold)\n\n # resize outputs\n if anomaly_map is not None:\n anomaly_map = cv2.resize(\n anomaly_map, (meta[\"original_shape\"][1], meta[\"original_shape\"][0])\n )\n\n if self.task == \"detection\":\n pred_boxes = self._get_boxes(pred_mask)\n\n return AnomalyResult(\n anomaly_map=anomaly_map,\n pred_boxes=pred_boxes,\n pred_label=pred_label,\n pred_mask=pred_mask,\n pred_score=pred_score.item(),\n )\n\n @classmethod\n def parameters(cls) -> dict:\n parameters = super().parameters()\n parameters.update(\n {\n \"image_threshold\": NumericalValue(\n description=\"Image threshold\", min=0.0, default_value=0.5\n ),\n \"pixel_threshold\": NumericalValue(\n description=\"Pixel threshold\", min=0.0, default_value=0.5\n ),\n \"normalization_scale\": NumericalValue(\n description=\"Value used for normalization\",\n ),\n \"task\": StringValue(\n description=\"Task type\", default_value=\"segmentation\"\n ),\n \"labels\": ListValue(description=\"List of class labels\"),\n }\n )\n return parameters\n\n def _normalize(self, tensor: np.ndarray, threshold: float) -> np.ndarray:\n \"\"\"Currently supports only min-max normalization.\"\"\"\n normalized = ((tensor - threshold) / self.normalization_scale) + 0.5\n normalized = np.clip(normalized, 0, 1)\n return normalized\n\n @staticmethod\n def _get_boxes(mask: np.ndarray) -> np.ndarray:\n \"\"\"Get bounding boxes from mask.\n\n Args:\n mask (np.ndarray): Input mask of shapw (H, W)\n\n Returns:\n np.ndarray: array of shape (N,4) containing the bounding box coordinates of the objects in the masks in\n format [x1, y1, x2, y2]\n \"\"\"\n contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n boxes = []\n for contour in contours:\n x, y, w, h = cv2.boundingRect(contour)\n boxes.append([x, y, x + w, y + h])\n return np.array(boxes)\n","repo_name":"openvinotoolkit/model_api","sub_path":"model_api/python/openvino/model_api/models/anomaly.py","file_name":"anomaly.py","file_ext":"py","file_size_in_byte":4213,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"3"}
+{"seq_id":"21448828715","text":"from django_elasticsearch_dsl_drf.serializers import DocumentSerializer\nfrom rest_framework import serializers\n\nfrom .documents import LectureDocument\nfrom .models import Lecture\n\n\nclass LectureSerializer(serializers.ModelSerializer):\n class Meta:\n model = Lecture\n fields = \"__all__\"\n\n\nclass LectureDocumentSerializer(DocumentSerializer):\n class Meta:\n document = LectureDocument\n fields = (\n \"lecture_id\",\n \"title\",\n \"date\",\n \"corporate_author\",\n \"abstract\",\n \"series\",\n \"speaker\",\n \"speaker_details\",\n \"event_details\",\n \"thumbnail_picture\",\n \"language\",\n \"subject_category\",\n \"lecture_note\",\n \"imprint\",\n \"license\",\n \"sponsor\",\n \"keywords\",\n \"types\",\n \"files\",\n \"video_parts\",\n )\n","repo_name":"cern-sis/cern-academic-training","sub_path":"backend/cds/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"19899401996","text":"# A program that allows the user to type in a phrase and then outputs the acronym for that phrase.\r\n\r\ndef main():\r\n\r\n phrase = input(\"Enter a pharse:\")\r\n acronym = \"\"\r\n\r\n for word in phrase.split():\r\n acronym = acronym+word[0]\r\n acronym = acronym.upper()\r\n\r\n print(\"The acronym of the phrase is\", acronym)\r\n\r\nmain()\r\n \r\n \r\n \r\n","repo_name":"marjana2019/python_problems","sub_path":"acronym.py","file_name":"acronym.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20120076312","text":"\nfrom numpy import TooHardError\nimport pygame\nfrom pygame import *\nfrom tkinter import *\nfrom tkinter import messagebox as mb\nimport json\n\npygame.init()\n\n\nclass Quiz:\n\n def __init__(self):\n\n self.q_no = 0\n\n self.display_title()\n self.display_question()\n\n self.opt_selected = IntVar()\n\n self.opts = self.radio_buttons()\n\n self.display_options()\n\n self.buttons()\n\n self.data_size = len(question)\n\n self.correct = 0\n\n def display_result(self):\n\n wrong_count = self.data_size - self.correct\n correct = f\"Correct: {self.correct}\"\n wrong = f\"Wrong: {wrong_count}\"\n\n score = int(self.correct / self.data_size * 100)\n result = f\"Score: {score}%\"\n\n mb.showinfo(\n \"Result\", f\"{result}\\n{correct}\\n{wrong}\\n\\n The Correct answers were: \\n 1.Dry Gravel \\n 2. Wet Asphalt \\n 3. Dry Asphalt \\n 4.Snow \\n 5. Ice \\n 6. Wet Gravel\")\n\n def check_ans(self, q_no):\n\n if self.opt_selected.get() == answer[q_no]:\n\n return True\n\n def next_btn(self):\n\n if self.check_ans(self.q_no):\n\n self.correct += 1\n\n self.q_no += 1\n print(self.q_no)\n\n if self.q_no == self.data_size:\n\n self.display_result()\n\n gui.destroy()\n else:\n\n self.display_question()\n self.display_options()\n\n def buttons(self):\n\n next_button = Button(gui, text=\"Next\", command=self.next_btn,\n width=10, bg=\"cyan\", fg=\"black\", font=(\"ariel\", 20, \"bold\"))\n\n next_button.place(x=300, y=380)\n\n quit_button = Button(gui, text=\"Quit\", command=gui.destroy,\n width=5, bg=\"cyan\", fg=\"black\", font=(\"ariel\", 16, \" bold\"))\n\n quit_button.place(x=700, y=700)\n\n def display_options(self):\n val = 0\n\n self.opt_selected.set(0)\n\n for option in options[self.q_no]:\n self.opts[val]['text'] = option\n val += 1\n\n def display_question(self):\n\n q_no = Label(gui, text=question[self.q_no], width=60,\n font=('ariel', 18, 'bold'), anchor='w')\n\n q_no.place(x=70, y=200)\n\n def display_title(self):\n\n title1 = Label(gui, text=\"Guess The Road Condition! By: Ice Riderzzz\",\n width=50, bg=\"cyan\", fg=\"black\", font=(\"ariel\", 20, \"bold\"))\n\n title1.place(x=0, y=2)\n\n\n title2 = Label(gui, text=r\"https://github.com/TheLonelyFighter/Capstone\",\n width=50, bg=\"white\", fg=\"black\", font=(\"Courier\", 10, \"italic\"))\n\n title2.place(x=0, y=600)\n\n\n def radio_buttons(self):\n\n q_list = []\n y_pos = 270\n x_pos = 140\n while len(q_list) < 6:\n radio_btn = Radiobutton(gui, text=\" \", variable=self.opt_selected,\n value=len(q_list)+1, font=(\"ariel\", 14))\n\n q_list.append(radio_btn)\n radio_btn.place(x=x_pos, y=y_pos)\n if len(q_list) == 1:\n x_pos += 150\n elif len(q_list) == 2:\n x_pos += 150\n elif len(q_list) == 3:\n y_pos +=40\n x_pos -= 300\n elif len(q_list) == 4:\n x_pos += 150\n elif len(q_list) == 5:\n x_pos +=150\n return q_list\n\n\ngui = Tk()\ngui.geometry('800x800')\n\nmyframe = Frame(gui)\nmyframe.pack()\n\ngui.title(\"Guess The Road Condition! By: Ice Riderzzz\")\n\n\nwith open('json_for_quiz_game.json') as f:\n data = json.load(f)\n\n\ndef unpause():\n pygame.mixer.music.unpause()\n\n\ndef pause():\n pygame.mixer.music.pause()\n\n\nquestion = (data['question'])\noptions = (data['options'])\nanswer = (data['answer'])\n\n\nquiz = Quiz()\n\n\ndef play_dry_gravel():\n pygame.mixer.music.load('dry_gravel.wav')\n pygame.mixer.music.play(-1)\n\n\ndef play_wet_asphalt():\n pygame.mixer.music.load('wet_asphalt_new.wav')\n pygame.mixer.music.play(-1)\n\n\ndef play_dry_asphalt():\n pygame.mixer.music.load('dry_asphalt_new.wav')\n pygame.mixer.music.play(-1)\n\ndef play_ice():\n pygame.mixer.music.load('ice.wav')\n pygame.mixer.music.play(-1) #continuous\n\ndef play_snow():\n pygame.mixer.music.load('snow.wav')\n pygame.mixer.music.play(-1)\n\ndef play_wet_gravel():\n pygame.mixer.music.load('wet_gravel.wav')\n pygame.mixer.music.play(-1)\n\n\n\n\nbutton1 = Button(gui, text=\"Sound 3\",\n command=play_dry_asphalt, width=20)\nbutton1.pack(pady=5)\nbutton1.place(x=460, y=100)\nbutton2 = Button(gui, text=\"Sound 2\",\n command=play_wet_asphalt, width=20)\nbutton2.pack(pady=10)\nbutton2.place(x=300, y=100)\nbutton3 = Button(gui, text=\"Sound 1\",\n command=play_dry_gravel, width=20)\nbutton3.pack(pady=15)\nbutton3.place(x=140, y=100)\n\nbutton6 = Button(gui, text=\"Sound 4\",\n command=play_snow, width=20)\nbutton6.pack(pady=15)\nbutton6.place(x=140, y=130)\n\nbutton7 = Button(gui, text=\"Sound 5\",\n command=play_ice, width=20)\nbutton7.pack(pady=15)\nbutton7.place(x=300, y=130)\n\nbutton7 = Button(gui, text=\"Sound 6\",\n command=play_wet_gravel, width=20)\nbutton7.pack(pady=15)\nbutton7.place(x=460, y=130)\n\n\nbutton4 = Button(gui, text=\"Continue\", command=unpause, width=15)\nbutton4.pack(pady=5)\nbutton4.place(x=240, y=70)\n\nbutton5 = Button(gui, text=\"Pause\", command=pause, width=15)\nbutton5.pack(pady=5)\nbutton5.place(x=360, y=70)\n\n\ngui.configure(background='grey')\ngui.mainloop()\n\n\n\n#TODO \n\"\"\" Pickle or sqlite3 for data collection,\nchange GUI friendlier \"\"\"\n\n\n","repo_name":"TheLonelyFighter/Capstone","sub_path":"guess_the_road_condition/quiz_game_road.py","file_name":"quiz_game_road.py","file_ext":"py","file_size_in_byte":5553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29183902107","text":"import logging\nimport yaml\n\nfrom library.python import resource as rs\nfrom collections import namedtuple\n\nyaml_loader = getattr(yaml, 'CSafeLoader', yaml.SafeLoader)\nResource = namedtuple('Resource', [\n 'vcpu', 'memory',\n 'capacity_hdd', 'disk_bandwidth_hdd',\n 'capacity_ssd', 'disk_bandwidth_ssd'])\n\n\ndef test_quota_capacity(request, account_id, gc_max_object_age_minutes):\n resources = []\n for name, data in rs.iteritems('/stage'):\n resource = _parse(data)\n logging.info('%s resource usage: %s', name, resource)\n resources.append(resource)\n logging.info('total resource usage: %s', _merge_resources(resources))\n\n # TODO: fetch it from target cluster?\n requested_quota = {\n 'vcpu': 12000, # 12c\n 'memory': 10737418240, # 10G\n 'capacity_hdd': 1099511627776, # 1T\n 'disk_bandwidth_hdd': 3145728, # 3M/s\n 'capacity_ssd': 1099511627776, # 1T\n 'disk_bandwidth_ssd': 3145728 # 3M/s\n }\n\n run_freq = 30\n test_split_factor = request.config.option.modulo\n def approx_quota_limit(value, factor=1.5):\n return factor * value * test_split_factor * (gc_max_object_age_minutes / run_freq)\n\n # well... this is pretty lame estimation.\n # better way is to look for split_factor consumers which\n # have max sum consumption (and assume some good metric)\n max_resource_vector_dict = _merge_resources(resources, max)._asdict()\n for key, quota in requested_quota.items():\n requested_quota = approx_quota_limit(max_resource_vector_dict[key])\n assert requested_quota <= quota, \\\n f\"Requested resource {key} could overwhelm allowed quota ({requested_quota} > {quota}). \" \\\n f\"Make sure that this is ok and re-write this check or consider enlarging quota for account {account_id}.\"\n\n quota_order = {}\n for key, quota in max_resource_vector_dict.items():\n quota_order[key] = approx_quota_limit(quota, 2.)\n logging.info(\"Quota order: %s\", quota_order)\n\n\ndef _parse(data):\n stage_yaml = data.decode(\"utf-8\") % {'yp_cluster': 'fake_cluster'}\n stage_dict = yaml.load(stage_yaml, Loader=yaml_loader)\n resources = list(_find_resources(stage_dict))\n return _merge_resources(resources)\n\n\ndef _merge_resources(resources, f=sum):\n return Resource(\n f([r.vcpu for r in resources]),\n f([r.memory for r in resources]),\n f([r.capacity_hdd for r in resources]),\n f([r.disk_bandwidth_hdd for r in resources]),\n f([r.capacity_ssd for r in resources]),\n f([r.disk_bandwidth_ssd for r in resources]),\n )\n\n\ndef _process_replica_sets(stage_dict, function, *args, **kwargs):\n for du_id, du_spec in stage_dict.get('spec', {}).get('deploy_units', {}).items():\n yield function(du_id, du_spec.get('multi_cluster_replica_set', {}).get('replica_set', {}), *args, **kwargs)\n yield function(du_id, du_spec.get('replica_set', {}).get('replica_set_template', {}), *args, **kwargs)\n\n\ndef _find_resources(stage_dict):\n default_bandwith = {\n 'hdd': 1048576, # 1M/s\n 'ssd': 1048576 # 1M/s\n }\n def _patch_resources_and_volumes(du_id, replica_set_dict):\n pod_spec = replica_set_dict.get('pod_template_spec', {}).get('spec', {})\n for volume_request in pod_spec.get('disk_volume_requests', []):\n quota_policy = volume_request.get('quota_policy', {})\n storage_type = volume_request.get('storage_class', 'hdd')\n yield Resource(**{\n 'vcpu': 0,\n 'memory': 0,\n 'capacity_hdd': 0,\n 'disk_bandwidth_hdd': default_bandwith['hdd'],\n 'capacity_ssd': 0,\n 'disk_bandwidth_ssd': default_bandwith['ssd'],\n 'disk_bandwidth_' + storage_type: quota_policy.get('bandwidth_limit', default_bandwith[storage_type]),\n 'capacity_' + storage_type: quota_policy['capacity']\n })\n\n resource_requests = pod_spec.get('resource_requests', {})\n yield Resource(resource_requests.get('vcpu_limit', 0),\n resource_requests.get('memory_limit', 0),\n 0, 0, 0, 0)\n\n for resource in _process_replica_sets(stage_dict, _patch_resources_and_volumes):\n for r in resource:\n yield r\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/test_quota_capacity.py","file_name":"test_quota_capacity.py","file_ext":"py","file_size_in_byte":4308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23001257304","text":"from datetime import timedelta\nfrom django.test import TestCase\nfrom django.contrib.auth import get_user_model\nfrom django.utils import timezone\nfrom accounts.models import Profile\nfrom models3d.models import Model3D\nfrom badges.constants import NB_VIEWS_FOR_STAR, TIMEDELTA_FOR_PIONNEER\nfrom badges.validator import BadgeValidator\nfrom mock import patch\n\nUser = get_user_model()\n\n\nclass ValidatorTests(TestCase):\n\n def setUp(self):\n user = User.objects.create_user(\n username=\"john\",\n email=\"john@beatles.com\",\n password=\"john123\")\n self.profile = Profile.objects.create(user=user)\n\n def test_check_star_badge(self):\n self.profile.nb_views = NB_VIEWS_FOR_STAR + 1\n self.profile.save()\n self.assertTrue(BadgeValidator.check_star_badge(self.profile))\n\n def test_check_star_badge_ko(self):\n self.profile.nb_views = NB_VIEWS_FOR_STAR - 1\n self.profile.save()\n self.assertFalse(BadgeValidator.check_star_badge(self.profile))\n\n @patch('badges.validator.NB_MODELS_FOR_COLLECTOR', 1)\n def test_check_collector_badge(self):\n Model3D.objects.create(profile=self.profile)\n self.assertTrue(BadgeValidator.check_collector_badge(self.profile))\n\n @patch('badges.validator.NB_MODELS_FOR_COLLECTOR', 1)\n def test_check_collector_badge_ko(self):\n self.assertFalse(BadgeValidator.check_collector_badge(self.profile))\n\n def test_check_pionneer_badge(self):\n self.profile.user.date_joined = timezone.now() - TIMEDELTA_FOR_PIONNEER - timedelta(seconds=1)\n self.profile.user.save()\n self.assertTrue(BadgeValidator.check_pionneer_badge(self.profile))\n\n def test_check_pionneer_badge_ko(self):\n self.profile.user.date_joined = timezone.now()\n self.profile.user.save()\n self.assertFalse(BadgeValidator.check_pionneer_badge(self.profile))\n","repo_name":"pleasedontbelong/badges","sub_path":"apps/badges/tests/test_validator.py","file_name":"test_validator.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34513028751","text":"import os\nimport vtk\n\nimport mooseutils\nfrom .. import base\n\nclass ImageAnnotationSource(base.ChiggerSource):\n \"\"\"\n Source for displaying images in 3D space.\n \"\"\"\n VTKACTOR_TYPE = vtk.vtkImageActor\n VTKMAPPER_TYPE = vtk.vtkImageSliceMapper\n\n @staticmethod\n def getOptions():\n \"\"\"\n Return default options for this object.\n \"\"\"\n opt = base.ChiggerSource.getOptions()\n opt.add('filename', None, \"The PNG file to read, this can be absolute or relative path to \"\n \"a PNG or just the name of a PNG located in the chigger/logos \"\n \"directory.\", vtype=str)\n return opt\n\n def __init__(self, **kwargs):\n super(ImageAnnotationSource, self).__init__(vtkactor_type=vtk.vtkImageActor,\n vtkmapper_type=vtk.vtkImageSliceMapper,\n **kwargs)\n\n self.__reader = vtk.vtkPNGReader()\n\n def getVTKSource(self):\n \"\"\"\n Return the image reader object. (override)\n \"\"\"\n return self.__reader\n\n def update(self, **kwargs):\n \"\"\"\n Updates the image reader. (override)\n \"\"\"\n super(ImageAnnotationSource, self).update(**kwargs)\n\n if self.isOptionValid('filename'):\n filename = self.getOption('filename')\n if not os.path.exists(filename):\n filename = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'logos',\n os.path.basename(filename)))\n if not os.path.exists(filename):\n raise mooseutils.MooseException('Unable to locate image file: {}'.format(filename))\n self.__reader.SetFileName(filename)\n self.__reader.Update()\n","repo_name":"mazajump/FEA-PhaseField-Moose","sub_path":"python/chigger/annotations/ImageAnnotationSource.py","file_name":"ImageAnnotationSource.py","file_ext":"py","file_size_in_byte":1853,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"4780781548","text":"\"\"\"\n#------------ Get OHLC top 15 cryto - exchange Kraken \nCryto\nbitcoin (btc), ethereum (eth), litecoin (ltc), polkadot (dot), monero (xmr), dogecoin (xdg), stellar lumens (xlm),\nripple (xrp), zcash (zec), nano (nano), tron (trx), bitcoin cash (bch), tezos (xtz), cardano (ada), orchid (oxt)\n \n\npath imports. \nadd the following bit of code to the beginning of your imports \n(as long as your notebook is inside a folder inside libs) it will tell Python to add the libs folder \nto the list of folders it looks for imports from \n(to see a list of folders Python is searching print sys.path after import.sys):\n\nimport sys\nimport os\nmodule_path = os.path.abspath(os.path.join('../..'))\nif module_path not in sys.path:\n sys.path.append(module_path)\n\nWhich then will allow you to import from our signals, data, or trading packages, i.e.:\nfrom libs.signals import signals.ewma_crossover\nfrom libs.trading import 'some_trading_function/library'\n\nto see a list of folders Python is searching print sys.path after import.sys):\n\n\nNote: you can remove a path by doing sys.path.remove('path_you_want_to_remove'), \nbut be really careful with that and don't remove any of the default paths!\nIf y'all would rather just move the files into the libs folder, \n\n\"\"\"\n\n# ---------- imports \nimport sys\nimport os\nmodule_path = os.path.abspath(os.path.join('../..'))\nif module_path not in sys.path:\n sys.path.append(module_path)\nsys.path\n\nfor p in (sys.path):\n print(p)\n\nos.getcwd()\n\nimport pandas as pd \nimport numpy as np\nimport datetime\nimport time, json, requests, sys\nfrom time import time, ctime\nfrom datetime import datetime\nimport pathlib\nfrom requests.exceptions import HTTPError\n#from libs.signals import signals \n\n\n\n#------------ functions \n\n#--- Get OHLC from Kraken\ndef Get_OHLC_Kraken(pair, interval='1440', since=''):\n response_json = {}\n url = 'https://api.kraken.com/0/public/OHLC' #only last 720 datapoints \n #pair = cryto\n #interval = '1440' # 1440 = 1 day \n #since = '' # return las 720 datapoints\n \n for i in range(3):\n try:\n response_kraken = requests.post(url, \n params=\n {'pair':pair,\n 'interval':interval, \n 'since':since}, \n headers={\"content-type\":\"application/json\"})\n \n # If the response was successful, no Exception will be raised\n response_kraken.raise_for_status()\n except HTTPError as http_err:\n print(f'HTTP error occurred: {http_err}') \n except Exception as err:\n print(f'Other error occurred: {err}') \n else:\n print('Success, URL found!')\n break\n print('-------------------- try #');print(i)\n time.sleep(5)\n\n if i == 2: sys.exit('URL error GGC') \n\n #print(response_kraken.json())\n #g = input(\"control 2 : \"); print (g)\n return response_kraken.json()\n\n\n# --- Process OHLC json into a list\ndef Process_Kraken_Jason(kraken_json, pair):\n i = 0\n l_price = []\n #pair = 'XXBTZUSD'\n \n while True:\n try:\n new_row = [ctime(kraken_json['result'][pair][i][0]), # Date \n kraken_json['result'][pair][i][1], # Open \n kraken_json['result'][pair][i][2], # High \n kraken_json['result'][pair][i][3], # Low \n kraken_json['result'][pair][i][4], # Close\n kraken_json['result'][pair][i][6] # Volume \n ] \n #print (new_row)\n l_price += [new_row]\n i += 1 \n\n except Exception as err:\n print(f'error Process_Kraken_Jason: {err}') \n break \n \n return l_price\n\n\n# --- tranform OHLC list into a DataFrame '\ndef l_price_into_df_price(l_price, pair):\n l_labels = ['Date', 'Open_'+pair[0:5], 'High_'+pair[0:5], 'Low_'+pair[0:5], 'Close_'+pair[0:5], 'Vol_'+pair[0:5]]\n\n df_price = pd.DataFrame.from_records(l_price, columns=l_labels) # l --> list, l_labels --> column names \n df_price['Date'] = pd.to_datetime(df_price['Date'])\n df_price.set_index('Date', inplace=True) \n\n return(df_price)\n\n\n# ---- concatenate pair dataframe with the global dataframe \ndef Concact_Prices(df_price, ft, df_all_prices):\n if ft == True:\n df_all_prices = pd.concat([df_price], axis=\"columns\", join=\"outer\")\n ft = False\n else:\n df_all_prices = pd.concat([df_all_prices, df_price], axis=\"columns\", join=\"outer\")\n\n return df_all_prices, ft\n\n\n# --- Export df to CSV \ndef export_df_csv(df_all_prices):\n #path_csv = 'Crypto_Bandyts/libs/data/'\n #name_csv = 'crypto_prices1.csv'\n #OHLC_csv = path_csv + name_csv\n\n path = pathlib.Path('/Users/gonzalogarciacontreras/rice15/Crypto_Bandyts/libs/data/crypto_pricesx.csv')\n\n df_all_prices.to_csv(path)\n\n return \n\n\n# --------- Main \n\ndef main():\n #bitcoin (btc), ethereum (eth), litecoin (ltc), polkadot (dot), monero (xmr), dogecoin (xdg), stellar lumens (xlm),\n #ripple (xrp), zcash (zec), nano (nano), tron (trx), bitcoin cash (bch), tezos (xtz), cardano (ada), orchid (oxt)\n #DOT, XDG, NANO, TRX, and OXT.\n \n cryto_list = ['XXBTZUSD']\n\n #cryto_list = ['XXBTZUSD', 'XETHZUSD', 'XLTCZUSD', 'DOTUSD', 'XXMRZUSD', 'XDGUSD', 'XXLMZUSD', 'XXRPZUSD', \n # 'XZECZUSD', 'NANOUSD', 'TRXUSD', 'BCHUSD', 'XTZUSD', 'ADAUSD', 'OXTUSD']\n\n\n interval = '1440'\n since = ''\n ft = True \n df_all_prices = pd.DataFrame()\n\n for pair in (cryto_list):\n kraken_json = Get_OHLC_Kraken(pair, interval, since)\n #print('\\n\\n')\n #print(kraken_json)\n\n l_price = Process_Kraken_Jason(kraken_json, pair)\n #print('\\n\\n')\n #print(l_price)\n\n df_price = l_price_into_df_price(l_price, pair)\n #print(df_price.head())\n\n df_all_prices, ft = Concact_Prices(df_price, ft, df_all_prices)\n \n print(df_all_prices.head())\n print(df_all_prices.tail())\n\n export_df_csv(df_all_prices)\n\n\n\nif __name__== \"__main__\":\n main()\n g = input(\"End Program .... Press any key : \"); print (g)\n\n \n","repo_name":"stew1922/Crypto_Bandyts","sub_path":"libs/notebooks/closings_kraken_btc.py","file_name":"closings_kraken_btc.py","file_ext":"py","file_size_in_byte":6332,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"73341014482","text":"import timeit\nstarttime = timeit.default_timer()\n\n# Hack to import the input file\nimport sys\nsys.path.append(\"../../../utils\")\nimport import_input\n\ndi = import_input.DailyImput(usr=\"mvp\")\ninput_file = di.get_input()\n\nasd = [\"$ cd /\",\n\"$ ls\",\n\"dir a\",\n\"14848514 b.txt\",\n\"8504156 c.dat\",\n\"dir d\",\n\"$ cd a\",\n\"$ ls\",\n\"dir e\",\n\"29116 f\",\n\"2557 g\",\n\"62596 h.lst\",\n\"$ cd e\",\n\"$ ls\",\n\"584 i\",\n\"$ cd ..\",\n\"$ cd ..\",\n\"$ cd d\",\n\"$ ls\",\n\"4060174 j\",\n\"8033020 d.log\",\n\"5626152 d.ext\",\n\"7214296 k\"]\n\n# Challenge\nimport re\nfile_pattern = re.compile(r\"(\\d*) (.*)\") # 14848514 b.txt\nimport sys\nsys.setrecursionlimit(2000)\n\nfolder = {\n \"parent\":None,\n \"size\":0,\n \"subfolders\":{},\n \"files\":{},\n \"name\":\"root\",\n}\n\ndef run_command(commands, current_folder):\n try:\n command = next(commands)\n except StopIteration:\n return 0\n if command == \"$ cd /\": #it just shows once\n run_command(commands, current_folder)\n elif command == \"$ ls\": #doesn't change anything, is just a header for the command list\n run_command(commands, current_folder) # ***\n elif command.startswith(\"dir\"): #creates a subfolder if it doesn't exist\n folder_name = command[4:]\n create_folder(folder_name, current_folder) # current_folder is a dict, representing the current folder\n run_command(commands, current_folder)\n elif command.startswith(\"$ cd ..\"): # goes up one folder\n run_command(commands, current_folder[\"parent\"])\n elif command.startswith(\"$ cd\"): # goes down one folder\n subfolder_name = command[5:]\n sub_folders_sum = run_command(commands, current_folder[\"subfolders\"][subfolder_name])\n current_folder[\"size\"] += sub_folders_sum # run the next command on the subfolder\n # current_folder[\"size\"] += run_command(commands, current_folder[\"subfolders\"][subfolder_name]) # run the next command on the subfolder\n elif file_pattern.match(command):\n file_size, file_name = file_pattern.match(command).groups()\n file_size = int(file_size)\n current_folder[\"files\"][file_name] = {\n \"size\":file_size,\n \"name\":file_name,\n }\n current_folder[\"size\"] += file_size + run_command(commands, current_folder)\n return current_folder[\"size\"]\n\ndef create_folder(subfolder_name, current_folder_dict):\n current_folder_dict[\"subfolders\"][subfolder_name] = {\n \"parent\":current_folder_dict,\n \"size\":0,\n \"subfolders\":{},\n \"files\":{},\n \"name\":subfolder_name,\n }\n # current_folder_dict[\"subfolders\"][subfolder_name][\"this\"] = current_folder_dict[\"subfolders\"][subfolder_name]\n\ninput69 = [\n\"$ cd /\",\n\"$ ls\",\n\"dir a\",\n\"$ cd a\",\n\"$ ls\",\n\"dir b\",\n\"$ cd b\",\n\"$ ls\",\n\"dir c\",\n\"dir d\",\n\"10 a.txt\",\n\"20 ba.txt\",\n\"30 ca.txt\",\n\"$ cd c\",\n\"$ ls\",\n\"30 asd.txt\",\n\"$ cd ..\",\n\"$ cd d\",\n\"$ ls\",\n\"10 absd.md\",\n\"90 pjq.m\"]\n\n'''\nroot (190)\n a (190)\n b (60 + 130 = 190)\n a.txt 10\n ba.txt 20\n ca.txt 30\n c (30)\n asd.txt 30\n d (100)\n absd.md 10\n pjq.m 90\n'''\n\ndef get_commands(command_list):\n for command in command_list:\n yield command.strip()\n\nrun_command(get_commands(input_file), folder)\n\ndef count_sizes_recursively(folder):\n #calculate recursively the sum of the folder size with the sum of all subfolders, but only if the folder or subfolder size is less or equan than 100000\n if folder[\"size\"] <= 100000:\n return folder[\"size\"] + sum(count_sizes_recursively(subfolder) for subfolder in folder[\"subfolders\"].values())\n else:\n return sum(count_sizes_recursively(subfolder) for subfolder in folder[\"subfolders\"].values())\n\nprint(count_sizes_recursively(folder))\nprint(f\"Root size = {folder['size']}\")\nprint(\"The time difference is :\", timeit.default_timer() - starttime)","repo_name":"astropingo/advent_of_code","sub_path":"challenges/07/c01/adv_2022_07_01.py","file_name":"adv_2022_07_01.py","file_ext":"py","file_size_in_byte":3862,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"30743063692","text":"from __future__ import annotations\n\nimport aiohttp\nimport asyncio\nimport enum\nimport sys\nimport typing\nfrom functools import partial\n\nfrom proxy_www.utils import get_logger, wrap_class_method\n\nlogger = get_logger('proxy_www')\n\n\n\"\"\"\nDEPRECATED OLD CODES.\nPRESERVED TO CHECK PREVIOUS IMPLEMENTATION LATER.\n\"\"\"\n\n\nclass HTTPMethod(enum.Enum):\n GET = enum.auto()\n HEAD = enum.auto()\n POST = enum.auto()\n PUT = enum.auto()\n DELETE = enum.auto()\n CONNECT = enum.auto()\n OPTIONS = enum.auto()\n TRACE = enum.auto()\n PATCH = enum.auto()\n\n\nfor http_mtd in HTTPMethod:\n setattr(sys.modules[__name__], http_mtd.name, http_mtd)\n\n\nclass ClassProxyMeta(type):\n def __new__(mcs, clsname: str, bases: tuple, attrs: dict):\n logger.debug('Creating new ClassProxy class with parameters : name={}, bases={}, attrs={}'.format(clsname, bases, attrs))\n\n # Preserve original __init__ function.\n if '__init__' in attrs:\n logger.debug(f'Found pre-defined __init__ method in class {clsname}')\n original_init = attrs['__init__']\n\n @wrap_class_method(clsname)\n def __init__(self, url: str):\n self.url = url\n self.args = []\n self.kwargs = {}\n # call original __init__\n original_init(self, url)\n else:\n @wrap_class_method(clsname)\n def __init__(self, url: str):\n self.url = url\n self.args = []\n self.kwargs = {}\n\n @wrap_class_method(clsname)\n def __getattr__(self, item):\n if item in self.__dict__:\n print(f'{item} is in self.__dict__!')\n return self.__dict__[item]\n if item.startswith('__'):\n print(super(ClassProxyMeta, self).__getattr__)\n return super(ClassProxyMeta, self).__getattr__(item)\n logger.debug('{} : getattr > item = {}'.format(self.url, item))\n self.url += '.{}'.format(item)\n return self\n\n @wrap_class_method(clsname)\n def __truediv__(self, other):\n if isinstance(other, str):\n self.url += '/{}'.format(other)\n print(self.url)\n return self\n else:\n return super().__truediv__(other)\n\n @wrap_class_method(clsname)\n def __call__(self, *args, **kwargs):\n self.args = args\n self.kwargs = kwargs\n return self\n\n @wrap_class_method(clsname)\n def __await__(self) -> aiohttp.ClientResponse:\n session = aiohttp.ClientSession()\n resp = yield from session._request(self.method, self.url, *self.args, **self.kwargs).__await__()\n yield from session.close().__await__()\n logger.debug('{} -> {} : session closed? > {}'.format(self.url, self.method, session.closed))\n return resp\n\n @wrap_class_method(clsname)\n def __repr__(self) -> str:\n return 'ClassProxy(class={}, url={}, method={})'.format(self.__class__.__name__, self.url, self.method)\n\n @wrap_class_method(clsname)\n def __getitem__(self, method: typing.Union[str, HTTPMethod]):\n if type(method) is str:\n method = method.upper()\n if method not in HTTPMethod.__members__:\n raise ValueError('HTTP Method must be one of valid HTTP methods, not {}'.format(method))\n self.method = method\n elif type(method) is HTTPMethod:\n self.method = method.name\n else:\n raise TypeError('HTTP Method must be HTTPMethod or string, not {}'.format(type(method)))\n\n return self\n\n attrs.update({\n '__await__': __await__,\n '__truediv__': __truediv__,\n '__getattr__': __getattr__,\n '__init__': __init__,\n '__repr__': __repr__,\n '__getitem__': __getitem__,\n 'method': 'GET'\n })\n\n logger.debug('Updated attrs for ClassProxy {}:'.format(clsname))\n logger.debug(attrs)\n logger.debug(super().__new__)\n cls = super(ClassProxyMeta, mcs).__new__(mcs, clsname, bases, attrs)\n return cls\n\n def __getattr__(self, item):\n logger.debug(f'{self}.__getattr__ > {item} in {self}.__dict__ == {item in self.__dict__}')\n if item in self.__dict__:\n return super().__getattr__(item)\n logger.debug('Creating {} proxy object for domain {}'.format(self.__name__, item))\n if self.__name__ == 'www':\n url = '{}://www.{}'.format('https' if self.https_default else 'http', item)\n else:\n url = '{}://{}'.format(self.__name__, item)\n instance = self(url)\n return instance\n\n def __repr__(self) -> str:\n return 'ClassProxy(class={})'.format(self.__name__)\n\n\nclass www(metaclass=ClassProxyMeta):\n url: str\n https_default: bool = False\n\n @property\n def is_secure(self) -> bool:\n return self.url.startswith('https://')\n\n def secure(self):\n if not self.is_secure:\n self.url = self.url.replace('http', 'https')\n return self\n\n def insecure(self):\n if self.is_secure:\n self.url = self.url.replace('https', 'http')\n return self\n\n def __call__(self):\n # return self.__sync_req__()\n raise NotImplementedError('Currently, sync request using ClassProxy.__call__ is WIP. Sorry for inconvenience :(')\n\n def __sync_req__(self):\n task_name: str = 'www.Future({} -> {})'.format(self.url, self.method)\n task: asyncio.Task = asyncio.create_task(self.__await__(), name=task_name)\n task.add_done_callback(partial(print, task_name))\n # result = next(future.__await__())\n # print(type(result), result)\n logger.debug('{}'.format(task_name, task))\n while True:\n if task.done():\n try:\n return task.result()\n except asyncio.CancelledError:\n return '{} has been cancelled :('.format(task_name, self.url)\n except Exception:\n raise task.exception()\n\n\nclass http(metaclass=ClassProxyMeta):\n @property\n def is_secure(self) -> bool:\n return False\n\n\nclass https(metaclass=ClassProxyMeta):\n @property\n def is_secure(self) -> bool:\n return True\n\n\nasync def test():\n async def async_test():\n print('async call')\n resp = await www.github.com\n print(resp)\n\n async def async_secure_test():\n print('secure in async call')\n req: www = www.github.com.secure()\n print('Request :', req)\n print('Is secure? :', req.is_secure)\n print(await req)\n\n async def div_test():\n http_member_req = http.www.github.com\n print(http_member_req)\n print(await http_member_req)\n div_path_req = www.github.com/'profile'\n print(div_path_req)\n print(await div_path_req)\n\n def sync_test():\n print('sync call')\n resp = www.github.com()\n print(resp)\n\n async def https_test():\n resp = await https.github.com\n print(resp)\n\n await div_test()\n\n\nif __name__ == '__main__':\n asyncio.get_event_loop().run_until_complete(test())\n","repo_name":"Lapis0875/proxy-www.py","sub_path":"proxy_www/old_implementation.py","file_name":"old_implementation.py","file_ext":"py","file_size_in_byte":7340,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"3"}
+{"seq_id":"31298212603","text":"import os\nimport time\nimport re\nimport stat\nimport errno\nimport fnmatch\n\ntry:\n import pwd, grp\nexcept ImportError:\n pwd = grp = None\n\nfrom zope.interface import Interface, implementer\n\n# Twisted Imports\nfrom twisted import copyright\nfrom twisted.internet import reactor, interfaces, protocol, error, defer\nfrom twisted.protocols import basic, policies\n\nfrom twisted.python import log, failure, filepath\nfrom twisted.python.compat import range, unicode\nfrom twisted.cred import error as cred_error, portal, credentials, checkers\n\n# constants\n# response codes\n\nRESTART_MARKER_REPLY = \"100\"\nSERVICE_READY_IN_N_MINUTES = \"120\"\nDATA_CNX_ALREADY_OPEN_START_XFR = \"125\"\nFILE_STATUS_OK_OPEN_DATA_CNX = \"150\"\n\nCMD_OK = \"200.1\"\nTYPE_SET_OK = \"200.2\"\nENTERING_PORT_MODE = \"200.3\"\nCMD_NOT_IMPLMNTD_SUPERFLUOUS = \"202\"\nSYS_STATUS_OR_HELP_REPLY = \"211.1\"\nFEAT_OK = '211.2'\nDIR_STATUS = \"212\"\nFILE_STATUS = \"213\"\nHELP_MSG = \"214\"\nNAME_SYS_TYPE = \"215\"\nSVC_READY_FOR_NEW_USER = \"220.1\"\nWELCOME_MSG = \"220.2\"\nSVC_CLOSING_CTRL_CNX = \"221.1\"\nGOODBYE_MSG = \"221.2\"\nDATA_CNX_OPEN_NO_XFR_IN_PROGRESS = \"225\"\nCLOSING_DATA_CNX = \"226.1\"\nTXFR_COMPLETE_OK = \"226.2\"\nENTERING_PASV_MODE = \"227\"\nENTERING_EPSV_MODE = \"229\"\nUSR_LOGGED_IN_PROCEED = \"230.1\" # v1 of code 230\nGUEST_LOGGED_IN_PROCEED = \"230.2\" # v2 of code 230\nREQ_FILE_ACTN_COMPLETED_OK = \"250\"\nPWD_REPLY = \"257.1\"\nMKD_REPLY = \"257.2\"\n\nUSR_NAME_OK_NEED_PASS = \"331.1\" # v1 of Code 331\nGUEST_NAME_OK_NEED_EMAIL = \"331.2\" # v2 of code 331\nNEED_ACCT_FOR_LOGIN = \"332\"\nREQ_FILE_ACTN_PENDING_FURTHER_INFO = \"350\"\n\nSVC_NOT_AVAIL_CLOSING_CTRL_CNX = \"421.1\"\nTOO_MANY_CONNECTIONS = \"421.2\"\nCANT_OPEN_DATA_CNX = \"425\"\nCNX_CLOSED_TXFR_ABORTED = \"426\"\nREQ_ACTN_ABRTD_FILE_UNAVAIL = \"450\"\nREQ_ACTN_ABRTD_LOCAL_ERR = \"451\"\nREQ_ACTN_ABRTD_INSUFF_STORAGE = \"452\"\n\nSYNTAX_ERR = \"500\"\nSYNTAX_ERR_IN_ARGS = \"501\"\nCMD_NOT_IMPLMNTD = \"502.1\"\nOPTS_NOT_IMPLEMENTED = '502.2'\nBAD_CMD_SEQ = \"503\"\nCMD_NOT_IMPLMNTD_FOR_PARAM = \"504\"\nNOT_LOGGED_IN = \"530.1\" # v1 of code 530 - please log in\nAUTH_FAILURE = \"530.2\" # v2 of code 530 - authorization failure\nNEED_ACCT_FOR_STOR = \"532\"\nFILE_NOT_FOUND = \"550.1\" # no such file or directory\nPERMISSION_DENIED = \"550.2\" # permission denied\nANON_USER_DENIED = \"550.3\" # anonymous users can't alter filesystem\nIS_NOT_A_DIR = \"550.4\" # rmd called on a path that is not a directory\nREQ_ACTN_NOT_TAKEN = \"550.5\"\nFILE_EXISTS = \"550.6\"\nIS_A_DIR = \"550.7\"\nPAGE_TYPE_UNK = \"551\"\nEXCEEDED_STORAGE_ALLOC = \"552\"\nFILENAME_NOT_ALLOWED = \"553\"\n\n\nRESPONSE = {\n # -- 100's --\n RESTART_MARKER_REPLY: '110 MARK yyyy-mmmm', # TODO: this must be fixed\n SERVICE_READY_IN_N_MINUTES: '120 service ready in %s minutes',\n DATA_CNX_ALREADY_OPEN_START_XFR: '125 Data connection already open, starting transfer',\n FILE_STATUS_OK_OPEN_DATA_CNX: '150 File status okay; about to open data connection.',\n\n # -- 200's --\n CMD_OK: '200 Command OK',\n TYPE_SET_OK: '200 Type set to %s.',\n ENTERING_PORT_MODE: '200 PORT OK',\n CMD_NOT_IMPLMNTD_SUPERFLUOUS: '202 Command not implemented, superfluous at this site',\n SYS_STATUS_OR_HELP_REPLY: '211 System status reply',\n FEAT_OK: ['211-Features:','211 End'],\n DIR_STATUS: '212 %s',\n FILE_STATUS: '213 %s',\n HELP_MSG: '214 help: %s',\n NAME_SYS_TYPE: '215 UNIX Type: L8',\n WELCOME_MSG: \"220 %s\",\n SVC_READY_FOR_NEW_USER: '220 Service ready',\n SVC_CLOSING_CTRL_CNX: '221 Service closing control connection',\n GOODBYE_MSG: '221 Goodbye.',\n DATA_CNX_OPEN_NO_XFR_IN_PROGRESS: '225 data connection open, no transfer in progress',\n CLOSING_DATA_CNX: '226 Abort successful',\n TXFR_COMPLETE_OK: '226 Transfer Complete.',\n ENTERING_PASV_MODE: '227 Entering Passive Mode (%s).',\n ENTERING_EPSV_MODE: '229 Entering Extended Passive Mode (|||%s|).', # where is epsv defined in the rfc's?\n USR_LOGGED_IN_PROCEED: '230 User logged in, proceed',\n GUEST_LOGGED_IN_PROCEED: '230 Anonymous login ok, access restrictions apply.',\n REQ_FILE_ACTN_COMPLETED_OK: '250 Requested File Action Completed OK', #i.e. CWD completed ok\n PWD_REPLY: '257 \"%s\"',\n MKD_REPLY: '257 \"%s\" created',\n\n # -- 300's --\n USR_NAME_OK_NEED_PASS: '331 Password required for %s.',\n GUEST_NAME_OK_NEED_EMAIL: '331 Guest login ok, type your email address as password.',\n NEED_ACCT_FOR_LOGIN: '332 Need account for login.',\n\n REQ_FILE_ACTN_PENDING_FURTHER_INFO: '350 Requested file action pending further information.',\n\n# -- 400's --\n SVC_NOT_AVAIL_CLOSING_CTRL_CNX: '421 Service not available, closing control connection.',\n TOO_MANY_CONNECTIONS: '421 Too many users right now, try again in a few minutes.',\n CANT_OPEN_DATA_CNX: \"425 Can't open data connection.\",\n CNX_CLOSED_TXFR_ABORTED: '426 Transfer aborted. Data connection closed.',\n\n REQ_ACTN_ABRTD_FILE_UNAVAIL: '450 Requested action aborted. File unavailable.',\n REQ_ACTN_ABRTD_LOCAL_ERR: '451 Requested action aborted. Local error in processing.',\n REQ_ACTN_ABRTD_INSUFF_STORAGE: '452 Requested action aborted. Insufficient storage.',\n\n # -- 500's --\n SYNTAX_ERR: \"500 Syntax error: %s\",\n SYNTAX_ERR_IN_ARGS: '501 syntax error in argument(s) %s.',\n CMD_NOT_IMPLMNTD: \"502 Command '%s' not implemented\",\n OPTS_NOT_IMPLEMENTED: \"502 Option '%s' not implemented.\",\n BAD_CMD_SEQ: '503 Incorrect sequence of commands: %s',\n CMD_NOT_IMPLMNTD_FOR_PARAM: \"504 Not implemented for parameter '%s'.\",\n NOT_LOGGED_IN: '530 Please login with USER and PASS.',\n AUTH_FAILURE: '530 Sorry, Authentication failed.',\n NEED_ACCT_FOR_STOR: '532 Need an account for storing files',\n FILE_NOT_FOUND: '550 %s: No such file or directory.',\n PERMISSION_DENIED: '550 %s: Permission denied.',\n ANON_USER_DENIED: '550 Anonymous users are forbidden to change the filesystem',\n IS_NOT_A_DIR: '550 Cannot rmd, %s is not a directory',\n FILE_EXISTS: '550 %s: File exists',\n IS_A_DIR: '550 %s: is a directory',\n REQ_ACTN_NOT_TAKEN: '550 Requested action not taken: %s',\n PAGE_TYPE_UNK: '551 Page type unknown',\n EXCEEDED_STORAGE_ALLOC: '552 Requested file action aborted, exceeded file storage allocation',\n FILENAME_NOT_ALLOWED: '553 Requested action not taken, file name not allowed'\n}\n\n\n\nclass InvalidPath(Exception):\n \"\"\"\n Internal exception used to signify an error during parsing a path.\n \"\"\"\n\n\n\ndef toSegments(cwd, path):\n \"\"\"\n Normalize a path, as represented by a list of strings each\n representing one segment of the path.\n \"\"\"\n if path.startswith('/'):\n segs = []\n else:\n segs = cwd[:]\n\n for s in path.split('/'):\n if s == '.' or s == '':\n continue\n elif s == '..':\n if segs:\n segs.pop()\n else:\n raise InvalidPath(cwd, path)\n elif '\\0' in s or '/' in s:\n raise InvalidPath(cwd, path)\n else:\n segs.append(s)\n return segs\n\n\ndef errnoToFailure(e, path):\n \"\"\"\n Map C{OSError} and C{IOError} to standard FTP errors.\n \"\"\"\n if e == errno.ENOENT:\n return defer.fail(FileNotFoundError(path))\n elif e == errno.EACCES or e == errno.EPERM:\n return defer.fail(PermissionDeniedError(path))\n elif e == errno.ENOTDIR:\n return defer.fail(IsNotADirectoryError(path))\n elif e == errno.EEXIST:\n return defer.fail(FileExistsError(path))\n elif e == errno.EISDIR:\n return defer.fail(IsADirectoryError(path))\n else:\n return defer.fail()\n\n\n_testTranslation = fnmatch.translate('TEST')\n\n\ndef _isGlobbingExpression(segments=None):\n \"\"\"\n Helper for checking if a FTPShell `segments` contains a wildcard Unix\n expression.\n\n Only filename globbing is supported.\n This means that wildcards can only be presents in the last element of\n `segments`.\n\n @type segments: C{list}\n @param segments: List of path elements as used by the FTP server protocol.\n\n @rtype: Boolean\n @return: True if `segments` contains a globbing expression.\n \"\"\"\n if not segments:\n return False\n\n # To check that something is a glob expression, we convert it to\n # Regular Expression.\n # We compare it to the translation of a known non-glob expression.\n # If the result is the same as the original expression then it contains no\n # globbing expression.\n globCandidate = segments[-1]\n globTranslations = fnmatch.translate(globCandidate)\n nonGlobTranslations = _testTranslation.replace('TEST', globCandidate, 1)\n\n if nonGlobTranslations == globTranslations:\n return False\n else:\n return True\n\n\nclass FTPCmdError(Exception):\n \"\"\"\n Generic exception for FTP commands.\n \"\"\"\n def __init__(self, *msg):\n Exception.__init__(self, *msg)\n self.errorMessage = msg\n\n\n def response(self):\n \"\"\"\n Generate a FTP response message for this error.\n \"\"\"\n return RESPONSE[self.errorCode] % self.errorMessage\n\n\n\nclass FileNotFoundError(FTPCmdError):\n \"\"\"\n Raised when trying to access a non existent file or directory.\n \"\"\"\n errorCode = FILE_NOT_FOUND\n\n\n\nclass AnonUserDeniedError(FTPCmdError):\n \"\"\"\n Raised when an anonymous user issues a command that will alter the\n filesystem\n \"\"\"\n\n errorCode = ANON_USER_DENIED\n\n\n\nclass PermissionDeniedError(FTPCmdError):\n \"\"\"\n Raised when access is attempted to a resource to which access is\n not allowed.\n \"\"\"\n errorCode = PERMISSION_DENIED\n\n\n\nclass IsNotADirectoryError(FTPCmdError):\n \"\"\"\n Raised when RMD is called on a path that isn't a directory.\n \"\"\"\n errorCode = IS_NOT_A_DIR\n\n\n\nclass FileExistsError(FTPCmdError):\n \"\"\"\n Raised when attempted to override an existing resource.\n \"\"\"\n errorCode = FILE_EXISTS\n\n\n\nclass IsADirectoryError(FTPCmdError):\n \"\"\"\n Raised when DELE is called on a path that is a directory.\n \"\"\"\n errorCode = IS_A_DIR\n\n\n\nclass CmdSyntaxError(FTPCmdError):\n \"\"\"\n Raised when a command syntax is wrong.\n \"\"\"\n errorCode = SYNTAX_ERR\n\n\n\nclass CmdArgSyntaxError(FTPCmdError):\n \"\"\"\n Raised when a command is called with wrong value or a wrong number of\n arguments.\n \"\"\"\n errorCode = SYNTAX_ERR_IN_ARGS\n\n\n\nclass CmdNotImplementedError(FTPCmdError):\n \"\"\"\n Raised when an unimplemented command is given to the server.\n \"\"\"\n errorCode = CMD_NOT_IMPLMNTD\n\n\n\nclass CmdNotImplementedForArgError(FTPCmdError):\n \"\"\"\n Raised when the handling of a parameter for a command is not implemented by\n the server.\n \"\"\"\n errorCode = CMD_NOT_IMPLMNTD_FOR_PARAM\n\n\n\nclass FTPError(Exception):\n pass\n\n\n\nclass PortConnectionError(Exception):\n pass\n\n\n\nclass BadCmdSequenceError(FTPCmdError):\n \"\"\"\n Raised when a client sends a series of commands in an illogical sequence.\n \"\"\"\n errorCode = BAD_CMD_SEQ\n\n\n\nclass AuthorizationError(FTPCmdError):\n \"\"\"\n Raised when client authentication fails.\n \"\"\"\n errorCode = AUTH_FAILURE\n\n\n\ndef debugDeferred(self, *_):\n log.msg('debugDeferred(): %s' % str(_), debug=True)\n\n\n# -- DTP Protocol --\n\n\n_months = [\n None,\n 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n\n\n@implementer(interfaces.IConsumer)\nclass DTP(protocol.Protocol, object):\n isConnected = False\n\n _cons = None\n _onConnLost = None\n _buffer = None\n _encoding = 'latin-1'\n\n def connectionMade(self):\n self.isConnected = True\n self.factory.deferred.callback(None)\n self._buffer = []\n\n def connectionLost(self, reason):\n self.isConnected = False\n if self._onConnLost is not None:\n self._onConnLost.callback(None)\n\n def sendLine(self, line):\n \"\"\"\n Send a line to data channel.\n\n @param line: The line to be sent.\n @type line: L{bytes}\n \"\"\"\n self.transport.write(line + b'\\r\\n')\n\n\n def _formatOneListResponse(self, name, size, directory, permissions, hardlinks, modified, owner, group):\n \"\"\"\n Helper method to format one entry's info into a text entry like:\n 'drwxrwxrwx 0 user group 0 Jan 01 1970 filename.txt'\n\n @param name: C{bytes} name of the entry (file or directory or link)\n @param size: C{int} size of the entry\n @param directory: evals to C{bool} - whether the entry is a directory\n @param permissions: L{twisted.python.filepath.Permissions} object\n representing that entry's permissions\n @param hardlinks: C{int} number of hardlinks\n @param modified: C{float} - entry's last modified time in seconds\n since the epoch\n @param owner: C{str} username of the owner\n @param group: C{str} group name of the owner\n\n @return: C{str} in the requisite format\n \"\"\"\n def formatDate(mtime):\n now = time.gmtime()\n info = {\n 'month': _months[mtime.tm_mon],\n 'day': mtime.tm_mday,\n 'year': mtime.tm_year,\n 'hour': mtime.tm_hour,\n 'minute': mtime.tm_min\n }\n if now.tm_year != mtime.tm_year:\n return '%(month)s %(day)02d %(year)5d' % info\n else:\n return '%(month)s %(day)02d %(hour)02d:%(minute)02d' % info\n\n format = ('%(directory)s%(permissions)s%(hardlinks)4d '\n '%(owner)-9s %(group)-9s %(size)15d %(date)12s '\n )\n\n msg = (format % {\n 'directory': directory and 'd' or '-',\n 'permissions': permissions.shorthand(),\n 'hardlinks': hardlinks,\n 'owner': owner[:8],\n 'group': group[:8],\n 'size': size,\n 'date': formatDate(time.gmtime(modified)),\n }).encode(self._encoding)\n return msg + name\n\n\n def sendListResponse(self, name, response):\n self.sendLine(self._formatOneListResponse(name, *response))\n\n # Proxy IConsumer to our transport\n def registerProducer(self, producer, streaming):\n return self.transport.registerProducer(producer, streaming)\n\n def unregisterProducer(self):\n self.transport.unregisterProducer()\n self.transport.loseConnection()\n\n def write(self, data):\n if self.isConnected:\n return self.transport.write(data)\n raise Exception(\"Crap damn crap damn crap damn\")\n\n\n # Pretend to be a producer, too.\n def _conswrite(self, bytes):\n try:\n self._cons.write(bytes)\n except:\n self._onConnLost.errback()\n\n def dataReceived(self, bytes):\n if self._cons is not None:\n self._conswrite(bytes)\n else:\n self._buffer.append(bytes)\n\n def _unregConsumer(self, ignored):\n self._cons.unregisterProducer()\n self._cons = None\n del self._onConnLost\n return ignored\n\n def registerConsumer(self, cons):\n assert self._cons is None\n self._cons = cons\n self._cons.registerProducer(self, True)\n for chunk in self._buffer:\n self._conswrite(chunk)\n self._buffer = None\n if self.isConnected:\n self._onConnLost = d = defer.Deferred()\n d.addBoth(self._unregConsumer)\n return d\n else:\n self._cons.unregisterProducer()\n self._cons = None\n return defer.succeed(None)\n\n def resumeProducing(self):\n self.transport.resumeProducing()\n\n def pauseProducing(self):\n self.transport.pauseProducing()\n\n def stopProducing(self):\n self.transport.stopProducing()\n\nclass DTPFactory(protocol.ClientFactory):\n \"\"\"\n Client factory for I{data transfer process} protocols.\n\n @ivar peerCheck: perform checks to make sure the ftp-pi's peer is the same\n as the dtp's\n @ivar pi: a reference to this factory's protocol interpreter\n\n @ivar _state: Indicates the current state of the DTPFactory. Initially,\n this is L{_IN_PROGRESS}. If the connection fails or times out, it is\n L{_FAILED}. If the connection succeeds before the timeout, it is\n L{_FINISHED}.\n\n @cvar _IN_PROGRESS: Token to signal that connection is active.\n @type _IN_PROGRESS: L{object}.\n\n @cvar _FAILED: Token to signal that connection has failed.\n @type _FAILED: L{object}.\n\n @cvar _FINISHED: Token to signal that connection was successfully closed.\n @type _FINISHED: L{object}.\n \"\"\"\n\n _IN_PROGRESS = object()\n _FAILED = object()\n _FINISHED = object()\n\n _state = _IN_PROGRESS\n\n # -- configuration variables --\n peerCheck = False\n\n # -- class variables --\n def __init__(self, pi, peerHost=None, reactor=None):\n \"\"\"\n Constructor\n\n @param pi: this factory's protocol interpreter\n @param peerHost: if peerCheck is True, this is the tuple that the\n generated instance will use to perform security checks\n \"\"\"\n self.pi = pi # the protocol interpreter that is using this factory\n self.peerHost = peerHost # the from FTP.transport.peerHost()\n self.deferred = defer.Deferred() # deferred will fire when instance is connected\n self.delayedCall = None\n if reactor is None:\n from twisted.internet import reactor\n self._reactor = reactor\n\n\n def buildProtocol(self, addr):\n log.msg('DTPFactory.buildProtocol', debug=True)\n\n if self._state is not self._IN_PROGRESS:\n return None\n self._state = self._FINISHED\n\n self.cancelTimeout()\n p = DTP()\n p.factory = self\n p.pi = self.pi\n self.pi.dtpInstance = p\n return p\n\n\n def stopFactory(self):\n log.msg('dtpFactory.stopFactory', debug=True)\n self.cancelTimeout()\n\n\n def timeoutFactory(self):\n log.msg('timed out waiting for DTP connection')\n if self._state is not self._IN_PROGRESS:\n return\n self._state = self._FAILED\n\n d = self.deferred\n self.deferred = None\n d.errback(\n PortConnectionError(defer.TimeoutError(\"DTPFactory timeout\")))\n\n\n def cancelTimeout(self):\n if self.delayedCall is not None and self.delayedCall.active():\n log.msg('cancelling DTP timeout', debug=True)\n self.delayedCall.cancel()\n\n\n def setTimeout(self, seconds):\n log.msg('DTPFactory.setTimeout set to %s seconds' % seconds)\n self.delayedCall = self._reactor.callLater(seconds, self.timeoutFactory)\n\n\n def clientConnectionFailed(self, connector, reason):\n if self._state is not self._IN_PROGRESS:\n return\n self._state = self._FAILED\n d = self.deferred\n self.deferred = None\n d.errback(PortConnectionError(reason))\n\n\n# -- FTP-PI (Protocol Interpreter) --\n\nclass ASCIIConsumerWrapper(object):\n def __init__(self, cons):\n self.cons = cons\n self.registerProducer = cons.registerProducer\n self.unregisterProducer = cons.unregisterProducer\n\n assert os.linesep == \"\\r\\n\" or len(os.linesep) == 1, \"Unsupported platform (yea right like this even exists)\"\n\n if os.linesep == \"\\r\\n\":\n self.write = cons.write\n\n def write(self, bytes):\n return self.cons.write(bytes.replace(os.linesep, \"\\r\\n\"))\n\n\n\n@implementer(interfaces.IConsumer)\nclass FileConsumer(object):\n \"\"\"\n A consumer for FTP input that writes data to a file.\n\n @ivar fObj: a file object opened for writing, used to write data received.\n @type fObj: C{file}\n \"\"\"\n def __init__(self, fObj):\n self.fObj = fObj\n\n\n def registerProducer(self, producer, streaming):\n self.producer = producer\n assert streaming\n\n\n def unregisterProducer(self):\n self.producer = None\n self.fObj.close()\n\n\n def write(self, bytes):\n self.fObj.write(bytes)\n\n\n\nclass FTPOverflowProtocol(basic.LineReceiver):\n \"\"\"FTP mini-protocol for when there are too many connections.\"\"\"\n _encoding = 'latin-1'\n\n def connectionMade(self):\n self.sendLine(RESPONSE[TOO_MANY_CONNECTIONS].encode(self._encoding))\n self.transport.loseConnection()\n\n\nclass FTP(basic.LineReceiver, policies.TimeoutMixin, object):\n \"\"\"\n Protocol Interpreter for the File Transfer Protocol\n\n @ivar state: The current server state. One of L{UNAUTH},\n L{INAUTH}, L{AUTHED}, L{RENAMING}.\n\n @ivar shell: The connected avatar\n @ivar binary: The transfer mode. If false, ASCII.\n @ivar dtpFactory: Generates a single DTP for this session\n @ivar dtpPort: Port returned from listenTCP\n @ivar listenFactory: A callable with the signature of\n L{twisted.internet.interfaces.IReactorTCP.listenTCP} which will be used\n to create Ports for passive connections (mainly for testing).\n\n @ivar passivePortRange: iterator used as source of passive port numbers.\n @type passivePortRange: C{iterator}\n\n @cvar UNAUTH: Command channel is not yet authenticated.\n @type UNAUTH: L{int}\n\n @cvar INAUTH: Command channel is in the process of being authenticated.\n @type INAUTH: L{int}\n\n @cvar AUTHED: Command channel was successfully authenticated.\n @type AUTHED: L{int}\n\n @cvar RENAMING: Command channel is between the renaming command sequence.\n @type RENAMING: L{int}\n \"\"\"\n\n disconnected = False\n\n # States an FTP can be in\n UNAUTH, INAUTH, AUTHED, RENAMING = range(4)\n\n # how long the DTP waits for a connection\n dtpTimeout = 10\n\n portal = None\n shell = None\n dtpFactory = None\n dtpPort = None\n dtpInstance = None\n binary = True\n PUBLIC_COMMANDS = ['FEAT', 'QUIT']\n FEATURES = ['FEAT', 'MDTM', 'PASV', 'SIZE', 'TYPE A;I']\n\n passivePortRange = range(0, 1)\n\n listenFactory = reactor.listenTCP\n _encoding = 'latin-1'\n\n def reply(self, key, *args):\n msg = RESPONSE[key] % args\n self.sendLine(msg)\n\n\n def sendLine(self, line):\n \"\"\"\n (Private) Encodes and sends a line\n\n @param line: L{bytes} or L{unicode}\n \"\"\"\n if isinstance(line, unicode):\n line = line.encode(self._encoding)\n super(FTP, self).sendLine(line)\n\n\n def connectionMade(self):\n self.state = self.UNAUTH\n self.setTimeout(self.timeOut)\n self.reply(WELCOME_MSG, self.factory.welcomeMessage)\n\n def connectionLost(self, reason):\n # if we have a DTP protocol instance running and\n # we lose connection to the client's PI, kill the\n # DTP connection and close the port\n if self.dtpFactory:\n self.cleanupDTP()\n self.setTimeout(None)\n if hasattr(self.shell, 'logout') and self.shell.logout is not None:\n self.shell.logout()\n self.shell = None\n self.transport = None\n\n def timeoutConnection(self):\n self.transport.loseConnection()\n\n def lineReceived(self, line):\n self.resetTimeout()\n self.pauseProducing()\n if bytes != str:\n line = line.decode(self._encoding)\n\n def processFailed(err):\n if err.check(FTPCmdError):\n self.sendLine(err.value.response())\n elif (err.check(TypeError) and any((\n msg in err.value.args[0] for msg in (\n 'takes exactly', 'required positional argument')))):\n self.reply(SYNTAX_ERR, \"%s requires an argument.\" % (cmd,))\n else:\n log.msg(\"Unexpected FTP error\")\n log.err(err)\n self.reply(REQ_ACTN_NOT_TAKEN, \"internal server error\")\n\n def processSucceeded(result):\n if isinstance(result, tuple):\n self.reply(*result)\n elif result is not None:\n self.reply(result)\n\n def allDone(ignored):\n if not self.disconnected:\n self.resumeProducing()\n\n spaceIndex = line.find(' ')\n if spaceIndex != -1:\n cmd = line[:spaceIndex]\n args = (line[spaceIndex + 1:],)\n else:\n cmd = line\n args = ()\n d = defer.maybeDeferred(self.processCommand, cmd, *args)\n d.addCallbacks(processSucceeded, processFailed)\n d.addErrback(log.err)\n\n # XXX It burnsss\n # LineReceiver doesn't let you resumeProducing inside\n # lineReceived atm\n from twisted.internet import reactor\n reactor.callLater(0, d.addBoth, allDone)\n\n\n def processCommand(self, cmd, *params):\n\n def call_ftp_command(command):\n method = getattr(self, \"ftp_\" + command, None)\n if method is not None:\n return method(*params)\n return defer.fail(CmdNotImplementedError(command))\n\n cmd = cmd.upper()\n\n if cmd in self.PUBLIC_COMMANDS:\n return call_ftp_command(cmd)\n\n elif self.state == self.UNAUTH:\n if cmd == 'USER':\n return self.ftp_USER(*params)\n elif cmd == 'PASS':\n return BAD_CMD_SEQ, \"USER required before PASS\"\n else:\n return NOT_LOGGED_IN\n\n elif self.state == self.INAUTH:\n if cmd == 'PASS':\n return self.ftp_PASS(*params)\n else:\n return BAD_CMD_SEQ, \"PASS required after USER\"\n\n elif self.state == self.AUTHED:\n return call_ftp_command(cmd)\n\n elif self.state == self.RENAMING:\n if cmd == 'RNTO':\n return self.ftp_RNTO(*params)\n else:\n return BAD_CMD_SEQ, \"RNTO required after RNFR\"\n\n\n def getDTPPort(self, factory):\n \"\"\"\n Return a port for passive access, using C{self.passivePortRange}\n attribute.\n \"\"\"\n for portn in self.passivePortRange:\n try:\n dtpPort = self.listenFactory(portn, factory)\n except error.CannotListenError:\n continue\n else:\n return dtpPort\n raise error.CannotListenError('', portn,\n \"No port available in range %s\" %\n (self.passivePortRange,))\n\n\n def ftp_USER(self, username):\n \"\"\"\n First part of login. Get the username the peer wants to\n authenticate as.\n \"\"\"\n if not username:\n return defer.fail(CmdSyntaxError('USER requires an argument'))\n\n self._user = username\n self.state = self.INAUTH\n if self.factory.allowAnonymous and self._user == self.factory.userAnonymous:\n return GUEST_NAME_OK_NEED_EMAIL\n else:\n return (USR_NAME_OK_NEED_PASS, username)\n\n # TODO: add max auth try before timeout from ip...\n # TODO: need to implement minimal ABOR command\n\n def ftp_PASS(self, password):\n \"\"\"\n Second part of login. Get the password the peer wants to\n authenticate with.\n \"\"\"\n if self.factory.allowAnonymous and self._user == self.factory.userAnonymous:\n # anonymous login\n creds = credentials.Anonymous()\n reply = GUEST_LOGGED_IN_PROCEED\n else:\n # user login\n creds = credentials.UsernamePassword(self._user, password)\n reply = USR_LOGGED_IN_PROCEED\n del self._user\n\n def _cbLogin(result):\n (interface, avatar, logout) = result\n assert interface is IFTPShell, \"The realm is busted, jerk.\"\n self.shell = avatar\n self.logout = logout\n self.workingDirectory = []\n self.state = self.AUTHED\n return reply\n\n def _ebLogin(failure):\n failure.trap(cred_error.UnauthorizedLogin, cred_error.UnhandledCredentials)\n self.state = self.UNAUTH\n raise AuthorizationError\n\n d = self.portal.login(creds, None, IFTPShell)\n d.addCallbacks(_cbLogin, _ebLogin)\n return d\n\n\n def ftp_PASV(self):\n \"\"\"\n Request for a passive connection\n\n from the rfc::\n\n This command requests the server-DTP to \\\"listen\\\" on a data port\n (which is not its default data port) and to wait for a connection\n rather than initiate one upon receipt of a transfer command. The\n response to this command includes the host and port address this\n server is listening on.\n \"\"\"\n # if we have a DTP port set up, lose it.\n if self.dtpFactory is not None:\n # cleanupDTP sets dtpFactory to none. Later we'll do\n # cleanup here or something.\n self.cleanupDTP()\n self.dtpFactory = DTPFactory(pi=self)\n self.dtpFactory.setTimeout(self.dtpTimeout)\n self.dtpPort = self.getDTPPort(self.dtpFactory)\n\n host = self.transport.getHost().host\n port = self.dtpPort.getHost().port\n self.reply(ENTERING_PASV_MODE, encodeHostPort(host, port))\n return self.dtpFactory.deferred.addCallback(lambda ign: None)\n\n\n def ftp_PORT(self, address):\n addr = tuple(map(int, address.split(',')))\n ip = '%d.%d.%d.%d' % tuple(addr[:4])\n port = addr[4] << 8 | addr[5]\n\n # if we have a DTP port set up, lose it.\n if self.dtpFactory is not None:\n self.cleanupDTP()\n\n self.dtpFactory = DTPFactory(pi=self, peerHost=self.transport.getPeer().host)\n self.dtpFactory.setTimeout(self.dtpTimeout)\n self.dtpPort = reactor.connectTCP(ip, port, self.dtpFactory)\n\n def connected(ignored):\n return ENTERING_PORT_MODE\n def connFailed(err):\n err.trap(PortConnectionError)\n return CANT_OPEN_DATA_CNX\n return self.dtpFactory.deferred.addCallbacks(connected, connFailed)\n\n\n def _encodeName(self, name):\n \"\"\"\n Encode C{name} to be sent over the wire.\n\n This encodes L{unicode} objects as UTF-8 and leaves L{bytes} as-is.\n\n As described by U{RFC 3659 section\n 2.2}::\n\n Various FTP commands take pathnames as arguments, or return\n pathnames in responses. When the MLST command is supported, as\n indicated in the response to the FEAT command, pathnames are to be\n transferred in one of the following two formats.\n\n pathname = utf-8-name / raw\n utf-8-name = \n raw = \n\n Which format is used is at the option of the user-PI or server-PI\n sending the pathname.\n\n @param name: Name to be encoded.\n @type name: L{bytes} or L{unicode}\n\n @return: Wire format of C{name}.\n @rtype: L{bytes}\n \"\"\"\n if isinstance(name, unicode):\n return name.encode('utf-8')\n return name\n\n\n def ftp_LIST(self, path=''):\n \"\"\" This command causes a list to be sent from the server to the\n passive DTP. If the pathname specifies a directory or other\n group of files, the server should transfer a list of files\n in the specified directory. If the pathname specifies a\n file then the server should send current information on the\n file. A null argument implies the user's current working or\n default directory.\n \"\"\"\n # Uh, for now, do this retarded thing.\n if self.dtpInstance is None or not self.dtpInstance.isConnected:\n return defer.fail(BadCmdSequenceError('must send PORT or PASV before RETR'))\n\n # Various clients send flags like -L or -al etc. We just ignore them.\n if path.lower() in ['-a', '-l', '-la', '-al']:\n path = ''\n\n def gotListing(results):\n self.reply(DATA_CNX_ALREADY_OPEN_START_XFR)\n for (name, attrs) in results:\n name = self._encodeName(name)\n self.dtpInstance.sendListResponse(name, attrs)\n self.dtpInstance.transport.loseConnection()\n return (TXFR_COMPLETE_OK,)\n\n try:\n segments = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n\n d = self.shell.list(\n segments,\n ('size', 'directory', 'permissions', 'hardlinks',\n 'modified', 'owner', 'group'))\n d.addCallback(gotListing)\n return d\n\n\n def ftp_NLST(self, path):\n \"\"\"\n This command causes a directory listing to be sent from the server to\n the client. The pathname should specify a directory or other\n system-specific file group descriptor. An empty path implies the current\n working directory. If the path is non-existent, send nothing. If the\n path is to a file, send only the file name.\n\n @type path: C{str}\n @param path: The path for which a directory listing should be returned.\n\n @rtype: L{Deferred}\n @return: a L{Deferred} which will be fired when the listing request\n is finished.\n \"\"\"\n # XXX: why is this check different from ftp_RETR/ftp_STOR? See #4180\n if self.dtpInstance is None or not self.dtpInstance.isConnected:\n return defer.fail(\n BadCmdSequenceError('must send PORT or PASV before RETR'))\n\n try:\n segments = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n\n def cbList(results, glob):\n \"\"\"\n Send, line by line, each matching file in the directory listing, and\n then close the connection.\n\n @type results: A C{list} of C{tuple}. The first element of each\n C{tuple} is a C{str} and the second element is a C{list}.\n @param results: The names of the files in the directory.\n\n @param glob: A shell-style glob through which to filter results (see\n U{http://docs.python.org/2/library/fnmatch.html}), or L{None}\n for no filtering.\n @type glob: L{str} or L{None}\n\n @return: A C{tuple} containing the status code for a successful\n transfer.\n @rtype: C{tuple}\n \"\"\"\n self.reply(DATA_CNX_ALREADY_OPEN_START_XFR)\n for (name, ignored) in results:\n if not glob or (glob and fnmatch.fnmatch(name, glob)):\n name = self._encodeName(name)\n self.dtpInstance.sendLine(name)\n self.dtpInstance.transport.loseConnection()\n return (TXFR_COMPLETE_OK,)\n\n def listErr(results):\n \"\"\"\n RFC 959 specifies that an NLST request may only return directory\n listings. Thus, send nothing and just close the connection.\n\n @type results: L{Failure}\n @param results: The L{Failure} wrapping a L{FileNotFoundError} that\n occurred while trying to list the contents of a nonexistent\n directory.\n\n @returns: A C{tuple} containing the status code for a successful\n transfer.\n @rtype: C{tuple}\n \"\"\"\n self.dtpInstance.transport.loseConnection()\n return (TXFR_COMPLETE_OK,)\n\n if _isGlobbingExpression(segments):\n # Remove globbing expression from path\n # and keep to be used for filtering.\n glob = segments.pop()\n else:\n glob = None\n\n d = self.shell.list(segments)\n d.addCallback(cbList, glob)\n # self.shell.list will generate an error if the path is invalid\n d.addErrback(listErr)\n return d\n\n\n def ftp_CWD(self, path):\n try:\n segments = toSegments(self.workingDirectory, path)\n except InvalidPath:\n # XXX Eh, what to fail with here?\n return defer.fail(FileNotFoundError(path))\n\n def accessGranted(result):\n self.workingDirectory = segments\n return (REQ_FILE_ACTN_COMPLETED_OK,)\n\n return self.shell.access(segments).addCallback(accessGranted)\n\n\n def ftp_CDUP(self):\n return self.ftp_CWD('..')\n\n\n def ftp_PWD(self):\n return (PWD_REPLY, '/' + '/'.join(self.workingDirectory))\n\n\n def ftp_RETR(self, path):\n \"\"\"\n This command causes the content of a file to be sent over the data\n transfer channel. If the path is to a folder, an error will be raised.\n\n @type path: C{str}\n @param path: The path to the file which should be transferred over the\n data transfer channel.\n\n @rtype: L{Deferred}\n @return: a L{Deferred} which will be fired when the transfer is done.\n \"\"\"\n if self.dtpInstance is None:\n raise BadCmdSequenceError('PORT or PASV required before RETR')\n\n try:\n newsegs = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n\n # XXX For now, just disable the timeout. Later we'll want to\n # leave it active and have the DTP connection reset it\n # periodically.\n self.setTimeout(None)\n\n # Put it back later\n def enableTimeout(result):\n self.setTimeout(self.factory.timeOut)\n return result\n\n # And away she goes\n if not self.binary:\n cons = ASCIIConsumerWrapper(self.dtpInstance)\n else:\n cons = self.dtpInstance\n\n def cbSent(result):\n return (TXFR_COMPLETE_OK,)\n\n def ebSent(err):\n log.msg(\"Unexpected error attempting to transmit file to client:\")\n log.err(err)\n if err.check(FTPCmdError):\n return err\n return (CNX_CLOSED_TXFR_ABORTED,)\n\n def cbOpened(file):\n # Tell them what to doooo\n if self.dtpInstance.isConnected:\n self.reply(DATA_CNX_ALREADY_OPEN_START_XFR)\n else:\n self.reply(FILE_STATUS_OK_OPEN_DATA_CNX)\n\n d = file.send(cons)\n d.addCallbacks(cbSent, ebSent)\n return d\n\n def ebOpened(err):\n if not err.check(PermissionDeniedError, FileNotFoundError, IsADirectoryError):\n log.msg(\"Unexpected error attempting to open file for transmission:\")\n log.err(err)\n if err.check(FTPCmdError):\n return (err.value.errorCode, '/'.join(newsegs))\n return (FILE_NOT_FOUND, '/'.join(newsegs))\n\n d = self.shell.openForReading(newsegs)\n d.addCallbacks(cbOpened, ebOpened)\n d.addBoth(enableTimeout)\n\n # Pass back Deferred that fires when the transfer is done\n return d\n\n\n def ftp_STOR(self, path):\n \"\"\"\n STORE (STOR)\n\n This command causes the server-DTP to accept the data\n transferred via the data connection and to store the data as\n a file at the server site. If the file specified in the\n pathname exists at the server site, then its contents shall\n be replaced by the data being transferred. A new file is\n created at the server site if the file specified in the\n pathname does not already exist.\n \"\"\"\n if self.dtpInstance is None:\n raise BadCmdSequenceError('PORT or PASV required before STOR')\n\n try:\n newsegs = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n\n # XXX For now, just disable the timeout. Later we'll want to\n # leave it active and have the DTP connection reset it\n # periodically.\n self.setTimeout(None)\n\n # Put it back later\n def enableTimeout(result):\n self.setTimeout(self.factory.timeOut)\n return result\n\n def cbOpened(file):\n \"\"\"\n File was open for reading. Launch the data transfer channel via\n the file consumer.\n \"\"\"\n d = file.receive()\n d.addCallback(cbConsumer)\n d.addCallback(lambda ignored: file.close())\n d.addCallbacks(cbSent, ebSent)\n return d\n\n def ebOpened(err):\n \"\"\"\n Called when failed to open the file for reading.\n\n For known errors, return the FTP error code.\n For all other, return a file not found error.\n \"\"\"\n if isinstance(err.value, FTPCmdError):\n return (err.value.errorCode, '/'.join(newsegs))\n log.err(err, \"Unexpected error received while opening file:\")\n return (FILE_NOT_FOUND, '/'.join(newsegs))\n\n def cbConsumer(cons):\n \"\"\"\n Called after the file was opended for reading.\n\n Prepare the data transfer channel and send the response\n to the command channel.\n \"\"\"\n if not self.binary:\n cons = ASCIIConsumerWrapper(cons)\n\n d = self.dtpInstance.registerConsumer(cons)\n\n # Tell them what to doooo\n if self.dtpInstance.isConnected:\n self.reply(DATA_CNX_ALREADY_OPEN_START_XFR)\n else:\n self.reply(FILE_STATUS_OK_OPEN_DATA_CNX)\n\n return d\n\n def cbSent(result):\n \"\"\"\n Called from data transport when tranfer is done.\n \"\"\"\n return (TXFR_COMPLETE_OK,)\n\n def ebSent(err):\n \"\"\"\n Called from data transport when there are errors during the\n transfer.\n \"\"\"\n log.err(err, \"Unexpected error received during transfer:\")\n if err.check(FTPCmdError):\n return err\n return (CNX_CLOSED_TXFR_ABORTED,)\n\n d = self.shell.openForWriting(newsegs)\n d.addCallbacks(cbOpened, ebOpened)\n d.addBoth(enableTimeout)\n\n # Pass back Deferred that fires when the transfer is done\n return d\n\n\n def ftp_SIZE(self, path):\n \"\"\"\n File SIZE\n\n The FTP command, SIZE OF FILE (SIZE), is used to obtain the transfer\n size of a file from the server-FTP process. This is the exact number\n of octets (8 bit bytes) that would be transmitted over the data\n connection should that file be transmitted. This value will change\n depending on the current STRUcture, MODE, and TYPE of the data\n connection or of a data connection that would be created were one\n created now. Thus, the result of the SIZE command is dependent on\n the currently established STRU, MODE, and TYPE parameters.\n\n The SIZE command returns how many octets would be transferred if the\n file were to be transferred using the current transfer structure,\n mode, and type. This command is normally used in conjunction with\n the RESTART (REST) command when STORing a file to a remote server in\n STREAM mode, to determine the restart point. The server-PI might\n need to read the partially transferred file, do any appropriate\n conversion, and count the number of octets that would be generated\n when sending the file in order to correctly respond to this command.\n Estimates of the file transfer size MUST NOT be returned; only\n precise information is acceptable.\n\n http://tools.ietf.org/html/rfc3659\n \"\"\"\n try:\n newsegs = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n\n def cbStat(result):\n (size,) = result\n return (FILE_STATUS, str(size))\n\n return self.shell.stat(newsegs, ('size',)).addCallback(cbStat)\n\n\n def ftp_MDTM(self, path):\n \"\"\"\n File Modification Time (MDTM)\n\n The FTP command, MODIFICATION TIME (MDTM), can be used to determine\n when a file in the server NVFS was last modified. This command has\n existed in many FTP servers for many years, as an adjunct to the REST\n command for STREAM mode, thus is widely available. However, where\n supported, the \"modify\" fact that can be provided in the result from\n the new MLST command is recommended as a superior alternative.\n\n http://tools.ietf.org/html/rfc3659\n \"\"\"\n try:\n newsegs = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n\n def cbStat(result):\n (modified,) = result\n return (FILE_STATUS, time.strftime('%Y%m%d%H%M%S', time.gmtime(modified)))\n\n return self.shell.stat(newsegs, ('modified',)).addCallback(cbStat)\n\n\n def ftp_TYPE(self, type):\n \"\"\"\n REPRESENTATION TYPE (TYPE)\n\n The argument specifies the representation type as described\n in the Section on Data Representation and Storage. Several\n types take a second parameter. The first parameter is\n denoted by a single Telnet character, as is the second\n Format parameter for ASCII and EBCDIC; the second parameter\n for local byte is a decimal integer to indicate Bytesize.\n The parameters are separated by a (Space, ASCII code\n 32).\n \"\"\"\n p = type.upper()\n if p:\n f = getattr(self, 'type_' + p[0], None)\n if f is not None:\n return f(p[1:])\n return self.type_UNKNOWN(p)\n return (SYNTAX_ERR,)\n\n def type_A(self, code):\n if code == '' or code == 'N':\n self.binary = False\n return (TYPE_SET_OK, 'A' + code)\n else:\n return defer.fail(CmdArgSyntaxError(code))\n\n def type_I(self, code):\n if code == '':\n self.binary = True\n return (TYPE_SET_OK, 'I')\n else:\n return defer.fail(CmdArgSyntaxError(code))\n\n def type_UNKNOWN(self, code):\n return defer.fail(CmdNotImplementedForArgError(code))\n\n\n\n def ftp_SYST(self):\n return NAME_SYS_TYPE\n\n\n def ftp_STRU(self, structure):\n p = structure.upper()\n if p == 'F':\n return (CMD_OK,)\n return defer.fail(CmdNotImplementedForArgError(structure))\n\n\n def ftp_MODE(self, mode):\n p = mode.upper()\n if p == 'S':\n return (CMD_OK,)\n return defer.fail(CmdNotImplementedForArgError(mode))\n\n\n def ftp_MKD(self, path):\n try:\n newsegs = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n return self.shell.makeDirectory(newsegs).addCallback(lambda ign: (MKD_REPLY, path))\n\n\n def ftp_RMD(self, path):\n try:\n newsegs = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n return self.shell.removeDirectory(newsegs).addCallback(lambda ign: (REQ_FILE_ACTN_COMPLETED_OK,))\n\n\n def ftp_DELE(self, path):\n try:\n newsegs = toSegments(self.workingDirectory, path)\n except InvalidPath:\n return defer.fail(FileNotFoundError(path))\n return self.shell.removeFile(newsegs).addCallback(lambda ign: (REQ_FILE_ACTN_COMPLETED_OK,))\n\n\n def ftp_NOOP(self):\n return (CMD_OK,)\n\n\n def ftp_RNFR(self, fromName):\n self._fromName = fromName\n self.state = self.RENAMING\n return (REQ_FILE_ACTN_PENDING_FURTHER_INFO,)\n\n\n def ftp_RNTO(self, toName):\n fromName = self._fromName\n del self._fromName\n self.state = self.AUTHED\n\n try:\n fromsegs = toSegments(self.workingDirectory, fromName)\n tosegs = toSegments(self.workingDirectory, toName)\n except InvalidPath:\n return defer.fail(FileNotFoundError(fromName))\n return self.shell.rename(fromsegs, tosegs).addCallback(lambda ign: (REQ_FILE_ACTN_COMPLETED_OK,))\n\n\n def ftp_FEAT(self):\n \"\"\"\n Advertise the features supported by the server.\n\n http://tools.ietf.org/html/rfc2389\n \"\"\"\n self.sendLine(RESPONSE[FEAT_OK][0])\n for feature in self.FEATURES:\n self.sendLine(' ' + feature)\n self.sendLine(RESPONSE[FEAT_OK][1])\n\n def ftp_OPTS(self, option):\n \"\"\"\n Handle OPTS command.\n\n http://tools.ietf.org/html/draft-ietf-ftpext-utf-8-option-00\n \"\"\"\n return self.reply(OPTS_NOT_IMPLEMENTED, option)\n\n def ftp_QUIT(self):\n self.reply(GOODBYE_MSG)\n self.transport.loseConnection()\n self.disconnected = True\n\n def cleanupDTP(self):\n \"\"\"\n Call when DTP connection exits\n \"\"\"\n log.msg('cleanupDTP', debug=True)\n\n log.msg(self.dtpPort)\n dtpPort, self.dtpPort = self.dtpPort, None\n if interfaces.IListeningPort.providedBy(dtpPort):\n dtpPort.stopListening()\n elif interfaces.IConnector.providedBy(dtpPort):\n dtpPort.disconnect()\n else:\n assert False, \"dtpPort should be an IListeningPort or IConnector, instead is %r\" % (dtpPort,)\n\n self.dtpFactory.stopFactory()\n self.dtpFactory = None\n\n if self.dtpInstance is not None:\n self.dtpInstance = None\n\n\nclass FTPFactory(policies.LimitTotalConnectionsFactory):\n \"\"\"\n A factory for producing ftp protocol instances\n\n @ivar timeOut: the protocol interpreter's idle timeout time in seconds,\n default is 600 seconds.\n\n @ivar passivePortRange: value forwarded to C{protocol.passivePortRange}.\n @type passivePortRange: C{iterator}\n \"\"\"\n protocol = FTP\n overflowProtocol = FTPOverflowProtocol\n allowAnonymous = True\n userAnonymous = 'anonymous'\n timeOut = 600\n\n welcomeMessage = \"Twisted %s FTP Server\" % (copyright.version,)\n\n passivePortRange = range(0, 1)\n\n def __init__(self, portal=None, userAnonymous='anonymous'):\n self.portal = portal\n self.userAnonymous = userAnonymous\n self.instances = []\n\n def buildProtocol(self, addr):\n p = policies.LimitTotalConnectionsFactory.buildProtocol(self, addr)\n if p is not None:\n p.wrappedProtocol.portal = self.portal\n p.wrappedProtocol.timeOut = self.timeOut\n p.wrappedProtocol.passivePortRange = self.passivePortRange\n return p\n\n def stopFactory(self):\n # make sure ftp instance's timeouts are set to None\n # to avoid reactor complaints\n [p.setTimeout(None) for p in self.instances if p.timeOut is not None]\n policies.LimitTotalConnectionsFactory.stopFactory(self)\n\n# -- Cred Objects --\n\n\nclass IFTPShell(Interface):\n \"\"\"\n An abstraction of the shell commands used by the FTP protocol for\n a given user account.\n\n All path names must be absolute.\n \"\"\"\n\n def makeDirectory(path):\n \"\"\"\n Create a directory.\n\n @param path: The path, as a list of segments, to create\n @type path: C{list} of C{unicode}\n\n @return: A Deferred which fires when the directory has been\n created, or which fails if the directory cannot be created.\n \"\"\"\n\n\n def removeDirectory(path):\n \"\"\"\n Remove a directory.\n\n @param path: The path, as a list of segments, to remove\n @type path: C{list} of C{unicode}\n\n @return: A Deferred which fires when the directory has been\n removed, or which fails if the directory cannot be removed.\n \"\"\"\n\n\n def removeFile(path):\n \"\"\"\n Remove a file.\n\n @param path: The path, as a list of segments, to remove\n @type path: C{list} of C{unicode}\n\n @return: A Deferred which fires when the file has been\n removed, or which fails if the file cannot be removed.\n \"\"\"\n\n\n def rename(fromPath, toPath):\n \"\"\"\n Rename a file or directory.\n\n @param fromPath: The current name of the path.\n @type fromPath: C{list} of C{unicode}\n\n @param toPath: The desired new name of the path.\n @type toPath: C{list} of C{unicode}\n\n @return: A Deferred which fires when the path has been\n renamed, or which fails if the path cannot be renamed.\n \"\"\"\n\n\n def access(path):\n \"\"\"\n Determine whether access to the given path is allowed.\n\n @param path: The path, as a list of segments\n\n @return: A Deferred which fires with None if access is allowed\n or which fails with a specific exception type if access is\n denied.\n \"\"\"\n\n\n def stat(path, keys=()):\n \"\"\"\n Retrieve information about the given path.\n\n This is like list, except it will never return results about\n child paths.\n \"\"\"\n\n\n def list(path, keys=()):\n \"\"\"\n Retrieve information about the given path.\n\n If the path represents a non-directory, the result list should\n have only one entry with information about that non-directory.\n Otherwise, the result list should have an element for each\n child of the directory.\n\n @param path: The path, as a list of segments, to list\n @type path: C{list} of C{unicode} or C{bytes}\n\n @param keys: A tuple of keys desired in the resulting\n dictionaries.\n\n @return: A Deferred which fires with a list of (name, list),\n where the name is the name of the entry as a unicode string or\n bytes and each list contains values corresponding to the requested\n keys. The following are possible elements of keys, and the\n values which should be returned for them:\n\n - C{'size'}: size in bytes, as an integer (this is kinda required)\n\n - C{'directory'}: boolean indicating the type of this entry\n\n - C{'permissions'}: a bitvector (see os.stat(foo).st_mode)\n\n - C{'hardlinks'}: Number of hard links to this entry\n\n - C{'modified'}: number of seconds since the epoch since entry was\n modified\n\n - C{'owner'}: string indicating the user owner of this entry\n\n - C{'group'}: string indicating the group owner of this entry\n \"\"\"\n\n\n def openForReading(path):\n \"\"\"\n @param path: The path, as a list of segments, to open\n @type path: C{list} of C{unicode}\n\n @rtype: C{Deferred} which will fire with L{IReadFile}\n \"\"\"\n\n\n def openForWriting(path):\n \"\"\"\n @param path: The path, as a list of segments, to open\n @type path: C{list} of C{unicode}\n\n @rtype: C{Deferred} which will fire with L{IWriteFile}\n \"\"\"\n\n\n\nclass IReadFile(Interface):\n \"\"\"\n A file out of which bytes may be read.\n \"\"\"\n\n def send(consumer):\n \"\"\"\n Produce the contents of the given path to the given consumer. This\n method may only be invoked once on each provider.\n\n @type consumer: C{IConsumer}\n\n @return: A Deferred which fires when the file has been\n consumed completely.\n \"\"\"\n\n\n\nclass IWriteFile(Interface):\n \"\"\"\n A file into which bytes may be written.\n \"\"\"\n\n def receive():\n \"\"\"\n Create a consumer which will write to this file. This method may\n only be invoked once on each provider.\n\n @rtype: C{Deferred} of C{IConsumer}\n \"\"\"\n\n def close():\n \"\"\"\n Perform any post-write work that needs to be done. This method may\n only be invoked once on each provider, and will always be invoked\n after receive().\n\n @rtype: C{Deferred} of anything: the value is ignored. The FTP client\n will not see their upload request complete until this Deferred has\n been fired.\n \"\"\"\n\ndef _getgroups(uid):\n \"\"\"\n Return the primary and supplementary groups for the given UID.\n\n @type uid: C{int}\n \"\"\"\n result = []\n pwent = pwd.getpwuid(uid)\n\n result.append(pwent.pw_gid)\n\n for grent in grp.getgrall():\n if pwent.pw_name in grent.gr_mem:\n result.append(grent.gr_gid)\n\n return result\n\n\ndef _testPermissions(uid, gid, spath, mode='r'):\n \"\"\"\n checks to see if uid has proper permissions to access path with mode\n\n @type uid: C{int}\n @param uid: numeric user id\n\n @type gid: C{int}\n @param gid: numeric group id\n\n @type spath: C{str}\n @param spath: the path on the server to test\n\n @type mode: C{str}\n @param mode: 'r' or 'w' (read or write)\n\n @rtype: C{bool}\n @return: True if the given credentials have the specified form of\n access to the given path\n \"\"\"\n if mode == 'r':\n usr = stat.S_IRUSR\n grp = stat.S_IRGRP\n oth = stat.S_IROTH\n amode = os.R_OK\n elif mode == 'w':\n usr = stat.S_IWUSR\n grp = stat.S_IWGRP\n oth = stat.S_IWOTH\n amode = os.W_OK\n else:\n raise ValueError(\"Invalid mode %r: must specify 'r' or 'w'\" % (mode,))\n\n access = False\n if os.path.exists(spath):\n if uid == 0:\n access = True\n else:\n s = os.stat(spath)\n if usr & s.st_mode and uid == s.st_uid:\n access = True\n elif grp & s.st_mode and gid in _getgroups(uid):\n access = True\n elif oth & s.st_mode:\n access = True\n\n if access:\n if not os.access(spath, amode):\n access = False\n log.msg(\"Filesystem grants permission to UID %d but it is inaccessible to me running as UID %d\" % (\n uid, os.getuid()))\n return access\n\n\n\n@implementer(IFTPShell)\nclass FTPAnonymousShell(object):\n \"\"\"\n An anonymous implementation of IFTPShell\n\n @type filesystemRoot: L{twisted.python.filepath.FilePath}\n @ivar filesystemRoot: The path which is considered the root of\n this shell.\n \"\"\"\n def __init__(self, filesystemRoot):\n self.filesystemRoot = filesystemRoot\n\n\n def _path(self, path):\n return self.filesystemRoot.descendant(path)\n\n\n def makeDirectory(self, path):\n return defer.fail(AnonUserDeniedError())\n\n\n def removeDirectory(self, path):\n return defer.fail(AnonUserDeniedError())\n\n\n def removeFile(self, path):\n return defer.fail(AnonUserDeniedError())\n\n\n def rename(self, fromPath, toPath):\n return defer.fail(AnonUserDeniedError())\n\n\n def receive(self, path):\n path = self._path(path)\n return defer.fail(AnonUserDeniedError())\n\n\n def openForReading(self, path):\n \"\"\"\n Open C{path} for reading.\n\n @param path: The path, as a list of segments, to open.\n @type path: C{list} of C{unicode}\n @return: A L{Deferred} is returned that will fire with an object\n implementing L{IReadFile} if the file is successfully opened. If\n C{path} is a directory, or if an exception is raised while trying\n to open the file, the L{Deferred} will fire with an error.\n \"\"\"\n p = self._path(path)\n if p.isdir():\n # Normally, we would only check for EISDIR in open, but win32\n # returns EACCES in this case, so we check before\n return defer.fail(IsADirectoryError(path))\n try:\n f = p.open('r')\n except (IOError, OSError) as e:\n return errnoToFailure(e.errno, path)\n except:\n return defer.fail()\n else:\n return defer.succeed(_FileReader(f))\n\n\n def openForWriting(self, path):\n \"\"\"\n Reject write attempts by anonymous users with\n L{PermissionDeniedError}.\n \"\"\"\n return defer.fail(PermissionDeniedError(\"STOR not allowed\"))\n\n\n def access(self, path):\n p = self._path(path)\n if not p.exists():\n # Again, win32 doesn't report a sane error after, so let's fail\n # early if we can\n return defer.fail(FileNotFoundError(path))\n # For now, just see if we can os.listdir() it\n try:\n p.listdir()\n except (IOError, OSError) as e:\n return errnoToFailure(e.errno, path)\n except:\n return defer.fail()\n else:\n return defer.succeed(None)\n\n\n def stat(self, path, keys=()):\n p = self._path(path)\n if p.isdir():\n try:\n statResult = self._statNode(p, keys)\n except (IOError, OSError) as e:\n return errnoToFailure(e.errno, path)\n except:\n return defer.fail()\n else:\n return defer.succeed(statResult)\n else:\n return self.list(path, keys).addCallback(lambda res: res[0][1])\n\n\n def list(self, path, keys=()):\n \"\"\"\n Return the list of files at given C{path}, adding C{keys} stat\n informations if specified.\n\n @param path: the directory or file to check.\n @type path: C{str}\n\n @param keys: the list of desired metadata\n @type keys: C{list} of C{str}\n \"\"\"\n filePath = self._path(path)\n if filePath.isdir():\n entries = filePath.listdir()\n fileEntries = [filePath.child(p) for p in entries]\n elif filePath.isfile():\n entries = [os.path.join(*filePath.segmentsFrom(self.filesystemRoot))]\n fileEntries = [filePath]\n else:\n return defer.fail(FileNotFoundError(path))\n\n results = []\n for fileName, filePath in zip(entries, fileEntries):\n ent = []\n results.append((fileName, ent))\n if keys:\n try:\n ent.extend(self._statNode(filePath, keys))\n except (IOError, OSError) as e:\n return errnoToFailure(e.errno, fileName)\n except:\n return defer.fail()\n\n return defer.succeed(results)\n\n\n def _statNode(self, filePath, keys):\n \"\"\"\n Shortcut method to get stat info on a node.\n\n @param filePath: the node to stat.\n @type filePath: C{filepath.FilePath}\n\n @param keys: the stat keys to get.\n @type keys: C{iterable}\n \"\"\"\n filePath.restat()\n return [getattr(self, '_stat_' + k)(filePath) for k in keys]\n\n\n def _stat_size(self, fp):\n \"\"\"\n Get the filepath's size as an int\n\n @param fp: L{twisted.python.filepath.FilePath}\n @return: C{int} representing the size\n \"\"\"\n return fp.getsize()\n\n\n def _stat_permissions(self, fp):\n \"\"\"\n Get the filepath's permissions object\n\n @param fp: L{twisted.python.filepath.FilePath}\n @return: L{twisted.python.filepath.Permissions} of C{fp}\n \"\"\"\n return fp.getPermissions()\n\n\n def _stat_hardlinks(self, fp):\n \"\"\"\n Get the number of hardlinks for the filepath - if the number of\n hardlinks is not yet implemented (say in Windows), just return 0 since\n stat-ing a file in Windows seems to return C{st_nlink=0}.\n\n (Reference:\n U{http://stackoverflow.com/questions/5275731/os-stat-on-windows})\n\n @param fp: L{twisted.python.filepath.FilePath}\n @return: C{int} representing the number of hardlinks\n \"\"\"\n try:\n return fp.getNumberOfHardLinks()\n except NotImplementedError:\n return 0\n\n\n def _stat_modified(self, fp):\n \"\"\"\n Get the filepath's last modified date\n\n @param fp: L{twisted.python.filepath.FilePath}\n @return: C{int} as seconds since the epoch\n \"\"\"\n return fp.getModificationTime()\n\n\n def _stat_owner(self, fp):\n \"\"\"\n Get the filepath's owner's username. If this is not implemented\n (say in Windows) return the string \"0\" since stat-ing a file in\n Windows seems to return C{st_uid=0}.\n\n (Reference:\n U{http://stackoverflow.com/questions/5275731/os-stat-on-windows})\n\n @param fp: L{twisted.python.filepath.FilePath}\n @return: C{str} representing the owner's username\n \"\"\"\n try:\n userID = fp.getUserID()\n except NotImplementedError:\n return \"0\"\n else:\n if pwd is not None:\n try:\n return pwd.getpwuid(userID)[0]\n except KeyError:\n pass\n return str(userID)\n\n\n def _stat_group(self, fp):\n \"\"\"\n Get the filepath's owner's group. If this is not implemented\n (say in Windows) return the string \"0\" since stat-ing a file in\n Windows seems to return C{st_gid=0}.\n\n (Reference:\n U{http://stackoverflow.com/questions/5275731/os-stat-on-windows})\n\n @param fp: L{twisted.python.filepath.FilePath}\n @return: C{str} representing the owner's group\n \"\"\"\n try:\n groupID = fp.getGroupID()\n except NotImplementedError:\n return \"0\"\n else:\n if grp is not None:\n try:\n return grp.getgrgid(groupID)[0]\n except KeyError:\n pass\n return str(groupID)\n\n\n def _stat_directory(self, fp):\n \"\"\"\n Get whether the filepath is a directory\n\n @param fp: L{twisted.python.filepath.FilePath}\n @return: C{bool}\n \"\"\"\n return fp.isdir()\n\n\n\n@implementer(IReadFile)\nclass _FileReader(object):\n def __init__(self, fObj):\n self.fObj = fObj\n self._send = False\n\n def _close(self, passthrough):\n self._send = True\n self.fObj.close()\n return passthrough\n\n def send(self, consumer):\n assert not self._send, \"Can only call IReadFile.send *once* per instance\"\n self._send = True\n d = basic.FileSender().beginFileTransfer(self.fObj, consumer)\n d.addBoth(self._close)\n return d\n\n\n\nclass FTPShell(FTPAnonymousShell):\n \"\"\"\n An authenticated implementation of L{IFTPShell}.\n \"\"\"\n\n def makeDirectory(self, path):\n p = self._path(path)\n try:\n p.makedirs()\n except (IOError, OSError) as e:\n return errnoToFailure(e.errno, path)\n except:\n return defer.fail()\n else:\n return defer.succeed(None)\n\n\n def removeDirectory(self, path):\n p = self._path(path)\n if p.isfile():\n # Win32 returns the wrong errno when rmdir is called on a file\n # instead of a directory, so as we have the info here, let's fail\n # early with a pertinent error\n return defer.fail(IsNotADirectoryError(path))\n try:\n os.rmdir(p.path)\n except (IOError, OSError) as e:\n return errnoToFailure(e.errno, path)\n except:\n return defer.fail()\n else:\n return defer.succeed(None)\n\n\n def removeFile(self, path):\n p = self._path(path)\n if p.isdir():\n # Win32 returns the wrong errno when remove is called on a\n # directory instead of a file, so as we have the info here,\n # let's fail early with a pertinent error\n return defer.fail(IsADirectoryError(path))\n try:\n p.remove()\n except (IOError, OSError) as e:\n return errnoToFailure(e.errno, path)\n except:\n return defer.fail()\n else:\n return defer.succeed(None)\n\n\n def rename(self, fromPath, toPath):\n fp = self._path(fromPath)\n tp = self._path(toPath)\n try:\n os.rename(fp.path, tp.path)\n except (IOError, OSError) as e:\n return errnoToFailure(e.errno, fromPath)\n except:\n return defer.fail()\n else:\n return defer.succeed(None)\n\n\n def openForWriting(self, path):\n \"\"\"\n Open C{path} for writing.\n\n @param path: The path, as a list of segments, to open.\n @type path: C{list} of C{unicode}\n @return: A L{Deferred} is returned that will fire with an object\n implementing L{IWriteFile} if the file is successfully opened. If\n C{path} is a directory, or if an exception is raised while trying\n to open the file, the L{Deferred} will fire with an error.\n \"\"\"\n p = self._path(path)\n if p.isdir():\n # Normally, we would only check for EISDIR in open, but win32\n # returns EACCES in this case, so we check before\n return defer.fail(IsADirectoryError(path))\n try:\n fObj = p.open('w')\n except (IOError, OSError) as e:\n return errnoToFailure(e.errno, path)\n except:\n return defer.fail()\n return defer.succeed(_FileWriter(fObj))\n\n\n\n@implementer(IWriteFile)\nclass _FileWriter(object):\n def __init__(self, fObj):\n self.fObj = fObj\n self._receive = False\n\n def receive(self):\n assert not self._receive, \"Can only call IWriteFile.receive *once* per instance\"\n self._receive = True\n # FileConsumer will close the file object\n return defer.succeed(FileConsumer(self.fObj))\n\n def close(self):\n return defer.succeed(None)\n\n\n\n@implementer(portal.IRealm)\nclass BaseFTPRealm:\n \"\"\"\n Base class for simple FTP realms which provides an easy hook for specifying\n the home directory for each user.\n \"\"\"\n def __init__(self, anonymousRoot):\n self.anonymousRoot = filepath.FilePath(anonymousRoot)\n\n\n def getHomeDirectory(self, avatarId):\n \"\"\"\n Return a L{FilePath} representing the home directory of the given\n avatar. Override this in a subclass.\n\n @param avatarId: A user identifier returned from a credentials checker.\n @type avatarId: C{str}\n\n @rtype: L{FilePath}\n \"\"\"\n raise NotImplementedError(\n \"%r did not override getHomeDirectory\" % (self.__class__,))\n\n\n def requestAvatar(self, avatarId, mind, *interfaces):\n for iface in interfaces:\n if iface is IFTPShell:\n if avatarId is checkers.ANONYMOUS:\n avatar = FTPAnonymousShell(self.anonymousRoot)\n else:\n avatar = FTPShell(self.getHomeDirectory(avatarId))\n return (IFTPShell, avatar,\n getattr(avatar, 'logout', lambda: None))\n raise NotImplementedError(\n \"Only IFTPShell interface is supported by this realm\")\n\n\n\nclass FTPRealm(BaseFTPRealm):\n \"\"\"\n @type anonymousRoot: L{twisted.python.filepath.FilePath}\n @ivar anonymousRoot: Root of the filesystem to which anonymous\n users will be granted access.\n\n @type userHome: L{filepath.FilePath}\n @ivar userHome: Root of the filesystem containing user home directories.\n \"\"\"\n def __init__(self, anonymousRoot, userHome='/home'):\n BaseFTPRealm.__init__(self, anonymousRoot)\n self.userHome = filepath.FilePath(userHome)\n\n\n def getHomeDirectory(self, avatarId):\n \"\"\"\n Use C{avatarId} as a single path segment to construct a child of\n C{self.userHome} and return that child.\n \"\"\"\n return self.userHome.child(avatarId)\n\n\n\nclass SystemFTPRealm(BaseFTPRealm):\n \"\"\"\n L{SystemFTPRealm} uses system user account information to decide what the\n home directory for a particular avatarId is.\n\n This works on POSIX but probably is not reliable on Windows.\n \"\"\"\n def getHomeDirectory(self, avatarId):\n \"\"\"\n Return the system-defined home directory of the system user account with\n the name C{avatarId}.\n \"\"\"\n path = os.path.expanduser('~' + avatarId)\n if path.startswith('~'):\n raise cred_error.UnauthorizedLogin()\n return filepath.FilePath(path)\n\n\n\n# --- FTP CLIENT -------------------------------------------------------------\n\n####\n# And now for the client...\n\n# Notes:\n# * Reference: http://cr.yp.to/ftp.html\n# * FIXME: Does not support pipelining (which is not supported by all\n# servers anyway). This isn't a functionality limitation, just a\n# small performance issue.\n# * Only has a rudimentary understanding of FTP response codes (although\n# the full response is passed to the caller if they so choose).\n# * Assumes that USER and PASS should always be sent\n# * Always sets TYPE I (binary mode)\n# * Doesn't understand any of the weird, obscure TELNET stuff (\\377...)\n# * FIXME: Doesn't share any code with the FTPServer\n\nclass ConnectionLost(FTPError):\n pass\n\nclass CommandFailed(FTPError):\n pass\n\nclass BadResponse(FTPError):\n pass\n\nclass UnexpectedResponse(FTPError):\n pass\n\nclass UnexpectedData(FTPError):\n pass\n\nclass FTPCommand:\n def __init__(self, text=None, public=0):\n self.text = text\n self.deferred = defer.Deferred()\n self.ready = 1\n self.public = public\n self.transferDeferred = None\n\n def fail(self, failure):\n if self.public:\n self.deferred.errback(failure)\n\n\nclass ProtocolWrapper(protocol.Protocol):\n def __init__(self, original, deferred):\n self.original = original\n self.deferred = deferred\n def makeConnection(self, transport):\n self.original.makeConnection(transport)\n def dataReceived(self, data):\n self.original.dataReceived(data)\n def connectionLost(self, reason):\n self.original.connectionLost(reason)\n # Signal that transfer has completed\n self.deferred.callback(None)\n\n\n\nclass IFinishableConsumer(interfaces.IConsumer):\n \"\"\"\n A Consumer for producers that finish.\n\n @since: 11.0\n \"\"\"\n\n def finish():\n \"\"\"\n The producer has finished producing.\n \"\"\"\n\n\n\n@implementer(IFinishableConsumer)\nclass SenderProtocol(protocol.Protocol):\n def __init__(self):\n # Fired upon connection\n self.connectedDeferred = defer.Deferred()\n\n # Fired upon disconnection\n self.deferred = defer.Deferred()\n\n #Protocol stuff\n def dataReceived(self, data):\n raise UnexpectedData(\n \"Received data from the server on a \"\n \"send-only data-connection\"\n )\n\n def makeConnection(self, transport):\n protocol.Protocol.makeConnection(self, transport)\n self.connectedDeferred.callback(self)\n\n def connectionLost(self, reason):\n if reason.check(error.ConnectionDone):\n self.deferred.callback('connection done')\n else:\n self.deferred.errback(reason)\n\n #IFinishableConsumer stuff\n def write(self, data):\n self.transport.write(data)\n\n def registerProducer(self, producer, streaming):\n \"\"\"\n Register the given producer with our transport.\n \"\"\"\n self.transport.registerProducer(producer, streaming)\n\n def unregisterProducer(self):\n \"\"\"\n Unregister the previously registered producer.\n \"\"\"\n self.transport.unregisterProducer()\n\n def finish(self):\n self.transport.loseConnection()\n\n\ndef decodeHostPort(line):\n \"\"\"\n Decode an FTP response specifying a host and port.\n\n @return: a 2-tuple of (host, port).\n \"\"\"\n abcdef = re.sub('[^0-9, ]', '', line)\n parsed = [int(p.strip()) for p in abcdef.split(',')]\n for x in parsed:\n if x < 0 or x > 255:\n raise ValueError(\"Out of range\", line, x)\n a, b, c, d, e, f = parsed\n host = \"%s.%s.%s.%s\" % (a, b, c, d)\n port = (int(e) << 8) + int(f)\n return host, port\n\ndef encodeHostPort(host, port):\n numbers = host.split('.') + [str(port >> 8), str(port % 256)]\n return ','.join(numbers)\n\ndef _unwrapFirstError(failure):\n failure.trap(defer.FirstError)\n return failure.value.subFailure\n\nclass FTPDataPortFactory(protocol.ServerFactory):\n \"\"\"\n Factory for data connections that use the PORT command\n\n (i.e. \"active\" transfers)\n \"\"\"\n noisy = 0\n def buildProtocol(self, addr):\n # This is a bit hackish -- we already have a Protocol instance,\n # so just return it instead of making a new one\n # FIXME: Reject connections from the wrong address/port\n # (potential security problem)\n self.protocol.factory = self\n self.port.loseConnection()\n return self.protocol\n\n\n\nclass FTPClientBasic(basic.LineReceiver):\n \"\"\"\n Foundations of an FTP client.\n \"\"\"\n debug = False\n _encoding = 'latin-1'\n\n def __init__(self):\n self.actionQueue = []\n self.greeting = None\n self.nextDeferred = defer.Deferred().addCallback(self._cb_greeting)\n self.nextDeferred.addErrback(self.fail)\n self.response = []\n self._failed = 0\n\n def fail(self, error):\n \"\"\"\n Give an error to any queued deferreds.\n \"\"\"\n self._fail(error)\n\n def _fail(self, error):\n \"\"\"\n Errback all queued deferreds.\n \"\"\"\n if self._failed:\n # We're recursing; bail out here for simplicity\n return error\n self._failed = 1\n if self.nextDeferred:\n try:\n self.nextDeferred.errback(failure.Failure(ConnectionLost('FTP connection lost', error)))\n except defer.AlreadyCalledError:\n pass\n for ftpCommand in self.actionQueue:\n ftpCommand.fail(failure.Failure(ConnectionLost('FTP connection lost', error)))\n return error\n\n def _cb_greeting(self, greeting):\n self.greeting = greeting\n\n\n def sendLine(self, line):\n \"\"\"\n Sends a line, unless line is None.\n\n @param line: Line to send\n @type line: L{bytes} or L{unicode}\n \"\"\"\n if line is None:\n return\n elif isinstance(line, unicode):\n line = line.encode(self._encoding)\n basic.LineReceiver.sendLine(self, line)\n\n\n def sendNextCommand(self):\n \"\"\"\n (Private) Processes the next command in the queue.\n \"\"\"\n ftpCommand = self.popCommandQueue()\n if ftpCommand is None:\n self.nextDeferred = None\n return\n if not ftpCommand.ready:\n self.actionQueue.insert(0, ftpCommand)\n reactor.callLater(1.0, self.sendNextCommand)\n self.nextDeferred = None\n return\n\n # FIXME: this if block doesn't belong in FTPClientBasic, it belongs in\n # FTPClient.\n if ftpCommand.text == 'PORT':\n self.generatePortCommand(ftpCommand)\n\n if self.debug:\n log.msg('<-- %s' % ftpCommand.text)\n self.nextDeferred = ftpCommand.deferred\n self.sendLine(ftpCommand.text)\n\n def queueCommand(self, ftpCommand):\n \"\"\"\n Add an FTPCommand object to the queue.\n\n If it's the only thing in the queue, and we are connected and we aren't\n waiting for a response of an earlier command, the command will be sent\n immediately.\n\n @param ftpCommand: an L{FTPCommand}\n \"\"\"\n self.actionQueue.append(ftpCommand)\n if (len(self.actionQueue) == 1 and self.transport is not None and\n self.nextDeferred is None):\n self.sendNextCommand()\n\n def queueStringCommand(self, command, public=1):\n \"\"\"\n Queues a string to be issued as an FTP command\n\n @param command: string of an FTP command to queue\n @param public: a flag intended for internal use by FTPClient. Don't\n change it unless you know what you're doing.\n\n @return: a L{Deferred} that will be called when the response to the\n command has been received.\n \"\"\"\n ftpCommand = FTPCommand(command, public)\n self.queueCommand(ftpCommand)\n return ftpCommand.deferred\n\n def popCommandQueue(self):\n \"\"\"\n Return the front element of the command queue, or None if empty.\n \"\"\"\n if self.actionQueue:\n return self.actionQueue.pop(0)\n else:\n return None\n\n def queueLogin(self, username, password):\n \"\"\"\n Login: send the username, send the password.\n\n If the password is L{None}, the PASS command won't be sent. Also, if\n the response to the USER command has a response code of 230 (User logged\n in), then PASS won't be sent either.\n \"\"\"\n # Prepare the USER command\n deferreds = []\n userDeferred = self.queueStringCommand('USER ' + username, public=0)\n deferreds.append(userDeferred)\n\n # Prepare the PASS command (if a password is given)\n if password is not None:\n passwordCmd = FTPCommand('PASS ' + password, public=0)\n self.queueCommand(passwordCmd)\n deferreds.append(passwordCmd.deferred)\n\n # Avoid sending PASS if the response to USER is 230.\n # (ref: http://cr.yp.to/ftp/user.html#user)\n def cancelPasswordIfNotNeeded(response):\n if response[0].startswith('230'):\n # No password needed!\n self.actionQueue.remove(passwordCmd)\n return response\n userDeferred.addCallback(cancelPasswordIfNotNeeded)\n\n # Error handling.\n for deferred in deferreds:\n # If something goes wrong, call fail\n deferred.addErrback(self.fail)\n # But also swallow the error, so we don't cause spurious errors\n deferred.addErrback(lambda x: None)\n\n def lineReceived(self, line):\n \"\"\"\n (Private) Parses the response messages from the FTP server.\n \"\"\"\n # Add this line to the current response\n if bytes != str:\n line = line.decode(self._encoding)\n\n if self.debug:\n log.msg('--> %s' % line)\n self.response.append(line)\n\n # Bail out if this isn't the last line of a response\n # The last line of response starts with 3 digits followed by a space\n codeIsValid = re.match(r'\\d{3} ', line)\n if not codeIsValid:\n return\n\n code = line[0:3]\n\n # Ignore marks\n if code[0] == '1':\n return\n\n # Check that we were expecting a response\n if self.nextDeferred is None:\n self.fail(UnexpectedResponse(self.response))\n return\n\n # Reset the response\n response = self.response\n self.response = []\n\n # Look for a success or error code, and call the appropriate callback\n if code[0] in ('2', '3'):\n # Success\n self.nextDeferred.callback(response)\n elif code[0] in ('4', '5'):\n # Failure\n self.nextDeferred.errback(failure.Failure(CommandFailed(response)))\n else:\n # This shouldn't happen unless something screwed up.\n log.msg('Server sent invalid response code %s' % (code,))\n self.nextDeferred.errback(failure.Failure(BadResponse(response)))\n\n # Run the next command\n self.sendNextCommand()\n\n def connectionLost(self, reason):\n self._fail(reason)\n\n\n\nclass _PassiveConnectionFactory(protocol.ClientFactory):\n noisy = False\n\n def __init__(self, protoInstance):\n self.protoInstance = protoInstance\n\n def buildProtocol(self, ignored):\n self.protoInstance.factory = self\n return self.protoInstance\n\n def clientConnectionFailed(self, connector, reason):\n e = FTPError('Connection Failed', reason)\n self.protoInstance.deferred.errback(e)\n\n\n\nclass FTPClient(FTPClientBasic):\n \"\"\"\n L{FTPClient} is a client implementation of the FTP protocol which\n exposes FTP commands as methods which return L{Deferred}s.\n\n Each command method returns a L{Deferred} which is called back when a\n successful response code (2xx or 3xx) is received from the server or\n which is error backed if an error response code (4xx or 5xx) is received\n from the server or if a protocol violation occurs. If an error response\n code is received, the L{Deferred} fires with a L{Failure} wrapping a\n L{CommandFailed} instance. The L{CommandFailed} instance is created\n with a list of the response lines received from the server.\n\n See U{RFC 959} for error code\n definitions.\n\n Both active and passive transfers are supported.\n\n @ivar passive: See description in __init__.\n \"\"\"\n connectFactory = reactor.connectTCP\n\n def __init__(self, username='anonymous',\n password='twisted@twistedmatrix.com',\n passive=1):\n \"\"\"\n Constructor.\n\n I will login as soon as I receive the welcome message from the server.\n\n @param username: FTP username\n @param password: FTP password\n @param passive: flag that controls if I use active or passive data\n connections. You can also change this after construction by\n assigning to C{self.passive}.\n \"\"\"\n FTPClientBasic.__init__(self)\n self.queueLogin(username, password)\n\n self.passive = passive\n\n def fail(self, error):\n \"\"\"\n Disconnect, and also give an error to any queued deferreds.\n \"\"\"\n self.transport.loseConnection()\n self._fail(error)\n\n def receiveFromConnection(self, commands, protocol):\n \"\"\"\n Retrieves a file or listing generated by the given command,\n feeding it to the given protocol.\n\n @param commands: list of strings of FTP commands to execute then receive\n the results of (e.g. C{LIST}, C{RETR})\n @param protocol: A L{Protocol} B{instance} e.g. an\n L{FTPFileListProtocol}, or something that can be adapted to one.\n Typically this will be an L{IConsumer} implementation.\n\n @return: L{Deferred}.\n \"\"\"\n protocol = interfaces.IProtocol(protocol)\n wrapper = ProtocolWrapper(protocol, defer.Deferred())\n return self._openDataConnection(commands, wrapper)\n\n def queueLogin(self, username, password):\n \"\"\"\n Login: send the username, send the password, and\n set retrieval mode to binary\n \"\"\"\n FTPClientBasic.queueLogin(self, username, password)\n d = self.queueStringCommand('TYPE I', public=0)\n # If something goes wrong, call fail\n d.addErrback(self.fail)\n # But also swallow the error, so we don't cause spurious errors\n d.addErrback(lambda x: None)\n\n def sendToConnection(self, commands):\n \"\"\"\n XXX\n\n @return: A tuple of two L{Deferred}s:\n - L{Deferred} L{IFinishableConsumer}. You must call\n the C{finish} method on the IFinishableConsumer when the file\n is completely transferred.\n - L{Deferred} list of control-connection responses.\n \"\"\"\n s = SenderProtocol()\n r = self._openDataConnection(commands, s)\n return (s.connectedDeferred, r)\n\n def _openDataConnection(self, commands, protocol):\n \"\"\"\n This method returns a DeferredList.\n \"\"\"\n cmds = [FTPCommand(command, public=1) for command in commands]\n cmdsDeferred = defer.DeferredList([cmd.deferred for cmd in cmds],\n fireOnOneErrback=True, consumeErrors=True)\n cmdsDeferred.addErrback(_unwrapFirstError)\n\n if self.passive:\n # Hack: use a mutable object to sneak a variable out of the\n # scope of doPassive\n _mutable = [None]\n def doPassive(response):\n \"\"\"Connect to the port specified in the response to PASV\"\"\"\n host, port = decodeHostPort(response[-1][4:])\n\n f = _PassiveConnectionFactory(protocol)\n _mutable[0] = self.connectFactory(host, port, f)\n\n pasvCmd = FTPCommand('PASV')\n self.queueCommand(pasvCmd)\n pasvCmd.deferred.addCallback(doPassive).addErrback(self.fail)\n\n results = [cmdsDeferred, pasvCmd.deferred, protocol.deferred]\n d = defer.DeferredList(results, fireOnOneErrback=True, consumeErrors=True)\n d.addErrback(_unwrapFirstError)\n\n # Ensure the connection is always closed\n def close(x, m=_mutable):\n m[0] and m[0].disconnect()\n return x\n d.addBoth(close)\n\n else:\n # We just place a marker command in the queue, and will fill in\n # the host and port numbers later (see generatePortCommand)\n portCmd = FTPCommand('PORT')\n\n # Ok, now we jump through a few hoops here.\n # This is the problem: a transfer is not to be trusted as complete\n # until we get both the \"226 Transfer complete\" message on the\n # control connection, and the data socket is closed. Thus, we use\n # a DeferredList to make sure we only fire the callback at the\n # right time.\n\n portCmd.transferDeferred = protocol.deferred\n portCmd.protocol = protocol\n portCmd.deferred.addErrback(portCmd.transferDeferred.errback)\n self.queueCommand(portCmd)\n\n # Create dummy functions for the next callback to call.\n # These will also be replaced with real functions in\n # generatePortCommand.\n portCmd.loseConnection = lambda result: result\n portCmd.fail = lambda error: error\n\n # Ensure that the connection always gets closed\n cmdsDeferred.addErrback(lambda e, pc=portCmd: pc.fail(e) or e)\n\n results = [cmdsDeferred, portCmd.deferred, portCmd.transferDeferred]\n d = defer.DeferredList(results, fireOnOneErrback=True, consumeErrors=True)\n d.addErrback(_unwrapFirstError)\n\n for cmd in cmds:\n self.queueCommand(cmd)\n return d\n\n def generatePortCommand(self, portCmd):\n \"\"\"\n (Private) Generates the text of a given PORT command.\n \"\"\"\n\n # The problem is that we don't create the listening port until we need\n # it for various reasons, and so we have to muck about to figure out\n # what interface and port it's listening on, and then finally we can\n # create the text of the PORT command to send to the FTP server.\n\n # FIXME: This method is far too ugly.\n\n # FIXME: The best solution is probably to only create the data port\n # once per FTPClient, and just recycle it for each new download.\n # This should be ok, because we don't pipeline commands.\n\n # Start listening on a port\n factory = FTPDataPortFactory()\n factory.protocol = portCmd.protocol\n listener = reactor.listenTCP(0, factory)\n factory.port = listener\n\n # Ensure we close the listening port if something goes wrong\n def listenerFail(error, listener=listener):\n if listener.connected:\n listener.loseConnection()\n return error\n portCmd.fail = listenerFail\n\n # Construct crufty FTP magic numbers that represent host & port\n host = self.transport.getHost().host\n port = listener.getHost().port\n portCmd.text = 'PORT ' + encodeHostPort(host, port)\n\n def escapePath(self, path):\n \"\"\"\n Returns a FTP escaped path (replace newlines with nulls).\n \"\"\"\n # Escape newline characters\n return path.replace('\\n', '\\0')\n\n def retrieveFile(self, path, protocol, offset=0):\n \"\"\"\n Retrieve a file from the given path\n\n This method issues the 'RETR' FTP command.\n\n The file is fed into the given Protocol instance. The data connection\n will be passive if self.passive is set.\n\n @param path: path to file that you wish to receive.\n @param protocol: a L{Protocol} instance.\n @param offset: offset to start downloading from\n\n @return: L{Deferred}\n \"\"\"\n cmds = ['RETR ' + self.escapePath(path)]\n if offset:\n cmds.insert(0, ('REST ' + str(offset)))\n return self.receiveFromConnection(cmds, protocol)\n\n retr = retrieveFile\n\n def storeFile(self, path, offset=0):\n \"\"\"\n Store a file at the given path.\n\n This method issues the 'STOR' FTP command.\n\n @return: A tuple of two L{Deferred}s:\n - L{Deferred} L{IFinishableConsumer}. You must call\n the C{finish} method on the IFinishableConsumer when the file\n is completely transferred.\n - L{Deferred} list of control-connection responses.\n \"\"\"\n cmds = ['STOR ' + self.escapePath(path)]\n if offset:\n cmds.insert(0, ('REST ' + str(offset)))\n return self.sendToConnection(cmds)\n\n stor = storeFile\n\n\n def rename(self, pathFrom, pathTo):\n \"\"\"\n Rename a file.\n\n This method issues the I{RNFR}/I{RNTO} command sequence to rename\n C{pathFrom} to C{pathTo}.\n\n @param: pathFrom: the absolute path to the file to be renamed\n @type pathFrom: C{str}\n\n @param: pathTo: the absolute path to rename the file to.\n @type pathTo: C{str}\n\n @return: A L{Deferred} which fires when the rename operation has\n succeeded or failed. If it succeeds, the L{Deferred} is called\n back with a two-tuple of lists. The first list contains the\n responses to the I{RNFR} command. The second list contains the\n responses to the I{RNTO} command. If either I{RNFR} or I{RNTO}\n fails, the L{Deferred} is errbacked with L{CommandFailed} or\n L{BadResponse}.\n @rtype: L{Deferred}\n\n @since: 8.2\n \"\"\"\n renameFrom = self.queueStringCommand('RNFR ' + self.escapePath(pathFrom))\n renameTo = self.queueStringCommand('RNTO ' + self.escapePath(pathTo))\n\n fromResponse = []\n\n # Use a separate Deferred for the ultimate result so that Deferred\n # chaining can't interfere with its result.\n result = defer.Deferred()\n # Bundle up all the responses\n result.addCallback(lambda toResponse: (fromResponse, toResponse))\n\n def ebFrom(failure):\n # Make sure the RNTO doesn't run if the RNFR failed.\n self.popCommandQueue()\n result.errback(failure)\n\n # Save the RNFR response to pass to the result Deferred later\n renameFrom.addCallbacks(fromResponse.extend, ebFrom)\n\n # Hook up the RNTO to the result Deferred as well\n renameTo.chainDeferred(result)\n\n return result\n\n\n def list(self, path, protocol):\n \"\"\"\n Retrieve a file listing into the given protocol instance.\n\n This method issues the 'LIST' FTP command.\n\n @param path: path to get a file listing for.\n @param protocol: a L{Protocol} instance, probably a\n L{FTPFileListProtocol} instance. It can cope with most common file\n listing formats.\n\n @return: L{Deferred}\n \"\"\"\n if path is None:\n path = ''\n return self.receiveFromConnection(['LIST ' + self.escapePath(path)], protocol)\n\n\n def nlst(self, path, protocol):\n \"\"\"\n Retrieve a short file listing into the given protocol instance.\n\n This method issues the 'NLST' FTP command.\n\n NLST (should) return a list of filenames, one per line.\n\n @param path: path to get short file listing for.\n @param protocol: a L{Protocol} instance.\n \"\"\"\n if path is None:\n path = ''\n return self.receiveFromConnection(['NLST ' + self.escapePath(path)], protocol)\n\n\n def cwd(self, path):\n \"\"\"\n Issues the CWD (Change Working Directory) command.\n\n @return: a L{Deferred} that will be called when done.\n \"\"\"\n return self.queueStringCommand('CWD ' + self.escapePath(path))\n\n\n def makeDirectory(self, path):\n \"\"\"\n Make a directory\n\n This method issues the MKD command.\n\n @param path: The path to the directory to create.\n @type path: C{str}\n\n @return: A L{Deferred} which fires when the server responds. If the\n directory is created, the L{Deferred} is called back with the\n server response. If the server response indicates the directory\n was not created, the L{Deferred} is errbacked with a L{Failure}\n wrapping L{CommandFailed} or L{BadResponse}.\n @rtype: L{Deferred}\n\n @since: 8.2\n \"\"\"\n return self.queueStringCommand('MKD ' + self.escapePath(path))\n\n\n def removeFile(self, path):\n \"\"\"\n Delete a file on the server.\n\n L{removeFile} issues a I{DELE} command to the server to remove the\n indicated file. Note that this command cannot remove a directory.\n\n @param path: The path to the file to delete. May be relative to the\n current dir.\n @type path: C{str}\n\n @return: A L{Deferred} which fires when the server responds. On error,\n it is errbacked with either L{CommandFailed} or L{BadResponse}. On\n success, it is called back with a list of response lines.\n @rtype: L{Deferred}\n\n @since: 8.2\n \"\"\"\n return self.queueStringCommand('DELE ' + self.escapePath(path))\n\n\n def removeDirectory(self, path):\n \"\"\"\n Delete a directory on the server.\n\n L{removeDirectory} issues a I{RMD} command to the server to remove the\n indicated directory. Described in RFC959.\n\n @param path: The path to the directory to delete. May be relative to\n the current working directory.\n @type path: C{str}\n\n @return: A L{Deferred} which fires when the server responds. On error,\n it is errbacked with either L{CommandFailed} or L{BadResponse}. On\n success, it is called back with a list of response lines.\n @rtype: L{Deferred}\n\n @since: 11.1\n \"\"\"\n return self.queueStringCommand('RMD ' + self.escapePath(path))\n\n\n def cdup(self):\n \"\"\"\n Issues the CDUP (Change Directory UP) command.\n\n @return: a L{Deferred} that will be called when done.\n \"\"\"\n return self.queueStringCommand('CDUP')\n\n\n def pwd(self):\n \"\"\"\n Issues the PWD (Print Working Directory) command.\n\n The L{getDirectory} does the same job but automatically parses the\n result.\n\n @return: a L{Deferred} that will be called when done. It is up to the\n caller to interpret the response, but the L{parsePWDResponse} method\n in this module should work.\n \"\"\"\n return self.queueStringCommand('PWD')\n\n\n def getDirectory(self):\n \"\"\"\n Returns the current remote directory.\n\n @return: a L{Deferred} that will be called back with a C{str} giving\n the remote directory or which will errback with L{CommandFailed}\n if an error response is returned.\n \"\"\"\n def cbParse(result):\n try:\n # The only valid code is 257\n if int(result[0].split(' ', 1)[0]) != 257:\n raise ValueError\n except (IndexError, ValueError):\n return failure.Failure(CommandFailed(result))\n path = parsePWDResponse(result[0])\n if path is None:\n return failure.Failure(CommandFailed(result))\n return path\n return self.pwd().addCallback(cbParse)\n\n\n def quit(self):\n \"\"\"\n Issues the I{QUIT} command.\n\n @return: A L{Deferred} that fires when the server acknowledges the\n I{QUIT} command. The transport should not be disconnected until\n this L{Deferred} fires.\n \"\"\"\n return self.queueStringCommand('QUIT')\n\n\n\nclass FTPFileListProtocol(basic.LineReceiver):\n \"\"\"\n Parser for standard FTP file listings\n\n This is the evil required to match::\n\n -rw-r--r-- 1 root other 531 Jan 29 03:26 README\n\n If you need different evil for a wacky FTP server, you can\n override either C{fileLinePattern} or C{parseDirectoryLine()}.\n\n It populates the instance attribute self.files, which is a list containing\n dicts with the following keys (examples from the above line):\n - filetype: e.g. 'd' for directories, or '-' for an ordinary file\n - perms: e.g. 'rw-r--r--'\n - nlinks: e.g. 1\n - owner: e.g. 'root'\n - group: e.g. 'other'\n - size: e.g. 531\n - date: e.g. 'Jan 29 03:26'\n - filename: e.g. 'README'\n - linktarget: e.g. 'some/file'\n\n Note that the 'date' value will be formatted differently depending on the\n date. Check U{http://cr.yp.to/ftp.html} if you really want to try to parse\n it.\n\n It also matches the following::\n -rw-r--r-- 1 root other 531 Jan 29 03:26 I HAVE\\ SPACE\n - filename: e.g. 'I HAVE SPACE'\n\n -rw-r--r-- 1 root other 531 Jan 29 03:26 LINK -> TARGET\n - filename: e.g. 'LINK'\n - linktarget: e.g. 'TARGET'\n\n -rw-r--r-- 1 root other 531 Jan 29 03:26 N S -> L S\n - filename: e.g. 'N S'\n - linktarget: e.g. 'L S'\n\n @ivar files: list of dicts describing the files in this listing\n \"\"\"\n fileLinePattern = re.compile(\n r'^(?P.)(?P.{9})\\s+(?P\\d*)\\s*'\n r'(?P\\S+)\\s+(?P\\S+)\\s+(?P\\d+)\\s+'\n r'(?P...\\s+\\d+\\s+[\\d:]+)\\s+(?P.{1,}?)'\n r'( -> (?P[^\\r]*))?\\r?$'\n )\n delimiter = b'\\n'\n _encoding = 'latin-1'\n\n def __init__(self):\n self.files = []\n\n def lineReceived(self, line):\n if bytes != str:\n line = line.decode(self._encoding)\n d = self.parseDirectoryLine(line)\n if d is None:\n self.unknownLine(line)\n else:\n self.addFile(d)\n\n def parseDirectoryLine(self, line):\n \"\"\"\n Return a dictionary of fields, or None if line cannot be parsed.\n\n @param line: line of text expected to contain a directory entry\n @type line: str\n\n @return: dict\n \"\"\"\n match = self.fileLinePattern.match(line)\n if match is None:\n return None\n else:\n d = match.groupdict()\n d['filename'] = d['filename'].replace(r'\\ ', ' ')\n d['nlinks'] = int(d['nlinks'])\n d['size'] = int(d['size'])\n if d['linktarget']:\n d['linktarget'] = d['linktarget'].replace(r'\\ ', ' ')\n return d\n\n def addFile(self, info):\n \"\"\"\n Append file information dictionary to the list of known files.\n\n Subclasses can override or extend this method to handle file\n information differently without affecting the parsing of data\n from the server.\n\n @param info: dictionary containing the parsed representation\n of the file information\n @type info: dict\n \"\"\"\n self.files.append(info)\n\n def unknownLine(self, line):\n \"\"\"\n Deal with received lines which could not be parsed as file\n information.\n\n Subclasses can override this to perform any special processing\n needed.\n\n @param line: unparsable line as received\n @type line: str\n \"\"\"\n pass\n\ndef parsePWDResponse(response):\n \"\"\"\n Returns the path from a response to a PWD command.\n\n Responses typically look like::\n\n 257 \"/home/andrew\" is current directory.\n\n For this example, I will return C{'/home/andrew'}.\n\n If I can't find the path, I return L{None}.\n \"\"\"\n match = re.search('\"(.*)\"', response)\n if match:\n return match.groups()[0]\n else:\n return None\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/twisted/protocols/ftp.py","file_name":"ftp.py","file_ext":"py","file_size_in_byte":105954,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"}
+{"seq_id":"12828285247","text":"import os, sys, praw, json, datetime\nimport pprint as p\n\n\nbanned_subs = (\n\t\"SuicideWatch\",\n\t\"depression\",\n\t\"test\", # would comment without being asked to because it replies to u/MarkdownShadowBot\n\t\"discordapp\", # those fuckers banned me so no need to waste cpu time here\n)\n\nif not os.path.isfile(\"checked_comments.json\"):\n\tchecked_comments = []\nelse:\n\twith open(\"checked_comments.json\", \"r\") as f:\n\t\tchecked_comments = json.loads(f.read())\n\nwith open(\"image_list.json\", \"r\") as f:\n\tblackholed_urls = json.loads(f.read())\n\n# allow client secret and password to be set manually or by environment for automating\ntry:\n\tsecret = os.environ[\"CLIENT_SECRET\"]\nexcept KeyError:\n\tsecret = input(\"secret: \")\n\ntry:\n\tpw = os.environ[\"REDDIT_PASSWORD\"]\nexcept KeyError:\n\tpw = input(\"password: \")\n\nreddit = praw.Reddit(\n\tclient_id=\"0V1g_2X1ayVenw\",\n\tclient_secret=secret,\n\tuser_agent=\"unix:4x_jpeg_bot:alpha (by /u/4x_jpeg)\",\n\tusername=\"4x_jpeg\",\n\tpassword=pw\n)\n\nmorejpeg_auto = reddit.redditor(\"morejpeg_auto\")\n\ntry:\n\ti = 0\n\tfor comment in morejpeg_auto.comments.new(limit=10):\n\t\ti += 1\n\t\tsys.stdout.write(\"\\rChecking: #%d (%s)\" % (i, comment.permalink))\n\t\tsys.stdout.flush()\n\n\t\t# no need to check already checked comments\n\t\tskip = False\n\t\tfor checked_comment in checked_comments:\n\t\t\tif checked_comment[\"id\"] == comment.id:\n\t\t\t\tskip = True\n\t\t\t\tbreak\n\t\tif skip:\n\t\t\tcontinue\n\n\t\tfor sub in banned_subs:\n\t\t\tif comment.subreddit.display_name == sub:\n\t\t\t\tskip = True\n\t\t\t\tbreak\n\t\tif skip:\n\t\t\tcontinue\n\n\t\t# if this comment is new and stuff might change, we need a way to tell \n\t\t# later parts of the code to not record this\n\t\ttoo_early_to_skip = False\n\n\t\t# don't skip next run if u/morejpeg_auto's comment is less than 10h old\n\t\tif int(datetime.datetime.utcnow().timestamp()) < comment.created_utc + 36000:\n\t\t\ttoo_early_to_skip = True\n\n\n\t\t# for the record, out of curiosity (lul)\n\t\tanswered_by_us = False\n\n\t\t# something is broken with comment.replies if we don't do .refresh()\n\t\t# also .refresh() throws if the comment was deleted, so well need to catch that\n\t\ttry:\n\t\t\tcomment.refresh()\n\n\t\t\tfor reply in comment.replies:\n\t\t\t\t# don't answer if the comment is pretty new, we want to\n\t\t\t\t# give u/morejpeg some time (at least 10min)\n\t\t\t\tif int(datetime.datetime.utcnow().timestamp()) < reply.created_utc + 600:\n\t\t\t\t\ttoo_early_to_skip = True\n\t\t\t\t\tcontinue\n\t\t\t\t\n\t\t\t\tfor line in iter(reply.body.splitlines()):\n\t\t\t\t\t# if someone writes \"morejpeg\" they probably are just talking about\n\t\t\t\t\t# the bot and not actually requesting more jpeg\n\t\t\t\t\tif ((\"more\" in line.lower() and \"jpeg\" in line.lower())\n\t\t\t\t\t\tor (\"needs\" in line.lower() and \"jpeg\" in line.lower())\n\t\t\t\t\t\t) and not \"morejpeg\" in line.lower():\n\n\t\t\t\t\t\t# check if it was answered by u/morejpeg_auto\n\t\t\t\t\t\twas_answered = False\n\t\t\t\t\t\tfor replyreply in reply.replies:\n\t\t\t\t\t\t\tif (replyreply.author.name == \"morejpeg_auto\" or\n\t\t\t\t\t\t\t\treplyreply.author.name == \"4x_jpeg\"):\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\twas_answered = True\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\n\t\t\t\t\t\tif not was_answered:\n\t\t\t\t\t\t\treply.reply(\n\t\t\t\t\t\t\t\t\">%s\\n\"\n\t\t\t\t\t\t\t\t\"\\n\"\n\t\t\t\t\t\t\t\t\"u/morejpeg_auto doesn't seem to answer you,\"\n\t\t\t\t\t\t\t\t\" so I'll help out:\\n\"\n\t\t\t\t\t\t\t\t\"[Here you go!](%s)\\n\"\n\t\t\t\t\t\t\t\t\"\\n\"\n\t\t\t\t\t\t\t\t\"\\n\"\n\t\t\t\t\t\t\t\t\"^(I am a bot and I don't answer to replies, though my master might.)\\n\"\n\t\t\t\t\t\t\t\t\"[GitHub]\"\n\t\t\t\t\t\t\t\t\"(https://github.com/Nunu-Willump/4x_jpeg_bot.git)\\n\"\n\t\t\t\t\t\t\t\t\"\"\n\t\t\t\t\t\t\t\t% (\n\t\t\t\t\t\t\t\t\tline,\n\n\t\t\t\t\t\t\t\t\t# add number of checked comments to index\n\t\t\t\t\t\t\t\t\t# to have it not repeat after each run\n\t\t\t\t\t\t\t\t\tblackholed_urls[(i + len(checked_comments))\n\t\t\t\t\t\t\t\t\t\t% len(blackholed_urls)]\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\tanswered_by_us = True\n\t\t\t\t\t\t\ttoo_early_to_skip = False\n\n\t\t\t\t\t\t# dont continue with this reply if the last line triggered the if\n\t\t\t\t\t\tcontinue\n\n\t\t# something is wrong with the comment, and that won't change as we already called\n\t\t# comment.refresh()\n\t\texcept AttributeError:\n\t\t\tpass\n\t\t# this happens at comment.refresh() if the comment was deleted\n\t\texcept praw.exceptions.ClientException:\n\t\t\tpass\n\n\t\tif not too_early_to_skip:\n\t\t\tchecked_comments.append({\"id\": comment.id,\n\t\t\t\t\"body\": comment.body, \"link\": \"https://reddit.com\" + comment.permalink,\n\t\t\t\t\"Answered\": answered_by_us})\n\nexcept Exception as e:\n\tsys.stdout.write(\"\\n\\nCaught %s.\" % repr(e))\n# also catch keyboard interrups and other shit so the json file always gets updated\nexcept:\n\tsys.stdout.write(\"\\n\\nCaught something. Idk though.\")\n\nprint(\"\\n\\nGimme a sec to update the json file...\")\nf = open(\"checked_comments.json\", \"w\")\nf.write(json.dumps(checked_comments, indent=4))\nf.close()\nexit(0)\n","repo_name":"llenck/4x_jpeg_bot","sub_path":"commenter.py","file_name":"commenter.py","file_ext":"py","file_size_in_byte":4542,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"7658062116","text":"\"\"\" Question 76:\nPlease write a program to output a random number, which is divisible by 5 and 7,\nbetween 0 and 10 inclusive using random module and list comprehension.\n\n\nHints:\nUse random.choice() to a random element from a list.\n\"\"\"\nimport random\n\nnum = None\nwhile True:\n num = random.randrange(0, 11)\n if num % 5 == 0 and num % 7 == 0:\n break\nprint(num)\n","repo_name":"neequole/my-python-programming-exercises","sub_path":"unsorted_solutions/question76.py","file_name":"question76.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30709565196","text":"class ApplicationState:\n instance = None\n\n def __init__(self):\n self.is_logged_in = False\n\n @staticmethod\n def get_app_state():\n if not ApplicationState.instance:\n ApplicationState.instance = ApplicationState()\n return ApplicationState.instance\n\n\nappstate1 = ApplicationState().get_app_state()\nprint(appstate1.is_logged_in)\nappstate1.is_logged_in = True\n\nprint(appstate1.is_logged_in)\nappstate2 = ApplicationState().get_app_state()\nprint(appstate2.is_logged_in)\n\nprint(appstate2 == appstate1)\n\n\n\n","repo_name":"pradeep-automation/Python-Practice","sub_path":"DesignPatterns/Singleton.py","file_name":"Singleton.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14386674872","text":"\"\"\"ProyectoCoder URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/4.1/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import include, path\nfrom AppCoder.views import *\nfrom AppCoder import views\nfrom django.contrib.auth.views import LogoutView\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('', inicio),\n path('movies/', views.Movies),\n path('add_form/', views.add_form, name='add_form'),\n path('favorite/', views.Movies),\n path('fav_form/', views.fav_form, name='fav_form'),\n path('reviews/', views.Reviews),\n path('review_form/', views.review_form, name='review_form'),\n path('find_movie/', views.find_movie, name='find_movie'),\n path('resultados/',views.Resultados,name='resultados'),\n path('busqueda/',views.Busqueda),\n path('login/',views.login_request),\n path('register/',views.register),\n path('register/',views.register, name=\"register\"),\n path('logout/',LogoutView.as_view(template_name='logout.html'), name=\"logout\"),\n]\n","repo_name":"belchus/pythoncopy","sub_path":"ProyectoCoder/ProyectoCoder/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19667828136","text":"# time: O(nlogk)\n# just same logic as k closest points to x\nimport math\nimport heapq\nclass Solution:\n def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]:\n heap= []\n for i in range(len(points)):\n dist= math.sqrt((points[i][0]**2)+(points[i][1]**2))\n heapq.heappush(heap,(-1*dist,points[i]))\n if len(heap)>k:\n heapq.heappop(heap)\n ans= []\n for i in range(len(heap)):\n ans.append(heap[i][1])\n return ans\n\n# better way of writing above code\nimport math\nimport heapq\nclass Solution:\n def kClosest(self, points: List[List[int]], k: int) -> List[List[int]]:\n heap= []\n for x,y in points:\n dist= sqrt(x**2 + y**2)\n heapq.heappush(heap,(-1*dist,x,y))\n if len(heap)>k:\n heapq.heappop(heap)\n return [(x,y) for (dist,x,y) in heap]\n\n\n# try later by quicksort method \n# https://leetcode.com/problems/k-closest-points-to-origin/discuss/219442/Python-with-quicksort-algorithm\n","repo_name":"Ravi-0412/DSA-Program-And-Notes","sub_path":"Heap/973_K_Closest_Point.py","file_name":"973_K_Closest_Point.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"41470859169","text":"class Solution:\n def restoreIpAddresses(self, s: str) -> List[str]:\n N = len(s)\n ans =[]\n def recurse(index, left, current):\n if left < 0:\n return\n \n if index == N:\n if left == 0:\n ans.append(\".\".join(current))\n return\n \n if s[index] == \"0\":\n current.append(\"0\")\n recurse(index + 1, left -1,current)\n current.pop()\n return\n for i in range(3):\n if index + i < N and 0 <= int(s[index:index + i + 1]) <= 255:\n current.append(s[index:index +i + 1])\n recurse(index + i + 1, left-1, current)\n current.pop()\n \n recurse(0, 4, [])\n return ans","repo_name":"Imsachin010/Leetcode-problems","sub_path":"restoring Ip address/93.py","file_name":"93.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"69900292562","text":"from pprint import pprint\nfrom collections import deque\n\nprint('===== day 23 =====')\n\ndef to_cups(i: str):\n\treturn [int(x) for x in i]\n\ndef step(cups):\n\tcurrent = cups[0]\n\tpicked_up = cups[1:4]\n\tdel cups[0:4]\n\tcups.append(current)\n\tdest = find_dest(current - 1, cups)\n\ti = cups.index(dest)\n\tnext = cups[0:i+1] + picked_up + cups[i+1:]\n\treturn next\n\nMAX = 9\ndef find_dest(n, ns):\n\t#assert ns != []\n\t# assert len([x for x in ns if x > 9 or x < 1]) == 0\n\tn = wrap(n)\n\twhile n not in ns:\n\t\tn = wrap(n - 1)\n\treturn n\n\ndef wrap(n):\n\tif n < 1:\n\t\tn = MAX\n\treturn n\n\ndef run(cups, steps):\n\t#seen = dict()\n\tfor i in range(steps):\n\t\tif (i % 1000) == 0:\n\t\t\tprint(i)\n\t\t#h = str(cups)\n\t\t#seen[h] = i\n\t\t\n\t\tcups = step(cups)\n\t\t\n\t\tif False:\n\t\t\th = str(cups)\n\t\t\tif h in seen:\n\t\t\t\tprev = seen[h]\n\t\t\t\tprint('repeat! previously saw on step', prev, 'and seen again on step', i, ', delta', i - prev)\n\t\t\tseen[h] = i\n\treturn cups\n\ndef solution(cups):\n\ti = cups.index(1)\n\tresult = cups[i+1:] + cups[0:i]\n\treturn ''.join((str(x) for x in result))\n\nexample = to_cups(\"389125467\")\nreal_input = to_cups(\"716892543\")\n\np1_answer = solution(run(real_input, 100))\nprint('part 1 answer:', p1_answer)\nassert p1_answer == '49725386'\n\nONE_MIL = 1_000_000\nMAX = ONE_MIL\n\ndef grow_input(cups):\n\tc = max(cups)\n\tprint(c)\n\trest = list(range(c + 1, ONE_MIL + 1))\n\tresult = cups + rest\n\tassert max(result) == 1_000_000, 'max was %s' % max(result)\n\tassert len(result) == 1_000_000, 'len was %s - cups len %s - rest len %d' % (len(result), len(cups), len(rest))\n\treturn result\n\ndef solution2(cups):\n\ti = cups.index(1)\n\ta = cups[(i + 1) % len(cups)]\n\tb = cups[(i + 2) % len(cups)]\n\tresult = a*b\n\tprint('a', a, '- b', b, ' - part 2 solution', result)\n\treturn result\n\nexample = to_cups(\"389125467\")\nreal_input = to_cups(\"716892543\")\ne2 = grow_input(example)\na2 = grow_input(real_input)\n\nten_mil = 10_000_000\n\ndef p2(cups):\n\tprint('solving part 2')\n\tafter = run(cups, ten_mil)\n\tresult = solution2(after)\n\tprint('done')\n\treturn result\n\nexpected = 149245887792\nassert p2(e2) == expected\n\np2(a2)\n","repo_name":"jeremy-w/adventofcode-2020","sub_path":"src/day23.py","file_name":"day23.py","file_ext":"py","file_size_in_byte":2042,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"19275545273","text":"\"\"\"\n# O(n) time and space complexity solution\nclass Solution:\n def backspaceCompare(self, s: str, t: str) -> bool:\n def get_text(text: str) -> str:\n cursor = 0\n text = list(text)\n for i in range(len(text)):\n if text[i] != '#':\n text[cursor] = text[i]\n cursor += 1\n else:\n cursor = max(0, cursor-1)\n return \"\".join(text[:cursor])\n\n s = get_text(s)\n t = get_text(t)\n\n return (s==t)\n\n\"\"\"\n\n# O(n) time complexity and O(1) space complexity\n\nclass Solution:\n def backspaceCompare(self, s: str, t: str) -> bool:\n def get_next_index(string, index):\n count = 0\n while (index > -1):\n if string[index] != '#':\n if (count > 0):\n index -= 1\n count -= 1\n else:\n break\n else:\n count += 1\n index -= 1\n\n return index\n \n i, j = len(s)-1, len(t)-1\n while True:\n i = get_next_index(s, i)\n j = get_next_index(t, j)\n if ((i > -1) and (j >= -1)) and (s[i] == t[j]):\n i -= 1\n j -= 1\n else:\n break\n return ((i == -1) and (j == -1))\n\n","repo_name":"connectwithprakash/Leet-Code","sub_path":"problems/844_backspace_string_compare.py","file_name":"844_backspace_string_compare.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15441512640","text":"numbers_example = 1+1 #оператор с числом\nstring_example = \"a\" * 4 #оператор с строкой\n\nlist_example = list()\nlist_example.append(1) #метод листа\n\narray1 = [1,2,4]\narray2 = [2,3,5]\nresult_array = array2 + array1 #оператор и массивами\n\ntuple1 = (1,2)\ntuple2 = (2,3) \nresult_tuple = tuple1+ tuple2 #оператор и кортежи\n\nprint(numbers_example, string_example, list_example, result_array, result_tuple)","repo_name":"Clankyyy/Lab-1","sub_path":"Zadanie1.py","file_name":"Zadanie1.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73215783761","text":"import random\n\ndef rolld100():\n \n result = 0\n bonusDice = 0\n penaltyDice = 0\n tens_die_rolls = []\n\n bonusDice = int(input('Any bonus dice?[0]') or \"0\")\n penaltyDice = int(input('Any penalty dice?[0]') or \"0\")\n resultDice = abs(bonusDice - penaltyDice) + 1\n\n # Roll the ones die\n d2 = random.randint(0,9)\n \n # Determine if bonus or penalty dice are to be rolled and roll the appropriate number of dice\n \n if bonusDice > penaltyDice:\n # Determine and print the number of Bonus dice to roll\n print('Rolling d100 with ' + str(resultDice -1) + ' bonus dice.\\n')\n\n for i in range(resultDice):\n tens_die_roll = random.randint(0,9)\n if d2 == 0 and tens_die_roll == 0:\n tens_die_roll =100 \n print('Tens die ' + str(i+1) + ': ' + str(tens_die_roll))\n tens_die_rolls.append(tens_die_roll)\n d1 = max(tens_die_rolls)\n\n elif penaltyDice > bonusDice:\n # Determine and print the number of Penalty dice to roll\n print('Rolling d100 with ' + str(resultDice -1) + ' penalty dice.\\n')\n \n for i in range(resultDice):\n tens_die_roll = random.randint(0,9)\n if d2 == 0 and tens_die_roll == 0:\n tens_die_roll =100 \n print('Tens die ' + str(i+1) + ': ' + str(tens_die_roll))\n tens_die_rolls.append(tens_die_roll)\n d1 = min(tens_die_rolls)\n\n else:\n # Determine and print the number of dice to roll\n print('Rolling d100 with no bonus or penalty dice.\\n')\n d1 = random.randint(0,9)\n print('Tens die: ' + str(d1))\n\n print (\"Ones die: \" + str(d2))\n\n if d1 == 100 and d2 == 0:\n result = 100\n else:\n result = d1 * 10 + d2\n return result\n\nprint(\"Final roll: \" + str(rolld100()))","repo_name":"dannable/DicePercent","sub_path":"dicepercent.py","file_name":"dicepercent.py","file_ext":"py","file_size_in_byte":1829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33550743283","text":"from models.gert_noeqv import make_model\nfrom models.heads import PPIFeedForward\nfrom sklearn.metrics import roc_auc_score, average_precision_score\nfrom utils import get_mask\nimport data.ppi.ppi_dataloader as dl\nimport data.ppi.noneqv_db5_dataloader as test_dl\nimport os\nimport time\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nPROT_ATOMS = ('C', 'O', 'N', 'S', 'P')\nRES_LABEL = ('LEU', 'ILE', 'VAL', 'TYR', 'ARG', 'GLU', 'PHE', 'ASP', 'THR', 'LYS', \n 'ALA', 'GLY', 'TRP', 'SER', 'PRO', 'ASN', 'GLN', 'HIS', 'MET', 'CYS')\n\n\ndef train(epoch, transformer_model, ff_model, loader, criterion, optimizer, device, max_train_iter, print_frequency):\n transformer_model.train()\n ff_model.train()\n\n start = time.time()\n\n losses = [] \n total = 0\n for it, (subunit1, subunit2) in enumerate(loader):\n tick = time.time()\n subunit1.label, subunit2.label = subunit1.label.to(device), subunit2.label.to(device)\n subunit1.pos, subunit2.pos = subunit1.pos.to(device), subunit2.pos.to(device)\n optimizer.zero_grad()\n\n mask1 = get_mask(subunit1.pos)\n out1 = transformer_model(subunit1.pos, mask1)\n mask2 = get_mask(subunit2.pos)\n out2 = transformer_model(subunit2.pos, mask2)\n output = ff_model(out1, out2)\n loss = criterion(output, subunit1.label.float())\n loss.backward()\n losses.append(loss.item())\n optimizer.step()\n\n if it % print_frequency == 0:\n elapsed = time.time() - start\n print(f'Epoch {epoch}, iter {it}, train loss {np.mean(losses)}, avg it/sec {print_frequency / elapsed}')\n start = time.time()\n if it == max_train_iter:\n return np.mean(losses)\n\n return np.mean(losses)\n\n\n@torch.no_grad()\ndef test(transformer_model, ff_model, loader, criterion, device, max_test_iter, print_frequency):\n transformer_model.eval()\n ff_model.eval()\n\n losses = []\n total = 0\n\n y_true = []\n y_pred = []\n for it, (subunit1, subunit2) in enumerate(loader):\n if subunit1.pos.shape[1] == 1:\n continue\n subunit1.label, subunit2.label = subunit1.label.to(device), subunit2.label.to(device)\n subunit1.pos, subunit2.pos = subunit1.pos.to(device), subunit2.pos.to(device)\n mask1 = get_mask(subunit1.pos)\n out1 = transformer_model(subunit1.pos, mask1)\n mask2 = get_mask(subunit2.pos)\n out2 = transformer_model(subunit2.pos, mask2)\n output = ff_model(out1, out2)\n loss = criterion(output, subunit1.label.float())\n losses.append(loss.item())\n y_true.extend(subunit1.label.tolist())\n y_pred.extend(output.tolist())\n if it % print_frequency == 0:\n print(f'iter {it}, loss {np.mean(losses)}')\n if it == max_test_iter:\n break\n\n y_true = np.array(y_true)\n y_pred = np.array(y_pred)\n auroc = roc_auc_score(y_true, y_pred)\n auprc = average_precision_score(y_true, y_pred)\n\n return np.mean(losses), auroc, auprc\n\n\ndef train_noneqv_ppi(ex, use_attention, data_dir, device, log_dir, checkpoint, num_epochs, batch_size,\n hidden_dim, learning_rate, workers, betas, eps, d_ff, d_atom,\n eta, max_radius, num_atoms, num_heads, max_train_iter, max_test_iter,\n print_frequency, test_mode=False):\n train_set = dl.PPI_Dataset(os.path.join(data_dir, 'pairs_pruned_train@1000'), max_radius=max_radius)\n train_loader = dl.DataLoader(train_set, batch_size=batch_size, num_workers=workers)\n val_set = dl.PPI_Dataset(os.path.join(data_dir, 'pairs_pruned_val@1000'), max_radius=max_radius)\n val_loader = dl.DataLoader(val_set, batch_size=batch_size, num_workers=workers)\n \n transformer_model = make_model(\n num_heads=num_heads, \n d_model=hidden_dim, \n d_ff=d_ff, \n d_atom=d_atom,\n eta=eta, \n Rc=max_radius, \n num_atoms=num_atoms, \n N=2,\n num_dense=2,\n use_attention=use_attention).to(device)\n ff_model = PPIFeedForward(hidden_dim).to(device)\n\n model_parameters = filter(lambda p: p.requires_grad, transformer_model.parameters())\n num_parameters = sum([np.prod(p.size()) for p in model_parameters])\n print('Number of trainable parameters:', num_parameters)\n \n best_val_loss = 999\n \n params = [x for x in transformer_model.parameters()] + [x for x in ff_model.parameters()]\n\n criterion = nn.BCELoss().to(device)\n optimizer = torch.optim.Adam(params, lr=learning_rate, betas=betas, eps=eps)\n scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min',\n factor=0.7, patience=2,\n min_lr=1e-6)\n\n if checkpoint:\n cpt = torch.load(checkpoint, map_location=device)\n transformer_model.load_state_dict(cpt['transformer_state_dict'])\n ff_model.load_state_dict(cpt['ff_state_dict'])\n optimizer.load_state_dict(cpt['optimizer_state_dict'])\n print('Loaded model from checkpoint')\n\n print(f'Training for {num_epochs} epochs')\n print('---------------------------------')\n for epoch in range(1, num_epochs+1):\n start = time.time()\n train_loss = train(\n epoch, \n transformer_model, \n ff_model, \n train_loader, \n criterion, \n optimizer,\n device,\n max_train_iter, \n print_frequency)\n ex.log_scalar('Train Loss', train_loss)\n print('Validating...')\n val_loss, auroc, auprc = test(\n transformer_model, \n ff_model, \n val_loader, \n criterion,\n device,\n max_test_iter,\n print_frequency)\n scheduler.step(val_loss)\n if val_loss < best_val_loss:\n torch.save({\n 'epoch': epoch,\n 'transformer_state_dict': transformer_model.state_dict(),\n 'ff_state_dict': ff_model.state_dict(),\n 'optimizer_state_dict': optimizer.state_dict(),\n 'loss': train_loss,\n }, os.path.join(log_dir, f'best_weights.pt'))\n best_val_loss = val_loss\n elapsed = (time.time() - start)\n ex.log_scalar('Validation Loss', val_loss)\n ex.log_scalar('Validation AUROC', auroc)\n ex.log_scalar('Validation AUPRC', auprc)\n print('Epoch: {:03d}, Time: {:.3f} s'.format(epoch, elapsed))\n print(f'\\tTrain loss {train_loss}, Val loss {val_loss}, Val AUROC {auroc}, Val AUPRC {auprc}')\n\n\n if test_mode:\n print(\"Testing on DIPS...\")\n test_set = dl.PPI_Dataset(os.path.join(data_dir, 'pairs_pruned_test@1000'), max_radius=max_radius)\n test_loader = dl.DataLoader(test_set, batch_size=batch_size, num_workers=workers)\n cpt = torch.load(os.path.join(log_dir, f'best_weights.pt'))\n transformer_model.load_state_dict(cpt['transformer_state_dict'])\n ff_model.load_state_dict(cpt['ff_state_dict'])\n test_loss, auroc, auprc = test(\n transformer_model, \n ff_model, \n test_loader, \n criterion,\n device,\n max_test_iter,\n print_frequency)\n ex.log_scalar('Test Loss', test_loss)\n ex.log_scalar('Test AUROC', auroc)\n ex.log_scalar('Test AUPRC', auprc)\n print(f'\\tTest loss {test_loss}, Test AUROC {auroc}, Test AUPRC {auprc}')\n\n print(\"Testing on DB5...\")\n db5_dir = '/oak/stanford/groups/rondror/projects/atom3d/protein_protein_interfaces/DB5/'\n test_set = test_dl.PPI_Dataset(os.path.join(db5_dir, 'pairs@10'), max_radius=max_radius)\n test_loader = test_dl.DataLoader(test_set, batch_size=batch_size, num_workers=workers)\n cpt = torch.load(os.path.join(log_dir, f'best_weights.pt'))\n transformer_model.load_state_dict(cpt['transformer_state_dict'])\n ff_model.load_state_dict(cpt['ff_state_dict'])\n test_loss, auroc, auprc = test(\n transformer_model, \n ff_model, \n test_loader, \n criterion,\n device,\n max_test_iter,\n print_frequency)\n ex.log_scalar('Test Loss', test_loss)\n ex.log_scalar('Test AUROC', auroc)\n ex.log_scalar('Test AUPRC', auprc)\n print(f'\\tTest loss {test_loss}, Test AUROC {auroc}, Test AUPRC {auprc}')\n return test_loss, auroc, auprc\n else:\n return best_val_loss","repo_name":"drorlab/gert","sub_path":"src/atom3d_combined/tasks/ppi/train_ppi.py","file_name":"train_ppi.py","file_ext":"py","file_size_in_byte":8540,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"1418071801","text":"import gradio as gr\ndict = {\n \"ko2en\" : {\n \"사과\" : \"apple\",\n \"사자\" : \"tiger\",\n \"사랑\" : \"love\"\n },\n \"en2ko\" : {\n \"apple\" : \"사과\",\n \"tiger\" : \"사자\",\n \"love\" : \"사랑\"\n }\n}\ndef trans(type, word):\n return dict[type][word]\nwith gr.Blocks() as app:\n result = gr.Textbox(label='결과')\n type = gr.Radio(label='종류', choices=['ko2en', 'en2ko'])\n word = gr.Textbox(label='단어', placeholder=\"한국어\")\n btn = gr.Button(value='실행')\n btn.click(\n fn = trans,\n inputs = [type, word],\n outputs = result\n )\napp.launch(debug=True, share=True)\n#public URL로 접근하면 전세계 어디서나 내 컴퓨터에 접속 가능\n#그러나! 72시간 후에 접속 안됨. 내 컴퓨터를 끄면 접속 안됨\n","repo_name":"gavy1004/AIHubWeb","sub_path":"gradioApp/block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26961765735","text":"\"\"\"\n26. Remove Duplicates from Sorted Array\n\nGiven a sorted array, remove the duplicates in place such that each element appear only once and return the new length.\n\nDo not allocate extra space for another array, you must do this in place with constant memory.\n\nFor example,\nGiven input array nums = [1,1,2],\n\nYour function should return length = 2, with the first two elements of nums being 1 and 2 respectively. It doesn't matter what you leave beyond the new length.\n\nHide Company Tags Microsoft Bloomberg Facebook\nShow Tags\nShow Similar Problems.\n\n\"\"\"\n\nclass Solution(object):\n def removeDuplicates(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n\n Own Solution: two pointer, first pointer will point to current location where value should be replaced, second pointer moves forward to find next non repetitive num\n \"\"\"\n if len(nums) < 2:\n return len(nums)\n\n start, end, current = 1, 1, nums[0]\n while end < len(nums):\n if nums[end] != current:\n current = nums[start] = nums[end]\n start += 1\n end += 1\n\n return start","repo_name":"tejamupparaju/LeetCode_Python","sub_path":"leet_code26.py","file_name":"leet_code26.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9535499194","text":"import inspect\nimport collections\nimport ipywidgets as ipyw\nfrom collections.abc import Sequence\nfrom collections import OrderedDict\nfrom operator import attrgetter\nfrom functools import wraps\n\nMISSING = set()\ntry:\n import dataclasses\n MISSING.add(dataclasses.MISSING)\nexcept ImportError:\n pass\n\ntry:\n import attr\n MISSING.add(attr.NOTHING)\nexcept ImportError:\n pass\n\nWIDGET_MAP = {\n int: (ipyw.IntSlider, 'value'),\n float: (ipyw.FloatSlider, 'value'),\n str: (ipyw.Text, 'value'),\n bool: (ipyw.Checkbox, 'value'),\n list: (ipyw.Dropdown, 'options'),\n tuple: (ipyw.Dropdown, 'options'),\n set: (ipyw.Dropdown, 'options')\n}\n\nATTRS_FIELDS = '__attrs_attrs__'\nDATACLASS_FIELDS = '__dataclass_fields__'\n\nWIDGET = '__widget__'\n\nATTRS_FIELDS = '__attrs_attrs__'\nDATACLASS_FIELDS = '__dataclass_fields__'\n\nWIDGET = '__widget__'\n\ndef _fields(instance):\n \"\"\"Generator that will yield\n attribute name, attribute field, attribute type\n\n Arguments:\n field_list {[type]} -- [description]\n\n Keyword Arguments:\n types {[type]} -- [description] (default: {None})\n \"\"\"\n fields = getattr(instance, ATTRS_FIELDS, None)\n if fields is None:\n fields = getattr(instance, DATACLASS_FIELDS, None)\n if fields is None:\n raise TypeError\n return dataclasses.fields(instance)\n else:\n try:\n return attr.fields(instance)\n except TypeError:\n return attr.fields(instance.__class__)\n raise TypeError(\"Not an instance of dataclass or attrs\")\n\ndef _observe_handlers(instance):\n \"\"\"Return a map of attributes and handlers\n\n Arguments:\n instance {[type]} -- [description]\n \"\"\"\n handlers = collections.defaultdict(list)\n for name, obj in inspect.getmembers(instance):\n if name.startswith('__') and name.endswith('__'):\n # skip magic methods\n continue\n\n if inspect.isfunction(obj):\n observees = getattr(obj, '_observes', {})\n for observee, kwargs in observees.items():\n handlers[observee].append((-1, obj, kwargs))\n\n return handlers\n\ndef create_widgets(instance, public_attributes_only=True):\n fields = _fields(instance)\n\n handlers = _observe_handlers(instance)\n widgets = []\n __widgets = OrderedDict()\n for field in fields:\n if public_attributes_only and field.name.startswith('_'):\n continue\n kwargs = {'description': field.name}\n kwargs.update(field.metadata.get('__widget', {}))\n\n try:\n _type = field.type\n widget_class, dattr = WIDGET_MAP[_type]\n except KeyError:\n if field.default not in MISSING:\n _type = type(field.default)\n widget_class, dattr = WIDGET_MAP[_type]\n elif field.default_factory not in MISSING:\n _type = type(field.default_factory())\n widget_class, dattr = WIDGET_MAP[_type]\n else:\n raise TypeError(\"Unable to find widget type for {}\".format(field.name))\n\n if field.default not in MISSING:\n kwargs['value'] = field.default\n\n widget = widget_class(**kwargs)\n for _t, observer, kwargs in handlers[field.name]:\n widget.observe(observer, names='value')\n\n widgets.append(widget)\n __widgets[field.name] = widget\n\n instance.__widgets = __widgets\n return widgets\n\n\ndef _observer(instance, attribute, name):\n def _observe(event):\n setattr(instance, attribute, event[name])\n return _observe\n\n\nclass Sync(object):\n def __init__(self, method, observes, updates):\n self.method = method\n self.observes = observes\n self.updates = updates\n\n def _sync_to_widgets(self, instance):\n _widgets = getattr(instance, '__widgets', {})\n if not _widgets:\n return\n for attribute, trait in self.updates.items():\n setattr(_widgets[attribute], trait, getattr(instance, attribute))\n\n def __get__(self, instance, owner):\n @wraps(self.method)\n def method(*args, **kwargs):\n rv = self.method(instance, *args, **kwargs)\n if self.updates:\n self._sync_to_widgets(instance)\n return rv\n method._observes = self.observes\n method._updates = self.updates\n return method\n\ndef sync(observes=None, updates=None):\n def _sync(method):\n return Sync(method, observes or {}, updates or {})\n return _sync\n","repo_name":"groutr/datawidgets","sub_path":"datawidgets/layout.py","file_name":"layout.py","file_ext":"py","file_size_in_byte":4514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39750262383","text":"import re\nfrom urllib.parse import urlparse\nfrom uuid import UUID\nfrom datetime import datetime\n\nimport pytest\n\nfrom django.shortcuts import reverse\n\nfrom users.tests.factories import USER_PASSWORD\nfrom media_items.models import MediaItem\n\n\n@pytest.mark.django_db\ndef test_sign_upload_request_success(authenticated_client, monkeypatch):\n c, u = authenticated_client\n monkeypatch.setenv('MEDIA_UPLOAD_BUCKET_NAME', 'abcdefgh')\n monkeypatch.setenv('AWS_ACCESS_KEY_ID', 'XXXX22XXXXXX4XX2XXXX')\n monkeypatch.setenv('AWS_SECRET_ACCESS_KEY', '0AbbbCtSAfgpoi71w8WERw8AviFYatdIV3xcPGry')\n mime_type = 'image/jpeg'\n request_url = reverse('upload-request')\n response = c.post(request_url, {'mime_type': mime_type})\n assert response.status_code == 201\n assert 'Location' in response\n assert urlparse(response['Location']) is not None\n\n\n@pytest.mark.django_db\ndef test_sign_upload_request_logged_out(client):\n url = reverse('upload-request')\n response = client.post(url, {}, secure=True)\n assert response.status_code == 403\n\n\n@pytest.mark.django_db\ndef test_sign_upload_request_mime_type_missing(authenticated_client):\n c, u = authenticated_client\n url = reverse('upload-request')\n response = c.post(url, {})\n assert response.status_code == 400\n assert 'required parameter' in str(response.content)\n\n\n@pytest.mark.django_db\ndef test_sign_upload_request_mime_type_unsupported(authenticated_client):\n c, u = authenticated_client\n url = reverse('upload-request')\n response = c.post(url, {'mime_type': 'foobar'})\n assert response.status_code == 400\n assert 'not supported' in str(response.content)\n","repo_name":"ebridges/elektrum","sub_path":"application/media_items/tests/test_upload_request.py","file_name":"test_upload_request.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38732893695","text":"import datetime\nfrom django.shortcuts import render_to_response, get_object_or_404\nfrom django.template import Context, loader\nfrom django.template.context import RequestContext\nfrom wic.models import *\nfrom wic.forms import *\nfrom wic.authentication import APIAuth\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom djangorestframework.renderers import JSONRenderer, JSONPRenderer, XMLRenderer\nfrom djangorestframework.views import ListModelView, ListOrCreateModelView\nfrom djangorestframework.permissions import IsAuthenticated\n\nclass AuthenticatedListModelView(ListModelView):\n \"\"\"\n Extends django-rest-framework's ListModelView to add our authentication methods to it.\n \"\"\"\n authentication = { APIAuth, }\n permissions = (IsAuthenticated, )\n renderers = (\n JSONRenderer,\n JSONPRenderer,\n XMLRenderer\n )\n\nclass AuthenticatedListOrCreateModelView(ListOrCreateModelView):\n \"\"\"\n Extends django-rest-framework's ListOrCreateModelView to add our authentication methods to it.\n \"\"\"\n authentication = { APIAuth, }\n permissions = (IsAuthenticated, )\n renderers = (\n JSONRenderer,\n JSONPRenderer,\n XMLRenderer\n )\n\ndef main_index(request):\n news_articles = NewsArticle.objects.all().order_by('-article_date')\n paginator = Paginator(news_articles, 8)\n\n page = request.GET.get('page')\n try:\n articles = paginator.page(page)\n except PageNotAnInteger:\n articles = paginator.page(1)\n except EmptyPage:\n articles = paginator.page(paginator.num_pages)\n\n return render_to_response( 'index.html' , {\"articles\": articles})\n\ndef view_article(request, slug):\n news_articles = NewsArticle.objects.all().order_by('-article_date')\n paginator = Paginator(news_articles, 8)\n articles = paginator.page(1)\n return render_to_response( 'newsarticle.html' , {\n 'post': get_object_or_404(NewsArticle, slug=slug),\n 'articles': articles\n })\n\ndef view_gig(request, slug):\n post = get_object_or_404(Gig, slug=slug)\n return render_to_response( 'gigs/index.html' , {'post': post, } )\n\ndef band_index(request):\n members = BandMember.objects.filter(is_active=True)\n t = loader.get_template( 'band/index.html' )\n c = Context({\n 'members': members,\n })\n return HttpResponse(t.render(c))\n\ndef gigs_index(request):\n gig_list = Gig.objects.filter(end_date__gte=datetime.date.today()).order_by('start_date')\n t = loader.get_template( 'gigs/index.html' )\n c = Context({\n 'gig_list': gig_list,\n })\n return HttpResponse(t.render(c))\n\ndef gigs_old(request):\n gig_list = Gig.objects.filter(end_date__lt=datetime.date.today()).order_by('start_date')\n t = loader.get_template( 'gigs/previous.html' )\n c = Context({\n 'gig_list': gig_list,\n })\n return HttpResponse(t.render(c))\n\ndef band_index(request):\n band_members = BandMember.objects.all().order_by('first_name')\n t = loader.get_template( 'band/index.html' )\n c = Context({\n 'members': band_members,\n })\n return HttpResponse(t.render(c))\n\ndef media_photos(request):\n photos = Photo.objects.all().order_by('-flickr_id')\n paginator = Paginator(photos, 27)\n\n page = request.GET.get('page')\n try:\n pics = paginator.page(page)\n except PageNotAnInteger:\n pics = paginator.page(1)\n except EmptyPage:\n pics = paginator.page(paginator.num_pages)\n\n return render_to_response( 'media/photos.html' , {\"photos\": pics})\n\ndef media_discography(request):\n albums = Album.objects.all().order_by('-release_date')\n\n t = loader.get_template( 'media/discography.html' )\n c = Context({\n 'albums': albums,\n })\n return HttpResponse(t.render(c))\n\ndef media_videos(request):\n t = loader.get_template( 'media/videos.html' )\n c = Context({})\n return HttpResponse(t.render(c))\n\ndef contact(request):\n if request.method == 'POST':\n form = ContactForm(request.POST)\n if form.is_valid():\n subject = form.cleaned_data['subject']\n message = form.cleaned_data['message']\n sender = form.cleaned_data['sender']\n\n recipients = ['walkincoma@hotmail.co.uk']\n\n from django.core.mail import send_mail\n send_mail(subject, message, sender, recipients)\n return HttpResponseRedirect('/thanks/')\n else:\n form = ContactForm()\n\n return render_to_response( 'contact.html' , {\n 'form': form,\n }, context_instance=RequestContext(request))","repo_name":"nickpack/WIC-Site","sub_path":"wic/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33484819451","text":"import os\nfrom pymacaron_unit import testcase\n\n\ndef load_port_host_token():\n \"\"\"Find out which host:port to run acceptance tests against,\n using the environment variables PYM_SERVER_HOST, PYM_SERVER_PORT\n \"\"\"\n\n server_host, server_port, token = (None, None, None)\n\n if 'PYM_SERVER_HOST' in os.environ:\n server_host = os.environ['PYM_SERVER_HOST']\n if 'PYM_SERVER_PORT' in os.environ:\n server_port = os.environ['PYM_SERVER_PORT']\n\n token = os.environ.get('PYM_JWT_TOKEN', None)\n\n if server_host:\n if server_host.startswith('http://'):\n server_host = server_host[7:]\n if server_host.startswith('https://'):\n server_host = server_host[8:]\n if server_host.endswith('/'):\n server_host = server_host[:-1]\n\n if not server_host or not server_port:\n raise Exception(\"Please set both of PYM_SERVER_HOST and PYM_SERVER_PORT envvironment variables\")\n\n return (server_host, server_port, token)\n\n\nclass PyMacaronTestCase(testcase.PyMacaronTestCase):\n\n token = None\n\n def setUp(self):\n super().setUp()\n self.maxDiff = None\n self.host, self.port, self.token = load_port_host_token()\n proto = 'https' if self.port in (443, '443') else 'http'\n self.base_url = '%s://%s:%s' % (proto, self.host, self.port)\n\n def assertIsVersion(self, j):\n self.assertTrue(type(j['apis']) is list)\n self.assertTrue(type(j['name']) is str)\n self.assertTrue(type(j['version']) is str)\n self.assertTrue(type(j['pym_env']) is str)\n\n def assertHasPing(self):\n return self.assertGetReturnOk('ping')\n\n def assertHasVersion(self, verify_ssl=True):\n j = self.assertGetReturnJson('version', 200, verify_ssl=verify_ssl)\n self.assertIsVersion(j)\n return j\n\n def assertHasAuthVersion(self, verify_ssl=True):\n self.assertGetReturnError('auth/version', 401, 'AUTHORIZATION_HEADER_MISSING', verify_ssl=verify_ssl)\n\n tests = [\n # header, status code, json code\n (\"\", 401, 'AUTHORIZATION_HEADER_MISSING'),\n (\"Bearer1234567890\", 401, 'TOKEN_INVALID'),\n (\"bearer foo bar\", 401, 'TOKEN_INVALID'),\n (\"Bearer 1234567890\", 401, 'TOKEN_INVALID'),\n ]\n\n for t in tests:\n token, status, error = t\n self.assertGetReturnError('auth/version', status, error, token, verify_ssl=verify_ssl)\n\n j = self.assertGetReturnJson('auth/version', 200, \"Bearer %s\" % self.token, verify_ssl=verify_ssl)\n self.assertIsVersion(j)\n return j\n","repo_name":"pymacaron/pymacaron","sub_path":"pymacaron/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2597,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"3"}
+{"seq_id":"38217920018","text":"# Write a program to check if two strings are a rotation of each other\r\n\r\ndef rotations(string1, string2):\r\n if len(string1) != len(string2):\r\n return False\r\n \r\n concatenated_strings = string1 + string1\r\n \r\n if string2 in concatenated_strings:\r\n return True\r\n \r\n return False\r\n\r\n# Example usage\r\nstr1 = \"yeshwenth\"\r\nstr2 = \"htnewhsey\"\r\nresult = rotations(str1, str2)\r\nprint(result)\r\n","repo_name":"yeshwenthgutta15/Assignment-1-Linear-Data-Structures-part1","sub_path":"program3.py","file_name":"program3.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8736308190","text":"from .models import Profile, FollowUser\n\n\ndef is_following(request, id):\n profile = Profile.objects.get(pk=id)\n try:\n following = FollowUser.objects.get(user=request.user, follow=profile)\n except FollowUser.DoesNotExist:\n following = None\n return following\n","repo_name":"chasedixon/chasedixon.github.io","sub_path":"portfolio/platter_social/mysite/users/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22572191376","text":"def mergeOverlappingIntervals(intervals):\n intervals = sorted(intervals, key=lambda x: x[0])\n print(f\"Sorted intervals: {intervals}\")\n for (idx, item) in enumerate(intervals[:-1]):\n next_item = intervals[idx+1]\n s_num, e_num = item\n n_s_num, n_e_num = next_item\n\n # two interval has overlap\n if e_num >= n_s_num and e_num < n_e_num:\n print(f\"merge {item} + {next_item} => [{s_num}, {n_e_num}]\")\n new_item = [s_num, n_e_num]\n # replace the merged interval(first)\n intervals[idx] = new_item\n # remove the merged interval(second)\n del intervals[idx+1]\n return mergeOverlappingIntervals(intervals)\n\n # next interval is subset of previous interval\n if e_num >= n_s_num and e_num >= n_e_num:\n del intervals[idx+1]\n return mergeOverlappingIntervals(intervals)\n\n return intervals","repo_name":"taigi0315/AlgoExpert","sub_path":"midium_questions/mergeOverlappingIntervals.py","file_name":"mergeOverlappingIntervals.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15304446290","text":"#!/usr/bin/python3\n\nimport rclpy\nfrom rclpy.node import Node\nfrom rclpy.time import Time\nfrom geometry_msgs.msg import Twist\nfrom sensor_msgs.msg import LaserScan\nfrom nav_msgs.msg import Odometry\n\nfrom math import cos, sin, degrees, radians, pi\nimport sys\nimport tf_transformations\nfrom tf2_ros import TransformBroadcaster\nfrom geometry_msgs.msg import TransformStamped\n\n# Change this path to your crazyflie-firmware folder\nsys.path.append('/home/Project/crazyflie-firmware')\nimport cffirmware\n\nclass sgbaDriver:\n\n def init(self, webots_node, properties):\n self.robot = webots_node.robot\n timestep = int(self.robot.getBasicTimeStep())\n ## Initialize motors\n self.m1_motor = self.robot.getDevice(\"m1_motor\")\n self.m1_motor.setPosition(float('inf'))\n self.m1_motor.setVelocity(-1)\n self.m2_motor = self.robot.getDevice(\"m2_motor\")\n self.m2_motor.setPosition(float('inf'))\n self.m2_motor.setVelocity(1)\n self.m3_motor = self.robot.getDevice(\"m3_motor\")\n self.m3_motor.setPosition(float('inf'))\n self.m3_motor.setVelocity(-1)\n self.m4_motor = self.robot.getDevice(\"m4_motor\")\n self.m4_motor.setPosition(float('inf'))\n self.m4_motor.setVelocity(1)\n\n self.target_twist = Twist()\n\n ## Initialize Sensors\n self.imu = self.robot.getDevice(\"inertial unit\")\n self.imu.enable(timestep)\n self.gps = self.robot.getDevice(\"gps\")\n self.gps.enable(timestep)\n self.gyro = self.robot.getDevice(\"gyro\")\n self.gyro.enable(timestep)\n self.range_front = self.robot.getDevice(\"range_front\")\n self.range_front.enable(timestep)\n self.range_left = self.robot.getDevice(\"range_left\")\n self.range_left.enable(timestep)\n self.range_back = self.robot.getDevice(\"range_back\")\n self.range_back.enable(timestep)\n self.range_right = self.robot.getDevice(\"range_right\")\n self.range_right.enable(timestep)\n\n #self.lidar = self.robot.getDevice(\"lidar\")\n #self.lidar.enable(timestep)\n\n ## Intialize Variables\n self.past_x_global = 0\n self.past_y_global = 0\n self.past_z_global = 0\n self.past_time = self.robot.getTime()\n\n self.first_pos = True\n self.first_x_global = 0.0\n self.first_y_global = 0.0\n\n\n cffirmware.controllerPidInit()\n\n rclpy.init(args=None)\n self.node = rclpy.create_node('sgba_controller')\n self.laser_publisher = self.node.create_publisher(LaserScan, 'scan', 10)\n self.odom_publisher = self.node.create_publisher(Odometry, 'odom', 10)\n\n self.tfbr = TransformBroadcaster(self.node)\n\n self.msg_laser = LaserScan()\n self.node.create_timer(1.0/30.0, self.publish_laserscan_data)\n\n def publish_laserscan_data(self):\n\n front_range = float(self.range_front.getValue()/1000.0)\n\n back_range = float(self.range_back.getValue()/1000.0)\n left_range = float(self.range_left.getValue()/1000.0)\n right_range = float(self.range_right.getValue()/1000.0)\n max_range = 3.49\n if front_range > max_range:\n front_range = 0.0\n if left_range > max_range:\n left_range = 0.0\n if right_range > max_range:\n right_range = 0.0\n if back_range > max_range:\n back_range = 0.0\n\n self.msg_laser = LaserScan()\n self.msg_laser.header.stamp = Time(seconds=self.robot.getTime()).to_msg()\n self.msg_laser.header.frame_id = 'base_link'\n self.msg_laser.range_min = 0.1\n self.msg_laser.range_max = max_range\n #print('print',back_range, left_range, front_range, right_range, back_range)\n self.msg_laser.ranges = [back_range, left_range, front_range, right_range, back_range]\n #self.msg_laser.ranges = [max_range, max_range, max_range, max_range, max_range]\n\n self.msg_laser.angle_min = 0.5 * 2*pi\n self.msg_laser.angle_max = -0.5 * 2*pi\n self.msg_laser.angle_increment = -1.0*pi/2\n self.laser_publisher.publish(self.msg_laser)\n \n def step(self):\n #rclpy.spin_once(self.node, timeout_sec=0)\n self.node.get_logger().error('pee')\n\n\n# def main(args=None):\n# rclpy.init(args=args)\n# sd = sgbaDriver()\n# rclpy.spin(sd)\n\n# rclpy.shutdown()\n# pass\n\n# if __name__ == '__main__':\n# main()","repo_name":"TomoCoder123/sgba-webots2.0","sub_path":"ros2_ws/src/crazyswarm2/crazyflie_ros2_sgba/crazyflie_ros2_sgba/sgba_controller.py","file_name":"sgba_controller.py","file_ext":"py","file_size_in_byte":4499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28922891491","text":"# -*- coding: utf-8 -*-\n\n__author__ = \"Petter Hetland\"\n__email__ = \"pehe@nmbu.no\"\n\n\n# Import pytest\nimport pytest\n\n\n# Median function sourced from ex03_pytest_intro.rst authored by\n# Yngve Mardal Moe\ndef median(data):\n \"\"\"\n Returns median of data.\n\n :param data: An iterable of containing numbers\n :return: Median of data\n \"\"\"\n\n sdata = sorted(data)\n n = len(sdata)\n if n % 2 == 1:\n return sdata[n // 2]\n else:\n return 0.5 * (sdata[n // 2 - 1] + sdata[n // 2])\n\n\ndef test_one_element_list():\n \"\"\"\"\". Tests that the median function returns\n the correct value for a one-element list.\n \"\"\"\n\n median_5 = median([5])\n assert median_5 == 5\n\n\ndef test_odd_n():\n \"\"\" Tests that the correct median is returned for lists with odd\n numbers of element.\n \"\"\"\n data = [5, 6, 8, 4, 2]\n data_median = median(data)\n assert data_median == 5\n\n\ndef test_even_n():\n \"\"\" Tests that the correct median is returned for lists with even\n numbers of elements.\n \"\"\"\n data = [5, 6, 8, 4, 2, 9]\n data_median = median(data)\n assert data_median == 5.5\n\n\ndef test_ordered_variations():\n \"\"\" Tests that the correct median is returned for lists with ordered,\n reverse-ordered and unordered elements\n \"\"\"\n data_1 = [1, 2, 3, 4, 5]\n data_2 = [5, 4, 3, 2, 1]\n data_3 = [4, 2, 3, 5, 1]\n data_1_median = median(data_1)\n data_2_median = median(data_2)\n data_3_median = median(data_3)\n assert data_1_median and data_2_median and data_3_median == 3\n\n\ndef test_empty_list():\n \"\"\" Tests that a IndexError is raised when\n the length of the given list is 0\n \"\"\"\n data = []\n with pytest.raises(IndexError):\n median(data)\n\n\ndef test_original_data():\n \"\"\" Tests that the median-function does not\n alter the original list of data\n \"\"\"\n data = [4, 5, 7, 6]\n data_median = median(data)\n assert data == [4, 5, 7, 6]\n\n\ndef test_tuples():\n \"\"\" Tests that the function works for tuples as well as lists\n \"\"\"\n tuple_data = (2, 4, 6, 8, 10)\n tuple_data_median = median(tuple_data)\n assert tuple_data_median == 6\n","repo_name":"pkhetland/Scientific-Python-exercises","sub_path":"Exercises/ex03_pytest_intro/test_median.py","file_name":"test_median.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"20667084959","text":"import tweepy\n\n# 認証キーの設定\nconsumer_key = \"hoge\"\nconsumer_secret = \"hoge\"\naccess_token = \"hoge\"\naccess_token_secret = \"hoge\"\n\n# OAuth認証\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\n\n# APIのインスタンスを生成\n# api = tweepy.API(auth)\napi = tweepy.API(\n auth,\n wait_on_rate_limit=True,\n wait_on_rate_limit_notify=True\n)\n\ndef getFollowers_ids(api, screen_name):\n followers_ids = tweepy.Cursor(api.followers_ids, screen_name=screen_name, cursor=-1).items()\n followers_ids_list = []\n try:\n followers_ids_list=[i for i in followers_ids]\n \n\n except tweepy.error.TweepError as e:\n print(e.reason)\n\n return followers_ids_list\n \n\n# Get Id list of followers\nfrom tqdm import tqdm\nimport time\nscreen_name=\"kyouyap\"\nfollowers_ids=getFollowers_ids(api, screen_name)\nname=[]\nscreen_name=[]\nurl=[]\ndescription=[]\nprotected=[]\nfolloerws_count=[]\nfriends＿count=[]\nlisted_count=[]\nstatuses_count=[]\ncreated_at=[]\ntry:\n for i in tqdm(range(len(followers_ids))):\n if (i%900==0)and(i!=0):\n time.sleep(600)\n center_info = api.get_user(id=followers_ids[i])\n name.append(center_info.name)\n screen_name.append(center_info.screen_name)\n url.append(center_info.url)\n description.append(center_info.description)\n protected.append(center_info.protected)\n folloerws_count.append(center_info.followers_count)\n friends＿count.append(center_info.friends_count)\n listed_count.append(center_info.listed_count)\n statuses_count.append(center_info.statuses_count)\n created_at.append(center_info.created_at)\n if (i%900==0)and(i!=0):\n time.sleep(900)\nexcept tweepy.error.TweepError as e:\n print(e.reason)\nimport pandas as pd\ndf=pd.DataFrame({\"name\":name,\"screen_name\":screen_name,\"url\":url\n ,\"description\":description,\"protected\":protected,\"folloerws_count\":folloerws_count,\"friends_count\":friends＿count,\"listed_count\":listed_count,\"statuses_count\":statuses_count,\"created_at\":created_at})\ndf.to_csv(\"folower_information.csv\",index=None,encoding='utf_8_sig')\n\ndf.description=df.description.fillna(\"\")\n\nimport japanize_matplotlib\nimport re\nimport MeCab\nimport numpy as np\n\n# 対象の品詞\nTARGET_POS1 = ['名詞']\n \n# 対象の詳細分類1\nTARGET_POS2 = ['サ変接続', 'ナイ形容詞語幹', '形容動詞語幹', '一般', '固有名詞']\n \n# ストップワード\nSTOP_WORDS = ['*']\n \ndef remove_blank(chapter):\n # 空白行と段落先頭の空白を削除\n \n lines = chapter.splitlines()\n \n # 空白行削除\n # 行頭の空白削除\n lines_cleaned = [l.strip() for l in lines if len(l) != 0]\n \n return '\\n'.join(lines_cleaned)\n \ndef chapter2bform(chapter_l):\n # 章ごとに形態素解析して単語の原型のリストを作成\n \n m = MeCab.Tagger('-d /usr/local/lib/mecab/dic/mecab-ipadic-neologd')\n m.parse('')\n \n bform_2l = []\n for i, chapter in enumerate(chapter_l):\n node = m.parseToNode(chapter)\n \n bform_l = []\n while node:\n feature_split = node.feature.split(',')\n \n pos1 = feature_split[0]\n pos2 = feature_split[1]\n base_form = feature_split[6]\n \n if pos1 in TARGET_POS1 and pos2 in TARGET_POS2 and base_form not in STOP_WORDS:\n bform_l.append(base_form)\n \n node = node.next\n \n bform_2l.append(bform_l)\n \n print('Term number of chapter {}: '.format(i+1), len(bform_l))\n return bform_2l\n\nfrom itertools import combinations, dropwhile\nfrom collections import Counter, OrderedDict\n \ndef bform2pair(bform_2l, min_cnt=5):\n # 単語ペアの出現章数をカウント\n \n # 全単語ペアのリスト\n pair_all = []\n \n for bform_l in bform_2l:\n # 章ごとに単語ペアを作成\n # combinationsを使うと順番が違うだけのペアは重複しない\n # ただし、同単語のペアは存在しえるのでsetでユニークにする\n pair_l = list(combinations(set(bform_l), 2))\n \n # 単語ペアの順番をソート\n for i,pair in enumerate(pair_l):\n pair_l[i] = tuple(sorted(pair))\n \n pair_all += pair_l\n \n # 単語ペアごとの出現章数\n pair_count = Counter(pair_all)\n \n # ペア数がmin_cnt以上に限定\n for key, count in dropwhile(lambda key_count: key_count[1] >= min_cnt, pair_count.most_common()):\n del pair_count[key]\n \n return pair_count\n \ndef pair2jaccard(pair_count, bform_2l, edge_th=0.4):\n # jaccard係数を計算\n \n # 単語ごとの出現章数\n word_count = Counter()\n for bform_l in bform_2l:\n word_count += Counter(set(bform_l))\n \n # 単語ペアごとのjaccard係数を計算\n jaccard_coef = []\n for pair, cnt in pair_count.items():\n jaccard_coef.append(cnt / (word_count[pair[0]] + word_count[pair[1]] - cnt))\n \n # jaccard係数がedge_th未満の単語ペアを除外\n jaccard_dict = OrderedDict()\n for (pair, cnt), coef in zip(pair_count.items(), jaccard_coef):\n if coef >= edge_th:\n jaccard_dict[pair] = coef\n print(pair, cnt, coef, word_count[pair[0]], word_count[pair[1]], sep='\\t')\n \n return jaccard_dict\n \n\nimport networkx as nx\n# matplotlibのターミナル対応\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n \ndef build_network(jaccard_dict):\n # 共起ネットワークを作成\n G = nx.Graph()\n \n # 接点／単語（node）の追加\n # ソートしないとネットワーク図の配置が実行ごとに変わる\n nodes = sorted(set([j for pair in jaccard_dict.keys() for j in pair]))\n G.add_nodes_from(nodes)\n \n print('Number of nodes =', G.number_of_nodes())\n \n # 線（edge）の追加\n for pair, coef in jaccard_dict.items():\n G.add_edge(pair[0], pair[1], weight=coef)\n \n print('Number of edges =', G.number_of_edges())\n \n plt.figure(figsize=(15, 15))\n \n # nodeの配置方法の指定\n seed = 0\n np.random.seed(seed)\n pos = nx.nx_agraph.graphviz_layout(\n G,\n prog='neato',\n args='-Goverlap=\"scalexy\" -Gsep=\"+6\" -Gnodesep=0.8 -Gsplines=\"polyline\" -GpackMode=\"graph\" -Gstart={}'.format(seed))\n\n \n # nodeの大きさと色をページランクアルゴリズムによる重要度により変える\n pr = nx.pagerank(G)\n nx.draw_networkx_nodes(\n G,\n pos,\n node_color=list(pr.values()),\n cmap=plt.cm.rainbow,\n alpha=0.7,\n node_size=[100000*v for v in pr.values()])\n \n # 日本語ラベルの設定\n nx.draw_networkx_labels(G, pos, fontsize=15, font_family='IPAexGothic', font_weight='bold')\n \n # エッジ太さをJaccard係数により変える\n edge_width = [d['weight'] * 8 for (u, v, d) in G.edges(data=True)]\n nx.draw_networkx_edges(G, pos, alpha=0.7, edge_color='darkgrey', width=edge_width, font_size=12, font_family='IPAexGothic')\n \n plt.axis('off')\n plt.tight_layout()\n plt.savefig('co-occurance.png', bbox_inches='tight')\n\n\n\n# 章ごとの単語原型リスト\nbform_2l = chapter2bform(list(df.description))\n\n# Jaccard係数の計算\npair_count = bform2pair(bform_2l, min_cnt=4)\njaccard_dict = pair2jaccard(pair_count, bform_2l, edge_th=0.4)\n\n# 共起ネットワーク作成\nbuild_network(jaccard_dict)","repo_name":"kyouyap/Co-occurrence_word","sub_path":"wordtwitter.py","file_name":"wordtwitter.py","file_ext":"py","file_size_in_byte":7420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14945481857","text":"# -*- coding: utf-8 -*-\n\nimport requests\nimport time\nimport os\nimport csv\nimport random\nimport json\n\nfrom bokeh.core.json_encoder import pd\nfrom bs4 import BeautifulSoup\nimport importlib\n\n\n# 要爬取热评的起始url\nurl = 'https://m.weibo.cn/comments/hotflow?id=4514782050289970&mid=4514782050289970&max_id_type=0'\n# cookie UA要换成自己的\nheaders = {\n 'Cookie': 'WEIBOCN_WM=9006_2001; WEIBOCN_FROM=1110006030; ALF=1597055017; SCF=AlFfFFXPNaDKgqx85KrtJ_D4OlIDpYusd-srzgU9N7wdCuyDFlppseRiusFDb6VRiMrfS6O-MldlxaC2qF-nBwM.; SUBP=0033WrSXqPxfM725Ws9jqgMF55529P9D9WhLoqxGA9SeGA8PL_rcQ1Pv5JpX5K-hUgL.Fo-fSonEe05R1h52dJLoIE5LxK.L1K.LBoqLxKqL1K.LBKnLxKqL1KMLB-Bc1KnReBtt; SSOLoginState=1594463025; SUB=_2A25yDeNtDeRhGeNK7VIW-CrLyDqIHXVR8Y0lrDV6PUJbkdAKLXDQkW1NSUzFjEmYMYOtxSc6SOoyOceWns7NlhqM; SUHB=0vJIObHKmWEkoX; _T_WM=73630556534; MLOGIN=1; M_WEIBOCN_PARAMS=sourceType%3Dqq%26from%3D10A7095010%26featurecode%3Dnewtitle%26oid%3D4514782050289970%26luicode%3D20000061%26lfid%3D4514782050289970%26uicode%3D20000061%26fid%3D4514782050289970; XSRF-TOKEN=b85cc4',\n 'Referer': 'https://m.weibo.cn/status/4514782050289970?sourceType=qq&from=10A7095010&wm=9006_2001&featurecode=newtitle',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36',\n 'X-Requested-With': 'XMLHttpRequest'\n}\n\ndef get_page(max_id, id_type):\n params = {\n 'max_id': max_id,\n 'max_id_type': id_type\n }\n try:\n r = requests.get(url, params=params, headers=headers)\n if r.status_code == 200:\n return r.json()\n except requests.ConnectionError as e:\n print('请检查网络', e.args)\n\n\ndef parse_page(jsondata):\n if jsondata:\n items = jsondata.get('data')\n global item_max_id\n item_max_id = {}\n try:\n if items != None:\n item_max_id['max_id'] = items['max_id']\n item_max_id['max_id_type'] = items['max_id_type']\n return item_max_id\n except TypeError:\n print('有可能是IP被封了，若之后一直出现此消息，请更换IP')\n\ndef write_csv(jsondata):\n dataone = jsondata.get('data')\n global datas\n try:\n if dataone != None:\n datas = dataone.get('data')\n except AttributeError:\n print('这页没有东西了')\n pass\n\n if datas != None:\n for data in datas:\n created_at = data.get(\"created_at\")\n like_count = data.get(\"like_count\")\n source = data.get(\"source\")\n total_number = data.get(\"total_number\")\n username = data.get(\"user\").get(\"screen_name\")\n gender = data.get(\"user\").get(\"gender\")\n if gender == 'f':\n gender = '女'\n elif gender == 'm':\n gender = '男'\n comment = data.get(\"text\")\n #print(comment)\n emoji = BeautifulSoup(comment, 'lxml').find('img')\n #print(emoji)\n if emoji is None:\n continue\n elif emoji is not None:\n emoj = emoji.get('alt')\n #if emoj is not None:\n #continue\n #print(emoj)\n # elif emoji is None:\n #continue\n #elif emoji == '':\n #ontinue\n #print(emoj)\n comment = BeautifulSoup(comment, 'lxml').get_text()\n writer.writerow([username, gender, created_at, like_count, total_number, emoj,\n json.dumps(comment, ensure_ascii=False)])\n\n# 存为csv\npath = \"C:\\\\Users\\\\pc\\\\Desktop\\\\mojitocomment2.csv\"\ncsvfile = open(path, 'w', encoding = 'utf-8-sig')\nwriter = csv.writer(csvfile)\nwriter.writerow(['用户名', '性别', '发表时间', '点赞数', '评论数', '表情', '评论内容'])\n\nmaxpage = 3100 #爬取的数量\nmax_id = 0\nid_type = 0\nfor page in range(0, maxpage):\n print(page)\n jsondata = get_page(max_id, id_type)\n write_csv(jsondata)\n results = parse_page(jsondata)\n x = random.randint(6,8)\n time.sleep(x)\n if results != None:\n max_id = results['max_id']\n id_type = results['max_id_type']\n else:\n print('翻页方式错误')\n time.sleep(10)\n max_id = max_id\n id_type = id_type\n\n\n","repo_name":"TaoChenXu/Python","sub_path":"weibocomment/weibocomment.py","file_name":"weibocomment.py","file_ext":"py","file_size_in_byte":4330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10657525094","text":"import math\nimport numpy as np\n\nclass Projector:\n \"\"\"Projects 2d points to 3d space defined by a homography matrix.\"\"\"\n \n def __init__(self, camera_matrix_file: str):\n\n # homography matrix: 3d -> 2d\n self.homography = np.zeros((3, 3))\n with open(camera_matrix_file, \"r\") as f:\n line = f.readline()\n PRE = \"Homography matrix:\"\n if line.startswith(PRE):\n line = line[len(PRE):]\n rows = line.split(\";\")\n for i, line in enumerate(rows):\n cols = line.strip().split()\n assert len(cols) == 3\n for j, x in enumerate(cols):\n self.homography[i, j] = float(x)\n self.inv_homography = np.linalg.inv(self.homography)\n \n def project3d(self, x, y):\n p = np.matmul(self.inv_homography, np.array([x, y, 1]))\n p /= p[2]\n return p[:2]\n\n\ndef dist(latlon1, latlon2) -> float:\n \"\"\"Distance between two gps coordinates.\n\n Uses the Haversine formula (https://www.movable-type.co.uk/scripts/latlong.html)\n\n Parameters:\n ----------\n latlon1: array_like[2]\n Latitude-longitude of point 1.\n latlon2: array_like[2]\n Latitude-longitude of point 2.\n \n Returns:\n -------\n d: float\n Distance between the two points in meters.\n \"\"\"\n R = 6371_000\n phi1 = latlon1[0] * math.pi / 180\n phi2 = latlon2[0] * math.pi / 180\n dphi = phi2 - phi1\n dlambda = (latlon2[1] - latlon1[1]) * math.pi / 180\n a = math.sin(dphi / 2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlambda / 2) ** 2\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n d = R * c\n return d\n\ndef dist_planar(latlon1, latlon2):\n \"\"\"Estimation of the distance between two points assuming planar earth.\"\"\"\n R = 6371_000\n y1 = latlon1[0] * math.pi / 180\n y2 = latlon2[0] * math.pi / 180\n x1 = latlon1[1] * math.pi / 180\n x2 = latlon2[1] * math.pi / 180\n\n dy = abs(y1 - y2) * R\n r_avg = R * math.cos((y1 + y2) / 2)\n dx = abs(x1 - x2) * r_avg\n return math.sqrt(dx ** 2 + dy ** 2)\n \n \n","repo_name":"regob/vehicle_mtmc","sub_path":"mot/projection_3d.py","file_name":"projection_3d.py","file_ext":"py","file_size_in_byte":2124,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"44254108021","text":"from __future__ import print_function\nimport numpy as np\n\n\nclass HMM:\n\n def __init__(self, pi, A, B, obs_dict, state_dict):\n \"\"\"\n - pi: (1*num_state) A numpy array of initial probailities. pi[i] = P(Z_1 = s_i)\n - A: (num_state*num_state) A numpy array of transition probailities. A[i, j] = P(Z_t = s_j|Z_t-1 = s_i)\n - B: (num_state*num_obs_symbol) A numpy array of observation probabilities. B[i, k] = P(X_t = o_k| Z_t = s_i)\n - obs_dict: (num_obs_symbol*1) A dictionary mapping each observation symbol to their index in B\n - state_dict: (num_state*1) A dictionary mapping each state to their index in pi and A\n \"\"\"\n self.pi = pi\n self.A = A\n self.B = B\n self.obs_dict = obs_dict\n self.state_dict = state_dict\n\n def forward(self, Osequence):\n \"\"\"\n Inputs:\n - self.pi: (1*num_state) A numpy array of initial probailities. pi[i] = P(Z_1 = s_i)\n - self.A: (num_state*num_state) A numpy array of transition probailities. A[i, j] = P(Z_t = s_j|Z_t-1 = s_i)\n - self.B: (num_state*num_obs_symbol) A numpy array of observation probabilities. B[i, k] = P(X_t = o_k| Z_t = s_i)\n - Osequence: (1*L) A numpy array of observation sequence with length L\n\n Returns:\n - alpha: (num_state*L) A numpy array alpha[i, t] = P(Z_t = s_i, x_1:x_t | λ)\n \"\"\"\n S = len(self.pi)\n L = len(Osequence)\n alpha = np.zeros([S, L])\n ###################################################\n # Edit here\n for i in range(S):\n alpha[i,0] = self.pi[i] * self.B[i,self.obs_dict[Osequence[0]]]\n\n for t in range(1,L):\n for i in range(S):\n alpha[i,t] = self.B[i,self.obs_dict[Osequence[t]]] * sum([self.A[k, i] * alpha[k, t - 1] for k in range(S)])\n ###################################################\n return alpha\n\n def backward(self, Osequence):\n \"\"\"\n Inputs:\n - self.pi: (1*num_state) A numpy array of initial probailities. pi[i] = P(Z_1 = s_i)\n - self.A: (num_state*num_state) A numpy array of transition probailities. A[i, j] = P(Z_t = s_j|Z_t-1 = s_i)\n - self.B: (num_state*num_obs_symbol) A numpy array of observation probabilities. B[i, k] = P(X_t = o_k| Z_t = s_i)\n - Osequence: (1*L) A numpy array of observation sequence with length L\n\n Returns:\n - beta: (num_state*L) A numpy array beta[i, t] = P(x_t+1:x_T | Z_t = s_i, λ)\n \"\"\"\n S = len(self.pi)\n L = len(Osequence)\n beta = np.zeros([S, L])\n ###################################################\n # Edit here\n for i in range(S):\n beta[i, L-1] = 1\n\n for t in reversed(range(L-1)):\n for i in range(S):\n beta[i,t] = sum([beta[j, t + 1] * self.A[i, j] * self.B[j, self.obs_dict[Osequence[t + 1]]] for j in range(S)])\n ###################################################\n return beta\n\n def sequence_prob(self, Osequence):\n \"\"\"\n Inputs:\n - Osequence: (1*L) A numpy array of observation sequence with length L\n\n Returns:\n - prob: A float number of P(x_1:x_T | λ)\n \"\"\"\n prob = 0\n ###################################################\n # Edit here\n alpha = self.forward(Osequence)\n prob = sum(alpha[:, -1])\n ###################################################\n return prob\n\n def posterior_prob(self, Osequence):\n \"\"\"\n Inputs:\n - Osequence: (1*L) A numpy array of observation sequence with length L\n\n Returns:\n - prob: (num_state*L) A numpy array of P(s_t = i|O, λ)\n \"\"\"\n S = len(self.pi)\n L = len(Osequence)\n prob = np.zeros([S, L])\n ###################################################\n # Edit here\n alpha = self.forward(Osequence)\n beta = self.backward(Osequence)\n seq_prob = self.sequence_prob(Osequence)\n for i in range(S):\n for j in range(L):\n prob[i,j] = alpha[i,j] * beta[i,j] / seq_prob\n ###################################################\n return prob\n #TODO:\n def likelihood_prob(self, Osequence):\n \"\"\"\n Inputs:\n - Osequence: (1*L) A numpy array of observation sequence with length L\n\n Returns:\n - prob: (num_state*num_state*(L-1)) A numpy array of P(X_t = i, X_t+1 = j | O, λ)\n \"\"\"\n S = len(self.pi)\n L = len(Osequence)\n prob = np.zeros([S, S, L - 1])\n ###################################################\n # Edit here\n alpha = self.forward(Osequence)\n beta = self.backward(Osequence)\n seq_prob = self.sequence_prob(Osequence)\n for i in range(S):\n for j in range(S):\n for k in range(L-1):\n prob[i,j,k] = alpha[i,k] * self.A[i,j] * self.B[j, self.obs_dict[Osequence[k+1]]] * beta[j,k+1] / seq_prob\n ###################################################\n return prob\n\n def viterbi(self, Osequence):\n \"\"\"\n Inputs:\n - Osequence: (1*L) A numpy array of observation sequence with length L\n\n Returns:\n - path: A List of the most likely hidden state path k* (return state instead of idx)\n \"\"\"\n path = []\n ###################################################\n # Q3.3 Edit here\n S = len(self.pi)\n L = len(Osequence)\n delta = np.zeros([S, L])\n Delta = np.zeros([S, L], dtype=\"int\")\n delta[:, 0] = self.pi * self.B[:, self.obs_dict[Osequence[0]]]\n\n for t in range(1, L):\n for i in range(S):\n delta[i, t] = self.B[i, self.obs_dict[Osequence[t]]] * np.max(self.A[:, i] * delta[:, t - 1])\n Delta[i, t] = np.argmax(self.A[:, i] * delta[:, t - 1])\n z = np.argmax(delta[:, L - 1])\n path.append(z)\n\n for t in range(L - 1, 0, -1):\n z = Delta[z, t]\n path.append(z)\n path = path[::-1]\n\n new_dict = {v : k for k, v in self.state_dict.items()}\n for i in range(len(path)):\n path[i] = new_dict[path[i]]\n\n ###################################################\n return path\n","repo_name":"russell-lin/HMM","sub_path":"hmm.py","file_name":"hmm.py","file_ext":"py","file_size_in_byte":6329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42853887700","text":"from . import HobokenTestCase, hoboken\nimport os\nimport yaml\nfrom hoboken.tests.compat import parametrize, parametrize_class, unittest\n\nfrom hoboken.application import Request\nfrom hoboken.six import iteritems, text_type\nMatcher = hoboken.matchers.HobokenRouteMatcher\n\n\nclass TestMethods(HobokenTestCase):\n def after_setup(self):\n for meth in self.app.SUPPORTED_METHODS:\n self.app.add_route(meth, '/', self.body_func)\n\n def test_successful_methods(self):\n methods = list(self.app.SUPPORTED_METHODS)\n methods.remove(\"HEAD\")\n for meth in methods:\n self.assert_body_is('request body', method=meth)\n\n def test_HEAD_method(self):\n self.assert_body_is('', method=\"HEAD\")\n\n def test_failed_methods(self):\n for meth in self.app.SUPPORTED_METHODS:\n self.assert_not_found(path='/somebadpath')\n\n\nclass TestHeadFallback(HobokenTestCase):\n def after_setup(self):\n @self.app.get('/')\n def get_func():\n self.app.response.headers['X-Custom-Header'] = b'foobar'\n return 'get body'\n\n def test_HEAD_fallback(self):\n r = Request.build('/')\n r.method = \"HEAD\"\n resp = r.get_response(self.app)\n\n self.assertEqual(resp.status_int, 200)\n self.assertEqual(len(resp.body), 0)\n self.assertEqual(resp.headers['X-Custom-Header'], b'foobar')\n\n\n# Load our list of test cases from our yaml file.\ncurr_dir = os.path.abspath(os.path.dirname(__file__))\ntest_file = os.path.join(curr_dir, 'routing_tests.yaml')\nwith open(test_file, 'rb') as f:\n file_data = f.read()\ntest_cases = list(yaml.load_all(file_data))\n\n\n@parametrize_class\nclass TestRouting(unittest.TestCase):\n @parametrize('param', test_cases)\n def test_route(self, param):\n if 'skip' in param:\n if hasattr(unittest, 'SkipTest'):\n raise unittest.SkipTest(param['skip'])\n return\n\n matcher = Matcher(param['path'])\n regex = param['regex'].encode('latin-1')\n self.assertEqual(matcher.match_re.pattern, regex)\n\n class FakeRequest(object):\n path_info = None\n\n for succ in param.get('successes', []):\n r = FakeRequest()\n r.path_info = succ['route'].encode('latin-1')\n matched, args, kwargs = matcher.match(r)\n\n expected_args = [x.encode('latin-1') for x in succ.get('args', [])]\n expected_kwargs_str = succ.get('kwargs', {})\n expected_kwargs = {}\n for k, v in iteritems(expected_kwargs_str):\n if isinstance(v, text_type):\n v = v.encode('latin-1')\n expected_kwargs[k] = v\n self.assertTrue(matched)\n self.assertEqual(args, expected_args)\n self.assertEqual(kwargs, expected_kwargs)\n\n for fail in param.get('failures', []):\n r = FakeRequest()\n r.path = fail\n matched, _, _ = matcher.match(r)\n\n self.assertFalse(matched)\n\n\ndef suite():\n suite = unittest.TestSuite()\n suite.addTest(unittest.makeSuite(TestMethods))\n suite.addTest(unittest.makeSuite(TestHeadFallback))\n suite.addTest(unittest.makeSuite(TestRouting))\n\n return suite\n\n","repo_name":"andrew-d/Hoboken","sub_path":"hoboken/tests/test_routing.py","file_name":"test_routing.py","file_ext":"py","file_size_in_byte":3239,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"19389625476","text":"\"\"\"\n LaTeX2IMG takes an expression, and name and an extension to convert that\n expression to a image file named \"name.extension\"\n\"\"\"\n\nimport sys\nimport os\nfrom urllib.parse import quote\nfrom urllib.request import urlopen\nfrom PIL import Image, ImageOps\n\n\ndef img2webp(path):\n \"\"\"\n Takes a path of an image and converts it to webp\n \"\"\"\n file, ext = os.path.splitext(path)\n image = Image.open(path).convert(\"RGBA\")\n image = ImageOps.expand(image, 75)\n image.save(file + \".webp\", \"WEBP\")\n os.remove(path)\n\ndef latex2img(expression, filename, extension):\n \"\"\"\n Convert expression to an image called filename.extension\n \"\"\"\n webp = False\n\n if extension not in (\"gif\", \"png\", \"pdf\", \"swf\", \"emf\", \"svg\", \"webp\"):\n print(\"Not supported extension, exiting...\")\n sys.exit(-1)\n\n if extension == \"webp\":\n webp = True\n extension = \"png\"\n\n # Preparing text strings\n server = \"http://latex.codecogs.com/\" + extension + \".download?\"\n fullname = filename + \".\" + extension\n size = \"%5Cdpi%7B300%7D%20\"\n\n # Quote expression\n expression = quote(expression)\n url = server + size + expression\n\n # Download file from url and save to output_file:\n with urlopen(url) as response, open(fullname, 'wb') as output_file:\n data = response.read() # Un objeto \"bytes\"\n output_file.write(data) # Se escribe en disco\n\n if webp:\n img2webp(fullname)\n extension = \"webp\"\n\n return filename + \".\" + extension\n","repo_name":"29antonioac/LaTeX2IMG","sub_path":"LaTeX2IMG/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"7480992864","text":"import random\nimport webbrowser\nimport os\nfrom time import sleep\nimport webbrowser\n\nvariable = [\"Wine Tasting\", 'Food', 'Skydiving', 'Symphony', 'Movie drive-in']\nrandom_choice = random.choice(variable)\n\n#placing in every location, along with the website for expense variable\ndef choices():\n #After random places is variable, it will go along with the if/elif system to correspond with its own function. \n print(\"After carefully selectings, we choose: \" + random_choice)\n if random_choice == 'Wine Tasting':\n # Function for Wine tasting, random will choose a key from Wine_choice and will depending what it is will open the webside automatically\n def Wine_Tasting():\n print('There are five basic steps in tasting wine: color, swirl, smell, taste, and savor.\\n These are also known as the \"five S\" steps: see, swirl, sniff, sip, savor.\\n Your job is to look for clarity, integration, expressiveness, complexity, and connectedness.\\n')\n Wine_choice = {\"Sable Gate Winery\": 'https://sablegatewinery.com/about/', 'Nice Winery':'https://www.nicewines.com/Visit-Us/Tastings', 'Decant Urban Winery': 'https://decantwinery.com','Ermarose Winery': 'https://ermarosewinery.com','Wild Stallion Vineyards': 'https://www.wildstallionvineyards.com'}\n sleep(10)\n entry = random.choice(list(Wine_choice))\n print('Your location for today\\'s adventure will be: ' + entry)\n print('A new page will open! Give me a sec for i can tranfer you over')\n sleep(10)\n\n while True:\n if entry == 'Sable Gate Winery':\n webbrowser.open('https://sablegatewinery.com/about')\n break\n elif entry == 'Nice Winery':\n webbrowser.open('https://www.nicewines.com')\n break\n elif entry == 'Decant Urban Winery':\n webbrowser.open('https://decantwinery.com')\n break\n elif entry == 'Ermarose Winery':\n webbrowser.open('https://ermarosewinery.com')\n break\n elif entry == 'Wild Stallion Vineyards':\n webbrowser.open('https://www.wildstallionvineyards.com')\n break\n Wine_Tasting()\n elif random_choice == 'Food':\n #function for food, this will place in a random answer from type of food and will go along with the if/elif to go with coressponding function\n def Food():\n os.system('clear')\n print('We have: Steak, Sushi, Italian')\n list = ['Steak','Sushi','Italian']\n Choice_food = random.choice(list)\n print(\"For today\\'s choice we have picked: \" + Choice_food)\n sleep(10)\n def Sushi():\n print(\"Sushi is a popular Japanese dish made from seasoned rice with fish, egg, or vegetables.\")\n Sushi_choice = ['Uchi', 'Hidden Omasake','MF Sushi','Kuu','Roka Akor']\n entry = random.choice(Sushi_choice)\n print('Your location for today\\'s adventure will be: ' + entry)\n print('A new page will open! Give me a sec for i can transfer you over')\n sleep(10)\n while True:\n if entry == 'Uchi':\n webbrowser.open('https://www.uchihouston.com')\n break\n elif entry == 'Hidden Omasake':\n webbrowser.open('https://hiddenomakase.com')\n break\n elif entry == 'MF Sushi':\n webbrowser.open('https://www.opentable.com/r/mf-sushi-houston')\n break\n elif entry == 'Kuu':\n webbrowser.open('https://kuurestaurant.com')\n break\n elif entry == 'Roka Akor':\n webbrowser.open('https://www.rokaakor.com')\n break\n\n def Steak():\n print(\"Steak is a food that can be described as tender, flavorful, juicy, and cooked to perfection\")\n Steak_choice = ['Vic & Anthony\\'s Steakhouse', 'B & B Butchers & Restaurant','Mastro\\'s Steakhouse','Tony\\'s','The Capital Grille']\n entry = random.choice(Steak_choice)\n print('Your location for today\\'s adventure will be: ' + entry + '!\\n' )\n print('A new page will open! Give me a sec for i can transfer you over')\n sleep(10)\n while True:\n if entry == 'Vic & Anthony\\'s Steakhouse':\n webbrowser.open('vicandanthonys.com')\n break\n elif entry == 'B & B Butchers & Restaurant':\n webbrowser.open('bbbutchers.com')\n break\n elif entry == 'Mastro\\'s Steakhouse':\n webbrowser.open('https://www.mastrosrestaurants.com')\n break\n elif entry == 'Tony\\'s':\n webbrowser.open('https://www.tonyshouston.com')\n break\n elif entry == 'The Capital Grille':\n webbrowser.open(' https://www.thecapitalgrille.com/locations/tx/houston/houston-the-galleria/8007')\n break\n\n def Italian():\n print(\"Squisito! If you want to pay someone the highest compliment for their cooking, an excellent alternative to buono is the word squisito meaning exquisite or delicious.\\n\")\n Sushi_choice = ['Sorrento Ristorante', 'Da Marco Italian Restaurant','La Griglia','Amore']\n entry = random.choice(Sushi_choice)\n print('Your location for today\\'s adventure will be: ' + entry + '!\\n' )\n print('A new page will open! Give me a sec for i can transfer you over')\n sleep(10)\n while True:\n if entry == 'Sorrento Ristorante':\n webbrowser.open('https://www.sorrentohouston.com')\n break\n elif entry == 'Da Marco Italian Restaurant':\n webbrowser.open('https://www.damarcohouston.com')\n break\n elif entry == 'La Griglia':\n webbrowser.open('https://www.lagrigliarestaurant.com')\n break\n elif entry == 'Amore':\n webbrowser.open('https://www.amorehouston.com')\n break\n#based om the option given it will go toward the elif and print that function\n if Choice_food == 'Sushi':\n Sushi()\n elif Choice_food == 'Steak':\n Steak()\n elif Choice_food == 'Italian':\n Italian()\n Food()\n elif random_choice == 'Skydiving':\n #skydiving function\n def Skydiving():\n print(\"Tired of something casual? Take it up to skies! \\n\")\n print('A new page will open! Give me a sec for i can transfer you over')\n sleep(10)\n webbrowser.open('https://www.skydivehouston.com')\n Skydiving()\n\n elif random_choice == 'Symphony':\n def Symphony():\n print(\"a large group of musicians who play together on a variety of string, wind and percussion instruments.\\n Find your sound, your melody, your rhythm, bring it to thy self!\")\n print('A new page will open! Give me a sec for i can transfer you over')\n sleep(10)\n webbrowser.open('https://houstonsymphony.org') \n Symphony()\n\n elif random_choice == 'Movie drive-in':\n def Movie_drive_in():\n print(\"Moviegoers are meant to drive into the theater and park their cars in front of a large screen that displays the movie.\")\n Movie_choice = ['Moonstruck', 'Showboat','Mastro\\'s Steakhouse','Rooftop Cinema Club','BlueMoon Cinemas']\n entry = random.choice(Movie_choice)\n print('Your location for today\\'s adventure will be: ' + entry + '!\\n' )\n print('A new page will open! Give me a sec for i can transfer you over')\n sleep(10)\n while True:\n if entry == 'Moonstruck':\n webbrowser.open('https://www.moonstruckdrivein.com')\n break\n elif entry == 'Showboat':\n webbrowser.open('https://www.showboathouston.com')\n break\n elif entry == 'Rooftop Cinema Club':\n webbrowser.open('https://rooftopcinemaclub.com/houston/')\n break\n elif entry == 'BlueMoon Cinemas':\n webbrowser.open('https://bmcinemas.com/index.html')\n break\n Movie_drive_in()\nchoices()","repo_name":"Jafet19/Help","sub_path":"options/expense.py","file_name":"expense.py","file_ext":"py","file_size_in_byte":9025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1889822481","text":"from fastapi import FastAPI\nfrom starlette.middleware.cors import CORSMiddleware\nimport uvicorn\nfrom fastapi.staticfiles import StaticFiles\n\napp = FastAPI()\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=[\"*\"],\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"]\n)\n\n\napp.mount(\"/web\", StaticFiles(directory=\"./web\", html=True), name=\"web\")\n\n\ndef main():\n uvicorn.run(__name__ + \":app\", host=\"192.168.1.1\", port=3001, reload=True)\n\n\nif __name__ == \"__main__\":\n\n main()\n#\n","repo_name":"Tand0/Flutter_Example","sub_path":"pythonweb/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21905840882","text":"def turnAround(proc,wt,tat,n):\r\n for i in range(n):\r\n tat[i]=wt[i]+proc[i][1]\r\n\r\ndef findWaiting(proc,wt,n):\r\n wt[0]=0\r\n for i in range(1,n):\r\n wt[i]=proc[i-1][1]+wt[i-1]\r\n\r\ndef findAvg(proc,n):\r\n wt=[0]*n\r\n tat=[0]*n\r\n findWaiting(proc,wt,n)\r\n turnAround(proc,wt,tat,n)\r\n for i in range(n):\r\n print(proc[i][1],\" \",wt[i],\" \",tat[i],\" \")\r\ndef findPriority(proc,n):\r\n proc=sorted(proc,key=lambda proc: proc[2], reverse=True)\r\n print(\"Shorted list which will executed:\")\r\n for i in proc:\r\n print(i[0],\" \")\r\n findAvg(proc,n)\r\n\r\nif __name__==\"__main__\":\r\n proc=[[1, 10, 1],\r\n [2, 5, 0],\r\n [3, 8, 1]]\r\n n=3\r\n findPriority(proc,n)\r\n","repo_name":"Nirob-0812/AllTaskOfVarsity","sub_path":"CSE310/fortest.py","file_name":"fortest.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9315484017","text":"import csv\nimport sys\nimport math\nfrom reddit import *\nfrom line import *\nfrom point import *\nfrom path import *\nfrom analytics_combined import *\n\ndef time_analysis(filename):\n # locations = store_locations(\"atlas.json\")\n locations = store_locations(\"atlas_complete.json\")\n project_time = dict()\n with open(filename,'r') as file:\n # Skip first line (header row)\n next(file, None)\n \n reader = csv.reader(file)\n\n for r in reader:\n time = int(r[0])\n \n x = float(r[2])\n y = float(r[3])\n\n boundary_list = create_sorted_lists(locations)\n filter_lists(Point(x,y), boundary_list)\n for boundary in boundary_list:\n pic_id = boundary[0]\n path = locations.get(pic_id)\n if ( path.pixel_is_in_image(Point(x,y))):\n if project_time.get(pic_id) is None:\n project_time[pic_id] = {\"oldest\": time, \"newest\": time}\n else:\n if time < project_time.get(pic_id)[\"oldest\"]:\n project_time[pic_id][\"oldest\"] = time\n if time > project_time.get(pic_id)[\"newest\"]:\n project_time[pic_id][\"newest\"] = time\n\n with open(\"times_per_project.csv\",'w') as fileOut:\n writer = csv.writer(fileOut, delimiter = \",\")\n writer.writerow([\"Pic ID\", \"Newest Time\", \"Oldest Time\", \"Duration\"])\n for pic_id in project_time:\n newest_time = project_time.get(pic_id).get(\"newest\")\n oldest_time = project_time.get(pic_id).get(\"oldest\")\n duration = newest_time - oldest_time\n writer.writerow([pic_id, newest_time, oldest_time, duration])\n\n\ntime_analysis(\"final_canvas_tile_placements.csv\")\n","repo_name":"meredithxu/reddit_place_project","sub_path":"Python_code/time_analytics.py","file_name":"time_analytics.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"74893034961","text":"#!/usr/bin/env python\n# encoding: utf-8\n\"\"\"\nAdvent of Code 2020 - Day 5 - Challenge 1\nhttps://adventofcode.com/2020/day/5\n\nSolution: 890\n\"\"\"\n\n__author__ = \"Filippo Corradino\"\n__email__ = \"filippo.corradino@gmail.com\"\n\n\ndef parse_tickets_id(ifile):\n with open(ifile) as file:\n for line in file:\n row = int(line[:7].replace('F', '0').replace('B', '1'), base=2)\n col = int(line[7:].replace('L', '0').replace('R', '1'), base=2)\n yield 8*row + col\n\n\ndef main(ifile='inputs/day_05_input.txt'):\n \"\"\"\n We'll use the fact that a ticket can be interpreted as two binary numbers\n E.g. FBFBBFF-RLR: FBFBBFF = 1010011 for the row, RLR = 101 for the column\n \"\"\"\n max_id = max(parse_tickets_id(ifile))\n print(f\"\\nThe max ticket ID is {max_id}\\n\")\n return max_id\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"filippocorradino/advent_of_code_2020","sub_path":"day05_1.py","file_name":"day05_1.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28008852","text":"# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality\n# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.\n# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.\n\nimport os\nimport warnings\nimport sys\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import ElasticNet\n\nimport mlflow\nimport mlflow.sklearn\nfrom time import time\nimport random\n\nimport logging\nlogging.basicConfig(level=logging.WARN)\nlogger = logging.getLogger(__name__)\n\n\ndef eval_metrics(actual, pred):\n rmse = np.sqrt(mean_squared_error(actual, pred))\n mae = mean_absolute_error(actual, pred)\n r2 = r2_score(actual, pred)\n return rmse, mae, r2\n\n\n\nif __name__ == \"__main__\":\n warnings.filterwarnings(\"ignore\")\n np.random.seed(42)\n\n # Read the wine-quality csv file from the URL\n FILENAME = \"winequality-red.csv\"\n csv_url = \\\n f\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/{FILENAME}\"\n\n if os.path.isfile(FILENAME):\n data = pd.read_csv(FILENAME, sep=\";\")\n else:\n try:\n data = pd.read_csv(csv_url, sep=\";\")\n except Exception as e:\n message = (f\"Unable to download training & test CSV, check your\\n\"\n \"internet connection. Error: {e}\") \n logger.exception(message)\n finally:\n sys.exit(1)\n\n\n # Split the data into training and test sets. (0.75, 0.25) split.\n train, test = train_test_split(data)\n\n # The predicted column is \"quality\" which is a scalar from [3, 9]\n train_x = train.drop([\"quality\"], axis=1)\n test_x = test.drop([\"quality\"], axis=1)\n train_y = train[[\"quality\"]]\n test_y = test[[\"quality\"]]\n\n # TALK NOTE Linear regression with combined L1 and L2 priors as regularizer.\n # alpha is weighting both losses ( how regularization we want) \n # 0 l1_ratio is mixing paramter, regularizing using ush L2 regularization\n # This\"ll set both params to 0.5 by default \n\n alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5 \n l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5\n\n # TALK NOTE couldn't get this to work well by just setting the env variable\n # remote_server_uri = os.environ.get(\"MLFLOW_TRACKING_URL\", None) # set to your server URI\n # mlflow.set_tracking_uri(remote_server_uri)\n\n # TALK NOTE mlflow.start_run() return a ActiveRun with serves as a context manager\n # also create mlruns/ dir that support the ui (if the folder doesn\"t exit)\n with mlflow.start_run(): # \n lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)\n lr.fit(train_x, train_y)\n \n # TALK NOTE let time these predictions\n start_time = time()\n predicted_qualities = lr.predict(test_x)\n time_cost = time() - start_time\n (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)\n\n print(\"Elasticnet model (alpha=%f, l1_ratio=%f):\" % (alpha, l1_ratio))\n print(\" RMSE: %s\" % rmse)\n print(\" MAE: %s\" % mae)\n print(\" R2: %s\" % r2)\n\n # TALK NOTE let make slightly interesting\n # by addding a random inference speed\n # which we calling 'time_cost'\n mlflow.log_param(\"alpha\", alpha)\n mlflow.log_param(\"l1_ratio\", l1_ratio)\n mlflow.log_metric(\"rmse\", rmse)\n mlflow.log_metric(\"r2\", r2)\n mlflow.log_metric(\"mae\", mae)\n mlflow.log_metric(\"time_cost\",\n random.randint(0, 10))\n\n # TALK NOTE we\"re also serializing the elastic net model to disk\n mlflow.sklearn.log_model(sk_model=lr, \n artifact_path=\"model\",\n # registered_model_name=\"WineClassifier\" \n )\n","repo_name":"orsonadams/experiment-tracking-talk","sub_path":"examples/sklearn_elasticnet_wine/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19541400660","text":"import pytest\nimport requests\nimport gmaps\nimport wikipedia\nimport json\nimport os\nimport geojson\nimport re\n\n\ndef test_search_api_google():\n api_gmap_key = (os.environ['api_google_key'])\n url_maps = 'https://www.google.com/maps/search/?api=1&'\n search = 'ecouen'\n parameters = 'findplacefromtext/json?input=' + search + '&inputtype=textquery&fields=formatted_address,geometry,rating'\n # url_maps_json = 'https://maps.googleapis.com/maps/api/geocode/json?address='\n url_place = \"https://maps.googleapis.com/maps/api/place/\"\n response = requests.get(url_maps + search)\n assert response.status_code == 200\n response = requests.get(\n url_place + parameters + '&key=' + api_gmap_key\n )\n response_body = response.json()\n assert response_body[\n 'candidates'][0]['formatted_address'] == '95440 Écouen, France'\n\ndef test_search_api_wikipedia():\n lat_ecouen = 49.018834\n lng_ecouen = 2.378926\n list_res = [\"Château d'Écouen\", 'Écouen']\n res_wiki = wikipedia.geosearch(\n lat_ecouen, lng_ecouen)\n assert all([a == b for a, b in zip(res_wiki, list_res)])\n","repo_name":"davidbarat/P7_GrandPy-Bot","sub_path":"tests/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":1125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"79511665","text":"# -*- coding: utf-8 -*-\r\n# @Author : ssbuild\r\n# @Time : 2022/11/4 13:31\r\n\r\nfrom transformers import AutoTokenizer, AutoConfig,AutoImageProcessor,AutoProcessor,AutoFeatureExtractor, CONFIG_MAPPING, PretrainedConfig\r\n\r\n__all__ = [\r\n 'load_tokenizer',\r\n 'load_configure',\r\n 'load_imageprocesser',\r\n 'load_processer',\r\n 'load_feature_extractor',\r\n]\r\n\r\ndef load_tokenizer(tokenizer_name,\r\n model_name_or_path=None,\r\n class_name = None,\r\n cache_dir=\"\",\r\n do_lower_case=None,\r\n use_fast_tokenizer=True,\r\n model_revision=\"main\",\r\n use_auth_token=None,\r\n **kwargs):\r\n tokenizer_kwargs = {\r\n \"cache_dir\": cache_dir,\r\n \"revision\": model_revision,\r\n \"use_auth_token\": True if use_auth_token else None,\r\n **kwargs\r\n }\r\n if do_lower_case is not None:\r\n tokenizer_kwargs['do_lower_case'] = do_lower_case\r\n\r\n if use_fast_tokenizer is not None:\r\n tokenizer_kwargs['use_fast'] = use_fast_tokenizer\r\n\r\n if class_name is not None:\r\n tokenizer = class_name.from_pretrained(tokenizer_name or model_name_or_path, **tokenizer_kwargs)\r\n elif tokenizer_name:\r\n tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, **tokenizer_kwargs)\r\n elif model_name_or_path:\r\n tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, **tokenizer_kwargs)\r\n else:\r\n raise ValueError(\r\n \"You are instantiating a new tokenizer from scratch. This is not supported by this script.\"\r\n \"You can do it from another script, save it, and load it from here, using --tokenizer_name.\"\r\n )\r\n return tokenizer\r\n\r\ndef load_configure(config_name,\r\n model_name_or_path=None,\r\n class_name = None,\r\n cache_dir=\"\",\r\n model_revision=\"main\",\r\n use_auth_token=None,\r\n model_type=None,\r\n config_overrides=None,\r\n bos_token_id=None,\r\n pad_token_id=None,\r\n eos_token_id=None,\r\n sep_token_id=None,\r\n return_dict=False,\r\n task_specific_params=None,\r\n **kwargs):\r\n config_kwargs = {\r\n \"cache_dir\": cache_dir,\r\n \"revision\": model_revision,\r\n \"use_auth_token\": True if use_auth_token else None,\r\n \"return_dict\": return_dict,\r\n **kwargs\r\n }\r\n tmp_kwargs = {\r\n \"bos_token_id\": bos_token_id,\r\n \"pad_token_id\": pad_token_id,\r\n \"eos_token_id\": eos_token_id,\r\n \"sep_token_id\": sep_token_id,\r\n \"task_specific_params\": task_specific_params,\r\n }\r\n for k in list(tmp_kwargs.keys()):\r\n if tmp_kwargs[k] is None:\r\n tmp_kwargs.pop(k)\r\n if tmp_kwargs:\r\n config_kwargs.update(tmp_kwargs)\r\n\r\n if class_name is not None:\r\n config = class_name.from_pretrained(config_name or model_name_or_path, **config_kwargs)\r\n elif isinstance(config_name,PretrainedConfig):\r\n for k,v in config_kwargs.items():\r\n setattr(config_name,k,v)\r\n config = config_name\r\n\r\n elif config_name:\r\n config = AutoConfig.from_pretrained(config_name, **config_kwargs)\r\n elif model_name_or_path:\r\n config = AutoConfig.from_pretrained(model_name_or_path, **config_kwargs)\r\n elif model_type:\r\n config = CONFIG_MAPPING[model_type].from_pretrained(model_name_or_path, **config_kwargs)\r\n else:\r\n raise ValueError(\r\n \"You are instantiating a new config_gpt2 from scratch. This is not supported by this script.\"\r\n \"You can do it from another script, save it, and load it from here, using --config_name.\"\r\n )\r\n if config_overrides is not None:\r\n config.update_from_string(config_overrides)\r\n return config\r\n\r\n\r\ndef load_imageprocesser(imageprocesser_name,\r\n model_name_or_path=None,\r\n class_name = None,\r\n cache_dir=\"\",\r\n model_revision=\"main\",\r\n use_auth_token=None,\r\n **kwargs):\r\n\r\n image_kwargs = {\r\n \"cache_dir\": cache_dir,\r\n \"revision\": model_revision,\r\n \"use_auth_token\": True if use_auth_token else None,\r\n **kwargs\r\n }\r\n\r\n if class_name is not None:\r\n image_processer = class_name.from_pretrained(imageprocesser_name or model_name_or_path, **image_kwargs)\r\n elif imageprocesser_name:\r\n image_processer = AutoImageProcessor.from_pretrained(imageprocesser_name, **image_kwargs)\r\n elif model_name_or_path:\r\n image_processer = AutoImageProcessor.from_pretrained(model_name_or_path, **image_kwargs)\r\n else:\r\n raise ValueError(\r\n \"You are instantiating a new imageprocesser from scratch. This is not supported by this script.\"\r\n \"You can do it from another script, save it, and load it from here, using --imageprocesser_name.\"\r\n )\r\n return image_processer\r\n\r\n\r\ndef load_processer(processer_name,\r\n model_name_or_path=None,\r\n class_name = None,\r\n cache_dir=\"\",\r\n model_revision=\"main\",\r\n use_auth_token=None,\r\n **kwargs):\r\n\r\n image_kwargs = {\r\n \"cache_dir\": cache_dir,\r\n \"revision\": model_revision,\r\n \"use_auth_token\": True if use_auth_token else None,\r\n **kwargs\r\n }\r\n\r\n if class_name is not None:\r\n processer = class_name.from_pretrained(processer_name or model_name_or_path, **image_kwargs)\r\n elif processer_name:\r\n processer = AutoProcessor.from_pretrained(processer_name, **image_kwargs)\r\n elif model_name_or_path:\r\n processer = AutoProcessor.from_pretrained(model_name_or_path, **image_kwargs)\r\n else:\r\n raise ValueError(\r\n \"You are instantiating a new processer from scratch. This is not supported by this script.\"\r\n \"You can do it from another script, save it, and load it from here, using --processer_name.\"\r\n )\r\n return processer\r\n\r\ndef load_feature_extractor(feature_extractor_name,\r\n model_name_or_path=None,\r\n class_name = None,\r\n cache_dir=\"\",\r\n model_revision=\"main\",\r\n use_auth_token=None,\r\n **kwargs):\r\n\r\n ft_kwargs = {\r\n \"cache_dir\": cache_dir,\r\n \"revision\": model_revision,\r\n \"use_auth_token\": True if use_auth_token else None,\r\n **kwargs\r\n }\r\n\r\n if class_name is not None:\r\n feature_extractor = class_name.from_pretrained(feature_extractor_name or model_name_or_path, **ft_kwargs)\r\n elif feature_extractor_name:\r\n feature_extractor = AutoFeatureExtractor.from_pretrained(feature_extractor_name, **ft_kwargs)\r\n elif model_name_or_path:\r\n feature_extractor = AutoFeatureExtractor.from_pretrained(model_name_or_path, **ft_kwargs)\r\n else:\r\n raise ValueError(\r\n \"You are instantiating a new processer from scratch. This is not supported by this script.\"\r\n \"You can do it from another script, save it, and load it from here, using --feature_extractor_name.\"\r\n )\r\n return feature_extractor\r\n\r\n","repo_name":"ssbuild/numpy_io","sub_path":"src/numpy_io/pytorch_loader/tokenizer_config_helper.py","file_name":"tokenizer_config_helper.py","file_ext":"py","file_size_in_byte":7480,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"32896809503","text":"from flask import Flask, render_template, url_for, request, redirect\nimport csv\napp = Flask(__name__)\n\n\n@app.route('/')\ndef my_home():\n return render_template(\"./index.html\")\n\n\n@app.route('/')\ndef html_page(page_name):\n return render_template(page_name)\n\n\ndef write_to_file(data):\n with open('database.txt', mode='a') as database:\n email = data['email']\n subject = data['subject']\n message = data['message']\n file = database.write(f'\\n{email},{subject},{message}')\n\n\ndef write_to_csv(data):\n with open('database.csv', mode='a', newline='') as database2:\n email = data['email']\n subject = data['subject']\n message = data['message']\n csv_writer = csv.writer(database2, delimiter=',',\n quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n csv_writer.writerow([email, subject, message])\n\n\n@app.route('/submit_form', methods=['POST', 'GET'])\ndef submit_form():\n if request.method == 'POST':\n try:\n data = request.form.to_dict()\n write_to_csv(data)\n return redirect('/thankyou.html')\n except:\n return 'Did not save to database'\n\n else:\n return 'something went wrong. Try again!'\n\n\n# app = Flask(__name__): to instantiate a member of the Flask class called app.\n# @app.route('/') is a generator used to create a directory on our website.\n# @app.route('/blog) is a generator used to create a sub-directory on our website.\n# Just save it as hello.py or something similar. Make sure to not call your application flask.py because this would conflict with Flask itself.\n# instead of just saving text documents into our server, flask has another function called render_template that can be used to serve html files.\n# but we need to create a folder called templates and save all the html files into this folder\n# render_template allows us to serve multiple html files at once.\n# in order to be able to serve our css and js files(flask static files),\n# we need to create a folder called static and save these files into this\n# folder.\n","repo_name":"codewithot/portfo","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40200452870","text":"import datetime as dt\nimport hashlib\nimport importlib\nimport inspect\nimport itertools\nimport logging\nimport os\nimport random\nimport re\nimport string\nimport sys\nimport traceback\nimport typing as t\nfrom collections import Iterable\nfrom contextlib import contextmanager\n\nimport netifaces\nimport requests\nimport six\nfrom cryptography.fernet import Fernet\nfrom flask import current_app\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass Singleton(type):\n \"\"\" Metaclass that creates a Singleton base type when called. \"\"\"\n _instances = {}\n\n def __call__(cls, *args, **kwargs):\n if cls not in cls._instances:\n cls._instances[cls] = super(Singleton, cls) \\\n .__call__(*args, **kwargs)\n return cls._instances[cls]\n\n\nclass AttributeDict(dict):\n __getattr__ = dict.__getitem__\n __setattr__ = dict.__setitem__\n\n\ndef is_string_types(var):\n return isinstance(var, six.string_types)\n\n\ndef is_iterable(var):\n return isinstance(var, Iterable)\n\n\ndef is_iterable_not_string(var):\n return is_iterable(var) and not is_string_types(var)\n\n\nUUID_PATTERN = re.compile(r'^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$')\n\n\ndef is_valid_uuid(var):\n # best performance with regular expression compiled and not using re.IGNORECASE\n if isinstance(var, str) and UUID_PATTERN.match(var):\n return True\n else:\n return False\n\n\ndef convert(d):\n for k, v in d.items():\n if isinstance(v, dict):\n d[k] = convert(v)\n return AttributeDict(d)\n\n\n# function to mock datetime.now\ndef get_now() -> dt.datetime:\n return dt.datetime.now(dt.timezone.utc)\n\n\ndef str_to_key(id_: str):\n key = []\n for x in id_.split('.'):\n try:\n x = int(x)\n except ValueError:\n pass\n finally:\n key.append(x)\n return tuple(key) if len(key) > 1 else key[0]\n\n\ndef key_to_str(key):\n if isinstance(key, t.Sequence):\n id_ = '.'.join([str(i) for i in key])\n else:\n id_ = str(key)\n return id_\n\n\ndef generate_url(destination, uri, protocol='https'):\n from dimensigon.domain.entities import Server\n try:\n forwarder = destination.mesh_best_route[0]\n except IndexError:\n forwarder = destination\n else:\n forwarder = Server.query.get(forwarder)\n\n return f\"{protocol}://{forwarder.name}:{forwarder.port}{uri}\"\n\n\ndef generate_symmetric_key():\n return Fernet.generate_key()\n\n\ndef encrypt_symmetric(data, key):\n cipher_suite = Fernet(key)\n return cipher_suite.encrypt(data)\n\n\ndef decrypt_symmetric(data, key):\n cipher_suite = Fernet(key)\n return cipher_suite.decrypt(data)\n\n\ndef encrypt(data: bytes, symmetric_key: bytes = None) -> \\\n t.Tuple[bytes, t.Optional[bytes]]:\n \"\"\"\n\n Parameters\n ----------\n data:\n data to encrypt.\n symmetric_key:\n symmetric key used for encrypting data. If set, cipher_key must be None\n\n Returns\n -------\n\n \"\"\"\n new_symmetric_key = None\n if not symmetric_key:\n symmetric_key = new_symmetric_key = generate_symmetric_key()\n cipher_data = encrypt_symmetric(data, symmetric_key)\n return cipher_data, new_symmetric_key\n\n\ndef decrypt(cipher_text: bytes, symmetric_key: bytes) -> bytes:\n \"\"\"\n\n Parameters\n ----------\n cipher_text:\n text to decrypt\n cipher_key:\n symmetric_key encrypted. If specified symmetric_key must be None (default)\n symmetric_key:\n symmetric_key. If specified cipher_token must be None (default)\n key:\n key used for decryption of cipher_key\n\n Returns\n -------\n decrypted data\n \"\"\"\n cipher_suite = Fernet(symmetric_key)\n dumped_data = cipher_suite.decrypt(cipher_text)\n return dumped_data\n\n\ndef get_logger(self=None):\n if self:\n name = '.'.join([\n self.__module__,\n self.__name__\n ])\n else:\n name = None\n try:\n current_app.logger\n except RuntimeError:\n logger = logging.getLogger(name)\n else:\n logger = current_app.logger\n return logger\n\n\ndef get_entities() -> t.List[t.Tuple['str', t.Any]]:\n from dimensigon.web import db\n entities = []\n for name, cls in inspect.getmembers(sys.modules['dimensigon.domain.entities'],\n lambda x: (inspect.isclass(x) and issubclass(x, db.Model))):\n entities.append((name, cls))\n\n return entities\n\n\ndef get_distributed_entities() -> t.List[t.Tuple['str', t.Any]]:\n from dimensigon.domain.entities.base import DistributedEntityMixin\n entities = []\n for name, cls in inspect.getmembers(sys.modules['dimensigon.domain.entities'],\n lambda x: (inspect.isclass(x) and issubclass(x, DistributedEntityMixin))):\n entities.append((name, cls)) if name == cls.__name__ else None\n\n\n return sorted(entities, key=lambda x: x[1].order or 99999)\n\n\ndef md5(fname):\n hash_md5 = hashlib.md5()\n with open(fname, \"rb\") as f:\n for chunk in iter(lambda: f.read(4096), b\"\"):\n hash_md5.update(chunk)\n return hash_md5.hexdigest()\n\n\ndef get_filename_from_cd(cd):\n \"\"\"\n Get filename from content-disposition\n \"\"\"\n if not cd:\n return None\n fname = re.findall('filename=(.+)', cd)\n if len(fname) == 0:\n return None\n return fname[0]\n\n\ndef generate_dimension(name):\n from cryptography.hazmat.backends import default_backend\n from cryptography.hazmat.primitives import serialization\n from cryptography.hazmat.primitives.asymmetric import rsa\n from dimensigon.domain.entities import Dimension\n\n private_key = rsa.generate_private_key(\n public_exponent=65537,\n key_size=4096,\n backend=default_backend()\n )\n priv_pem = private_key.private_bytes(encoding=serialization.Encoding.PEM,\n format=serialization.PrivateFormat.TraditionalOpenSSL,\n encryption_algorithm=serialization.NoEncryption())\n pub_pem = private_key.public_key().public_bytes(encoding=serialization.Encoding.PEM,\n format=serialization.PublicFormat.PKCS1)\n\n return Dimension(name=name, private=priv_pem, public=pub_pem)\n\n\ndef remove_prefix(s, prefix):\n return s[len(prefix):] if s.startswith(prefix) else s\n\n\ndef get_config_from_filename(filename):\n if not os.path.exists(filename):\n raise RuntimeError(\"%r doesn't exist\" % filename)\n\n ext = os.path.splitext(filename)[1]\n\n try:\n module_name = '__config__'\n if ext in [\".py\", \".pyc\"]:\n spec = importlib.util.spec_from_file_location(module_name, filename)\n else:\n msg = \"configuration file should have a valid Python extension.\\n\"\n loader_ = importlib.machinery.SourceFileLoader(module_name, filename)\n spec = importlib.util.spec_from_file_location(module_name, filename, loader=loader_)\n mod = importlib.util.module_from_spec(spec)\n sys.modules[module_name] = mod\n spec.loader.exec_module(mod)\n except Exception:\n print(\"Failed to read config file: %s\" % filename, file=sys.stderr)\n traceback.print_exc()\n sys.stderr.flush()\n sys.exit(1)\n\n return vars(mod)\n\n\nSQLITE_URL_PREFIX = \"sqlite://\"\n\n\ndef validate_sqlite_database(dbpath: str) -> bool:\n \"\"\"Run a quick check on an sqlite database to see if it is corrupt.\"\"\"\n import sqlite3 # pylint: disable=import-outside-toplevel\n\n dbpath = dbpath[len(SQLITE_URL_PREFIX):]\n\n if not os.path.exists(dbpath):\n # Database does not exist yet, this is OK\n return True\n\n try:\n conn = sqlite3.connect(dbpath)\n conn.cursor().execute(\"PRAGMA QUICK_CHECK\")\n conn.close()\n except sqlite3.DatabaseError:\n # _LOGGER.exception(\"The database at %s is corrupt or malformed.\", dbpath)\n return False\n\n return True\n\n\ndef get_ips(ipv4=True, ipv6=False) -> t.List[t.Tuple[str, int]]:\n ips = []\n\n if ipv4:\n ips.extend(list(itertools.chain(\n *[[ip['addr'] for ip in netifaces.ifaddresses(iface).get(netifaces.AF_INET, [])] for iface in\n netifaces.interfaces()])))\n if ipv6:\n iter_ips = itertools.chain(\n *[[ip['addr'] for ip in netifaces.ifaddresses(iface).get(netifaces.AF_INET6, [])] for iface in\n netifaces.interfaces()])\n\n for ip in iter_ips:\n if ip:\n if '%' in ip:\n ips.append(ip.rsplit('%')[0])\n else:\n ips.append(ip)\n return ips\n\n\ndef bind2gate(bind: t.List[str]) -> t.List[t.Tuple[str, int]]:\n from dimensigon import defaults\n specified_gates = set()\n for b in bind:\n if ':' in b:\n gate = b.rsplit(':')\n gate = (gate[0], int(gate[1]))\n else:\n gate = (b, defaults.DEFAULT_PORT)\n if gate[0] == '0.0.0.0':\n ips = get_ips(ipv4=True, ipv6=False)\n for ip in ips:\n specified_gates.update([(ip, int(gate[1]))])\n elif gate[0] == '::':\n ips = get_ips(ipv4=False, ipv6=True)\n for ip in ips:\n specified_gates.update([(ip, int(gate[1]))])\n else:\n specified_gates.update([gate])\n return list(specified_gates)\n\n\ndef clean_string(incoming_string):\n replace_char = '_'\n newstring = incoming_string\n newstring = newstring.replace(\"!\", replace_char)\n newstring = newstring.replace(\"@\", replace_char)\n newstring = newstring.replace(\"#\", replace_char)\n newstring = newstring.replace(\"$\", replace_char)\n newstring = newstring.replace(\"%\", replace_char)\n newstring = newstring.replace(\"^\", replace_char)\n newstring = newstring.replace(\"&\", replace_char)\n newstring = newstring.replace(\"*\", replace_char)\n newstring = newstring.replace(\"(\", replace_char)\n newstring = newstring.replace(\")\", replace_char)\n newstring = newstring.replace(\"+\", replace_char)\n newstring = newstring.replace(\"=\", replace_char)\n newstring = newstring.replace(\"?\", replace_char)\n newstring = newstring.replace(\"\\'\", replace_char)\n newstring = newstring.replace(\"\\\"\", replace_char)\n newstring = newstring.replace(\"{\", replace_char)\n newstring = newstring.replace(\"}\", replace_char)\n newstring = newstring.replace(\"[\", replace_char)\n newstring = newstring.replace(\"]\", replace_char)\n newstring = newstring.replace(\"<\", replace_char)\n newstring = newstring.replace(\">\", replace_char)\n newstring = newstring.replace(\"~\", replace_char)\n newstring = newstring.replace(\"`\", replace_char)\n newstring = newstring.replace(\":\", replace_char)\n newstring = newstring.replace(\";\", replace_char)\n newstring = newstring.replace(\"|\", replace_char)\n newstring = newstring.replace(\"\\\\\", replace_char)\n newstring = newstring.replace(\"/\", replace_char)\n newstring = newstring.replace(\"-\", replace_char)\n return newstring\n\n\n@contextmanager\ndef session_scope(session=None):\n \"\"\"Provide a transactional scope around a series of operations.\"\"\"\n if session is None:\n raise RuntimeError(\"Session required\")\n\n need_rollback = False\n try:\n yield session\n if session.transaction:\n need_rollback = True\n session.commit()\n except Exception as err:\n _LOGGER.error(\"Error executing query: %s\", err)\n if need_rollback:\n session.rollback()\n raise\n finally:\n session.close()\n\n\ndef format_exception(exc: Exception, with_traceback=True) -> str:\n if with_traceback:\n return ''.join(traceback.format_exception(exc, exc, exc.__traceback__))\n else:\n return str(exc) or exc.__qualname__\n\n\ndef str_resp(resp: requests.Response):\n try:\n return resp.json()\n except ValueError:\n return resp.text\n\n\ndef get_root(path: str):\n parent_path = os.path.dirname(os.path.abspath(os.path.expanduser(path)))\n if path == parent_path:\n return parent_path\n else:\n return get_root(parent_path)\n\n\ndef remove_root(path: str):\n return path.lstrip(get_root(path))\n\n\ndef get_random_string(length=8):\n letters = string.ascii_lowercase + string.digits\n return ''.join(random.choice(letters) for i in range(length))","repo_name":"dimensigon/dimensigon","sub_path":"dimensigon/utils/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":12339,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"36137646175","text":"import logging\nimport tkinter as tk\nfrom gui import gui_summarize\nfrom gui import gui_helpers as helper\nfrom gui import gui_analyze\nfrom gui import gui_research\n\nlogger = logging.getLogger(__name__)\n\n\nclass GUIMainMenu(tk.Tk):\n \"\"\"\n Class for create the main Form\n \"\"\"\n def __init__(self):\n \"\"\"\n Constructor for GUIMainMenu class\n \"\"\"\n super().__init__()\n\n self_width = 800\n self_height = 500\n\n center_x, center_y = helper.center_form(self, self_width, self_height) # Get center coordinates\n self.geometry(f\"{self_width}x{self_height}+{center_x}+{center_y}\")\n self.title(\"Research Tool\")\n self.configure(bg=\"#EEEEEE\")\n\n # *Build Canvas*\n\n canvas = tk.Canvas(\n self,\n bg=\"#EEEEEE\",\n height=500,\n width=800,\n bd=0,\n highlightthickness=0,\n relief=\"ridge\"\n )\n \n canvas.place(x=0, y=0)\n canvas.create_rectangle(\n 0.0,\n 0.0,\n 400.0,\n 500.0,\n fill=\"#00ADB5\",\n outline=\"\")\n \n # *Title, Description (Texts) and Images on Left Side*\n \n canvas.create_text(\n 50.0,\n 56.0,\n anchor=\"nw\",\n text=\"Research Tool\",\n fill=\"#EEEEEE\",\n font=(\"Roboto\", 48 * -1)\n )\n \n self.image_checkbox = tk.PhotoImage(\n file=helper.assets(\"img_checkbox.png\")\n )\n\n canvas.create_image(\n 96.0,\n 208.0,\n image=self.image_checkbox\n )\n \n canvas.create_image(\n 96.0,\n 273.0,\n image=self.image_checkbox\n )\n \n canvas.create_image(\n 96.0,\n 340.0,\n image=self.image_checkbox\n )\n canvas.create_image(\n 96.0,\n 406.0,\n image=self.image_checkbox\n\n )\n \n canvas.create_text(\n 125.0,\n 194.0,\n anchor=\"nw\",\n text=\"Summarize Text\",\n fill=\"#FFFFFF\",\n font=(\"Roboto\", 24 * -1)\n )\n \n canvas.create_text(\n 125.0,\n 324.0,\n anchor=\"nw\",\n text=\"Analyze Keywords\",\n fill=\"#FFFFFF\",\n font=(\"Roboto\", 24 * -1)\n )\n \n canvas.create_text(\n 125.0,\n 257.0,\n anchor=\"nw\",\n text=\"Identify Keywords\",\n fill=\"#FFFFFF\",\n font=(\"Roboto\", 24 * -1)\n )\n \n canvas.create_text(\n 125.0,\n 390.0,\n anchor=\"nw\",\n text=\"Further Research\",\n fill=\"#FFFFFF\",\n font=(\"Roboto\", 24 * -1)\n )\n \n canvas.create_text(\n 7.0,\n 481.0,\n anchor=\"nw\",\n text=\"© (2022) Christoph Beckmann\",\n fill=\"#FFFFFF\",\n font=(\"Roboto\", 12 * -1)\n )\n \n # Right Side: Subtitle and Buttons\n \n canvas.create_text(\n 415.0,\n 66.0,\n anchor=\"nw\",\n text=\"What should be done?\",\n fill=\"#222831\",\n font=(\"Roboto\", 36 * -1)\n )\n \n # *Buttons*\n\n # Summarized\n self.btn_image_Summarize = tk.PhotoImage(\n file=helper.assets(\"btn_Summarize.png\")\n )\n \n btn_summarize = tk.Button(\n self,\n image=self.btn_image_Summarize,\n borderwidth=0,\n highlightthickness=0,\n command=lambda: self.open_gui_summarize(),\n relief=\"flat\"\n )\n btn_summarize.place(\n x=500.0,\n y=197.0,\n width=200.0,\n height=58.0\n )\n\n # Keyword Analysis\n self.btn_image_Analyze = tk.PhotoImage(\n file=helper.assets(\"btn_Analyze.png\")\n )\n btn_analyze = tk.Button(\n self,\n image=self.btn_image_Analyze,\n borderwidth=0,\n highlightthickness=0,\n command=lambda: self.open_gui_analyze(),\n relief=\"flat\"\n )\n \n btn_analyze.place(\n x=500.0,\n y=288.0,\n width=200.0,\n height=58.0\n )\n\n # Further Research\n self.btn_image_Research = tk.PhotoImage(\n file=helper.assets(\"btn_Research.png\")\n )\n\n btn_research = tk.Button(\n self,\n image=self.btn_image_Research,\n borderwidth=0,\n highlightthickness=0,\n command=lambda: self.open_gui_research(),\n relief=\"flat\"\n )\n btn_research.place(\n x=500.0,\n y=379.0,\n width=200.0,\n height=58.0\n )\n\n self.btn_image_exit = tk.PhotoImage(\n file=helper.assets(\"btn_back.png\"))\n\n # Close Form\n btn_exit = tk.Button(\n self,\n image=self.btn_image_exit,\n borderwidth=0,\n highlightthickness=0,\n command=lambda: helper.close_window(self),\n relief=\"flat\"\n )\n btn_exit.place(\n x=367.0,\n y=442.0,\n width=65.0,\n height=58.0\n )\n\n self.resizable(False, False)\n\n # Functions to call TopLevel Windows\n\n def open_gui_summarize(self):\n \"\"\"\n Function to open TopLevel GUI Summarize\n \"\"\"\n form_summarize = gui_summarize.GUISummarize(self)\n form_summarize.grab_set()\n\n def open_gui_analyze(self):\n \"\"\"\n Function to open TopLevel GUI Analyze\n \"\"\"\n form_analyze = gui_analyze.GUIAnalyze(self)\n form_analyze.grab_set()\n\n def open_gui_research(self):\n \"\"\"\n Function to open TopLevel GUI Research\n \"\"\"\n form_research = gui_research.GUIResearch(self)\n form_research.grab_set()\n\n\n########################################################################################################################\n\n\nif __name__ == \"__main__\":\n # Set special variables. Python interpreter reads this first.\n # Prevent parts of code from being run when the modules are imported.\n # To get a deeper insight I used logging Package for debug, info and warning messages.\n import logging.config\n logging_config = helper.PROJECT_DIR / \"logging.conf\"\n logging.config.fileConfig(logging_config, disable_existing_loggers=False)\n\n # Create Class\n mainmenu = GUIMainMenu()\n mainmenu.mainloop()\n","repo_name":"Christoph-Beckmann/Research-Tool","sub_path":"gui/gui_mainmenu.py","file_name":"gui_mainmenu.py","file_ext":"py","file_size_in_byte":6679,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"32768940424","text":"from __future__ import division, print_function\nimport json\n\nimport numpy as np\nimport pandas as pd\nimport cooler\nimport h5py\nimport time\n\nTILESIZE = 256\n\nwhere = np.flatnonzero\nchromsizes = cooler.read_chromsizes(\n \"http://s3.amazonaws.com/pkerp/data/mm9/chromInfo.txt\"\n) # defaults to reading chr#,X,Y,M\nchromosomes = list(chromsizes.keys())\nchromid_map = dict(zip(chromosomes, range(len(chromosomes))))\ncumul_lengths = np.r_[0, np.cumsum(chromsizes)]\n\n\ndef absCoord2bin(c, pos):\n try:\n cid = where(cumul_lengths > pos)[0] - 1\n except IndexError:\n return c.info[\"nbins\"]\n chrom = chromosomes[cid]\n relPos = pos - cumul_lengths[cid]\n return c.offset((chrom, relPos, chromsizes[chrom]))\n\n\ndef getData(FILEPATH, zoomLevel, startPos1, endPos1, startPos2, endPos2):\n\n groupname = str(zoomLevel)\n\n with h5py.File(FILEPATH, \"r\") as f:\n c = cooler.Cooler(f[groupname])\n\n i0 = absCoord2bin(c, startPos1)\n i1 = absCoord2bin(c, endPos1)\n j0 = absCoord2bin(c, startPos2)\n j1 = absCoord2bin(c, endPos2)\n mat = c.matrix(balance=True)[i0:i1, j0:j1]\n\n flat = list(mat.toarray().ravel())\n\n return json.dumps({\"dense\": flat})\n\n\ndef getData2(cooler_matrix, zoomLevel, startPos1, endPos1, startPos2, endPos2):\n\n c = cooler_matrix[\"cooler\"]\n matrix = cooler_matrix[\"matrix\"]\n\n i0 = absCoord2bin(c, startPos1)\n i1 = absCoord2bin(c, endPos1)\n j0 = absCoord2bin(c, startPos2)\n j1 = absCoord2bin(c, endPos2)\n\n if (i1 - i0) == 0 or (j1 - j0) == 0:\n return pd.DataFrame(columns=[\"genome_start\", \"genome_end\", \"balanced\"])\n\n t1 = time.time()\n pixels = matrix[i0:i1, j0:j1]\n cid1 = pixels[\"chrom1\"].apply(chromid_map.get)\n cid2 = pixels[\"chrom2\"].apply(chromid_map.get)\n pixels[\"genome_start\"] = cumul_lengths[cid1] + pixels[\"start1\"]\n pixels[\"genome_end\"] = cumul_lengths[cid2] + pixels[\"end2\"]\n\n print(\"z:\", zoomLevel, \"t:\", time.time() - t1)\n return pixels[[\"genome_start\", \"genome_end\", \"balanced\"]]\n\n\ndef getData3(cooler_matrix, zoomLevel, startPos1, endPos1, startPos2, endPos2):\n c = cooler_matrix[\"cooler\"]\n\n i0 = absCoord2bin(c, startPos1)\n i1 = absCoord2bin(c, endPos1)\n j0 = absCoord2bin(c, startPos2)\n j1 = absCoord2bin(c, endPos2)\n\n if (i1 - i0) == 0 or (j1 - j0) == 0:\n return pd.DataFrame(columns=[\"genome_start\", \"genome_end\", \"balanced\"])\n\n pixels = c.matrix(as_pixels=True, max_chunk=np.inf)[i0:i1, j0:j1]\n\n if not len(pixels):\n return pd.DataFrame(columns=[\"genome_start\", \"genome_end\", \"balanced\"])\n\n lo = min(i0, j0)\n hi = max(i1, j1)\n bins = c.bins()[[\"chrom\", \"start\", \"end\", \"weight\"]][lo:hi]\n bins[\"chrom\"] = bins[\"chrom\"].cat.codes\n pixels = cooler.annotate(pixels, bins)\n pixels[\"genome_start\"] = cumul_lengths[pixels[\"chrom1\"]] + pixels[\"start1\"]\n pixels[\"genome_end\"] = cumul_lengths[pixels[\"chrom2\"]] + pixels[\"end2\"]\n pixels[\"balanced\"] = pixels[\"count\"] * pixels[\"weight1\"] * pixels[\"weight2\"]\n\n return pixels[[\"genome_start\", \"genome_end\", \"balanced\"]]\n\n\ndef getInfo(FILEPATH):\n\n with h5py.File(FILEPATH, \"r\") as f:\n total_length = int(cumul_lengths[-1])\n binsize = int(f[\"0\"].attrs[\"bin-size\"])\n binsize = 1000\n n_tiles = total_length / binsize / TILESIZE\n print(\"total_length:\", total_length, binsize, TILESIZE)\n n_zooms = int(np.ceil(np.log2(n_tiles)))\n max_width = binsize * TILESIZE * 2 ** n_zooms\n\n info = {\n \"min_pos\": [0.0, 0.0],\n \"max_pos\": [total_length, total_length],\n \"max_zoom\": n_zooms,\n \"max_width\": max_width,\n \"bins_per_dimension\": TILESIZE,\n }\n\n return info\n\n return json.dumps(info)\n","repo_name":"higlass/clodius","sub_path":"clodius/higlass_getter.py","file_name":"higlass_getter.py","file_ext":"py","file_size_in_byte":3740,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"3"}
+{"seq_id":"29027551237","text":"import pytest\n\nfrom django.conf import settings\nfrom django.core.management import call_command\nfrom django.utils import timezone\n\nfrom intranet.crt.core.models import PrivateKey, Certificate\nfrom intranet.crt.constants import CERT_TYPE, TASK_TYPE, ACTION_TYPE\nfrom __tests__.utils.common import create_certificate\nfrom __tests__.utils.ssl import generate_private_key\n\npytestmark = pytest.mark.django_db\n\n\ndef test_remove_private_keys_from_yav(users, certificate_types):\n cert1 = create_certificate(\n users['normal_user'],\n certificate_types[CERT_TYPE.HOST],\n uploaded_to_yav=False,\n private_key=PrivateKey.objects.create(data=generate_private_key()),\n )\n cert2 = create_certificate(\n users['normal_user'],\n certificate_types[CERT_TYPE.HOST],\n uploaded_to_yav=True,\n private_key=PrivateKey.objects.create(data=generate_private_key()),\n )\n\n old_time = timezone.now() - timezone.timedelta(settings.CRT_DAYS_TO_REMOVE_PRIVATE_KEYS + 1)\n Certificate.objects.filter(pk=cert2.pk).update(issued=old_time)\n\n call_command(TASK_TYPE.REMOVE_PRIVATE_KEYS)\n\n cert1.refresh_from_db()\n cert2.refresh_from_db()\n\n assert cert1.private_key_id\n assert not cert2.private_key_id\n assert cert1.actions.filter(type=ACTION_TYPE.CERT_PRIVATE_KEY_DELETED).count() == 0\n assert cert2.actions.filter(type=ACTION_TYPE.CERT_PRIVATE_KEY_DELETED).count() == 1\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/tasks/test_remove_private_keys.py","file_name":"test_remove_private_keys.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31746199660","text":"from tkinter import *\r\n\r\n\r\nwindow=Tk()\r\n\r\ndef convert():\r\n print(e1_value.get())\r\n grams= float(e1_value.get())*1000\r\n pounds= float(e1_value.get())*2.20462\r\n ounces= float(e1_value.get())*35.274 \r\n t1.delete(\"1.0\", END) # Deletes the content of the Text box from start to END\r\n t1.insert(END,grams) # Fill in the text box with the value of gram variable\r\n t2.delete(\"1.0\", END)\r\n t2.insert(END,pounds)\r\n t3.delete(\"1.0\", END)\r\n t3.insert(END,ounces)\r\nb1= Button(window, text=\"convert\", command=convert)\r\nb1.grid(row=0,column=4)\r\n \r\n\r\ne2=Label(window,text=\"Kg\")\r\ne2.grid(row=0,column=0) \r\n\r\ne1_value=StringVar()\r\ne1= Entry(window, textvariable=e1_value)\r\ne1.grid(row=0, column=1)\r\n\r\nt1=Text(window, height=1, width=20)\r\nt1.grid(row=1, column=0)\r\n\r\nt2=Text(window, height=1, width=20)\r\nt2.grid(row=1, column=1)\r\n\r\nt3=Text(window, height=1, width=20)\r\nt3.grid(row=1, column=2)\r\n\r\nwindow.mainloop()","repo_name":"sakshi0710/Python_Application","sub_path":"LibraryApp/gui_sample.py","file_name":"gui_sample.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16480597608","text":"import numpy as np\nimport tensorflow as tf\nimport os\nfrom model.darknet import DarkNet448\nfrom utils.bbox_utils import bbox_iou\nimport time\n\ndarknet_cfg_path = './pre_weight/darknet19_448.cfg'\n\ndef create_cfg_dict():\n cfg = {}\n count = 0\n section_name = ''\n lines = open(darknet_cfg_path).readlines()\n for line in lines:\n line = line.strip()\n if line.startswith('['):\n section_name = line.strip('[]') + '_' + str(count)\n cfg[section_name] = {}\n count += 1\n elif line == '':\n continue\n else:\n if section_name == '':\n raise LookupError('The file format is unsupproted!')\n else:\n para_name, para_value = line.split('=')\n cfg[section_name][para_name.strip()] = para_value.strip()\n return cfg\n\n# d = create_cfg_dict()\n# print(d)\n\n\n# # test the bn initialization\n# os.environ[\"CUDA_VISIBLE_DEVICES\"] = '2'\n#\n# x = np.random.randn(1,3,3,3).astype('float32')\n# print(x.shape[-1:])\n#\n# gamma = np.array([1,2,3]).astype('float32')\n# beta = np.array([0,0,0]).astype('float32')\n# mean = np.array([0,0,0]).astype('float32')\n# var = np.array([1,1,1]).astype('float32')\n#\n# y1 = gamma*((x-mean)/np.sqrt(var+0.001))+beta\n# print(y1)\n#\n# y3 = tf.nn.batch_normalization(x, mean, var, beta, gamma, 0.001)\n#\n# gamma_init = tf.constant_initializer(gamma)\n# beta_init = tf.constant_initializer(beta)\n# mean_init = tf.constant_initializer(mean)\n# var_init = tf.constant_initializer(var)\n# y2 = tf.contrib.layers.batch_norm(x, center = True, scale = True, is_training = False,\n# param_initializers = {'gamma':gamma_init, 'beta':beta_init, 'moving_mean':mean_init, 'moving_variance':var_init})\n# with tf.Session() as sess:\n# sess.run(tf.global_variables_initializer())\n# y2 = sess.run(y2)\n# print(y2)\n# # print(y2 == x)\n# y3 = sess.run(y3)\n# print(y3)\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = '2'\n# a = DarkNet448()\n# a.save_ckpt()\n# print(isinstance(a, DarkNet448))\n\n# a=[0,1,2,3]\n# b=[0,1,2,3,4]\n# c=tf.meshgrid(b,a)\n# e = tf.expand_dims(c[0], axis=0)\n\na = tf.ones((2,3))\nb = tf.cast(a>0, dtype=tf.float32)\nwith tf.Session() as sess:\n print(sess.run(b))\n # d = sess.run(c)\n # print(d[0])\n # print(d[1])\n # print(sess.run(e).shape)\n\n# a = np.array([[-1.2,-2.1, 1.2, 2.1], [-4.2,-3.1, 4.2, 3.1]])\n# b = np.array([[-1.4,-2.3, 1.4, 2.3], [-4.1,-3.2, 4.1, 3.2], [-4.0,-3.3, 4.0, 3.3]])\n# start = time.time()\n# iou = bbox_iou(a,b)\n# idx = np.argmax(iou, axis=1)\n# end = time.time()\n# print(iou)\n# print(idx)\n# print(end-start)","repo_name":"AGroupofProbiotocs/YOLO_V2_in_Tensorflow","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2623,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"74036601041","text":"from events.models import Event\nfrom events.event_types import EventTypes\nfrom posts.models import Post\n\n\nclass EventHandler:\n def __init__(self, events):\n self.events = events\n\n def process(self):\n # Update the event store.\n # Update the application state.\n # This should be a transaction.\n\n for event in self.events:\n # Event store update.\n event.save()\n\n if event.event_type == EventTypes.post_created_event:\n # Create the application state\n Post.objects.create(\n id=event.post_id,\n title=event.event_data.title,\n content=event.event_data.content,\n datetime=event.event_time\n )\n elif event.event_type == EventTypes.post_updated_event:\n # Get the post and update it.\n post = Post.objects.get(id=event.post_id)\n post.title = event.event_data.title\n post.content = event.event_data.content\n post.datetime = event.event_time\n\n post.save()\n","repo_name":"Dineshs91/django-cqrs","sub_path":"blog/events/event_handler.py","file_name":"event_handler.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"3"}
+{"seq_id":"6009116322","text":"from som.topologies import GridTopology\nfrom som import SelfOrganizingMap\n\nimport numpy as np\nimport os\nfrom collections import namedtuple\n\nSEED = 0\n\nSetup = namedtuple('Setup', 'dataset d grid_shape periodicities image_shape' )\n\nsetup_1 = Setup(dataset='uniform_colors', d=1, grid_shape=(64, 1, 1), periodicities=[True, False, False], image_shape=None)\nsetup_2 = Setup(dataset='uniform_colors', d=2, grid_shape=(8, 8, 1), periodicities=[False, False, False], image_shape=None)\nsetup_3 = Setup(dataset='uniform_colors', d=2, grid_shape=(8, 8, 1), periodicities=[True, False, False], image_shape=None)\nsetup_4 = Setup(dataset='uniform_colors', d=3, grid_shape=(4, 4, 4), periodicities=[False, False, False], image_shape=None)\nsetup_5 = Setup(dataset='color_blobs', d=1, grid_shape=(64, 1, 1), periodicities=[True, False, False], image_shape=None)\nsetup_6 = Setup(dataset='olivetti_faces', d=2, grid_shape=(20, 20, 1), periodicities=[False, False, False], image_shape=(64, 64))\nsetup_7 = Setup(dataset='cifar10', d=2, grid_shape=(16, 16, 1), periodicities=[False, False, False], image_shape=(32, 32))\nsetup_8 = Setup(dataset='digits', d=2, grid_shape=(8, 8, 1), periodicities=[False, False, False], image_shape=(8, 8))\n\nsetups = [setup_1, setup_2, setup_3, setup_4, setup_5, setup_6, setup_7, setup_8]\n\ndef load_dataset(dataset):\n DATASETS = ['uniform_colors', 'color_blobs', 'olivetti_faces', 'cifar10', 'digits']\n assert dataset in DATASETS\n\n if dataset == 'uniform_colors':\n N = 250\n\n X = np.random.rand(N, 3)\n return X, np.zeros(len(X), dtype=int)\n\n if dataset == 'color_blobs':\n from sklearn.datasets import make_blobs\n N = 250\n\n X, y = make_blobs(n_samples=N, centers=0.7*np.array([[255, 0, 0], [0, 255, 0], [0, 0, 255]]), cluster_std=10, n_features=3)\n min = X.min()\n max = X.max()\n X = (X - min) / (max - min)\n return X, y\n\n if dataset == 'olivetti_faces':\n from sklearn.datasets import fetch_olivetti_faces\n N = None\n\n X, y = fetch_olivetti_faces(return_X_y=True)\n X, y = X[:N], y[:N]\n return X, y\n\n if dataset == 'cifar10':\n import os\n os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n import torchvision\n import torchvision.transforms as transforms\n N = 5000\n\n transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n X = dataset.data.reshape(len(dataset.data), -1) / 255\n y = np.array(dataset.targets)\n\n X, y = X[:N], y[:N]\n # classes = dataset.classes\n return X, y\n\n if dataset == 'digits':\n from sklearn.datasets import load_digits\n N = None\n\n X, y = load_digits(return_X_y=True)\n X /= X.max()\n\n X, y = X[:N], y[:N]\n return X, y\n\n\n\nif __name__ == '__main__':\n from matplotlib import cm\n\n for s in setups:\n\n np.random.seed(SEED)\n X, y = load_dataset(dataset=s.dataset)\n\n identifier = f\"{s.dataset}_{s.d}d_{'x'.join(list(map(str, s.grid_shape[:s.d])))}_p{'x'.join([str(int(b)) for b in s.periodicities[:s.d]])}\"\n print(\"-\"*128)\n print(\"Experiment:\\t\", identifier)\n print(\"Dataset:\\t X:\", X.shape, \"y:\", y.shape)\n print(\"-\"*128)\n\n if not os.path.isdir(os.path.join(\"imgs\", identifier)):\n os.makedirs(os.path.join(\"imgs\", identifier))\n else:\n continue\n\n NUM_COLORS = len(np.unique(y))\n colors = np.array([cm.gist_rainbow(1.*i / NUM_COLORS) for i in range(NUM_COLORS)])[:,:3]\n\n topo = GridTopology(*s.grid_shape, d=s.d, periodicities=s.periodicities)\n som = SelfOrganizingMap(topo, metric='euclidian')\n\n try:\n som.fit_and_animate(X, filename=f'{identifier}_fit_animation', rotate=True)\n except AssertionError:\n som.fit(X)\n\n som.plot_map(image_shape=s.image_shape, title=None, filename=f'imgs/{identifier}/map', animate=True)\n\n try:\n som.plot_nodes(title=None, filename=f'imgs/{identifier}/nodes', animate=True)\n except:\n pass\n\n som.plot_unified_distance_map(title=None, filename=f'imgs/{identifier}/umap', animate=False)\n som.plot_class_representation_map(y, colors, mode=\"discrete\", title=None, filename=f'imgs/{identifier}/crm', animate=False)\n","repo_name":"jonasgrebe/py-self-organizing-maps","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4470,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"5057967222","text":"from django.contrib import messages\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.shortcuts import render , get_object_or_404 , redirect\nfrom .models import OrderProducts , Order\nfrom products.models import Product\nfrom django.utils import timezone\nfrom django.http import HttpResponseRedirect\nfrom django.views.generic import DetailView ,View\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\n\n# Create your views here.\n@login_required\ndef add_to_cart(request,product_id):\n item = get_object_or_404(Product,id=product_id)\n order_item,created = OrderProducts.objects.get_or_create(\n product=item,\n user=request.user,\n orderd = False)\n order_qs = Order.objects.filter(user=request.user,orderd=False)\n if order_qs.exists():\n order = order_qs[0]\n if order.items.filter(product__id=item.id).exists():\n order_item.quantity +=1\n order_item.save()\n messages.info(request,'item quantity was updated')\n else:\n messages.info(request,'item added to your shoping cart')\n order.items.add(order_item)\n else:\n order_date = timezone.now()\n order = Order.objects.create(user=request.user,order_date=order_date)\n order.items.add(order_item)\n messages.info(request,'item added to your shoping cart')\n\n order.promo_discount = None\n order.save()\n \n return redirect('order-summary')\n\n@login_required\ndef remove_from_cart(request,product_id):\n item = get_object_or_404(Product,id=product_id)\n order_qs = Order.objects.filter(user=request.user,orderd=False)\n if order_qs.exists():\n order = order_qs[0]\n if order.items.filter(product__id=item.id).exists():\n order_item = OrderProducts.objects.filter(product=item,user=request.user,orderd = False)[0]\n messages.info(request,'your item removed properly')\n if order_item.quantity > 1:\n order_item.quantity -= 1\n order_item.save()\n else:\n order_item.delete()\n else:\n # item dose not exists\n messages.info(request,'item dose not exsist')\n # return redirect('product',product_id=product_id)\n return redirect('order-summary')\n else:\n # order dose not exist\n messages.info(request,'your cart is empty')\n return redirect('order-summary')\n # return redirect('product',product_id=product_id)\n order.promo_discount = None\n order.save()\n return redirect('order-summary')\n #return redirect('product',product_id=product_id)\n\n@login_required\ndef Delete_product_from_cart(request,product_id):\n item = get_object_or_404(Product,id=product_id)\n order_qs = Order.objects.filter(user=request.user,orderd=False)\n if order_qs.exists():\n order = order_qs[0]\n if order.items.filter(product__id=item.id).exists():\n order_item = OrderProducts.objects.filter(product=item,user=request.user,orderd = False)[0]\n messages.info(request,'your item Deleted')\n order_item.delete()\n else:\n # item dose not exists\n messages.info(request,'item dose not exsist')\n # return redirect('product',product_id=product_id)\n return redirect('order-summary')\n else:\n # order dose not exist\n messages.info(request,'your cart is empty')\n return redirect('order-summary')\n # return redirect('product',product_id=product_id)\n order.promo_discount = None\n order.save()\n return redirect('order-summary')\n #return redirect('product',product_id=product_id)\n\nclass AddToCart(LoginRequiredMixin,View):\n def post(self,*args,**kwargs):\n pass\n\n\nclass OrderSummaryView(LoginRequiredMixin,View):\n def get(self,*args,**kwargs):\n try:\n order = Order.objects.get(user=self.request.user,orderd=False)\n context = {\n 'object':order,\n }\n return render(self.request,'cart/order_summary.html',context=context)\n except ObjectDoesNotExist:\n return redirect('index')\n","repo_name":"mahanfarzaneh2000/DjangoEcommerce","sub_path":"cart/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4207,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"13644646359","text":"import re\r\nimport pkg_resources\r\n\r\ndef find_bank(context):\r\n # 用正則找到判決書中的車牌並回傳位置\r\n # context: string 整篇或部分判決書\r\n resource_filename = pkg_resources.resource_filename(__name__, 'assets/banklist.txt') # 尋找package底下的檔案路徑\r\n f = open(resource_filename, 'r',encoding='utf-8')\r\n bank_rgs = r\"(\"\r\n for line in f.readlines():\r\n bank_rgs += line.replace('\\n','') + '|'\r\n bank_rgs = bank_rgs[:-1] + ')'\r\n regex = \"((.{0,10})(銀行|郵局|郵政|信託|世華|金庫|商銀|企銀|開發|信合社|漁會|農會|信用合作社|中央信託局)(.{0,5})(帳號|帳戶)?(?:(?!年|元|月|萬|千|百|第)\\w)(.{0,15})(?:(?!年|元|月|萬|千|百|第)\\w)[0-9]{1,7}(-|─|－|—|–)?(?:(?!年|元|月|萬|千|百|第)\\w)[0-9]{3,15}(.{0,9})(-|─|－|—|–)?[0-9]{0,3}(帳戶|存簿))|((.{0,8})(銀行|郵局|郵政|信託|世華|金庫|商銀|企銀|開發|信合社|漁會|農會|信用合作社|中央信託局)+(.{0,15})(帳號|帳戶|局號)(.{0,15})(?:(?!年|元|月|萬|千|百|第)\\w)[0-9]{1,7}(-|─|－|—|–)?(?:(?!年|元|月|萬|千|百|第)\\w)[0-9]{3,15}[^至](-|─|－|—|–)?[0-9]{0,3}(?:(?!年|元|月|萬|千|百|第)\\w)(號)?(帳戶)?(、)?[0-9]*)\"\r\n\r\n\r\n match = re.finditer(regex, context)\r\n \r\n bank_list = []\r\n \r\n for one in match:\r\n# print(one)\r\n bank_name=''\r\n start=one.span()[0]\r\n orgin_start=start\r\n end=one.span()[1]\r\n orgin_end=end\r\n value=context[start:end]\r\n if value.find('函') !=-1 or value.find('明細') !=-1:\r\n continue\r\n \r\n \r\n regex_bankaccount=\"[0-9]{0,3}(-|─|－|—|–)?[0-9]{0,3}(-|─|－|—|–)?[0-9]{1,7}(-|─|－|—|–)?(?:(?!年|元|月|萬|千|百|第)\\w)[0-9]{3,15}(-|─|－|—|–)?[0-9]{0,8}\"\r\n match_bankaccount=re.finditer(regex_bankaccount, value)\r\n bank_accounts=[]\r\n for i in match_bankaccount:\r\n account_start=i.span()[0]\r\n account_end=i.span()[1]\r\n bank_accounts.append(value[account_start:account_end])\r\n\r\n start+=account_start\r\n end=start+account_end-account_start\r\n branch=''\r\n bank_name_start=0\r\n match_bankname=re.finditer(bank_rgs, value)\r\n for j in match_bankname:\r\n# print(j)\r\n \r\n if bank_name_start= len(input_fields):\n result = self._opts.get('missing', lambda s: None)(input_fields)\n else:\n result = (input_fields[input_fieldnum]\n or self._opts.get('empty',\n lambda s: '')(input_fields))\n if result is not None:\n if 'noboundaries' in self._opts:\n # sys.stderr.write(result)\n result = self._make_lemma_without_boundaries(\n result,\n input_fields[self._input_field_nums.get('word')])\n # sys.stderr.write(' -> ' + result + '\\n')\n if 'set' in self._opts or 'setconvert' in self._opts:\n result = fix_feature_set_attr(result,\n self._opts.get('unique'))\n elif 'setfirst' in self._opts:\n if result.startswith('|'):\n result = result[1:]\n result = result.split('|', 1)[0]\n elif 'setlast' in self._opts:\n if result.endswith('|'):\n result = result[:-1]\n result = result.split('|')[-1]\n if self._opts.get('setconvert'):\n result = result.replace('|', self._opts['setconvert'])\n if 'set' in self._opts:\n if self._char_encode_maps['set']:\n result = replace_substrings(\n result, self._char_encode_maps['set'])\n elif self._char_encode_maps['base']:\n result = replace_substrings(\n result, self._char_encode_maps['base'])\n return result\n\n def _make_lemma_without_boundaries_simple(self, lemma, wordform):\n return (self._compound_boundary_re.sub('', lemma)\n if len(lemma) > 2\n else lemma)\n\n # Adapted from vrt-add-parses.py\n def _make_lemma_without_boundaries_tdt(self, lemma, wordform):\n if len(lemma) < 3 or not self._compound_boundary_re.search(lemma):\n return lemma\n elif '-' not in wordform:\n return self._compound_boundary_re.sub('', lemma)\n else:\n # In some cases, the lemma has - replaced with a |; in\n # other cases not\n wordform_parts = wordform.split('-')\n lemma_parts = self._compound_boundary_re.split(lemma)\n if (len(wordform_parts) == len(lemma_parts)\n and '-' not in lemma):\n return self._compound_boundary_re.sub('-', lemma)\n else:\n lemma_without_boundaries = [lemma_parts[0]]\n lemma_prefix_len = len(lemma_parts[0])\n wf_prefix_len = len(wordform_parts[0])\n wf_partnr = 1\n for lemma_part in lemma_parts[1:]:\n if wf_partnr >= len(wordform_parts):\n lemma_without_boundaries.append(lemma_part)\n elif (lemma_part[:2] == wordform_parts[wf_partnr][:2]\n and abs(wf_prefix_len - lemma_prefix_len) <= 2):\n # FIXME: Devise a better heuristic\n lemma_without_boundaries.extend(['-', lemma_part])\n wf_prefix_len += len(wordform_parts[wf_partnr])\n wf_partnr += 1\n else:\n lemma_without_boundaries.append(lemma_part)\n lemma_prefix_len += len(lemma_part)\n return ''.join(lemma_without_boundaries)\n\n def get_input_fieldnum(self):\n return self._input_fieldnum\n\n def __repr__(self):\n return str(self._input_fieldnum) + ':' + repr(self._opts)\n\n def __init__(self, input_fields, output_fields, strip=False,\n empty_values=None, missing_values=None,\n copy_extra_fields=None, char_encode_maps=None,\n compound_boundary_marker=None,\n compound_boundary_hyphen=False):\n self._compound_boundary_marker = compound_boundary_marker\n self._compound_boundary_hyphen = compound_boundary_hyphen\n self._make_input_fields(input_fields)\n self._copy_extra_fields = value_or_default(copy_extra_fields,\n not output_fields)\n self._empty_field_values = self._make_default_field_values(empty_values)\n self._missing_field_values = self._make_default_field_values(\n missing_values)\n self._make_output_fields(output_fields, char_encode_maps)\n output_field_fieldnums = [output_field.get_input_fieldnum()\n for output_field in self._output_fields]\n self._max_fieldnum = max((output_field_fieldnums\n + list(self._empty_field_values.keys())\n + list(self._missing_field_values.keys()))\n or [-1])\n # print self._output_fields, output_field_fieldnums, self._max_fieldnum\n for num in range(max(output_field_fieldnums or [-1]) + 1,\n self._max_fieldnum + 1):\n opts = {}\n if num in self._set_fields:\n opts['set'] = True\n self._output_fields.append(\n self.OutputFieldSpec(\n str(num + 1), self._input_fields,\n compound_boundary_marker=self._compound_boundary_marker,\n compound_boundary_hyphen=self._compound_boundary_hyphen,\n options=opts))\n for type_, values in [('empty', self._empty_field_values),\n ('missing', self._missing_field_values)]:\n self._add_default_fieldvals(type_, values)\n # print self._output_fields\n self._strip = strip\n\n def _make_input_fields(self, fields):\n if fields:\n fieldlist = ' '.join(fields).strip().split()\n self._set_fields = set(num for num, name in enumerate(fieldlist)\n if name[-1] == '/')\n self._input_fields = dict((name, num)\n for num, name in enumerate(fieldlist))\n self._input_fields.update(dict((name.strip('/'), num)\n for num, name in enumerate(fieldlist)\n if name[-1] == '/'))\n else:\n self._set_fields = set()\n self._input_fields = {}\n\n def _make_default_field_values(self, fieldspec):\n if not fieldspec:\n return {}\n fieldvals_list = re.split(r'\\s*;\\s*', fieldspec)\n fieldvals = {}\n for field_numval in fieldvals_list:\n (fieldnumstr, fieldval) = re.split(r'\\s*:\\s*', field_numval)\n fieldnums = self._extract_fieldnums(fieldnumstr)\n for fieldnum in fieldnums:\n fieldvals[fieldnum] = fieldval\n return fieldvals\n\n def _extract_fieldnums(self, fieldnumstr):\n result = []\n for fieldrange in re.split(r'\\s*,\\s*', fieldnumstr):\n if fieldrange == '*':\n result.append(-1)\n else:\n if '-' in fieldrange:\n (start, end) = fieldrange.split('-', 1)\n else:\n start = end = fieldrange\n start = self._get_fieldnum(start)\n end = self._get_fieldnum(end)\n result.extend(list(range(start, end + 1)))\n return result\n\n def _get_fieldnum(self, num_or_name):\n if re.match(r'^\\d+$', num_or_name):\n return int(num_or_name) - 1\n else:\n return self._input_fields.get(num_or_name)\n\n def _make_output_fields(self, output_fieldslist, char_encode_maps):\n self._output_fields = []\n output_field_kwargs = dict(\n char_encode_maps=char_encode_maps,\n compound_boundary_marker=self._compound_boundary_marker,\n compound_boundary_hyphen=self._compound_boundary_hyphen)\n if output_fieldslist:\n for fields in output_fieldslist:\n fieldspecs = re.findall(r'(?:\\S|\\'[^\\']+\\'|\"[^\\\"]+\")+', fields)\n # print repr(fieldspecs)\n for fieldspec in fieldspecs:\n self._output_fields.append(\n self.OutputFieldSpec(\n fieldspec, self._input_fields,\n **output_field_kwargs))\n self._extra_output_field = self.OutputFieldSpec(\n '0', self._input_fields, **output_field_kwargs)\n\n def _add_default_fieldvals(self, type_, values):\n for fieldnum, fieldval in list(values.items()):\n if fieldnum != -1 and fieldnum <= self._max_fieldnum:\n self._output_fields[fieldnum].set_option(type_, fieldval)\n if -1 in values:\n for field in self._output_fields + [self._extra_output_field]:\n field.set_option(type_, values[-1], override=True)\n\n def convert_line(self, line, fieldsep='\\t'):\n return fieldsep.join(self.convert(line.strip().split(fieldsep)))\n\n def convert(self, fields):\n outfields = []\n\n def add_output_field(fields, outfield_spec, fieldnum=None):\n outfield = outfield_spec.make_field(fields, fieldnum)\n if outfield is not None:\n outfields.append(outfield)\n\n # print fields, self._output_fields, self._copy_extra_fields\n if self._strip:\n fields = [field.strip() for field in fields]\n for output_field in self._output_fields:\n add_output_field(fields, output_field)\n if self._copy_extra_fields:\n for fieldnum in range(self._max_fieldnum + 1, len(fields)):\n add_output_field(fields, self._extra_output_field, fieldnum)\n return outfields\n\n\nclass AttributeFixer:\n\n _xml_char_entities = {'quot': '\"',\n 'amp': '&',\n 'apos': '\\'',\n 'lt': '<',\n 'gt': '>'}\n _xml_char_entities_rev = dict(\n (val, key) for key, val in _xml_char_entities.items())\n _xml_char_entity_name_regex = \\\n r'#x[0-9a-fA-F]+|#[0-9]+|' + '|'.join(list(_xml_char_entities.keys()))\n\n def __init__(self, opts):\n self._opts = opts\n if self._opts.angle_brackets:\n self._opts.angle_brackets = self._opts.angle_brackets.split(',', 1)\n self._split_lines = (self._opts.compound_boundaries != 'keep'\n or self._opts.strip\n or self._opts.empty_field_values\n or self._opts.missing_field_values\n or self._opts.input_fields\n or self._opts.output_fields)\n self._encode_posattrs = (self._opts.encode_special_chars\n in ['all', 'pos'])\n self._encode_structattrs = (self._opts.encode_special_chars\n in ['all', 'struct'])\n self._special_char_encode_maps = {}\n self._special_char_encode_maps['base'] = [\n (c, (opts.encoded_special_char_prefix\n + chr(i + opts.encoded_special_char_offset)))\n for (i, c) in enumerate(opts.special_chars)]\n self._special_char_encode_maps['set'] = [\n (key, val) for key, val in self._special_char_encode_maps['base']\n if key != '|']\n if self._split_lines:\n self._make_pos_attr_converter()\n self._elem_renames = {}\n self._elem_ids = {}\n for rename_elem_str in self._opts.rename_element:\n for rename_spec in re.split(r'\\s*[,\\s]\\s*', rename_elem_str):\n oldname, newname = rename_spec.split(':')\n self._elem_renames[oldname] = newname\n for elemnames_str in self._opts.add_element_id:\n elemnames = re.split(r'\\s*[,\\s]\\s*', elemnames_str)\n for elemname in elemnames:\n self._elem_ids[elemname] = 0\n self._set_struct_attrs = self._init_set_struct_attrs()\n self._init_struct_attr_copy_info()\n\n def _make_pos_attr_converter(self):\n\n def make_field_list(last):\n return ' '.join(str(fieldnum + 1) for fieldnum in range(last))\n\n copy_extra_fields = None\n output_fields = self._opts.output_fields\n if not self._opts.output_fields:\n copy_extra_fields = True\n if self._opts.compound_boundaries == 'remove':\n output_fields = [(make_field_list(self._opts.lemma_field)\n + ':noboundaries')]\n elif self._opts.compound_boundaries == 'new':\n output_fields = [(\n make_field_list(self._opts.noncompound_lemma_field - 1)\n + ' ' + str(self._opts.lemma_field) + ':noboundaries')]\n # print repr(output_fields)\n char_encode_maps = (\n self._special_char_encode_maps if self._encode_posattrs\n else None)\n self._pos_attr_converter = PosAttrConverter(\n self._opts.input_fields, output_fields, strip=self._opts.strip,\n empty_values=self._opts.empty_field_values,\n missing_values=self._opts.missing_field_values,\n copy_extra_fields=copy_extra_fields,\n char_encode_maps=char_encode_maps,\n compound_boundary_marker=self._opts.compound_boundary_marker,\n compound_boundary_hyphen=self._opts.compound_boundary_hyphen)\n\n def _init_set_struct_attrs(self):\n set_struct_attrs = {}\n if self._opts.set_struct_attributes:\n for attr_spec_list in self._opts.set_struct_attributes:\n for attr_spec in attr_spec_list.split():\n if ':' in attr_spec:\n elemname, attrnames_str = attr_spec.split(':', 1)\n attrnames = set(re.split(r'[,+]', attrnames_str))\n elem_attrs = set_struct_attrs.setdefault(elemname,\n set())\n elem_attrs |= attrnames\n elif '_' in attr_spec:\n elemname, attrname = attr_spec.split('_', 1)\n elem_attrs = set_struct_attrs.setdefault(elemname,\n set())\n elem_attrs |= set([attrname])\n return set_struct_attrs\n\n def _init_struct_attr_copy_info(self):\n self._struct_attr_values = defaultdict(dict)\n self._struct_attr_copy_sources = defaultdict(set)\n self._struct_attr_copy_targets = defaultdict(list)\n for copyspec in self._opts.copy_struct_attribute:\n target, sources = re.split(r'\\s*:\\s*', copyspec, 1)\n sourcelist = re.split(r'\\s*;\\s*', sources)\n for source in sourcelist:\n elem, attrs = re.split(r'\\s*/\\s*', source, 1)\n attrlist = re.split(r'\\s*,\\s*', attrs)\n for attr in attrlist:\n self._struct_attr_copy_sources[elem].add(attr)\n self._struct_attr_copy_targets[target].append((elem, attr))\n # print self._struct_attr_copy_sources, self._struct_attr_copy_targets\n\n def process_files(self, files):\n if isinstance(files, list):\n for file_ in files:\n self.process_files(file_)\n elif isinstance(files, str):\n with open(files, 'r', encoding='utf-8-sig',\n errors=self._opts.encoding_errors) as f:\n self._fix_input(f)\n else:\n self._fix_input(files)\n\n def _fix_input(self, f):\n linenr = 1\n try:\n for line in korputil.whole_line_reader(f):\n sys.stdout.write(self._make_fixed_line(line))\n linenr += 1\n except SyntaxError:\n raise\n except Exception:\n sys.stderr.write(\n ('{prog}: An exception occurred while processing file {fname}'\n ', line {linenr} (approximately):\\n')\n .format(prog=sys.argv[0], fname=f.name, linenr=linenr))\n raise\n\n def _make_fixed_line(self, line):\n if re.match(r'^\\\"]|\\\"[^\\\"]*\\\")+)?>$',\n line.rstrip()):\n line = self._fix_structattrs(line.rstrip()) + '\\n'\n if self._encode_structattrs and line[1] != '/':\n return self._encode_special_chars_in_struct_attrs(line)\n else:\n return line\n elif line[0] == '<' and line[1] in '!?':\n return line\n else:\n return self._fix_posattrs(line)\n\n def _fix_posattrs(self, line):\n if self._opts.replace_xml_character_entities != 'none':\n line = self._replace_character_entities(line)\n if self._split_lines:\n attrs = line[:-1].split('\\t')\n attrs = self._pos_attr_converter.convert(attrs)\n line = '\\t'.join(attrs) + '\\n'\n if self._opts.space:\n line = line.replace(' ', self._opts.space)\n if self._opts.angle_brackets:\n line = (line.replace('<', self._opts.angle_brackets[0])\n .replace('>', self._opts.angle_brackets[1]))\n if self._encode_posattrs and not self._split_lines:\n line = self._encode_special_chars(line)\n line = self._fix_ampersands(line)\n return line\n\n def _encode_special_chars(self, s, encode_map_key='base'):\n \"\"\"Encode the special characters in s.\"\"\"\n return replace_substrings(\n s, self._special_char_encode_maps.get(encode_map_key, []))\n\n def _encode_special_chars_in_struct_attrs(self, s):\n \"\"\"Encode the special characters in the double- or single-quoted\n substrings of s.\n \"\"\"\n\n def encode_attr(mo, encode_map_key=None, elemname=None):\n encode_map_key = encode_map_key or (\n 'set' if (mo.group(1) in\n self._set_struct_attrs.get(elemname, set()))\n else 'base')\n # Strip possible whitespace around the equals sign.\n return (mo.group(1) + '='\n + self._encode_special_chars(mo.group(2), encode_map_key))\n\n elemname = re.search(r'(\\w+)', s).group(1)\n encode_map_key = None if elemname in self._set_struct_attrs else 'base'\n return re.sub(r'''(\\w+)\\s*=\\s*(\".*?\"|'.*?')''',\n lambda mo: encode_attr(mo, elemname=elemname,\n encode_map_key=encode_map_key), s)\n\n def _replace_character_entities(self, line, retain=None):\n numeric_only = (self._opts.replace_xml_character_entities\n in ['numeric', 'correctnumeric'])\n retain = retain or []\n\n def replace_char_entity_ref(matchobj):\n name = matchobj.group(1)\n if (name in self._xml_char_entities and not numeric_only\n and name not in retain):\n return self._xml_char_entities[name]\n elif name[0] == '#':\n chrval = (int(name[2:], base=16)\n if name[1] == 'x'\n else int(name[1:]))\n if chrval < 32:\n return name\n try:\n char = chr(chrval)\n except ValueError:\n return name\n if numeric_only and char in self._xml_char_entities_rev:\n return '&' + self._xml_char_entities_rev[char] + ';'\n else:\n return char\n else:\n return '&' + name + ';'\n\n line = re.sub(r'&(' + self._xml_char_entity_name_regex + r');',\n replace_char_entity_ref, line)\n if self._opts.replace_xml_character_entities in ['all', 'numeric']:\n line = re.sub(r'&(' + self._xml_char_entity_name_regex + r')(?=\\s)',\n replace_char_entity_ref, line)\n return line\n\n def _fix_ampersands(self, line):\n \"\"\"Replace ampersands with &: they always need to be encoded\"\"\"\n\n if '&' not in line:\n return line\n # First encode & in valid (numeric or predefined) XML\n # character entity references as \\x01, then replace the rest\n # with & and finally restore the correct ampersands. Could\n # we do this with a single regexp substitution and an\n # appropriate substitution function?\n line = re.sub(\n r'&(?=(?:amp|apos|gt|lt|quot|#(?:[0-9]+|x[0-9a-fA-F]+));)',\n '\\x01', line)\n line = line.replace('&', '&')\n return line.replace('\\x01', '&')\n\n def _fix_structattrs(self, line):\n if self._elem_renames:\n line = self._rename_elem(line)\n if line.startswith('|')\n optparser.add_option('--encoded-special-char-offset',\n '--special-char-offset', default='0x7F')\n optparser.add_option('--encoded-special-char-prefix',\n '--special-char-prefix', default='')\n optparser.add_option(\n '--set-struct-attributes', '--feature-set-valued-struct-attributes',\n action='append', default=[],\n help=('Treat the structural attributes specified in the attribute'\n ' specification ATTRSPEC as feature set attributes and do not'\n ' convert the vertical bar characters in them.'\n ' ATTRSPEC is a space-separated list of element definitions, of'\n ' the form ELEMNAME_ATTRNAME or ELEMNAME:ATTRNAMELIST, where'\n ' ELEMNAME is the name of the XML element, ATTRNAME is a single'\n ' attribute name and ATTRNAMELIST is a list of attribute names'\n ' separated by commas or pluses.'\n ' The first form can be used to specify multiple feature set'\n ' attributes for a single element type: for example, the values'\n ' \"elem_attr1 elem_attr2\" and \"elem:attr1,attr2\" are equivalent.'\n ' This option can be repeated.'),\n metavar='ATTRSPEC')\n optparser.add_option('--empty-field-values')\n optparser.add_option('--missing-field-values')\n optparser.add_option('--rename-element', action='append', default=[])\n optparser.add_option('--add-element-id', '--add-elem-id', action='append',\n default=[])\n optparser.add_option('--replace-xml-character-entities', type='choice',\n choices=['none', 'correct', 'all', 'numeric',\n 'correctnumeric'],\n default='correctnumeric')\n optparser.add_option('--copy-struct-attribute', action='append', default=[])\n optparser.add_option('--encoding-errors', '--character-encoding-errors',\n type='choice', choices=['strict', 'replace', 'ignore'],\n default='strict')\n (opts, args) = optparser.parse_args()\n if opts.noncompound_lemma_field is None:\n if opts.compound_boundaries == 'remove':\n opts.noncompound_lemma_field = opts.lemma_field\n else:\n opts.noncompound_lemma_field = opts.lemma_field + 1\n if opts.space == '':\n opts.space = ':'\n if opts.angle_brackets == '':\n opts.angle_brackets = '[,]'\n opts.encoded_special_char_offset = int(opts.encoded_special_char_offset,\n base=0)\n if not opts.compound_boundary_regexp:\n opts.compound_boundary_marker = re.escape(opts.compound_boundary_marker)\n return (opts, args)\n\n\ndef main_main():\n input_encoding = 'utf-8-sig'\n output_encoding = 'utf-8'\n korputil.set_sys_stream_encodings(\n input_encoding, output_encoding, output_encoding)\n (opts, args) = getopts()\n attr_fixer = AttributeFixer(opts)\n attr_fixer.process_files(args if args else sys.stdin)\n\n\ndef main():\n try:\n main_main()\n except IOError as e:\n if e.errno == errno.EPIPE:\n sys.stderr.write('Broken pipe\\n')\n else:\n sys.stderr.write(str(e) + '\\n')\n exit(1)\n except KeyboardInterrupt as e:\n sys.stderr.write('Interrupted\\n')\n exit(1)\n except:\n raise\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"CSCfi/Kielipankki-utilities","sub_path":"scripts/vrt-fix-attrs.py","file_name":"vrt-fix-attrs.py","file_ext":"py","file_size_in_byte":33937,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"71597600721","text":"#\n# Avent of Code 2016, Day 3\n# http://adventofcode.com/2016/day/3\n#\n\ndef check(a, b, c):\n return (a < (b + c)) and (b < (a + c)) and (c < (a + b)) \n\ndef part1():\n\n input = open(\"data\\input.txt\", \"r\") \n\n count = 0\n good = 0\n\n for line in input:\n \n l = line.split()\n \n if len(l) != 3:\n raise ValueError(\"input not parsed to three values\", line, \": \", l)\n\n a = int(l[0])\n b = int(l[1])\n c = int(l[2])\n\n if check(a, b, c): \n good = good + 1\n\n count = count + 1\n \n print(\"part 1: \", good, \"/\", count)\n\ndef part2():\n\n input = open(\"data\\input.txt\", \"r\") \n\n count = 0\n good = 0\n i = 0\n t = [0,0,0],[0,0,0],[0,0,0]\n\n for line in input:\n \n l = line.split()\n \n t[0][i] = int(l[0])\n t[1][i] = int(l[1])\n t[2][i] = int(l[2])\n\n if (i == 2): \n for j in range(0, 3): \n if check(t[j][0], t[j][1], t[j][2]): \n good = good + 1\n #print(t[j][0], t[j][1], t[j][2]) \n i = 0\n else:\n i = i + 1\n\n count = count + 1\n \n print(\"part 2: \", good, \"/\", count)\n\npart1()\npart2()","repo_name":"davidvidmar/AdventOfCode2016","sub_path":"Day 3/Day_3.py","file_name":"Day_3.py","file_ext":"py","file_size_in_byte":1309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12355875513","text":"from __future__ import annotations\nfrom math import acos, atan2, sqrt, cos, sin, atan\nfrom typing import Tuple\n\nimport numpy as np\n\n\nclass Point:\n x: float\n y: float\n\n def __init__(self, x: float, y: float):\n self.x = x\n self.y = y\n\n def __repr__(self) -> str:\n return f\"({self.x:.3f}, {self.y:.3f})\"\n\n def distance_to(self, other: Point) -> float:\n return sqrt((self.x - other.x) ** 2 + (self.y - other.y) ** 2)\n\n def oriented_angle_to(self, other: Point) -> float:\n return atan2(other.y - self.y, other.x - self.x)\n\n def __add__(self, other: Point) -> Point:\n return Point(self.x + other.x, self.y + other.y)\n\n def __sub__(self, other: Point) -> Point:\n return Point(self.x - other.x, self.y - other.y)\n\n def __mul__(self, scale: float) -> Point:\n return Point(self.x * scale, self.y * scale)\n\n def __truediv__(self, scale: float) -> Point:\n return Point(self.x / scale, self.y / scale)\n\n def ha1(self, s: float, v: float):\n return Point(\n self.x + s * cos(v),\n self.y + s * sin(v),\n )\n\n def ha2(self, other: Point):\n s = self.distance_to(other)\n v = self.oriented_angle_to(other)\n return s, v\n\n\n# Aus 3 Längen eines Dreiecks, ergibt die 3 (gegenüberliegenden) Winkel dieses Dreiecks\ndef Halbwinkelsatz(\n a: float,\n b: float,\n c: float,\n):\n s = (a + b + c) / 2\n alpha = 2 * atan(sqrt((s - b) * (s - c) / (s * (s - a))))\n beta = 2 * atan(sqrt((s - c) * (s - a) / (s * (s - b))))\n gamma = 2 * atan(sqrt((s - a) * (s - b) / (s * (s - c))))\n return alpha, beta, gamma\n\n\n# Bogenschnitt\n# Aus zwei Punkten (A, B) und den Längen ausgehend von diesen Punkten,\n# ergibt den Schnittpunkt (C) zweier Kreise mit diesen Radien.\n# Achtung: ABC ist im Uhrzeigersinn. Tauscht man A und B, gäbe es noch eine andere Lösung.\ndef Bogenschnitt(\n A: Point,\n B: Point,\n sAC: float,\n sBC: float,\n) -> Point:\n sAB, vAB = A.ha2(B)\n alpha, beta, gamma = Halbwinkelsatz(sBC, sAC, sAB)\n\n vAC = vAB + alpha\n C = A.ha1(sAC, vAC)\n\n # Alternativ:\n # vBC = vAB + (gon(200) - beta)\n # C = B.ha1(sBC, vBC)\n\n return C\n\n\n# Aus zwei Punkten und den orientierten Richtungen ausgehend von diesen Punkten, ergibt\n# den Schnittpunkt dieser Geraden.\ndef Vorwärtsschnitt_Richtung(\n A: Point,\n B: Point,\n vAC: float,\n vBC: float,\n) -> Point:\n sAB, vAB = A.ha2(B)\n\n sAC = sAB * sin(vBC - vAB) / sin(vBC - vAC)\n C = A.ha1(sAC, vAC)\n\n # Alternative:\n # sBC = sAB * sin(vAC - vAB) / sin(vBC - vAC)\n # C = B.ha1(sBC, vBC)\n\n return C\n\n\n# Aus zwei Punkten und den unorientierten Richtungen ausgehend von diesen Punkten, ergibt\n# den Schnittpunkt C dieser Geraden.\n# A, B, C sind im Uhrzeigersinn.\ndef Vorwärtsschnitt_Winkel(\n A: Point,\n B: Point,\n alpha: float,\n beta: float,\n) -> Point:\n\n sAB, vAB = A.ha2(B)\n sAC = sAB * sin(beta) / sin(alpha + beta)\n sBC = sAB * sin(alpha) / sin(alpha + beta)\n\n vAC = vAB + alpha\n C = A.ha1(sAC, vAC)\n\n # Alternative:\n # vBC = vAB + (pi - beta)\n # C = B.ha1(sBC, vBC)\n\n return C\n\n\n# Aus 3 Punkten (L, M, R), und zwei Winkeln von N zu den drei Punkten,\n# berechne den Punkt N.\ndef Rückwärtsschnitt(\n L: Point,\n M: Point,\n R: Point,\n rNL: float,\n rNM: float,\n rNR: float,\n) -> Point:\n alpha = rNM - rNL\n beta = rNR - rNM\n\n sML, vML = M.ha2(L)\n sMR, vMR = M.ha2(R)\n\n a = sin(alpha) / sML\n b = sin(-beta) / sMR\n\n va = vMR - beta\n vb = vML + alpha\n base = sin(vML - vMR + alpha + beta)\n gamma = (a * cos(va) - b * cos(vb)) / base\n mu = (a * sin(va) - b * sin(vb)) / base\n\n sMN_sq = 1 / (gamma**2 + mu**2)\n\n return Point(\n M.x + sMN_sq * gamma,\n M.y + sMN_sq * mu,\n )\n\n\n# Peripheriewinkelsatz!!\ndef Rückwärtsschnitt_Collins(\n L: Point,\n M: Point,\n R: Point,\n rNL: float,\n rNM: float,\n rNR: float,\n) -> Point:\n alpha = rNM - rNL\n beta = rNR - rNM\n\n H = Vorwärtsschnitt_Winkel(R, L, alpha, beta)\n sHL, vHL = H.ha2(L)\n sHM, vHM = H.ha2(M)\n sHR, vHR = H.ha2(R)\n\n gamma = vHM - vHR\n delta = vHL - vHM\n\n N = Vorwärtsschnitt_Winkel(L, R, gamma, delta)\n return N\n\n\nclass CoordinateTransformation:\n scale: float\n rotation: float\n translation: Point\n\n def __str__(self) -> str:\n return f\"scale={self.scale}, rotation={self.rotation}, translation={self.translation}\"\n\n def transform(self, point: Point) -> Point:\n point = point - self.translation\n point = point * self.scale\n\n point = Point(\n point.x * cos(self.rotation) - point.y * sin(self.rotation),\n point.x * sin(self.rotation) + point.y * cos(self.rotation),\n )\n\n return point\n\n\n# Given a pair of points in the coordinate system 1, and a pair of points in the coordinate system 2,\n# returns the transformation from cs1 -> cs2.\ndef HelmertTransform(\n points_cs1: Tuple[Point, Point],\n points_cs2: Tuple[Point, Point],\n) -> CoordinateTransformation:\n x1 = points_cs1[0].x\n y1 = points_cs1[0].y\n x2 = points_cs1[1].x\n y2 = points_cs1[1].y\n x1_ = points_cs2[0].x\n y1_ = points_cs2[0].y\n x2_ = points_cs2[1].x\n y2_ = points_cs2[1].y\n\n [a, b, c, d] = np.linalg.solve(\n [\n [x1_, -y1_, 1, 0],\n [y1_, x1_, 0, 1],\n [x2_, -y2_, 1, 0],\n [y2_, x2_, 0, 1],\n ],\n [x1, y1, x2, y2],\n )\n\n mu = 1 / (a**2 + b**2) ** 0.5\n phi = acos(a * mu)\n dx = c\n dy = d\n\n return CoordinateTransformation(\n scale=mu,\n rotation=phi,\n translation=Point(dx, dy),\n )\n","repo_name":"pothos-dev/study","sub_path":"Geomathematik I/lib2d.py","file_name":"lib2d.py","file_ext":"py","file_size_in_byte":5697,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29138825777","text":"from typing import Union, Optional\n\nimport sys\n\nsys.path.append(\"schema\")\nsys.path.append(\"../schema\")\n\nimport pytest\nfrom rest_api import Resources\nfrom rest_api.calculator import StatCalculator\nfrom vsml_common.proto import ProtoSerializable\nfrom rest_api.region_api import RegionTree\nfrom rest_api.catalog import Catalog\n\nimport schema.auto.api.response_model_pb2 as proto_response\nimport schema.auto.api.stats_model_pb2 as proto\n\nfrom rest_api.model import ResponseStatus, StatsMarkResponseV3, StatsModelResponseV3, StatsMark, StatsMarkModel, \\\n ModelPhotos, Price, DeprecatedPrice, AgeWithPrice, StatsModel, Model, TechParams, MostPopularTechParam, \\\n DurationOfSale, DataSource\n\ndatasource_mapping = {1: DataSource.MARK,\n 2: DataSource.MODEL,\n 3: DataSource.GENERATION,\n 4: DataSource.CONFIGURATION,\n 5: DataSource.TECH_PARAM,\n 6: DataSource.COMPLECTATION}\n\nresponse_status_mapping = {\n 0: ResponseStatus.SUCCESS,\n 1: ResponseStatus.ERROR\n}\n\n\n@pytest.fixture(scope='module')\ndef resources():\n resources = Resources()\n region_tree = RegionTree('data/geobase_export.json')\n resources.region_tree = region_tree\n catalog = Catalog('data/catalog.json')\n resources.catalog = catalog\n calculator = StatCalculator('data/auto_stats.tsv', resources)\n resources.calculator = calculator\n return resources\n\n\ndef _parse_mark(response: proto.MarkStats,\n status: proto_response.ResponseStatus) -> StatsMarkResponseV3:\n models = []\n for p_model in response.models:\n p_photo = p_model.photo\n photo = None\n if p_photo.ByteSize():\n photo = ModelPhotos(sizes=ModelPhotos.ModelPhotosSizes(\n cattouch=p_photo.sizes['cattouch'],\n cattouchret=p_photo.sizes['cattouchret']\n ))\n\n price = _parse_price(p_model.price)\n deprecation = _parse_deprecation(p_model.deprecation)\n\n model = StatsMarkModel(\n model=p_model.model,\n photo=photo,\n year_from=p_model.year_from,\n year_to=p_model.year_to,\n price=price,\n deprecation=deprecation\n )\n models.append(model)\n\n return StatsMarkResponseV3(status=response_status_mapping[status],\n stats=StatsMark(\n mark=StatsMark.StatsMarkInner(\n models=models\n )\n ))\n\n\ndef _parse_price(p_price: proto.PriceBlock) -> Optional[Price]:\n if p_price.ByteSize():\n return Price(\n data_source=datasource_mapping[p_price.data_source],\n min_price=p_price.min_price,\n max_price=p_price.max_price,\n average_price=p_price.average_price,\n offers_count=p_price.offers_count\n )\n\n\ndef _parse_deprecation(p_deprecation: proto.DeprecationPriceBlock) -> Optional[DeprecatedPrice]:\n if p_deprecation.ByteSize():\n price_percentage_diff = []\n for ic, age_diff in enumerate(p_deprecation.price_percentage_diff):\n diff = age_diff.price_percentage_diff\n if ic == 0 and diff == 0:\n diff = None\n price_percentage_diff.append(AgeWithPrice(\n age=age_diff.age,\n price=age_diff.price,\n price_percentage_diff=diff\n ))\n return DeprecatedPrice(\n data_source=datasource_mapping[p_deprecation.data_source],\n avg_in_percentage=p_deprecation.avg_in_percentage,\n price_percentage_diff=price_percentage_diff # or None\n )\n\n\ndef _parse_techparam(p_tech_params: proto.TechParamsBlock) -> Optional[TechParams]:\n if p_tech_params.ByteSize():\n p_most_popular = p_tech_params.most_popular_tech_param\n most_popular_tech_param = None\n if p_most_popular.ByteSize():\n most_popular_tech_param = MostPopularTechParam(\n mark=p_most_popular.mark,\n mark_name=p_most_popular.mark_name,\n model=p_most_popular.model,\n model_name=p_most_popular.model_name,\n super_gen_id=str(p_most_popular.super_gen_id),\n super_gen_name=p_most_popular.super_gen_name,\n configuration_id=str(p_most_popular.configuration_id),\n tech_param_id=str(p_most_popular.tech_param_id),\n body_type=p_most_popular.body_type,\n engine_type=p_most_popular.engine_type,\n displacement=p_most_popular.displacement,\n transmission=p_most_popular.transmission,\n horse_power=p_most_popular.horse_power\n )\n\n displacement_segments = p_tech_params.displacement_segments\n if displacement_segments:\n displacement_segments = {str(k): v for k, v in displacement_segments.items()}\n return TechParams(\n data_source=datasource_mapping[p_tech_params.data_source],\n most_popular_tech_param=most_popular_tech_param,\n displacement_segments=displacement_segments,\n engine_type_segments=p_tech_params.engine_type_segments,\n transmission_segments=p_tech_params.transmission_segments,\n gear_type_segments=p_tech_params.gear_type_segments\n )\n\n\ndef _parse_duration(p_duration: proto.DurationOfSaleBlock) -> Optional[DurationOfSale]:\n if p_duration.ByteSize():\n return DurationOfSale(\n data_source=datasource_mapping[p_duration.data_source],\n avg=p_duration.avg,\n vas=p_duration.vas,\n cert=p_duration.cert\n )\n\n\ndef _parse_model(response: proto.ModelStats,\n status: proto_response.ResponseStatus) -> StatsModelResponseV3:\n price = _parse_price(response.price)\n deprecation = _parse_deprecation(response.deprecation)\n tech_params = _parse_techparam(response.tech_params)\n duration_of_sale = _parse_duration(response.duration_of_sale)\n model = Model(\n price=price,\n deprecation=deprecation,\n tech_params=tech_params,\n duration_of_sale=duration_of_sale\n )\n\n return StatsModelResponseV3(status=response_status_mapping[status],\n stats=StatsModel(\n model=model\n ))\n\n\ndef parse_response_from_protobuf(\n response: proto_response.StatsSummaryResponse\n) -> Optional[Union[StatsMarkResponseV3, StatsModelResponseV3]]:\n mark = response.stats.mark\n model = response.stats.model\n status = response.status\n if response.stats.WhichOneof('data') == 'mark':\n return _parse_mark(mark, status)\n elif response.stats.WhichOneof('data') == 'model':\n return _parse_model(model, status)\n\n\ndef serde(response: ProtoSerializable):\n serialized = response.to_proto().SerializeToString()\n proto_deserialized = proto_response.StatsSummaryResponse()\n proto_deserialized.ParseFromString(serialized)\n return parse_response_from_protobuf(proto_deserialized)\n\n\ndef _test_query_v3(resources,\n rid: int,\n mark: str,\n model: str = None,\n super_gen: str = None,\n configuration_id: str = None,\n tech_param_id: str = None):\n expected_response = resources.calculator.stat_v3(rid=rid,\n mark=mark,\n model=model,\n super_gen=super_gen,\n configuration_id=configuration_id,\n tech_param_id=tech_param_id)\n\n deserialized_response = serde(expected_response)\n assert deserialized_response == expected_response\n\n\ndef _test_all_models_v3(resources, rid, mark):\n mark = resources.catalog.tree.get(mark)\n if not mark:\n return True\n for model_id in mark.models:\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id)\n\n # these tests does not match catalog tree, thus should accept properly only mark\n # (all other params are just silently dropped)\n _test_query_v3(resources, rid=rid, mark=mark.mark, model='does-not-exist')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model='3')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model='3',\n super_gen='7754738')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model='3',\n super_gen='7754738', configuration_id='7754744')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model='3',\n super_gen='7754738', configuration_id='7754744', tech_param_id='7754757')\n\n\ndef _test_all_supergen_v3(resources, rid, mark):\n mark = resources.catalog.tree.get(mark)\n if not mark:\n return True\n for model_id in mark.models:\n model = mark.models[model_id]\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen='does-not-exist')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen='7754738')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen='7754738', configuration_id='7754744')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen='7754738', configuration_id='7754744', tech_param_id='7754757')\n\n for supergen_id in model.supergens:\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id, super_gen=supergen_id)\n\n\ndef _test_all_configuration_v3(resources, rid, mark):\n mark = resources.catalog.tree.get(mark)\n if not mark:\n return True\n for model_id, model in mark.models.items():\n for supergen_id, supergen in model.supergens.items():\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen=supergen_id, configuration_id='does-not-exist')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen=supergen_id, configuration_id='7754744')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen=supergen_id, configuration_id='7754744', tech_param_id='7754757')\n for ic, (configuration_id, configuration) in enumerate(supergen.configurations.items()):\n if ic % 2 == 1: continue\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen=supergen_id, configuration_id=configuration_id)\n\n\ndef _test_all_techparam_v3(resources, rid, mark):\n mark = resources.catalog.tree.get(mark)\n if not mark:\n return True\n for model_id, model in mark.models.items():\n for supergen_id, supergen in model.supergens.items():\n for ic, (configuration_id, configuration) in enumerate(supergen.configurations.items()):\n if ic % 2 == 0: continue\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen=supergen_id, configuration_id=configuration_id, tech_param_id='does-not-exist')\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen=supergen_id, configuration_id=configuration_id, tech_param_id='7754757')\n for jc, (tech_param_id, tech_param) in enumerate(configuration.techparams.items()):\n if jc % 2 == 0: continue\n _test_query_v3(resources, rid=rid, mark=mark.mark, model=model_id,\n super_gen=supergen_id, configuration_id=configuration_id,\n tech_param_id=tech_param_id)\n\n\ndef test_mark_vaz_whole_earth(resources: Resources):\n _test_query_v3(resources, rid=10000, mark='VAZ')\n\n\ndef test_mark_vaz_spb(resources: Resources):\n _test_query_v3(resources, rid=2, mark='VAZ')\n\n\ndef test_mark_vaz_small_city(resources: Resources):\n _test_query_v3(resources, rid=121713, mark='VAZ')\n\n\ndef test_mark_bmw_whole_earth(resources: Resources):\n _test_query_v3(resources, rid=10000, mark='BMW')\n\n\ndef test_mark_bmw_moscow(resources: Resources):\n _test_query_v3(resources, rid=1, mark='BMW')\n\n\ndef test_mark_bmw_small_city(resources: Resources):\n _test_query_v3(resources, rid=159972, mark='BMW')\n\n\ndef test_mark_ac_whole_earth(resources: Resources):\n _test_query_v3(resources, rid=10000, mark='AC')\n\n\ndef test_mark_ac_moscow(resources: Resources):\n _test_query_v3(resources, rid=1, mark='AC')\n\n\ndef test_mark_ac_small_city(resources: Resources):\n _test_query_v3(resources, rid=159972, mark='AC')\n\n\ndef test_mark_unknown_mark_whole_earth(resources: Resources):\n _test_query_v3(resources, rid=10000, mark='BLABLABLA')\n\n\ndef test_mark_opel_unknown_region(resources: Resources):\n _test_query_v3(resources, rid=123321123, mark='OPEL')\n\n\ndef test_model_vaz_whole_earth(resources: Resources):\n _test_all_models_v3(resources, rid=10000, mark='VAZ')\n\n\ndef test_model_vaz_spb(resources: Resources):\n _test_all_models_v3(resources, rid=2, mark='VAZ')\n\n\ndef test_model_vaz_small_city(resources: Resources):\n _test_all_models_v3(resources, rid=121713, mark='VAZ')\n\n\ndef test_model_bmw_whole_earth(resources: Resources):\n _test_all_models_v3(resources, rid=10000, mark='BMW')\n\n\ndef test_model_bmw_moscow(resources: Resources):\n _test_all_models_v3(resources, rid=1, mark='BMW')\n\n\ndef test_model_bmw_small_city(resources: Resources):\n _test_all_models_v3(resources, rid=159972, mark='BMW')\n\n\ndef test_model_ac_whole_earth(resources: Resources):\n _test_all_models_v3(resources, rid=10000, mark='AC')\n\n\ndef test_model_ac_moscow(resources: Resources):\n _test_all_models_v3(resources, rid=1, mark='AC')\n\n\ndef test_model_ac_small_city(resources: Resources):\n _test_all_models_v3(resources, rid=159972, mark='AC')\n\n\ndef test_model_unknown_mark_whole_earth(resources: Resources):\n _test_all_models_v3(resources, rid=10000, mark='BLABLABLA')\n\n\ndef test_model_opel_unknown_region(resources: Resources):\n _test_all_models_v3(resources, rid=123321123, mark='OPEL')\n\n\n####################\n# SUPERGEN\n####################\n\ndef test_supergen_vaz_whole_earth(resources: Resources):\n _test_all_supergen_v3(resources, rid=10000, mark='VAZ')\n\n\ndef test_supergen_vaz_spb(resources: Resources):\n _test_all_supergen_v3(resources, rid=2, mark='VAZ')\n\n\ndef test_supergen_vaz_small_city(resources: Resources):\n _test_all_supergen_v3(resources, rid=121713, mark='VAZ')\n\n\ndef test_supergen_bmw_whole_earth(resources: Resources):\n _test_all_supergen_v3(resources, rid=10000, mark='BMW')\n\n\ndef test_supergen_bmw_moscow(resources: Resources):\n _test_all_supergen_v3(resources, rid=1, mark='BMW')\n\n\ndef test_supergen_bmw_small_city(resources: Resources):\n _test_all_supergen_v3(resources, rid=159972, mark='BMW')\n\n\ndef test_supergen_ac_whole_earth(resources: Resources):\n _test_all_supergen_v3(resources, rid=10000, mark='AC')\n\n\ndef test_supergen_ac_moscow(resources: Resources):\n _test_all_supergen_v3(resources, rid=1, mark='AC')\n\n\ndef test_supergen_ac_small_city(resources: Resources):\n _test_all_supergen_v3(resources, rid=159972, mark='AC')\n\n\ndef test_supergen_unknown_mark_whole_earth(resources: Resources):\n _test_all_supergen_v3(resources, rid=10000, mark='BLABLABLA')\n\n\ndef test_supergen_opel_unknown_region(resources: Resources):\n _test_all_supergen_v3(resources, rid=123321123, mark='OPEL')\n\n\n####################\n# configuration\n####################\n\ndef test_configuration_vaz_whole_earth(resources: Resources):\n _test_all_configuration_v3(resources, rid=10000, mark='VAZ')\n\n\ndef test_configuration_vaz_spb(resources: Resources):\n _test_all_configuration_v3(resources, rid=2, mark='VAZ')\n\n\ndef test_configuration_vaz_small_city(resources: Resources):\n _test_all_configuration_v3(resources, rid=121713, mark='VAZ')\n\n\ndef test_configuration_bmw_whole_earth(resources: Resources):\n _test_all_configuration_v3(resources, rid=10000, mark='BMW')\n\n\ndef test_configuration_bmw_moscow(resources: Resources):\n _test_all_configuration_v3(resources, rid=1, mark='BMW')\n\n\ndef test_configuration_bmw_small_city(resources: Resources):\n _test_all_configuration_v3(resources, rid=159972, mark='BMW')\n\n\ndef test_configuration_ac_whole_earth(resources: Resources):\n _test_all_configuration_v3(resources, rid=10000, mark='AC')\n\n\ndef test_configuration_ac_moscow(resources: Resources):\n _test_all_configuration_v3(resources, rid=1, mark='AC')\n\n\ndef test_configuration_ac_small_city(resources: Resources):\n _test_all_configuration_v3(resources, rid=159972, mark='AC')\n\n\ndef test_configuration_unknown_mark_whole_earth(resources: Resources):\n _test_all_configuration_v3(resources, rid=10000, mark='BLABLABLA')\n\n\ndef test_configuration_opel_unknown_region(resources: Resources):\n _test_all_configuration_v3(resources, rid=123321123, mark='OPEL')\n\n\n####################\n# techparam\n####################\n\ndef test_techparam_vaz_whole_earth(resources: Resources):\n _test_all_techparam_v3(resources, rid=10000, mark='VAZ')\n\n\ndef test_techparam_vaz_spb(resources: Resources):\n _test_all_techparam_v3(resources, rid=2, mark='VAZ')\n\n\ndef test_techparam_vaz_small_city(resources: Resources):\n _test_all_techparam_v3(resources, rid=121713, mark='VAZ')\n\n\ndef test_techparam_bmw_whole_earth(resources: Resources):\n _test_all_techparam_v3(resources, rid=10000, mark='BMW')\n\n\ndef test_techparam_bmw_moscow(resources: Resources):\n _test_all_techparam_v3(resources, rid=1, mark='BMW')\n\n\ndef test_techparam_bmw_small_city(resources: Resources):\n _test_all_techparam_v3(resources, rid=159972, mark='BMW')\n\n\ndef test_techparam_ac_whole_earth(resources: Resources):\n _test_all_techparam_v3(resources, rid=10000, mark='AC')\n\n\ndef test_techparam_ac_moscow(resources: Resources):\n _test_all_techparam_v3(resources, rid=1, mark='AC')\n\n\ndef test_techparam_ac_small_city(resources: Resources):\n _test_all_techparam_v3(resources, rid=159972, mark='AC')\n\n\ndef test_techparam_unknown_mark_whole_earth(resources: Resources):\n _test_all_techparam_v3(resources, rid=10000, mark='BLABLABLA')\n\n\ndef test_techparam_opel_unknown_region(resources: Resources):\n _test_all_techparam_v3(resources, rid=123321123, mark='OPEL')\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"classfields/tests/test_protobuf.py","file_name":"test_protobuf.py","file_ext":"py","file_size_in_byte":18661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20505176711","text":"#pathfinding\nimport pygame as pg\nfrom collections import deque\nfrom os import path\nvec = pg.math.Vector2\n\nTILESIZE = 32\nTILEWIDTH = 32\nTILEHEIGHT = 16\n\nWIDTH = TILEWIDTH * TILESIZE\nHEIGHT = TILEHEIGHT * TILESIZE\n\nFPS = 60\n\nWHITE = (255,255,255)\nBLACK = (0,0,0)\nRED = (255,0,0)\nBLUE = (0,0,255)\nGREEN = (0,255,0)\nGREY = (100,100,100)\nDARKGREY = (40,40,40)\nLIGHTGREY = (150,150,150)\nYELLOW = (255,255,0)\n\npremade_lab = [(0, 2), (1, 2), (2, 2), (2, 3), (2, 4), (2, 5), (5, 0), (5, 2), (5, 1), (5, 3), (6, 3), (7, 3), (8, 3), (8, 4),\n(8, 5), (5, 8), (5, 9), (4, 9), (3, 9), (2, 9), (2, 10), (2, 11), (2, 12), (1, 12), (0, 12), (3, 6), (2, 6), (6, 14), (7, 14),\n(8, 14), (9, 14), (10, 14), (10, 13), (10, 12), (10, 11), (10, 10), (10, 9), (10, 8), (11, 8), (12, 8), (13, 8), (6, 15),\n(12, 5), (12, 4), (12, 3), (12, 2), (13, 1), (12, 1), (14, 1), (15, 1), (11, 5), (16, 1), (17, 1), (18, 1), (19, 1), (20, 1),\n(20, 2), (20, 3), (20, 5), (20, 4), (19, 5), (18, 5), (17, 5), (16, 5), (16, 4), (16, 3), (23, 0), (23, 1), (24, 1), (25, 1),\n(25, 2), (25, 3), (26, 3), (27, 3), (28, 3), (28, 2), (31, 5), (30, 5), (29, 5), (29, 6), (29, 7), (28, 7), (27, 7), (12, 15),\n(12, 14), (13, 14), (13, 13), (13, 12), (13, 11), (14, 11), (15, 11), (16, 11), (16, 10), (16, 9), (16, 8), (16, 7), (17, 7),\n(18, 7), (21, 9), (20, 9), (20, 10), (20, 11), (20, 12), (19, 13), (20, 13), (18, 13), (18, 14), (18, 15), (17, 15), (28, 1),\n(26, 7), (26, 8), (26, 9), (26, 10), (27, 11), (26, 11), (28, 11), (29, 11), (29, 10), (31, 13), (30, 13), (29, 13), (29, 14),\n(26, 15), (26, 14), (26, 13), (25, 13), (24, 13), (23, 13), (21, 15), (22, 15), (24, 11), (24, 10), (24, 9), (24, 8), (24, 7),\n(23, 7), (22, 7), (21, 7), (20, 7), (22, 3), (22, 4), (22, 5), (23, 5), (24, 5), (4, 15), (4, 14), (4, 13), (4, 12), (5, 12),\n(6, 12), (7, 12), (7, 11), (7, 10), (7, 9), (7, 8), (8, 0), (8, 1), (9, 1), (10, 1), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9)]\n\npg.init()\nscreen = pg.display.set_mode((WIDTH,HEIGHT))\nclock = pg.time.Clock()\nrunning = True\n\n#dibuja la cuadricula de tiles\ndef draw_grid():\n for x in range(0,WIDTH,TILESIZE):\n pg.draw.line(screen,GREY,(x,0),(x,HEIGHT))\n for y in range(0,HEIGHT,TILESIZE):\n pg.draw.line(screen,GREY,(0,y),(WIDTH,y))\n\n#mapa que es conformado por cuadrados de igual area (tilemap)\nclass Squaregrid():\n def __init__(self, width, height):\n self.width = width #tile width of the map\n self.height = height #tile height of the map\n self.directions = [vec(1,0), vec(-1,0), vec(0,1), vec(0,-1)] #+ [vec(-1,-1), vec(1,-1), vec(-1,1), vec(1,1)]#allowed directions to move between nodes\n self.walls = []#all non passable nodes (tiles)\n\n def in_bounds(self, node):#check if a node is inside the grid\n if 0 <= node.x < self.width and 0 <= node.y < self.height:\n return True\n\n def passable(self, node):#check if a node is passable (is not a wall)\n if node not in self.walls:\n return True\n\n def find_neighbors(self, node):#find all the possible neighbors of a node\n neighbors = [node + direction for direction in self.directions]\n #fitrando los vecinos del nodo:\n neighbors = filter(self.in_bounds,neighbors)#verificando que los vecino esten dentro del mapa (in-bound (dentro de las esquinas))\n neighbors = filter(self.passable,neighbors)#verificando que se pueda pasar por los vecinos (que no sean walls)\n return neighbors\n\n def draw_walls(self):\n for wall in self.walls:\n rect = pg.Rect(wall * TILESIZE,(TILESIZE,TILESIZE))\n pg.draw.rect(screen,GREY,rect)\n\ndef vec2int(vector):#converts a vector object into a int tuple\n return (int(vector.x),int(vector.y))\n\ndef breadth_first_search(graph,goal,start):#encuentra el camino desde el nodo start hacia el goal\n #nota: el BFS hace la busqueda desde el goal hasta el start es decir al reves\n frontier = deque()\n frontier.append(goal)\n path = {}\n path[vec2int(goal)] = None\n\n while len(frontier) > 0:\n current = frontier.popleft()\n if current == start:\n break\n for next in graph.find_neighbors(current):\n if vec2int(next) not in path:\n frontier.append(next)\n path[vec2int(next)] = current - next\n return path\n\n#loading graphics\nimg_folder = path.join(path.dirname(__file__),\"icons\")\n\narrows = {}\narrow = pg.image.load(path.join(img_folder,\"arrowRight.png\"))\narrow = pg.transform.scale(arrow,(50,50))\nfor dir in [(1,0), (-1,0), (0,1), (0,-1), (-1,-1), (1,-1), (-1,1), (1,1)]:\n arrows[dir] = pg.transform.rotate(arrow, vec(dir).angle_to(vec(1,0)))\n\ngoal_img = pg.image.load(path.join(img_folder,\"home.png\"))\ngoal_img = pg.transform.scale(goal_img,(50,50))\ngoal_img_rect = goal_img.get_rect()\n\nstart_img = pg.image.load(path.join(img_folder,\"cross.png\"))\nstart_img = pg.transform.scale(start_img,(50,50))\nstart_img_rect = start_img.get_rect()\n\ndef draw_visited_nodes_to_find_path():\n for node in path:\n rect = pg.Rect(vec(node) * TILESIZE,(TILESIZE,TILESIZE))\n pg.draw.rect(screen,LIGHTGREY,rect)\n\n#charging graph (map of nodes)\nG = Squaregrid(TILEWIDTH,TILEHEIGHT)\nG.walls = [vec(cord) for cord in premade_lab]\n\n#calculando el path desde el start al goal\ngoal = vec(5,6)\nstart = vec(7,4)\npath = breadth_first_search(G,goal,start)\n\nwhile running:\n clock.tick(FPS)\n #events\n for event in pg.event.get():\n if event.type == pg.QUIT:\n running = False\n\n #adding walls by clicking\n if event.type == pg.MOUSEBUTTONDOWN:\n mouse_pos = vec(pg.mouse.get_pos()) // TILESIZE #getting the tile coordinates of the mouse_pos\n if event.button == 1:\n #spawning or destroying a wall\n if mouse_pos in G.walls:\n G.walls.remove(mouse_pos)\n else:\n G.walls.append(mouse_pos)\n if event.button == 3:\n #change the current goal of the path\n goal = mouse_pos\n #recalculating the path\n path = breadth_first_search(G,goal,start)\n\n if event.type == pg.KEYDOWN:\n if event.key == pg.K_p:\n if len(G.walls) > 0:\n #elimina todas las paredes\n G.walls = []\n else:\n #spawnea las paredes del mapa incial\n G.walls = [vec(cord) for cord in premade_lab]\n #recalculation the path\n path = breadth_first_search(G,goal,start)\n\n if event.key == pg.K_m:\n #print the list of all the walls\n print([(int(wall.x),int(wall.y)) for wall in G.walls])\n\n #chage the staring point\n if event.key == pg.K_q:\n mouse_pos = vec(pg.mouse.get_pos()) // TILESIZE\n start = mouse_pos\n\n #recalculating the path\n path = breadth_first_search(G,goal,start)\n #update\n\n #drawing\n screen.fill(DARKGREY)\n draw_visited_nodes_to_find_path()\n pg.display.set_caption(\"{:.0f}\".format(clock.get_fps()))\n draw_grid()\n G.draw_walls()\n\n #drawing goal and start points\n x = (goal.x * TILESIZE) + TILESIZE//2\n y = (goal.y * TILESIZE) + TILESIZE//2\n goal_img_rect.center = (x,y)\n screen.blit(goal_img,goal_img_rect)\n\n x = (start.x * TILESIZE) + TILESIZE//2\n y = (start.y * TILESIZE) + TILESIZE//2\n start_img_rect.center = (x,y)\n screen.blit(start_img,start_img_rect)\n\n #drawing path from start to goal\n if vec2int(start) in path:\n current = start + path[vec2int(start)]#dibuja flechas a partir del siguiente nodo al start\n while current != goal:\n x = (current.x * TILESIZE) + TILESIZE//2\n y = current.y * TILESIZE + TILESIZE//2\n image = arrows[vec2int(path[vec2int(current)])]\n rect = image.get_rect(center=(x,y))\n\n screen.blit(image,rect)\n\n current += path[vec2int(current)]\n\n\n\n pg.display.flip()\n\npg.quit()\n","repo_name":"joaobose/public-repo","sub_path":"GameDev/Python/python-pygame-pathFinding-algorithms/BFS-shortest-path-and-optimazation.py","file_name":"BFS-shortest-path-and-optimazation.py","file_ext":"py","file_size_in_byte":8051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30829232535","text":"import scrapy\r\n\r\nfrom scrapy.utils.response import get_base_url\r\nfrom scrapy.utils.url import urljoin_rfc\r\n\r\nfrom scrapy_splash import SplashRequest\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom pyvirtualdisplay import Display\r\nimport traceback\r\n\r\nclass sjtuSpider(scrapy.Spider):\r\n name = \"sjtu\"\r\n\r\n def __init__(self, category=None, *args, **kwargs):\r\n super(sjtuSpider, self).__init__(*args, **kwargs)\r\n\r\n display = Display(visible=0, size=(800, 600))\r\n display.start()\r\n\r\n self.driver = webdriver.Chrome()\r\n self.driver.set_page_load_timeout(10)\r\n\r\n\r\n def start_requests(self):\r\n urls = [\r\n 'http://news.nju.edu.cn/mtjj.html'\r\n ]\r\n for url in urls:\r\n yield SplashRequest(url, self.parse,args={'wait': 0.5})\r\n\r\n def parse(self,response):\r\n url_set = set() # 话题url的集合\r\n\r\n print(response.url)\r\n print(response.css(\"a#next\").extract_first())\r\n self.driver.get(response.url)\r\n\r\n url_sum_lists = []\r\n while True:\r\n try:\r\n wait = WebDriverWait(self.driver, 10)\r\n #wait.until(lambda driver: driver.find_element_by_xpath('//div[@class=\"content\"]/ul/li/a'))\r\n wait.until(lambda driver: driver.find_element_by_xpath('//li[@class=\"t\"]/a'))\r\n except:\r\n pass\r\n\r\n sel_list = self.driver.find_elements_by_xpath('//li[@class=\"t\"]/a')\r\n\r\n url_list = []\r\n for sel in sel_list:\r\n try:\r\n url_list.append(sel.get_attribute(\"href\"))\r\n except:\r\n continue\r\n\r\n #url_list = [sel for sel in sel_list]\r\n url_sum_lists += url_list\r\n url_set |= set(url_list)\r\n\r\n print(len(url_set))\r\n\r\n\r\n\r\n try:\r\n wait = WebDriverWait(self.driver, 10)\r\n wait.until(lambda driver: driver.find_element_by_xpath(\"//a[@id='next']\"))\r\n next_page = self.driver.find_element_by_xpath(\"//a[@id='next']\")\r\n #print(\"@#@@#@!#!#!#\"+next_page.get_attribute('href'))\r\n #print(\"+++++++++++++++++++++++++++++++++++++++++++++++++++++++\\n\\n\\n\\n\")\r\n #print(next_page)\r\n if(int(next_page.get_attribute('pn')) == 201):\r\n break\r\n self.driver.execute_script(\"arguments[0].click();\", next_page)\r\n\r\n #next_page.click() # 模拟点击下一页\r\n except Exception as e :\r\n traceback.print_exc()\r\n info = traceback.format_exc()\r\n print(\"#####Arrive thelast page.#####\")\r\n break\r\n\r\n #for url in url_sum_lists:\r\n #print(url)\r\n #print(\"\\n\\n\\nlen:\"+str(len(url_set)))\r\n\r\n print(\"len of sum is :\"+str(len(url_sum_lists)))\r\n for url in url_sum_lists:\r\n print(url)\r\n\r\n #print(\"\".join(response.css(\".next\").extract()))\r\n #yield response.css(\".next\").extract_first()\r\n #yield response.css(\".whj_border\").extract_first()\r\n def parse_news(self,response):\r\n yield {\"title\":response.css(\".article_title\").extract_first()}\r\n\r\n","repo_name":"zhengyima/scraper-school","sub_path":"tutorial/tutorial/spiders/sjtuSpider.py","file_name":"sjtuSpider.py","file_ext":"py","file_size_in_byte":3286,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"2581311124","text":"from django import forms\nfrom django.forms import ModelForm\nfrom django.forms.widgets import DateInput\nfrom .models import *\nimport datetime\n\n# Form for the add and update a product\nclass addProductForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['proBoughtDate'].initial = datetime.date.today()\n\n class Meta:\n model = t_products\n fields = ['proName', 'proNote', 'proImage', 'proBoughtPrice', 'proBoughtDate', 'fkType' ,'fkCategory']\n labels = {\n 'proName': 'Nom de produit :',\n 'proNote': 'Description :',\n 'proImage': 'Image :',\n 'proBoughtPrice': 'Prix d\\'achat :',\n 'proBoughtDate': 'Date d\\'achat :',\n 'fkCategory': 'Categorie : ',\n 'fkType': 'Type'\n }\n widgets = {\n 'proBoughtDate': DateInput(format=('%Y-%m-%d'),attrs={'type': 'date', 'class': 'dateForm'}),\n }\n \nclass articleForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n \n class Meta:\n model = t_article\n fields = ['artName', 'artNote','fkRoom','fkProduct', 'fkCupboard']\n labels = {\n 'artName': 'Label : ',\n 'fkProduct': 'Produit : ',\n 'artNote': 'Note : ',\n 'fkRoom': 'Labo : ',\n 'fkCupboard': 'Armoire : '\n }\n\nclass borrowForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields['borDate'].initial = datetime.date.today()\n self.fields['borReturnDate'].initial = datetime.date.today() + datetime.timedelta(days=30)\n\n class Meta:\n model = t_borrow\n fields = ['borLocation', 'borDate', 'borReturnDate', 'borUser', 'fkArticle']\n labels = {\n 'borLocation': 'Lieu demprunt :',\n 'borDate': 'Date demprunt :',\n 'borReturnDate': 'Date de retour :',\n 'borUser': 'Emprunté par :',\n 'fkArticle': 'Article :',\n }\n widgets = {\n 'borDate': DateInput(format=('%Y-%m-%d'),attrs={'type': 'date', 'class': 'dateForm'}),\n 'borReturnDate': DateInput(format=('%Y-%m-%d'),attrs={'type': 'date', 'class': 'dateForm'}),\n }\n ","repo_name":"alessdangelo/tpi_gestock","sub_path":"app_gestock/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8063135692","text":"from old_ant import *\nfrom graph_data import *\n\n\n# g = Graph(\"O\", (\"O\", \"N1\", \"N2\", \"N3\", \"N4\", \"N5\", \"N6\", \"N7\"), ((\"O\", \"N1\", 3), (\"O\", \"N2\", 4), (\"O\", \"N3\", 3),\n# (\"N1\", \"N2\", 2), (\"N2\", \"N3\", 1), (\"N2\", \"N4\", 2),\n# (\"N3\", \"N5\", 5), (\"N2\", \"N3\", 1), (\"N4\", \"N5\", 1),\n# (\"N4\", \"N6\", 6), (\"N5\", \"N7\", 3), (\"N6\", \"N7\", 2)))\n\norigin, nodes, edges = generate_graph(100, 2000, 3, 10)\nwrite_graph(\"100-graph.json\", origin, nodes, edges)\n# origin, nodes, edges = read_graph(\"100-graph.json\")\n\ng = Graph(origin, nodes, edges)\n\nclny = Colony(10, 60, 600, g)\nclny.iterate(1)\n\nfor vehicle in clny.vehicles:\n print(vehicle.path)\n print(g.calculate_len(vehicle.path))\nprint(\"\\n\")\n\nclny.iterate(10)\n\nfor vehicle in clny.vehicles:\n print(vehicle.path)\n print(g.calculate_len(vehicle.path))\n\no = clny.graph.find_node(\"O\")\nfor e in o.edges:\n print(e.node1.name, e.node2.name, e.pheromone)\nfor _ in range(5):\n print(o.choose_path()[1].name)\n\nclny.iterate(100000)\n\nprint(\"\\n\")\nfor vehicle in clny.vehicles:\n print(vehicle.path)\n print(g.calculate_len(vehicle.path))\n\no = clny.graph.find_node(\"O\")\nfor e in o.edges:\n print(e.node1.name, e.node2.name, e.pheromone)\nfor _ in range(5):\n print(o.choose_path()[1].name)\n","repo_name":"neonbevz/AdFontes","sub_path":"Python/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38176219802","text":"import filecmp\nfrom pathlib import Path\n\nimport pytest\n\nfrom mocasin.common.graph import DataflowChannel, DataflowProcess, DataflowGraph\nfrom mocasin.mapper.partial import ComFullMapper, ProcPartialMapper\nfrom mocasin.platforms.odroid import DesignerPlatformOdroid\nfrom mocasin.platforms.platformDesigner import genericProcessor\nfrom mocasin.util.mapping_table import MappingTableReader, MappingTableWriter\n\n\n@pytest.fixture\ndef table_file():\n return Path(__file__).parent.absolute().joinpath(\"mapping_table.csv\")\n\n\n@pytest.fixture\ndef expected_csv():\n return (\n Path(__file__).parent.absolute().joinpath(\"expected_mapping_table.csv\")\n )\n\n\n@pytest.fixture\ndef graph():\n k = DataflowGraph(\"graph\")\n channel = DataflowChannel(\"ch\", 1)\n k.add_channel(channel)\n process_a = DataflowProcess(\"a\")\n process_a.connect_to_outgoing_channel(channel)\n process_b = DataflowProcess(\"b\")\n process_b.connect_to_incomming_channel(channel)\n k.add_process(DataflowProcess(\"a\"))\n k.add_process(DataflowProcess(\"b\"))\n return k\n\n\n@pytest.fixture\ndef platform():\n pe_little = genericProcessor(\"proc_type_0\")\n pe_big = genericProcessor(\"proc_type_1\")\n p = DesignerPlatformOdroid(pe_little, pe_big)\n return p\n\n\ndef test_mapping_table_reader(platform, graph, table_file):\n reader = MappingTableReader(\n platform, graph, table_file, attributes=[\"attribute1\"]\n )\n mappings = reader.form_mappings()\n assert len(mappings) == 4\n assert mappings[0][0].to_list() == [1, 3]\n assert mappings[0][0].metadata.exec_time == 10.23\n assert mappings[0][0].metadata.energy == 24.43\n assert mappings[0][1] == \"a\"\n assert mappings[1][0].to_list() == [1, 1]\n assert mappings[1][0].metadata.exec_time == 14.43\n assert mappings[1][0].metadata.energy == 21.56\n assert mappings[1][1] == \"b\"\n assert mappings[2][0].to_list() == [0, 1]\n assert mappings[2][1] == \"c\"\n assert mappings[3][0].to_list() == [0, 6]\n\n\ndef num_resources(mapping):\n return len(mapping.get_used_processors())\n\n\ndef test_mapping_table_writer(platform, graph, tmpdir, expected_csv):\n output_file = Path(tmpdir).joinpath(\"output_table.csv\")\n com_mapper = ComFullMapper(platform)\n mapper = ProcPartialMapper(graph, platform, com_mapper)\n\n mapping1 = mapper.generate_mapping([0, 0])\n mapping1.metadata.exec_time = 10.2\n mapping1.metadata.energy = 21.45\n\n mapping2 = mapper.generate_mapping([0, 3])\n mapping2.metadata.exec_time = 5.2\n mapping2.metadata.energy = 31.15\n\n attributes = {\"num_resources\": num_resources}\n writer = MappingTableWriter(\n platform, graph, output_file, attributes=attributes\n )\n writer.open()\n writer.write_header()\n writer.write_mapping(mapping1)\n writer.write_mapping(mapping2)\n writer.close()\n assert filecmp.cmp(output_file, expected_csv, shallow=False)\n\n\ndef test_mapping_table_writer_with(platform, graph, tmpdir, expected_csv):\n output_file = Path(tmpdir).joinpath(\"output_table.csv\")\n com_mapper = ComFullMapper(platform)\n mapper = ProcPartialMapper(graph, platform, com_mapper)\n\n mapping1 = mapper.generate_mapping([0, 0])\n mapping1.metadata.exec_time = 10.2\n mapping1.metadata.energy = 21.45\n\n mapping2 = mapper.generate_mapping([0, 3])\n mapping2.metadata.exec_time = 5.2\n mapping2.metadata.energy = 31.15\n\n attributes = {\"num_resources\": num_resources}\n with MappingTableWriter(\n platform, graph, output_file, attributes=attributes\n ) as writer:\n writer.write_header()\n writer.write_mapping(mapping1)\n writer.write_mapping(mapping2)\n assert filecmp.cmp(output_file, expected_csv, shallow=False)\n","repo_name":"tud-ccc/mocasin","sub_path":"mocasin/util/test/test_mapping_table.py","file_name":"test_mapping_table.py","file_ext":"py","file_size_in_byte":3686,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"30118112207","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING\nfrom dm.skills.Common._common import CommonSkill\nfrom utilities import SkillEffect, CooldownType\n\nif TYPE_CHECKING:\n from dm.core.contexts import AttackContext\n from dm.core.game.game import DMGame\n from dm.core.objects.unit import DMUnit\n################################################################################\n\n__all__ = (\"ScarletMoon\",)\n\n################################################################################\nclass ScarletMoon(CommonSkill):\n\n def __init__(self, state: DMGame, parent: DMUnit = None):\n\n super().__init__(\n state, parent,\n _id=\"SKL-185\",\n name=\"Scarlet Moon\",\n description=(\n \"Apply 8 (+0.8*ATK) Vampire, 2 Bloodlust to all allies in \"\n \"the room.\"\n ),\n rank=4,\n cooldown=CooldownType.RoomWide,\n effect=SkillEffect(base=8, scalar=0.8)\n )\n\n################################################################################\n def execute(self, ctx: AttackContext) -> None:\n\n # For each ally in the room\n for unit in ctx.room.units_of_type(self.owner):\n # Apply Vampire\n unit.add_status(\"Vampire\", self.effect, self)\n # Apply Bloodlust\n unit.add_status(\"Bloodlust\", 2, self)\n\n################################################################################\n","repo_name":"AllegroVivo/DungeonDefense","sub_path":"dm/skills/Common/SRank/ScarletMoon.py","file_name":"ScarletMoon.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5006270963","text":"array = [7,5,9,0,3,1,6,2,9,1,4,8,0,5,2]\nN = len(array)\n\ncount = [0] * (N+1)\n\nfor i in range(N):\n count[array[i]] += 1\n\nfor i in range(len(count)):\n for j in range(count[i]):\n print(i,end=' ')","repo_name":"sskong777/Algorithm","sub_path":"이것이코딩테스트다/정렬/6_6_계수정렬.py","file_name":"6_6_계수정렬.py","file_ext":"py","file_size_in_byte":204,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"14289700375","text":"import logging\nimport pickle\nimport time\n\nfrom golem.core.service import LoopingCallService\nfrom golem.core.variables import PAYMENT_DEADLINE\nfrom golem.ethereum.exceptions import NotEnoughFunds\n\nlogger = logging.getLogger(__name__)\n\n\nclass TaskFundsLock: # pylint: disable=too-few-public-methods\n def __init__(self, task):\n self.price = task.subtask_price\n self.num_tasks = task.total_tasks\n self.task_deadline = task.header.deadline\n\n @property\n def gnt_lock(self):\n return self.price * self.num_tasks\n\n\nclass FundsLocker(LoopingCallService):\n def __init__(self, transaction_system, datadir, persist=True,\n interval_seconds=60):\n super().__init__(interval_seconds)\n self.task_lock = {}\n self.transaction_system = transaction_system\n self.dump_path = datadir / \"fundslockv1.pickle\"\n self.persist = persist\n self.restore()\n\n def lock_funds(self, task):\n task_id = task.header.task_id\n if self.task_lock.get(task_id) is not None:\n logger.error(\"Tried to duplicate lock_fund with same \"\n \"task_id %r\", task_id)\n return\n\n tfl = TaskFundsLock(task)\n logger.info(\n 'Locking funds for task: %r price: %f num: %d',\n task_id,\n tfl.price,\n tfl.num_tasks,\n )\n self.task_lock[task_id] = tfl\n self.dump_locks()\n self.transaction_system.lock_funds_for_payments(\n tfl.price,\n tfl.num_tasks,\n )\n\n def remove_old(self):\n time_ = time.time()\n for task_id, task in list(self.task_lock.items()):\n if task.task_deadline + PAYMENT_DEADLINE < time_:\n del self.task_lock[task_id]\n self.dump_locks()\n\n def _run(self):\n self.remove_old()\n\n def restore(self):\n if not self.persist:\n return\n if not self.dump_path.exists():\n return\n with self.dump_path.open('rb') as f:\n try:\n self.task_lock = pickle.load(f)\n except (pickle.UnpicklingError, EOFError, AttributeError, KeyError):\n logger.exception(\"Problem restoring dumpfile: %s\",\n self.dump_path)\n return\n for task_id, task in self.task_lock.items():\n logger.info('Restoring old tasks locks: %r', task_id)\n # Bandait solution for increasing gas price\n try:\n self.transaction_system.lock_funds_for_payments(\n task.price,\n task.num_tasks,\n )\n except NotEnoughFunds:\n pass\n\n def dump_locks(self):\n if not self.persist:\n return\n with self.dump_path.open('wb') as f:\n pickle.dump(self.task_lock, f)\n\n def remove_subtask(self, task_id):\n task_lock = self.task_lock.get(task_id)\n if task_lock is None:\n logger.warning(\"I can't remove payment lock for subtask from task\"\n \"%r: unkown task.\", task_id)\n return\n logger.info('Removing subtask lock for task %r', task_id)\n task_lock.num_tasks -= 1\n self.dump_locks()\n self.transaction_system.unlock_funds_for_payments(task_lock.price, 1)\n\n def remove_task(self, task_id):\n task_lock = self.task_lock.get(task_id)\n if task_lock is None:\n logger.warning(\"I can't remove payment lock from task\"\n \"%r: unkown task.\", task_id)\n return\n logger.info('Removing task lock %r', task_id)\n del self.task_lock[task_id]\n self.dump_locks()\n self.transaction_system.unlock_funds_for_payments(\n task_lock.price,\n task_lock.num_tasks,\n )\n","repo_name":"albert19882016/golem","sub_path":"golem/ethereum/fundslocker.py","file_name":"fundslocker.py","file_ext":"py","file_size_in_byte":3852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"29183767607","text":"from infra.ya_salt.lib.packages import action, transaction, transaction_validator, stateutil\n\nLO_STATE = [\n {\n \"name\": \"nvidia_driver_installed\",\n \"install_recommends\": False,\n \"refresh\": False,\n \"state\": \"pkg\",\n \"__id__\": \"nvidia_driver_installed\",\n \"fun\": \"installed\",\n \"__env__\": \"search_runtime\",\n \"__sls__\": \"components.nvidia.driver-418-v2\",\n \"order\": 10199,\n \"pkgs\": [\n {\n \"nvidia-418\": \"418.67-0ubuntu1\"\n },\n {\n \"nvidia-418-dev\": \"418.67-0ubuntu1\"\n },\n {\n \"nvidia-modprobe\": \"418.67-0ubuntu1\"\n },\n {\n \"libcuda1-418\": \"418.67-0ubuntu1\"\n },\n \"nvidia-common\",\n {\n \"libc6-i386\": \"2.23-0ubuntu10\"\n },\n {\n \"libc6-dev\": \"2.23-0ubuntu10\"\n },\n {\n \"libc6\": \"2.23-0ubuntu10\"\n },\n {\n \"libc-bin\": \"2.23-0ubuntu10\"\n },\n {\n \"libc-dev-bin\": \"2.23-0ubuntu10\"\n },\n {\n \"locales\": \"2.23-0ubuntu10\"\n },\n {\n \"multiarch-support\": \"2.23-0ubuntu10\"\n }\n ]\n },\n {\n \"name\": \"yandex-hbf-agent-purged\",\n \"state\": \"pkg\",\n \"pkgs\": [\n \"yandex-hbf-agent-static\",\n \"yandex-hbf-agent-init\"\n ],\n \"fun\": \"purged\",\n \"__env__\": \"search_runtime\",\n \"__sls__\": \"services.yandex-hbf-agent\",\n \"order\": 10229,\n \"__id__\": \"yandex-hbf-agent-purged\"\n }\n]\n\n\nclass TestTransaction(object):\n def test_install(self):\n tx = transaction.Transaction()\n tx.installed(action.Install('pkg-mock', 'ver-mock', 'src-mock'))\n assert len(tx.get_installed()) == 1\n assert tx.get_installed()['pkg-mock'][0].name == 'pkg-mock'\n assert tx.get_installed()['pkg-mock'][0].version == 'ver-mock'\n assert tx.get_installed()['pkg-mock'][0].src == 'src-mock'\n\n def test_remove(self):\n tx = transaction.Transaction()\n tx.removed(action.Remove('pkg-mock', 'src-mock'))\n assert len(tx.get_removed()) == 1\n assert tx.get_removed()['pkg-mock'][0].name == 'pkg-mock'\n assert tx.get_removed()['pkg-mock'][0].src == 'src-mock'\n\n def test_get_pkg_installed_version_of_installed_package(self):\n tx = transaction.Transaction()\n tx.installed(action.Install('pkg-mock', 'ver-mock', 'src-mock'))\n assert transaction.get_pkg_installed_version('pkg-mock', tx) == ('ver-mock', None,)\n\n def test_get_pkg_installed_version_of_not_installed_package(self):\n tx = transaction.Transaction()\n tx.installed(action.Install('pkg-mock', 'ver-mock', 'src-mock'))\n ver, err = transaction.get_pkg_installed_version('not-installed-pkg-mock', tx)\n assert ver is None\n assert err == 'package \"not-installed-pkg-mock\" is not scheduled to be installed'\n\n\nclass TestTransactionValidator(object):\n def test_validate_packages_loop(self):\n tx = transaction.Transaction()\n tx.installed(action.Install('pkg-mock', 'ver-mock', 'src-mock'))\n tx.removed(action.Remove('pkg-mock', 'other-src-mock'))\n messages = transaction_validator.validate_packages_loop(tx)\n expected_messages = [\n 'install-remove loop detected for package \"pkg-mock\" - '\n 'Remove(\"pkg-mock\", src: \"other-src-mock\"), '\n 'Install(\"pkg-mock\"=\"ver-mock\", src: \"src-mock\")'\n ]\n assert messages == expected_messages\n\n def test_validate_packages_versions_loop(self):\n tx = transaction.Transaction()\n tx.installed(action.Install('pkg-mock', 'v1', 'src1-mock'))\n tx.installed(action.Install('pkg-mock', 'v2', 'src2-mock'))\n messages = transaction_validator.validate_packages_versions_loop(tx)\n expected_messages = [\n 'version loop detected for package \"pkg-mock\" - '\n 'Install(\"pkg-mock\"=\"v1\", src: \"src1-mock\"), '\n 'Install(\"pkg-mock\"=\"v2\", src: \"src2-mock\")'\n ]\n assert messages == expected_messages\n\n def test_validate_packages_versions_present(self):\n tx = transaction.Transaction()\n tx.installed(action.Install('pkg-mock1', None, 'src1-mock'))\n tx.installed(action.Install('pkg-mock2', '', 'src2-mock'))\n messages = transaction_validator.validate_packages_versions_present(tx)\n expected_messages = sorted([\n 'package action Install(\"pkg-mock1\"=\"None\", src: \"src1-mock\") has no version',\n 'package action Install(\"pkg-mock2\"=\"\", src: \"src2-mock\") has no version',\n ])\n assert sorted(messages) == expected_messages\n\n def test_validate_transaction(self):\n tx = transaction.Transaction()\n tx.removed(action.Remove('pkg-mock', 'other-src-mock'))\n tx.installed(action.Install('pkg-mock', 'v1', 'src-mock'))\n tx.installed(action.Install('pkg-mock', 'v2', 'src2-mock'))\n tx.installed(action.Install('pkg-mock3', None, 'src3-mock'))\n messages = transaction_validator.validate_transaction(tx)\n expected_messages = [\n 'package action Install(\"pkg-mock3\"=\"None\", src: \"src3-mock\") has no version',\n\n 'install-remove loop detected for package \"pkg-mock\" - '\n 'Remove(\"pkg-mock\", src: \"other-src-mock\"), '\n 'Install(\"pkg-mock\"=\"v1\", src: \"src-mock\"), '\n 'Install(\"pkg-mock\"=\"v2\", src: \"src2-mock\")',\n\n 'version loop detected for package \"pkg-mock\" - '\n 'Install(\"pkg-mock\"=\"v1\", src: \"src-mock\"), '\n 'Install(\"pkg-mock\"=\"v2\", src: \"src2-mock\")'\n ]\n assert messages == expected_messages\n\n\nclass TestStatUtil(object):\n def test_extract_lo_packages_to_transaction(self):\n tx = transaction.Transaction()\n stateutil.extract_lo_packages_to_tx(LO_STATE, tx)\n assert len(tx.get_installed().keys()) == 12\n assert len(tx.get_removed().keys()) == 2\n assert transaction.get_pkg_installed_version('nvidia-common', tx) == (None, None,)\n assert transaction.get_pkg_installed_version('nvidia-418', tx) == ('418.67-0ubuntu1', None,)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/test_packages.py","file_name":"test_packages.py","file_ext":"py","file_size_in_byte":6336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42484093403","text":"\"\"\"\nhttps://stepik.org/lesson/24470/step/10?unit=6776\n\n\nВам дана последовательность строк.\nВыведите строки, содержащие обратный слеш \"\\\".\n\nSample Input:\n\n\\w denotes word character\nNo slashes here\n\nSample Output:\n\n\\w denotes word character\n\"\"\"\nimport re\nimport sys\n\npattern = r'.*\\\\.*'\n\nfor line in sys.stdin:\n line = line.rstrip()\n result = re.findall(pattern, line)\n for each_line in result:\n print(each_line)\n","repo_name":"DorogAD/stepik","sub_path":"stepik_tasks/python_basics_and_usage/unit3.2/task3-2-10.py","file_name":"task3-2-10.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12526918999","text":"import re\n\ndef parse_markdown(markdown):\n lines = markdown.split('\\n')\n response, in_list = '', False\n for line in lines:\n line = markdown_emphasis(line)\n line, in_list = markdown_list(line, in_list)\n if not in_list:\n line = markdown_titles(line)\n line = markdown_paragraph(line)\n response += line\n if in_list:\n response += ''\n return response\n\n\ndef markdown_emphasis(line):\n #bold\n for found in re.findall(r'\\b__.*__\\b', line):\n line = line.replace(found, f'{found[2:-2]}')\n\n #italic\n for found in re.findall(r'\\s_.*_\\s', line):\n line = line.replace(found, f' {found[2:-2]} ')\n\n return line\n\ndef markdown_titles(line):\n hashtags = re.match(r'^[#]+\\s', line)\n if hashtags:\n number = hashtags.group().count('#')\n line = f'' + line[number + 1:] + f' '\n return line\n\ndef markdown_list(line, in_list):\n if re.match(r'^\\*\\s', line):\n line = '' + line[2:] + ' '\n if not in_list:\n line = '' + line\n in_list = True\n else:\n if in_list:\n in_list = False\n line += '
'\n return line, in_list\n\ndef markdown_paragraph(line):\n if not re.match('{line}'\n return line","repo_name":"paulozava/exerciscm","sub_path":"python/markdown/markdown.py","file_name":"markdown.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19122694093","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\n\nclass Attention_block(nn.Module):\n def __init__(self,F_g,F_l,F_int):\n super(Attention_block, self).__init__()\n self.W_g = nn.Sequential(\n nn.Conv3d(F_g, F_int, kernel_size =3, stride =1, padding =1),\n nn.BatchNorm3d(F_int)\n )\n self.W_x = nn.Sequential(\n nn.Conv3d(F_l, F_int, kernel_size =3, stride =1, padding =1),\n nn.BatchNorm3d(F_int)\n )\n self.psi = nn.Sequential(\n nn.Conv3d(F_int, 1, kernel_size =3, stride =1, padding =1),\n nn.BatchNorm3d(1),\n nn.Sigmoid()\n )\n self.relu = nn.ReLU(inplace = True)\n def forward(self, g, x):\n g1 = self.W_g(g)\n x1 = self.W_x(x)\n #print(g1.shape)\n #print(x1.shape)\n g1 = F.interpolate(g1, scale_factor = (x1.shape[2]/g1.shape[2],x1.shape[3]/g1.shape[3],x1.shape[4]/g1.shape[4]), mode = 'trilinear')\n #print(g1.shape)\n psi = self.relu(g1+x1)\n psi = self.psi(psi)\n #print(psi.shape)\n\n return x*psi\n\nclass conv_block(nn.Module):\n def __init__(self, ch_in, ch_out):\n super(conv_block,self).__init__()\n self.conv = nn.Sequential(\n nn.Conv3d(ch_in, ch_out, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(ch_out),\n nn.ReLU(inplace = True),\n nn.Conv3d(ch_in, ch_out, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(ch_out),\n nn.ReLU(inplace = True)\n )\n def forward(self,x):\n x = self.conv(x)\n return x\n \nclass up_conv(nn.Module):\n def __init__(self,ch_in,ch_out):\n super(up_conv, self).__init__()\n self.up = nn.Sequential(\n nn.Upsample(scale_factor =2),\n nn.Conv3d(ch_in,ch_out,3,stride =1 ,padding =1),\n nn.BatchNorm3d(ch_out),\n nn.ReLU(inpalce =True)\n )\n def forward(self, x):\n x =self.up(x)\n return x \n\n\n\nclass Att_Unet(nn.Module):\n def __init__(self, in_channel = 1, out_channel = 1, training = True):\n super(Att_Unet, self).__init__()\n self.trianing = training\n # encoder section\n self.encoder1 = nn.Sequential(\n nn.Conv3d(in_channel, 16, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(16),\n nn.ReLU(inplace = True),\n nn.Conv3d(16, 32, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(32),\n nn.ReLU(inplace = True)\n ).to(\"cuda:0\")\n self.encoder2 = nn.Sequential(\n nn.Conv3d(32, 32, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(32),\n nn.ReLU(inplace = True),\n nn.Conv3d(32, 64, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(64),\n nn.ReLU(inplace = True)\n ).to(\"cuda:0\")\n\n self.encoder3 = nn.Sequential(\n nn.Conv3d(64, 64, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(64),\n nn.ReLU(inplace = True),\n nn.Conv3d(64, 128, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(128),\n nn.ReLU(inplace = True)\n ).to(\"cuda:0\")\n\n self.encoder4 = nn.Sequential(\n nn.Conv3d(128, 128, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(128),\n nn.ReLU(inplace = True)\n ).to(\"cuda:0\")\n\n self.att1 = Attention_block(F_g =128, F_l =128, F_int =128).to(\"cuda:0\")\n\n self.encoder5 = nn.Sequential(\n nn.Conv3d(128, 256, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(256),\n nn.ReLU(inplace = True)\n ).to(\"cuda:0\")\n\n # decoder section\n self.decoder1 = nn.Sequential(\n nn.Conv3d(384, 64, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(64),\n nn.ReLU(inplace = True)\n ).to('cuda:0')\n self.att2 = Attention_block(F_g=64, F_l=64, F_int =64).to(\"cuda:0\")\n self.decoder2 = nn.Sequential(\n nn.Conv3d(64, 64, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(64),\n nn.ReLU(inplace = True)\n ).to('cuda:0')\n self.decoder3 = nn.Sequential(\n nn.Conv3d(128, 32, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(32),\n nn.ReLU(inplace = True)\n ).to('cuda:1')\n self.att3 = Attention_block(F_g =32, F_l=32, F_int =32).to(\"cuda:1\")\n self.decoder4 = nn.Sequential(\n nn.Conv3d(32, 32, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(32),\n nn.ReLU(inplace = True)\n ).to('cuda:1')\n self.decoder5 = nn.Sequential(\n nn.Conv3d(64, 32, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(32),\n nn.ReLU(inplace = True)\n ).to('cuda:1')\n self.decoder6 = nn.Sequential(\n nn.Conv3d(32, 2, 3, stride = 1, padding = 1),\n nn.BatchNorm3d(2),\n nn.ReLU(inplace = True)\n ).to('cuda:1')\n self.decoder7 = nn.Sequential(\n nn.Conv3d(2, out_channel, 1)\n ).to('cuda:1')\n\n\n \n def forward(self, x):\n # encoder section\n x = x.to(\"cuda:0\")\n print('origin:',x.shape)\n x = self.encoder1(x).to(\"cuda:0\")# relu(4->8)\n f1 = x #(8)\n f1= f1.to(\"cuda:1\")\n print('f1:',x.shape)\n #print(\"encoder1_size:\",f1.shape)\n x = F.max_pool3d(x,kernel_size = 2,stride = 2,padding = 0).to(\"cuda:0\")# maxpool(8->8)\n #print(\"test\",x.shape)\n x = self.encoder2(x).to(\"cuda:0\")# relu(8->16)\n f2 = x #(16)\n f2 = f2.to(\"cuda:1\")\n print('f2:',x.shape)\n #print(\"encoder2_size:\",f2.shape)\n x = F.max_pool3d(x,kernel_size = 2,stride = 2,padding = 0).to(\"cuda:0\")# maxpool(16->16)\n \n x = self.encoder3(x).to(\"cuda:0\")# relu(16->32)\n f3 = x #(32)\n f3 =f3.to(\"cuda:0\")\n print('f3:',x.shape)\n #print(\"encoder3_size:\",f3.shape)\n x = F.max_pool3d(x,kernel_size = 2,stride = 2,padding = 0).to(\"cuda:0\")# maxpool(32->32)\n x = self.encoder4(x).to(\"cuda:0\")# relu(32->32)\n a1 = x\n x=x.to(\"cuda:0\")\n a1=a1.to(\"cuda:0\")\n print('x:',x.shape)\n A1 = self.att1(g = f3, x =a1).to(\"cuda:0\") #attention block1\n print('A1:',A1.shape)\n x = self.encoder5(x).to(\"cuda:0\") #relu(32->64)\n print('x:',x.shape)\n \n # decoder section\n\n x = F.interpolate(x, scale_factor = (f3.shape[2]/x.shape[2],f3.shape[3]/x.shape[3],f3.shape[4]/x.shape[4]), mode = 'trilinear').to(\"cuda:0\")# upsample（16->16)\n print('x:',x.shape)\n A1 = F.interpolate(A1, scale_factor = (f3.shape[2]/A1.shape[2],f3.shape[3]/A1.shape[3],f3.shape[4]/A1.shape[4]), mode = 'trilinear').to(\"cuda:0\")# upsample（16->16)\n print('A1:',A1.shape)\n x = torch.cat((x,A1),1).to(\"cuda:0\") #(64+32 = 96)\n print('x:',x.shape)\n x = x.to(\"cuda:0\")\n x = self.decoder1(x).to(\"cuda:0\") # relu(96 ->32)\n print('x:',x.shape)\n\n a2 = x #attention block2, size = 32\n a2 = a2.to(\"cuda:0\")\n print('a2:',a2.shape)\n print('f2:',f2.shape)\n f2 = f2.to('cuda:0')\n A2 = self.att2(g =f2,x=a2).to(\"cuda:0\") # size =32\n print('A2',A2.shape)\n A2 = F.interpolate(A2, scale_factor = (f2.shape[2]/A2.shape[2],f2.shape[3]/A2.shape[3],f2.shape[4]/A2.shape[4]), mode = 'trilinear').to(\"cuda:1\") # upsample（16->16)\n # x = x.to(\"cuda:1\")\n #print(\"decoder1_size:\",x.shape)\n # x = x.to(\"cuda:1\")\n x = self.decoder2(x).to(\"cuda:0\") #relu(32->32)\n print('x:',x.shape)\n x= x.to(\"cuda:1\")\n x = F.interpolate(x, scale_factor =(f2.shape[2]/x.shape[2],f2.shape[3]/x.shape[3],f2.shape[4]/x.shape[4]), mode = 'trilinear').to(\"cuda:1\") # upsample(256->256)\n print('A2:',A2.shape)\n x = torch.cat((x,A2),1).to(\"cuda:1\") #(4+4 = 8)\n # x = x.to(\"cuda:1\")\n print('x:',x.shape)\n x = self.decoder3(x).to(\"cuda:1\") # relu(8 ->2)\n print('x:',x.shape)\n #print(\"decoder2_size:\",x.shape)\n a3 = x#attention block2\n print('a3:',a3.shape)\n f1 = f1.to(\"cuda:1\")\n a3 = a3.to(\"cuda:1\")\n A3 = self.att3(g =f1,x=a3).to(\"cuda:1\")\n print('x:',x.shape)\n A3 = F.interpolate(A3, scale_factor = (f1.shape[2]/A3.shape[2],f1.shape[3]/A3.shape[3],f1.shape[4]/A3.shape[4]), mode = 'trilinear').to(\"cuda:1\") # upsample（16->16)\n print('A3:',A3.shape)\n # x = x.to(\"cuda:1\")\n x = self.decoder4(x).to(\"cuda:1\") #relu(2->2)\n print('x:',x.shape)\n x = F.interpolate(x, scale_factor =(f1.shape[2]/x.shape[2],f1.shape[2]/x.shape[2],f1.shape[2]/x.shape[2]), mode = 'trilinear').to(\"cuda:1\") # upsample(128->128)\n print('x:',x.shape)\n x = torch.cat((x,A3),1).to(\"cuda:1\") #(2+2 = 4)\n print('x:',x.shape)\n # x= x.to(\"cuda:2\")\n x = self.decoder5(x).to(\"cuda:1\") # relu(4 ->2)\n print('x:',x.shape)\n #print(\"decoder3_size:\",x.shape)\n x = self.decoder6(x).to(\"cuda:1\")\n print('x:',x.shape)\n x = self.decoder7(x).to(\"cuda:1\")\n print('x:',x.shape)\n x = F.interpolate(x, scale_factor = (f1.shape[2]/x.shape[2],f1.shape[3]/x.shape[3],f1.shape[4]/x.shape[4]), mode = 'trilinear').to(\"cuda:1\")\n\n\n return x","repo_name":"BrunoFANG1/3D-Laision-Seg","sub_path":"model/Baseline.py","file_name":"Baseline.py","file_ext":"py","file_size_in_byte":9335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34616752537","text":"import tools\nfrom tools import get_arrays_median, single_dags, dags\nimport matplotlib.pyplot as plt\n\nkwargs = {\"marker\": \"\", \"markersize\": 2}\n\nsingle_dags_paths = get_arrays_median(single_dags, \"paths\")\nsingle_dags_colors = get_arrays_median(single_dags, \"colors\")\nhash_dags_paths = get_arrays_median(dags, \"paths\")\nhash_dags_colors = get_arrays_median(dags, \"colors\")\n\nplt.xlabel(\"Frame\")\nplt.ylabel(\"Time (ms)\")\n\n# plt.xlim(0, 1000)\n# plt.ylim(0, 5)\n\nplt.plot(tools.indices, single_dags_paths, color=\"red\", label=\"paths (DAG)\", **kwargs)\nplt.plot(tools.indices, single_dags_colors, color=\"orange\", label=\"colors (DAG)\", **kwargs)\nplt.plot(tools.indices, hash_dags_paths, color=\"green\", label=\"paths (Hash DAG)\", **kwargs)\nplt.plot(tools.indices, hash_dags_colors, color=\"cyan\", label=\"colors (Hash DAG)\", **kwargs)\n\nplt.legend()\nplt.show()\n\n# plt.plot(tools.indices, (hash_dags_paths - single_dags_paths) / single_dags_paths, color=\"blue\", **kwargs)\n# plt.plot(tools.indices, (hash_dags_colors - single_dags_colors) / single_dags_colors, color=\"purple\", **kwargs)\n# plt.show()","repo_name":"Phyronnaz/HashDAG","sub_path":"python/plot_frames.py","file_name":"plot_frames.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":99,"dataset":"github-code","pt":"3"}
+{"seq_id":"25826720159","text":"# generic imports\nimport os\nimport numpy as np\nimport pandas as pd\n\n# torch imports\nimport torch\nfrom torch.utils.data import Dataset\n\n# pyg imports\nimport torch_geometric.data as PyGData\n\n# torch cluster imports\nfrom torch_cluster import knn_graph\n\n\nclass BasicDataset(Dataset):\n def __init__(self, batches, auxiliary=False, k_neighbors=1, scale=None):\n\n self.PATH_DATASET = '/mnt/d/waleed/kaggle/IceCube/data/'\n if batches is not None:\n self.batches = batches\n else:\n raise TypeError(\"List of files should contain at least 1 train parquet file\")\n\n self.scale = scale\n self.initialize()\n\n self.event_ids = np.unique(self.batches.index)\n self.items = dict(zip(np.arange(self.event_ids.shape[0]), self.event_ids))\n\n self.auxiliary = auxiliary\n self.k_neighbors = k_neighbors\n\n def initialize(self):\n self.meta_data = pd.read_parquet(os.path.join(self.PATH_DATASET, 'train_meta.parquet'))\n self.geometry = pd.read_csv(os.path.join(self.PATH_DATASET, 'sensor_geometry.csv'))\n if isinstance(self.batches, list):\n self.batches = pd.concat(self.batches, ignore_index=True)\n\n def getdata(self):\n df = self.data[self.features]\n x, y = df.iloc[:, 0:-1].to_numpy(), df.iloc[:, -1].to_numpy()\n if self.scale is not None:\n x /= self.scale\n\n return x, y\n\n def __getitem__(self, item):\n event_num = self.items.get(item)\n position = self.geometry.iloc[self.batches.loc[event_num].sensor_id.tolist(), 1:].to_numpy()\n features = self.batches.loc[event_num,['time', 'charge', 'auxiliary']].astype(float).to_numpy()\n\n data = np.hstack([position, features])\n target = self.meta_data[self.meta_data.event_id==event_num][['azimuth', 'zenith']].to_numpy().flatten()\n\n if self.auxiliary:\n good_idx = np.where(data[:,-1]==0)[0]\n data = data[good_idx][:, 1:]\n\n x = torch.tensor(data, dtype=torch.float32)\n y = self.meta_data[self.meta_data.event_id==event_num][['azimuth', 'zenith']].to_numpy().flatten()\n y = torch.tensor(y, dtype=torch.float32)\n # graph connecvtivety by k_nn algorithm\n knn_graph_edge_index = knn_graph(x[:, 0:3], k=self.k_neighbors)\n\n return {\"data\": PyGData.Data(x=x, y=y, edge_index=knn_graph_edge_index), \"targets\": y}\n\n def __len__(self):\n return self.event_ids.shape[0]\n","repo_name":"wesmail/IceCubeChallenge","sub_path":"data_handling.py","file_name":"data_handling.py","file_ext":"py","file_size_in_byte":2472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35946026724","text":"import random\r\n\r\n# Dafault values\r\nplayer_choice = None\r\nwinner = \"Tie\"\r\n\r\n# Turns player letter selection into the selected word.\r\n\r\n\r\ndef translator(choice):\r\n if choice == \"R\":\r\n return \"Rock\"\r\n elif choice == \"P\":\r\n return \"Paper\"\r\n elif choice == \"S\":\r\n return \"Scissors\"\r\n\r\n\r\n# Determines the winner of the game.\r\ndef winner_calculator(player_selection, computer_selection):\r\n if player_selection == \"Rock\":\r\n if computer_selection == \"Rock\":\r\n return \"Tie\"\r\n elif computer_selection == \"Paper\":\r\n return \"Computer wins\"\r\n elif computer_selection == \"Scissors\":\r\n return \"Player wins\"\r\n elif player_selection == \"Paper\":\r\n if computer_selection == \"Rock\":\r\n return \"Player wins\"\r\n elif computer_selection == \"Paper\":\r\n return \"Tie\"\r\n elif computer_selection == \"Scissors\":\r\n return \"Computer wins\"\r\n elif player_selection == \"Scissors\":\r\n if computer_selection == \"Rock\":\r\n return \"Computer wins\"\r\n elif computer_selection == \"Paper\":\r\n return \"Player wins\"\r\n elif computer_selection == \"Scissors\":\r\n return \"Tie\"\r\n\r\n\r\n# Game loop. Goes until one player wins.\r\nwhile winner == \"Tie\":\r\n # Player makes selection\r\n print(\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nChoose rock (r), paper (p), or scissors (s).\")\r\n player_choice = input()\r\n player_choice = player_choice.upper()\r\n\r\n # Error handling for when the selection is not rock, paper, or scissors.\r\n while player_choice != \"R\" and player_choice != \"P\" and player_choice != \"S\":\r\n print(\"Invalid choice. Please choose again.\")\r\n print(\"Choose rock (r), paper (p), or scissors (s).\")\r\n player_choice = input()\r\n player_choice = player_choice.upper()\r\n\r\n # Turns player letter selection into the selected word.\r\n player_selection = translator(player_choice)\r\n\r\n # Computer Selection\r\n computer_choice_list = [\"R\", \"P\", \"S\"]\r\n computer_choice = computer_choice_list[random.randint(0, 2)]\r\n computer_selection = translator(computer_choice)\r\n\r\n # Player and computer printed results.\r\n print(f\"You chose {player_selection}\")\r\n print(f\"The computer chose {computer_selection}\")\r\n\r\n # Calculates winner.\r\n winner = winner_calculator(player_selection, computer_selection)\r\n print(winner_calculator(player_selection, computer_selection))\r\n if winner == \"Tie\":\r\n print(\"Play again.\")\r\n","repo_name":"BradyL271/Python-Mini-projects","sub_path":"RockPaperScissors.py","file_name":"RockPaperScissors.py","file_ext":"py","file_size_in_byte":2507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19668452216","text":"# logic: when we are standing at any node then to have same path value to leaf from the current node\n# cost value to leaf from both the subtree must be same.\n# if not same then we will incr the value of node in subtree having lower value by their diff i.e abs(l- r)'.\n\n# Reason: current node will common to both (left and right subtree), so ans will depend on the left and right subtree value only.\n\n# will go bottom up.\n\n# After that at each function call(at each node), we will return the cost to leaf from the current node.\n\nclass Solution:\n def minIncrements(self, n: int, cost: List[int]) -> int:\n self.count= 0\n \n def dfs(index):\n # if leaf node\n if 2*index >= n and 2*index + 1 >= n:\n return cost[index -1]\n l= dfs(2*index)\n r= dfs(2*index + 1)\n # to make the both subtree to have same weight till leaf \n self.count+= abs(l - r)\n return cost[index -1] + max(l, r) # return the cost to leaf from this node\n \n dfs(1) # calling from index '1'.\n return self.count\n","repo_name":"Ravi-0412/DSA-Program-And-Notes","sub_path":"Tree/Binary Tree/2673. Make Costs of Paths Equal in a Binary Tree.py","file_name":"2673. Make Costs of Paths Equal in a Binary Tree.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"14882931292","text":"import numpy as np\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nimport random\nimport pandas as pd\nfrom collections import Counter\nimport numpy as np\nfrom sklearn.datasets import fetch_20newsgroups\nfrom collections import Counter, defaultdict\nfrom nltk.corpus import stopwords\nfrom sklearn.model_selection import train_test_split\nimport re\nfrom sklearn.utils import shuffle\nimport os\nfrom tqdm import tqdm, trange\nfrom torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\nfrom keras.preprocessing.sequence import pad_sequences\nfrom transformers import AutoTokenizer\nfrom torch.optim import Adam\nfrom transformers import BertTokenizer, BertConfig\nfrom transformers import AdamW, BertForSequenceClassification, get_linear_schedule_with_warmup\nfrom torch.cuda.amp import autocast, GradScaler\nimport wandb\n\ndef set_seed(args):\n random.seed(args.seed)\n np.random.seed(args.seed)\n torch.manual_seed(args.seed)\n\ndef corrupt_dataset(args, data, num_labels):\n total_number = len(data)\n new_data = data.clone()\n random_indices = torch.rand(int(args.noise_ratio * total_number)) * total_number\n random_indices = random_indices.int()\n new_labels = torch.rand(int(len(random_indices))) * num_labels\n new_labels = new_labels.int()\n for i in range(len(random_indices)):\n temp_label = data[random_indices[i]]\n while temp_label == new_labels[i]:\n new_labels[i] = (torch.rand(1)[0] * num_labels).int()\n new_data[random_indices[i]] = new_labels[i]\n return new_data \n\ndef corrupt_dataset_asymmetric(args, data, num_labels):\n total_number = len(data)\n new_data = data.clone()\n random_indices = torch.rand(int(args.noise_ratio * total_number)) * total_number\n random_indices = random_indices.int()\n for i in range(len(random_indices)):\n temp_label = data[random_indices[i]]\n new_data[random_indices[i]] = (new_data[random_indices[i]] + 1) % num_labels\n return new_data \n\ndef load_dataset(args, dataset):\n\n if dataset == 'sst':\n df_train = pd.read_csv(\"/localscratch/yzhuang43/datasets/SST-2/train.tsv\", delimiter='\\t', header=0)\n \n df_val = pd.read_csv(\"/localscratch/yzhuang43/datasets/SST-2/dev.tsv\", delimiter='\\t', header=0)\n \n df_test = pd.read_csv(\"/localscratch/yzhuang43/datasets/SST-2/sst-test.tsv\", delimiter='\\t', header=None, names=['sentence', 'label'])\n\n train_sentences = df_train.sentence.values\n val_sentences = df_val.sentence.values\n test_sentences = df_test.sentence.values\n train_labels = df_train.label.values\n val_labels = df_val.label.values\n test_labels = df_test.label.values \n \n\n if dataset == '20news':\n \n VALIDATION_SPLIT = 0.8\n newsgroups_train = fetch_20newsgroups(data_home=args.path, subset='train', shuffle=True, random_state=0)\n print(newsgroups_train.target_names)\n print(len(newsgroups_train.data))\n\n newsgroups_test = fetch_20newsgroups(data_home=args.path, subset='test', shuffle=False)\n\n print(len(newsgroups_test.data))\n\n train_len = int(VALIDATION_SPLIT * len(newsgroups_train.data))\n\n train_sentences = newsgroups_train.data[:train_len]\n val_sentences = newsgroups_train.data[train_len:]\n test_sentences = newsgroups_test.data\n train_labels = newsgroups_train.target[:train_len]\n val_labels = newsgroups_train.target[train_len:]\n test_labels = newsgroups_test.target \n \n return train_sentences, val_sentences, test_sentences, train_labels, val_labels, test_labels\n\ndef read_data(args, num_labels):\n # load dataset\n \n train_sentences, val_sentences, test_sentences, train_labels, val_labels, test_labels = load_dataset(args, args.dataset)\n\n tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)\n\n train_input_ids = []\n val_input_ids = []\n test_input_ids = []\n \n if args.dataset == '20news':\n MAX_LEN = 150\n else:\n MAX_LEN = 256\n\n for sent in train_sentences:\n # `encode` will:\n # (1) Tokenize the sentence.\n # (2) Prepend the `[CLS]` token to the start.\n # (3) Append the `[SEP]` token to the end.\n # (4) Map tokens to their IDs.\n encoded_sent = tokenizer.encode(\n sent, # Sentence to encode.\n add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n # This function also supports truncation and conversion\n # to pytorch tensors, but we need to do padding, so we\n # can't use these features :( .\n max_length = MAX_LEN, # Truncate all sentences.\n #return_tensors = 'pt', # Return pytorch tensors.\n )\n # Add the encoded sentence to the list.\n train_input_ids.append(encoded_sent)\n \n\n for sent in val_sentences:\n encoded_sent = tokenizer.encode(\n sent, \n add_special_tokens = True, \n max_length = MAX_LEN, \n )\n val_input_ids.append(encoded_sent)\n\n for sent in test_sentences:\n encoded_sent = tokenizer.encode(\n sent, \n add_special_tokens = True, \n max_length = MAX_LEN, \n )\n test_input_ids.append(encoded_sent)\n\n # Pad our input tokens\n train_input_ids = pad_sequences(train_input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")\n val_input_ids = pad_sequences(val_input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")\n test_input_ids = pad_sequences(test_input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")\n # Create attention masks\n train_attention_masks = []\n val_attention_masks = []\n test_attention_masks = []\n\n # Create a mask of 1s for each token followed by 0s for padding\n for seq in train_input_ids:\n seq_mask = [float(i>0) for i in seq]\n train_attention_masks.append(seq_mask)\n for seq in val_input_ids:\n seq_mask = [float(i>0) for i in seq]\n val_attention_masks.append(seq_mask)\n for seq in test_input_ids:\n seq_mask = [float(i>0) for i in seq]\n test_attention_masks.append(seq_mask)\n\n # Convert all of our data into torch tensors, the required datatype for our model\n\n train_inputs = torch.tensor(train_input_ids)\n validation_inputs = torch.tensor(val_input_ids)\n train_labels = torch.tensor(train_labels)\n validation_labels = torch.tensor(val_labels)\n train_masks = torch.tensor(train_attention_masks)\n validation_masks = torch.tensor(val_attention_masks)\n test_inputs = torch.tensor(test_input_ids)\n test_labels = torch.tensor(test_labels)\n test_masks = torch.tensor(test_attention_masks)\n\n if args.noise_type == 's':\n noisy_train_labels = corrupt_dataset(args, train_labels, num_labels)\n noisy_validation_labels = corrupt_dataset(args, validation_labels, num_labels)\n else:\n noisy_train_labels = corrupt_dataset_asymmetric(args, train_labels, num_labels)\n noisy_validation_labels = corrupt_dataset_asymmetric(args, validation_labels, num_labels)\n \n # Create an iterator of our data with torch DataLoader. \n return train_inputs, train_masks, train_labels, noisy_train_labels, validation_inputs, validation_masks, validation_labels, noisy_validation_labels, test_inputs, test_masks, test_labels\n\n \n\ndef create_dataset(args, inputs, masks, labels, batch_size, noisy_labels=None):\n if noisy_labels != None:\n data = TensorDataset(inputs, masks, labels, noisy_labels)\n else:\n data = TensorDataset(inputs, masks, labels)\n \n sampler = SequentialSampler(data)\n dataloader = DataLoader(data, sampler=sampler, batch_size=batch_size)\n return sampler, dataloader","repo_name":"night-chen/DyGen","sub_path":"src/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8131,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"32092720241","text":"__author__ = 'sindre'\n\nimport glob\nimport logging\nimport os\n\nimport shutil\nimport subprocess\nimport sys\nimport zipfile\n\nimport tool\n\nlogger = logging.getLogger(__name__)\n\n\nclass InstallerBuilder(object):\n \"\"\"控制构建安装程序。这包括三个主要步骤:\n\n 1.在构建目录中安排必要的文件\n 2.填写模板NSI文件以控制NSIS\n 3.运行 “makensis” 来构建安装程序\n\n \"\"\"\n python_template = \"\"\"\n\n \n {extra_preamble}\n \n if __name__ == '__main__':\n from {module} import {func}\n {func}() \n \n \n \"\"\"\n\n def __init__(self,\n name,\n version,\n requirementstxt,\n entry_point,\n source_path,\n icon=\"config/logo.ico\",\n author=\"SindreYang\",\n licence=\"config/licence.rtf\",\n onefile=1,\n tobinary=1,\n thirdpriority=1,\n build_dir=\"./build_py2nsis\",\n python_version=None):\n self.appname =name.replace(\" \",\"\")\n self.version = version\n self.requirementsTxT = os.path.abspath(requirementstxt)\n self.icon = os.path.abspath(icon)\n self.author = os.path.abspath(author)\n self.licence = os.path.abspath(licence)\n self.OneFile = bool(int(onefile))\n self.ToBinary = bool(int(tobinary))\n self.ThirdPriority = bool(int(thirdpriority))\n self.build_dir = os.path.abspath(build_dir)\n self.third_dir = os.path.abspath(os.path.join(build_dir, \"third\"))\n self.entry_point = entry_point\n self.source_path = os.path.abspath(source_path)\n self.work_dir = os.getcwd()\n self.tmp_path = os.path.abspath(os.path.join(self.build_dir, \"tmp\"))\n self.python_path = os.path.abspath(os.path.join(self.build_dir, \"py\"))\n if python_version is None:\n version = sys.version_info\n version_number = f\"{version.major}.{version.minor}.{version.micro}\"\n self.python_version= version_number\n else:\n self.python_version = python_version\n\n\n\n def install_third(self):\n from pip._internal import main as pip_main\n #pip_main(['install', \"pyinstaller\", '--target', self.tmp_path])\n pip_main(['install', \"pip\", '--target', self.third_dir])\n # 读取 requirements.txt 文件\n with open(self.requirementsTxT, 'r') as file:\n requirements = file.readlines()\n name_str = self.entry_point.split(':')\n import_name, fun_name = name_str[0], name_str[1]\n\n if self.ThirdPriority:\n\n # 安装所有的whl文件到指定目录下\n for requirement in requirements:\n pip_main(['install', requirement.strip(), '--target', self.third_dir])\n code = f\"\"\"\nimport sys, os\nimport site\nscriptdir, script = os.path.split(os.path.abspath(__file__))\ninstalldir = scriptdir\nthirddir = os.path.join(scriptdir, 'third')\nappdir =os.path.join(scriptdir, 'app')\nsys.path.insert(0, thirddir)\nsys.path.insert(0, appdir)\nsite.addsitedir(thirddir)\nsite.addsitedir(appdir)\n\n\nif __name__ == '__main__':\n import qtmain\n {import_name}.{fun_name}()\n\"\"\"\n with open(os.path.join(self.build_dir, 'app.py', ), 'w', encoding=\"utf-8\") as f:\n f.write(code)\n\n else:\n shutil.copy(self.requirementsTxT, self.build_dir)\n # 否则写入脚本中，用户启动时在安装\n code = f\"\"\"\nimport sys, os\nimport site\nscriptdir, script = os.path.split(os.path.abspath(__file__))\ninstalldir = scriptdir\nthirddir = os.path.join(scriptdir, 'third')\nappdir =os.path.join(scriptdir, 'app')\nsys.path.insert(0, thirddir)\nsys.path.insert(0, appdir)\nsite.addsitedir(thirddir)\nsite.addsitedir(appdir)\n\n\nif __name__ == '__main__':\n try:\n import qtmain\n except Exception as e:\n print(\"import error:\",sys.path)\n try:\n from pip._internal import main as pip_main\n pip_main(['install', '-r', 'requirements.txt', '--target','third'])\n except Exception as e:\n print(\"pip error:\",e)\n {import_name}.{fun_name}()\n\"\"\"\n\n with open(os.path.join(self.build_dir, 'app.py', ), 'w', encoding=\"utf-8\") as f:\n f.write(code)\n\n def py2pyd(self):\n target_path = os.path.join(self.build_dir, \"app\")\n try:\n shutil.rmtree(target_path)\n except FileNotFoundError:\n pass\n shutil.copytree(self.source_path, target_path)\n if self.ToBinary:\n from setuptools import Extension, setup\n from Cython.Build import cythonize\n extensions = []\n py_files = []\n # 遍历目录下的所有文件\n for root, dirs, files in os.walk(target_path):\n for file in files:\n # 判断文件名是否以 .py 结尾\n if file.endswith('.py'):\n # 构建文件的完整路径\n file_path = os.path.join(root, file)\n py_files.append(file_path)\n\n # 构建扩展模块名称\n module_name = os.path.splitext(file)[0]\n\n # 构建扩展模块对象\n extension = Extension(module_name, sources=[file_path])\n extensions.append(extension)\n\n setup(\n ext_modules=cythonize(extensions, compiler_directives={'language_level': \"3\"}),\n script_args=[\"build_ext\", \"--inplace\", \"--build-lib\", f\"{target_path}\", \"--build-temp\",\n f\"{self.tmp_path}\"])\n\n # 删除.c和.py文件\n for file in glob.glob(os.path.join(target_path, \"**/*\"), recursive=True):\n if file.endswith((\".c\", \".py\")):\n os.remove(file)\n\n def py2exe_pyinstaller(self):\n # 调用命令行执行PyInstaller编译代码\n os.chdir(self.build_dir)\n check_file = \"onedir\"\n if self.OneFile:\n check_file = \"onefile\"\n\n code = f\"\"\"\nimport os\nimport sys\nscriptdir, script = os.path.split(os.path.abspath(__file__))\nthirddir = os.path.join(scriptdir, fR\"{self.tmp_path}\")\nthirddir2 = os.path.join(scriptdir, 'third')\nsys.path.insert(0, thirddir)\nsys.path.insert(0, thirddir2)\nimport PyInstaller.__main__\nPyInstaller.__main__.run([\n 'app.py',\n f'--{check_file}',\n '--add-data=app;app',\n '--add-data=third;third',\n '--specpath=./',\n '--distpath=./',\n '--workpath=./tmp',\n]) \n \n \"\"\"\n with open('installer.py', 'w', encoding=\"utf-8\") as f:\n f.write(code)\n\n subprocess.run([\"./py/python.exe\", \"./installer.py\"])\n def py2exe(self):\n # 调用命令行执行PyInstaller编译代码\n os.chdir(self.build_dir)\n code_c = R\"\"\"\n\n#include \n\nint main() {\n // 获取当前可执行文件的路径\n char exePath[MAX_PATH];\n GetModuleFileNameA(NULL, exePath, MAX_PATH);\n\n // 去除文件名部分，只保留目录路径\n char drive[_MAX_DRIVE];\n char dir[MAX_PATH];\n _splitpath_s(exePath, drive, sizeof(drive), dir, sizeof(dir), NULL, 0, NULL, 0);\n\n // 组合目录路径\n char dirPath[MAX_PATH];\n sprintf_s(dirPath, sizeof(dirPath), \"%s%s\", drive, dir);\n\n // 构建app.py的绝对路径\n char appPath[MAX_PATH];\n sprintf_s(appPath, sizeof(appPath), \"%sapp.py\", dirPath);\n\n // 构建python的绝对路径\n char pythonPath[MAX_PATH];\n sprintf_s(pythonPath, sizeof(pythonPath), \"%spy/python.exe\", dirPath);\n\n // 构建启动命令\n char command[MAX_PATH * 2];\n sprintf_s(command, sizeof(command), \"\\\"%s\\\" \\\"%s\\\"\", pythonPath, appPath);\n\n // 创建进程并执行命令\n STARTUPINFOA si = { sizeof(si) };\n PROCESS_INFORMATION pi;\n \n if (CreateProcessA(NULL, command, NULL, NULL, FALSE, 0, NULL, NULL, &si, &pi)) {\n // 等待进程结束\n WaitForSingleObject(pi.hProcess, INFINITE);\n \n // 关闭进程和线程的句柄\n CloseHandle(pi.hProcess);\n CloseHandle(pi.hThread);\n }\n\n return 0;\n}\n \n \"\"\"\n with open('app.c', 'w', encoding=\"utf-8\") as f:\n f.write(code_c)\n code_steup = \"\"\"\nfrom distutils.ccompiler import new_compiler\nimport distutils.sysconfig\nimport sys\nimport os\nfrom pathlib import Path\n\ndef compile(src):\n src = Path(src)\n cc = new_compiler()\n exe = src.stem\n cc.add_include_dir(distutils.sysconfig.get_python_inc())\n cc.add_library_dir(os.path.join(sys.base_exec_prefix, 'libs'))\n # First the CLI executable\n objs = cc.compile([str(src)])\n cc.link_executable(objs, exe)\n # Now the GUI executable\n cc.define_macro('WINDOWS')\n objs = cc.compile([str(src)])\n cc.link_executable(objs, exe + 'w')\n\nif __name__ == \"__main__\":\n compile(\"app.c\") \n \n \"\"\"\n with open('app_setup.py', 'w', encoding=\"utf-8\") as f:\n f.write(code_steup)\n\n\n subprocess.run([\"./py/python.exe\", \"./app_setup.py\"])\n\n def exe2nsis(self):\n # 获取当前脚本的绝对路径\n os.chdir(os.path.dirname(os.path.realpath(__file__)))\n print(f\"exe2nsis工作目录：{os.getcwd()}\")\n self.exe_7z_path = os.path.abspath(\"../bin/7z/7z.exe\")\n self.exe_nsis_path = os.path.abspath(\"../bin/NSIS/makensis.exe\")\n self.config_path = os.path.abspath(\"../config\")\n # 压缩app目录\n files_to_compress =[f\"{os.path.abspath(self.build_dir)}/{i}\" for i in [\"app\", \"py\",\"third\",\"app.exe\",\"app.py\",\"requirements.txt\"]]\n subprocess.run([f\"{self.exe_7z_path}\", \"a\",f\"{self.work_dir}/app.7z\"]+files_to_compress)\n # 替换文件\n nsis_code =f\"\"\"\n# ====================== 自定义宏 产品信息==============================\n!define PRODUCT_NAME \t\t\"{self.appname}\"\n!define PRODUCT_PATHNAME \t\"{self.appname}\" #安装卸载项用到的KEY\n!define INSTALL_APPEND_PATH \"{self.appname}\" #安装路径追加的名称 \n!define INSTALL_DEFALT_SETUPPATH \"\" #默认生成的安装路径 \n!define EXE_NAME \t\t\"app.exe\" # 指定主运行程序，快捷方式也是用此程序生成\n!define PRODUCT_VERSION \t\t\"{self.version}\"\n!define PRODUCT_PUBLISHER \t\t\"{self.author}\"\n!define PRODUCT_LEGAL \t\t\"\"${{PRODUCT_PUBLISHER}} Copyright（c）2023\"\"\n!define INSTALL_OUTPUT_NAME \t\t\"{self.appname}_V{self.version}.exe\"\n\n# ====================== 自定义宏 安装信息==============================\n!define INSTALL_7Z_PATH \t \t\t\"{self.work_dir}\\\\app.7z\"\n!define INSTALL_7Z_NAME \t \t\t\"app.7z\"\n!define INSTALL_RES_PATH \t\t\"skin.zip\"\n!define INSTALL_LICENCE_FILENAME \"{self.licence}\"\n!define INSTALL_ICO \t\t\t\t\"{self.icon}\"\n\n\n!include \"ui.nsh\"\n\n# ==================== NSIS属性 ================================\n\n# 针对Vista和win7 的UAC进行权限请求.\n# RequestExecutionLevel none|user|highest|admin\nRequestExecutionLevel admin\n\n#SetCompressor zlib\n\n; 安装包名字.\nName \"${{PRODUCT_NAME}}\"\n\n# 安装程序文件名.\n\nOutFile \"{self.work_dir}\\\\{self.appname}_V{self.version}.exe\"\n\nInstallDir \"1\"\n\n# 安装和卸载程序图标\nIcon \"${{INSTALL_ICO}}\"\nUninstallIcon \"uninst.ico\"\n\n \n \"\"\"\n\n # 执行封装命令\n os.chdir(self.config_path)\n with open(\"output.nsi\", \"w\") as file:\n file.write(nsis_code)\n subprocess.run([f\"{self.exe_nsis_path}\", \"./output.nsi\"])\n\n\n\n\n def run(self,clean=False):\n \"\"\"\n 运行构建安装程序的所有步骤。\n \"\"\"\n\n try:\n # 从干净的构建目录开始\n shutil.rmtree(self.build_dir)\n except FileNotFoundError:\n pass\n os.makedirs(self.build_dir)\n os.makedirs(self.tmp_path)\n\n # 安装python\n tool.python_installer(self.python_path,version=self.python_version)\n\n\n #转换二进制\n self.py2pyd()\n\n # 安装依赖到指定文件夹\n self.install_third()\n\n # 生成单文件\n self.py2exe()\n\n\n # # 封装nsis\n self.exe2nsis()\n\n if clean:\n shutil.rmtree(self.build_dir)\n os.remove(f\"{self.work_dir}\\\\app.7z\")\n","repo_name":"SindreYang/py2nsis","sub_path":"py2nsis/src/installer_pipeline.py","file_name":"installer_pipeline.py","file_ext":"py","file_size_in_byte":12365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31243236911","text":"import itertools\nimport logging\nimport random\n\nfrom src.const import *\nfrom pygame.locals import *\n\nfrom src.paddle import Paddle\n\n\nclass Ball(pygame.sprite.Sprite):\n width = 50\n height = 50\n\n def __init__(self, game, pos, control=0):\n super().__init__()\n self.game = game\n self.x, self.y = pos\n self.dx, self.dy = 0, 0\n self.image = pygame.Surface((Ball.width, Ball.height))\n self.image.fill(D_GREY)\n self.rect = self.image.get_rect()\n self.rect.center = self.x, self.y\n self.send_delay = 3\n\n def update(self):\n self._handle_input()\n self.x += self.dx\n self.y += self.dy\n self.rect.center = self.x, self.y\n\n # rebound horizontally hitting paddle\n collidee = self.collide_with_paddle()\n if collidee:\n self.dx *= -1.5\n if abs(self.dx) > MAX_BALL_SPEED:\n self.dx = sign(self.dx) * MAX_BALL_SPEED\n self.dy += collidee.dy * 0.5\n\n # rebound vertically hitting floor or ceiling\n if self.rect.top < 0 or self.rect.bottom > DISPLAY_HEIGHT:\n self.dy *= -1\n # reset upon leaving horizontally\n if self.rect.right < 0 or self.rect.left > DISPLAY_WIDTH:\n self.game.ball.reset()\n if self.rect.right < 0:\n self.game.client.send_msg('win player2')\n else:\n self.game.client.send_msg('win player1')\n\n if self.game.control_type == 1:\n if self.send_delay == 0:\n self.game.client.send_msg(f'ball {self.x} {self.y}')\n self.send_delay = 3\n else:\n self.send_delay -= 1\n\n def draw(self):\n self.game.surface.blit(self.image, self.rect)\n\n def _handle_input(self):\n # single press\n for event in self.game.events:\n if event.type == KEYDOWN:\n key = event.key\n if key == pygame.K_SPACE:\n self.kick()\n\n def kick(self):\n rand_vel = (random.choice([-1, 1]) * random.randint(2, 4),\n random.choice([-1, 1]) * random.randint(2, 4))\n print(rand_vel)\n self.game.client.send_msg(f'b {rand_vel[0]} {rand_vel[1]}')\n self.dx = rand_vel[0]\n self.dy = rand_vel[1]\n\n def reset(self):\n self.x = DISPLAY_WIDTH / 2\n self.y = DISPLAY_HEIGHT / 2\n self.dx = 0\n self.dy = 0\n\n def collide_with_paddle(self):\n for sprite in self.game.entities:\n if self.rect.colliderect(sprite.rect) and isinstance(sprite, Paddle):\n return sprite\n","repo_name":"ozcer/PongPing","sub_path":"src/ball.py","file_name":"ball.py","file_ext":"py","file_size_in_byte":2634,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29038217013","text":"from django.test import TestCase\nfrom django.contrib.auth import get_user_model\nfrom django.urls import reverse\n\nfrom core.models import User, Apartment\nfrom rest_framework.test import APIClient\nfrom rest_framework import status\n\nAPARTMENTS_URL = reverse('apartment:apartment-list')\n\n\ndef detail_url(object_id):\n \"\"\"Return data_for_annotation detail URL\"\"\"\n return reverse('apartment:apartment-detail', args=[object_id])\n\n\ndef create_user(**params):\n \"\"\"Helper function to create new user\"\"\"\n return get_user_model().objects.create_user(**params)\n\n\ndef create_apartment(user, **params):\n \"\"\"Helper function to create new apartment\"\"\"\n return Apartment.objects.create(user= user,**params)\n\n\nclass ApartmentApiTestsForRealtor(TestCase):\n \"\"\"Test the apartments API for realtor\"\"\"\n\n def setUp(self):\n self.client = APIClient()\n self.user = create_user(\n email='test@test.com',\n password='testpass',\n name='name'\n )\n self.user.role = User.REALTOR\n self.user.save()\n self.client.force_authenticate(user=self.user)\n\n def test_all_apartments_api(self):\n \"\"\"Test geting all apartments using realtor user\"\"\"\n res = self.client.get(APARTMENTS_URL)\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n\n def test_add_apartment_api(self):\n \"\"\"Test adding all apartments using realtor user\"\"\"\n payload = {\n 'date': '2021-01-01',\n 'name': 'test name',\n 'description': 'test description',\n 'floor_area_size': 123.5,\n 'price_per_month': 123,\n 'number_of_rooms': 2,\n 'latitude': 123.3423,\n 'longitude': 123.3422,\n 'rented': False\n }\n res = self.client.post(APARTMENTS_URL, payload)\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n self.assertIn('name', res.data)\n self.assertIn('description', res.data)\n self.assertIn('floor_area_size', res.data)\n self.assertIn('price_per_month', res.data)\n self.assertIn('number_of_rooms', res.data)\n self.assertIn('latitude', res.data)\n self.assertIn('longitude', res.data)\n\n def test_update_apartment_api(self):\n \"\"\"Test updating all apartments using realtor user\"\"\"\n payload = {\n 'date': '2021-01-01',\n 'name': 'test name',\n 'description': 'test description',\n 'floor_area_size': 123.5,\n 'price_per_month': 123,\n 'number_of_rooms': 2,\n 'latitude': 123.34234,\n 'longitude': 123.34234,\n 'rented': True\n }\n apartment = create_apartment(self.user,**payload)\n payload['description'] = 'changed description'\n url = detail_url(apartment.pk)\n res = self.client.put(url, payload)\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n self.assertEqual(res.data['description'], payload['description'])\n\n def test_delete_apartment_api(self):\n \"\"\"Test deleting the apartment using realtor user\"\"\"\n payload = {\n \"name\": \"test name\",\n \"date\": \"2021-01-01\",\n \"description\": \"test description\",\n \"floor_area_size\": 123.5,\n \"price_per_month\": 123,\n \"number_of_rooms\": 2,\n \"latitude\": \"123.342300\",\n \"longitude\": \"123.342200\",\n \"rented\": False\n }\n apartment = create_apartment(self.user,**payload)\n url = detail_url(apartment.id)\n res = self.client.delete(url)\n self.assertEqual(res.status_code, status.HTTP_204_NO_CONTENT)\n","repo_name":"baghoyanlevon/toptal_2","sub_path":"server/app/apartment/tests/test_apartments_for_realtor_api.py","file_name":"test_apartments_for_realtor_api.py","file_ext":"py","file_size_in_byte":3667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27057204243","text":"import logging\nimport datetime\nfrom dataclasses import dataclass\nfrom decimal import Decimal\nfrom types import NoneType\nfrom typing import List, Optional, Literal, Collection, Iterable\n\nfrom web3.types import BlockIdentifier\n\nfrom eth_defi.aave_v3.rates import SECONDS_PER_YEAR\n\nfrom tradeexecutor.backtest.simulated_wallet import SimulatedWallet\nfrom tradeexecutor.ethereum.wallet import ReserveUpdateEvent\nfrom tradeexecutor.state.balance_update import BalanceUpdate\nfrom tradeexecutor.state.identifier import AssetIdentifier\nfrom tradeexecutor.state.position import TradingPosition\nfrom tradeexecutor.state.state import State\nfrom tradeexecutor.state.types import JSONHexAddress, BlockNumber\nfrom tradeexecutor.strategy.interest import update_interest, update_leveraged_position_interest\nfrom tradeexecutor.strategy.pricing_model import PricingModel\nfrom tradeexecutor.strategy.sync_model import SyncModel, OnChainBalance\nfrom tradeexecutor.strategy.trading_strategy_universe import TradingStrategyUniverse\nfrom tradeexecutor.testing.dummy_wallet import apply_sync_events\n\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass FundFlowEvent:\n \"\"\"One simulated deposit/redemption in backtest.\n\n - Events can be triggered any time with :py:meth:`BacktestSyncModel.simulate_funding`\n\n - Fund flow is added to the reserves during :py:meth:`BacktestSyncModel.sync_treasury`\n as it would be with live trading\n \"\"\"\n\n\n timestamp: datetime.datetime\n\n amount: Decimal\n\n\nclass BacktestSyncModel(SyncModel):\n \"\"\"Backtest sync model.\n\n Simulate deposit events to the backtest wallet.\"\"\"\n\n def __init__(self, wallet: SimulatedWallet, initial_deposit_amount: Decimal):\n assert isinstance(initial_deposit_amount, Decimal)\n self.wallet = wallet\n\n #: Simulated deposit/redemption events pending to be processed\n self.fund_flow_queue: List[FundFlowEvent] = []\n if initial_deposit_amount > 0:\n self.fund_flow_queue.append(FundFlowEvent(datetime.datetime.utcnow(), initial_deposit_amount))\n\n def get_token_storage_address(self) -> Optional[JSONHexAddress]:\n return None\n\n def sync_initial(self, state: State):\n \"\"\"Set up the initial sync details.\n\n For backtesting these are irrelevant.\n \"\"\"\n deployment = state.sync.deployment\n deployment.chain_id = None\n deployment.address = None\n deployment.block_number = None\n deployment.tx_hash = None\n deployment.block_mined_at = None\n deployment.vault_token_name = None\n deployment.vault_token_symbol = None\n\n def sync_treasury(\n self,\n strategy_cycle_ts: datetime.datetime,\n state: State,\n supported_reserves: Optional[List[AssetIdentifier]] = None,\n end_block: BlockNumber | NoneType = None,\n ) -> List[BalanceUpdate]:\n \"\"\"Apply the balance sync before each strategy cycle.\n\n .. warning::\n\n Old legacy code with wrong return signature compared to the parent class\n \"\"\"\n\n assert len(supported_reserves) == 1\n reserve_token = supported_reserves[0]\n\n assert end_block is None, \"Cannot use block ranges with backtesting\"\n\n reserve_update_events = [] # TODO: Legacy\n\n for funding_event in self.fund_flow_queue:\n\n past_balance = self.wallet.get_balance(reserve_token.address)\n\n # Update wallet\n self.wallet.update_balance(reserve_token.address, funding_event.amount)\n\n # Generate a deposit event\n reserve_update_events.append(\n ReserveUpdateEvent(\n asset=reserve_token,\n past_balance=past_balance,\n new_balance=self.wallet.get_balance(reserve_token.address),\n updated_at=strategy_cycle_ts,\n mined_at=funding_event.timestamp,\n )\n )\n\n balance_update_events = apply_sync_events(state, reserve_update_events)\n\n # Clear our pending funding simulation events\n self.fund_flow_queue = []\n\n return balance_update_events\n\n def simulate_funding(self, timestamp: datetime.datetime, amount: Decimal):\n \"\"\"Simulate a funding flow event.\n\n Call for the test to cause deposit or redemption for the backtest.\n The event goes to a queue and is processed in next `tick()`\n through `sync_portfolio()`.\n\n :param amount:\n Positive for deposit, negative for redemption\n\n \"\"\"\n self.fund_flow_queue.append(\n FundFlowEvent(timestamp, amount)\n )\n\n def create_transaction_builder(self) -> None:\n \"\"\"Backtesting does not need to care about how to build blockchain transactions.\"\"\"\n\n def calculate_accrued_interest(\n self,\n universe: TradingStrategyUniverse,\n position: TradingPosition,\n timestamp: datetime.datetime,\n interest_type: Literal[\"collateral\"] | Literal[\"borrow\"],\n ) -> Decimal:\n \"\"\"Calculate accrued interest of a position since last update.\"\"\"\n # get relevant candles for the position period since last update until now\n if interest_type == \"collateral\":\n interest = position.loan.collateral_interest\n amount = Decimal(interest.last_token_amount)\n elif interest_type == \"borrow\":\n interest = position.loan.borrowed_interest\n amount = Decimal(interest.last_token_amount)\n\n previous_update_at = interest.last_event_at\n\n df = universe.data_universe.lending_candles.supply_apr.df.copy()\n supply_df = df[\n (df[\"timestamp\"] >= previous_update_at)\n & (df[\"timestamp\"] <= timestamp)\n ].copy()\n\n if len(supply_df) == 0:\n # TODO: this is a temporary hack, we should make it better\n supply_df = df[\n (df[\"timestamp\"] >= position.opened_at)\n & (df[\"timestamp\"] <= timestamp)\n ].copy()\n\n assert len(supply_df) > 0, f\"No lending data for {position} from {previous_update_at} to {timestamp}\"\n\n # get average APR from high and low\n supply_df[\"avg\"] = supply_df[[\"high\", \"low\"]].mean(axis=1)\n avg_apr = Decimal(supply_df[\"avg\"].mean() / 100)\n\n duration = Decimal((timestamp - previous_update_at).total_seconds())\n accrued_interest_estimation = amount * avg_apr * duration / SECONDS_PER_YEAR\n\n return accrued_interest_estimation\n\n def sync_interests(\n self,\n timestamp: datetime.datetime,\n state: State,\n universe: TradingStrategyUniverse,\n positions: List[TradingPosition],\n pricing_model: PricingModel,\n ) -> List[BalanceUpdate]:\n\n assert universe.has_lending_data(), \"Cannot update credit positions if no data is available\"\n\n events = []\n for p in positions:\n if p.is_credit_supply():\n assert len(p.trades) <= 2, \"This interest calculation does not support increase/reduce position\"\n\n accrued = self.calculate_accrued_interest(\n universe,\n p,\n timestamp,\n \"collateral\",\n )\n\n new_amount = p.loan.collateral_interest.last_token_amount + accrued\n\n # TODO: the collateral is stablecoin so this can be hardcode for now\n # but make sure to fetch it from somewhere later\n price = 1.0\n\n evt = update_interest(\n state,\n p,\n p.pair.base,\n new_token_amount=new_amount,\n event_at=timestamp,\n asset_price=price,\n )\n events.append(evt)\n\n # Make atokens magically appear in the simulated\n # backtest wallet. The amount must be updated, or\n # otherwise we get errors when closing the position.\n self.wallet.update_balance(p.pair.base.address, accrued)\n elif p.is_leverage() and p.is_short():\n assert len(p.trades) <= 2, \"This interest calculation does not support increase/reduce position\"\n\n accrued_collateral_interest = self.calculate_accrued_interest(\n universe,\n p,\n timestamp,\n \"collateral\",\n )\n accrued_borrow_interest = self.calculate_accrued_interest(\n universe,\n p,\n timestamp,\n \"borrow\",\n )\n\n new_atoken_amount = p.loan.collateral_interest.last_token_amount + accrued_collateral_interest\n new_vtoken_amount = p.loan.borrowed_interest.last_token_amount + accrued_borrow_interest\n\n atoken_price = 1.0\n\n vtoken_price_structure = pricing_model.get_sell_price(\n timestamp,\n p.pair.get_pricing_pair(),\n p.loan.borrowed.quantity,\n )\n vtoken_price = vtoken_price_structure.price\n\n vevt, aevt = update_leveraged_position_interest(\n state,\n p,\n new_vtoken_amount=new_vtoken_amount,\n new_token_amount=new_atoken_amount,\n vtoken_price=vtoken_price,\n atoken_price=atoken_price,\n event_at=timestamp,\n )\n events.append(vevt)\n events.append(aevt)\n\n # Make aToken and vToken magically appear in the simulated\n # backtest wallet. The amount must be updated, or\n # otherwise we get errors when closing the position.\n self.wallet.rebase(p.pair.base.address, new_vtoken_amount)\n self.wallet.rebase(p.pair.quote.address, new_atoken_amount)\n\n return events\n\n def fetch_onchain_balances(\n self,\n assets: Collection[AssetIdentifier],\n filter_zero=True,\n block_identifier: BlockIdentifier = None,\n ) -> Iterable[OnChainBalance]:\n raise NotImplementedError(\"Backtesting does not know about on-chain balances\")","repo_name":"tradingstrategy-ai/trade-executor","sub_path":"tradeexecutor/backtest/backtest_sync.py","file_name":"backtest_sync.py","file_ext":"py","file_size_in_byte":10335,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"3"}
+{"seq_id":"71847400400","text":"num_as_str_in_list = input().split()\nintegers_to_remove = int(input())\nnum_list = [int(num_str) for num_str in num_as_str_in_list]\nsmallest_num = 0\nfor num in range(integers_to_remove):\n smallest_num = min(num_list)\n num_list.remove(smallest_num)\n#for element in num_list:\n# print(element, end= \" \")\nremaining_integers = [str(num) for num in num_list]\nresult_str = ', '.join(remaining_integers)\n\nprint(result_str)","repo_name":"MarioPetkov/Python_SoftUni","sub_path":"List basics/survival_of_the_biggest.py","file_name":"survival_of_the_biggest.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13975333433","text":"'''Ler uma estrutura (lista, tupla ou conjunto), R de 5 elementos, inteiros, contendo o resultado da LOTO. \nA seguir ler outra estrutura (lista, tupla ou conjunto), A de 10 elementos inteiros contendo uma aposta. \nA seguir imprima quantos pontos fez o apostador.\n'''\n\nr = set()\na = set()\nx = set()\n\nfor i in range(0, 6):\n r.add(int(input(\"Valores da loto: \")))\n\nfor j in range(0, 9):\n a.add(int(input(\"Valores apostados: \")))\n\nx = r & a\n\nprint(f\"O número de pontos é {len(x)}\")","repo_name":"isaberamos/BSI-Algoritmos","sub_path":"Listas2/resultado-loto-conjuntos.py.py","file_name":"resultado-loto-conjuntos.py.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73086285842","text":"from math import floor, sqrt\r\n\r\nx: int = int(input())\r\n\r\nif x == 0:\r\n print(\"inf\")\r\nelse:\r\n factors = 0\r\n for f in range(1, floor(sqrt(x)) + 1):\r\n q, r = divmod(x, f)\r\n if r == 0:\r\n factors += 1 + (q != f)\r\n print(factors)\r\n","repo_name":"xtevenx/sjudge","sub_path":"src/solutions/num_factors.py","file_name":"num_factors.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31297682673","text":"from __future__ import absolute_import, division\n\nimport time\nimport re\nimport base64\nimport socket\nimport os\nimport random\nimport binascii\nimport warnings\n\nfrom email.utils import parseaddr\n\nfrom zope.interface import implementer\n\nfrom twisted import cred\nfrom twisted.copyright import longversion\nfrom twisted.protocols import basic\nfrom twisted.protocols import policies\nfrom twisted.internet import protocol\nfrom twisted.internet import defer\nfrom twisted.internet import error\nfrom twisted.internet import reactor\nfrom twisted.internet.interfaces import ITLSTransport, ISSLTransport\nfrom twisted.python import log\nfrom twisted.python import util\nfrom twisted.python.compat import (_PY3, range, long, unicode, networkString,\n nativeString, iteritems, _keys, _bytesChr,\n iterbytes)\nfrom twisted.python.runtime import platform\n\nfrom twisted.mail.interfaces import (IClientAuthentication,\n IMessageSMTP as IMessage,\n IMessageDeliveryFactory, IMessageDelivery)\nfrom twisted.mail._cred import (CramMD5ClientAuthenticator, LOGINAuthenticator,\n LOGINCredentials as _lcredentials)\nfrom twisted.mail._except import (\n AUTHDeclinedError, AUTHRequiredError, AddressError,\n AuthenticationError, EHLORequiredError, ESMTPClientError,\n SMTPAddressError, SMTPBadRcpt, SMTPBadSender, SMTPClientError,\n SMTPConnectError, SMTPDeliveryError, SMTPError, SMTPServerError,\n SMTPTimeoutError, SMTPTLSError as TLSError, TLSRequiredError,\n SMTPProtocolError)\n\n\nfrom io import BytesIO\n\n\n__all__ = [\n 'AUTHDeclinedError', 'AUTHRequiredError', 'AddressError',\n 'AuthenticationError', 'EHLORequiredError', 'ESMTPClientError',\n 'SMTPAddressError', 'SMTPBadRcpt', 'SMTPBadSender', 'SMTPClientError',\n 'SMTPConnectError', 'SMTPDeliveryError', 'SMTPError', 'SMTPServerError',\n 'SMTPTimeoutError', 'TLSError', 'TLSRequiredError', 'SMTPProtocolError',\n\n 'IClientAuthentication', 'IMessage', 'IMessageDelivery',\n 'IMessageDeliveryFactory',\n\n 'CramMD5ClientAuthenticator', 'LOGINAuthenticator', 'LOGINCredentials',\n 'PLAINAuthenticator',\n\n 'Address', 'User', 'sendmail', 'SenderMixin',\n 'ESMTP', 'ESMTPClient', 'ESMTPSender', 'ESMTPSenderFactory',\n 'SMTP', 'SMTPClient', 'SMTPFactory', 'SMTPSender', 'SMTPSenderFactory',\n\n 'idGenerator', 'messageid', 'quoteaddr', 'rfc822date', 'xtextStreamReader',\n 'xtextStreamWriter', 'xtext_codec', 'xtext_decode', 'xtext_encode'\n]\n\n\n# Cache the hostname (XXX Yes - this is broken)\nif platform.isMacOSX():\n # On macOS, getfqdn() is ridiculously slow - use the\n # probably-identical-but-sometimes-not gethostname() there.\n DNSNAME = socket.gethostname()\nelse:\n DNSNAME = socket.getfqdn()\n\n# Encode the DNS name into something we can send over the wire\nDNSNAME = DNSNAME.encode('ascii')\n\n# Used for fast success code lookup\nSUCCESS = dict.fromkeys(range(200, 300))\n\n\n\ndef rfc822date(timeinfo=None, local=1):\n \"\"\"\n Format an RFC-2822 compliant date string.\n\n @param timeinfo: (optional) A sequence as returned by C{time.localtime()}\n or C{time.gmtime()}. Default is now.\n @param local: (optional) Indicates if the supplied time is local or\n universal time, or if no time is given, whether now should be local or\n universal time. Default is local, as suggested (SHOULD) by rfc-2822.\n\n @returns: A L{bytes} representing the time and date in RFC-2822 format.\n \"\"\"\n if not timeinfo:\n if local:\n timeinfo = time.localtime()\n else:\n timeinfo = time.gmtime()\n if local:\n if timeinfo[8]:\n # DST\n tz = -time.altzone\n else:\n tz = -time.timezone\n\n (tzhr, tzmin) = divmod(abs(tz), 3600)\n if tz:\n tzhr *= int(abs(tz)//tz)\n (tzmin, tzsec) = divmod(tzmin, 60)\n else:\n (tzhr, tzmin) = (0, 0)\n\n return networkString(\"%s, %02d %s %04d %02d:%02d:%02d %+03d%02d\" % (\n ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'][timeinfo[6]],\n timeinfo[2],\n ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',\n 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'][timeinfo[1] - 1],\n timeinfo[0], timeinfo[3], timeinfo[4], timeinfo[5],\n tzhr, tzmin))\n\n\n\ndef idGenerator():\n i = 0\n while True:\n yield i\n i += 1\n\n_gen = idGenerator()\n\n\n\ndef messageid(uniq=None, N=lambda: next(_gen)):\n \"\"\"\n Return a globally unique random string in RFC 2822 Message-ID format\n\n \n\n Optional uniq string will be added to strengthen uniqueness if given.\n \"\"\"\n datetime = time.strftime('%Y%m%d%H%M%S', time.gmtime())\n pid = os.getpid()\n rand = random.randrange(2**31-1)\n if uniq is None:\n uniq = ''\n else:\n uniq = '.' + uniq\n\n return '<%s.%s.%s%s.%s@%s>' % (datetime, pid, rand, uniq, N(), DNSNAME)\n\n\n\ndef quoteaddr(addr):\n \"\"\"\n Turn an email address, possibly with realname part etc, into\n a form suitable for and SMTP envelope.\n \"\"\"\n\n if isinstance(addr, Address):\n return b'<' + bytes(addr) + b'>'\n\n if isinstance(addr, bytes):\n addr = addr.decode('ascii')\n\n res = parseaddr(addr)\n\n if res == (None, None):\n # It didn't parse, use it as-is\n return b'<' + bytes(addr) + b'>'\n else:\n return b'<' + res[1].encode('ascii') + b'>'\n\nCOMMAND, DATA, AUTH = 'COMMAND', 'DATA', 'AUTH'\n\n\n# Character classes for parsing addresses\natom = br\"[-A-Za-z0-9!\\#$%&'*+/=?^_`{|}~]\"\n\nclass Address:\n \"\"\"Parse and hold an RFC 2821 address.\n\n Source routes are stipped and ignored, UUCP-style bang-paths\n and %-style routing are not parsed.\n\n @type domain: C{bytes}\n @ivar domain: The domain within which this address resides.\n\n @type local: C{bytes}\n @ivar local: The local (\\\"user\\\") portion of this address.\n \"\"\"\n\n tstring = re.compile(br'''( # A string of\n (?:\"[^\"]*\" # quoted string\n |\\\\. # backslash-escaped characted\n |''' + atom + br''' # atom character\n )+|.) # or any single character''', re.X)\n atomre = re.compile(atom) # match any one atom character\n\n\n def __init__(self, addr, defaultDomain=None):\n if isinstance(addr, User):\n addr = addr.dest\n if isinstance(addr, Address):\n self.__dict__ = addr.__dict__.copy()\n return\n elif not isinstance(addr, bytes):\n addr = str(addr).encode('ascii')\n\n self.addrstr = addr\n\n # Tokenize\n atl = list(filter(None, self.tstring.split(addr)))\n local = []\n domain = []\n\n while atl:\n if atl[0] == b'<':\n if atl[-1] != b'>':\n raise AddressError(\"Unbalanced <>\")\n atl = atl[1:-1]\n elif atl[0] == b'@':\n atl = atl[1:]\n if not local:\n # Source route\n while atl and atl[0] != b':':\n # remove it\n atl = atl[1:]\n if not atl:\n raise AddressError(\"Malformed source route\")\n atl = atl[1:] # remove :\n elif domain:\n raise AddressError(\"Too many @\")\n else:\n # Now in domain\n domain = [b'']\n elif (len(atl[0]) == 1 and\n not self.atomre.match(atl[0]) and\n atl[0] != b'.'):\n raise AddressError(\"Parse error at %r of %r\" % (atl[0], (addr, atl)))\n else:\n if not domain:\n local.append(atl[0])\n else:\n domain.append(atl[0])\n atl = atl[1:]\n\n self.local = b''.join(local)\n self.domain = b''.join(domain)\n if self.local != b'' and self.domain == b'':\n if defaultDomain is None:\n defaultDomain = DNSNAME\n self.domain = defaultDomain\n\n dequotebs = re.compile(br'\\\\(.)')\n\n\n def dequote(self, addr):\n \"\"\"\n Remove RFC-2821 quotes from address.\n \"\"\"\n res = []\n\n if not isinstance(addr, bytes):\n addr = str(addr).encode('ascii')\n\n atl = filter(None, self.tstring.split(addr))\n\n for t in atl:\n if t[0] == b'\"' and t[-1] == b'\"':\n res.append(t[1:-1])\n elif '\\\\' in t:\n res.append(self.dequotebs.sub(br'\\1', t))\n else:\n res.append(t)\n\n return b''.join(res)\n\n if _PY3:\n def __str__(self):\n return nativeString(bytes(self))\n else:\n def __str__(self):\n return self.__bytes__()\n\n\n def __bytes__(self):\n if self.local or self.domain:\n return b'@'.join((self.local, self.domain))\n else:\n return b''\n\n\n def __repr__(self):\n return \"%s.%s(%s)\" % (self.__module__, self.__class__.__name__,\n repr(str(self)))\n\n\n\nclass User:\n \"\"\"\n Hold information about and SMTP message recipient,\n including information on where the message came from\n \"\"\"\n def __init__(self, destination, helo, protocol, orig):\n try:\n host = protocol.host\n except AttributeError:\n host = None\n self.dest = Address(destination, host)\n self.helo = helo\n self.protocol = protocol\n if isinstance(orig, Address):\n self.orig = orig\n else:\n self.orig = Address(orig, host)\n\n\n def __getstate__(self):\n \"\"\"\n Helper for pickle.\n\n protocol isn't picklabe, but we want User to be, so skip it in\n the pickle.\n \"\"\"\n return { 'dest' : self.dest,\n 'helo' : self.helo,\n 'protocol' : None,\n 'orig' : self.orig }\n\n\n def __str__(self):\n return nativeString(bytes(self.dest))\n\n\n def __bytes__(self):\n return bytes(self.dest)\n\n\n\nclass SMTP(basic.LineOnlyReceiver, policies.TimeoutMixin):\n \"\"\"\n SMTP server-side protocol.\n\n @ivar host: The hostname of this mail server.\n @type host: L{bytes}\n \"\"\"\n\n timeout = 600\n portal = None\n\n # Control whether we log SMTP events\n noisy = True\n\n # A factory for IMessageDelivery objects. If an\n # avatar implementing IMessageDeliveryFactory can\n # be acquired from the portal, it will be used to\n # create a new IMessageDelivery object for each\n # message which is received.\n deliveryFactory = None\n\n # An IMessageDelivery object. A new instance is\n # used for each message received if we can get an\n # IMessageDeliveryFactory from the portal. Otherwise,\n # a single instance is used throughout the lifetime\n # of the connection.\n delivery = None\n\n # Cred cleanup function.\n _onLogout = None\n\n def __init__(self, delivery=None, deliveryFactory=None):\n self.mode = COMMAND\n self._from = None\n self._helo = None\n self._to = []\n self.delivery = delivery\n self.deliveryFactory = deliveryFactory\n self.host = DNSNAME\n\n\n @property\n def host(self):\n return self._host\n\n\n @host.setter\n def host(self, toSet):\n if not isinstance(toSet, bytes):\n toSet = str(toSet).encode('ascii')\n self._host = toSet\n\n\n\n def timeoutConnection(self):\n msg = self.host + b' Timeout. Try talking faster next time!'\n self.sendCode(421, msg)\n self.transport.loseConnection()\n\n\n def greeting(self):\n return self.host + b' NO UCE NO UBE NO RELAY PROBES'\n\n\n def connectionMade(self):\n # Ensure user-code always gets something sane for _helo\n peer = self.transport.getPeer()\n try:\n host = peer.host\n except AttributeError: # not an IPv4Address\n host = str(peer)\n self._helo = (None, host)\n self.sendCode(220, self.greeting())\n self.setTimeout(self.timeout)\n\n\n def sendCode(self, code, message=b''):\n \"\"\"\n Send an SMTP code with a message.\n \"\"\"\n lines = message.splitlines()\n lastline = lines[-1:]\n for line in lines[:-1]:\n self.sendLine(networkString('%3.3d-' % (code,)) + line)\n self.sendLine(networkString('%3.3d ' % (code,)) +\n (lastline and lastline[0] or b''))\n\n\n def lineReceived(self, line):\n self.resetTimeout()\n return getattr(self, 'state_' + self.mode)(line)\n\n\n def state_COMMAND(self, line):\n # Ignore leading and trailing whitespace, as well as an arbitrary\n # amount of whitespace between the command and its argument, though\n # it is not required by the protocol, for it is a nice thing to do.\n line = line.strip()\n\n parts = line.split(None, 1)\n if parts:\n method = self.lookupMethod(parts[0]) or self.do_UNKNOWN\n if len(parts) == 2:\n method(parts[1])\n else:\n method(b'')\n else:\n self.sendSyntaxError()\n\n\n def sendSyntaxError(self):\n self.sendCode(500, b'Error: bad syntax')\n\n\n def lookupMethod(self, command):\n \"\"\"\n\n @param command: The command to get from this class.\n @type command: L{str}\n @return: The function which executes this command.\n \"\"\"\n if not isinstance(command, str):\n command = nativeString(command)\n\n return getattr(self, 'do_' + command.upper(), None)\n\n\n def lineLengthExceeded(self, line):\n if self.mode is DATA:\n for message in self.__messages:\n message.connectionLost()\n self.mode = COMMAND\n del self.__messages\n self.sendCode(500, b'Line too long')\n\n\n def do_UNKNOWN(self, rest):\n self.sendCode(500, b'Command not implemented')\n\n\n def do_HELO(self, rest):\n peer = self.transport.getPeer()\n try:\n host = peer.host\n except AttributeError:\n host = str(peer)\n\n if not isinstance(host, bytes):\n host = host.encode('idna')\n\n self._helo = (rest, host)\n self._from = None\n self._to = []\n self.sendCode(250,\n self.host + b' Hello ' + host + b', nice to meet you')\n\n\n def do_QUIT(self, rest):\n self.sendCode(221, b'See you later')\n self.transport.loseConnection()\n\n # A string of quoted strings, backslash-escaped character or\n # atom characters + '@.,:'\n qstring = br'(\"[^\"]*\"|\\\\.|' + atom + br'|[@.,:])+'\n\n mail_re = re.compile(br'''\\s*FROM:\\s*(?P<> # Empty <>\n |<''' + qstring + br'''> # \n |''' + qstring + br''' # addr\n )\\s*(\\s(?P.*))? # Optional WS + ESMTP options\n $''', re.I|re.X)\n rcpt_re = re.compile(br'\\s*TO:\\s*(?P<' + qstring + br'''> # \n |''' + qstring + br''' # addr\n )\\s*(\\s(?P.*))? # Optional WS + ESMTP options\n $''', re.I|re.X)\n\n def do_MAIL(self, rest):\n if self._from:\n self.sendCode(503, b\"Only one sender per message, please\")\n return\n # Clear old recipient list\n self._to = []\n m = self.mail_re.match(rest)\n if not m:\n self.sendCode(501, b\"Syntax error\")\n return\n\n try:\n addr = Address(m.group('path'), self.host)\n except AddressError as e:\n self.sendCode(553, networkString(str(e)))\n return\n\n validated = defer.maybeDeferred(self.validateFrom, self._helo, addr)\n validated.addCallbacks(self._cbFromValidate, self._ebFromValidate)\n\n\n def _cbFromValidate(self, fromEmail, code=250,\n msg=b'Sender address accepted'):\n self._from = fromEmail\n self.sendCode(code, msg)\n\n\n def _ebFromValidate(self, failure):\n if failure.check(SMTPBadSender):\n self.sendCode(failure.value.code,\n (b'Cannot receive from specified address ' +\n quoteaddr(failure.value.addr) + b': ' +\n networkString(failure.value.resp)))\n elif failure.check(SMTPServerError):\n self.sendCode(failure.value.code,\n networkString(failure.value.resp))\n else:\n log.err(failure, \"SMTP sender validation failure\")\n self.sendCode(\n 451,\n b'Requested action aborted: local error in processing')\n\n\n def do_RCPT(self, rest):\n if not self._from:\n self.sendCode(503, b\"Must have sender before recipient\")\n return\n m = self.rcpt_re.match(rest)\n if not m:\n self.sendCode(501, b\"Syntax error\")\n return\n\n try:\n user = User(m.group('path'), self._helo, self, self._from)\n except AddressError as e:\n self.sendCode(553, networkString(str(e)))\n return\n\n d = defer.maybeDeferred(self.validateTo, user)\n d.addCallbacks(\n self._cbToValidate,\n self._ebToValidate,\n callbackArgs=(user,)\n )\n\n\n def _cbToValidate(self, to, user=None, code=250,\n msg=b'Recipient address accepted'):\n if user is None:\n user = to\n self._to.append((user, to))\n self.sendCode(code, msg)\n\n\n def _ebToValidate(self, failure):\n if failure.check(SMTPBadRcpt, SMTPServerError):\n self.sendCode(failure.value.code,\n networkString(failure.value.resp))\n else:\n log.err(failure)\n self.sendCode(\n 451,\n b'Requested action aborted: local error in processing'\n )\n\n\n def _disconnect(self, msgs):\n for msg in msgs:\n try:\n msg.connectionLost()\n except:\n log.msg(\"msg raised exception from connectionLost\")\n log.err()\n\n\n def do_DATA(self, rest):\n if self._from is None or (not self._to):\n self.sendCode(503, b'Must have valid receiver and originator')\n return\n self.mode = DATA\n helo, origin = self._helo, self._from\n recipients = self._to\n\n self._from = None\n self._to = []\n self.datafailed = None\n\n msgs = []\n for (user, msgFunc) in recipients:\n try:\n msg = msgFunc()\n rcvdhdr = self.receivedHeader(helo, origin, [user])\n if rcvdhdr:\n msg.lineReceived(rcvdhdr)\n msgs.append(msg)\n except SMTPServerError as e:\n self.sendCode(e.code, e.resp)\n self.mode = COMMAND\n self._disconnect(msgs)\n return\n except:\n log.err()\n self.sendCode(550, b\"Internal server error\")\n self.mode = COMMAND\n self._disconnect(msgs)\n return\n self.__messages = msgs\n\n self.__inheader = self.__inbody = 0\n self.sendCode(354, b'Continue')\n\n if self.noisy:\n fmt = 'Receiving message for delivery: from=%s to=%s'\n log.msg(fmt % (origin, [str(u) for (u, f) in recipients]))\n\n\n def connectionLost(self, reason):\n # self.sendCode(421, 'Dropping connection.') # This does nothing...\n # Ideally, if we (rather than the other side) lose the connection,\n # we should be able to tell the other side that we are going away.\n # RFC-2821 requires that we try.\n if self.mode is DATA:\n try:\n for message in self.__messages:\n try:\n message.connectionLost()\n except:\n log.err()\n del self.__messages\n except AttributeError:\n pass\n if self._onLogout:\n self._onLogout()\n self._onLogout = None\n self.setTimeout(None)\n\n\n def do_RSET(self, rest):\n self._from = None\n self._to = []\n self.sendCode(250, b'I remember nothing.')\n\n\n def dataLineReceived(self, line):\n if line[:1] == b'.':\n if line == b'.':\n self.mode = COMMAND\n if self.datafailed:\n self.sendCode(self.datafailed.code,\n self.datafailed.resp)\n return\n if not self.__messages:\n self._messageHandled(\"thrown away\")\n return\n defer.DeferredList([\n m.eomReceived() for m in self.__messages\n ], consumeErrors=True).addCallback(self._messageHandled\n )\n del self.__messages\n return\n line = line[1:]\n\n if self.datafailed:\n return\n\n try:\n # Add a blank line between the generated Received:-header\n # and the message body if the message comes in without any\n # headers\n if not self.__inheader and not self.__inbody:\n if b':' in line:\n self.__inheader = 1\n elif line:\n for message in self.__messages:\n message.lineReceived(b'')\n self.__inbody = 1\n\n if not line:\n self.__inbody = 1\n\n for message in self.__messages:\n message.lineReceived(line)\n except SMTPServerError as e:\n self.datafailed = e\n for message in self.__messages:\n message.connectionLost()\n state_DATA = dataLineReceived\n\n\n def _messageHandled(self, resultList):\n failures = 0\n for (success, result) in resultList:\n if not success:\n failures += 1\n log.err(result)\n if failures:\n msg = 'Could not send e-mail'\n resultLen = len(resultList)\n if resultLen > 1:\n msg += ' (%d failures out of %d recipients)'.format(\n failures, resultLen)\n self.sendCode(550, networkString(msg))\n else:\n self.sendCode(250, b'Delivery in progress')\n\n\n def _cbAnonymousAuthentication(self, result):\n \"\"\"\n Save the state resulting from a successful anonymous cred login.\n \"\"\"\n (iface, avatar, logout) = result\n if issubclass(iface, IMessageDeliveryFactory):\n self.deliveryFactory = avatar\n self.delivery = None\n elif issubclass(iface, IMessageDelivery):\n self.deliveryFactory = None\n self.delivery = avatar\n else:\n raise RuntimeError(\"%s is not a supported interface\" % (iface.__name__,))\n self._onLogout = logout\n self.challenger = None\n\n\n # overridable methods:\n def validateFrom(self, helo, origin):\n \"\"\"\n Validate the address from which the message originates.\n\n @type helo: C{(bytes, bytes)}\n @param helo: The argument to the HELO command and the client's IP\n address.\n\n @type origin: C{Address}\n @param origin: The address the message is from\n\n @rtype: C{Deferred} or C{Address}\n @return: C{origin} or a C{Deferred} whose callback will be\n passed C{origin}.\n\n @raise SMTPBadSender: Raised of messages from this address are\n not to be accepted.\n \"\"\"\n if self.deliveryFactory is not None:\n self.delivery = self.deliveryFactory.getMessageDelivery()\n\n if self.delivery is not None:\n return defer.maybeDeferred(self.delivery.validateFrom,\n helo, origin)\n\n # No login has been performed, no default delivery object has been\n # provided: try to perform an anonymous login and then invoke this\n # method again.\n if self.portal:\n\n result = self.portal.login(\n cred.credentials.Anonymous(),\n None,\n IMessageDeliveryFactory, IMessageDelivery)\n\n def ebAuthentication(err):\n \"\"\"\n Translate cred exceptions into SMTP exceptions so that the\n protocol code which invokes C{validateFrom} can properly report\n the failure.\n \"\"\"\n if err.check(cred.error.UnauthorizedLogin):\n exc = SMTPBadSender(origin)\n elif err.check(cred.error.UnhandledCredentials):\n exc = SMTPBadSender(\n origin, resp=\"Unauthenticated senders not allowed\")\n else:\n return err\n return defer.fail(exc)\n\n result.addCallbacks(\n self._cbAnonymousAuthentication, ebAuthentication)\n\n def continueValidation(ignored):\n \"\"\"\n Re-attempt from address validation.\n \"\"\"\n return self.validateFrom(helo, origin)\n\n result.addCallback(continueValidation)\n return result\n\n raise SMTPBadSender(origin)\n\n\n def validateTo(self, user):\n \"\"\"\n Validate the address for which the message is destined.\n\n @type user: L{User}\n @param user: The address to validate.\n\n @rtype: no-argument callable\n @return: A C{Deferred} which becomes, or a callable which\n takes no arguments and returns an object implementing C{IMessage}.\n This will be called and the returned object used to deliver the\n message when it arrives.\n\n @raise SMTPBadRcpt: Raised if messages to the address are\n not to be accepted.\n \"\"\"\n if self.delivery is not None:\n return self.delivery.validateTo(user)\n raise SMTPBadRcpt(user)\n\n\n def receivedHeader(self, helo, origin, recipients):\n if self.delivery is not None:\n return self.delivery.receivedHeader(helo, origin, recipients)\n\n heloStr = b\"\"\n if helo[0]:\n heloStr = b\" helo=\" + helo[0]\n domain = networkString(self.transport.getHost().host)\n\n from_ = b\"from \" + helo[0] + b\" ([\" + helo[1] + b\"]\" + heloStr + b\")\"\n by = b\"by %s with %s (%s)\" % (domain,\n self.__class__.__name__,\n longversion)\n for_ = b\"for %s; %s\" % (' '.join(map(str, recipients)),\n rfc822date())\n return b\"Received: \" + from_ + b\"\\n\\t\" + by + b\"\\n\\t\" + for_\n\n\n\nclass SMTPFactory(protocol.ServerFactory):\n \"\"\"\n Factory for SMTP.\n \"\"\"\n\n # override in instances or subclasses\n domain = DNSNAME\n timeout = 600\n protocol = SMTP\n\n portal = None\n\n def __init__(self, portal = None):\n self.portal = portal\n\n\n def buildProtocol(self, addr):\n p = protocol.ServerFactory.buildProtocol(self, addr)\n p.portal = self.portal\n p.host = self.domain\n return p\n\n\n\nclass SMTPClient(basic.LineReceiver, policies.TimeoutMixin):\n \"\"\"\n SMTP client for sending emails.\n\n After the client has connected to the SMTP server, it repeatedly calls\n L{SMTPClient.getMailFrom}, L{SMTPClient.getMailTo} and\n L{SMTPClient.getMailData} and uses this information to send an email.\n It then calls L{SMTPClient.getMailFrom} again; if it returns L{None}, the\n client will disconnect, otherwise it will continue as normal i.e. call\n L{SMTPClient.getMailTo} and L{SMTPClient.getMailData} and send a new email.\n \"\"\"\n\n # If enabled then log SMTP client server communication\n debug = True\n\n # Number of seconds to wait before timing out a connection. If\n # None, perform no timeout checking.\n timeout = None\n\n def __init__(self, identity, logsize=10):\n if isinstance(identity, unicode):\n identity = identity.encode('ascii')\n\n self.identity = identity or b''\n self.toAddressesResult = []\n self.successAddresses = []\n self._from = None\n self.resp = []\n self.code = -1\n self.log = util.LineLog(logsize)\n\n\n def sendLine(self, line):\n # Log sendLine only if you are in debug mode for performance\n if self.debug:\n self.log.append(b'>>> ' + line)\n\n basic.LineReceiver.sendLine(self,line)\n\n\n def connectionMade(self):\n self.setTimeout(self.timeout)\n\n self._expected = [ 220 ]\n self._okresponse = self.smtpState_helo\n self._failresponse = self.smtpConnectionFailed\n\n\n def connectionLost(self, reason=protocol.connectionDone):\n \"\"\"\n We are no longer connected\n \"\"\"\n self.setTimeout(None)\n self.mailFile = None\n\n\n def timeoutConnection(self):\n self.sendError(\n SMTPTimeoutError(\n -1, b\"Timeout waiting for SMTP server response\",\n self.log.str()))\n\n\n def lineReceived(self, line):\n self.resetTimeout()\n\n # Log lineReceived only if you are in debug mode for performance\n if self.debug:\n self.log.append(b'<<< ' + line)\n\n why = None\n\n try:\n self.code = int(line[:3])\n except ValueError:\n # This is a fatal error and will disconnect the transport\n # lineReceived will not be called again.\n self.sendError(SMTPProtocolError(-1,\n \"Invalid response from SMTP server: {}\".format(line),\n self.log.str()))\n return\n\n if line[0:1] == b'0':\n # Verbose informational message, ignore it\n return\n\n self.resp.append(line[4:])\n\n if line[3:4] == b'-':\n # Continuation\n return\n\n if self.code in self._expected:\n why = self._okresponse(self.code, b'\\n'.join(self.resp))\n else:\n why = self._failresponse(self.code, b'\\n'.join(self.resp))\n\n self.code = -1\n self.resp = []\n return why\n\n\n def smtpConnectionFailed(self, code, resp):\n self.sendError(SMTPConnectError(code, resp, self.log.str()))\n\n\n def smtpTransferFailed(self, code, resp):\n if code < 0:\n self.sendError(SMTPProtocolError(code, resp, self.log.str()))\n else:\n self.smtpState_msgSent(code, resp)\n\n\n def smtpState_helo(self, code, resp):\n self.sendLine(b'HELO ' + self.identity)\n self._expected = SUCCESS\n self._okresponse = self.smtpState_from\n\n\n def smtpState_from(self, code, resp):\n self._from = self.getMailFrom()\n self._failresponse = self.smtpTransferFailed\n if self._from is not None:\n self.sendLine(b'MAIL FROM:' + quoteaddr(self._from))\n self._expected = [250]\n self._okresponse = self.smtpState_to\n else:\n # All messages have been sent, disconnect\n self._disconnectFromServer()\n\n\n def smtpState_disconnect(self, code, resp):\n self.transport.loseConnection()\n\n\n def smtpState_to(self, code, resp):\n self.toAddresses = iter(self.getMailTo())\n self.toAddressesResult = []\n self.successAddresses = []\n self._okresponse = self.smtpState_toOrData\n self._expected = range(0, 1000)\n self.lastAddress = None\n return self.smtpState_toOrData(0, b'')\n\n\n def smtpState_toOrData(self, code, resp):\n if self.lastAddress is not None:\n self.toAddressesResult.append((self.lastAddress, code, resp))\n if code in SUCCESS:\n self.successAddresses.append(self.lastAddress)\n try:\n self.lastAddress = next(self.toAddresses)\n except StopIteration:\n if self.successAddresses:\n self.sendLine(b'DATA')\n self._expected = [ 354 ]\n self._okresponse = self.smtpState_data\n else:\n return self.smtpState_msgSent(code,'No recipients accepted')\n else:\n self.sendLine(b'RCPT TO:' + quoteaddr(self.lastAddress))\n\n\n def smtpState_data(self, code, resp):\n s = basic.FileSender()\n d = s.beginFileTransfer(\n self.getMailData(), self.transport, self.transformChunk)\n def ebTransfer(err):\n self.sendError(err.value)\n d.addCallbacks(self.finishedFileTransfer, ebTransfer)\n self._expected = SUCCESS\n self._okresponse = self.smtpState_msgSent\n\n\n def smtpState_msgSent(self, code, resp):\n if self._from is not None:\n self.sentMail(code, resp, len(self.successAddresses),\n self.toAddressesResult, self.log)\n\n self.toAddressesResult = []\n self._from = None\n self.sendLine(b'RSET')\n self._expected = SUCCESS\n self._okresponse = self.smtpState_from\n\n\n ##\n ## Helpers for FileSender\n ##\n def transformChunk(self, chunk):\n \"\"\"\n Perform the necessary local to network newline conversion and escape\n leading periods.\n\n This method also resets the idle timeout so that as long as process is\n being made sending the message body, the client will not time out.\n \"\"\"\n self.resetTimeout()\n return chunk.replace(b'\\n', b'\\r\\n').replace(b'\\r\\n.', b'\\r\\n..')\n\n\n def finishedFileTransfer(self, lastsent):\n if lastsent != b'\\n':\n line = b'\\r\\n.'\n else:\n line = b'.'\n self.sendLine(line)\n\n\n ##\n # these methods should be overridden in subclasses\n def getMailFrom(self):\n \"\"\"\n Return the email address the mail is from.\n \"\"\"\n raise NotImplementedError\n\n\n def getMailTo(self):\n \"\"\"\n Return a list of emails to send to.\n \"\"\"\n raise NotImplementedError\n\n\n def getMailData(self):\n \"\"\"\n Return file-like object containing data of message to be sent.\n\n Lines in the file should be delimited by '\\\\n'.\n \"\"\"\n raise NotImplementedError\n\n\n def sendError(self, exc):\n \"\"\"\n If an error occurs before a mail message is sent sendError will be\n called. This base class method sends a QUIT if the error is\n non-fatal and disconnects the connection.\n\n @param exc: The SMTPClientError (or child class) raised\n @type exc: C{SMTPClientError}\n \"\"\"\n if isinstance(exc, SMTPClientError) and not exc.isFatal:\n self._disconnectFromServer()\n else:\n # If the error was fatal then the communication channel with the\n # SMTP Server is broken so just close the transport connection\n self.smtpState_disconnect(-1, None)\n\n\n def sentMail(self, code, resp, numOk, addresses, log):\n \"\"\"\n Called when an attempt to send an email is completed.\n\n If some addresses were accepted, code and resp are the response\n to the DATA command. If no addresses were accepted, code is -1\n and resp is an informative message.\n\n @param code: the code returned by the SMTP Server\n @param resp: The string response returned from the SMTP Server\n @param numOK: the number of addresses accepted by the remote host.\n @param addresses: is a list of tuples (address, code, resp) listing\n the response to each RCPT command.\n @param log: is the SMTP session log\n \"\"\"\n raise NotImplementedError\n\n\n def _disconnectFromServer(self):\n self._expected = range(0, 1000)\n self._okresponse = self.smtpState_disconnect\n self.sendLine(b'QUIT')\n\n\n\nclass ESMTPClient(SMTPClient):\n \"\"\"\n A client for sending emails over ESMTP.\n\n @ivar heloFallback: Whether or not to fall back to plain SMTP if the C{EHLO}\n command is not recognised by the server. If L{requireAuthentication} is\n C{True}, or L{requireTransportSecurity} is C{True} and the connection is\n not over TLS, this fallback flag will not be honored.\n @type heloFallback: L{bool}\n\n @ivar requireAuthentication: If C{True}, refuse to proceed if authentication\n cannot be performed. Overrides L{heloFallback}.\n @type requireAuthentication: L{bool}\n\n @ivar requireTransportSecurity: If C{True}, refuse to proceed if the\n transport cannot be secured. If the transport layer is not already\n secured via TLS, this will override L{heloFallback}.\n @type requireAuthentication: L{bool}\n\n @ivar context: The context factory to use for STARTTLS, if desired.\n @type context: L{ssl.ClientContextFactory}\n\n @ivar _tlsMode: Whether or not the connection is over TLS.\n @type _tlsMode: L{bool}\n \"\"\"\n heloFallback = True\n requireAuthentication = False\n requireTransportSecurity = False\n context = None\n _tlsMode = False\n\n def __init__(self, secret, contextFactory=None, *args, **kw):\n SMTPClient.__init__(self, *args, **kw)\n self.authenticators = []\n self.secret = secret\n self.context = contextFactory\n\n\n def __getattr__(self, name):\n if name == \"tlsMode\":\n warnings.warn(\n \"tlsMode attribute of twisted.mail.smtp.ESMTPClient \"\n \"is deprecated since Twisted 13.0\",\n category=DeprecationWarning, stacklevel=2)\n return self._tlsMode\n else:\n raise AttributeError(\n '%s instance has no attribute %r' % (\n self.__class__.__name__, name,))\n\n\n def __setattr__(self, name, value):\n if name == \"tlsMode\":\n warnings.warn(\n \"tlsMode attribute of twisted.mail.smtp.ESMTPClient \"\n \"is deprecated since Twisted 13.0\",\n category=DeprecationWarning, stacklevel=2)\n self._tlsMode = value\n else:\n self.__dict__[name] = value\n\n\n def esmtpEHLORequired(self, code=-1, resp=None):\n \"\"\"\n Fail because authentication is required, but the server does not support\n ESMTP, which is required for authentication.\n\n @param code: The server status code from the most recently received\n server message.\n @type code: L{int}\n\n @param resp: The server status response from the most recently received\n server message.\n @type resp: L{bytes}\n \"\"\"\n self.sendError(EHLORequiredError(502, b\"Server does not support ESMTP \"\n b\"Authentication\", self.log.str()))\n\n\n def esmtpAUTHRequired(self, code=-1, resp=None):\n \"\"\"\n Fail because authentication is required, but the server does not support\n any schemes we support.\n\n @param code: The server status code from the most recently received\n server message.\n @type code: L{int}\n\n @param resp: The server status response from the most recently received\n server message.\n @type resp: L{bytes}\n \"\"\"\n tmp = []\n\n for a in self.authenticators:\n tmp.append(a.getName().upper())\n\n auth = b\"[%s]\" % b\", \".join(tmp)\n\n self.sendError(AUTHRequiredError(502, b\"Server does not support Client \"\n b\"Authentication schemes %s\" % auth, self.log.str()))\n\n\n def esmtpTLSRequired(self, code=-1, resp=None):\n \"\"\"\n Fail because TLS is required and the server does not support it.\n\n @param code: The server status code from the most recently received\n server message.\n @type code: L{int}\n\n @param resp: The server status response from the most recently received\n server message.\n @type resp: L{bytes}\n \"\"\"\n self.sendError(TLSRequiredError(502, b\"Server does not support secure \"\n b\"communication via TLS / SSL\", self.log.str()))\n\n\n def esmtpTLSFailed(self, code=-1, resp=None):\n \"\"\"\n Fail because the TLS handshake wasn't able to be completed.\n\n @param code: The server status code from the most recently received\n server message.\n @type code: L{int}\n\n @param resp: The server status response from the most recently received\n server message.\n @type resp: L{bytes}\n \"\"\"\n self.sendError(TLSError(code, b\"Could not complete the SSL/TLS \"\n b\"handshake\", self.log.str()))\n\n\n def esmtpAUTHDeclined(self, code=-1, resp=None):\n \"\"\"\n Fail because the authentication was rejected.\n\n @param code: The server status code from the most recently received\n server message.\n @type code: L{int}\n\n @param resp: The server status response from the most recently received\n server message.\n @type resp: L{bytes}\n \"\"\"\n self.sendError(AUTHDeclinedError(code, resp, self.log.str()))\n\n\n def esmtpAUTHMalformedChallenge(self, code=-1, resp=None):\n \"\"\"\n Fail because the server sent a malformed authentication challenge.\n\n @param code: The server status code from the most recently received\n server message.\n @type code: L{int}\n\n @param resp: The server status response from the most recently received\n server message.\n @type resp: L{bytes}\n \"\"\"\n self.sendError(AuthenticationError(501, b\"Login failed because the \"\n b\"SMTP Server returned a malformed Authentication Challenge\",\n self.log.str()))\n\n\n def esmtpAUTHServerError(self, code=-1, resp=None):\n \"\"\"\n Fail because of some other authentication error.\n\n @param code: The server status code from the most recently received\n server message.\n @type code: L{int}\n\n @param resp: The server status response from the most recently received\n server message.\n @type resp: L{bytes}\n \"\"\"\n self.sendError(AuthenticationError(code, resp, self.log.str()))\n\n\n def registerAuthenticator(self, auth):\n \"\"\"\n Registers an Authenticator with the ESMTPClient. The ESMTPClient will\n attempt to login to the SMTP Server in the order the Authenticators are\n registered. The most secure Authentication mechanism should be\n registered first.\n\n @param auth: The Authentication mechanism to register\n @type auth: L{IClientAuthentication} implementor\n\n @return: L{None}\n \"\"\"\n self.authenticators.append(auth)\n\n\n def connectionMade(self):\n \"\"\"\n Called when a connection has been made, and triggers sending an C{EHLO}\n to the server.\n \"\"\"\n self._tlsMode = ISSLTransport.providedBy(self.transport)\n SMTPClient.connectionMade(self)\n self._okresponse = self.esmtpState_ehlo\n\n\n def esmtpState_ehlo(self, code, resp):\n \"\"\"\n Send an C{EHLO} to the server.\n\n If L{heloFallback} is C{True}, and there is no requirement for TLS or\n authentication, the client will fall back to basic SMTP.\n\n @param code: The server status code from the most recently received\n server message.\n @type code: L{int}\n\n @param resp: The server status response from the most recently received\n server message.\n @type resp: L{bytes}\n\n @return: L{None}\n \"\"\"\n self._expected = SUCCESS\n\n self._okresponse = self.esmtpState_serverConfig\n self._failresponse = self.esmtpEHLORequired\n\n if self._tlsMode:\n needTLS = False\n else:\n needTLS = self.requireTransportSecurity\n\n if self.heloFallback and not self.requireAuthentication and not needTLS:\n self._failresponse = self.smtpState_helo\n\n self.sendLine(b\"EHLO \" + self.identity)\n\n\n def esmtpState_serverConfig(self, code, resp):\n \"\"\"\n Handle a positive response to the I{EHLO} command by parsing the\n capabilities in the server's response and then taking the most\n appropriate next step towards entering a mail transaction.\n \"\"\"\n items = {}\n for line in resp.splitlines():\n e = line.split(None, 1)\n if len(e) > 1:\n items[e[0]] = e[1]\n else:\n items[e[0]] = None\n\n self.tryTLS(code, resp, items)\n\n\n def tryTLS(self, code, resp, items):\n \"\"\"\n Take a necessary step towards being able to begin a mail transaction.\n\n The step may be to ask the server to being a TLS session. If TLS is\n already in use or not necessary and not available then the step may be\n to authenticate with the server. If TLS is necessary and not available,\n fail the mail transmission attempt.\n\n This is an internal helper method.\n\n @param code: The server status code from the most recently received\n server message.\n @type code: L{int}\n\n @param resp: The server status response from the most recently received\n server message.\n @type resp: L{bytes}\n\n @param items: A mapping of ESMTP extensions offered by the server. Keys\n are extension identifiers and values are the associated values.\n @type items: L{dict} mapping L{bytes} to L{bytes}\n\n @return: L{None}\n \"\"\"\n\n # has tls can tls must tls result\n # t t t authenticate\n # t t f authenticate\n # t f t authenticate\n # t f f authenticate\n\n # f t t STARTTLS\n # f t f STARTTLS\n # f f t esmtpTLSRequired\n # f f f authenticate\n\n hasTLS = self._tlsMode\n canTLS = self.context and b\"STARTTLS\" in items\n mustTLS = self.requireTransportSecurity\n\n if hasTLS or not (canTLS or mustTLS):\n self.authenticate(code, resp, items)\n elif canTLS:\n self._expected = [220]\n self._okresponse = self.esmtpState_starttls\n self._failresponse = self.esmtpTLSFailed\n self.sendLine(b\"STARTTLS\")\n else:\n self.esmtpTLSRequired()\n\n\n def esmtpState_starttls(self, code, resp):\n \"\"\"\n Handle a positive response to the I{STARTTLS} command by starting a new\n TLS session on C{self.transport}.\n\n Upon success, re-handshake with the server to discover what capabilities\n it has when TLS is in use.\n \"\"\"\n try:\n self.transport.startTLS(self.context)\n self._tlsMode = True\n except:\n log.err()\n self.esmtpTLSFailed(451)\n\n # Send another EHLO once TLS has been started to\n # get the TLS / AUTH schemes. Some servers only allow AUTH in TLS mode.\n self.esmtpState_ehlo(code, resp)\n\n\n def authenticate(self, code, resp, items):\n if self.secret and items.get(b'AUTH'):\n schemes = items[b'AUTH'].split()\n tmpSchemes = {}\n\n #XXX: May want to come up with a more efficient way to do this\n for s in schemes:\n tmpSchemes[s.upper()] = 1\n\n for a in self.authenticators:\n auth = a.getName().upper()\n\n if auth in tmpSchemes:\n self._authinfo = a\n\n # Special condition handled\n if auth == b\"PLAIN\":\n self._okresponse = self.smtpState_from\n self._failresponse = self._esmtpState_plainAuth\n self._expected = [235]\n challenge = base64.b64encode(\n self._authinfo.challengeResponse(self.secret, 1))\n self.sendLine(b\"AUTH %s %s\" % (auth, challenge))\n else:\n self._expected = [334]\n self._okresponse = self.esmtpState_challenge\n # If some error occurs here, the server declined the\n # AUTH before the user / password phase. This would be\n # a very rare case\n self._failresponse = self.esmtpAUTHServerError\n self.sendLine(b'AUTH ' + auth)\n return\n\n if self.requireAuthentication:\n self.esmtpAUTHRequired()\n else:\n self.smtpState_from(code, resp)\n\n\n def _esmtpState_plainAuth(self, code, resp):\n self._okresponse = self.smtpState_from\n self._failresponse = self.esmtpAUTHDeclined\n self._expected = [235]\n challenge = base64.b64encode(\n self._authinfo.challengeResponse(self.secret, 2))\n self.sendLine(b'AUTH PLAIN ' + challenge)\n\n\n def esmtpState_challenge(self, code, resp):\n self._authResponse(self._authinfo, resp)\n\n\n def _authResponse(self, auth, challenge):\n self._failresponse = self.esmtpAUTHDeclined\n try:\n challenge = base64.b64decode(challenge)\n except binascii.Error:\n # Illegal challenge, give up, then quit\n self.sendLine(b'*')\n self._okresponse = self.esmtpAUTHMalformedChallenge\n self._failresponse = self.esmtpAUTHMalformedChallenge\n else:\n resp = auth.challengeResponse(self.secret, challenge)\n self._expected = [235, 334]\n self._okresponse = self.smtpState_maybeAuthenticated\n self.sendLine(base64.b64encode(resp))\n\n\n def smtpState_maybeAuthenticated(self, code, resp):\n \"\"\"\n Called to handle the next message from the server after sending a\n response to a SASL challenge. The server response might be another\n challenge or it might indicate authentication has succeeded.\n \"\"\"\n if code == 235:\n # Yes, authenticated!\n del self._authinfo\n self.smtpState_from(code, resp)\n else:\n # No, not authenticated yet. Keep trying.\n self._authResponse(self._authinfo, resp)\n\n\n\nclass ESMTP(SMTP):\n ctx = None\n canStartTLS = False\n startedTLS = False\n\n authenticated = False\n\n def __init__(self, chal=None, contextFactory=None):\n SMTP.__init__(self)\n if chal is None:\n chal = {}\n self.challengers = chal\n self.authenticated = False\n self.ctx = contextFactory\n\n\n def connectionMade(self):\n SMTP.connectionMade(self)\n self.canStartTLS = ITLSTransport.providedBy(self.transport)\n self.canStartTLS = self.canStartTLS and (self.ctx is not None)\n\n\n def greeting(self):\n return SMTP.greeting(self) + b' ESMTP'\n\n\n def extensions(self):\n \"\"\"\n SMTP service extensions\n\n @return: the SMTP service extensions that are supported.\n @rtype: L{dict} with L{bytes} keys and a value of either L{None} or a\n L{list} of L{bytes}.\n \"\"\"\n ext = {b'AUTH': _keys(self.challengers)}\n if self.canStartTLS and not self.startedTLS:\n ext[b'STARTTLS'] = None\n return ext\n\n\n def lookupMethod(self, command):\n command = nativeString(command)\n\n m = SMTP.lookupMethod(self, command)\n if m is None:\n m = getattr(self, 'ext_' + command.upper(), None)\n return m\n\n\n def listExtensions(self):\n r = []\n for (c, v) in iteritems(self.extensions()):\n if v is not None:\n if v:\n # Intentionally omit extensions with empty argument lists\n r.append(c + b' ' + b' '.join(v))\n else:\n r.append(c)\n\n return b'\\n'.join(r)\n\n\n def do_EHLO(self, rest):\n peer = self.transport.getPeer().host\n\n if not isinstance(peer, bytes):\n peer = peer.encode('idna')\n\n self._helo = (rest, peer)\n self._from = None\n self._to = []\n self.sendCode(\n 250,\n (self.host + b' Hello ' + peer + b', nice to meet you\\n' +\n self.listExtensions())\n )\n\n\n def ext_STARTTLS(self, rest):\n if self.startedTLS:\n self.sendCode(503, b'TLS already negotiated')\n elif self.ctx and self.canStartTLS:\n self.sendCode(220, b'Begin TLS negotiation now')\n self.transport.startTLS(self.ctx)\n self.startedTLS = True\n else:\n self.sendCode(454, b'TLS not available')\n\n\n def ext_AUTH(self, rest):\n if self.authenticated:\n self.sendCode(503, b'Already authenticated')\n return\n parts = rest.split(None, 1)\n chal = self.challengers.get(parts[0].upper(), lambda: None)()\n if not chal:\n self.sendCode(504, b'Unrecognized authentication type')\n return\n\n self.mode = AUTH\n self.challenger = chal\n\n if len(parts) > 1:\n chal.getChallenge() # Discard it, apparently the client does not\n # care about it.\n rest = parts[1]\n else:\n rest = None\n self.state_AUTH(rest)\n\n\n def _cbAuthenticated(self, loginInfo):\n \"\"\"\n Save the state resulting from a successful cred login and mark this\n connection as authenticated.\n \"\"\"\n result = SMTP._cbAnonymousAuthentication(self, loginInfo)\n self.authenticated = True\n return result\n\n\n def _ebAuthenticated(self, reason):\n \"\"\"\n Handle cred login errors by translating them to the SMTP authenticate\n failed. Translate all other errors into a generic SMTP error code and\n log the failure for inspection. Stop all errors from propagating.\n\n @param reason: Reason for failure.\n \"\"\"\n self.challenge = None\n if reason.check(cred.error.UnauthorizedLogin):\n self.sendCode(535, b'Authentication failed')\n else:\n log.err(reason, \"SMTP authentication failure\")\n self.sendCode(\n 451,\n b'Requested action aborted: local error in processing')\n\n\n def state_AUTH(self, response):\n \"\"\"\n Handle one step of challenge/response authentication.\n\n @param response: The text of a response. If None, this\n function has been called as a result of an AUTH command with\n no initial response. A response of '*' aborts authentication,\n as per RFC 2554.\n \"\"\"\n if self.portal is None:\n self.sendCode(454, b'Temporary authentication failure')\n self.mode = COMMAND\n return\n\n if response is None:\n challenge = self.challenger.getChallenge()\n encoded = base64.b64encode(challenge)\n self.sendCode(334, encoded)\n return\n\n if response == b'*':\n self.sendCode(501, b'Authentication aborted')\n self.challenger = None\n self.mode = COMMAND\n return\n\n try:\n uncoded = base64.b64decode(response)\n except (TypeError, binascii.Error):\n self.sendCode(501, b'Syntax error in parameters or arguments')\n self.challenger = None\n self.mode = COMMAND\n return\n\n self.challenger.setResponse(uncoded)\n if self.challenger.moreChallenges():\n challenge = self.challenger.getChallenge()\n coded = base64.b64encode(challenge)\n self.sendCode(334, coded)\n return\n\n self.mode = COMMAND\n result = self.portal.login(\n self.challenger, None,\n IMessageDeliveryFactory, IMessageDelivery)\n result.addCallback(self._cbAuthenticated)\n result.addCallback(lambda ign: self.sendCode(235,\n b'Authentication successful.'))\n result.addErrback(self._ebAuthenticated)\n\n\n\nclass SenderMixin:\n \"\"\"\n Utility class for sending emails easily.\n\n Use with SMTPSenderFactory or ESMTPSenderFactory.\n \"\"\"\n done = 0\n\n def getMailFrom(self):\n if not self.done:\n self.done = 1\n return str(self.factory.fromEmail)\n else:\n return None\n\n\n def getMailTo(self):\n return self.factory.toEmail\n\n\n def getMailData(self):\n return self.factory.file\n\n\n def sendError(self, exc):\n # Call the base class to close the connection with the SMTP server\n SMTPClient.sendError(self, exc)\n\n # Do not retry to connect to SMTP Server if:\n # 1. No more retries left (This allows the correct error to be returned to the errorback)\n # 2. retry is false\n # 3. The error code is not in the 4xx range (Communication Errors)\n\n if (self.factory.retries >= 0 or\n (not exc.retry and not (exc.code >= 400 and exc.code < 500))):\n self.factory.sendFinished = True\n self.factory.result.errback(exc)\n\n\n def sentMail(self, code, resp, numOk, addresses, log):\n # Do not retry, the SMTP server acknowledged the request\n self.factory.sendFinished = True\n if code not in SUCCESS:\n errlog = []\n for addr, acode, aresp in addresses:\n if acode not in SUCCESS:\n errlog.append((addr + b\": \" +\n networkString(\"%03d\" % (acode,)) +\n b\" \" + aresp))\n\n errlog.append(log.str())\n\n exc = SMTPDeliveryError(code, resp, b'\\n'.join(errlog), addresses)\n self.factory.result.errback(exc)\n else:\n self.factory.result.callback((numOk, addresses))\n\n\n\nclass SMTPSender(SenderMixin, SMTPClient):\n \"\"\"\n SMTP protocol that sends a single email based on information it\n gets from its factory, a L{SMTPSenderFactory}.\n \"\"\"\n\n\n\nclass SMTPSenderFactory(protocol.ClientFactory):\n \"\"\"\n Utility factory for sending emails easily.\n\n @type currentProtocol: L{SMTPSender}\n @ivar currentProtocol: The current running protocol returned by\n L{buildProtocol}.\n\n @type sendFinished: C{bool}\n @ivar sendFinished: When the value is set to True, it means the message has\n been sent or there has been an unrecoverable error or the sending has\n been cancelled. The default value is False.\n \"\"\"\n\n domain = DNSNAME\n protocol = SMTPSender\n\n def __init__(self, fromEmail, toEmail, file, deferred, retries=5,\n timeout=None):\n \"\"\"\n @param fromEmail: The RFC 2821 address from which to send this\n message.\n\n @param toEmail: A sequence of RFC 2821 addresses to which to\n send this message.\n\n @param file: A file-like object containing the message to send.\n\n @param deferred: A Deferred to callback or errback when sending\n of this message completes.\n @type deferred: L{defer.Deferred}\n\n @param retries: The number of times to retry delivery of this\n message.\n\n @param timeout: Period, in seconds, for which to wait for\n server responses, or None to wait forever.\n \"\"\"\n assert isinstance(retries, (int, long))\n\n if isinstance(toEmail, unicode):\n toEmail = [toEmail.encode('ascii')]\n elif isinstance(toEmail, bytes):\n toEmail = [toEmail]\n else:\n toEmailFinal = []\n for _email in toEmail:\n if not isinstance(_email, bytes):\n _email = _email.encode('ascii')\n\n toEmailFinal.append(_email)\n toEmail = toEmailFinal\n\n self.fromEmail = Address(fromEmail)\n self.nEmails = len(toEmail)\n self.toEmail = toEmail\n self.file = file\n self.result = deferred\n self.result.addBoth(self._removeDeferred)\n self.sendFinished = False\n self.currentProtocol = None\n\n self.retries = -retries\n self.timeout = timeout\n\n\n def _removeDeferred(self, result):\n del self.result\n return result\n\n\n def clientConnectionFailed(self, connector, err):\n self._processConnectionError(connector, err)\n\n\n def clientConnectionLost(self, connector, err):\n self._processConnectionError(connector, err)\n\n\n def _processConnectionError(self, connector, err):\n self.currentProtocol = None\n if (self.retries < 0) and (not self.sendFinished):\n log.msg(\"SMTP Client retrying server. Retry: %s\" % -self.retries)\n\n # Rewind the file in case part of it was read while attempting to\n # send the message.\n self.file.seek(0, 0)\n connector.connect()\n self.retries += 1\n elif not self.sendFinished:\n # If we were unable to communicate with the SMTP server a ConnectionDone will be\n # returned. We want a more clear error message for debugging\n if err.check(error.ConnectionDone):\n err.value = SMTPConnectError(-1, \"Unable to connect to server.\")\n self.result.errback(err.value)\n\n\n def buildProtocol(self, addr):\n p = self.protocol(self.domain, self.nEmails*2+2)\n p.factory = self\n p.timeout = self.timeout\n self.currentProtocol = p\n self.result.addBoth(self._removeProtocol)\n return p\n\n\n def _removeProtocol(self, result):\n \"\"\"\n Remove the protocol created in C{buildProtocol}.\n\n @param result: The result/error passed to the callback/errback of\n L{defer.Deferred}.\n\n @return: The C{result} untouched.\n \"\"\"\n if self.currentProtocol:\n self.currentProtocol = None\n return result\n\n\n\nclass LOGINCredentials(_lcredentials):\n \"\"\"\n L{LOGINCredentials} generates challenges for I{LOGIN} authentication.\n\n For interoperability with Outlook, the challenge generated does not exactly\n match the one defined in the\n U{draft specification}.\n \"\"\"\n\n def __init__(self):\n _lcredentials.__init__(self)\n self.challenges = [b'Password:', b'Username:']\n\n\n\n@implementer(IClientAuthentication)\nclass PLAINAuthenticator:\n def __init__(self, user):\n self.user = user\n\n\n def getName(self):\n return b\"PLAIN\"\n\n\n def challengeResponse(self, secret, chal=1):\n if chal == 1:\n return self.user + b'\\0' + self.user + b'\\0' + secret\n else:\n return b'\\0' + self.user + b'\\0' + secret\n\n\n\nclass ESMTPSender(SenderMixin, ESMTPClient):\n\n requireAuthentication = True\n requireTransportSecurity = True\n\n def __init__(self, username, secret, contextFactory=None, *args, **kw):\n self.heloFallback = 0\n self.username = username\n\n if contextFactory is None:\n contextFactory = self._getContextFactory()\n\n ESMTPClient.__init__(self, secret, contextFactory, *args, **kw)\n\n self._registerAuthenticators()\n\n\n def _registerAuthenticators(self):\n # Register Authenticator in order from most secure to least secure\n self.registerAuthenticator(CramMD5ClientAuthenticator(self.username))\n self.registerAuthenticator(LOGINAuthenticator(self.username))\n self.registerAuthenticator(PLAINAuthenticator(self.username))\n\n\n def _getContextFactory(self):\n if self.context is not None:\n return self.context\n try:\n from twisted.internet import ssl\n except ImportError:\n return None\n else:\n try:\n context = ssl.ClientContextFactory()\n context.method = ssl.SSL.TLSv1_METHOD\n return context\n except AttributeError:\n return None\n\n\n\nclass ESMTPSenderFactory(SMTPSenderFactory):\n \"\"\"\n Utility factory for sending emails easily.\n\n @type currentProtocol: L{ESMTPSender}\n @ivar currentProtocol: The current running protocol as made by\n L{buildProtocol}.\n \"\"\"\n protocol = ESMTPSender\n\n def __init__(self, username, password, fromEmail, toEmail, file,\n deferred, retries=5, timeout=None,\n contextFactory=None, heloFallback=False,\n requireAuthentication=True,\n requireTransportSecurity=True):\n\n SMTPSenderFactory.__init__(self, fromEmail, toEmail, file, deferred, retries, timeout)\n self.username = username\n self.password = password\n self._contextFactory = contextFactory\n self._heloFallback = heloFallback\n self._requireAuthentication = requireAuthentication\n self._requireTransportSecurity = requireTransportSecurity\n\n\n def buildProtocol(self, addr):\n \"\"\"\n Build an L{ESMTPSender} protocol configured with C{heloFallback},\n C{requireAuthentication}, and C{requireTransportSecurity} as specified\n in L{__init__}.\n\n This sets L{currentProtocol} on the factory, as well as returning it.\n\n @rtype: L{ESMTPSender}\n \"\"\"\n p = self.protocol(self.username, self.password, self._contextFactory,\n self.domain, self.nEmails*2+2)\n p.heloFallback = self._heloFallback\n p.requireAuthentication = self._requireAuthentication\n p.requireTransportSecurity = self._requireTransportSecurity\n p.factory = self\n p.timeout = self.timeout\n self.currentProtocol = p\n self.result.addBoth(self._removeProtocol)\n return p\n\n\n\ndef sendmail(smtphost, from_addr, to_addrs, msg, senderDomainName=None, port=25,\n reactor=reactor, username=None, password=None,\n requireAuthentication=False, requireTransportSecurity=False):\n \"\"\"\n Send an email.\n\n This interface is intended to be a replacement for L{smtplib.SMTP.sendmail}\n and related methods. To maintain backwards compatibility, it will fall back\n to plain SMTP, if ESMTP support is not available. If ESMTP support is\n available, it will attempt to provide encryption via STARTTLS and\n authentication if a secret is provided.\n\n @param smtphost: The host the message should be sent to.\n @type smtphost: L{bytes}\n\n @param from_addr: The (envelope) address sending this mail.\n @type from_addr: L{bytes}\n\n @param to_addrs: A list of addresses to send this mail to. A string will\n be treated as a list of one address.\n @type to_addr: L{list} of L{bytes} or L{bytes}\n\n @param msg: The message, including headers, either as a file or a string.\n File-like objects need to support read() and close(). Lines must be\n delimited by '\\\\n'. If you pass something that doesn't look like a file,\n we try to convert it to a string (so you should be able to pass an\n L{email.message} directly, but doing the conversion with\n L{email.generator} manually will give you more control over the process).\n\n @param senderDomainName: Name by which to identify. If None, try to pick\n something sane (but this depends on external configuration and may not\n succeed).\n @type senderDomainName: L{bytes}\n\n @param port: Remote port to which to connect.\n @type port: L{int}\n\n @param username: The username to use, if wanting to authenticate.\n @type username: L{bytes} or L{unicode}\n\n @param password: The secret to use, if wanting to authenticate. If you do\n not specify this, SMTP authentication will not occur.\n @type password: L{bytes} or L{unicode}\n\n @param requireTransportSecurity: Whether or not STARTTLS is required.\n @type requireTransportSecurity: L{bool}\n\n @param requireAuthentication: Whether or not authentication is required.\n @type requireAuthentication: L{bool}\n\n @param reactor: The L{reactor} used to make the TCP connection.\n\n @rtype: L{Deferred}\n @returns: A cancellable L{Deferred}, its callback will be called if a\n message is sent to ANY address, the errback if no message is sent. When\n the C{cancel} method is called, it will stop retrying and disconnect\n the connection immediately.\n\n The callback will be called with a tuple (numOk, addresses) where numOk\n is the number of successful recipient addresses and addresses is a list\n of tuples (address, code, resp) giving the response to the RCPT command\n for each address.\n \"\"\"\n if not hasattr(msg, 'read'):\n # It's not a file\n msg = BytesIO(bytes(msg))\n\n\n def cancel(d):\n \"\"\"\n Cancel the L{twisted.mail.smtp.sendmail} call, tell the factory not to\n retry and disconnect the connection.\n\n @param d: The L{defer.Deferred} to be cancelled.\n \"\"\"\n factory.sendFinished = True\n if factory.currentProtocol:\n factory.currentProtocol.transport.abortConnection()\n else:\n # Connection hasn't been made yet\n connector.disconnect()\n\n d = defer.Deferred(cancel)\n\n if isinstance(username, unicode):\n username = username.encode(\"utf-8\")\n if isinstance(password, unicode):\n password = password.encode(\"utf-8\")\n\n factory = ESMTPSenderFactory(username, password, from_addr, to_addrs, msg,\n d, heloFallback=True, requireAuthentication=requireAuthentication,\n requireTransportSecurity=requireTransportSecurity)\n\n if senderDomainName is not None:\n factory.domain = networkString(senderDomainName)\n\n connector = reactor.connectTCP(smtphost, port, factory)\n\n return d\n\n\n\nimport codecs\ndef xtext_encode(s, errors=None):\n r = []\n for ch in iterbytes(s):\n o = ord(ch)\n if ch == '+' or ch == '=' or o < 33 or o > 126:\n r.append(networkString('+%02X' % (o,)))\n else:\n r.append(_bytesChr(o))\n return (b''.join(r), len(s))\n\n\n\ndef xtext_decode(s, errors=None):\n \"\"\"\n Decode the xtext-encoded string C{s}.\n\n @param s: String to decode.\n @param errors: codec error handling scheme.\n @return: The decoded string.\n \"\"\"\n r = []\n i = 0\n while i < len(s):\n if s[i:i+1] == b'+':\n try:\n r.append(chr(int(bytes(s[i + 1:i + 3]), 16)))\n except ValueError:\n r.append(ord(s[i:i + 3]))\n i += 3\n else:\n r.append(bytes(s[i:i+1]).decode('ascii'))\n i += 1\n return (''.join(r), len(s))\n\n\n\nclass xtextStreamReader(codecs.StreamReader):\n def decode(self, s, errors='strict'):\n return xtext_decode(s)\n\n\n\nclass xtextStreamWriter(codecs.StreamWriter):\n def decode(self, s, errors='strict'):\n return xtext_encode(s)\n\n\n\ndef xtext_codec(name):\n if name == 'xtext':\n return (xtext_encode, xtext_decode, xtextStreamReader, xtextStreamWriter)\ncodecs.register(xtext_codec)\n","repo_name":"wistbean/learn_python3_spider","sub_path":"stackoverflow/venv/lib/python3.6/site-packages/twisted/mail/smtp.py","file_name":"smtp.py","file_ext":"py","file_size_in_byte":71392,"program_lang":"python","lang":"en","doc_type":"code","stars":14022,"dataset":"github-code","pt":"3"}
+{"seq_id":"30753603107","text":"# -*- coding: utf-8 -*-\nimport json\nimport recipe\nimport requests\nfrom datetime import timedelta\nfrom django.db import IntegrityError\n\nfrom django.db.models import Q\nfrom django.core.files import File\nfrom os.path import basename\nfrom tempfile import TemporaryFile\nfrom urllib.parse import urlsplit\n\nfrom pathlib import Path\n\nfrom users.models import User\n\nfrom recipe.models import Ingredient, Recipe, RecipeImage\nfrom recipe.enums import Cuisines, Diets, RecipeTypes, \\\n Units, UNITS_KEYS\nfrom utils.helper import strip_links\n\nfrom django.core.management.base import BaseCommand\n\n\nclass Command(BaseCommand):\n help = \"Add recipes from json files in the data directory\"\n\n def add_arguments(self, parser):\n parser.add_argument(\"--user-id\", type=int, help=\"User ID to assign recipes\", required=True)\n parser.add_argument(\"--data-dir\", type=str, help=\"Data directory\")\n\n def handle(self, *args, **options):\n\n DIR = options.get('data_dir') or 'recipe_data'\n\n try:\n user = User.objects.get(pk=options.get('user_id'))\n except User.DoesNotExist as e:\n raise Exception(f\"User with id {options['user_id']} not found\") from e\n\n files = [f for f in Path.cwd().joinpath(DIR).iterdir() if f.suffix == '.json']\n\n for f in files:\n self.stdout.write(str(f))\n try:\n with open(f, 'r') as jf:\n recipe_data = json.load(jf)\n\n rc = RecipeCreator(recipe_data=recipe_data, user=user)\n recipe = rc.add_recipe()\n except IntegrityError:\n self.stdout.write(self.style.WARNING(f\"Recipe already exists\"))\n except Exception as e:\n self.stdout.write(self.style.ERROR(f\"Error: {e}\"))\n else:\n self.stdout.write(self.style.SUCCESS(f\"Recipe #{recipe.pk} added\"))\n\n\nclass RecipeCreator:\n \"\"\"\n Created Recipe models from necessary API by matching params\n \"\"\"\n\n def __init__(self, recipe_data: dict, user: User):\n self.recipe_data = recipe_data\n self.user = user\n\n def add_recipe(self):\n\n description = self.recipe_data.get('instructions') or self.recipe_data.get('summary', '-')\n\n recipe = Recipe.objects.create(\n title=self.recipe_data['title'],\n description=strip_links(description),\n user=self.user,\n cooking_time=timedelta(minutes=self.recipe_data['readyInMinutes']),\n cuisines=self._get_valid_cuisines(self.recipe_data['cuisines']),\n cooking_methods=self._get_valid_cooking_methods(),\n types=self._get_valid_types(self.recipe_data['dishTypes']),\n diet_restrictions=self._get_diet_restrictions(self.recipe_data),\n language='English',\n caption='unknown',\n status=Recipe.Status.ACCEPTED,\n publish_status=Recipe.PublishStatus.PUBLISHED,\n source_id=int(self.recipe_data['id']),\n source_url=self.recipe_data['sourceUrl'],\n is_parsed=True\n )\n\n if self.recipe_data.get('image'):\n self.download_to_recipe_image(recipe, self.recipe_data['image'])\n\n self.add_ingredients(\n recipe,\n self.recipe_data.get('extendedIngredients', [])\n )\n\n return recipe\n\n def download_to_recipe_image(self, recipe, url):\n \"\"\"\n https://goodcode.io/articles/django-download-url-to-file-field/\n \"\"\"\n ri = RecipeImage()\n ri.recipe = recipe\n ri.user = self.user\n with TemporaryFile() as tf:\n r = requests.get(url, stream=True)\n for chunk in r.iter_content(chunk_size=4096):\n tf.write(chunk)\n tf.seek(0)\n ri.file = File(tf, name=basename(urlsplit(url).path))\n ri.save()\n\n def add_ingredients(self, recipe, ingredients_list: list):\n\n def _get_unit(unit: str):\n for keys, unit_value in UNITS_KEYS.items():\n if unit in keys:\n return unit_value\n for choice in Units.choices:\n if unit == choice[0]:\n return unit\n return Units.EMPTY\n\n for ingredient_data in ingredients_list:\n Ingredient.objects.create(\n recipe=recipe,\n title=ingredient_data['originalName'],\n quantity=round(ingredient_data['amount'], 3),\n unit=_get_unit(ingredient_data['unit'])\n )\n\n def _get_valid_cuisines(self, cuisines_to_load: list) -> list:\n res = [v for v in Cuisines if v.label in cuisines_to_load]\n return res\n\n def _get_valid_cooking_methods(self) -> list:\n return []\n\n def _get_valid_types(self, dish_types:list) -> list:\n \"\"\"\n can have 'main course', 'main dish' values\n \"\"\"\n return [v for v in RecipeTypes if v.label.lower() in dish_types]\n\n def _get_diet_restrictions(self, recipe_data: dict) -> int:\n \"\"\"\n can have 'lacto ovo vegetarian'\n \"\"\"\n res = []\n if recipe_data.get('dairyFree'):\n res.append(Diets.DAIRY_FREE)\n if recipe_data.get('glutenFree'):\n res.append(Diets.GLUTEN_FREE)\n if recipe_data.get('lowFodmap'):\n res.append(Diets.FODMAP)\n if recipe_data.get('vegan'):\n res.append(Diets.VEGAN)\n if recipe_data.get('vegetarian'):\n res.append(Diets.VEGETARIAN)\n if 'pescatarian' in recipe_data['diets']:\n res.append(Diets.PESCETARIAN)\n if 'paleolitic' in recipe_data['diets']:\n res.append(Diets.PALEO)\n if 'primal' in recipe_data['diets']:\n res.append(Diets.PRIMAL)\n return res\n\n","repo_name":"Kartos102/eatchef_server","sub_path":"recipe/management/commands/add_recipes.py","file_name":"add_recipes.py","file_ext":"py","file_size_in_byte":5774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29568722834","text":"from rest_framework import serializers\nfrom api.models import Question, Option, Quiz\n\n\nclass OptionSerializer(serializers.ModelSerializer):\n class Meta:\n model = Option\n fields = '__all__'\n read_only_fields = [\n 'question',\n ]\n\n\nclass QuestionSerializer(serializers.ModelSerializer):\n options = OptionSerializer(many=True)\n\n class Meta:\n model = Question\n fields = '__all__'\n\n def create(self,validated_data):\n options_data = validated_data.pop('options')\n question = Question.objects.create(**validated_data)\n\n for option in options_data:\n Option.objects.create(question=question,**option)\n \n return question\n\nclass QuestionListSerializer(serializers.ModelSerializer):\n class Meta:\n model = Question\n fields = '__all__'\n\n\nclass QuizRetrieveSerializer(serializers.ModelSerializer):\n questions = QuestionSerializer(many=True)\n class Meta:\n model = Quiz\n fields = '__all__'\n read_only_fields = [\n 'slug',\n 'id',\n 'number_of_questions',\n 'created_by',\n ]\n\nclass QuizListCreateSerializer(serializers.ModelSerializer):\n url = serializers.HyperlinkedIdentityField(view_name='quiz-detail-update',lookup_field='slug')\n class Meta:\n model = Quiz\n fields = [\n 'id',\n 'title',\n 'slug',\n 'number_of_questions',\n 'url'\n ]\n read_only_fields = [\n 'slug',\n 'id',\n 'number_of_questions'\n ]","repo_name":"toptechschool/backend","sub_path":"api/serializers/ranking.py","file_name":"ranking.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72341973843","text":"import argparse\nimport os\nimport sys\nsys.path.append(os.getcwd())\nimport time\nimport numpy as np\nimport random\nfrom PIL import Image\nimport imageio\n\nimport yaml, shutil\nimport torch\nimport torch.nn as nn\nfrom tqdm import tqdm\nfrom torch.utils.data import DataLoader\nfrom torch.optim.lr_scheduler import MultiStepLR\nimport torch.nn.functional as F\n\nimport torchvision\n\nimport datasets\nimport models, losses\nimport utils\nimport matplotlib.pyplot as plt\nfrom einops import rearrange, repeat, reduce, parse_shape\n\nimport refine_svg\nfrom svgpathtools import svg2paths, parse_path, wsvg, Line, QuadraticBezier, CubicBezier, Path\nimport pydiffvg\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--outdir', required=True, type=str)\nparser.add_argument('--resume', required=True, type=str)\nparser.add_argument('--seed', default=42, type=int)\nparser.add_argument('--n-sample', default=20, type=int)\nparser.add_argument('--begin-index', default=0, type=int)\n\nargs = parser.parse_args()\n\ndef seed_all(seed):\n random.seed(seed) # Python\n np.random.seed(seed) # cpu vars\n torch.manual_seed(seed) # cpu vars\n\n if torch.cuda.is_available(): \n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed) # gpu vars\n torch.backends.cudnn.deterministic = True #needed\n torch.backends.cudnn.benchmark = False\n\ndef make_data_loader(spec, tag=''):\n if spec is None:\n return None\n\n dataset = datasets.make(spec['dataset'])\n\n loader = DataLoader(dataset, batch_size=spec['batch_size'],\n shuffle=spec['shuffle'], num_workers=12, pin_memory=True)\n return loader\n\ndef make_data_loaders(config):\n val_loader = make_data_loader(config.get('val_dataset'), tag='val')\n return val_loader\n\nref_char_list = [0,1]\n\nconfig_str = f'''\nloss:\n name: local-loss\n args:\n lam_render: 1\n lam_reg: 1.e-6\n'''\n\nseed = args.seed\nseed_all(seed)\nprint('seed:', seed)\n\nconfig = yaml.load(config_str, Loader=yaml.FullLoader)\noutput_dir = args.outdir\nos.makedirs(output_dir, exist_ok=True)\nos.makedirs(os.path.join(output_dir, 'all_rec'), exist_ok=True)\n\nsv_file = torch.load(args.resume)\n\nsystem = models.make(sv_file['model'], load_sd=True).cuda()\nsystem.init()\nsystem.eval()\nmodels.freeze(system)\n\nsidelength = 256\nloss_fn = losses.make(config['loss'])\nchar_nums = 52\n\nn_sample = args.n_sample\nsample_begin = args.begin_index\n\nwith open(os.path.join(output_dir, 'seed.txt'), 'w') as f:\n f.write(str(seed))\n\nfor sample_i in tqdm(range(sample_begin, sample_begin + n_sample)):\n parent_dir = os.path.join(output_dir, f'{sample_i:04d}')\n os.makedirs(parent_dir, exist_ok=True)\n\n with torch.no_grad():\n tgt_char_idx = torch.arange(char_nums).cuda()\n z_style = torch.randn(1, 256).cuda() * 1.5\n torch.save(z_style.detach().cpu(), os.path.join(parent_dir, 'z_style.pt'))\n\n z_style = z_style.expand(char_nums, -1)\n emb_char = system.cls_token(tgt_char_idx)\n z = system.merge(torch.cat([z_style, emb_char], dim=-1))\n curves = system.decoder(z)\n\n img_rec = system.decode_image_from_latent_vector(z)['rec']\n\n n = curves.shape[0]\n curves_np = curves.detach().cpu().numpy()\n curves_np = (curves_np + 1) * sidelength / 2\n targets = img_rec\n\n torchvision.utils.save_image(targets, os.path.join(output_dir, 'all_rec', f'{sample_i:04d}_rec.png'), nrow=13)\n\n for i in range(n):\n path_prefix = os.path.join(output_dir, f'{sample_i:04d}', f'{i:02d}')\n if os.path.exists(path_prefix + '_init.svg'):\n continue\n\n target = targets[i, 0]\n utils.tensor_to_image(img_rec[i, 0], path_prefix + '_rec.png')\n\n curve_np = curves_np[i]\n d_string_list = [models.gutils.path_d_from_control_points(cp, xy_flip=True) for cp in curve_np]\n path, d_string = refine_svg.merge_d_string(d_string_list)\n\n cps_list = refine_svg.convert_path_to_control_points(path, pruned=True)\n\n if len(cps_list) == 0:\n continue\n refine_svg.write_path_to_svg(cps_list, path_prefix + '_init.svg')\n continue","repo_name":"thuliu-yt16/dualvector","sub_path":"eval/sample_font.py","file_name":"sample_font.py","file_ext":"py","file_size_in_byte":4072,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"}
+{"seq_id":"7837676102","text":"# -*- coding:utf-8 -*-\n#\n# author: yangmqglobe\n# file: accessible.py\n# time: 2021/02/16\nfrom argparse import ArgumentParser\nimport pysam\n\nFRAGMENT = {\n 99: 'reference_start', 147: 'reference_end', 163: 'reference_start', 83: 'reference_start'\n}\n\nSHIFT_FACTOR = {99: 4, 147: -5, 163: 4, 83: -5}\n\n\ndef read_chromosome_sizes(filename):\n chromosome_sizes = dict()\n with open(filename) as f:\n for line in f:\n chromosome, sizes = line.strip().split('\\t')[:2]\n chromosome_sizes[chromosome] = int(sizes)\n return chromosome_sizes\n\n\ndef shift(read, chromosome_sizes, length=25):\n try:\n shift_pos = getattr(\n read, FRAGMENT[read.flag]) + SHIFT_FACTOR[read.flag]\n except KeyError:\n return None\n strand = '-' if read.is_reverse else '+'\n shift_start = max(shift_pos - length, 0)\n shift_end = min(shift_pos + length, chromosome_sizes[read.reference_name])\n return read.reference_name, shift_start, shift_end, '.', '.', strand\n\n\ndef run(in_bam, out_bed, chromosome_sizes_file, include=2, exclude=1024, mapq=10, length=25):\n chromosome_sizes = read_chromosome_sizes(chromosome_sizes_file)\n with pysam.AlignmentFile(in_bam, 'rb') as bam, open(out_bed, 'w') as bed:\n for chrom in chromosome_sizes.keys():\n for read in bam.fetch(chrom):\n if not read.flag & include:\n continue\n if read.flag & exclude:\n continue\n if read.mapq < mapq:\n continue\n record = shift(read, chromosome_sizes, length)\n if record is not None:\n bed.write('{}\\t{}\\t{}\\t{}\\t{}\\t{}\\n'.format(*record))\n\n\ndef main():\n parser = ArgumentParser(\n description='Shift reads to cut sizes for ATAC-Seq')\n parser.add_argument(\n '-l', dest='length', help='extend length of cut sizes', default=25, type=int\n )\n parser.add_argument(\n '-f', dest='include', help='only include reads with all of the FLAGs in INT present',\n default=2\n )\n parser.add_argument(\n '-F', dest='exclude', help='only include reads with none of the FLAGS in INT present',\n default=1028\n )\n parser.add_argument(\n '-q', dest='mapq', help='only include reads with mapping quality >= INT', default=10, type=int\n )\n parser.add_argument('', help='chromosome sizes file')\n parser.add_argument('', help='input bam file')\n parser.add_argument('', help='output bed file')\n args = vars(parser.parse_args())\n run(\n args[''], args[''], args[''],\n args['include'], args['exclude'], args['length']\n )\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"suda-huanglab/circlehunter","sub_path":"tools/accessible.py","file_name":"accessible.py","file_ext":"py","file_size_in_byte":2773,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"73168249362","text":"\"\"\"Configuration for the dj_core_drf app.\"\"\"\nfrom datetime import timedelta\n\nfrom dj_core.config import Config as BaseConfig, DefaultProxy\n\n\nclass Config(BaseConfig):\n \"\"\"Override config to inject our defaults.\"\"\"\n\n defaults = BaseConfig.defaults.copy()\n defaults.update({\n 'ACCOUNT_AUTHENTICATION_METHOD': 'email',\n 'ACCOUNT_EMAIL_REQUIRED': True,\n 'ACCOUNT_EMAIL_VERIFICATION': 'none',\n 'ACCOUNT_USER_MODEL_USERNAME_FIELD': None,\n 'ACCOUNT_USERNAME_REQUIRED': False,\n 'ACCOUNT_REGISTRATION': 'enabled',\n 'REST_USE_JWT': True,\n 'SWAGGER_SETTINGS': {'api_version': '1'},\n 'JWT_AUTH': {\n 'JWT_EXPIRATION_DELTA': timedelta(hours=24),\n 'JWT_AUTH_HEADER_PREFIX': 'Token',\n },\n 'REST_FRAMEWORK': DefaultProxy({}, 'get_drf_settings'),\n 'REST_AUTH_SERIALIZERS': DefaultProxy({}, 'get_rest_auth_serializers'),\n 'REST_AUTH_REGISTER_SERIALIZERS':\n DefaultProxy({}, 'get_rest_auth_register_serializers'),\n })\n defaults.INSTALLED_APPS_REQUIRED = [\n 'dj_core_drf',\n 'rest_framework',\n 'rest_framework.authtoken',\n 'rest_framework_swagger',\n 'rest_auth',\n 'allauth',\n 'allauth.account',\n 'allauth.socialaccount',\n 'rest_auth.registration',\n 'django_filters',\n ] + defaults.INSTALLED_APPS_REQUIRED\n defaults.INSTALLED_APPS_OPTIONAL += [\n 'revproxy',\n ]\n\n # pylint: disable=unused-argument,no-self-use\n def get_drf_settings(self, settings):\n \"\"\"Return a default settings dict for DRF.\"\"\"\n return {\n 'DEFAULT_PERMISSION_CLASSES': [\n 'rest_framework.permissions.IsAuthenticatedOrReadOnly'\n ],\n 'DEFAULT_AUTHENTICATION_CLASSES': [\n 'rest_framework_jwt.authentication.JSONWebTokenAuthentication',\n 'rest_framework.authentication.BasicAuthentication',\n 'rest_framework.authentication.SessionAuthentication',\n ],\n 'DEFAULT_PAGINATION_CLASS':\n 'dj_core_drf.pagination.ThousandMaxLimitOffsetPagination',\n 'DEFAULT_FILTER_BACKENDS': [\n 'rest_framework_filters.backends.DjangoFilterBackend',\n ],\n 'DEFAULT_METADATA_CLASS':\n 'dj_core_drf.metadata.ModelChoicesMetadata',\n }\n\n # pylint: disable=unused-argument,no-self-use\n def get_rest_auth_serializers(self, settings):\n \"\"\"Return a default settings dict for rest_auth.\"\"\"\n return {\n 'USER_DETAILS_SERIALIZER':\n 'dj_core_drf.serializers.UserDetailsSerializer',\n 'PASSWORD_RESET_SERIALIZER':\n 'dj_core_drf.serializers.PasswordResetSerializer',\n 'LOGIN_SERIALIZER': 'dj_core_drf.serializers.LoginSerializer',\n }\n\n # pylint: disable=unused-argument,no-self-use\n def get_rest_auth_register_serializers(self, settings):\n \"\"\"Return a default settings dict for rest_auth.registration.\"\"\"\n return {\n 'REGISTER_SERIALIZER':\n 'dj_core_drf.serializers.RegisterSerializer',\n }\n","repo_name":"ionata/dj-core-drf","sub_path":"dj_core_drf/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16186785053","text":"import logging\nimport random\n\nfrom boltons.iterutils import chunked\nimport interactions\nfrom sqlalchemy import and_\nfrom sqlalchemy.exc import IntegrityError\n\nfrom bridge_discord import datastore\nfrom bridge_discord.extensions import utilities\n\n\ndef bbo_user_option_factory(description):\n return interactions.Option(\n name=\"bbo_user\",\n description=description,\n type=interactions.OptionType.STRING\n )\n\n\nclass TeamRRManagerExtension(interactions.Extension):\n @interactions.extension_command(\n name=\"create\",\n description=\"Creates a team round robin tournament\",\n default_member_permissions=interactions.Permissions.MANAGE_MESSAGES,\n options=[\n interactions.Option(\n name=\"tournament_name\",\n description=\"The name of the new tournament.\",\n type=interactions.OptionType.STRING,\n required=True\n )\n ]\n )\n async def create(self, ctx, tournament_name):\n with datastore.Session() as session:\n session.add(\n datastore.TeamRRTournament(state=datastore.TournamentState.SIGNUP, tournament_name=tournament_name)\n )\n session.commit()\n await ctx.send(\"Successfully created a new team round robin tournament!\", ephemeral=True)\n\n @interactions.extension_command(\n name=\"signup\",\n description=\"Sign up for the currently active team round robin tournament.\",\n options=[\n bbo_user_option_factory(\"BBO user if signing up for someone else.\"),\n ]\n )\n @utilities.SessionedGuard(\n active_tournament=utilities.assert_tournament_exists,\n bbo_user=utilities.assert_bbo_rep\n )\n async def signup(self, ctx, *, bbo_user=None):\n guard = self.signup.coro\n if guard.active_tournament.state is not datastore.TournamentState.SIGNUP:\n await utilities.failed_guard(ctx, \"The active tournament is not currently accepting signups.\")\n guard.session.add(\n datastore.TeamRREntry(\n tournament_id=guard.active_tournament.tournament_id,\n bbo_user=guard.bbo_user\n )\n )\n try:\n guard.session.commit()\n await ctx.send(\"Signed up for the upcoming tournament!\", ephemeral=True)\n except IntegrityError:\n await ctx.send(\"You are already signed up for the upcoming tournament.\", ephemeral=True)\n\n @interactions.extension_command(\n name=\"drop\",\n description=\"Drop registration from the currently active team round robin tournament.\",\n options=[\n bbo_user_option_factory(\"BBO user if dropping for someone else.\"),\n ]\n )\n @utilities.SessionedGuard(\n active_tournament=utilities.assert_tournament_exists,\n bbo_user=utilities.assert_bbo_rep\n )\n async def drop(self, ctx, *, bbo_user=None):\n guard = self.drop_tournament.coro\n entry_model = guard.session.get(datastore.TeamRREntry, (guard.active_tournament.tournament_id, guard.bbo_user))\n if not entry_model:\n await ctx.send(\n f\"{guard.bbo_user} is not currently signed up for the upcoming tournament.\",\n ephemeral=True\n )\n guard.session.delete(entry_model)\n guard.session.commit()\n await ctx.send(\"Successfully dropped out from the upcoming tournament!\", ephemeral=True)\n\n @interactions.extension_command(\n name=\"info\",\n description=\"Displays information about the currently active team round robin tournament.\",\n )\n @utilities.SessionedGuard(active_tournament=utilities.assert_tournament_exists)\n async def info(self, ctx):\n guard = self.tournament_info.coro\n\n profile_embed = interactions.Embed(title=f\"Team RR Tournament: {guard.active_tournament.tournament_name}\")\n profile_embed.add_field(\n name=\"Tournament Details\",\n value=\"\\n\".join(\n f\"{key}: {value}\"\n for key, value in {\n \"Challenge format\": guard.active_tournament.scoring_method,\n \"Boards per match\": guard.active_tournament.segment_boards,\n \"Created at\": guard.active_tournament.created_at\n }.items()\n )\n )\n if guard.active_tournament.state is datastore.TournamentState.SIGNUP:\n participant_strings = [\n await entry_model.bbo_profile.to_str_with_linked_mention(self.client)\n for entry_model in guard.active_tournament.participants\n ]\n profile_embed.add_field(name=\"Currently Registered Players\", value=\"\\n\".join(participant_strings))\n elif guard.active_tournament.state is datastore.TournamentState.STARTED:\n for team_number in range(guard.active_tournament.number_of_teams):\n team_members = guard.session.query(\n datastore.TeamRREntry\n ).join(datastore.BBOProfile).filter(\n and_(\n datastore.TeamRREntry.tournament_id == guard.active_tournament.tournament_id,\n datastore.TeamRREntry.team_number == team_number\n )\n ).order_by(datastore.BBOProfile.conservative_mmr_estimate).all()\n profile_embed.add_field(\n name=f\"Team {team_number + 1}\",\n value=\"\\n\".join(f\"•{member.bbo_user}\" for member in team_members),\n inline=team_number % 3 != 0 or team_number == 0\n )\n await ctx.send(embeds=profile_embed)\n\n @interactions.extension_command(\n name=\"start\",\n description=\"Ends the signup phase, starts matches.\",\n default_member_permissions=interactions.Permissions.MANAGE_MESSAGES\n )\n @utilities.SessionedGuard(active_tournament=utilities.assert_tournament_exists)\n async def start(self, ctx):\n guard = self.start_tournament.coro\n\n sorted_participants = sorted(\n guard.active_tournament.participants, key=lambda p: p.bbo_profile.conservative_mmr_estimate, reverse=True)\n for pot in chunked(sorted_participants, guard.active_tournament.number_of_teams):\n random.shuffle(pot)\n for ix, participant in enumerate(pot):\n participant.team_number = ix\n guard.active_tournament.state = datastore.TournamentState.STARTED\n guard.session.commit()\n await ctx.send(\"Tournament has been started and teams have been assigned.\", ephemeral=True)\n\n\ndef setup(client):\n datastore.setup_connection()\n with datastore.Session() as session:\n active_tournament = session.query(\n datastore.TeamRRTournament\n ).where(datastore.TeamRRTournament.state != datastore.TournamentState.INACTIVE).all()\n if active_tournament and len(active_tournament) > 1:\n logging.critical(\"There can only be one active Team RR tournament.\")\n raise ValueError(\"Failed setup assumptions.\")\n\n TeamRRManagerExtension(client)\n","repo_name":"edgy-raven/bridge-discord","sub_path":"bridge_discord/extensions/tournament.py","file_name":"tournament.py","file_ext":"py","file_size_in_byte":7105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38506164446","text":"from django.db.models import Q\nfrom rest_framework import permissions, viewsets, generics, status\nfrom django.core.exceptions import ValidationError\nfrom django.contrib.auth import get_user_model\nfrom .models import Offer, OfferStatus, OfferType, Contract\nfrom apps.children.models import Child\nfrom apps.adverts.models import Advert, AdvertType\nfrom .serializers import OfferSerializer, ContractSerializer\nfrom .permissions import IsSenderOrReceiver\nfrom rest_framework.response import Response\nimport pickle\nimport base64\n# Create your views here.\n\n\nclass OfferViewSet(viewsets.ModelViewSet):\n\n serializer_class = OfferSerializer\n\n http_method_names = ['get', 'head', 'delete', 'post']\n\n permission_classes = [IsSenderOrReceiver]\n\n def perform_create(self, serializer):\n if self.request.data.get('recipient'):\n #Vulnerable to SQL injection, user has direct access to application SQL - Eivind\n #Tested with input (hei' OR 1=1--) wich returned all usernames in database.\n result = get_user_model().objects.raw(\n \"SELECT * from users_user WHERE username = '%s'\" % self.request.data['recipient'])\n\n if self.request.user.username == self.request.data['recipient'].strip():\n raise ValidationError(\"Cannot send offer to yourself\")\n\n if len(result) > 0: # Check if a user exist with the given username\n r = \"\"\n for u in result:\n r += u.username + \" \"\n serializer.save(\n recipient=r.strip(), sender=self.request.user.username)\n else:\n raise ValidationError(\"User does not exist\")\n else:\n raise ValidationError(\"Missing parameter: recipient\")\n\n def get_queryset(self):\n queryset = Offer.objects.filter(\n Q(sender=self.request.user.username) | Q(recipient=self.request.user.username))\n\n return queryset\n\n\nclass ContractViewSet(viewsets.ModelViewSet):\n\n serializer_class = ContractSerializer\n\n http_method_names = ['get', 'head']\n\n def get_queryset(self):\n queryset = Contract.objects.filter(\n Q(parent=self.request.user) | Q(sitter=self.request.user))\n\n return queryset\n\n\nclass FinishContractView(generics.GenericAPIView):\n permission_classes = [permissions.IsAuthenticated]\n\n def post(self, request):\n if request.data.get('contractId'):\n # Pickle could be voulnrable when deserializing data\n # Quote from https://davidhamann.de/2020/04/05/exploiting-python-pickle/:\n # \"Warning: The pickle module is not secure. Only unpickle data you trust\".\n cId = base64.b64decode(request.data.get('contractId'))\n cId = pickle.loads(cId)\n contract = Contract.objects.get(id=cId)\n if request.user == contract.parent:\n contract.finished = True\n contract.save()\n return Response({'success': True, 'message': 'Contract finished'}, status=status.HTTP_200_OK)\n\n return Response({'success': False, 'message': 'Only parents can mark the contract as finished.'}, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass AnswerOfferView(generics.GenericAPIView):\n # Missing permission class here, all users can accept others' guardian offers - Eivind\n\n def post(self, request):\n\n if request.data.get('offerId') and request.data.get('status'):\n\n offer = Offer.objects.get(id=request.data['offerId'])\n state = request.data['status']\n\n if offer.status != OfferStatus.PENDING:\n return Response({'success': False, 'message': 'Offer already answered'}, status=status.HTTP_400_BAD_REQUEST)\n\n if state == 'D': # Declined offer\n offer.status = OfferStatus.DECLINED\n offer.save()\n return Response({'success': True, 'message': 'Offer declined'}, status=status.HTTP_200_OK)\n\n if state == 'A': # Accepted offer\n offer.status = OfferStatus.ACCEPTED\n\n if offer.offerType == OfferType.GUARDIAN_OFFER:\n parent = get_user_model().objects.get(username=offer.recipient)\n children = Child.objects.filter(parent=parent)\n sender = get_user_model().objects.get(username=offer.sender)\n\n for child in children:\n child.guardians.add(sender)\n child.save()\n if offer.offerType == OfferType.JOB_OFFER:\n ad = offer.advert # Advert.objects.get(offer.advert)\n if ad.advertType == AdvertType.IS_SITTER:\n parent = get_user_model().objects.get(username=offer.sender)\n sitter = get_user_model().objects.get(username=offer.recipient)\n\n if ad.advertType == AdvertType.NEED_SITTER:\n parent = get_user_model().objects.get(username=offer.recipient)\n sitter = get_user_model().objects.get(username=offer.sender)\n\n contract = Contract(\n parent=parent, sitter=sitter, date=ad.date, start_time=ad.start_time, end_time=ad.end_time, content=ad.content)\n ad.save()\n contract.save()\n\n offer.save()\n return Response({'success': True, 'message': 'Offer accepted'}, status=status.HTTP_200_OK)\n\n return Response({'success': False, 'message': 'Missing offerId or invalid status'}, status=status.HTTP_400_BAD_REQUEST)\n","repo_name":"edalholt/TDT4237-trustedSitters-Vulnerable-Application","sub_path":"backend/apps/offers/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73349022480","text":"#!/usr/bin/env python\n#\n# Code to plot the quantitiave comparisons of the Y image slices\n# between different codes\n#\n# 17 Jun 2016: written (Karl Gordon)\n#\nimport os.path\nimport argparse\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport matplotlib as mpl\n\nfrom astropy.table import Table\n\ndef plot_indiv_slice(ax, tau, waves, angles, run_tag,\n dcol, fontsize=16, irwaves=False):\n\n sym = ['-','--','-.',':']\n for n, wave in enumerate(waves):\n for k, angle in enumerate(angles):\n\n tab_name = 'dat/slab_t' + tau + '_i' + angle + '_w' + wave + \\\n '_image_comp' + run_tag\n \n cur_table = Table.read(tab_name+'.dat',\n format='ascii.commented_header')\n\n # initialize and get model names\n if n == 0 and k == 0:\n mvals = np.array(cur_table['model'])\n pvals = np.empty((len(mvals),len(angles)))\n \n pvals[:,k] = np.array(cur_table['cut1_stddev'])\n\n not_used_models = [\"DART-ray\",\"TRADING\"]\n for i, cmodel in enumerate(mvals):\n trans = 1.0\n if (tau == '1e1') and irwaves:\n if cmodel in not_used_models:\n trans = 0.25\n ax.plot(angles, pvals[i,:], dcol[cmodel]+sym[n], alpha=trans,\n label=cmodel + r' $\\lambda = ' + wave + '$')\n\n indxs = np.arange(len(mvals))\n if (tau == '1e1') and irwaves:\n for nmod in not_used_models:\n nindxs, = np.where(mvals[indxs] != nmod)\n indxs = indxs[nindxs]\n\n print('Y slice: ', tau, wave, np.max(pvals[indxs,:]))\n \n #sones = np.full((len(angles)),1.0)\n #ax.plot(angles,sones,'k--')\n #ax.plot(angles,5.0*sones,'k-.')\n\n ax.set_yscale('log') \n ax.set_ylim(0.5e-1,1e4)\n ax.set_ylabel(r'$\\bar{\\Delta}$ [%]')\n ax.set_xlabel(r'$\\theta$')\n\n ax.set_title(r'$\\tau_z =$ ' + tau + ' (Y slice)')\n\n return mvals\n\n #ax.legend(loc=3)\n\ndef plot_slice_all_taus(args, good_angles, waves, ax, dispstr,\n fontsize=14, plegend=True, irwaves=False):\n\n # setup the plot\n taus = ['1e0','1e1']\n tax = ax\n\n col = ['b','g','r','c','m','y','k']\n models = ['CRT','DART-ray','DIRTY','Hyperion','SKIRT',\n 'TRADING','SOC']\n dcol = dict(zip(models, col))\n\n for k, cur_ax in enumerate(tax):\n pmodels = plot_indiv_slice(cur_ax, taus[k], waves, good_angles, \n dispstr,dcol, irwaves=irwaves)\n if k == 0:\n save_pmodels = pmodels\n\n # Create two custom legends (more compact)\n\n # models\n if plegend:\n arts = [plt.Line2D((0,1),(0,0), color=dcol[cmodel], linestyle='-') \n for cmodel in save_pmodels]\n leg1 = tax[0].legend(arts,\n save_pmodels,\n fontsize=1.25*fontsize,\n loc='upper left',\n ncol=2)\n leg1.get_frame().set_linewidth(2)\n\n # Add the legend manually to the current Axes.\n #tax[0].add_artist(leg1)\n\n # waves\n if args.sto and not args.uvopt:\n arts = [plt.Line2D((0,1),(0,0), color='k', linestyle='-'),\n plt.Line2D((0,1),(0,0), color='k', linestyle='--'),\n plt.Line2D((0,1),(0,0), color='k', linestyle='-.')]\n labs = [r'$\\lambda = '+waves[0]+'$',\n r'$\\lambda = '+waves[1]+'$',\n r'$\\lambda = '+waves[2]+'$']\n else:\n arts = [plt.Line2D((0,1),(0,0), color='k', linestyle='-'),\n plt.Line2D((0,1),(0,0), color='k', linestyle='--')]\n labs = [r'$\\lambda = '+waves[0]+'$',\n r'$\\lambda = '+waves[1]+'$']\n \n leg2 = tax[1].legend(arts, labs,\n title='Y Slice',\n fontsize=1.25*fontsize,\n loc='upper center', \n bbox_to_anchor=(-0.05,0.97))\n leg2.get_title().set_fontsize(fontsize*1.5)\n leg2.get_frame().set_linewidth(2)\n\n # make y axis nice\n\n tax[1].yaxis.tick_right()\n tax[1].yaxis.set_ticks_position('both')\n tax[1].yaxis.set_label_position(\"right\")\n\nif __name__ == \"__main__\":\n\n good_angles = ['000','030','060','090','120','150','180']\n\n # commandline parser\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--uvopt\", action=\"store_true\",\n help=\"Display UV/optical results\")\n parser.add_argument(\"--eff\", action=\"store_true\",\n help=\"Display results for the effective grain \" + \\\n \"emission approximation [Default]\")\n parser.add_argument(\"--equ\", action=\"store_true\",\n help=\"Display results for the equilibrium heating \" + \\\n \"only approximation\")\n parser.add_argument(\"--sto\", action=\"store_true\",\n help=\"Display results for the full heating solution \" + \\\n \"(equilibrium + non-equilibrium)\")\n parser.add_argument(\"--eps\", help=\"Save the plot as an \" + \\\n \"encapsulated file\",\n action=\"store_true\")\n parser.add_argument(\"--pdf\", help=\"Save the plot as a \" + \\\n \"portable document file file\",\n action=\"store_true\")\n parser.add_argument(\"--png\", help=\"Save the plot as a \" + \\\n \"portable network graphics file\",\n action=\"store_true\")\n args = parser.parse_args()\n\n # setup the plots\n fontsize = 14\n font = {'size' : fontsize}\n\n mpl.rc('font', **font)\n\n mpl.rc('lines', linewidth=2)\n mpl.rc('axes', linewidth=2)\n mpl.rc('xtick.major', width=2)\n mpl.rc('ytick.major', width=2)\n\n if args.uvopt:\n waves = ['000.15','000.53']\n irwaves = False\n else:\n if args.sto:\n waves = ['008.11','023.10','151.99']\n else:\n waves = ['035.11','151.99']\n irwaves = True\n\n dispstr = 'eff'\n tagstr = ''\n if args.equ:\n dispstr = 'equ'\n tagstr = '_equ'\n elif args.sto:\n dispstr = 'sto'\n tagstr = '_sto'\n\n fig, ax = plt.subplots(ncols=2,figsize=(15,6))\n\n plot_slice_all_taus(args, good_angles, waves, ax, tagstr)\n\n fig.tight_layout()\n\n save_name = 'slab_qcomp_image_' + dispstr\n\n if args.uvopt:\n save_name += '_uvopt'\n else:\n save_name += '_ir'\n\n if args.png:\n fig.savefig(save_name+'.png')\n fig.savefig(save_name+'_small.png',dpi=11)\n elif args.eps:\n fig.savefig(save_name+'.eps')\n elif args.pdf:\n fig.savefig(save_name+'.pdf')\n else:\n plt.show()\n\n plt.close(fig)\n","repo_name":"karllark/trust_slab","sub_path":"plot_slab_quant_comp_image.py","file_name":"plot_slab_quant_comp_image.py","file_ext":"py","file_size_in_byte":6839,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"8280842848","text":"import numpy as np\nimport casadi as ca\nimport csv\n\nclass Near():\n def __init__(self, path=None):\n \n opt_option = {\n 'verbose': False,\n 'ipopt.tol': 1e-2,\n 'ipopt.acceptable_tol': 1e-2,\n 'ipopt.max_iter': 20,\n # 'ipopt.warm_start_init_point': 'yes',\n 'ipopt.print_level': 0,\n }\n\n if path==None:\n t = ca.SX.sym('t')\n traj_ploy = ca.SX.sym(\"traj_ploy\", 12, 2)\n dts = ca.SX.sym(\"dts\",2)\n pos = ca.SX.sym('pos', 3)\n\n obj = (self.traj_ploy(traj_ploy, dts, t)-pos).T@(self.traj_ploy(traj_ploy, dts, t)-pos)\n\n nlp_dect = {\n 'f': obj,\n 'x': t,\n 'p': ca.vertcat(traj_ploy[:,0], traj_ploy[:,1], dts, pos)\n }\n self._opt_solver = ca.nlpsol('opt', 'ipopt', nlp_dect, opt_option)\n else:\n self._opt_solver = ca.nlpsol('opt', 'ipopt', path, opt_option)\n\n def solve(self, coef1, coef2, dt1, dt2, pos):\n p = np.zeros(12*2+2+3)\n p[:12] = coef1\n p[12:24] = coef2\n p[24] = dt1\n p[25] = dt2\n p[26:] = pos\n\n res = self._opt_solver(\n x0=0,\n p=p\n )\n\n return res['x'].full().flatten()\n\n\n def traj_ploy(self, ploy_coef, dts, t):\n p=0\n\n a0=ploy_coef[:3,0]\n a1=ploy_coef[3:6,0]\n a2=ploy_coef[6:9,0]\n a3=ploy_coef[9:12,0]\n dt = dts[0]+t\n dtdt = dt*dt\n dtdtdt = dtdt*dt\n p+= (t<=0)*( a0+a1*dt+a2*dtdt+a3*dtdtdt )\n \n a0=ploy_coef[:3,1]\n a1=ploy_coef[3:6,1]\n a2=ploy_coef[6:9,1]\n a3=ploy_coef[9:12,1]\n dt = t\n dtdt = dt*dt\n dtdtdt = dtdt*dt\n p+= (t>0)*( a0+a1*dt+a2*dtdt+a3*dtdtdt )\n\n return p\n\nclass Trajectory():\n def __init__(self, csv_f=None, near_path=None):\n if csv_f != None:\n t = []\n pos = []\n vel = []\n quaternion = []\n omega = []\n self._N = 0\n ploynomials = []\n u = []\n acc = []\n with open(csv_f, 'r') as f:\n traj_reader = csv.DictReader(f)\n for s in traj_reader:\n t.append(float(s['t']))\n pos.append([ float(s[\"p_x\"]), float(s[\"p_y\"]), float(s[\"p_z\"]) ])\n vel.append([ float(s[\"v_x\"]), float(s[\"v_y\"]), float(s[\"v_z\"]), np.sqrt(float(s[\"v_x\"])*float(s[\"v_x\"])+float(s[\"v_y\"])*float(s[\"v_y\"])+float(s[\"v_z\"])*float(s[\"v_z\"])) ])\n quaternion.append([ float(s[\"q_w\"]), float(s[\"q_x\"]), float(s[\"q_y\"]), float(s[\"q_z\"]) ])\n omega.append([ float(s[\"w_x\"]), float(s[\"w_y\"]), float(s[\"w_z\"]) ])\n if \"u_1\" in s.keys():\n u.append([float(s[\"u_1\"]), float(s[\"u_2\"]), float(s[\"u_3\"]), float(s[\"u_4\"])])\n if \"a_x\" in s.keys():\n acc.append([float(s[\"a_x\"]), float(s[\"a_y\"]), float(s[\"a_z\"])])\n \n self._t = np.array(t)\n self._pos = np.array(pos)\n self._vel = np.array(vel)\n self._quaternion = np.array(quaternion)\n self._omega = np.array(omega)\n self._u = np.array(u)\n self._acc = np.array(acc)\n \n self._N = self._t.shape[0]-1\n assert(self._N>0)\n self._dt = self._t[1:]-self._t[:-1]\n for i in range(self._N):\n a0, a1, a2, a3 = self._ploynomial(self._pos[i], self._pos[i+1], self._vel[i,:3], self._vel[i+1,:3], self._dt[i])\n ploynomials.append( np.concatenate((a0,a1,a2,a3)) )\n self._ploynomials = np.array(ploynomials)\n\n else:\n self._t = np.array([])\n self._pos = np.array([])\n self._vel = np.array([])\n self._quaternion = np.array([])\n self._omega = np.array([])\n self._u = np.array([])\n self._acc = np.array([])\n self._N = 0\n self._dt = np.array([])\n self._ploynomials = np.array([])\n \n self._near = Near(near_path)\n \n def load_data(self, pos, vel, quat, omega, dt):\n self._pos = pos\n self._quaternion = quat\n self._omega = omega\n self._dt = dt\n self._N = self._pos.shape[0]-1\n\n self._vel = np.zeros([self._N+1, 4])\n self._vel[0,:3] = vel[0,:]\n self._vel[0,3] = np.linalg.norm(vel[0,:])\n self._t = np.zeros([self._N+1])\n self._t[0] = 0\n ploynomials = []\n for i in range(self._N):\n self._vel[i+1,:3] = vel[i+1,:]\n self._vel[i+1,3] = np.linalg.norm(vel[i+1,:])\n self._t[i+1] = self._t[i]+dt[i]\n \n a0, a1, a2, a3 = self._ploynomial(self._pos[i], self._pos[i+1], self._vel[i,:3], self._vel[i+1,:3], self._dt[i])\n ploynomials.append( np.concatenate((a0,a1,a2,a3)) )\n self._ploynomials = np.array(ploynomials)\n \n\n def __getitem__(self, idx):\n traj = Trajectory()\n traj._t = self._t[idx]\n traj._pos = self._pos[idx]\n traj._vel = self._vel[idx]\n traj._quaternion = self._quaternion[idx]\n traj._omega = self._omega[idx]\n traj._dt = traj._t[1:] - traj._t[:-1]\n traj._ploynomials = self._ploynomials[idx]\n if self._u.shape[0]!=0:\n traj._u = self._u[idx]\n if self._acc.shape[0]!=0:\n traj._acc = self._acc[idx]\n traj._N = traj._t.shape[0]-1\n return traj\n\n def _ploynomial(self, p1, p2, v1, v2, dt):\n a0 = p1\n a1 = v1\n a2 = (3*p2-3*p1-2*v1*dt-v2*dt)/dt/dt\n a3 = (2*p1-2*p2+v2*dt+v1*dt)/dt/dt/dt\n return a0, a1, a2, a3\n \n def _seg_pos_vel(self, poly_coef, dt):\n a0 = poly_coef[:3]\n a1 = poly_coef[3:6]\n a2 = poly_coef[6:9]\n a3 = poly_coef[9:12]\n\n dtdt = dt*dt\n dtdtdt = dtdt*dt\n pos = a0 + a1*dt + a2*dtdt +a3*dtdtdt\n vel = a1 + 2*a2*dt + 3*a3*dtdt\n return pos, vel\n\n def sample(self, n, pos):\n idx = np.argmin(np.linalg.norm(self._pos-pos, axis=1))\n idx -= 1 #\n\n traj_seg = np.zeros((n, 3))\n traj_v_seg = np.zeros((n, 3))\n traj_dt_seg = np.zeros(n)\n traj_polynomial_seg = np.zeros((n, 12))\n for i in range(n):\n traj_seg[i,:] = self._pos[(idx+int(i*1.0))%self._N]\n traj_v_seg[i,:] = self._vel[(idx+int(i*1.0))%self._N,:3]\n traj_dt_seg[i] = self._dt[(idx+int(i*1.0))%self._N]\n traj_polynomial_seg[i, :] = self._ploynomials[(idx+int(i*1.0))%self._N]\n\n return traj_seg, traj_v_seg, traj_dt_seg, traj_polynomial_seg\n \n def sample_dt_reset(self):\n self._sample_time_idx = 0\n self._sample_time = 0\n \n def sample_t(self, time_dt, dt, n, loop=True):\n self._sample_time += time_dt\n \n while self._sample_time - self._dt[self._sample_time_idx]>0:\n self._sample_time -= self._dt[self._sample_time_idx]\n self._sample_time_idx += 1\n if self._sample_time_idx >= self._N:\n if loop:\n self._sample_time_idx = 0\n else:\n self._sample_time_idx = self._N-1\n break\n \n traj_seg = np.zeros((n, 3))\n traj_v_seg = np.zeros((n, 3))\n for i in range(n):\n s_time = self._sample_time + (i+1)*dt\n s_idx = self._sample_time_idx\n while s_time - self._dt[s_idx]>0:\n s_time -= self._dt[s_idx]\n s_idx += 1\n if s_idx >= self._N:\n if loop:\n s_idx = 0\n else:\n s_idx = self._N-1\n break\n \n traj_seg[i,:], traj_v_seg[i,:] = self._seg_pos_vel(self._ploynomials[s_idx], s_time)\n \n return traj_seg, traj_v_seg\n \n def distance(self, pos):\n idx = np.argmin(np.linalg.norm(self._pos-pos, axis=1))\n if idx == 0:\n idx = 1\n ddt = self._near.solve(self._ploynomials[idx-1], self._ploynomials[idx], self._dt[idx-1], self._dt[idx], pos)\n idx_dt = 0\n if ddt<0:\n idx_dt = self._dt[idx-1]+ddt\n idx = idx-1\n else:\n idx_dt = ddt\n \n p, v = self._seg_pos_vel(self._ploynomials[idx], idx_dt)\n return np.linalg.norm(p-pos)\n \n def divide_loops(self, pos):\n loop_idx = []\n flag1 = 0\n flag2 = 0\n for i in range(self._N):\n if np.linalg.norm(self._pos[i]-pos)< 1:\n if flag1 == 0:\n l = np.linalg.norm(self._pos[i] - pos)\n flag1 = 1\n else:\n if l 0:\n warnings.warn(f\"There are {df.index.duplicated().sum()} duplicates in the index after cleaning\", RuntimeWarning)\n\n return df\n\n\ndef save_data(df, database_filename):\n \"\"\"\n Save messages and categories in dataframe to SQLite DB table 'messages'\n :param df: DataFrame with messages and category columns\n :param database_filename: Filename of the SQLite DB\n :return:\n \"\"\"\n engine = create_engine(f'sqlite:///{database_filename}')\n df.to_sql('messages', engine, index=False, if_exists='replace')\n\n\ndef main():\n if len(sys.argv) == 4:\n\n messages_filepath, categories_filepath, database_filepath = sys.argv[1:]\n\n print('Loading data...\\n MESSAGES: {}\\n CATEGORIES: {}'\n .format(messages_filepath, categories_filepath))\n messages, categories = load_data(messages_filepath, categories_filepath)\n\n print('Cleaning data...')\n df = clean_data(messages, categories)\n \n print('Saving data...\\n DATABASE: {}'.format(database_filepath))\n save_data(df, database_filepath)\n \n print('Cleaned data saved to database!')\n \n else:\n print('Please provide the filepaths of the messages and categories '\\\n 'datasets as the first and second argument respectively, as '\\\n 'well as the filepath of the database to save the cleaned data '\\\n 'to as the third argument. \\n\\nExample: python process_data.py '\\\n 'disaster_messages.csv disaster_categories.csv '\\\n 'DisasterResponse.db')\n\n\nif __name__ == '__main__':\n main()","repo_name":"schilli/disaster-response","sub_path":"data/process_data.py","file_name":"process_data.py","file_ext":"py","file_size_in_byte":4087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39710652127","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 14 16:29:28 2017\n\n@author: Tim\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport Praktikum\n\n\ndata = Praktikum.lese_lab_datei('lab/Parallell_unendlich2.lab')\n\nf = data[:,-2]\nphi = data[:,-7]\nI = data[:,3]\nZ = data[:,-1]\nIC = data[:,-3]\nIL = data[:,-6]\n\n\nplt.figure(0)\nax1 = plt.subplot(111)\nplt.plot(f,I,label='I')\nplt.ylabel('I/A')\nplt.xlabel('f/Hz')\n\nax2 = ax1.twinx()\nplt.plot(f,Z,color = 'r',label='Z')\nplt.ylabel('Z/$\\Omega$')\n\nplt.show()\n\nplt.figure(1)\nplt.plot(f,phi,marker='.')\nplt.ylabel('$\\phi$/deg')\nplt.xlabel('f/Hz')\nplt.axhline(0)\nplt.show()\n\nplt.figure()\nplt.plot(f,IC)\nplt.plot(f,IL)\nplt.plot(f,I)\nplt.show()\n\n\n","repo_name":"thewhitecat/grundpraktikum1","sub_path":"grundpraktikum2/E-Lehre/Gruppe B/Parallel.py","file_name":"Parallel.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42516082715","text":"########################################################################################################################\n#\n# Create service API call\n#\n# Date created: 01.06.2020\n# Date last modified: 10.02.2021\n#\n# __author__ = Michel Schwandner (schwandner@geoville.com)\n# __version__ = 21.02\n#\n########################################################################################################################\n\nfrom error_classes.http_error_400.http_error_400 import BadRequestError\nfrom error_classes.http_error_500.http_error_500 import InternalServerErrorAPI\nfrom error_classes.http_error_503.http_error_503 import ServiceUnavailableError\nfrom flask_restx import Resource\nfrom geoville_ms_database.geoville_ms_database import execute_database\nfrom geoville_ms_logging.geoville_ms_logging import gemslog, LogLevel\nfrom init.init_env_variables import database_config_file, database_config_section_api\nfrom init.namespace_constructor import crm_namespace as api\nfrom lib.auth_header import auth_header_parser\nfrom lib.database_helper import check_service_name_existence\nfrom lib.hashing_helper import generate_service_id_hash\nfrom models.models_crm.service_models.service_models import service_creation_model, service_id_model\nfrom models.models_error.http_error_400 import error_400_model\nfrom models.models_error.http_error_401 import error_401_model\nfrom models.models_error.http_error_403 import error_403_model\nfrom models.models_error.http_error_500 import error_500_model\nfrom models.models_error.http_error_503 import error_503_model\nfrom oauth.oauth2 import require_oauth\nimport traceback\n\n\n########################################################################################################################\n# Resource definition for the create customer API call\n########################################################################################################################\n\n@api.expect(service_creation_model)\n@api.header('Content-Type', 'application/json')\nclass CreateService(Resource):\n \"\"\" Class for handling the POST request\n\n This class defines the API call for the create customer script. The class consists of one method which accepts a\n POST request. For the POST request a JSON with several parameters is required and defined in the corresponding model.\n\n \"\"\"\n\n ####################################################################################################################\n # Method for handling the POST request\n ####################################################################################################################\n\n @require_oauth(['admin'])\n @api.expect(auth_header_parser)\n @api.response(201, 'Operation successful', service_id_model)\n @api.response(400, 'Validation Error', error_400_model)\n @api.response(401, 'Unauthorized', error_401_model)\n @api.response(403, 'Forbidden', error_403_model)\n @api.response(500, 'Internal Server Error', error_500_model)\n @api.response(503, 'Service Unavailable', error_503_model)\n def post(self):\n \"\"\" POST definition for creating a new service\n\n This method defines the handler for the POST request of the create service\n script. It returns a message wrapped into a dictionary about the process of the insertion operation.
\n\n
Description:\n \n\n
Request headers:\n \n Authorization: Bearer token in the format \"Bearer XXXX\"
\n\n
Request payload:\n \n service_name (str):
service_validity (str):
service_owner_geoville (str):
service_comment (str):
external (str):
\n\n
Result:\n \n\n \"\"\"\n\n try:\n req_args = api.payload\n gemslog(LogLevel.INFO, f'Request payload: {req_args}', 'API-create_service')\n\n if check_service_name_existence(req_args['service_name'], database_config_file, database_config_section_api):\n error = BadRequestError('Service name exists already', '', '')\n gemslog(LogLevel.WARNING, f\"'message': {error.to_dict()}\", 'API-create_service')\n return {'message': error.to_dict()}, 400\n\n service_comment = None if 'service_comment' not in req_args else req_args['service_comment']\n service_id = generate_service_id_hash(req_args['service_name'], req_args['service_owner_geoville'])\n\n db_query = \"\"\"INSERT INTO customer.services\n ( \n service_id, service_name, service_validity, service_owner_geoville, service_comment, external\n ) \n VALUES\n (\n %s, %s, %s, %s, %s, %s\n );\n \"\"\"\n\n execute_database(db_query, (service_id, req_args['service_name'], req_args['service_validity'],\n req_args['service_owner_geoville'], service_comment, req_args['external']),\n database_config_file, database_config_section_api, True)\n\n except KeyError as err:\n error = BadRequestError(f'Key error resulted in a BadRequest: {err}', api.payload, traceback.format_exc())\n gemslog(LogLevel.WARNING, f\"'message': {error.to_dict()}\", 'API-create_service')\n return {'message': error.to_dict()}, 400\n\n except AttributeError:\n error = ServiceUnavailableError('Could not connect to the database server', '', '')\n gemslog(LogLevel.ERROR, f\"'message': {error.to_dict()}\", 'API-create_service')\n return {'message': error.to_dict()}, 503\n\n except Exception:\n error = InternalServerErrorAPI('Unexpected error occurred', api.payload, traceback.format_exc())\n gemslog(LogLevel.ERROR, f\"'message': {error.to_dict()}\", 'API-create_service')\n return {'message': error.to_dict()}, 500\n\n else:\n gemslog(LogLevel.INFO, 'Successfully created new service', 'API-create_service')\n return {'service_id': service_id}, 201\n","repo_name":"eea/CLMS_Production_System","sub_path":"03_Service_and_Data_Dissemination_API/services/backend_api/src/resources/resources_crm/create_service/create_service.py","file_name":"create_service.py","file_ext":"py","file_size_in_byte":6419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10665549287","text":"# -*- coding：utf-8 -*-\r\n# &Author AnFany\r\n\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndata = pd.read_csv(r'C:\\Users\\GWT9\\Desktop\\PRSA_data_2010.1.1-2014.12.31.csv')\r\n\r\n# 因为决策树回归和决策树分类程序，相似性很大，唯一的不同在于分类计算基尼系数，而回归计算MSE。\r\n# 因此回归的数据结构在此处和分类的相似\r\n\r\n# 因为原始数据中有些变量的值是离散的，但是是整数表示，因此这里需要将其变为字符串，\r\n# 这主要涉及的变量包括：year\tmonth\tday\thour\r\n\r\n'''第一部分：缺失值的处理'''\r\n# 因为Pm2.5是目标数据，对于缺失值直接删除这一条记录\r\n\r\n# 定义删除目标值为空值的行的函数, 其他列为缺失值则自动填充的函数,并将目标变量放置在数据集最后一列\r\ndef DeleteTargetNan(exdata, targetstr):\r\n # 首先判断目标字段是否有缺失值\r\n if exdata[targetstr].isnull().any():\r\n # 首先确定缺失值的行数\r\n loc = exdata[targetstr][data[targetstr].isnull().values == True].index.tolist()\r\n # 然后删除这些行\r\n exdata = exdata.drop(loc)\r\n # 凡是有缺失值的再一起利用此行的均值填充\r\n exdata = exdata.fillna(exdata.mean())\r\n # 将目标字段至放在最后的一列\r\n targetnum = exdata[targetstr].copy()\r\n del exdata[targetstr]\r\n exdata[targetstr] = targetnum\r\n return exdata\r\n\r\n# 因为此数据集中的year，month, day, hour均为整数型，再程序中会被当做连续的变量\r\n\r\n# 删除原始数据中不需要的字段名\r\ndef Shanchu(exdata, aiduan=['No']):\r\n for ai in aiduan:\r\n if ai in exdata.keys():\r\n del exdata[ai]\r\n return exdata\r\n\r\n\r\n# 因此这里将这4个变量变为字符串的形式\r\ndef TeshuHandle(exdata, ziduan=['year', 'month', 'day', 'hour'], tianjiastr=['年', '月', '日', '时']):\r\n for j, k in zip(ziduan, tianjiastr):\r\n if j in exdata.keys():\r\n exdata[j] = ['%d%s' % (j, k) for j in exdata[j]]\r\n return exdata\r\n\r\n# 数据处理后最终的数据集\r\n\r\nfirst = DeleteTargetNan(data, 'pm2.5')\r\ntwo = Shanchu(first)\r\nthird = TeshuHandle(two)\r\n\r\n# 将数据集按照7；1.5:1.5的比例分为训练，测试、预测数据集\r\n\r\ndef fenge(exdata, per=[0.15, 0.15]):\r\n # 总长度\r\n lent = len(exdata)\r\n alist = np.arange(lent)\r\n np.random.shuffle(alist)\r\n\r\n # 验证\r\n shu = int(lent * per[0])\r\n yu = int(lent * per[1])\r\n\r\n yanzheng = np.random.choice(alist, shu, replace=False)\r\n\r\n # 预测\r\n shengxai = np.array([i for i in alist if i not in yanzheng])\r\n\r\n yuce = np.random.choice(shengxai, yu, replace=False)\r\n\r\n # 训练\r\n train = np.array([j for j in alist if j not in yanzheng and j not in yuce])\r\n\r\n # 返回数据集\r\n dadata = {}\r\n dadata[0] = {}\r\n\r\n dadata[0]['train'] = exdata[train]\r\n dadata[0]['test'] = exdata[yanzheng]\r\n\r\n return dadata, exdata[yuce]\r\n\r\ndeeer = fenge(third.values)\r\n\r\n# 数据\r\ndt_data = deeer[0]\r\ntest_data = deeer[1]\r\n\r\n\r\n# 数据结构和决策树分类是相同的，\r\n","repo_name":"Anfany/Machine-Learning-for-Beginner-by-Python3","sub_path":"Decision Tree/DT_Regression/Data_DT_Regression.py","file_name":"Data_DT_Regression.py","file_ext":"py","file_size_in_byte":3105,"program_lang":"python","lang":"zh","doc_type":"code","stars":405,"dataset":"github-code","pt":"3"}
+{"seq_id":"27769196130","text":"import requests\r\nimport json\r\n\r\n#To get the latest randomness from cloudflare:\r\nr = requests.get('https://drand.cloudflare.com/public/latest', headers={ 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:31.0) Gecko/20130401 Firefox/31.0'})\r\ndata = r.json()\r\nlatest_randomness= data[\"randomness\"]\r\n\r\n#Digits is a list containing the hex's individual digits:\r\ndigits = list(latest_randomness.strip(' '))\r\n\r\nnum_length = len(digits)\r\n\r\n#Pairs is a list containing the pairs of digits that will later be converted:\r\npairs = [digits[i] + digits[i+1] for i in range(0, num_length-1, 2)]\r\n\r\nif (num_length % 2 == 1):\r\n pairs.append(digits[num_length-1])\r\n\r\n#Converts hex pairs to integers modulo 80:\r\nfor i in range(len(pairs)):\r\n pairs[i]= int(pairs[i],16) % 80\r\n \r\n#To remove any duplicate numbers:\r\npairs= list(set(pairs))\r\n\r\n#To get the last KINO winning numbers from the given link\r\nreq=requests.get(\"https://api.opap.gr/draws/v3.0/1100/last-result-and-active\")\r\ndata=req.json()\r\nkino_numbers= data[\"last\"][\"winningNumbers\"][\"list\"]\r\n\r\ncount=0 #How many of the pairs' numbers would win in the last KINO drawing\r\n\r\nfor i in range(len(pairs)):\r\n for k in range(len(kino_numbers)):\r\n if (pairs[i]==kino_numbers[k]):\r\n count+=1\r\n kino_numbers.pop(k) #If a number is found, remove it to make the search faster\r\n break\r\n continue\r\n\r\nprint(\"{} of the generated numbers would win in the last KINO drawing\".format(count))","repo_name":"MarinaTsl/Assignment_13","sub_path":"latest_randomness.py","file_name":"latest_randomness.py","file_ext":"py","file_size_in_byte":1475,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"2931046250","text":"# pylint: disable=too-many-locals,too-many-statements,too-many-nested-blocks\n\"\"\"\nGet optimal resolution method is defined here.\n\"\"\"\nfrom __future__ import annotations\n\nimport itertools as it\n\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\nfrom loguru import logger\nfrom matplotlib.axes import Axes\nfrom networkx.algorithms.community import greedy_modularity_communities\n\nfrom derevo.compatability import get_compatability_graph\n\n\ndef get_best_resolution(\n plants: pd.DataFrame,\n plants_with_limitations_resistance: pd.DataFrame,\n plants_suitable_for_light: pd.DataFrame,\n cohabitation_attributes: pd.DataFrame,\n graph_axes: Axes | None = None,\n) -> pd.DataFrame:\n \"\"\"\n Return dataframe with calculated best resolutions for current collection of species\n and limitation factors / light variants.\n\n If `graph_axes` is given, scatter plot will be drawn on it.\n \"\"\"\n plants = plants.copy()\n plants_with_lim_resist = plants_with_limitations_resistance.copy()\n plants_with_lig_resist = plants_suitable_for_light.copy()\n compatability_graph = get_compatability_graph(plants, cohabitation_attributes)\n lig_list = [1, 2, 3]\n lim_list = [1, 2, 3, 4, 5, 6]\n\n res_list = [x / 10 for x in range(0, 21, 1)]\n result: list[list] = []\n for lig in range(len(lig_list) + 1):\n for lig_subset in it.combinations(lig_list, lig):\n if len(list(lig_subset)) == 0:\n continue\n filtered_plants = plants_with_lig_resist[plants_with_lig_resist.light_type_id.isin(list(lig_subset))]\n light_comp = list(pd.unique(filtered_plants[\"name_ru\"]))\n for lim in range(len(lim_list) + 1):\n for lim_subset in it.combinations(lim_list, lim):\n filtered_plants = plants_with_lim_resist[\n plants_with_lim_resist.limitation_factor_id.isin(list(lim_subset))\n ]\n limitations_comp = filtered_plants.groupby(\"name_ru\").count().reset_index()\n limitations_comp = limitations_comp[\n limitations_comp.limitation_factor_id == limitations_comp.limitation_factor_id.max()\n ]\n limitations_comp = list(pd.unique(limitations_comp[\"name_ru\"]))\n\n # filtering species by environmental factors\n df_comp = plants[plants[\"name_ru\"].isin(light_comp)]\n if len(lim_subset) > 0:\n df_comp = df_comp[df_comp[\"name_ru\"].isin(limitations_comp)]\n\n comp_graph = compatability_graph.copy()\n comp_graph = comp_graph.subgraph(df_comp[\"name_ru\"])\n default_size_of_community = comp_graph.number_of_nodes()\n default_number_of_negative_edges = len(nx.to_pandas_edgelist(comp_graph).query(\"weight == 1\"))\n for res in res_list:\n communities_list = greedy_modularity_communities(comp_graph, weight=\"weight\", resolution=res)\n size_of_biggest_community = max(len(com) for com in communities_list)\n composition = [list(com) for com in communities_list if len(com) == size_of_biggest_community]\n if len(composition) == default_size_of_community:\n c_graph = comp_graph.subgraph(list(it.chain.from_iterable(composition)))\n number_of_remaining_nodes = c_graph.number_of_nodes()\n number_of_negative_edges = len(nx.to_pandas_edgelist(c_graph).query(\"weight == 1\"))\n tag = \"One node community\"\n result.append(\n [\n \", \".join(map(str, list(lig_subset)))\n + \"; \"\n + \", \".join(map(str, list(lim_subset))),\n list(pd.unique(df_comp[\"name_ru\"])),\n communities_list,\n number_of_remaining_nodes,\n number_of_negative_edges,\n res,\n default_size_of_community,\n default_number_of_negative_edges,\n tag,\n ]\n )\n elif len(composition) > 1:\n for comp in composition:\n c_graph = comp_graph.subgraph(comp)\n number_of_remaining_nodes = c_graph.number_of_nodes()\n number_of_negative_edges = len(nx.to_pandas_edgelist(c_graph).query(\"weight == 1\"))\n tag = \"Variable community\"\n result.append(\n [\n \", \".join(map(str, list(lig_subset)))\n + \"; \"\n + \", \".join(map(str, list(lim_subset))),\n list(pd.unique(df_comp[\"name_ru\"])),\n communities_list,\n number_of_remaining_nodes,\n number_of_negative_edges,\n res,\n default_size_of_community,\n default_number_of_negative_edges,\n tag,\n ]\n )\n else:\n c_graph = comp_graph.subgraph(list(it.chain.from_iterable(composition)))\n number_of_remaining_nodes = c_graph.number_of_nodes()\n number_of_negative_edges = len(nx.to_pandas_edgelist(c_graph).query(\"weight == 1\"))\n tag = \"Default community\"\n result.append(\n [\n \", \".join(map(str, list(lig_subset)))\n + \"; \"\n + \", \".join(map(str, list(lim_subset))),\n list(pd.unique(df_comp[\"name_ru\"])),\n communities_list,\n number_of_remaining_nodes,\n number_of_negative_edges,\n res,\n default_size_of_community,\n default_number_of_negative_edges,\n tag,\n ]\n )\n logger.debug(\"lim_subset done: {}\", list(lim_subset))\n logger.debug(\"lig_subset done: {}\", list(lig_subset))\n df_result = pd.DataFrame(\n result,\n columns=[\n \"variant_id\",\n \"precomposition\",\n \"communities_list\",\n \"size\",\n \"negative_edges\",\n \"resolution\",\n \"total_size\",\n \"total_edge_number\",\n \"tag\",\n ],\n )\n df_result[\"share_of_nodes\"] = df_result[\"size\"] / df_result[\"total_size\"]\n df_result[\"share_of_negative_edges\"] = df_result[\"negative_edges\"] / df_result[\"total_edge_number\"]\n df_result[\"composition_improvement\"] = df_result[\"share_of_nodes\"] - df_result[\"share_of_negative_edges\"]\n df_result[\"communities\"] = df_result.communities_list.map(\n lambda x: [list(com) for com in x if len(com) == max(len(com) for com in x)]\n )\n df_result[\"communities\"] = df_result.communities.map(lambda x: list(it.chain.from_iterable(x)))\n df_result[\"size\"] = df_result.communities.map(len)\n\n if graph_axes is not None:\n x_axis = list(df_result[\"resolution\"])\n y_axis = list(df_result[\"composition_improvement\"])\n\n graph_axes.scatter(x_axis, y_axis, marker=0)\n graph_axes.set_xticks(np.arange(min(x_axis), max(x_axis), 0.1), rotation=90)\n graph_axes.set_yticks(np.arange(min(y_axis), max(y_axis), 0.1))\n graph_axes.grid(True)\n graph_axes.set_xlabel(\"resolution\")\n graph_axes.set_ylabel(\"composition improvement\")\n\n return df_result\n","repo_name":"egov-itmo/derevo","sub_path":"method/derevo/optimal_resolution.py","file_name":"optimal_resolution.py","file_ext":"py","file_size_in_byte":8529,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"72733722320","text":"from openerp import api, fields, models, _\nfrom urllib import urlencode\nfrom urlparse import urljoin\nfrom openerp.exceptions import UserError\n\n\nclass SaleOrderExtend(models.Model):\n _inherit = 'sale.order'\n\n @api.multi\n def unlink(self):\n for order in self:\n if order.state not in ['draft','to_accept']:\n raise UserError(_('You can only delete draft quotations or Quotations Awaiting Acceptance!'))\n self.env.cr.execute(\"delete from sale_order where id = %s\" % order.id)\n\n\n @api.multi\n def copy(self, default=None):\n self.ensure_one()\n if default is None:\n default = {}\n res = super(SaleOrderExtend, self).copy(default)\n res.onchange_reach_limit()\n return res\n\n @api.multi\n def action_send_to_manager(self, msg):\n company_email = self.env.user.company_id.email.strip()\n sender_person_email = self.env.user.partner_id.email.strip()\n sender_person_name = self.env.user.name\n res = {}\n self.state = 'to_accept'\n\n if company_email and sender_person_email:\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_sale_send_to_manager')\n ctx = {}\n ctx.update({'sale_id': self.id})\n the_url = self._get_sale_order_url('sale', 'menu_sale_order', 'action_orders', ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_show_receive_send_to_manager')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {'system_email': company_email,\n 'sender_person_name': sender_person_name,\n 'sender_person_email': sender_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url': the_url,\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n\n #Simulates Notification Dialog Box\n res = {\n 'name': 'Send to Manager Notification',\n 'view_mode': 'form',\n 'res_model': 'send.manager.wizard',\n 'type': 'ir.actions.act_window',\n 'target': 'new'\n }\n\n return res\n\n\n @api.multi\n def action_disapprove(self,msg):\n reason_for_dispproval = msg\n self.disapproved_by_user_id = self.env.user\n self.state = 'no_sale'\n\n # Send Email\n company_email = self.env.user.company_id.email.strip()\n disapprove_person_email = self.disapproved_by_user_id.partner_id.email.strip()\n disapprove_person_name = self.disapproved_by_user_id.name\n\n if company_email and disapprove_person_email:\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_sale_disapproved')\n ctx = {}\n ctx.update({'sale_id': self.id})\n the_url = self._get_sale_order_url('sale', 'menu_sale_order', 'action_orders', ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_disapprove_sale_order_email')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {'system_email': company_email,\n 'disapprove_person_name': disapprove_person_name,\n 'disapprove_person_email': disapprove_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url': the_url,\n 'reason_for_dispproval': reason_for_dispproval,\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n\n if user_ids :\n self.message_subscribe_users(user_ids=user_ids)\n # For Similar Odoo Kind of Email. Works fine\n #self.message_post( _(\"Sales Order has been Disapproved with reason: \" + reason_for_dispproval + \"\") ,subject='Please See the Disapproved Sales Order', subtype='mail.mt_comment')\n\n #Just Log the Note Only\n self.message_post(_(\"Sales Order has been Disapproved with reason: \" + reason_for_dispproval + \"\"), subject='Please See the Disapproved Sales Order')\n\n @api.multi\n def action_approve(self):\n res = super(SaleOrderExtend, self).action_confirm()\n self.approved_by_user_id = self.env.user\n\n # Send Email\n company_email = self.env.user.company_id.email.strip()\n approve_person_email = self.approved_by_user_id.partner_id.email.strip()\n approve_person_name = self.approved_by_user_id.name\n\n if company_email and approve_person_email:\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_sale_approved')\n ctx = {}\n ctx.update({'sale_id': self.id})\n the_url = self._get_sale_order_url('sale', 'menu_sale_order', 'action_orders', ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_approve_sale_order_email')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {'system_email': company_email,\n 'approve_person_name': approve_person_name,\n 'approve_person_email': approve_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url': the_url\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n if user_ids :\n self.message_subscribe_users(user_ids=user_ids)\n\n return res\n\n\n @api.multi\n def action_confirm(self):\n self.state = 'so_to_approve'\n self.confirmed_by_user_id = self.env.user\n\n # Give a SO ID\n if self.so_name:\n self.name = self.so_name\n else:\n self.quote_name = self.name\n self.name = self.env['ir.sequence'].get('so_id_code')\n self.so_name = self.name\n\n #Send FYI email notification\n company_email = self.env.user.company_id.email and self.env.user.company_id.email.strip()\n confirm_person_email = self.confirmed_by_user_id.partner_id.email.strip()\n confirm_person_name = self.confirmed_by_user_id.partner_id.name\n\n if company_email and confirm_person_email :\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_quote_confirmed')\n ctx = {}\n ctx.update({'sale_id':self.id})\n the_url = self._get_sale_order_url('sale','menu_sale_order','action_orders',ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_quotation_confirmed_email')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {\n 'system_email': company_email,\n 'confirm_person_name': confirm_person_name ,\n 'confirm_person_email' :confirm_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url':the_url\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n if user_ids:\n self.message_subscribe_users(user_ids=user_ids)\n\n #Send email for approval or disapproval\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_quote_confirmed_to_approve')\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_quotation_confirmed_email_to_approve')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {\n 'system_email': company_email,\n 'confirm_person_name': confirm_person_name,\n 'confirm_person_email': confirm_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url': the_url\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n if user_ids :\n self.message_subscribe_users(user_ids=user_ids)\n\n return\n\n # It acts as a helper for other sub classess to call the parent action_confirm() method\n @api.multi\n def action_confirm_subclass(self):\n res = super(SaleOrderExtend, self).action_confirm()\n\n @api.multi\n def action_cancel(self):\n res = super(SaleOrderExtend,self).action_cancel()\n\n #Send Email\n company_email = self.env.user.company_id.email.strip()\n sales_person_email = self.user_id.partner_id.email.strip()\n confirm_person_email = self.env.user.partner_id.email.strip()\n\n if company_email and sales_person_email and confirm_person_email and (sales_person_email != confirm_person_email ):\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_sale_canceled')\n ctx = {}\n ctx.update({'sale_id':self.id})\n the_url = self._get_sale_order_url('sale','menu_sale_order','action_orders',ctx)\n\n ctx = {'system_email': company_email,\n 'confirm_person_name': self.env.user.name ,\n 'confirm_person_email' :confirm_person_email,\n 'url':the_url\n }\n mail_template.with_context(ctx).send_mail(self.id,force_send=False)\n return res\n\n def onchange_reach_limit(self):\n for order in self:\n amount_total = order.amount_total\n sale_confirm_limit = self.env.user.sale_confirm_limit\n is_use_sale_confirm_limit = self.env.user.is_use_sale_confirm_limit\n if (amount_total < sale_confirm_limit) and is_use_sale_confirm_limit and order.state in ['draft','sent','to_accept']:\n self.env.cr.execute(\"update sale_order set state = 'so_to_approve' where id = %s\" % (self.id))\n elif ((amount_total >= sale_confirm_limit) and is_use_sale_confirm_limit and order.state in ['draft','sent']) :\n self.env.cr.execute(\"update sale_order set state = 'to_accept' where id = %s\" % (self.id))\n return\n\n\n\n @api.model\n def create(self, vals):\n order = super(SaleOrderExtend, self).create(vals)\n return order\n\n def _get_sale_order_url(self, module_name,menu_id,action_id, context=None):\n fragment = {}\n res = {}\n model_data = self.env['ir.model.data']\n base_url = self.env['ir.config_parameter'].get_param('web.base.url')\n fragment['menu_id'] = model_data.get_object_reference(module_name,menu_id)[1]\n fragment['model'] = 'sale.order'\n fragment['view_type'] = 'form'\n fragment['action']= model_data.get_object_reference(module_name,action_id)[1]\n query = {'db': self.env.cr.dbname}\n\n# for displaying tree view. Remove if you want to display form view\n# fragment['page'] = '0'\n# fragment['limit'] = '80'\n# res = urljoin(base_url, \"?%s#%s\" % (urlencode(query), urlencode(fragment)))\n\n\n # For displaying a single record. Remove if you want to display tree view\n\n fragment['id'] = context.get(\"sale_id\")\n res = urljoin(base_url, \"?%s#%s\" % (urlencode(query), urlencode(fragment)))\n return res\n\n @api.multi\n def action_send_for_confirm(self):\n\n for order in self:\n order.state = 'waiting'\n order.is_show_btn_send_for_confirm = False\n order.is_show_btn_approve = False\n order.is_show_btn_confirm = True\n\n is_po_check = order.env.user.company_id.is_po_check\n # is PO Check\n if is_po_check:\n if order.client_order_ref:\n client_order_ref = order.client_order_ref.strip()\n\n if len(client_order_ref) <= 0:\n raise UserError(\n 'Please Ensure that the Quote is confirmed from the customer and that the PO reference is set. e.g. you may put the po number, email, contact name, number of the customer that confirmed the Quote on the PO reference field')\n\n else:\n raise UserError(\n 'Please Ensure that the Quote is confirmed from the customer and that the PO reference is set. e.g. you may put the po number, email, contact name, number of the customer that confirmed the Quote')\n\n #Send Email\n company_email = order.env.user.company_id.email and order.env.user.company_id.email.strip()\n sales_person_email = order.user_id.partner_id.email.strip()\n\n if company_email and sales_person_email :\n # Custom Email Template\n mail_template = order.env.ref('kin_sales_double_validation.mail_templ_send_for_confirmation')\n ctx = {}\n ctx.update({'sale_id':order.id})\n the_url = order._get_sale_order_url('sale','menu_sale_order','action_orders',ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_send_for_confirm_email')\n for user in group_obj.users :\n if user.partner_id.email and user.partner_id.email.strip() :\n user_ids.append(user.id)\n ctx = {'system_email': company_email,\n 'confirm_person_email':user.partner_id.email,\n 'confirm_person_name': user.partner_id.name ,\n 'url':the_url\n }\n mail_template.with_context(ctx).send_mail(order.id,force_send=False)\n\n if user_ids :\n order.message_subscribe_users(user_ids=user_ids)\n\n #self.message_post( _('Sales Order has been created %s.') % (self.name),subject='Please See the Created Sales Order', subtype='mail.mt_comment')\n\n partner_id = fields.Many2one(states={'draft': [('readonly', False)], 'sent': [('readonly', False)], 'to_accept': [('readonly', False)], 'waiting': [('readonly', False)], 'so_to_approve': [('readonly', False)]})\n partner_invoice_id = fields.Many2one(states={'draft': [('readonly', False)], 'sent': [('readonly', False)], 'to_accept': [('readonly', False)], 'waiting': [('readonly', False)], 'so_to_approve': [('readonly', False)]}, track_visibility='always')\n partner_shipping_id = fields.Many2one(states={'draft': [('readonly', False)], 'sent': [('readonly', False)], 'to_accept': [('readonly', False)], 'waiting': [('readonly', False)], 'so_to_approve': [('readonly', False)]}, track_visibility='always')\n pricelist_id = fields.Many2one('product.pricelist', string='Pricelist', required=True, readonly=True,\n states={'draft': [('readonly', False)],'to_accept': [('readonly', False)], 'sent': [('readonly', False)]},\n help=\"Pricelist for current sales order.\")\n #state = fields.Selection(selection_add=[('waiting', 'Waiting Approval')]) # it worked but , i could not sequence it\n state = fields.Selection([\n ('draft', 'Quotation'),\n ('sent', 'Quote Sent'),\n ('to_accept', 'Quote Awaiting Acceptance'),\n ('waiting', 'Quote Awaiting Conversion to SO'),\n ('so_to_approve', 'Sale Order To Approve'),\n ('sale', 'Sale Order Approved'),\n ('no_sale', 'Sale Order Disapproved'),\n ('credit_limit_by_pass_request', 'Credit Limit By Pass Request'),\n ('credit_limit_by_pass_confirm', 'Credit Limit By Pass Confirmed'),\n ('credit_limit_by_pass_approve', 'Credit Limit By Pass Approved'),\n ('credit_limit_by_pass_disapprove', 'Credit Limit By Pass DisApproved'),\n ('credit_limit_by_pass_cancel', 'Credit Limit By Pass Cancelled'),\n ('done', 'Done'),\n ('cancel', 'Cancelled'),\n ], string='Status', readonly=True, copy=False, index=True, track_visibility='onchange', default='draft')\n\n confirmed_by_user_id = fields.Many2one('res.users',string= 'Confirmed By')\n approved_by_user_id = fields.Many2one('res.users', string='Approved By')\n disapproved_by_user_id = fields.Many2one('res.users', string='Disapproved By')\n date_order = fields.Datetime(string='Order Date', required=True, readonly=True, index=True,\n states={'draft': [('readonly', False), ('required', False)],\n 'sent': [('readonly', False)], 'waiting': [('readonly', False)],\n 'so_to_approve': [('readonly', False)]}, copy=False)\n\n\n\n\nclass ResUsersExtend(models.Model):\n _inherit = 'res.users'\n\n sale_confirm_limit = fields.Float('Sales Confirmation Limit',help='If user exceeds this limit, expose the confirmation button for validation')\n is_use_sale_confirm_limit = fields.Boolean('Use Sales Confirm Limit')\n\n","repo_name":"kingsleyuk2003/ODEX","sub_path":"addons/kin_sales_double_validation/sale.py","file_name":"sale.py","file_ext":"py","file_size_in_byte":18270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38397716932","text":"from io import BytesIO\n\nimport requests\nfrom starlette.responses import StreamingResponse\nfrom fastapi import FastAPI, Request, Form, File, UploadFile\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi.templating import Jinja2Templates\nfrom fastapi.logger import logger\n\napp = FastAPI()\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n\ntemplates = Jinja2Templates(directory=\"templates\")\n\n@app.get(\"/\")\nasync def get_root(request: Request):\n return templates.TemplateResponse(\"index.html\", {\"request\": request})\n\n\n@app.post(\"/process_text\")\nasync def process_text(text: str = Form(...)):\n req = requests.get(\"http://text2speech/text2speech\", params={\"text\": text})\n fd = BytesIO(req.content)\n\n return StreamingResponse(fd, headers=req.headers, media_type=req.headers['Content-Type'])\n\n\n@app.post(\"/process_audio\")\nasync def process_audio(audio: UploadFile = File(...)):\n\n file_data = {'file': (audio.filename, audio.file, audio.content_type)}\n req = requests.post(\"http://speech2text/\", files=file_data)\n\n response = {\"text\": req.text}\n logger.info(response)\n return response\n\n\n@app.post(\"/translate\")\nasync def translate(text: str = Form(...), source_language: str = Form(...), target_language: str = Form(...)):\n data = {\"source\": source_language, \"target\": target_language, \"q\": text}\n req = requests.get(\"http://taln.upf.edu/mmt-es_en/translate\", params=data)\n\n json_response = req.json()\n text = json_response['data']['translation']\n \n response = {\"text\": text}\n logger.info(response)\n return response\n\n\nif __name__ == \"__main__\":\n import uvicorn\n uvicorn.run(app, host=\"0.0.0.0\", port=8000)","repo_name":"rcarlini-upf/ingenious","sub_path":"integration/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33747446750","text":"import os\r\nimport cv2\r\nfrom visander.util import ImageSampler\r\nfrom visander.core import Entity\r\nfrom visander.color import BgrIntensityComponent, GrayscaleIntensityComponent,\\\r\n get_intensity, ensure_grayscale\r\n\r\nclass DirectoryBasedImageSource(object):\r\n '''\r\n An image source which is given a directory and will walk \r\n all files in the directory and all subdirectory and read in and\r\n return those files which look like images (based on the file extension)\r\n '''\r\n \r\n def __init__(self, root_dir):\r\n self.root_dir = root_dir\r\n self.image_extensions = ['.jpg','.png','.bmp','.tif','.tiff']\r\n \r\n @property\r\n def entities(self):\r\n '''\r\n A generator function which returns all of the images in this image source.\r\n '''\r\n for root,_,filenames in os.walk(self.root_dir):\r\n for filename in filenames:\r\n _,extension = os.path.splitext(filename)\r\n if extension.lower() in self.image_extensions:\r\n image = cv2.imread(os.path.join(root,filename))\r\n if image.shape[2] == 3:\r\n yield Entity(BgrIntensityComponent(image))\r\n else:\r\n yield Entity(GrayscaleIntensityComponent(image)) \r\n","repo_name":"westonpace/visander","sub_path":"src/visander/io/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17256756838","text":"#utility file for langevin_sim_mfpt_opt.py\n#bt TW 9th Oct 2023.\n# Path: langevin_approach/util.py\n\nimport numpy as np\nfrom scipy.linalg import eig\nfrom scipy.linalg import expm\nfrom scipy.linalg import inv\nfrom scipy.optimize import minimize\nfrom math import pi\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\nfrom tqdm import tqdm\n\nimport openmm\nimport config\n\n\"\"\"\n\ndef gaussian_2d(x, y, ax, bx, by, cx, cy): #deprecated\n return ax * np.exp(-((x-bx)**2/(2*cx**2) + (y-by)**2/(2*cy**2)))\n\"\"\"\ndef gaussian_2D(params, x, y):\n A, x0, y0, sigma_x, sigma_y = params\n return A * np.exp(-((x - x0)**2 / (2 * sigma_x**2) + (y - y0)**2 / (2 * sigma_y**2)))\n\ndef random_initial_bias_2d(initial_position, num_gaussians = 20):\n # initial position is a list e.g. [3,3]\n # note this is in \n #returns a set of random ax,ay, bx, by, cx, cy for the 2d Gaussian function\n\n #we convert the initial position from openmm quantity object to array with just the value.\n initial_position = initial_position.value_in_unit_system(openmm.unit.md_unit_system)[0] #this is in nm.\n rng = np.random.default_rng()\n a = np.ones(num_gaussians) * 0.1#* 4 #\n #ay = np.ones(num_gaussians) * 0.1 #there's only one amplitude!\n bx = rng.uniform(initial_position[0]-1, initial_position[0]+1, num_gaussians)\n by = rng.uniform(initial_position[1]-1, initial_position[1]+1, num_gaussians)\n cx = rng.uniform(1.0, 5.0, num_gaussians)\n cy = rng.uniform(1.0, 5.0, num_gaussians)\n #\"gaussian_param should be in A, x0, y0, sigma_x, sigma_y format.\"\n return np.concatenate((a, bx, by, cx, cy))\n\ndef get_total_bias_2d(x,y, gaussian_params):\n \"\"\"\n here we get the total bias at x,y.\n note: we used the transposed K matrix, we need to apply transposed total gaussian bias.\n \"\"\"\n N = x.shape[0] #N is the number of grid points.\n total_bias = np.zeros((N,N))\n num_gaussians = len(gaussian_params)//5\n a = gaussian_params[:num_gaussians]\n bx = gaussian_params[num_gaussians:2*num_gaussians]\n by = gaussian_params[2*num_gaussians:3*num_gaussians]\n cx = gaussian_params[3*num_gaussians:4*num_gaussians]\n cy = gaussian_params[4*num_gaussians:5*num_gaussians]\n for i in range(num_gaussians):\n total_bias = total_bias + gaussian_2D([a[i], bx[i], by[i], cx[i], cy[i]], x, y,)\n\n return total_bias\n\ndef compute_free_energy(K, kT):\n \"\"\"\n In 2D senario, we just need to reshape the peq and F.\n\n K is the transition matrix\n kT is the thermal energy\n peq is the stationary distribution #note this was defined as pi in Simian's code.\n F is the free energy\n eigenvectors are the eigenvectors of K\n\n first we calculate the eigenvalues and eigenvectors of K\n then we use the eigenvalues to calculate the equilibrium distribution: peq.\n then we use the equilibrium distribution to calculate the free energy: F = -kT * ln(peq)\n \"\"\"\n N = int(np.sqrt(K.shape[0]))\n evalues, evectors = eig(K)\n\n #sort the eigenvalues and eigenvectors\n index = np.argsort(evalues) #sort the eigenvalues, the largest eigenvalue is at the end of the list\n evalues_sorted = evalues[index] #sort the eigenvalues based on index\n\n #calculate the equilibrium distribution\n peq = evectors[:, index[-1]].T #normalize the eigenvector\n peq = peq / np.sum(peq)\n peq = peq.real\n #take the real part of the eigenvector i.e. the probability distribution at equilibrium.\n #calculate the free energy\n F = -kT * np.log(peq) #add a small number to avoid log(0))\n #F = F.reshape(N, N)\n return [peq, F, evectors, evalues, evalues_sorted, index]\n\ndef compute_free_energy_power_method(K, kT=0.5981):\n \"\"\"\n this use the power method to calculate the equilibrium distribution.\n num_iter is the number of iterations.\n \"\"\"\n num_iter = 1000\n N = K.shape[0]\n peq = np.ones(N) / N #initialise the peq\n for i in range(num_iter):\n peq = np.dot(peq, K)\n peq = peq / np.sum(peq)\n F = -kT * np.log(peq)\n return [peq, F]\n\ndef kemeny_constant_check(mfpt, peq):\n N2 = mfpt.shape[0]\n kemeny = np.zeros((N2, 1))\n for i in range(N2):\n for j in range(N2):\n kemeny[i] = kemeny[i] + mfpt[i, j] * peq[j]\n #print(\"Performing Kemeny constant check...\")\n #print(\"the min/max of the Kemeny constant is:\", np.min(kemeny), np.max(kemeny))\n \"\"\"\n if np.max(kemeny) - np.min(kemeny) > 1e-6:\n print(\"Kemeny constant check failed!\")\n raise ValueError(\"Kemeny constant check failed!\")\"\"\"\n return kemeny\n\ndef mfpt_calc(peq, K):\n \"\"\"\n peq is the probability distribution at equilibrium.\n K is the transition matrix.\n N is the number of states.\n \"\"\"\n N = K.shape[0] #K is a square matrix.\n onevec = np.ones((N, 1)) #, dtype=np.float64\n Qinv = np.linalg.inv(peq.T * onevec - K.T)\n\n mfpt = np.zeros((N, N)) #, dtype=np.float64\n for j in range(N):\n for i in range(N):\n #to avoid devided by zero error:\n if peq[j] == 0:\n mfpt[i, j] = 0\n else:\n mfpt[i, j] = 1 / peq[j] * (Qinv[j, j] - Qinv[i, j])\n \n #result = kemeny_constant_check(N, mfpt, peq)\n return mfpt\n\ndef Markov_mfpt_calc(peq, M):\n N = M.shape[0]\n onevec = np.ones((N, 1))\n Idn = np.diag(onevec[:, 0])\n A = (peq.reshape(-1, 1)) @ onevec.T #was peq.T @ onevec.T\n A = A.T\n Qinv = inv(Idn + A - M)\n mfpt = np.zeros((N, N))\n for j in range(N):\n for i in range(N):\n term1 = Qinv[j, j] - Qinv[i, j] + Idn[i, j]\n if peq[j] * term1 == 0:\n mfpt[i, j] = 1000000000000\n else:\n mfpt[i, j] = 1/peq[j] * term1\n #result = kemeny_constant_check(N, mfpt, peq)\n return mfpt\n\ndef try_and_optim_M(M, working_indices, N=20, num_gaussian=10, start_index=0, end_index=0, plot = False):\n \"\"\"\n here we try different gaussian params 1000 times\n and use the best one (lowest mfpt) to local optimise the gaussian_params\n \n returns the best gaussian params\n\n input:\n M: the working transition matrix, square matrix.\n working_indices: the indices of the working states.\n num_gaussian: number of gaussian functions to use.\n start_state: the starting state. note this has to be converted into the index space.\n end_state: the ending state. note this has to be converted into the index space.\n index_offset: the offset of the index space. e.g. if the truncated M (with shape [20, 20]) matrix starts from 13 to 33, then the index_offset is 13.\n \"\"\"\n #here we find the index of working_indices.\n # e.g. the starting index in the working_indices is working_indices[start_state_working_index]\n # and the end is working_indices[end_state_working_index]\n \n start_state_working_index = np.argmin(np.abs(working_indices - start_index))\n end_state_working_index = np.argmin(np.abs(working_indices - end_index))\n \n start_state_working_index_xy = np.unravel_index(working_indices[start_state_working_index], (N, N), order='C')\n end_state_working_index_xy = np.unravel_index(working_indices[end_state_working_index], (N, N), order='C')\n print(\"Try and Optim from state:\", start_state_working_index_xy, \"to state:\", end_state_working_index_xy)\n\n #now our M/working_indices could be incontinues. #N = M.shape[0]\n x,y = np.meshgrid(np.linspace(0, 2*np.pi, N), np.linspace(0, 2*np.pi, N)) #hard coded here. we need to change this.\n best_mfpt = 1e20 #initialise the best mfpt np.inf\n\n #here we find the x,y maximum and minimun in xy coordinate space, with those working index\n #we use this to generate the random gaussian params.\n working_indices_xy = np.unravel_index(working_indices, (N, N), order='C')\n\n for try_num in range(1000):\n rng = np.random.default_rng()\n a = np.ones(num_gaussian) * 2\n bx = rng.uniform(0, 2*np.pi, num_gaussian)\n by = rng.uniform(0, 2*np.pi, num_gaussian)\n cx = rng.uniform(0.3, 1.5, num_gaussian)\n cy = rng.uniform(0.3, 1.5, num_gaussian)\n gaussian_params = np.concatenate((a, bx, by, cx, cy))\n\n total_bias = get_total_bias_2d(x,y, gaussian_params)\n M_biased = np.zeros_like(M)\n\n #we truncate the total_bias to the working index.\n working_bias = total_bias[working_indices_xy] #say M is in shape[51,51], working bias will be in [51] shape.\n\n #now we have a discontinues M matrix. we need to apply the bias to the working index.\n for i in range(M.shape[0]):\n for j in range(M.shape[1]):\n u_ij = working_bias[j] - working_bias[i]\n M_biased[i,j] = M[i,j] * np.exp(-u_ij / (2*0.5981))\n M_biased[i,i] = M[i,i]\n #epsilon_offset = 1e-15\n #M_biased = M_biased / (np.sum(M_biased, axis=0)[:, None] + 1e-15)\n for i in range(M_biased.shape[0]):\n if np.sum(M_biased[i, :]) > 0:\n M_biased[i, :] = M_biased[i, :] / np.sum(M_biased[i, :])\n else:\n M_biased[i, :] = 0\n \n #note our M_biased is in working index. M.shape = (num_working_states, num_working_states)\n peq,F,_,_,_,_ = compute_free_energy(M_biased.T.astype(np.float64), kT=0.5981)\n #print(peq)\n #print(sum(peq))\n\n mfpts_biased = Markov_mfpt_calc(peq, M_biased)\n mfpt_biased = mfpts_biased[start_state_working_index, end_state_working_index]\n\n if try_num % 100 == 0:\n kemeny_constant_check(mfpts_biased, peq)\n print(\"random try:\", try_num, \"mfpt:\", mfpt_biased)\n if best_mfpt > mfpt_biased:\n best_mfpt = mfpt_biased\n best_params = gaussian_params\n\n print(\"best mfpt:\", best_mfpt)\n \n #now we use the best params to local optimise the gaussian params\n\n def mfpt_helper(gaussian_params, M, start_state_working_index = start_state_working_index, end_state_working_index = end_state_working_index, kT=0.5981, working_indices=working_indices):\n #print(\"Try and Optim from state:\", start_state_working_index_xy, \"to state:\", end_state_working_index_xy)\n total_bias = get_total_bias_2d(x,y, gaussian_params)\n M_biased = np.zeros_like(M)\n\n #we truncate the total_bias to the working index.\n working_bias = total_bias[working_indices_xy] #say M is in shape[51,51], working bias will be in [51] shape.\n\n #now we have a discontinues M matrix. we need to apply the bias to the working index.\n for i in range(M.shape[0]):\n for j in range(M.shape[1]):\n u_ij = working_bias[j] - working_bias[i]\n M_biased[i,j] = M[i,j] * np.exp(-u_ij / (2*0.5981))\n M_biased[i,i] = M[i,i]\n #epsilon_offset = 1e-15\n #M_biased = M_biased / (np.sum(M_biased, axis=0)[:, None] + 1e-15)\n for i in range(M_biased.shape[0]):\n if np.sum(M_biased[i, :]) > 0:\n M_biased[i, :] = M_biased[i, :] / np.sum(M_biased[i, :])\n else:\n M_biased[i, :] = 0\n \n #note our M_biased is in working index. M.shape = (num_working_states, num_working_states)\n peq,F,_,_,_,_ = compute_free_energy(M_biased.T.astype(np.float64), kT=0.5981)\n \n mfpts_biased = Markov_mfpt_calc(peq, M_biased)\n mfpt_biased = mfpts_biased[start_state_working_index, end_state_working_index]\n return mfpt_biased\n\n \n res = minimize(mfpt_helper, \n best_params, \n args=(M,\n start_state_working_index, \n end_state_working_index,\n working_indices), \n method='Nelder-Mead', \n bounds= [(0.1, 2)]*num_gaussian + [(0, 2*np.pi)]*num_gaussian + [(0, 2*np.pi)]*num_gaussian + [(0.3, 1.5)]*num_gaussian + [(0.3, 1.5)]*num_gaussian,\n tol=1e0)\n \n #print(\"local optimisation result:\", res.x)\n return res.x\n\ndef save_CV_total(CV_total, time_tag, prop_index):\n np.save(f\"./data/{time_tag}_{prop_index}_CV_total.npy\", CV_total[-1])\n\ndef save_gaussian_params(gaussian_params, time_tag, prop_index):\n np.save(f\"./data/{time_tag}_{prop_index}_gaussian_params.npy\", gaussian_params)\n\n\n\ndef apply_fes(system, particle_idx, gaussian_param=None, pbc = False, name = \"FES\", amp = 7, mode = \"gaussian\", plot = False, plot_path = \"./fes_visualization.png\"):\n \"\"\"\n this function apply the bias given by the gaussian_param to the system.\n \"\"\"\n pi = np.pi #we need convert this into nm.\n #at last we add huge barrier at the edge of the box. since we are not using pbc.\n #this is to prevent the particle from escaping the box.\n # if x<0, push the atom back to x=0\n\n\n k = 5 # Steepness of the sigmoid curve\n max_barrier = \"1e2\" # Scaling factor for the potential maximum\n offset = 0.5 #the offset of the boundary energy barrier.\n # Defining the potentials using a sigmoid function\n left_pot = openmm.CustomExternalForce(f\"{max_barrier} * (1 / (1 + exp({k} * x - (-{offset}))))\")\n right_pot = openmm.CustomExternalForce(f\"{max_barrier} * (1 / (1 + exp(-{k} * (x - (2 * {pi} + {offset})))))\")\n bottom_pot = openmm.CustomExternalForce(f\"{max_barrier} * (1 / (1 + exp({k} * y - (-{offset}))))\")\n top_pot = openmm.CustomExternalForce(f\"{max_barrier} * (1 / (1 + exp(-{k} * (y - (2 * {pi} + {offset})))))\")\n\n left_pot.addParticle(particle_idx)\n right_pot.addParticle(particle_idx)\n bottom_pot.addParticle(particle_idx)\n top_pot.addParticle(particle_idx)\n\n system.addForce(left_pot)\n system.addForce(right_pot)\n system.addForce(bottom_pot)\n system.addForce(top_pot)\n\n \n #unpack gaussian parameters\n if mode == \"gaussian\":\n num_gaussians = int(len(gaussian_param)/5)\n A = gaussian_param[0::5] * amp #*7\n x0 = gaussian_param[1::5]\n y0 = gaussian_param[2::5]\n sigma_x = gaussian_param[3::5]\n sigma_y = gaussian_param[4::5]\n\n #now we add the force for all gaussians.\n energy = \"0\"\n force = openmm.CustomExternalForce(energy)\n for i in range(num_gaussians):\n if pbc:\n energy = f\"A{i}*exp(-periodicdistance(x,0,0, x0{i},0,0)^2/(2*sigma_x{i}^2) - periodicdistance(0,y,0, 0,y0{i},0)^2/(2*sigma_y{i}^2))\"\n force = openmm.CustomExternalForce(energy)\n else:\n energy = f\"A{i}*exp(-(x-x0{i})^2/(2*sigma_x{i}^2) - (y-y0{i})^2/(2*sigma_y{i}^2))\"\n force = openmm.CustomExternalForce(energy)\n\n #examine the current energy term within force.\n\n print(force.getEnergyFunction())\n\n force.addGlobalParameter(f\"A{i}\", A[i])\n force.addGlobalParameter(f\"x0{i}\", x0[i])\n force.addGlobalParameter(f\"y0{i}\", y0[i])\n force.addGlobalParameter(f\"sigma_x{i}\", sigma_x[i])\n force.addGlobalParameter(f\"sigma_y{i}\", sigma_y[i])\n force.addParticle(particle_idx)\n #we append the force to the system.\n system.addForce(force)\n if plot:\n #plot the fes.\n x = np.linspace(0, 2*np.pi, 100)\n y = np.linspace(0, 2*np.pi, 100)\n X, Y = np.meshgrid(x, y)\n Z = np.zeros_like(X)\n for i in range(num_gaussians):\n Z += A[i] * np.exp(-(X-x0[i])**2/(2*sigma_x[i]**2) - (Y-y0[i])**2/(2*sigma_y[i]**2))\n plt.figure()\n plt.imshow(Z, cmap=\"coolwarm\", extent=[0, 2*np.pi,0, 2*np.pi], vmin=0, vmax=amp *12/7, origin=\"lower\")\n plt.xlabel(\"x\")\n plt.xlim([-1, 2*np.pi+1])\n plt.ylim([-1, 2*np.pi+1])\n plt.ylabel(\"y\")\n plt.title(\"FES mode = gaussian, pbc=False\")\n plt.colorbar()\n plt.savefig(plot_path)\n plt.close()\n fes = Z\n\n if mode == \"multiwell\":\n \"\"\"\n here we create a multiple well potential.\n essentially we deduct multiple gaussians from a flat surface, \n with a positive gaussian acting as an additional barrier.\n note we have to implement this into openmm CustomExternalForce.\n the x,y is [0, 2pi]\n eq:\n U(x,y) = amp * (1 #flat surface\n - A_i*exp(-(x-x0i)^2/(2*sigma_xi^2) - (y-y0i)^2/(2*sigma_yi^2))) ... #deduct gaussians\n + A_j * exp(-(x-x0j)^2/(2*sigma_xj^2) - (y-y0j)^2/(2*sigma_yj^2)) #add a sharp positive gaussian\n \"\"\"\n if pbc:\n raise NotImplementedError(\"pbc not implemented for multi-well potential.\")\n else:\n num_wells = 9\n num_barrier = 1\n\n #here's the well params\n A_i = np.array([0.9, 0.3, 0.5, 1, 0.2, 0.4, 0.9, 0.9, 0.9]) * amp #this is in kcal/mol.\n x0_i = [1.12, 1, 3, 4.15, 4, 5.27, 5.5, 6, 1] # this is in nm.\n y0_i = [1.34, 2.25, 2.31, 3.62, 5, 4.14, 4.5, 1.52, 5]\n sigma_x_i = [0.5, 0.3, 0.4, 2, 0.9, 1, 0.3, 0.5, 0.5]\n sigma_y_i = [0.5, 0.3, 1, 0.8, 0.2, 0.3, 1, 0.6, 0.7]\n\n #here's the barrier params\n # for example we define a diagonal barrier at x = pi\n A_j = np.array([0.3]) * amp\n x0_j = [np.pi]\n y0_j = [np.pi]\n sigma_x_j = [3]\n sigma_y_j = [0.3]\n\n #now we add the force for all gaussians.\n #note all energy is in Kj/mol unit.\n energy = str(amp * 4.184) #flat surface\n force = openmm.CustomExternalForce(energy)\n force.addParticle(particle_idx)\n system.addForce(force)\n for i in range(num_wells):\n energy = f\"-A{i}*exp(-(x-x0{i})^2/(2*sigma_x{i}^2) - (y-y0{i})^2/(2*sigma_y{i}^2))\"\n force = openmm.CustomExternalForce(energy)\n\n #examine the current energy term within force.\n\n print(force.getEnergyFunction())\n\n force.addGlobalParameter(f\"A{i}\", A_i[i] * 4.184) #convert kcal to kj\n force.addGlobalParameter(f\"x0{i}\", x0_i[i])\n force.addGlobalParameter(f\"y0{i}\", y0_i[i])\n force.addGlobalParameter(f\"sigma_x{i}\", sigma_x_i[i])\n force.addGlobalParameter(f\"sigma_y{i}\", sigma_y_i[i])\n force.addParticle(particle_idx)\n #we append the force to the system.\n system.addForce(force)\n \n for i in range(num_barrier):\n energy = f\"A{i+num_wells}*exp(-(x-x0{i+num_wells})^2/(2*sigma_x{i+num_wells}^2) - (y-y0{i+num_wells})^2/(2*sigma_y{i+num_wells}^2))\"\n force = openmm.CustomExternalForce(energy)\n\n #examine the current energy term within force.\n\n print(force.getEnergyFunction())\n\n force.addGlobalParameter(f\"A{i+num_wells}\", A_j[i])\n force.addGlobalParameter(f\"x0{i+num_wells}\", x0_j[i])\n force.addGlobalParameter(f\"y0{i+num_wells}\", y0_j[i])\n force.addGlobalParameter(f\"sigma_x{i+num_wells}\", sigma_x_j[i])\n force.addGlobalParameter(f\"sigma_y{i+num_wells}\", sigma_y_j[i])\n force.addParticle(particle_idx)\n #we append the force to the system.\n system.addForce(force)\n \n if plot:\n #plot the fes.\n x = np.linspace(0, 2*np.pi, 100)\n y = np.linspace(0, 2*np.pi, 100)\n X, Y = np.meshgrid(x, y)\n Z = np.zeros_like(X)\n Z += amp * 4.184 #flat surface\n for i in range(num_wells):\n Z -= A_i[i] * np.exp(-(X-x0_i[i])**2/(2*sigma_x_i[i]**2) - (Y-y0_i[i])**2/(2*sigma_y_i[i]**2))\n for i in range(num_barrier):\n Z += A_j[i] * np.exp(-(X-x0_j[i])**2/(2*sigma_x_j[i]**2) - (Y-y0_j[i])**2/(2*sigma_y_j[i]**2))\n \n #add the x,y boundary energy barrier.\n total_energy_barrier = np.zeros_like(X)\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (X - (-offset))))) #left\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (X - (2 * pi + offset))))) #right\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (Y - (-offset)))))\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (Y - (2 * pi + offset)))))\n Z += total_energy_barrier\n\n plt.figure()\n plt.imshow(Z, cmap=\"coolwarm\", extent=[0, 2*np.pi,0, 2*np.pi], vmin=0, vmax=amp* 12/7 * 4.184, origin=\"lower\")\n plt.xlabel(\"x\")\n plt.xlim([-1, 1+2*np.pi])\n plt.ylim([-1, 1+2*np.pi])\n plt.ylabel(\"y\")\n plt.title(\"FES mode = multiwell, pbc=False\")\n plt.colorbar()\n plt.savefig(plot_path)\n plt.close()\n fes = Z\n \n if mode == \"funnel\":\n \"\"\"\n this is funnel like potential.\n we start wtih a flat fes, then add/deduct sphrical gaussians\n eq:\n U = 0.7* amp * cos(2 * p * (sqrt((x-pi)^2 + (y-pi)^2))) #cos function. periodicity determines the num of waves.\n - amp exp(-((x-pi)^2+(y-pi)^2))\n + 0.4*amp*((x-pi/8)^2 + (y-pi/8)^2)\n \"\"\"\n if pbc:\n raise NotImplementedError(\"pbc not implemented for funnel potential.\")\n else:\n plot_3d = False\n periodicity = 8\n energy = f\"0.7*{amp} * cos({periodicity} * (sqrt((x-{pi})^2 + (y-{pi})^2))) - 0.6* {amp} * exp(-((x-{pi})^2+(y-{pi})^2)) + 0.4*{amp}*((x-{pi}/8)^2 + (y-{pi}/8)^2)\"\n \n force = openmm.CustomExternalForce(energy)\n force.addParticle(particle_idx)\n system.addForce(force)\n if plot:\n if plot_3d:\n import plotly.graph_objs as go\n\n # Define the x, y, and z coordinates\n x = np.linspace(0, 2*np.pi, 100)\n y = np.linspace(0, 2*np.pi, 100)\n X, Y = np.meshgrid(x, y)\n Z = np.zeros_like(X)\n Z += 0.9* amp * np.cos(periodicity * (np.sqrt((X-np.pi)**2 + (Y-np.pi)**2))) #cos function. periodicity determines the num of waves.\n Z -= 0.6* amp * np.exp(-((X-np.pi)**2/0.5+(Y-np.pi)**2)/0.5)\n Z += 0.4*amp*(((X-np.pi)/8)**2 + ((Y-np.pi)/8)**2)\n\n #add the x,y boundary energy barrier.\n total_energy_barrier = np.zeros_like(X)\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (X - 0)))) #left\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (X - 2 * pi)))) #right\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (Y - 0))))\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (Y - 2 * pi))))\n Z += total_energy_barrier\n\n # Create the 3D contour plot\n fig = go.Figure(data=[go.Surface(z=Z, x=X, y=Y, cmin = 0, cmax = amp *12/7)])\n fig.update_traces(contours_z=dict(show=True, usecolormap=True,\n highlightcolor=\"limegreen\", project_z=True))\n fig.update_layout(title='FES mode = funnel, pbc=False', autosize=True,\n width=800, height=800,\n scene = {\n \"xaxis\": {\"nticks\": 5},\n \"yaxis\": {\"nticks\": 5},\n \"zaxis\": {\"nticks\": 5},\n \"camera_eye\": {\"x\": 1, \"y\": 1, \"z\": 0.4},\n \"aspectratio\": {\"x\": 1, \"y\": 1, \"z\": 0.4}\n }\n )\n #margin=dict(l=65, r=50, b=65, t=90))\n #save fig.\n fig.write_image(plot_path)\n fes = Z\n \n else:\n #plot the fes.\n x = np.linspace(0, 2*np.pi, 100)\n y = np.linspace(0, 2*np.pi, 100)\n X, Y = np.meshgrid(x, y)\n Z = np.zeros_like(X)\n Z += 0.4* amp * np.cos(periodicity * (np.sqrt((X-np.pi)**2 + (Y-np.pi)**2))) #cos function. periodicity determines the num of waves.\n Z += 0.7* amp * np.exp(-((X-np.pi)**2/0.5+(Y-np.pi)**2/0.5))\n Z += 0.2*amp*(((X-np.pi)/8)**2 + ((Y-np.pi)/8)**2)\n\n #add the x,y boundary energy barrier.\n total_energy_barrier = np.zeros_like(X)\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (X - 0)))) #left\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (X - 2 * pi)))) #right\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (Y - 0))))\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (Y - 2 * pi))))\n Z += total_energy_barrier\n\n plt.figure()\n plt.imshow(Z, cmap=\"coolwarm\", extent=[0, 2*np.pi,0, 2*np.pi], vmin=0, vmax=amp *12/7, origin=\"lower\")\n plt.xlabel(\"x\")\n plt.xlim([-1, 2*np.pi+1])\n plt.ylim([-1, 2*np.pi+1])\n plt.ylabel(\"y\")\n plt.title(\"FES mode = funnel, pbc=False\")\n plt.colorbar()\n plt.savefig(plot_path)\n plt.close()\n fes = Z\n\n return system, fes #return the system and the fes (2D array for plotting.)\n\ndef sum_of_gaussians(params, x, y, n_gaussians, N=100):\n total = np.zeros((N,N))\n \n A = params[0::5]\n x0 = params[1::5]\n y0 = params[2::5]\n sigma_x = params[3::5]\n sigma_y = params[4::5]\n\n for i in range(n_gaussians):\n total += gaussian_2D([A[i], x0[i], y0[i], sigma_x[i], sigma_y[i]], x, y)\n return total\n\ndef apply_bias(system, particle_idx, gaussian_param, pbc = False, name = \"BIAS\", num_gaussians = 20):\n \"\"\"\n this applies a bias using customexternal force class. similar as apply_fes.\n note this leaves a set of global parameters Ag, x0g, y0g, sigma_xg, sigma_yg.\n as these parameters can be called and updated later.\n note this is done while preparing the system before assembling the context.\n \"\"\"\n assert len(gaussian_param) == 5 * num_gaussians, \"gaussian_param should be in A, x0, y0, sigma_x, sigma_y format.\"\n\n #unpack gaussian parameters gaussian_params = np.concatenate((a, bx, by, cx, cy))\n num_gaussians = len(gaussian_param)//5\n A = gaussian_param[:num_gaussians]\n x0 = gaussian_param[num_gaussians:2*num_gaussians]\n y0 = gaussian_param[2*num_gaussians:3*num_gaussians]\n sigma_x = gaussian_param[3*num_gaussians:4*num_gaussians]\n sigma_y = gaussian_param[4*num_gaussians:5*num_gaussians]\n\n #now we add the force for all gaussians. with num_gaussians terms.\n energy = \"0\"\n force = openmm.CustomExternalForce(energy)\n for i in range(num_gaussians):\n if pbc:\n raise NotImplementedError(\"pbc not implemented for gaussian potential.\")\n energy = f\"Ag{i}*exp(-periodicdistance(x,0,0, x0g{i},0,0)^2/(2*sigma_xg{i}^2) - periodicdistance(0,y,0, 0,y0g{i},0)^2/(2*sigma_yg{i}^2))\"\n force = openmm.CustomExternalForce(energy)\n else:\n energy = f\"Ag{i}*exp(-(x-x0g{i})^2/(2*sigma_xg{i}^2) - (y-y0g{i})^2/(2*sigma_yg{i}^2))\" #in openmm unit, kj/mol, nm.\n force = openmm.CustomExternalForce(energy)\n\n #examine the current energy term within force.\n\n print(force.getEnergyFunction())\n\n force.addGlobalParameter(f\"Ag{i}\", A[i] * 4.184) #convert to kJ/mol\n force.addGlobalParameter(f\"x0g{i}\", x0[i]) #convert to nm\n force.addGlobalParameter(f\"y0g{i}\", y0[i])\n force.addGlobalParameter(f\"sigma_xg{i}\", sigma_x[i])\n force.addGlobalParameter(f\"sigma_yg{i}\", sigma_y[i])\n force.addParticle(particle_idx)\n #we append the force to the system.\n system.addForce(force)\n \n print(\"system added with bias.\")\n return system\n\ndef update_bias(simulation, gaussian_param, name = \"BIAS\", num_gaussians = 20):\n \"\"\"\n given the gaussian_param, update the bias\n note this requires the context object. or a simulation object.\n # the context object can be accessed by simulation.context.\n \"\"\"\n assert len(gaussian_param) == 5 * num_gaussians, \"gaussian_param should be in A, x0, y0, sigma_x, sigma_y format.\"\n\n #unpack gaussian parameters gaussian_params = np.concatenate((a, bx, by, cx, cy))\n num_gaussians = len(gaussian_param)//5\n A = gaussian_param[:num_gaussians]\n x0 = gaussian_param[num_gaussians:2*num_gaussians]\n y0 = gaussian_param[2*num_gaussians:3*num_gaussians]\n sigma_x = gaussian_param[3*num_gaussians:4*num_gaussians]\n sigma_y = gaussian_param[4*num_gaussians:5*num_gaussians]\n\n #now we update the GlobalParameter for all gaussians. with num_gaussians terms. and update them in the system.\n #note globalparameter does NOT need to be updated in the context.\n for i in range(num_gaussians):\n simulation.context.setParameter(f\"Ag{i}\", A[i] * 4.184) #convert to kJ/mol\n simulation.context.setParameter(f\"x0g{i}\", x0[i]) #convert to nm\n simulation.context.setParameter(f\"y0g{i}\", y0[i])\n simulation.context.setParameter(f\"sigma_xg{i}\", sigma_x[i])\n simulation.context.setParameter(f\"sigma_yg{i}\", sigma_y[i])\n \n print(\"system bias updated\")\n return simulation\n","repo_name":"DerienFe/RL_MFPT","sub_path":"2D_langevin_sim/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":30034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41294610209","text":"import math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# number of points\nnum_samples = 500\n\ndef painting(xc, yc, xr, yr):\n fig, ax = plt.subplots()\n\n # make a simple unit circle\n theta = np.linspace(0, 2*np.pi, num_samples)\n a, b = 1*np.cos(theta), 1*np.sin(theta)\n plt.plot(a, b, linestyle='-', linewidth=2, color='black', label='Circle')\n\n # painting the points\n plt.scatter(xr, yr, color='blue')\n plt.scatter(xc, yc, color='red')\n plt.axis([-1.5, 1.5, -1.5, 1.5])\n ax.set(xlabel='$x_1$', ylabel='$x_2$', title='Train data')\n plt.show()\n\ndef generate_points(num_samples):\n # generate the num_samples points within a rectangle [-1,5,1.5, -1.5, 1.5]\n point = -1.5+np.random.rand(num_samples, 2)*3\n px, py = list(point[:,0]), list(point[:,1])\n\n # divide points into two classes: in the circle and not.\n xc, yc = [], [] # points in the unit circle\n xr, yr = [], [] # points not in the unit circle\n for i in range(len(px)):\n if math.sqrt(px[i]**2+py[i]**2) <= 1:\n xc.append(px[i])\n yc.append(py[i])\n else:\n xr.append(px[i])\n yr.append(py[i])\n\n return xc, yc, xr, yr\n\ndef main():\n xc, yc, xr, yr = generate_points(num_samples)\n painting(xc, yc, xr, yr)\n\nif __name__ == '__main__':\n main()\n","repo_name":"htma/nn-trainer","sub_path":"plot_points.py","file_name":"plot_points.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30755007287","text":"# all global variables\n\ndatabase_path = '' # path for database\nmat_prefs_path = '' # path for mat_prefs\nmat_prefs = '' # actual mat_prefs student/section prefs\nsec_sec_matrix_path = '' # path for section/section prefs\nmatrix_sections = '' # actual sec_sec_matrix\n\nparameters = ''\npara_path = ''\n\n# conflict and preference parameters\nsec_time_con = -10000000\nsec_sameclass_pref = 2000\nsec_sameprof_pref = 2000\n\nstu_time_con = -5000\nstu_class_con = -500\nstu_prof_con = -500\nstu_year_pref = 1000\nstu_div_pref = 50000\nstu_skill_pref = 1000\nstu_time_pref = 500\nstu_class_pref = 500\nstu_prof_pref = 500\n","repo_name":"Katiedaisey/scheduling","sub_path":"interface/globalvars.py","file_name":"globalvars.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"7483335856","text":"import aiohttp\nimport asyncio\nimport urllib.parse\nimport os\nimport logging\nfrom discord import File, TextChannel, User\nfrom discord.errors import NotFound\nimport config\nfrom utils.image import modify_avatar\nfrom utils import tag\n\n\nclass AvatarManager:\n def __init__(\n self,\n loop,\n db,\n http_session: aiohttp.ClientSession,\n fetch_channel\n ):\n self.loop = loop\n self.db = db\n self.http_session = http_session\n self.fetch_channel = fetch_channel\n\n\n async def fetch(self, user: User) -> str:\n res = self.db.execute(\n \"\"\"\n SELECT original_avatar_url,\n modified_avatar_url,\n modified_avatar_message_id\n FROM users WHERE id = ?\n \"\"\",\n (user.id,)\n )\n data = res.fetchone()\n\n avatar_channel = await self.fetch_channel(config.AVATAR_STORE_CHANNEL_ID)\n\n if data is not None:\n (\n original_avatar_url,\n modified_avatar_url,\n modified_avatar_message_id\n ) = data\n\n # User hasn't changed their avatar since last time they did\n # |imitate, so we can use the cached modified avatar.\n if self._avatar_url_id(user.display_avatar.url) == self._avatar_url_id(original_avatar_url):\n return modified_avatar_url\n\n # Else, user has changed their avatar.\n # Respect the user's privacy by deleting the message with their old\n # avatar.\n # Don't wait for this operation to complete before continuing.\n asyncio.create_task(\n self._delete_message(avatar_channel, modified_avatar_message_id)\n )\n\n # User has changed their avatar since last time they did |imitate or has\n # not done |imitate before, so we must create a modified version of\n # their avatar.\n # We employ the classic Discord As A CDN method to cache the modified\n # avatars by posting them to a Discord channel and storing the message\n # IDs for later.\n modified_avatar, file_format = await modify_avatar(user)\n message = await avatar_channel.send(\n file=File(modified_avatar, f\"{user.id}.{file_format}\")\n )\n\n # Update the avatar database with the new avatar URL.\n self.db.execute(\n \"\"\"\n UPDATE users\n SET original_avatar_url = ?,\n modified_avatar_url = ?,\n modified_avatar_message_id = ?\n WHERE id = ?\n \"\"\",\n (\n user.display_avatar.url,\n message.attachments[0].url,\n message.id,\n user.id\n )\n )\n return message.attachments[0].url\n\n\n async def _delete_message(\n self,\n channel: TextChannel,\n message_id: int\n ) -> None:\n try:\n message = await channel.fetch_message(message_id)\n except NotFound:\n logging.warn(\n f\"Tried to delete message {message_id} from the avatar store, \"\n \"but it doesn't exist.\"\n )\n else:\n await message.delete()\n\n\n def _avatar_url_id(self, url: str) -> str:\n path = urllib.parse.urlparse(url).path\n return os.path.splitext(path)[0]\n","repo_name":"garlic-os/parrot","sub_path":"database/avatar_manager.py","file_name":"avatar_manager.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"6767440779","text":"import cv2\nimport numpy as np\nimport os\nfrom matplotlib import pyplot as plot\n\n\n########################################\n# Ler imagem original\nimg_path = \"Resources/Semaforo_verm_5.jpg\"\nimg = cv2.imread(img_path)\n\nredPixels=0\nyellowPixels=0\ngreenPixels=0\nnumero_redlights=0\nnumero_greenlights=0\nnumero_yellowlights=0\n\n\n# Aplicar Blur\nimg_blur = cv2.medianBlur(img,5)\n\n# Converter para HSV\nimg_hsv = cv2.cvtColor(img_blur, cv2.COLOR_BGR2HSV)\n\n# Criar Array com o numpy para consultar dados recebidos doa HSV\nimg_HSV_values = np.array(img_hsv)\n\n# # Mostrar dados array valores HSV\n# print(img_HSV_values)\n\n\n\n\n# Mostrar imagem original\ncv2.imshow('Original', img)\n\ncv2.waitKey()\n\n\n\n########################################\n# Red Color Range de HSV\nlower_range_red = np.array([161, 155, 84])\nupper_range_red = np.array([179, 255, 255])\n\n# Criar mascara à volta da zona RED\nred_mask = cv2.inRange(img_hsv, lower_range_red, upper_range_red)\n\n# Função para apenas a zona vermelha\nred_only = cv2.bitwise_and(img_hsv, img, mask=red_mask)\n\n# Mostrar mascaras aplicadas à zona red\ncv2.imshow(\"mascara red\", red_mask)\n#cv2.imshow(\"mascara red\", red_only)\nredPixels = cv2.countNonZero(red_mask)\n\nprint(\"Pixels Vermelhos \" + str(redPixels))\ncv2.waitKey()\n\n\n\n########################################\n# Green Color Range de HSV\nlower_range_green = np.array([48, 208, 0])\nupper_range_green = np.array([83, 255, 255])\n\n# Criar máscara à volta da zona Green\ngreen_mask = cv2.inRange(img_hsv, lower_range_green, upper_range_green)\n\n# Mostrar apenas a zona Green\ngreen_only = cv2.bitwise_and(img_hsv, img, mask=green_mask)\n\n# Mostrar mascaras aplicadas à zona red\ncv2.imshow(\"mascara Green\", green_mask)\n#cv2.imshow(\"mascara Green\", green_only)\ngreenPixels = cv2.countNonZero(green_mask)\n\nprint(\"Pixels Verdes \" + str(greenPixels))\ncv2.waitKey()\n\n\n\n########################################\n# Yellow Color Range de HSV\nlower_range_yellow = np.array([12, 173, 155])\nupper_range_yellow = np.array([42, 212, 255])\n\n# Criar máscara à volta da zona yellow\nyellow_mask = cv2.inRange(img_hsv, lower_range_yellow, upper_range_yellow)\n\n# Mostrar apenas a zona Green\nyellow_only = cv2.bitwise_and(img_hsv, img, mask=yellow_mask)\n\n# Mostrar mascaras aplicadas à zona red\ncv2.imshow(\"mascara Yellow\", yellow_mask)\n#cv2.imshow(\"mascara Yellow\", yellow_only)\nyellowPixels = cv2.countNonZero(yellow_mask)\n\nprint(\"Pixels Amarelos \" + str(yellowPixels))\ncv2.waitKey()\n\n\nif redPixels > 0:\n numero_redlights = numero_redlights + 1\n print (\"Está Amarelo em \"+str(numero_redlights)+\" Imagens\")\n\nif greenPixels > 0:\n numero_redlights = numero_redlights + 1\n print (\"Está Verde em \"+str(numero_greenlights)+\" Imagens\")\n\nif yellowPixels > 0:\n print (\"Está Amarelo em \"+str(numero_yellowlights)+\" Imagens\")\n\ncv2.waitKey()\n\n\n\n","repo_name":"blamelas/python-color-detection","sub_path":"ColorDetection.py","file_name":"ColorDetection.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12598112543","text":"\"\"\"\nDashboard view and supporting methods\n\"\"\"\n\n\nimport datetime\nimport logging\nfrom collections import defaultdict\n\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import redirect\nfrom django.urls import reverse\nfrom django.utils.translation import gettext as _\nfrom django.views.decorators.csrf import ensure_csrf_cookie\nfrom edx_django_utils import monitoring as monitoring_utils\nfrom edx_django_utils.plugins import get_plugins_view_context\nfrom edx_toggles.toggles import WaffleFlag\nfrom ipware.ip import get_client_ip\nfrom opaque_keys.edx.keys import CourseKey\nfrom openedx_filters.learning.filters import DashboardRenderStarted\nfrom pytz import UTC\n\nfrom lms.djangoapps.bulk_email.api import is_bulk_email_feature_enabled\nfrom lms.djangoapps.bulk_email.models import Optout\nfrom common.djangoapps.course_modes.models import CourseMode\nfrom common.djangoapps.edxmako.shortcuts import render_to_response, render_to_string\nfrom common.djangoapps.entitlements.models import CourseEntitlement\nfrom lms.djangoapps.commerce.utils import EcommerceService\nfrom lms.djangoapps.courseware.access import has_access\nfrom lms.djangoapps.learner_home.waffle import should_redirect_to_learner_home_mfe\nfrom lms.djangoapps.experiments.utils import get_dashboard_course_info, get_experiment_user_metadata_context\nfrom lms.djangoapps.verify_student.services import IDVerificationService\nfrom openedx.core.djangoapps.catalog.utils import (\n get_programs,\n get_pseudo_session_for_entitlement,\n get_visible_sessions_for_entitlement\n)\nfrom openedx.core.djangoapps.credit.email_utils import get_credit_provider_attribute_values, make_providers_strings\nfrom openedx.core.djangoapps.plugins.constants import ProjectType\nfrom openedx.core.djangoapps.programs.models import ProgramsApiConfig\nfrom openedx.core.djangoapps.programs.utils import ProgramDataExtender, ProgramProgressMeter\nfrom openedx.core.djangoapps.site_configuration import helpers as configuration_helpers\nfrom openedx.core.djangoapps.user_api.accounts.utils import is_secondary_email_feature_enabled\nfrom openedx.core.djangoapps.util.maintenance_banner import add_maintenance_banner\nfrom openedx.core.djangolib.markup import HTML, Text\nfrom openedx.features.content_type_gating.models import ContentTypeGatingConfig\nfrom openedx.features.course_duration_limits.access import get_user_course_duration, get_user_course_expiration_date\nfrom openedx.features.enterprise_support.api import (\n get_dashboard_consent_notification,\n get_enterprise_learner_portal_context,\n)\nfrom openedx.features.enterprise_support.utils import is_enterprise_learner\nfrom openedx.core.djangoapps.geoinfo.api import country_code_from_ip\nfrom common.djangoapps.student.api import COURSE_DASHBOARD_PLUGIN_VIEW_NAME\nfrom common.djangoapps.student.helpers import cert_info, check_verify_status_by_course, get_resume_urls_for_enrollments\nfrom common.djangoapps.student.models import (\n AccountRecovery,\n CourseEnrollment,\n CourseEnrollmentAttribute,\n DashboardConfiguration,\n PendingSecondaryEmailChange,\n UserProfile\n)\nfrom common.djangoapps.util.milestones_helpers import get_pre_requisite_courses_not_completed\nfrom xmodule.modulestore.django import modulestore # lint-amnesty, pylint: disable=wrong-import-order\n\nlog = logging.getLogger(\"edx.student\")\n\nEXPERIMENTS_NAMESPACE = 'student.experiments'\n\n\ndef get_org_black_and_whitelist_for_site():\n \"\"\"\n Returns the org blacklist and whitelist for the current site.\n\n Returns:\n (org_whitelist, org_blacklist): A tuple of lists of orgs that serve as\n either a blacklist or a whitelist of orgs for the current site. The\n whitelist takes precedence, and the blacklist is used if the\n whitelist is None.\n \"\"\"\n # Default blacklist is empty.\n org_blacklist = None\n # Whitelist the orgs configured for the current site. Each site outside\n # of edx.org has a list of orgs associated with its configuration.\n org_whitelist = configuration_helpers.get_current_site_orgs()\n\n if not org_whitelist:\n # If there is no whitelist, the blacklist will include all orgs that\n # have been configured for any other sites. This applies to edx.org,\n # where it is easier to blacklist all other orgs.\n org_blacklist = configuration_helpers.get_all_orgs()\n\n return org_whitelist, org_blacklist\n\n\ndef _get_recently_enrolled_courses(course_enrollments):\n \"\"\"\n Given a list of enrollments, filter out all but recent enrollments.\n\n Args:\n course_enrollments (list[CourseEnrollment]): A list of course enrollments.\n\n Returns:\n list[CourseEnrollment]: A list of recent course enrollments.\n \"\"\"\n seconds = DashboardConfiguration.current().recent_enrollment_time_delta\n time_delta = (datetime.datetime.now(UTC) - datetime.timedelta(seconds=seconds))\n return [\n enrollment for enrollment in course_enrollments\n # If the enrollment has no created date, we are explicitly excluding the course\n # from the list of recent enrollments.\n if enrollment.is_active and enrollment.created > time_delta\n ]\n\n\ndef _create_recent_enrollment_message(course_enrollments, course_modes): # lint-amnesty, pylint: disable=unused-argument\n \"\"\"\n Builds a recent course enrollment message.\n\n Constructs a new message template based on any recent course enrollments\n for the student.\n\n Args:\n course_enrollments (list[CourseEnrollment]): a list of course enrollments.\n course_modes (dict): Mapping of course ID's to course mode dictionaries.\n\n Returns:\n A string representing the HTML message output from the message template.\n None if there are no recently enrolled courses.\n\n \"\"\"\n recently_enrolled_courses = _get_recently_enrolled_courses(course_enrollments)\n\n if recently_enrolled_courses:\n enrollments_count = len(recently_enrolled_courses)\n course_name_separator = ', '\n # If length of enrolled course 2, join names with 'and'\n if enrollments_count == 2:\n course_name_separator = _(' and ')\n\n course_names = course_name_separator.join(\n [enrollment.course_overview.display_name for enrollment in recently_enrolled_courses]\n )\n\n platform_name = configuration_helpers.get_value('platform_name', settings.PLATFORM_NAME)\n\n return render_to_string(\n 'enrollment/course_enrollment_message.html',\n {\n 'course_names': course_names,\n 'enrollments_count': enrollments_count,\n 'platform_name': platform_name,\n 'course_id': recently_enrolled_courses[0].course_overview.id if enrollments_count == 1 else None\n }\n )\n\n\ndef get_course_enrollments(user, org_whitelist, org_blacklist, course_limit=None):\n \"\"\"\n Given a user, return a filtered set of their course enrollments.\n\n Arguments:\n user (User): the user in question.\n org_whitelist (list[str]): If not None, ONLY courses of these orgs will be returned.\n org_blacklist (list[str]): Courses of these orgs will be excluded.\n course_limit: Number courses to load in dashboard if set to None then all the courses would be load.\n\n Returns:\n generator[CourseEnrollment]: a sequence of enrollments to be displayed\n on the user's dashboard.\n \"\"\"\n for enrollment in CourseEnrollment.enrollments_for_user_with_overviews_preload(user, course_limit):\n\n # If the course is missing or broken, log an error and skip it.\n course_overview = enrollment.course_overview\n if not course_overview:\n log.error(\n \"User %s enrolled in broken or non-existent course %s\",\n user.username,\n enrollment.course_id\n )\n continue\n\n # Filter out anything that is not in the whitelist.\n if org_whitelist and course_overview.location.org not in org_whitelist:\n continue\n\n # Conversely, filter out any enrollments in the blacklist.\n elif org_blacklist and course_overview.location.org in org_blacklist:\n continue\n\n # Else, include the enrollment.\n else:\n yield enrollment\n\n\ndef get_filtered_course_entitlements(user, org_whitelist, org_blacklist):\n \"\"\"\n Given a user, return a filtered set of their course entitlements.\n\n Arguments:\n user (User): the user in question.\n org_whitelist (list[str]): If not None, ONLY entitlements of these orgs will be returned.\n org_blacklist (list[str]): CourseEntitlements of these orgs will be excluded.\n\n Returns:\n generator[CourseEntitlement]: a sequence of entitlements to be displayed\n on the user's dashboard.\n \"\"\"\n course_entitlement_available_sessions = {}\n unfulfilled_entitlement_pseudo_sessions = {}\n course_entitlements = list(CourseEntitlement.get_active_entitlements_for_user(user))\n filtered_entitlements = []\n pseudo_session = None\n course_run_key = None\n\n for course_entitlement in course_entitlements:\n course_entitlement.update_expired_at()\n available_runs = get_visible_sessions_for_entitlement(course_entitlement)\n\n if not course_entitlement.enrollment_course_run:\n # Unfulfilled entitlements need a mock session for metadata\n pseudo_session = get_pseudo_session_for_entitlement(course_entitlement)\n unfulfilled_entitlement_pseudo_sessions[str(course_entitlement.uuid)] = pseudo_session\n\n # Check the org of the Course and filter out entitlements that are not available.\n if course_entitlement.enrollment_course_run:\n course_run_key = course_entitlement.enrollment_course_run.course_id\n elif available_runs:\n course_run_key = CourseKey.from_string(available_runs[0]['key'])\n elif pseudo_session:\n course_run_key = CourseKey.from_string(pseudo_session['key'])\n\n if course_run_key:\n # If there is no course_run_key at this point we will be unable to determine if it should be shown.\n # Therefore it should be excluded by default.\n if org_whitelist and course_run_key.org not in org_whitelist:\n continue\n elif org_blacklist and course_run_key.org in org_blacklist:\n continue\n\n course_entitlement_available_sessions[str(course_entitlement.uuid)] = available_runs\n filtered_entitlements.append(course_entitlement)\n\n return filtered_entitlements, course_entitlement_available_sessions, unfulfilled_entitlement_pseudo_sessions\n\n\ndef complete_course_mode_info(course_id, enrollment, modes=None):\n \"\"\"\n We would like to compute some more information from the given course modes\n and the user's current enrollment\n\n Returns the given information:\n - whether to show the course upsell information\n - numbers of days until they can't upsell anymore\n \"\"\"\n if modes is None:\n modes = CourseMode.modes_for_course_dict(course_id)\n\n mode_info = {'show_upsell': False, 'days_for_upsell': None}\n # we want to know if the user is already enrolled as verified or credit and\n # if verified is an option.\n if CourseMode.VERIFIED in modes and enrollment.mode in CourseMode.UPSELL_TO_VERIFIED_MODES:\n mode_info['show_upsell'] = True\n mode_info['verified_sku'] = modes['verified'].sku\n mode_info['verified_bulk_sku'] = modes['verified'].bulk_sku\n # if there is an expiration date, find out how long from now it is\n if modes['verified'].expiration_datetime:\n today = datetime.datetime.now(UTC).date()\n mode_info['days_for_upsell'] = (modes['verified'].expiration_datetime.date() - today).days\n mode_info['expiration_datetime'] = modes['verified'].expiration_datetime.date()\n\n return mode_info\n\n\ndef get_verification_error_reasons_for_display(verification_error_codes):\n \"\"\"\n Returns the display text for the given verification error codes.\n \"\"\"\n verification_errors = []\n verification_error_map = {\n 'photos_mismatched': _('Photos are mismatched'),\n 'id_image_missing_name': _('Name missing from ID photo'),\n 'id_image_missing': _('ID photo not provided'),\n 'id_invalid': _('ID is invalid'),\n 'user_image_not_clear': _('Learner photo is blurry'),\n 'name_mismatch': _('Name on ID does not match name on account'),\n 'user_image_missing': _('Learner photo not provided'),\n 'id_image_not_clear': _('ID photo is blurry'),\n }\n\n for error in verification_error_codes:\n error_text = verification_error_map.get(error)\n if error_text:\n verification_errors.append(error_text)\n\n return verification_errors\n\n\ndef reverification_info(statuses):\n \"\"\"\n Returns reverification-related information for *all* of user's enrollments whose\n reverification status is in statuses.\n\n Args:\n statuses (list): a list of reverification statuses we want information for\n example: [\"must_reverify\", \"denied\"]\n\n Returns:\n dictionary of lists: dictionary with one key per status, e.g.\n dict[\"must_reverify\"] = []\n dict[\"must_reverify\"] = [some information]\n \"\"\"\n reverifications = defaultdict(list)\n\n # Sort the data by the reverification_end_date\n for status in statuses:\n if reverifications[status]:\n reverifications[status].sort(key=lambda x: x.date)\n return reverifications\n\n\ndef credit_statuses(user, course_enrollments):\n \"\"\"\n Retrieve the status for credit courses.\n\n A credit course is a course for which a user can purchased\n college credit. The current flow is:\n\n 1. User becomes eligible for credit (submits verifications, passes the course, etc.)\n 2. User purchases credit from a particular credit provider.\n 3. User requests credit from the provider, usually creating an account on the provider's site.\n 4. The credit provider notifies us whether the user's request for credit has been accepted or rejected.\n\n The dashboard is responsible for communicating the user's state in this flow.\n\n Arguments:\n user (User): The currently logged-in user.\n course_enrollments (list[CourseEnrollment]): List of enrollments for the\n user.\n\n Returns: dict\n\n The returned dictionary has keys that are `CourseKey`s and values that\n are dictionaries with:\n\n * eligible (bool): True if the user is eligible for credit in this course.\n * deadline (datetime): The deadline for purchasing and requesting credit for this course.\n * purchased (bool): Whether the user has purchased credit for this course.\n * provider_name (string): The display name of the credit provider.\n * provider_status_url (string): A URL the user can visit to check on their credit request status.\n * provider_id (string): A unique alphanumeric (plus hyphens) string identifying the provider.\n * request_status (string): Either \"pending\", \"approved\", or \"rejected\"\n * error (bool): If true, an unexpected error occurred when retrieving the credit status,\n so the user should contact the support team.\n\n Example:\n >>> credit_statuses(user, course_enrollments)\n {\n CourseKey.from_string(\"edX/DemoX/Demo_Course\"): {\n \"course_key\": \"edX/DemoX/Demo_Course\",\n \"eligible\": True,\n \"deadline\": 2015-11-23 00:00:00 UTC,\n \"purchased\": True,\n \"provider_name\": \"Hogwarts\",\n \"provider_status_url\": \"http://example.com/status\",\n \"provider_id\": \"HSWW\",\n \"request_status\": \"pending\",\n \"error\": False\n }\n }\n\n \"\"\"\n from openedx.core.djangoapps.credit import api as credit_api\n\n # Feature flag off\n if not settings.FEATURES.get(\"ENABLE_CREDIT_ELIGIBILITY\"):\n return {}\n\n request_status_by_course = {\n request[\"course_key\"]: request[\"status\"]\n for request in credit_api.get_credit_requests_for_user(user.username)\n }\n\n credit_enrollments = {\n enrollment.course_id: enrollment\n for enrollment in course_enrollments\n if enrollment.mode == \"credit\"\n }\n\n # When a user purchases credit in a course, the user's enrollment\n # mode is set to \"credit\" and an enrollment attribute is set\n # with the ID of the credit provider. We retrieve *all* such attributes\n # here to minimize the number of database queries.\n purchased_credit_providers = {\n attribute.enrollment.course_id: attribute.value\n for attribute in CourseEnrollmentAttribute.objects.filter(\n namespace=\"credit\",\n name=\"provider_id\",\n enrollment__in=list(credit_enrollments.values())\n ).select_related(\"enrollment\")\n }\n\n provider_info_by_id = {\n provider[\"id\"]: provider\n for provider in credit_api.get_credit_providers()\n }\n\n statuses = {}\n for eligibility in credit_api.get_eligibilities_for_user(user.username):\n course_key = CourseKey.from_string(str(eligibility[\"course_key\"]))\n providers_names = get_credit_provider_attribute_values(course_key, 'display_name')\n status = {\n \"course_key\": str(course_key),\n \"eligible\": True,\n \"deadline\": eligibility[\"deadline\"],\n \"purchased\": course_key in credit_enrollments,\n \"provider_name\": make_providers_strings(providers_names),\n \"provider_status_url\": None,\n \"provider_id\": None,\n \"request_status\": request_status_by_course.get(course_key),\n \"error\": False,\n }\n\n # If the user has purchased credit, then include information about the credit\n # provider from which the user purchased credit.\n # We retrieve the provider's ID from the an \"enrollment attribute\" set on the user's\n # enrollment when the user's order for credit is fulfilled by the E-Commerce service.\n if status[\"purchased\"]:\n provider_id = purchased_credit_providers.get(course_key)\n if provider_id is None:\n status[\"error\"] = True\n log.error(\n \"Could not find credit provider associated with credit enrollment \"\n \"for user %s in course %s. The user will not be able to see their \"\n \"credit request status on the student dashboard. This attribute should \"\n \"have been set when the user purchased credit in the course.\",\n user.id, course_key\n )\n else:\n provider_info = provider_info_by_id.get(provider_id, {})\n status[\"provider_name\"] = provider_info.get(\"display_name\")\n status[\"provider_status_url\"] = provider_info.get(\"status_url\")\n status[\"provider_id\"] = provider_id\n\n if not status[\"provider_name\"] and not status[\"provider_status_url\"]:\n status[\"error\"] = True\n log.error(\n \"Could not find credit provider info for [%s] in [%s]. The user will not \"\n \"be able to see their credit request status on the student dashboard.\",\n provider_id, provider_info_by_id\n )\n\n statuses[course_key] = status\n\n return statuses\n\n\ndef show_load_all_courses_link(user, course_limit, course_enrollments):\n \"\"\"\n By default dashboard will show limited courses based on the course limit\n set in configuration.\n\n A link would be provided provided at the bottom to load all the courses if there are any courses.\n \"\"\"\n\n if course_limit is None:\n return False\n\n total_enrollments = CourseEnrollment.enrollments_for_user(user).count()\n return len(course_enrollments) < total_enrollments\n\n\ndef get_dashboard_course_limit():\n \"\"\"\n get course limit from configuration\n \"\"\"\n course_limit = getattr(settings, 'DASHBOARD_COURSE_LIMIT', None)\n return course_limit\n\n\ndef check_for_unacknowledged_notices(context):\n \"\"\"\n Checks the notices apps plugin context to see if there are any unacknowledged notices the user needs to take action\n on. If so, build a redirect url to the first unack'd notice.\n \"\"\"\n notice_url = None\n\n notices = context.get(\"plugins\", {}).get(\"notices\", {}).get(\"unacknowledged_notices\")\n if notices:\n # We will only show one notice to the user one at a time. Build a redirect URL to the first notice in the\n # list of unacknowledged notices.\n notice_url = f\"{settings.LMS_ROOT_URL}{notices[0]}?next={settings.LMS_ROOT_URL}/dashboard/\"\n\n return notice_url\n\n\n@login_required\n@ensure_csrf_cookie\n@add_maintenance_banner\ndef student_dashboard(request): # lint-amnesty, pylint: disable=too-many-statements\n \"\"\"\n Provides the LMS dashboard view\n\n TODO: This is lms specific and does not belong in common code.\n Note:\n To load the all courses set course_limit=None as parameter in GET. If its not None then default course\n limit will be used that is set in configuration\n Arguments:\n request: The request object.\n\n Returns:\n The dashboard response.\n\n \"\"\"\n user = request.user\n if not UserProfile.objects.filter(user=user).exists():\n return redirect(reverse('account_settings'))\n\n if should_redirect_to_learner_home_mfe(user):\n return redirect(settings.LEARNER_HOME_MICROFRONTEND_URL)\n\n platform_name = configuration_helpers.get_value(\"platform_name\", settings.PLATFORM_NAME)\n\n enable_verified_certificates = configuration_helpers.get_value(\n 'ENABLE_VERIFIED_CERTIFICATES',\n settings.FEATURES.get('ENABLE_VERIFIED_CERTIFICATES')\n )\n display_course_modes_on_dashboard = configuration_helpers.get_value(\n 'DISPLAY_COURSE_MODES_ON_DASHBOARD',\n settings.FEATURES.get('DISPLAY_COURSE_MODES_ON_DASHBOARD', True)\n )\n activation_email_support_link = configuration_helpers.get_value(\n 'ACTIVATION_EMAIL_SUPPORT_LINK', settings.ACTIVATION_EMAIL_SUPPORT_LINK\n ) or settings.SUPPORT_SITE_LINK\n hide_dashboard_courses_until_activated = configuration_helpers.get_value(\n 'HIDE_DASHBOARD_COURSES_UNTIL_ACTIVATED',\n settings.FEATURES.get('HIDE_DASHBOARD_COURSES_UNTIL_ACTIVATED', False)\n )\n empty_dashboard_message = configuration_helpers.get_value(\n 'EMPTY_DASHBOARD_MESSAGE', None\n )\n disable_unenrollment = configuration_helpers.get_value(\n 'DISABLE_UNENROLLMENT',\n settings.FEATURES.get('DISABLE_UNENROLLMENT')\n )\n\n disable_course_limit = request and 'course_limit' in request.GET\n course_limit = get_dashboard_course_limit() if not disable_course_limit else None\n\n # Get the org whitelist or the org blacklist for the current site\n site_org_whitelist, site_org_blacklist = get_org_black_and_whitelist_for_site()\n course_enrollments = list(get_course_enrollments(user, site_org_whitelist, site_org_blacklist, course_limit))\n\n # Get the entitlements for the user and a mapping to all available sessions for that entitlement\n # If an entitlement has no available sessions, pass through a mock course overview object\n (course_entitlements,\n course_entitlement_available_sessions,\n unfulfilled_entitlement_pseudo_sessions) = get_filtered_course_entitlements(\n user,\n site_org_whitelist,\n site_org_blacklist\n )\n\n # Record how many courses there are so that we can get a better\n # understanding of usage patterns on prod.\n monitoring_utils.accumulate('num_courses', len(course_enrollments))\n\n # Sort the enrollment pairs by the enrollment date\n course_enrollments.sort(key=lambda x: x.created, reverse=True)\n\n # Retrieve the course modes for each course\n enrolled_course_ids = [enrollment.course_id for enrollment in course_enrollments]\n __, unexpired_course_modes = CourseMode.all_and_unexpired_modes_for_courses(enrolled_course_ids)\n course_modes_by_course = {\n course_id: {\n mode.slug: mode\n for mode in modes\n }\n for course_id, modes in unexpired_course_modes.items()\n }\n\n # Check to see if the student has recently enrolled in a course.\n # If so, display a notification message confirming the enrollment.\n enrollment_message = _create_recent_enrollment_message(\n course_enrollments, course_modes_by_course\n )\n course_optouts = Optout.objects.filter(user=user).values_list('course_id', flat=True)\n\n # Display activation message\n activate_account_message = ''\n if not user.is_active:\n activate_account_message = Text(_(\n \"Check your {email_start}{email}{email_end} inbox for an account activation link from {platform_name}. \"\n \"If you need help, contact {link_start}{platform_name} Support{link_end}.\"\n )).format(\n platform_name=platform_name,\n email_start=HTML(\"\"),\n email_end=HTML(\"\"),\n email=user.email,\n link_start=HTML(\"\").format(\n activation_email_support_link=activation_email_support_link,\n ),\n link_end=HTML(\"\"),\n )\n\n enterprise_message = get_dashboard_consent_notification(request, user, course_enrollments)\n\n recovery_email_message = recovery_email_activation_message = None\n if is_secondary_email_feature_enabled():\n try:\n pending_email = PendingSecondaryEmailChange.objects.get(user=user) # lint-amnesty, pylint: disable=unused-variable\n except PendingSecondaryEmailChange.DoesNotExist:\n try:\n account_recovery_obj = AccountRecovery.objects.get(user=user) # lint-amnesty, pylint: disable=unused-variable\n except AccountRecovery.DoesNotExist:\n recovery_email_message = Text(\n _(\n \"Add a recovery email to retain access when single-sign on is not available. \"\n \"Go to {link_start}your Account Settings{link_end}.\")\n ).format(\n link_start=HTML(\"\").format(\n account_setting_page=reverse('account_settings'),\n ),\n link_end=HTML(\"\")\n )\n else:\n recovery_email_activation_message = Text(\n _(\n \"Recovery email is not activated yet. \"\n \"Kindly visit your email and follow the instructions to activate it.\"\n )\n )\n\n # Disable lookup of Enterprise consent_required_course due to ENT-727\n # Will re-enable after fixing WL-1315\n consent_required_courses = set()\n\n # Account activation message\n account_activation_messages = [\n message for message in messages.get_messages(request) if 'account-activation' in message.tags\n ]\n\n # Global staff can see what courses encountered an error on their dashboard\n staff_access = False\n errored_courses = {}\n if has_access(user, 'staff', 'global'):\n # Show any courses that encountered an error on load\n staff_access = True\n errored_courses = modulestore().get_errored_courses()\n\n show_courseware_links_for = {\n enrollment.course_id: has_access(request.user, 'load', enrollment.course_overview)\n for enrollment in course_enrollments\n }\n\n # Find programs associated with course runs being displayed. This information\n # is passed in the template context to allow rendering of program-related\n # information on the dashboard.\n meter = ProgramProgressMeter(request.site, user, enrollments=course_enrollments)\n ecommerce_service = EcommerceService()\n inverted_programs = meter.invert_programs()\n\n urls, programs_data = {}, {}\n bundles_on_dashboard_flag = WaffleFlag(f'{EXPERIMENTS_NAMESPACE}.bundles_on_dashboard', __name__) # lint-amnesty, pylint: disable=toggle-missing-annotation\n\n # TODO: Delete this code and the relevant HTML code after testing LEARNER-3072 is complete\n if bundles_on_dashboard_flag.is_enabled() and inverted_programs and list(inverted_programs.items()):\n if len(course_enrollments) < 4:\n for program in inverted_programs.values():\n try:\n program_uuid = program[0]['uuid']\n program_data = get_programs(uuid=program_uuid)\n program_data = ProgramDataExtender(program_data, request.user).extend()\n skus = program_data.get('skus')\n checkout_page_url = ecommerce_service.get_checkout_page_url(*skus)\n program_data['completeProgramURL'] = checkout_page_url + '&bundle=' + program_data.get('uuid')\n programs_data[program_uuid] = program_data\n except: # pylint: disable=bare-except\n pass\n\n # Construct a dictionary of course mode information\n # used to render the course list. We re-use the course modes dict\n # we loaded earlier to avoid hitting the database.\n course_mode_info = {\n enrollment.course_id: complete_course_mode_info(\n enrollment.course_id, enrollment,\n modes=course_modes_by_course[enrollment.course_id]\n )\n for enrollment in course_enrollments\n }\n\n # Determine the per-course verification status\n # This is a dictionary in which the keys are course locators\n # and the values are one of:\n #\n # VERIFY_STATUS_NEED_TO_VERIFY\n # VERIFY_STATUS_SUBMITTED\n # VERIFY_STATUS_APPROVED\n # VERIFY_STATUS_MISSED_DEADLINE\n #\n # Each of which correspond to a particular message to display\n # next to the course on the dashboard.\n #\n # If a course is not included in this dictionary,\n # there is no verification messaging to display.\n verify_status_by_course = check_verify_status_by_course(user, course_enrollments)\n cert_statuses = {\n enrollment.course_id: cert_info(request.user, enrollment)\n for enrollment in course_enrollments\n }\n\n # only show email settings for Mongo course and when bulk email is turned on\n show_email_settings_for = frozenset(\n enrollment.course_id for enrollment in course_enrollments if (\n is_bulk_email_feature_enabled(enrollment.course_id)\n )\n )\n\n # Verification Attempts\n # Used to generate the \"you must reverify for course x\" banner\n verification_status = IDVerificationService.user_status(user)\n verification_errors = get_verification_error_reasons_for_display(verification_status['error'])\n\n # Gets data for midcourse reverifications, if any are necessary or have failed\n statuses = [\"approved\", \"denied\", \"pending\", \"must_reverify\"]\n reverifications = reverification_info(statuses)\n\n enrolled_courses_either_paid = frozenset(\n enrollment.course_id for enrollment in course_enrollments\n if enrollment.is_paid_course()\n )\n\n # Checks if a course enrollment redeemed using a voucher is refundable\n enrolled_courses_voucher_refundable = frozenset(\n enrollment.course_id for enrollment in course_enrollments\n if enrollment.is_order_voucher_refundable()\n )\n\n # If there are *any* denied reverifications that have not been toggled off,\n # we'll display the banner\n denied_banner = any(item.display for item in reverifications[\"denied\"])\n\n # get list of courses having pre-requisites yet to be completed\n courses_having_prerequisites = frozenset(\n enrollment.course_id for enrollment in course_enrollments\n if enrollment.course_overview.pre_requisite_courses\n )\n courses_requirements_not_met = get_pre_requisite_courses_not_completed(user, courses_having_prerequisites)\n\n if 'notlive' in request.GET:\n redirect_message = _(\"The course you are looking for does not start until {date}.\").format(\n date=request.GET['notlive']\n )\n elif 'course_closed' in request.GET:\n redirect_message = _(\"The course you are looking for is closed for enrollment as of {date}.\").format(\n date=request.GET['course_closed']\n )\n elif 'access_response_error' in request.GET:\n # This can be populated in a generalized way with fields from access response errors\n redirect_message = request.GET['access_response_error']\n else:\n redirect_message = ''\n\n # Filter out any course enrollment course cards that are associated with fulfilled entitlements\n for entitlement in [e for e in course_entitlements if e.enrollment_course_run is not None]:\n course_enrollments = [\n enr for enr in course_enrollments if entitlement.enrollment_course_run.course_id != enr.course_id\n ]\n\n show_account_activation_popup = request.COOKIES.get(settings.SHOW_ACTIVATE_CTA_POPUP_COOKIE_NAME, None)\n\n enrollments_fbe_is_on = []\n for enrollment in course_enrollments:\n course_key = CourseKey.from_string(str(enrollment.course_id))\n gated_content = ContentTypeGatingConfig.enabled_for_enrollment(\n user=user,\n course_key=course_key\n )\n duration = get_user_course_duration(user, enrollment.course)\n deadline = duration and get_user_course_expiration_date(user, enrollment.course)\n fbe_is_on = deadline and gated_content\n if fbe_is_on:\n enrollments_fbe_is_on.append(course_key)\n\n ip_address = get_client_ip(request)[0]\n country_code = country_code_from_ip(ip_address).upper()\n\n context = {\n 'urls': urls,\n 'programs_data': programs_data,\n 'enterprise_message': enterprise_message,\n 'consent_required_courses': consent_required_courses,\n 'enrollment_message': enrollment_message,\n 'redirect_message': Text(redirect_message),\n 'account_activation_messages': account_activation_messages,\n 'activate_account_message': activate_account_message,\n 'course_enrollments': course_enrollments,\n 'course_entitlements': course_entitlements,\n 'course_entitlement_available_sessions': course_entitlement_available_sessions,\n 'unfulfilled_entitlement_pseudo_sessions': unfulfilled_entitlement_pseudo_sessions,\n 'course_optouts': course_optouts,\n 'staff_access': staff_access,\n 'errored_courses': errored_courses,\n 'show_courseware_links_for': show_courseware_links_for,\n 'all_course_modes': course_mode_info,\n 'cert_statuses': cert_statuses,\n 'credit_statuses': credit_statuses(user, course_enrollments),\n 'show_email_settings_for': show_email_settings_for,\n 'reverifications': reverifications,\n 'verification_display': verification_status['should_display'],\n 'verification_status': verification_status['status'],\n 'verification_expiry': verification_status['verification_expiry'],\n 'verification_status_by_course': verify_status_by_course,\n 'verification_errors': verification_errors,\n 'denied_banner': denied_banner,\n 'billing_email': settings.PAYMENT_SUPPORT_EMAIL,\n 'show_account_activation_popup': show_account_activation_popup,\n 'user': user,\n 'logout_url': reverse('logout'),\n 'platform_name': platform_name,\n 'enrolled_courses_either_paid': enrolled_courses_either_paid,\n 'enrolled_courses_voucher_refundable': enrolled_courses_voucher_refundable,\n 'provider_states': [],\n 'courses_requirements_not_met': courses_requirements_not_met,\n 'nav_hidden': True,\n 'inverted_programs': inverted_programs,\n 'show_program_listing': ProgramsApiConfig.is_enabled(),\n 'show_dashboard_tabs': True,\n 'disable_courseware_js': True,\n 'display_course_modes_on_dashboard': enable_verified_certificates and display_course_modes_on_dashboard,\n 'display_sidebar_account_activation_message': not(user.is_active or hide_dashboard_courses_until_activated),\n 'display_dashboard_courses': (user.is_active or not hide_dashboard_courses_until_activated),\n 'empty_dashboard_message': empty_dashboard_message,\n 'enrollments_fbe_is_on': enrollments_fbe_is_on,\n 'recovery_email_message': recovery_email_message,\n 'recovery_email_activation_message': recovery_email_activation_message,\n 'show_load_all_courses_link': show_load_all_courses_link(user, course_limit, course_enrollments),\n # TODO START: clean up as part of REVEM-199 (START)\n 'course_info': get_dashboard_course_info(user, course_enrollments),\n # TODO START: clean up as part of REVEM-199 (END)\n 'disable_unenrollment': disable_unenrollment,\n 'country_code': country_code,\n # TODO: clean when experiment(Merchandise 2U LOBs - Dashboard) would be stop. [VAN-1097]\n 'is_enterprise_user': is_enterprise_learner(user),\n }\n\n # Include enterprise learner portal metadata and messaging\n enterprise_learner_portal_context = get_enterprise_learner_portal_context(request)\n context.update(enterprise_learner_portal_context)\n\n context_from_plugins = get_plugins_view_context(\n ProjectType.LMS,\n COURSE_DASHBOARD_PLUGIN_VIEW_NAME,\n context\n )\n context.update(context_from_plugins)\n\n notice_url = check_for_unacknowledged_notices(context)\n if notice_url:\n return redirect(notice_url)\n\n course = None\n context.update(\n get_experiment_user_metadata_context(\n course,\n user,\n )\n )\n if ecommerce_service.is_enabled(request.user):\n context.update({\n 'use_ecommerce_payment_flow': True,\n 'ecommerce_payment_page': ecommerce_service.payment_page_url(),\n })\n\n # Gather urls for course card resume buttons.\n resume_button_urls = ['' for entitlement in course_entitlements]\n for url in get_resume_urls_for_enrollments(user, course_enrollments).values():\n resume_button_urls.append(url)\n # There must be enough urls for dashboard.html. Template creates course\n # cards for \"enrollments + entitlements\".\n context.update({\n 'resume_button_urls': resume_button_urls\n })\n\n dashboard_template = 'dashboard.html'\n try:\n # .. filter_implemented_name: DashboardRenderStarted\n # .. filter_type: org.openedx.learning.dashboard.render.started.v1\n context, dashboard_template = DashboardRenderStarted.run_filter(\n context=context, template_name=dashboard_template,\n )\n except DashboardRenderStarted.RenderInvalidDashboard as exc:\n response = render_to_response(exc.dashboard_template, exc.template_context)\n except DashboardRenderStarted.RedirectToPage as exc:\n response = HttpResponseRedirect(exc.redirect_to or reverse('account_settings'))\n except DashboardRenderStarted.RenderCustomResponse as exc:\n response = exc.response\n else:\n response = render_to_response(dashboard_template, context)\n\n if show_account_activation_popup:\n response.delete_cookie(\n settings.SHOW_ACTIVATE_CTA_POPUP_COOKIE_NAME,\n domain=settings.SESSION_COOKIE_DOMAIN,\n path='/',\n )\n\n return response\n","repo_name":"openedx/edx-platform","sub_path":"common/djangoapps/student/views/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":39601,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"24045700912","text":"# Author: \n# Assignment #2 - TextAnalyzer\n# Date due: 2021-03-11\n# I pledge that I have completed this assignment without\n# collaborating with anyone else, in conformance with the\n# NYU School of Engineering Policies and Procedures on\n# Academic Misconduct.\n\n####### DO NOT EDIT FUNCTIONS BELOW ########\n\n#identifying functions\ndef character_is_digit(char):\n \"\"\"Indicates whether the value referenced by char parameter\n is a digit character\n\n :param char: character to check\n :return: True when char is a digit character, False otherwise\n\n >>> test_char = 'b'\n >>> character_is_digit(test_char)\n False\n >>> test_char = '2'\n >>> character_is_digit(test_char)\n True\n \"\"\"\n #true if character is a digit\n return char.isdigit()\n\n\ndef character_is_letter(char):\n \"\"\"Indicates whether the value referenced by char parameter\n is a letter\n\n :param char: character to check\n :return: True when char is a letter, False otherwise\n\n >>> test_char = 'b'\n >>> character_is_letter(test_char)\n True\n >>> test_char = '2'\n >>> character_is_letter(test_char)\n False\n \"\"\"\n #true if character is a letter\n return char.isalpha()\n\n\n####### DO NOT EDIT FUNCTIONS ABOVE ########\n\ndef character_is_whitespace(char):\n \"\"\"Indicates whether the value referenced by char parameter\n is a whitespace character (' ', '\\\\n', '\\\\t')\n\n :param char: character to check\n :return: True when char is space character, False otherwise\n\n >>> test_char = ' '\n >>> character_is_whitespace(test_char)\n True\n >>> test_char = '#'\n >>> character_is_whitespace(test_char)\n False\n >>> test_char = '\\\\n'\n >>> character_is_whitespace(test_char)\n True\n >>> test_char = '\\\\t'\n >>> character_is_whitespace(test_char)\n True\n \"\"\"\n #true if character is a whitespace character\n return char.isspace()\n\n\ndef character_ends_sentence(char):\n \"\"\"Indicates whether the value referenced by char parameter\n is a period, question mark, or exclamation point\n\n :param char: character to check\n :return: True when char ends sentence, False otherwise\n\n >>> test_char = 'k'\n >>> character_ends_sentence(test_char)\n False\n >>> test_char = '.'\n >>> character_ends_sentence(test_char)\n True\n >>> test_char = '?'\n >>> character_ends_sentence(test_char)\n True\n >>> test_char = '!'\n >>> character_ends_sentence(test_char)\n True\n \"\"\"\n #true if the character ends a sentence\n if char.endswith(\".\"):\n return True\n elif char.endswith(\"!\"):\n return True\n elif char.endswith(\"?\"):\n return True\n else:\n return False\n\n\ndef print_results(num_chars, num_spaces, num_digits, num_letters, num_sentences):\n \"\"\"Prints the number of total characters, spaces, digits, letters,\n and sentences identified in the text being analyzed.\n\n :param num_chars: number of total characters in text\n :param num_spaces: number of spaces in text\n :param num_digits: number of digits in text\n :param num_letters: number of letters in text\n :param num_sentences: number of sentences in text\n :return: None\n\n >>> num_chars = 234\n >>> num_spaces = 14\n >>> num_digits = 16\n >>> num_letters = 201\n >>> num_sentences = 21\n >>> print_results(num_chars, num_spaces, num_digits, num_letters, num_sentences)\n \n Count of characters: 234\n Count of spaces: 14\n Count of digits: 16\n Count of letters: 201\n Count of sentences: 21\n \n \"\"\"\n #output the results\n print()\n print('Count of characters:', num_chars)\n print('Count of spaces:', num_spaces)\n print('Count of digits:', num_digits)\n print('Count of letters:', num_letters)\n print('Count of sentences:', num_sentences)\n print()\n\n\ndef analyze_text():\n \"\"\"Calls the functions to compute the number of total characters,\n spaces, digits, letters, and sentences in user-supplied text and to\n output the final counts when text input by user.\n\n :return: True when text provided, False when no text provided\n \"\"\"\n #takes in a string\n txt = input('Please enter text to analyze (press ENTER/return without text to exit): ')\n\n #total variables\n num_letters = 0\n num_digits = 0\n num_spaces = 0\n num_chars = 0\n num_sentences = 0\n\n #loops through every single character in the inputed string\n #for each character it checks it against all of our previously written identifying functions\n #when any of the identifying functions come back as true it adds one to the total variables defined above\n #at the end it increases the iteration variable 'i' in order to move on and check the next character untill the entire string has been worked through\n for i in range(len(txt)):\n if character_is_letter(txt[i]):\n num_letters += 1\n elif character_is_digit(txt[i]):\n num_digits += 1\n elif character_ends_sentence(txt[i]):\n num_sentences += 1\n elif character_is_whitespace(txt[i]):\n num_spaces += 1\n num_chars += 1\n i += 1\n\n #if the inputed string is empty return false\n #else print the results and return true\n if len(txt) == 0:\n return False\n else:\n print_results(num_chars, num_spaces, num_digits, num_letters, num_sentences)\n return True\n\n\ndef run_text_analyzer():\n \"\"\"Runs the Text Analyzer as a repeated sequence of\n prompting the user for input text and outputting the\n character counts computed from the input\n\n :return: None\n \"\"\"\n #displays welcome message\n print('Welcome to the Text Analyzer!')\n print()\n\n #while there continue to be inputs we will analyze the text\n #else the input is empty and we say goodbye and end the program\n while analyze_text():\n continue\n else:\n print()\n print('Goodbye.')\n\n\ndef main():\n \"\"\"Runs a program for analyzing character counts from\n input text\n \"\"\"\n\n # calls run_text_analyzer()\n run_text_analyzer()\n\n\n####### DO NOT REMOVE IF STATEMENT BELOW ########\n\nif __name__ == '__main__':\n # Remove comments for next 4 lines to run doctests\n # print(\"Running doctests...\")\n # import doctest\n # doctest.testmod(verbose=True)\n\n # print(\"\\nRunning program...\\n\")\n\n main()\n","repo_name":"tywenrick3/Calculator_and_TextAnalyzer","sub_path":"text_analyzer.py","file_name":"text_analyzer.py","file_ext":"py","file_size_in_byte":6325,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"40429425582","text":"import typing\nimport numpy\nimport matplotlib.pyplot\nimport imgproc.stats.hist._hist\n\nclass Hist:\n \"\"\"[summary]\n \"\"\"\n def __init__(self,\n hist: numpy.ndarray,\n histr: numpy.ndarray,\n histg: numpy.ndarray,\n histb: numpy.ndarray):\n \"\"\"[summary]\n\n Args:\n hist (numpy.ndarray): [description]\n histr (numpy.ndarray): [description]\n histg (numpy.ndarray): [description]\n histb (numpy.ndarray): [description]\n \"\"\"\n self.hist = hist\n self.histr = histr\n self.histg = histg\n self.histb = histb\n def __str__(self) -> str:\n \"\"\"[summary]\n\n Returns:\n str: [description]\n \"\"\"\n return f\"Hist:\\n{self.hist}\\n\" +\\\n f\"Histr:\\n{self.histr}\\n\" +\\\n f\"Histg:\\n{self.histg}\\n\" +\\\n f\"Histb:\\n{self.histb}\\n\"\n def __repr__(self) -> str:\n \"\"\"[summary]\n\n Returns:\n str: [description]\n \"\"\"\n return f\"Hist:\\n{self.hist}\\n\" +\\\n f\"Histr:\\n{self.histr}\\n\" +\\\n f\"Histg:\\n{self.histg}\\n\" +\\\n f\"Histb:\\n{self.histb}\\n\"\n def plot(self, fn: typing.Union[None, str]=None):\n \"\"\"[summary]\n\n Args:\n fn (typing.Union[None, str], optional): [description].\n Defaults to None.\n \"\"\"\n matplotlib.pyplot.clf()\n matplotlib.pyplot.bar(numpy.arange(256), self.hist)\n if (fn):\n matplotlib.pyplot.savefig(fn)\n\n\ndef hist(fn: str) -> Hist:\n \"\"\"[summary]\n\n Args:\n fn (str): [description]\n\n Returns:\n Hist: [description]\n \"\"\"\n hist = numpy.empty(256, dtype=numpy.uintc)\n histr = numpy.empty(256, dtype=numpy.uintc)\n histg = numpy.empty(256, dtype=numpy.uintc)\n histb = numpy.empty(256, dtype=numpy.uintc)\n imgproc.stats.hist._hist.hist_base(fn, hist, histr, histg, histb)\n return Hist(hist, histr, histg, histb)\n","repo_name":"leafyao8621/imgproc","sub_path":"package/imgproc/stats/hist/_hist_wrapper.py","file_name":"_hist_wrapper.py","file_ext":"py","file_size_in_byte":2010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12616907569","text":"\"\"\"\n\nServidor da aplicação Distriduino, \ndesenvolvida na disciplina de Computação Distribuída, 2023.2.\nBrenda Silva Machado\n\n\"\"\"\n\n\nimport socket\nimport pika\n\nHOST = \"127.0.0.1\" # localhost\nPORT = 65432 # Porta para o listen\n\nserver = socket.socket(socket.AF_INET,\n socket.SOCK_STREAM)\nserver.bind((HOST, PORT))\n\nconnection = pika.BlockingConnection(pika.ConnectionParameters(HOST))\nchannel = connection.channel()\nchannel.queue_declare(queue='fila')\n\nserver.listen(1)\n\nprint(\"Calculadora iniciou\")\nprint(\"Esperando pela operacao..\")\n\nclientConnection, clientAddress = server.accept()\nprint(\"Cliente conectado :\", clientAddress)\nmsg = ''\n\ndef callback(ch, method, properties, body):\n while True:\n if body.decode() == \"fim da operação\":\n break\n print(\"Mensagem terminou de ser exibida no mensageiro\")\n channel.stop_consuming()\n return 0\n\n# Recebimento da autenticação\ntoken = clientConnection.recv(1024)\nif token.decode() == \"123456\":\n clientConnection.send(\"1\".encode())\n while True:\n data = clientConnection.recv(1024)\n msg = data.decode()\n if msg == 'q':\n print(\"Conexao encerrada pelo cliente\")\n channel.basic_publish(exchange='',\n routing_key='fila',\n body='fim')\n break\n \n else:\n \n result = 0\n operation_list = msg.split()\n oprnd1 = operation_list[0]\n operation = operation_list[1]\n oprnd2 = operation_list[2]\n \n # Conversao de string para int\n num1 = (int(oprnd1, 2))\n num2 = (int(oprnd2, 2))\n\n # Operacoes básicas\n if operation == \"+\":\n result = bin(num1 + num2)\n elif operation == \"-\":\n result = bin(num1 - num2)\n \n # Conversao para string\n # Envio para o cliente\n output = str(result[2:])\n channel.basic_publish(exchange='',\n routing_key='fila',\n body=output)\n clientConnection.send(output.encode())\n print(\"Resultado enviado para o mensageiro\")\n\n # Limitação: esperar a confirmação que o mensageiro exibiu a mensagem antes de uma nova operação\n # Recebimento da confirmação\n channel.basic_consume(queue='fila', on_message_callback=callback, auto_ack=True)\n\n channel.start_consuming()\n\n clientConnection.send(\"Mensageiro finalizou, pode iniciar nova operação\".encode())\n\n clientConnection.close()\n\n # Destruir a fila\n channel.queue_delete(queue='fila')\nelse:\n clientConnection.send(\"0\".encode())\n clientConnection.close()\n exit()\n","repo_name":"Brenda-Machado/Distriduino","sub_path":"src/servidor.py","file_name":"servidor.py","file_ext":"py","file_size_in_byte":2792,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10749361623","text":"import time\n\nara = (u'في حال واجهتك مشكلة راسلني على الخاص')\nara2 = (u'تم الانتهاء من ضغط السيارات بنجاح')\n\nprint(f\"By ! TankOsama#1792\\n {ara}\")\n\n\nprint('Whit 5 Sec...')\ntime.sleep(2.5)\n\n\nprint('Program is run')\n\nimport tkinter as tk\nfrom tkinter import filedialog\n\nroot = tk.Tk()\nroot.withdraw()\nOld_filepath = filedialog.askdirectory()\n\nNew_filePath = filedialog.askdirectory()\n\nimport os\n\narr = os.listdir(rf\"{Old_filepath}\")\nd = 0\n#print(arr)\n#for i in arr:\n # d = d +1\n # b = str(d)\n # os.rename(rf\"C:\\Users\\iosam\\Desktop\\كل ما يتعلف بفايف ام\\marrigecars\\TankFBI{i}\",rf'C:\\Users\\iosam\\Desktop\\كل ما يتعلف بفايف ام\\marrigecars\\TankFBI\\TankCars{i}'+b)\nfd = (fr\"{New_filePath}\\__resource.lua\")\nold = []\nfor j in arr:\n d = d +1\n b = str(d)\n arry=(os.listdir(rf\"{Old_filepath}\\{j}\"))\n for i in arry:\n if i == 'stream' or i == '__resource.lua' or i == 'Stream':\n continue\n os.rename(rf\"{Old_filepath}\\{j}\\{i}\",rf'{New_filePath}\\{b}{i}')\n arryd =(os.listdir(rf\"{Old_filepath}\\{j}\\stream\"))\n for z in arryd:\n try:\n\n os.rename(rf\"{Old_filepath}\\{j}\\stream\\{z}\",rf'{New_filePath}\\stream\\{z}')\n print(f'suuccfule{z}')\n except:\n print(f\"Error{j,z}\")\n\n\n\nf = open(rf\"{New_filePath}\\__resource.lua\", \"a\")\nttd = 0\nf.write(\"\"\"resource_manifest_version '77731fab-63ca-442c-a67b-abc70f28dfa5'\\n\\n\\n\\n\"\"\")\nf.write(\"files {\")\nfor r in arr:\n ttd = ttd +1\n tt = str(ttd)\n f.write(f\"\"\"\\n '{tt}vehicles.meta',\\n '{tt}carvariations.meta',\\n '{tt}carcols.meta',\\n '{tt}handling.meta',\"\"\")\n # f.write(f\"\"\"'{tt}vehicles.meta',\\n'{tt}carvariations.meta',\\n'{tt}carcols.meta',\\n'{tt}handling.meta' \\n\\n\\n\\n data_file 'HANDLING_FILE' '{tt}handling.meta' \\n\n#data_file 'VEHICLE_METADATA_FILE''{tt}vehicles.meta'\\ndata_file 'CARCOLS_FILE' '{tt}carcols.meta' \\ndata_file 'VEHICLE_VARIATION_FILE' '{tt}carvariations.meta' \"\"\")\nf.write(\"\\n}\")\ntred = 0\nfor q in arr:\n tred = tred +1\n tre = str(tred)\n f.write(f\"\"\"\\n\\n\\ndata_file 'HANDLING_FILE' '{tre}handling.meta' \\ndata_file 'VEHICLE_METADATA_FILE''{tre}vehicles.meta'\\ndata_file 'CARCOLS_FILE' '{tre}carcols.meta' \\ndata_file 'VEHICLE_VARIATION_FILE' '{tre}carvariations.meta'\\n\\n\\n\"\"\")\nf.close()\n\n\nprint(f'----------{ara2}----------')\n\n","repo_name":"TankOsama/FiveM-Merge-cars","sub_path":"MargeCars.py","file_name":"MargeCars.py","file_ext":"py","file_size_in_byte":2408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5879396378","text":"import subprocess\nimport psutil\n\ntry:\n from pynvml import *\n nvmlPY = \"OK\"\nexcept ImportError:\n nvmlPY = None\n\ndef getCpuInfo():\n \"\"\"Returns CPU/s information.\n\n Notes:\n - All values set to zero when called the first time\n - Values stat updating on the second call\n - This can be changed by specifying an interval. In that case the\n calls will be blocking\n \"\"\"\n return {\n 'usage': psutil.cpu_percent(interval=None, percpu=True),\n 'global_usage': psutil.cpu_percent(interval=None),\n 'cpu_times': vars(psutil.cpu_times()),\n }\n\ndef getCudaInfo():\n \"\"\"Returns CUDA device/s information\n \"\"\"\n def get_attribute(att):\n return subprocess.check_output(\n ['nvidia-smi', '--query-gpu={}'.format(att),\n '--format=csv,noheader,nounits'])\n\n mem_used = get_attribute(\"memory.used\")\n mem_total = get_attribute(\"memory.total\")\n mem_per = int(mem_used) * 100 / int(mem_total)\n\n if nvmlPY is not None:\n nvmlInit()\n try:\n handle = nvmlDeviceGetHandleByIndex(0)\n (enc_util, ssize) = nvmlDeviceGetEncoderUtilization(handle)\n except NVMLError as err:\n enc_util = err\n nvmlShutdown()\n else:\n enc_util = \"-1\"\n\n return {\n 'memory.used': mem_used,\n 'memory.free': get_attribute(\"memory.free\"),\n 'memory.total': mem_total,\n 'memory.percent': mem_per,\n 'utilization.perf_state': get_attribute(\"pstate\"),\n 'utilization.gpu': get_attribute(\"utilization.gpu\"),\n 'utilization.enc': enc_util,\n 'utilization.memory': get_attribute(\"utilization.memory\"),\n 'name': get_attribute(\"gpu_name\"),\n }\n\n\ndef getMemoryInfo():\n \"\"\"Returns information of the memory modules in the system\n \"\"\"\n vmem = psutil.virtual_memory()\n swap = psutil.swap_memory()\n vmem = vars(vmem)\n swap = vars(swap)\n\n return {\n 'virtual': vmem,\n 'swap': swap,\n }\n","repo_name":"stitchEm/stitchEm","sub_path":"lib/bindings/samples/server/utils/performance.py","file_name":"performance.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"en","doc_type":"code","stars":246,"dataset":"github-code","pt":"3"}
+{"seq_id":"10659962324","text":"#!/usr/bin/env python\r\n__author__ = \"@KaosNeverDied forgettable.eth\"\r\n__copyright__ = \"whatever\"\r\n__credits__ = [\"@kaosNeverdied\"]\r\n__license__ = \"GPL\"\r\n__version__ = \"1.0.1\"\r\n__maintainer__ = \"@KaosNeverdied\"\r\n__status__ = \"Prototype\"\r\n\r\nimport logging\r\nimport time\r\n\r\nfrom seasnake import *\r\n\r\nlogger = logging.getLogger()\r\nlogger.setLevel(logging.DEBUG) #NOTE: minimum logging level - set this to whatever suits your needs\r\n\r\n#config logging\r\nstream_handler = logging.StreamHandler() \r\nformatter = logging.Formatter('%(asctime)s %(levelname)s :: %(message)s')\r\nstream_handler.setFormatter(formatter)\r\nstream_handler.setLevel(logging.INFO) \r\nfile_handler = logging.FileHandler('info.log')\r\nfile_handler.setFormatter(formatter)\r\nfile_handler.setLevel(logging.DEBUG) \r\nlogger.addHandler(stream_handler)\r\nlogger.addHandler(file_handler)\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n #NOTE: Basic example of use\r\n asset_contract_address = \"0x06012c8cf97bead5deae237070f9587f8e7a266d\"\r\n token_id = \"1\"\r\n opensea = OpenSea()\r\n asset = opensea.get_asset_single(asset_contract_address=asset_contract_address, token_id=token_id)\r\n print(f'id: {asset.id}')\r\n print(f'token_id: {asset.token_id}')\r\n print(f'background_color: {asset.background_color}')\r\n print(f'img_url: {asset.image_url}')\r\n","repo_name":"kaosneverdied/seasnake","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"42194038771","text":"\ndef suma(num_1:int,num_2:int):\n return num_1+num_2\ndef resta(num_1:int,num_2:int):\n return num_1-num_2\ndef multiplicacion(num_1:int,num_2:int):\n return num_1*num_2\ndef division(num_1:int,num_2:int):\n return num_1/num_2\n\n\ninicio=True\n\nwhile inicio:\n print(\"1. Sumar\")\n print(\"2. Restar\")\n print(\"3. Multiplicar\")\n print(\"4. Dividir\")\n print(\"5. Salir\")\n\n opcion = int(input(\"Seleccione la opción deseada: \"))\n\n if opcion ==1:\n print(\"Ha seleccionado la opcion Sumar\")\n num_1=int(input(\"Seleccione primer numero a sumar: \"))\n num_2=int(input(\"Seleccione segundo numero a sumar: \"))\n print(\"El resultado de la suma es: \", suma(num_1,num_2))\n\n if opcion ==2:\n print(\"Ha seleccionado la opcion Resta\")\n print(\"Ha seleccionado la opcion Resta\")\n num_1=int(input(\"Seleccione primer numero a restar: \"))\n num_2=int(input(\"Seleccione segundo numero a restar: \"))\n print(\"El resultado de la resta es: \", resta(num_1,num_2))\n\n\n\n if opcion ==3:\n print(\"Ha seleccionado la opcion Multiplica\")\n num_1=int(input(\"Seleccione primer numero a multiplicar: \"))\n num_2=int(input(\"Seleccione segundo numero a multiplicar: \"))\n print(\"El resultado de la resta es: \", multiplicacion(num_1,num_2))\n\n\n if opcion ==4:\n print(\"Ha seleccionado la opcion Dividir\")\n num_1=int(input(\"Seleccione primer numero a dividir: \"))\n num_2=int(input(\"Seleccione segundo numero a dividir: \"))\n print(\"El resultado de la resta es: \", division(num_1,num_2))\n\n\n if opcion ==5:\n print(\"Ha seleccionado Salir\")\ninicio=False\n\n\n\n","repo_name":"ClauMurua/funciones","sub_path":"Calculadora.py","file_name":"Calculadora.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70682620883","text":"import win32gui\nimport win32con\nimport time\n #不知道这个咋玩的\ndef exe_is_active():\n while True:\n time.sleep(0.2)\n try:\n # 关闭文件或目录，使用模糊查询\n hld = FindWinHwnd(u\" D:\\Android\")\n if str(hld) != \"0\":\n time.sleep(0.2)\n win32gui.PostMessage(hld, win32con.WM_CLOSE, 0, 0)\n time.sleep(0.1)\n print(\"已关闭txt\")\n\n # 关闭文件或目录，精准查询\n hld = win32gui.FindWindow(None, u\"公用\")\n if str(hld) != \"0\":\n time.sleep(0.2)\n win32gui.PostMessage(hld, win32con.WM_CLOSE, 0, 0)\n time.sleep(0.1)\n print(\"已关闭目录\")\n except:\n print(\"速度过快报错\")\n\n# 模糊查询\ndef FindWinHwnd(title, top=True):\n titles = []\n def foo(hwnd, mouse):\n if top:\n if win32gui.IsWindow(hwnd) and win32gui.IsWindowEnabled(hwnd) and win32gui.IsWindowVisible(hwnd):\n if title in win32gui.GetWindowText(hwnd):\n titles.append(hwnd)\n return titles[0]\n else:\n if title in win32gui.GetWindowText(hwnd):\n titles.append(hwnd)\n win32gui.EnumWindows(foo, 0)\n if titles:\n return titles[0]\n else:\n return 0\n\n\nif __name__ == '__main__':\n exe_is_active()","repo_name":"jihaikang/2021-","sub_path":"5_29/wuai_3关闭打开某个程序或目录.py","file_name":"wuai_3关闭打开某个程序或目录.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29039809493","text":"from datetime import datetime\n\nfrom django.db import models\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.contrib.contenttypes.generic import GenericForeignKey\n\nfrom ganeti_webmgr.utils import get_rapi\nfrom ganeti_webmgr.utils.client import GanetiApiError\nfrom ganeti_webmgr.clusters.models import CachedClusterObject\n\n\nclass JobManager(models.Manager):\n \"\"\"\n Custom manager for Ganeti Jobs model\n \"\"\"\n def create(self, **kwargs):\n \"\"\" helper method for creating a job with disabled cache \"\"\"\n job = Job(ignore_cache=True, **kwargs)\n job.save(force_insert=True)\n return job\n\n\nclass Job(CachedClusterObject):\n \"\"\"\n model representing a job being run on a ganeti Cluster. This includes\n operations such as creating or delting a virtual machine.\n\n Jobs are a special type of CachedClusterObject. Job's run once then become\n immutable. The lazy cache is modified to become permanent once a complete\n status (success/error) has been detected. The cache can be disabled by\n settning ignore_cache=True.\n \"\"\"\n\n job_id = models.IntegerField()\n content_type = models.ForeignKey(ContentType, related_name=\"+\")\n object_id = models.IntegerField()\n obj = GenericForeignKey('content_type', 'object_id')\n cluster = models.ForeignKey('clusters.Cluster', related_name='jobs',\n editable=False)\n cluster_hash = models.CharField(max_length=40, editable=False)\n\n finished = models.DateTimeField(null=True, blank=True)\n status = models.CharField(max_length=10)\n op = models.CharField(max_length=50)\n\n objects = JobManager()\n\n def save(self, *args, **kwargs):\n \"\"\"\n sets the cluster_hash for newly saved instances\n \"\"\"\n if self.id is None or self.cluster_hash == '':\n self.cluster_hash = self.cluster.hash\n\n super(Job, self).save(*args, **kwargs)\n\n @models.permalink\n def get_absolute_url(self):\n job = '%s/job/(?P\\d+)' % self.cluster\n\n return 'ganeti_web.views.jobs.detail', (), {'job': job}\n\n @property\n def rapi(self):\n return get_rapi(self.cluster_hash, self.cluster_id)\n\n def _refresh(self):\n return self.rapi.GetJobStatus(self.job_id)\n\n def load_info(self):\n \"\"\"\n Load info for class. This will load from ganeti if ignore_cache==True,\n otherwise this will always load from the cache.\n \"\"\"\n if self.id and (self.ignore_cache or self.info is None):\n try:\n self.refresh()\n except GanetiApiError as e:\n # if the Job has been archived then we don't know whether it\n # was successful or not. Mark it as unknown.\n if e.code == 404:\n self.status = 'unknown'\n self.save()\n else:\n # its possible the cluster or crednetials are bad. fail\n # silently\n pass\n\n def refresh(self):\n info = self._refresh()\n valid = self.valid_job(info)\n if valid:\n self.info = info\n self.save()\n # else:\n # Job.objects.get(job_id=self.info['id']).delete()\n\n @classmethod\n def valid_job(cls, info):\n status = info.get('status')\n ops = info.get('ops')\n return not (ops is None and status is None)\n\n @classmethod\n def parse_op(cls, info):\n ops = info['ops']\n op = None\n if ops:\n # Return the most recent operation\n op = ops[-1]['OP_ID']\n return op\n\n @classmethod\n def parse_persistent_info(cls, info):\n \"\"\"\n Parse status and turn off cache bypass flag if job has finished\n \"\"\"\n if not cls.valid_job(info):\n return {}\n op = cls.parse_op(info)\n data = {'status': info['status'], 'op': op}\n if data['status'] in ('error', 'success'):\n data['ignore_cache'] = False\n if info['end_ts']:\n data['finished'] = cls.parse_end_timestamp(info)\n return data\n\n @staticmethod\n def parse_end_timestamp(info):\n sec, micro = info['end_ts']\n return datetime.fromtimestamp(sec + (micro / 1000000.0))\n\n def parse_transient_info(self):\n pass\n\n @property\n def current_operation(self):\n \"\"\"\n Jobs may consist of multiple commands/operations. This helper\n method will return the operation that is currently running or errored\n out, or the last operation if all operations have completed\n\n @returns raw name of the current operation\n \"\"\"\n info = self.info\n index = 0\n for i in range(len(info['opstatus'])):\n if info['opstatus'][i] != 'success':\n index = i\n break\n return info['ops'][index]['OP_ID']\n\n @property\n def operation(self):\n \"\"\"\n Returns the last operation, which is generally the primary operation.\n \"\"\"\n return self.parse_op(self.info)\n\n def __repr__(self):\n return \"\" % (self.id, self.job_id,\n self.status)\n\n __unicode__ = __repr__\n","repo_name":"osuosl/ganeti_webmgr","sub_path":"ganeti_webmgr/jobs/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5260,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"3"}
+{"seq_id":"35399260014","text":"#This file reads all our feature sets and assembles \n#a feature set.\n\n#Imports\nimport rasterio\nimport gdal\nimport numpy as np\nimport os\nimport shutil\nimport subprocess\n\n\n#This function returns a dict. with point values and location\ndef return_points(taskID):\n #Get all the files to run\n filenames = os.listdir(taskID + '/individuals')\n\n #Run for each file\n point_data = {} \n for filename in filenames:\n #Open the input raster\n raster = rasterio.open(taskID + '/individuals/' + filename)\n array = raster.read(1)\n\n #Get the index for the min value (the datapoint)\n flat_index = np.argmin(array)\n index = np.unravel_index(flat_index, array.shape)\n \n #Write the data to the dictionary\n key = os.path.splitext(filename)[0]\n point_data[key] = {'index': index, 'value': array[index]}\n\n return point_data\n\n\n#This function returns a dict. with buffer arrays\ndef return_buffers(taskID):\n #Get all the files to run\n filenames = os.listdir(taskID + '/buffers')\n\n #Run for each file\n buffers = {} \n for filename in filenames:\n #Open the input raster\n raster = rasterio.open(taskID + '/buffers/' + filename)\n array = raster.read(1)\n \n #Write the data to the dictionary\n key = os.path.splitext(filename)[0]\n buffers[key] = array\n\n return buffers\n\n\ndef return_topo(taskID):\n \n #Define stack\n arrays = list()\n labels = list()\n\n #Import Elevation\n ##########################\n elev_raster = rasterio.open(taskID + '/topo/elev.tif')\n elev = elev_raster.read(1)\n arrays.append(elev)\n labels.append('Elevation')\n\n #Import Multi-Neighborhood curvatures\n ##########################\n curvlist = os.listdir(taskID + '/topo/curvatures')\n for instance in curvlist:\n curve_raster = rasterio.open(taskID + '/topo/curvatures/' + instance)\n curve = curve_raster.read(1)\n arrays.append(curve)\n labels.append(os.path.splitext(instance)[0])\n\n return arrays #, labels\n\n\ndef template(feature_set, taskID):\n\n #Get template data\n raster_shape = feature_set[0].shape\n raster = gdal.Open(taskID + '/topo/elev.tif')\n geotrans = raster.GetGeoTransform()\n proj = raster.GetProjection()\n\n return raster_shape, geotrans, proj\n\ndef cleanup(taskID):\n if os.path.isfile(taskID + '/rootdata/boundary.shp'):\n subprocess.call('rm ' + taskID + '/rootdata/boundary.*', shell=True)\n if os.path.isfile(taskID + '/rootdata/buffered_boundary.shp'):\n subprocess.call('rm ' + taskID + '/rootdata/buffered_boundary.*', shell=True)\n #if os.path.isfile(taskID + '/topo/elev.tif'):\n #subprocess.call('rm ' + taskID + '/topo/elev.tif', shell=True)\n #if os.path.isdir(taskID + '/topo/curvatures/'):\n #shutil.rmtree(taskID + '/topo/curvatures')\n #subprocess.call('mkdir ' + taskID + '/topo/curvatures/', shell=True)\n if os.path.isdir(taskID + '/buffers/'):\n shutil.rmtree(taskID + '/buffers/')\n subprocess.call('mkdir ' + taskID + '/buffers/', shell=True)\n if os.path.isdir(taskID + '/individuals/'):\n shutil.rmtree(taskID + '/individuals/')\n subprocess.call('mkdir ' + taskID + '/individuals/', shell=True)\n\n\n","repo_name":"jafiecht/soil-docker","sub_path":"server/scripts/stack.py","file_name":"stack.py","file_ext":"py","file_size_in_byte":3074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35113186650","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 14 22:23:34 2023\n@author: ayaha\n\"\"\"\nimport os\nimport time\n\nimport joblib\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import metrics, svm\nfrom sklearn.model_selection import train_test_split\n\nfrom PSLogger import psLog\nfrom PSUtils import OUT_DIR, get_pics\n\n\ndef psSVM(save=False, kernel=\"linear\", sea=1, shape='ovr', size_img=100, show_cm=False, probability=False):\n \"\"\"\n Higher C decreases the amount of misclassified data points in the trainng set\n but may increase misclassification in test data. C is log\n \"\"\"\n total_time = time.time()\n\n start_time = time.time()\n psLog.debug(\"Loading data...\")\n Y = np.loadtxt(os.path.join(OUT_DIR, 'Y.txt'), dtype=str)\n X = get_pics(size_img)\n\n X_train, X_test, y_train, y_test = train_test_split(\n X, Y, test_size=0.2, shuffle=False)\n psLog.debug(\"Loaded data (%.2fs)\", (time.time()-start_time))\n\n clf = svm.SVC(kernel=kernel, C=sea,\n decision_function_shape=shape, probability=probability)\n psLog.debug(\"Training model...\")\n start_time = time.time()\n clf.fit(X_train, y_train)\n psLog.debug(\"Model trained (%.2fs)\", (time.time()-start_time))\n\n psLog.debug(\"Verifying accuracy...\")\n start_time = time.time()\n predicted = clf.predict(X_test)\n error_rate = 1 - metrics.accuracy_score(y_test, predicted)\n elapsed_time = time.time() - start_time\n\n psLog.debug(\"Classification complete. (%.2fs)\", elapsed_time)\n psLog.debug(f\"Classification error: {error_rate}\")\n\n model_settings = [kernel, sea, shape, size_img]\n if save:\n psLog.debug(\"Saving model...\")\n start_time = time.time()\n joblib.dump((clf, model_settings), 'PSSVMSaved.jbl')\n psLog.debug(\"Model saved (%.2fs)\", (time.time()-start_time))\n\n elapsed_time = time.time() - total_time\n psLog.debug(\"Total time: %.2fs\", time.time()-total_time)\n\n if show_cm:\n disp = metrics.ConfusionMatrixDisplay.from_predictions(\n y_test, predicted)\n disp.figure_.suptitle(\"Confusion Matrix\")\n plt.show()\n\n if probability:\n probabilities = clf.predict_proba(X_test)\n max_probabilities = np.max(probabilities, axis=1)\n confidence = max_probabilities * 100\n labels = clf.classes_[np.argmax(probabilities, axis=1)]\n for conf, label in zip(confidence, labels):\n print(f\"SVM Confidence: {conf:.2f}%, Label: {label}\")\n\n return [model_settings, elapsed_time, error_rate, (clf, model_settings)]\n","repo_name":"ayah-uhhh/ParrotSourML","sub_path":"PSSVM.py","file_name":"PSSVM.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9446936248","text":"import requests\nimport json\nimport influxdb_client\n\ndef getInverterValues(config):\n measurement_points = []\n \n #get config\n debug_output = config[\"global\"][\"debug_output\"]\n hostname = config[\"inverter\"][\"hostname\"]\n measurement = config[\"inverter\"][\"measurement\"]\n\n try:\n #get data from inverter\n url = requests.get(\"http://\" + hostname + \"/solar_api/v1/GetPowerFlowRealtimeData.fcgi\", timeout=10)\n\n try:\n #parse data\n json_data = json.loads(url.text)\n inverter_list = json_data['Body']['Data']['Inverters']\n for inverter_number in inverter_list:\n inverter_data = json_data['Body']['Data']['Inverters'][inverter_number]\n total_kWh = inverter_data['E_Total'] / 1000\n point = influxdb_client.Point(measurement).tag(\"unit\", \"kWh\").field(\"inverter_\" + str(inverter_number), total_kWh)\n measurement_points.append(point)\n if debug_output == \"true\":\n print(\"Total current: \" + str(total_kWh) + \" kWh\")\n\n except:\n if debug_output == \"true\":\n print(\"failed to parse inverter data\")\n\n except:\n if debug_output == \"true\":\n print(\"failed to get inverter data, is server offline?\")\n\n return measurement_points\n","repo_name":"Flipper189/solarflux","sub_path":"inverter.py","file_name":"inverter.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1731572475","text":"from multiprocessing import context\nfrom django.shortcuts import render, redirect\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Q\nfrom .models import Room, Topic, Message\nfrom .forms import RoomForm, UserForm\nfrom django.http import HttpResponse, HttpResponseRedirect\n\n\n#basic views...\ndef home(request):\n rooms = Room.objects.all()\n topics = Topic.objects.all()[0:5]\n comments = Message.objects.all()\n if request.method == 'GET':\n q = request.GET.get('q')\n if q != '' and q != None:\n rooms = Room.objects.filter(\n Q(topic__name__icontains=q) |\n Q(name__icontains=q)|\n Q(description__icontains=q) \n )\n comments = Message.objects.filter(\n Q(room__topic__name__icontains =q)|\n Q(user__username__icontains=q)\n )\n else:\n rooms = Room.objects.all()\n \n room_count = rooms.count()\n \n \n context = {'rooms': rooms, 'topics': topics, 'room_count': room_count, 'comments': comments}\n return render(request, 'IndexApp/home.html', context)\n\ndef rooms(request, pk):\n \n room = Room.objects.get(id =pk)\n comments = room.comments.all().order_by('-created')\n participants = room.participants.all()\n \n if request.method == 'POST':\n comment = Message.objects.create(\n user = request.user,\n room = room,\n body = request.POST.get('body')\n )\n\n comment.save()\n room.participants.add(request.user)\n return redirect('room', pk)\n \n \n \n context ={'room': room, 'comments': comments, 'participants': participants}\n return render(request, 'IndexApp/rooms.html', context)\n\ndef userProfile(request, pk):\n user = User.objects.get(id = pk)\n comments = user.message_set.all()\n rooms = user.room_set.all()\n topics = Topic.objects.all()\n \n \n context = {'comments': comments, 'rooms': rooms, 'topics': topics, 'user': user}\n return render(request, 'IndexApp/userProfile.html', context)\n\n\n@login_required(login_url='loginPage')\ndef room_form(request):\n form = RoomForm()\n topics=Topic.objects.all()\n if request.method == 'POST':\n topic_name = request.POST.get('topic')\n topic, created = Topic.objects.get_or_create(name = topic_name)\n \n Room.objects.create(\n host = request.user,\n topic = topic,\n name = request.POST.get('name'),\n description = request.POST.get('description'),\n )\n # form = RoomForm(request.POST)\n # if form.is_valid():\n # room = form.save(commit=False)\n # room.host = request.User\n # room.save()\n return redirect('home')\n \n \n form = RoomForm()\n context = {'form': form, 'topics': topics}\n return render(request, 'IndexApp/room_form.html', context)\n\n\n\n#Update or edit comments\n@login_required(login_url='loginPage')\ndef updateRoom(request, pk):\n room = Room.objects.get(id=pk)\n form = RoomForm(instance = room)\n topics = Topic.objects.all()\n if (request.user != room.host):\n return HttpResponse('you are not allowed to update this room')\n if request.method == 'POST':\n topic_name = request.POST.get('topic')\n topic, created = Topic.objects.get_or_create(name = topic_name)\n \n room.name = request.POST.get('name')\n room.topic = topic\n room.description = request.POST.get('description')\n room.save()\n \n # form = RoomForm(request.POST, instance=room)\n # if form.is_valid():\n # temp = form.save(commit=False)\n # temp.topic = topic\n return redirect('home') \n \n context = {'form': form, \"topics\":topics, 'room':room}\n \n return render(request, 'IndexApp/room_form.html', context)\n\n@login_required(login_url='loginPage')\ndef deleteRoom(request, pk):\n page = 'room'\n room = Room.objects.get(id=pk)\n \n if (request.user != room.host):\n return HttpResponse('you are not allowed to delete this room')\n \n if request.method == 'POST':\n room.delete()\n return redirect('room', pk)\n \n \n context = {'room': room, 'page': page}\n return render(request, 'IndexApp/deleteRoom.html', context)\n\n\n#Login views\ndef loginPage(request):\n page = 'login'\n if request.user.is_authenticated:\n return redirect('home')\n if request.method == 'POST':\n username = request.POST.get('username').lower()\n password = request.POST.get('password')\n \n \n user = authenticate(username = username, password = password)\n \n if user is not None:\n login(request, user)\n return redirect('home')\n \n else:\n messages.error(request, 'Invalid username or password')\n \n context = {'page': page}\n return render(request, 'IndexApp/login_registration.html', context)\n\n\ndef registerUser(request):\n page = 'register'\n form = UserCreationForm()\n \n if request.method == 'POST':\n form = UserCreationForm(request.POST)\n if form.is_valid():\n user = form.save(commit=False)\n user.username = user.username.lower()\n user.save()\n login(request, user)\n return redirect('home')\n \n else:\n messages.error(request, 'There was an error registering the User. Please try again later')\n return redirect('regUser')\n \n \n \n \n context = {'page': page, 'form': form}\n return render(request, 'IndexApp/login_registration.html', context)\n\ndef logoutPage(request):\n logout(request)\n return redirect('loginPage')\n \n#delete comments \n@login_required(login_url='loginPage')\ndef deleteComment(request, pk):\n page = 'comments'\n comment = Message.objects.get(id=pk)\n \n if (request.user != comment.user):\n return HttpResponse('you are not allowed to delete this room')\n \n if request.method == 'POST':\n comment.delete()\n return redirect('room', pk=comment.room.id)\n \n \n context = {'page': page, 'comment' : comment}\n return render(request, 'IndexApp/deleteRoom.html', context)\n\n\n@login_required(login_url='loginPage')\ndef updateUser(request):\n user = request.user\n form = UserForm(instance=user)\n \n if request.method == 'POST':\n form = UserForm(request.POST, instance=user)\n if form.is_valid():\n form.save()\n return redirect('userProfile', pk = request.user.id)\n \n \n \n context = {'form': form}\n return render(request, 'IndexApp/update_user.html', context)\n\ndef topicsPage(request):\n q = request.GET.get('q') if request.GET.get('q') != None else''\n topics = Topic.objects.filter(name__icontains=q)\n \n context = {'topics': topics, 'rooms': rooms}\n return render(request, 'IndexApp/topics.html', context)\n\n\ndef activityPage(request):\n rooms= Room.objects.all()\n comments = Message.objects.all()\n \n context = {'rooms': rooms, 'comments': comments}\n return render(request, 'IndexApp/activity.html', context)\n","repo_name":"Kayphaz007/WeChat","sub_path":"IndexApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7425,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21182809231","text":"#!/usr/bin/env python\n\n\"\"\" Histogram of user function run-times (completed & killed).\n\nScript to produce a histogram plot giving a count of user function (sim or\ngen) calls by run-time intervals. Color shows completed versus killed versus\nfailed/exception.\n\nThis plot is produced from the libE_stats.txt file. Status is taken from the\ncalc_status returned by user functions.\n\nThe plot is written to a file.\n\n\"\"\"\n\nimport sys\n\nimport matplotlib\nimport numpy as np\n\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\n# Basic options ---------------------------------------------------------------\n\ninfile = \"libE_stats.txt\"\ntime_key = \"Time:\"\nstatus_key = \"Status:\"\nsim_only = True # Ignore generator times\nmax_bins = 40\nran_ok = [\"Completed\"] # List of OK states\nrun_killed = [\"killed\"] # Searches for this word in status string\nrun_exception = [\"Exception\", \"Failed\"]\n\n# -----------------------------------------------------------------------------\n\n\ndef search_for_keyword(in_list, kw_list):\n for i, val in enumerate(in_list):\n if val.endswith(\":\"):\n break # New key word found\n else:\n if val in kw_list:\n return True\n return False\n\n\ndef append_to_list(mylst, glob_list, found_time):\n # Assumes Time comes first - else have to modify\n if found_time:\n mylst.append(glob_list[-1])\n else:\n print(\"Error Status found before time - exiting\")\n sys.exit()\n\n\nactive_line_count = 0\nin_times = []\nin_times_ran = []\nin_times_kill = []\nin_times_exception = []\n\n\n# Read straight from libEnsemble summary file.\nwith open(infile) as f:\n for line in f:\n lst = line.split()\n found_time = False\n found_status = False\n for i, val in enumerate(lst):\n if val == time_key:\n if sim_only and lst[i - 1] != \"sim\":\n break\n in_times.append(lst[i + 1])\n found_time = True\n if val == status_key:\n if lst[i + 1] in ran_ok:\n append_to_list(in_times_ran, in_times, found_time) # Assumes Time comes first\n elif search_for_keyword(lst[i + 1 : len(lst)], run_killed):\n append_to_list(in_times_kill, in_times, found_time) # Assumes Time comes first\n elif search_for_keyword(lst[i + 1 : len(lst)], run_exception):\n exceptions = True\n append_to_list(in_times_exception, in_times, found_time) # Assumes Time comes first\n else:\n print(f\"Error: Unknown status - rest of line: {lst[i + 1:len(lst)]}\")\n sys.exit()\n found_status = True\n if found_time and found_status:\n active_line_count += 1\n break\n\nprint(f\"Processed {active_line_count} calcs\")\n\ntimes = np.asarray(in_times, dtype=float)\ntimes_ran = np.asarray(in_times_ran, dtype=float)\ntimes_kill = np.asarray(in_times_kill, dtype=float)\ntimes_exc = np.asarray(in_times_exception, dtype=float)\n\nnum_bins = min(active_line_count, max_bins)\nbinwidth = (times.max() - times.min()) / num_bins\nbins = np.arange(min(times), max(times) + binwidth, binwidth)\n\n# Completed is the top segment\nplt_times = [times_kill, times_ran]\nlabels = [\"Killed\", \"Completed\"]\ncolors = [\"#FF7F0e\", \"#1F77B4\"] # Orange, Blue\n\n# Insert at bottom (Dont want label if no exceptions)\nif in_times_exception:\n plt_times.insert(0, times_exc)\n labels.insert(0, \"Except/Failed\")\n colors.insert(0, \"#d62728\") # Red\n\nplt.hist(plt_times, bins, edgecolor=\"black\", linewidth=1.5, stacked=True, label=labels, color=colors)\n\nif sim_only:\n calc = \"sim\"\nelse:\n calc = \"calc\"\n\ntitl = \"Histogram of \" + calc + \" times\" + \" (\" + str(active_line_count) + \" user calcs)\" + str(num_bins) + \" bins\"\n\nplt.title(titl)\nplt.xlabel(\"Calculation run time (sec)\", fontsize=14)\nplt.ylabel(\"Count\", fontsize=14)\nplt.grid(axis=\"y\")\nplt.legend(loc=\"best\", fontsize=14)\n\nplt.xticks(fontsize=12)\nplt.yticks(fontsize=12)\n\n# plt.show()\nplt.savefig(\"hist_completed_v_killed.png\")\n# plt.savefig(\"hist_completed_v_killed.png\", bbox_inches=\"tight\", transparent=True)\n","repo_name":"Libensemble/libensemble","sub_path":"scripts/plot_libe_histogram.py","file_name":"plot_libe_histogram.py","file_ext":"py","file_size_in_byte":4169,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"3"}
+{"seq_id":"17503147475","text":"# @Author: Farhan Rehman\n# Purpose: Converts SQLite queries response to JSON\n\n# Imports\nfrom rest_framework.serializers import ModelSerializer, ReadOnlyField\nfrom rest_framework import serializers\n\nfrom .models import CongressTrade, CongressPerson, Ticker, SummaryStat\n\n# Abstraction is integrated due to django within all of these classes\nclass TickerSerializer(serializers.ModelSerializer):\n class Meta:\n # Database table\n model = Ticker\n # Fields to appear on the response\n fields = ('ticker', 'company', 'marketcap', 'sector', 'industry', 'totalTransactions', 'totalVolumeTransactions', 'purchases', 'sales',)\n\nclass CongressPersonSerializer(serializers.ModelSerializer):\n class Meta:\n # Database table\n model = CongressPerson\n # Fields to appear on the response\n fields = ('fullName', 'currentParty', 'currentChamber', 'currentState', 'image', 'totalTransactions', 'totalVolumeTransactions', 'purchases', 'sales',)\n\nclass CongressTradeSerializer(serializers.ModelSerializer):\n # Gets ForignKey field to appear on the response, refer to ERD Diagram to get a better understanding of the connection between the models\n name = ReadOnlyField(source='name.fullName')\n ticker = ReadOnlyField(source='ticker.ticker')\n\n if ticker == None:\n ticker = \"-\"\n\n class Meta:\n # Database table\n model = CongressTrade\n # Fields to appear on the response\n # fields = ('name', 'bioguide','firstName','lastName', 'ticker', 'transactionDate', 'assetType', 'transactionType', 'amount', 'ptrLink')\n fields = ('name', 'ticker', 'transactionDate', 'assetType', 'transactionType', 'amount', 'ptrLink')\n\nclass SummaryStatSerializer(serializers.ModelSerializer):\n class Meta:\n # Database table\n model = SummaryStat\n # Fields to appear on the response\n # __all__ includes all fields in the model in the response\n fields = \"__all__\"\n","repo_name":"InsiderUnlocked/Backend","sub_path":"congress/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1966,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"74833671761","text":"# -*- coding: utf-8 -*-\nimport typing\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\nclass CustomComboBox(QtWidgets.QWidget): \n def __init__(self, \n parent: typing.Optional[QtWidgets.QWidget] = ...,\n init: str = \"No Connection\"\n ) -> None: \n super(QtWidgets.QWidget, self).__init__(parent)\n\n self.DOT_STYLE = (\"QWidget {\\n\"\n \"border-radius: 6px;\\n\"\n \"background-color: {OPTION};\\n\"\n \"}\")\n\n self.setupUi(init)\n\n def setupUi(self, initial_text: str = \"No Connection\"):\n\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n sizePolicy.setHorizontalStretch(0)\n sizePolicy.setVerticalStretch(0)\n sizePolicy.setHeightForWidth(self.sizePolicy().hasHeightForWidth())\n self.setSizePolicy(sizePolicy)\n self.setMinimumSize(QtCore.QSize(0, 30))\n\n self.setStyleSheet(\"QComboBox {\\n\"\n\"background-color: #FFF;\\n\"\n\"color: #B5BFC6; \\n\"\n\"border-radius: 14px;\\n\"\n\"text-align: center; \\n\"\n\"\\n\"\n\"selection-background-color: #FFF; /*ECECEC*/\\n\"\n\"selection-color: #0F5BCD;\\n\"\n\"outline: 0;\\n\"\n\"padding-left: 8px;\\n\"\n\"}\\n\"\n\"QComboBox:hover {\\n\"\n\" background-color: #F8F9FB; \\n\"\n\"}\\n\"\n\"QComboBox:on { /* shift the text when the popup opens */\\n\"\n\" color: #B5BFC6\\n\"\n\"}\\n\"\n\"QComboBox::drop-down {\\n\"\n\"border: 0px;\\n\"\n\"padding-right: 5px;\\n\"\n\"}\\n\"\n\"QComboBox::down-arrow {\\n\"\n\"/* image: url(:/icons/icons/edit.png); width: 16px; height: 16px; */\\n\"\n\"}\\n\"\n\"\\n\"\n\"QComboBox QListView {\\n\"\n\"background-color: #F8F9FB;\\n\"\n\"color: #B5BFC6; \\n\"\n\"border-radius: 8px;\\n\"\n\"padding: 5px; margin-top: 5px;\\n\"\n\"outline: 1px;\\n\"\n\"}\\n\"\n\"QComboBox QAbstractItemView {\\n\"\n\" selection-background-color: #EDF0F6;\\n\"\n\" selection-color: #4487EB;\\n\"\n\"}\\n\"\n\"\\n\")\n self.setObjectName(\"comboBoxFrame\")\n\n self.setLocale(QtCore.QLocale(QtCore.QLocale.English, QtCore.QLocale.UnitedStates))\n\n #########################\n # Layout #\n #########################\n self.__layout = QtWidgets.QHBoxLayout(self)\n self.__layout.setContentsMargins(12, 0, 0, 0)\n self.__layout.setSpacing(4)\n self.__layout.setObjectName(\"layout\")\n\n #########################\n # Status Dot #\n #########################\n self.__dot = QtWidgets.QWidget(self)\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Fixed, QtWidgets.QSizePolicy.Fixed)\n sizePolicy.setHorizontalStretch(0)\n sizePolicy.setVerticalStretch(0)\n sizePolicy.setHeightForWidth(self.__dot.sizePolicy().hasHeightForWidth())\n self.__dot.setSizePolicy(sizePolicy)\n self.__dot.setMinimumSize(QtCore.QSize(12, 12))\n self.__dot.setMaximumSize(QtCore.QSize(12, 12))\n self.__dot.setObjectName(\"dot\")\n self.__layout.addWidget(self.__dot)\n self.setStatusIdle()\n \n #########################\n # ComboBox #\n #########################\n self.comboBox = QtWidgets.QComboBox(self)\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)\n sizePolicy.setHorizontalStretch(0)\n sizePolicy.setVerticalStretch(0)\n sizePolicy.setHeightForWidth(self.comboBox.sizePolicy().hasHeightForWidth())\n self.comboBox.setSizePolicy(sizePolicy)\n self.comboBox.setMinimumSize(QtCore.QSize(0, 30))\n\n font = QtGui.QFont()\n font.setFamily(\"Montserrat SemiBold\")\n font.setPointSize(10)\n font.setBold(False)\n font.setWeight(50)\n self.comboBox.setFont(font)\n\n self.comboBox.addItem(initial_text)\n self.comboBox.setSizeAdjustPolicy(QtWidgets.QComboBox.AdjustToContents)\n self.comboBox.setObjectName(\"comboBox\")\n self.__layout.addWidget(self.comboBox)\n \n def setStatusIdle(self) -> None: self.__dot.setStyleSheet( \n self.DOT_STYLE.replace('{OPTION}', '#B5BFC6') \n )\n def setStatusConnected(self) -> None: self.__dot.setStyleSheet( \n self.DOT_STYLE.replace('{OPTION}', '#04B56E') \n )\n def setStatusConnecting(self) -> None: self.__dot.setStyleSheet( \n self.DOT_STYLE.replace('{OPTION}', '#F99500') \n )\n def setStatusDisconnected(self) -> None: self.__dot.setStyleSheet( \n self.DOT_STYLE.replace('{OPTION}', '#F25858') \n )\n\n\n def addItem(self, text: str, userData: typing.Any = ...) -> None: \n self.comboBox.addItem(text, userData)\n def addItems(self, texts: typing.Iterable[str]) -> None:\n self.comboBox.addItems(texts)\n def clear(self) -> None: \n self.comboBox.clear()\n\n\nif __name__ == '__main__':\n\n app = QtWidgets.QApplication([])\n \n window = QtWidgets.QWidget()\n window.setStyleSheet(\"QWidget {background-color: #FFF;}\")\n\n layout = QtWidgets.QHBoxLayout(window)\n layout.setContentsMargins(12, 12, 12, 12)\n layout.setSpacing(12)\n layout.setObjectName(\"layout\")\n\n #TUTORIAL ######################################\n statusBox1 = CustomComboBox(window)\n\n statusBox1.addItem(\"Connection A\")\n statusBox1.addItems(\n [\"Connection B\", \"Connection C\"])\n \n statusBox1.comboBox.activated.connect(print) # Print the index after comboBox get clicked\n\n statusBox2 = CustomComboBox(window, init=\"Connecting\")\n statusBox2.setStatusConnecting()\n\n statusBox3 = CustomComboBox(window, init=\"Connected\")\n statusBox3.setStatusConnected()\n\n statusBox4 = CustomComboBox(window, init=\"Error\")\n statusBox4.setStatusDisconnected()\n\n layout.addWidget(statusBox1)\n layout.addWidget(statusBox2)\n layout.addWidget(statusBox3)\n layout.addWidget(statusBox4)\n window.update()\n ################################################\n\n spacer = QtWidgets.QSpacerItem(261, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)\n layout.addItem(spacer)\n window.show()\n app.exec_()\n","repo_name":"defmylife/Qt-templates","sub_path":"StatusButtons/ComboBox.py","file_name":"ComboBox.py","file_ext":"py","file_size_in_byte":5973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2037490038","text":"import sys\nN = int(sys.stdin.readline())\nwhole_img = []\nres = ''\n\nfor i in range(N):\n line = list(sys.stdin.readline())\n whole_img.append(line)\n\ndef compression(start_row, start_col, length):\n global res\n\n if length == 1:\n res += whole_img[start_row][start_col]\n return\n\n first = ''\n for i in range(start_row, start_row+length):\n for j in range(start_col, start_col+length):\n if i == start_row and j == start_col:\n first = whole_img[i][j]\n continue\n else:\n if whole_img[i][j] != first:\n res += '('\n compression(start_row, start_col, int(length/2))\n compression(start_row, start_col + int(length / 2), int(length / 2))\n compression(start_row + int(length/2), start_col, int(length/2))\n compression(start_row + int(length/2), start_col + int(length/2), int(length/2))\n res += ')'\n return\n\n res += first\n\ncompression(0, 0, N)\n\nprint(res)","repo_name":"kayjayk/Algorithms_py","sub_path":"Exam1992.py","file_name":"Exam1992.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33024404274","text":"import pygame\nfrom pygame.locals import (\n K_w,\n K_a,\n K_s,\n K_d,\n K_q,\n K_e,\n K_ESCAPE,\n KEYDOWN,\n QUIT,\n)\nimport config as CONFIG\n\nclass Hud:\n '''Represents the heads up display elements on the canvs.'''\n\n def __init__(self):\n '''Initialize the robot class'''\n\n # Indicator color (initial shade of gray)\n self.indicator_color = 240\n\n # Create the indicator rectangle\n self.ind_pos = CONFIG.border_pixels/4\n self.ind_size = CONFIG.border_pixels/2\n self.indicator = pygame.Rect(self.ind_pos, self.ind_pos, self.ind_size, self.ind_size)\n\n # Position of pressed key indicators\n self.key_ind_pos = {\n K_w: 1 * (self.ind_size + 2 * self.ind_pos) + self.ind_pos,\n K_a: 2 * (self.ind_size + 2 * self.ind_pos) + self.ind_pos,\n K_s: 3 * (self.ind_size + 2 * self.ind_pos) + self.ind_pos,\n K_d: 4 * (self.ind_size + 2 * self.ind_pos) + self.ind_pos,\n K_q: 5 * (self.ind_size + 2 * self.ind_pos) + self.ind_pos,\n K_e: 6 * (self.ind_size + 2 * self.ind_pos) + self.ind_pos,\n }\n\n # Color of pressed key indicators\n self.key_ind_colors = {\n K_w: (255, 0, 0),\n K_a: (0, 255, 0),\n K_s: (0, 0, 255),\n K_d: (255, 255, 0),\n K_q: (0, 255, 255),\n K_e: (255, 0, 255),\n 'none': (0, 0, 0,)\n }\n\n # Define pressed key indicator rectangles\n self.key_ind = {\n K_w: pygame.Rect(self.ind_pos, self.key_ind_pos[K_w], self.ind_size, self.ind_size),\n K_a: pygame.Rect(self.ind_pos, self.key_ind_pos[K_a], self.ind_size, self.ind_size),\n K_s: pygame.Rect(self.ind_pos, self.key_ind_pos[K_s], self.ind_size, self.ind_size),\n K_d: pygame.Rect(self.ind_pos, self.key_ind_pos[K_d], self.ind_size, self.ind_size),\n K_q: pygame.Rect(self.ind_pos, self.key_ind_pos[K_q], self.ind_size, self.ind_size),\n K_e: pygame.Rect(self.ind_pos, self.key_ind_pos[K_e], self.ind_size, self.ind_size)\n }\n\n # Clock for managing game framerate\n self.clock = pygame.time.Clock()\n\n def check_input(self, events):\n '''Check for keyboard inputs'''\n\n # Look at every event in the queue\n for event in events:\n # Did the user hit a key?\n if event.type == KEYDOWN:\n # Was it the Escape key? If so, stop the loop.\n if event.key == K_ESCAPE:\n return False\n\n # Did the user click the window close button? If so, stop the loop.\n elif event.type == QUIT:\n return False\n\n return True\n\n def draw_frame_indicator(self, canvas):\n '''Draws the HUD frame indicator.'''\n\n # Update the color value\n self.indicator_color -= int(240/CONFIG.frame_rate)\n if self.indicator_color <= 0:\n self.indicator_color = 240\n\n # Create an RGB color tuple\n color_tuple = (self.indicator_color, self.indicator_color, self.indicator_color)\n\n # Draw the indicator on the canvas\n pygame.draw.rect(canvas, color_tuple, self.indicator)\n\n def draw_keys(self, canvas, keypress):\n '''Draws indicators showing the currently pressed wasd-qe keys'''\n\n for [key, value] in self.key_ind.items():\n if keypress[key]:\n pygame.draw.rect(canvas, self.key_ind_colors[key], self.key_ind[key])\n else:\n pygame.draw.rect(canvas, self.key_ind_colors['none'], self.key_ind[key])\n\n def get_exec_time(self):\n '''Gets the frame calculation time'''\n return self.clock.get_rawtime()\n","repo_name":"CherL01/KirB","sub_path":"interface/hud.py","file_name":"hud.py","file_ext":"py","file_size_in_byte":3721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72239739282","text":"'''\n1. 기준 테이터를 설정하고 그 기준보다 큰 데이터와 작은 데이터의 위치를 바꾸는 방법\n2. 병합 정렬과 더불어 일반적인 상황에서 가장 많이 사용된다.\n3. 쉽게 하려면 첫 번째 데이터를 기준으로 선택한다\n4. 왼쪽의 데이터에서 피벗보다 큰 값을 선택하고, 오른쪽에선 작은 값을 선택한다.\n5. 선택이 완료되면 둘을 스왑한다.\n6. start와 end가 엇갈리면 왼쪽 배열의 마지막 값과 피벗을 스왑한다.\n'''\n\n\ndef qsort_pythonic(target):\n if len(target) <= 1:\n return target\n pivot = target[0]\n\n left = [i for i in target if i < pivot]\n mid = [i for i in target if i == pivot]\n right = [i for i in target if i > pivot]\n return qsort_pythonic(left) + mid + qsort_pythonic(right)\n\n\ndef qsort_while(target, start, end):\n # 재귀에서 다루는 배열은 같은 배열임을 생각하자.\n # start, end 포인터만 변경해가면서 정렬을 수행하는 것이다.\n # 따라서 종료조건은 target의 length가 아니라 start와 end의 차이로 지정해야 한다.\n # 여기서 start와 end는 둘다 포함되는 범위이므로 range(start, end)와 다름을 생각하자.\n\n if start >= end:\n return\n pivot = (start + end) // 2 # 피벗도 인덱스로 지정한다. 인덱스와 실제 값을 헷갈리지말자\n target[pivot], target[start] = target[start], target[pivot]\n left = start + 1\n right = end\n # 두 개의 포인터가 교차하면 멈춰야 한다.\n while left <= right:\n # 왼쪽은 피벗보다 큰 숫자를 찾을 때 까지 계속 증가시켜준다.\n while left <= end and target[left] <= target[pivot]:\n left += 1\n # 오른쪽은 피벗보다 작은 숫자를 찾을 때 까지 증가시킨다.\n while right > start and target[right] >= target[pivot]:\n right -= 1\n # 만약 left와 right가 교차했다면 특수한 처리를 해줘야한다.\n # right가 멈춰 선 곳에 위치한 원소는 pivot보다 작거나 같은 원소이므로\n # 제일 첫 번째 원소인 pivot과 스위치해줘도 퀵정렬의 조건과 일치한다.\n if left > right:\n target[right], target[pivot] = target[pivot], target[right]\n # 교차하지 않았다면 left, right를 바꿔주면 된다.\n else:\n target[left], target[right] = target[right], target[left]\n # while문 안에서 피벗을 기준으로 한 정렬이 완료되었다.\n # 따라서 피벗을 제외한 양 옆의 배열을 대상으로 재귀적으로 퀵정렬을 호출해준다.\n # array 자체는 변화하지 않고 start, end인덱스 기준으로 퀵정렬을 수행할 구간을 지정하는 형식이므로\n # 이 형식에 맞게 호출해주어야한다.\n qsort_while(target, start, right - 1)\n qsort_while(target, right + 1, end)\n # partition 함수는 주어진 범위에서 피벗값을 기준으로 작은 값들과 큰 값들을 분리하는 역할을 수행하고, 피벗값의 위치를 반환합니다.\n\n\ndef partition(arr, low, high):\n # 피벗 인덱스를 중간값으로 설정합니다.\n pivot_index = (low + high) // 2\n # 피벗 인덱스의 값을 배열의 마지막 요소와 교환합니다.\n arr[pivot_index], arr[high] = arr[high], arr[pivot_index]\n pivot = arr[high]\n\n # i는 작은 값들을 추적하는 인덱스로 사용됩니다.\n i = low - 1\n\n # low부터 high까지 반복하며 피벗값과 비교합니다.\n for j in range(low, high):\n if arr[j] <= pivot:\n # 작은 값들을 좌측에 유지하고 인덱스 i를 증가시킵니다.\n i += 1\n arr[i], arr[j] = arr[j], arr[i]\n\n # 마지막으로 피벗 값을 올바른 위치로 옮깁니다.\n arr[i + 1], arr[high] = arr[high], arr[i + 1]\n return i + 1\n\n# quick_sort 함수는 재귀적으로 분할 정복 방식으로 정렬을 수행합니다.\n\n\ndef quick_sort(arr, low, high):\n if low < high:\n # 주어진 범위를 분할하여 피벗 인덱스를 얻습니다.\n pivot_index = partition(arr, low, high)\n # 피벗 인덱스를 기준으로 왼쪽 부분 배열을 정렬합니다.\n quick_sort(arr, low, pivot_index - 1)\n # 피벗 인덱스를 기준으로 오른쪽 부분 배열을 정렬합니다.\n quick_sort(arr, pivot_index + 1, high)\n\n\narray = [3, 6, 8, 10, 1, 2, 1, -1]\nprint(\"Original array:\", array)\nquick_sort(array, 0, len(array) - 1)\nprint(\"Sorted array:\", array)\nprint('----------------')\n\narray = [3, 3, 3, 1, 1, 2, 1, 0, -1, -1, 0]\nprint(\"WHILE Original array:\", array)\nqsort_while(array, 0, len(array) - 1)\nprint(\"WHILE Sorted array:\", array)\nprint('----------------')\n\narray = [3, 6, 8, 10, 1, 2, 1, -1]\nprint(\"Original array:\", array)\nprint(\"Sorted array:\", qsort_pythonic(array))\n","repo_name":"atoye1/ThisIsCodingTest","sub_path":"Sorting/quick_sort.py","file_name":"quick_sort.py","file_ext":"py","file_size_in_byte":4920,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43023255394","text":"# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport pretend\nimport pytest\n\nfrom pyramid.httpexceptions import HTTPBadRequest\n\nfrom warehouse.admin.views import journals as views\n\nfrom ....common.db.accounts import UserFactory\nfrom ....common.db.packaging import JournalEntryFactory, ProjectFactory\n\n\nclass TestProjectList:\n def test_no_query(self, db_request):\n journals = sorted(\n JournalEntryFactory.create_batch(30),\n key=lambda j: (j.submitted_date, j.id),\n reverse=True,\n )\n result = views.journals_list(db_request)\n\n assert result == {\"journals\": journals[:25], \"query\": None}\n\n def test_with_page(self, db_request):\n journals = sorted(\n JournalEntryFactory.create_batch(30),\n key=lambda j: (j.submitted_date, j.id),\n reverse=True,\n )\n db_request.GET[\"page\"] = \"2\"\n result = views.journals_list(db_request)\n\n assert result == {\"journals\": journals[25:], \"query\": None}\n\n def test_with_invalid_page(self):\n request = pretend.stub(params={\"page\": \"not an integer\"})\n\n with pytest.raises(HTTPBadRequest):\n views.journals_list(request)\n\n def test_query_basic(self, db_request):\n project0 = ProjectFactory.create()\n project1 = ProjectFactory.create()\n journals0 = sorted(\n JournalEntryFactory.create_batch(30, name=project0.normalized_name),\n key=lambda j: (j.submitted_date, j.id),\n reverse=True,\n )\n JournalEntryFactory.create_batch(30, name=project1.normalized_name)\n\n db_request.GET[\"q\"] = f\"{project0.name}\"\n result = views.journals_list(db_request)\n\n assert result == {\n \"journals\": journals0[:25],\n \"query\": f\"{project0.name}\",\n }\n\n def test_query_term_project(self, db_request):\n project0 = ProjectFactory.create()\n project1 = ProjectFactory.create()\n journals0 = sorted(\n JournalEntryFactory.create_batch(30, name=project0.normalized_name),\n key=lambda j: (j.submitted_date, j.id),\n reverse=True,\n )\n JournalEntryFactory.create_batch(30, name=project1.normalized_name)\n\n db_request.GET[\"q\"] = f\"project:{project0.name}\"\n result = views.journals_list(db_request)\n\n assert result == {\n \"journals\": journals0[:25],\n \"query\": f\"project:{project0.name}\",\n }\n\n def test_query_term_user(self, db_request):\n user0 = UserFactory.create()\n user1 = UserFactory.create()\n journals0 = sorted(\n JournalEntryFactory.create_batch(30, submitted_by=user0),\n key=lambda j: (j.submitted_date, j.id),\n reverse=True,\n )\n JournalEntryFactory.create_batch(30, submitted_by=user1)\n\n db_request.GET[\"q\"] = f\"user:{user0.username}\"\n result = views.journals_list(db_request)\n\n assert result == {\n \"journals\": journals0[:25],\n \"query\": f\"user:{user0.username}\",\n }\n\n def test_query_term_version(self, db_request):\n journals = JournalEntryFactory.create_batch(10)\n\n db_request.GET[\"q\"] = f\"version:{journals[0].version}\"\n result = views.journals_list(db_request)\n\n assert result == {\n \"journals\": [journals[0]],\n \"query\": f\"version:{journals[0].version}\",\n }\n","repo_name":"pypi/warehouse","sub_path":"tests/unit/admin/views/test_journals.py","file_name":"test_journals.py","file_ext":"py","file_size_in_byte":3921,"program_lang":"python","lang":"en","doc_type":"code","stars":3382,"dataset":"github-code","pt":"3"}
+{"seq_id":"35124568046","text":"import warnings\n\nwarnings.filterwarnings(\"ignore\")\n\nimport joblib\nimport numpy as np\nimport pandas as pd\nfrom nbsvm_classifier import NBSVMClassifier\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score, confusion_matrix, matthews_corrcoef\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import MultinomialNB\n\n\nclass TrainClassifier:\n def __init__(self, field=\"Summary\", vec_type=\"tfidf\"):\n self.RANDOM_STATE = 42\n self.field = field\n self.vec_type = vec_type\n\n def get_data(self, path):\n df = pd.read_csv(path)\n df = df.drop_duplicates()\n df = df.dropna(subset=[self.field])\n X, y = df[self.field], df[\"Class\"]\n return X, y\n\n def get_splits(self, X, y):\n X_train, X_valid, y_train, y_valid = train_test_split(\n X,\n y,\n test_size=0.2,\n random_state=self.RANDOM_STATE,\n stratify=y,\n )\n return X_train, X_valid, y_train, y_valid\n\n def print_configs(self):\n print(f\"FIELD: {self.field}\")\n print(f\"VECTORIZER: {self.vec_type.upper()}\", end=\"\\n\\n\")\n\n def print_metrics(self, y_true, y_pred):\n acc = accuracy_score(y_true, y_pred)\n mcc = matthews_corrcoef(y_true, y_pred)\n print(f\"ACCURACY: {acc:.3f}\")\n print(f\"MCC: {mcc:.3f}\")\n tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n print(f\"TP: {tp}\\tTN: {tn}\\tFP: {fp}\\tFN: {fn}\", end=\"\\n\\n\")\n return acc, mcc, tp, tn, fp, fn\n\n def get_count_vectors(self, X_train, X_valid):\n vec = CountVectorizer()\n train_term_doc = vec.fit_transform(X_train)\n valid_term_doc = vec.transform(X_valid)\n joblib.dump(vec, f\"../pickles/count_vectorizer_{self.field}.joblib\")\n return train_term_doc, valid_term_doc\n\n def get_tfidf_vectors(self, X_train, X_valid):\n vec = TfidfVectorizer()\n train_term_doc = vec.fit_transform(X_train)\n valid_term_doc = vec.transform(X_valid)\n joblib.dump(vec, f\"../pickles/tfidf_vectorizer_{self.field}.joblib\")\n return train_term_doc, valid_term_doc\n\n def train(self, path):\n accs, mccs, tps, tns, fps, fns = [], [], [], [], [], []\n\n # load data\n X, y = self.get_data(path)\n\n # get splits\n X_train, X_valid, y_train, y_valid = self.get_splits(X, y)\n\n # get train & valid vectors\n if self.vec_type == \"tfidf\":\n train_term_doc, valid_term_doc = self.get_tfidf_vectors(X_train, X_valid)\n elif self.vec_type == \"count\":\n train_term_doc, valid_term_doc = self.get_count_vectors(X_train, X_valid)\n\n # iterate over each model\n for model_name in [\"naive_bayes\", \"nbsvm\", \"logistic_regression\"]:\n print(\"*\" * 60)\n print(\" \" * 25 + f\"{model_name.upper()}\")\n print(\"*\" * 60)\n # create an object\n if model_name == \"naive_bayes\":\n model = MultinomialNB()\n elif model_name == \"nbsvm\":\n model = NBSVMClassifier()\n elif model_name == \"logistic_regression\":\n model = LogisticRegression()\n\n # fit the model on training data\n model.fit(train_term_doc, y_train)\n\n # predict on train & validation\n train_preds = model.predict(train_term_doc)\n valid_preds = model.predict(valid_term_doc)\n\n # print & store training metrics\n print(f\"TRAINING METRICS\")\n acc, mcc, tp, tn, fp, fn = self.print_metrics(y_train, train_preds)\n accs.append(acc)\n mccs.append(mcc)\n tps.append(tp)\n tns.append(tn), fps.append(fp), fns.append(fn)\n\n # print & store validation metrics\n print(f\"VALIDATION METRICS\")\n acc, mcc, tp, tn, fp, fn = self.print_metrics(y_valid, valid_preds)\n accs.append(acc)\n mccs.append(mcc)\n tps.append(tp)\n tns.append(tn), fps.append(fp), fns.append(fn)\n print(\"\\n\\n\")\n\n # save model\n joblib.dump(\n model, f\"../models/{model_name}_{self.field}_{self.vec_type}.joblib\"\n )\n index = pd.MultiIndex.from_product(\n [[\"naive_bayes\", \"nbsvm\", \"logistic_regression\"], [\"Train\", \"Valid\"]],\n names=[\"Split\", \"Model\"],\n )\n metric_df = pd.DataFrame.from_dict(\n {\"ACC\": accs, \"MCC\": mccs, \"TP\": tps, \"TN\": tns, \"FP\": fps, \"FN\": fns},\n )\n metric_df.index = index\n print(metric_df.to_markdown())\n\n\nif __name__ == \"__main__\":\n path = \"../data/processed/processed_food_reviews.csv\"\n for vec_type in [\"count\", \"tfidf\"]:\n for field in [\"Summary\", \"Text\"]:\n classifier = TrainClassifier(field=field, vec_type=vec_type)\n classifier.print_configs()\n classifier.train(path)\n print(\"-\" * 80, end=\"\\n\")\n print(\">\" * 80, end=\"\\n\")\n","repo_name":"adimyth/customer_sentiment_analysis","sub_path":"scripts/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22062081462","text":"from setuptools import setup\n\n\nwith open('requirements.txt') as f:\n requirements = f.read().splitlines()\n\n\nwith open('requirements.txt') as f:\n required = f.read().splitlines()\n\nsetup(\n name=\"smaystr-crawling-platform\",\n version=\"0.0.1\",\n install_requires=required,\n packages=[\"app\"],\n url=\"\",\n download_url=\"\",\n description=\"\",\n long_description=\"\",\n license=\"\",\n keywords=\"\",\n classifiers=[\n \"Intended Audience :: Developers\",\n \"Programming Language :: Python\",\n \"Natural Language :: English\",\n \"License :: OSI Approved :: MIT License\",\n \"Programming Language :: Python :: 3.7\",\n ],\n)\n\n","repo_name":"smaystr/dtk_modus","sub_path":"data_crawling/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27634908642","text":"import scrapy\n\nfrom pep_parse.items import PepParseItem\nfrom pep_parse.settings import SPIDER_NAME, ALLOWED_DOMAINS, START_URLS\n\n\nclass PepSpider(scrapy.Spider):\n name = SPIDER_NAME\n allowed_domains = ALLOWED_DOMAINS\n start_urls = START_URLS\n\n def parse(self, response):\n rows = response.css('section#numerical-index tbody tr')\n for row in rows:\n pep_link = row.css('a::attr(href)').get()\n yield response.follow(pep_link, callback=self.parse_pep)\n\n def parse_pep(self, response):\n number, name = response.css('h1.page-title::text').get().split(' – ')\n data = {\n 'number': int(number.replace('PEP ', '')),\n 'name': name,\n 'status': response.css('dd abbr::text').get()\n }\n yield PepParseItem(data)\n","repo_name":"bvsvrvb/praktikum-scrapy-parser","sub_path":"pep_parse/spiders/pep.py","file_name":"pep.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44023319388","text":"from __future__ import print_function\n\nimport argparse\nimport logging\nimport random\nimport time\nimport os\nimport numpy as np\n\nfrom carla.client import make_carla_client\nfrom carla.sensor import Camera, Lidar\nfrom carla.settings import CarlaSettings\nfrom carla.tcp import TCPConnectionError\nfrom carla.util import print_over_same_line\nfrom carla.transform import Transform,Scale\n\n\ndef makeadir(filename):\n dirname = os.path.dirname(filename)\n if not os.path.isdir(dirname):\n os.makedirs(dirname)\n\n\n\n\ndef run_carla_client(args):\n # Here we will run 3 episodes with 300 frames each.\n number_of_episodes = 1\n frames_per_episode =600\n\n # We assume the CARLA server is already waiting for a client to connect at\n # host:port. To create a connection we can use the `make_carla_client`\n # context manager, it creates a CARLA client object and starts the\n # connection. It will throw an exception if something goes wrong. The\n # context manager makes sure the connection is always cleaned up on exit.\n with make_carla_client(args.host, args.port) as client:\n print('CarlaClient connected')\n\n for episode in range(0, number_of_episodes):\n # Start a new episode.\n\n\n\n if 1:\n\n # Create a CarlaSettings object. This object is a wrapper around\n # the CarlaSettings.ini file. Here we set the configuration we\n # want for the new episode.\n settings = CarlaSettings()\n settings.set(\n SynchronousMode=True,\n SendNonPlayerAgentsInfo=True,\n NumberOfVehicles=0,\n NumberOfPedestrians=0,\n WeatherId=1,\n QualityLevel='low')\n settings.randomize_seeds()\n\n # Now we want to add a couple of cameras to the player vehicle.\n # We will collect the images produced by these cameras every\n # frame.\n\n\n\n #add camera easily,ver1.0\n Cameralist={}\n Lidarlist={}\n\n\n\n def add_camera(cameraname,pitch,yaw,roll):\n\n def Add_camera(full_name,pitch,yaw,roll,type):\n camera0 = Camera(full_name)\n # Set image resolution in pixels.\n camera0.set_image_size(1024,1024)\n # Set its position relative to the car in meters.\n camera0.set_position(5,0,4)\n camera0.set_rotation(pitch, yaw, roll)\n camera0.set_PostProcessing(type)\n settings.add_sensor(camera0)\n Cameralist[full_name]=camera0\n\n\n Add_camera(os.path.join('RGB',cameraname),pitch,yaw,roll,'None')\n Add_camera(os.path.join('Semantic',cameraname),pitch,yaw,roll,'SemanticSegmentation')\n # Add_camera(os.path.join('Depth',cameraname),pitch,yaw,roll,'Depth')\n\n instrincs_name = args.out_filename_format.format('pose/instrincs/'+cameraname , 0)\n\n cam_instrincs=Cameralist[os.path.join('RGB',cameraname)].get_instrincs()\n makeadir(instrincs_name)\n np.savetxt(instrincs_name, cam_instrincs)\n\n\n add_camera('Camera_F0',0,0,0)\n add_camera('Camera_F1',-90,0,0)\n add_camera('Camera_L0',0,-90,0)\n add_camera('Camera_R0',0,90,0)\n add_camera('Camera_B0',0,180,0)\n # add_camera('Camera_A',0,45,0)\n # add_camera('Camera_B',0,135,0)\n # add_camera('Camera_C',0,-45,0)\n # add_camera('Camera_D',0,-135,0)\n #add_camera('Camera_F1',0,0,0)\n #add_camera('Camera_L1',0,-90, 0)\n #add_camera('Camera_R1',0,90, 0)\n\n\n\n\n\n\n\n\n #\n #\n # # Let's add another camera producing ground-truth depth.\n # camera1 = Camera('CameraDepth', PostProcessing='Depth')\n # camera1.set_image_size(800, 600)\n # camera1.set_position(0.30, 0, 1.30)\n # settings.add_sensor(camera1)\n\n Lidarname='Lidar/Lidar64_0'\n lidar = Lidar(Lidarname)\n lidar.set_position(5,0,4)\n lidar.set_rotation(-80, 0, 0)\n lidar.set(\n Channels=64,\n Range=30,\n PointsPerSecond=2000000,\n RotationFrequency=10,\n UpperFovLimit=30,\n LowerFovLimit=-30)\n settings.add_sensor(lidar)\n Lidarlist[Lidarname]=lidar\n\n\n Lidarname = 'Lidar/Lidar64_1'\n lidar = Lidar(Lidarname)\n lidar.set_position(5, 0, 4) #(5,0,4)\n lidar.set_rotation(0, 0, 0)\n lidar.set(\n Channels=64,\n Range=30,\n PointsPerSecond=2000000,\n RotationFrequency=10,\n UpperFovLimit=30,\n LowerFovLimit=-30)\n settings.add_sensor(lidar)\n Lidarlist[Lidarname] = lidar\n\n\n\n\n else:\n\n # Alternatively, we can load these settings from a file.\n with open(args.settings_filepath, 'r') as fp:\n settings = fp.read()\n\n # Now we load these settings into the server. The server replies\n # with a scene description containing the available start spots for\n # the player. Here we can provide a CarlaSettings object or a\n # CarlaSettings.ini file as string.\n scene = client.load_settings(settings)\n\n # Choose one player start at random.\n number_of_player_starts = len(scene.player_start_spots)\n #player_start = random.randint(0, max(0, number_of_player_starts - 1))\n player_start = 133\n\n # Notify the server that we want to start the episode at the\n # player_start index. This function blocks until the server is ready\n # to start the episode.\n print('Starting new episode at %r...' % scene.map_name)\n client.start_episode(player_start)\n\n\n\n\n \n \n \n # Iterate every frame in the episode.\n for frame in range(0, frames_per_episode):\n\n # Read the data produced by the server this frame.\n measurements, sensor_data = client.read_data()\n\n # Print some of the measurements.\n if frame>=50:\n frame-=50\n posefilename = args.out_filename_format.format('pose/txt/Lidar', frame)\n print_and_save_measurements(measurements,posefilename)\n # Save the images to disk if requested.\n if args.save_images_to_disk:\n for name, measurement in sensor_data.items():\n extrincs_name = args.out_filename_format.format('pose/extrincs/'+name.split('/')[1], frame)\n makeadir(extrincs_name)\n filename = args.out_filename_format.format('Data/'+name, frame)\n\n\n if 'Lidar' in name:\n world_transform = Transform(\n measurements.player_measurements.transform,'l'\n )\n sensor_to_car_transform=Lidarlist[name].get_unreal_transform()\n else:\n world_transform = Transform(\n measurements.player_measurements.transform, 'c'\n )\n sensor_to_car_transform = Cameralist[name].get_unreal_transform()\n\n Camera_extrincs=Transform(Scale(x=-1))*world_transform*sensor_to_car_transform\n np.savetxt(extrincs_name,Camera_extrincs.matrix)\n\n\n if 'Lidar' in name:\n measurement.apply_transform(Camera_extrincs)\n\n\n measurement.save_to_disk(filename)\n\n\n\n\n # We can access the encoded data of a given image as numpy\n # array using its \"data\" property. For instance, to get the\n # depth value (normalized) at pixel X, Y\n #\n # depth_array = sensor_data['CameraDepth'].data\n # value_at_pixel = depth_array[Y, X]\n #\n\n # Now we have to send the instructions to control the vehicle.\n # If we are in synchronous mode the server will pause the\n # simulation until we send this control.\n\n if not args.autopilot:\n\n client.send_control(\n steer=random.uniform(-1.0, 1.0),\n throttle=0.5,\n brake=0.0,\n hand_brake=False,\n reverse=False)\n\n else:\n\n # Together with the measurements, the server has sent the\n # control that the in-game autopilot would do this frame. We\n # can enable autopilot by sending back this control to the\n # server. We can modify it if wanted, here for instance we\n # will add some noise to the steer.\n\n control = measurements.player_measurements.autopilot_control\n #control.steer += random.uniform(-0.1, 0.1)\n control.throttle = 0.4\n client.send_control(control)\n\n\ndef print_and_save_measurements(measurements,filename):\n folder = os.path.dirname(filename)\n if not os.path.isdir(folder):\n os.makedirs(folder)\n\n filename=filename+'.txt'\n with open(filename,'w') as fp:\n\n number_of_agents = len(measurements.non_player_agents)\n player_measurements = measurements.player_measurements\n message = 'Vehicle at ({pos_x:.1f}, {pos_y:.1f}), '\n message += 'rotation:{pitch:.2f},{roll:.2f},{yaw:.2f} '\n message += '{speed:.0f} km/h, '\n message += 'Collision: {{vehicles={col_cars:.0f}, pedestrians={col_ped:.0f}, other={col_other:.0f}}}, '\n message += '{other_lane:.0f}% other lane, {offroad:.0f}% off-road, '\n message += '({agents_num:d} non-player agents in the scene)'\n message = message.format(\n pos_x=-player_measurements.transform.location.x,\n pos_y=player_measurements.transform.location.y,\n pitch=player_measurements.transform.rotation.pitch,\n roll=player_measurements.transform.rotation.roll,\n yaw=player_measurements.transform.rotation.yaw,\n speed=player_measurements.forward_speed * 3.6, # m/s -> km/h\n col_cars=player_measurements.collision_vehicles,\n col_ped=player_measurements.collision_pedestrians,\n col_other=player_measurements.collision_other,\n other_lane=100 * player_measurements.intersection_otherlane,\n offroad=100 * player_measurements.intersection_offroad,\n agents_num=number_of_agents)\n print_over_same_line(message)\n\n\n write_message = 'x:{pos_x:.2f}\\ny:{pos_y:.2f}\\nz:{pos_z:.2f}\\npitch:{pitch:.2f}\\nroll:{roll:.2f}\\nyaw:{yaw:.2f}\\n'\n write_message = write_message.format(\n pos_x=-player_measurements.transform.location.x,\n pos_y=player_measurements.transform.location.y,\n pos_z=player_measurements.transform.location.z,\n pitch=player_measurements.transform.rotation.pitch,\n roll=player_measurements.transform.rotation.roll,\n yaw=player_measurements.transform.rotation.yaw,\n )\n fp.write(write_message)\n\n\ndef main():\n argparser = argparse.ArgumentParser(description=__doc__)\n argparser.add_argument(\n '-v', '--verbose',\n action='store_true',\n dest='debug',\n help='print debug information')\n argparser.add_argument(\n '--host',\n metavar='H',\n default='localhost',\n help='IP of the host server (default: localhost)')\n argparser.add_argument(\n '-p', '--port',\n metavar='P',\n default=2000,\n type=int,\n help='TCP port to listen to (default: 2000)')\n argparser.add_argument(\n '-a', '--autopilot',\n action='store_true',\n help='enable autopilot')\n argparser.add_argument(\n '-l', '--lidar',\n action='store_true',\n help='enable Lidar')\n argparser.add_argument(\n '-q', '--quality-level',\n choices=['Low', 'Epic'],\n type=lambda s: s.title(),\n default='low',\n help='graphics quality level, a lower level makes the simulation run considerably faster.')\n argparser.add_argument(\n '-i', '--images-to-disk',\n action='store_true',\n dest='save_images_to_disk',\n help='save images (and Lidar data if active) to disk')\n argparser.add_argument(\n '-c', '--carla-settings',\n metavar='PATH',\n dest='settings_filepath',\n default='',\n help='Path to a \"CarlaSettings.ini\" file')\n\n args = argparser.parse_args()\n\n log_level = logging.DEBUG if args.debug else logging.INFO\n logging.basicConfig(format='%(levelname)s: %(message)s', level=log_level)\n\n logging.info('listening to server %s:%s', args.host, args.port)\n\n args.out_filename_format = 'Data/out_lidar_vedio_test/episode_18/{:s}/{:0>6d}'\n\n while True:\n try:\n\n run_carla_client(args)\n\n print('Done.')\n return\n\n except TCPConnectionError as error:\n logging.error(error)\n time.sleep(1)\n\n\nif __name__ == '__main__':\n\n try:\n main()\n except KeyboardInterrupt:\n print('\\nCancelled by user. Bye!')\n","repo_name":"visg-robox/2.5D-semantic","sub_path":"dataset_scipt/carla_scipt/data_simulate/getmydata.py","file_name":"getmydata.py","file_ext":"py","file_size_in_byte":14137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25335658130","text":"# Runtime: 124 ms, faster than 82.13% of Python3 online submissions for Range Sum Query 2D - Immutable.\r\n# Memory Usage: 16.7 MB, less than 16.67% of Python3 online submissions for Range Sum Query 2D - Immutable.\r\nclass NumMatrix:\r\n\r\n def __init__(self, matrix: List[List[int]]):\r\n if not matrix:\r\n return\r\n len_row, len_col = len(matrix), len(matrix[0])\r\n self.mat = [[0] * (len_col + 1) for _ in range(len_row + 1)]\r\n for row in range(1, len_row + 1):\r\n for col in range(1, len_col + 1):\r\n self.mat[row][col] = matrix[row - 1][col - 1] + self.mat[row][col - 1] + self.mat[row - 1][col] - self.mat[row - 1][col - 1]\r\n\r\n def sumRegion(self, row1: int, col1: int, row2: int, col2: int) -> int:\r\n return self.mat[row2 + 1][col2 + 1] - self.mat[row1][col2 + 1] - self.mat[row2 + 1][col1] + self.mat[row1][col1]\r\n\r\n\r\n# Your NumMatrix object will be instantiated and called as such:\r\n# obj = NumMatrix(matrix)\r\n# param_1 = obj.sumRegion(row1,col1,row2,col2)\r\n","repo_name":"daidai21/Leetcode","sub_path":"Algorithms/Python3.x/304-Range_Sum_Query_2D_-_Immutable.py","file_name":"304-Range_Sum_Query_2D_-_Immutable.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"42811705809","text":"from typing import List\n\n\nclass Solution:\n def isPerfectSquare(self, num: int) -> bool:\n i = 1\n while i <= num // i:\n if i * i == num:\n return True\n i += 1\n return False\n\n\nif __name__ == \"__main__\":\n s = Solution()\n result = s.isPerfectSquare(16)\n print(result)\n","repo_name":"kenwoov/PlayLeetCode","sub_path":"Algorithms/Easy/367. Valid Perfect Square/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36970481015","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 7 17:44:57 2019\n\n@author: robin\n\"\"\"\n\nimport os\nfrom random import sample\nfrom shutil import copy\nimport numpy as np\n\n\ndef partition_title_list(list_in, tronc = 0, is_test_set = False):\n ''' Partition the curves files names into train, test and vaidation sets. Be careful that it shuffles the original list\n list_in (list): The original list of files names to partition\n tronc (int): If positive keep only elements of the list\n is_test_set (bool): whether or not to create a test set\n ------------------------------------------------------------------------\n returns (list of list): list_in partitioned in n random lists\n '''\n assert type(list_in) == list\n \n n = 4 # Split the list in four parts: the first two parts (half of the files names) constitute the training set. The other two parts represent the valid and train set\n \n tronc_list = sample(list_in, tronc) if tronc > 0 else list_in\n while len(tronc_list) % n != 0: # To avoid lists of different size pad the sequences with 'None' strings\n tronc_list.append('None')\n four_splitted_list = [tronc_list[i::n] for i in range(n)] \n files_partition = {}\n if is_test_set:\n files_partition['train'] = four_splitted_list[0] + four_splitted_list[1]\n files_partition['test'] = four_splitted_list[2]\n files_partition['valid'] = four_splitted_list[3]\n else:\n files_partition['train'] = four_splitted_list[0] + four_splitted_list[1] + four_splitted_list[2]\n files_partition['test'] = []\n files_partition['valid'] = four_splitted_list[3]\n return files_partition\n\n \ndef imgs_train_test_valid_split(root_dir, balanced = False, is_test_set = False):\n ''' Split the curves contained in the subdirectory of the into 3 folders: train, test and validation folders\n For the moment, I sample the same number of curves for each class (equal to the lowest umber of curves available)\n root_dir (str): The path to the folder containing the 4 folders: all, train, test and validation set (If everithing is ok root_directory is the 'curves' directory)\n ---------------------------------------------------------------------------------------------------------------\n returns (None): The data rightly split in three sub-datasets stored on hard disk\n '''\n curves_types = [f for f in os.listdir(root_dir + '/all')]\n\n if balanced:\n min_nb_curves = min([len(os.listdir(root_dir + '/all/' + ctype + '/' + cclass)) for ctype in curves_types for cclass in os.listdir(root_dir + '/all/' + ctype)]) \n min_nb_curves -= min_nb_curves % 4 # Ensures that the all the plankton types have the same number of observations\n \n else: # Extract all available curves\n min_nb_curves = 0 #np.inf\n \n for ctype in curves_types:\n print(ctype)\n cluster_classes = [f for f in os.listdir(root_dir + '/all/' + ctype)]\n for cclass in cluster_classes:\n curves_files = [f for f in os.listdir(root_dir + '/all/' + ctype + '/' + cclass)]\n\n files_partition = partition_title_list(curves_files, min_nb_curves, is_test_set)\n for folder, files in files_partition.items():\n for file in files:\n if file != 'None': \n copy(root_dir + '/all/' + ctype + '/' + cclass + '/' + file, root_dir + '/' + folder + '/' + ctype + '/' + cclass + '/' + file)\n \ndef nb_available_imgs(root_dir):\n ''' Compute the number of available images in the folders '''\n # Assuming that all 5 ctypes repos have the same number of observations\n nb_train = np.sum([len(os.listdir(root_dir + '/train/Curvature/' + cclass)) for cclass in os.listdir(root_dir + '/train/Curvature')]) \n nb_test = np.sum([len(os.listdir(root_dir + '/test/Curvature/' + cclass)) for cclass in os.listdir(root_dir + '/test/Curvature')]) \n nb_valid = np.sum([len(os.listdir(root_dir + '/valid/Curvature/' + cclass)) for cclass in os.listdir(root_dir + '/valid/Curvature')]) \n\n return nb_train, nb_test, nb_valid\n\n# Write a spec to ensure ok to fit NN on the curves: Same number of pic in each ctype subfolders ","repo_name":"RobeeF/planktonPipeline","sub_path":"extract_Pulse_curves/cnn_functions.py","file_name":"cnn_functions.py","file_ext":"py","file_size_in_byte":4255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34550341814","text":"try:\n import gi\n\n gi.require_version('Aravis', '0.4')\n from gi.repository import Aravis\n import numpy as np\n from PIL import Image\n import time\n\nexcept ImportError as importError:\n print(importError)\n\nclass Gige_camera():\n def __init__(self, name=None, camResolution=None, frameRate=15, mode = 'mono8' ):\n '''\n\t\t:param name: datatype: string, optional\n\t\t:param camResolution: datatype: tuple, optional\n\t\t:param frameRate: data type: int, optional\n\t\t'''\n self._name = name if (type(name) == str or name == None) else str(name)\n self._camResolution = camResolution if type(camResolution) == tuple else None\n self._frameRate = frameRate if type(frameRate) == int else int(frameRate)\n self.threshTimeTrigExitDanger =0.0005\n try:\n if name is None:\n self._camera = Aravis.Camera.new()\n\n else:\n self._camera = Aravis.Camera.new(name)\n\n sensorSize = self._camera.get_sensor_size()\n if self._camResolution is None or self._camResolution[0] > sensorSize[0] or self._camResolution[1] > \\\n sensorSize[1]:\n self._camResolution = (sensorSize[0], sensorSize[1])\n print('name: ', name, '\\n', 'cameraResolution: ', self._camResolution[0], 'x', self._camResolution[\n 1], '\\n', 'frameRate: ', frameRate)\n self._CameraInit(self._camera, self._camResolution, frameRate, mode)\n except Exception as error:\n if str(error) == 'constructor returned NULL':\n print('no camera found')\n else:\n print(error)\n\n def _CameraInit(self,a,b,c,d):\n try:\n camResolution=(320,480)\n frameRate=15\n mode='mono8'\n camera=self._camera\n camera.set_region(x=0, y=0, width=camResolution[0], height=camResolution[1])\n camera.set_frame_rate(frameRate)\n if mode == 'mono8':\n try:\n camera.set_pixel_format(Aravis.PIXEL_FORMAT_MONO_8)\n except Exception as error:\n print(error)\n elif mode == 'BRG8':\n try:\n camera.set_pixel_format(Aravis.PIXEL_FORMAT_BAYER_RG_8)\n except Exception as error:\n print(error)\n else:\n pass\n self._payload = camera.get_payload()\n self._stream = camera.create_stream()\n self._stream.push_buffer(Aravis.Buffer.new_allocate(self._payload))\n camera.start_acquisition()\n print(\"Camera vendor : %s\" % (camera.get_vendor_name()))\n except Exception as error:\n print(error)\n\n\n def Capture(self):\n try:\n self._camera.software_trigger()\n tic = time.time()\n print(\"waiting for pop\")\n while True:\n print(\"hi\")\n imageBuffer = self._stream.pop_buffer()\n\n print(imageBuffer)\n if imageBuffer:\n break\n elif (time.time() - tic) > self.threshTimeTrigExitDanger:\n break\n print('pop done')\n if imageBuffer:\n self._stream.push_buffer(imageBuffer)\n dataFromBuffer = imageBuffer.get_data()\n print(dataFromBuffer)\n if imageBuffer:\n print(\"test 1\")\n ImageIpl = Image.frombytes('L', (self._camResolution[0], self._camResolution[1]), dataFromBuffer, 'raw')\n print(ImageIpl)\n imageArray = np.asarray(ImageIpl)\n imgBayer = imageArray\n cv2.imwrite('fkjshfs.jpg', imgBayer)\n print(\"image saved\")\n cv2.waitKey(0)\n return True, imgBayer\n\n else:\n return False, None\n except Exception as error:\n return False, error\n\n\n def SetTriggerMode(self, mode=\"Software\"):\n try:\n if mode == 'software':\n self._camera.set_trigger(\"Software\")\n elif mode == 'external':\n self._camera.set_trigger(\"Line1\")\n else:\n self._camera.set_trigger(\"Any\")\n print ('mode should either \\'software\\' or \\'external\\'.',)\n print ('current Triggering Mode: ', self._camera.get_trigger_source())\n except Exception as error:\n print (error)\n\n\n def SetExposure(self, exposure=None):\n if exposure == None:\n print ('Exposure time: ', self._camera.get_exposure_time())\n return\n try:\n self._camera.set_exposure_time(exposure)\n print ('Exposure time: ', self._camera.get_exposure_time())\n except Exception as error:\n print (error)\ng=Gige_camera()\ng.Capture()\n#\n#name=None, camResolution=None, frameRate=15, mode = 'BRG8'\n\n#\n# camera, camResolution, frameRate, mode\n\n","repo_name":"darshil97/python","sub_path":"gigeCamera.py","file_name":"gigeCamera.py","file_ext":"py","file_size_in_byte":5000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5479148661","text":"#!/usr/bin/env python2.4\n\"\"\"\nReturns all positions of a maf with any pwm score > threshold\nThe positions are projected onto human coordinates\n\"\"\"\n\nimport sys\n\nfrom bx import intervals\nfrom bx.align import maf as align_maf\nfrom bx.pwm.pwm_score_maf import MafMotifScorer\n\n\ndef isnan(x):\n return not x == x\n\n\ndef main():\n if len(sys.argv) < 5:\n print(\"%s bedfile inmaf spec1,spec2,... string [string2,...]\" % sys.argv[0], file=sys.stderr)\n sys.exit(0)\n\n # read in intervals\n regions = {}\n for line in open(sys.argv[1]):\n if line.startswith(\"#\"):\n continue\n fields = line.strip().split()\n chrom, start, end = fields[0], int(fields[1]), int(fields[2])\n try:\n name = fields[3]\n except IndexError:\n name = None\n if chrom not in regions:\n regions[chrom] = intervals.Intersecter()\n regions[chrom].add(start, end, name)\n\n motif_strings = sys.argv[4:]\n if not isinstance(motif_strings, list):\n motif_strings = [motif_strings]\n inmaf = open(sys.argv[2])\n threshold = 0.5\n\n species = []\n\n for sp in sys.argv[3].split(\",\"):\n species.append(sp)\n\n for maf in align_maf.Reader(inmaf):\n mafchrom = maf.components[0].src.split(\".\")[1]\n mafstart = maf.components[0].start\n mafend = maf.components[0].end\n reftext = maf.components[0].text\n r = regions[mafchrom].find(mafstart, mafend)\n if mafchrom not in regions or len(r) == 0:\n continue\n\n # maf block scores for each matrix\n for scoremax, width, headers in MafMotifScorer(species, maf, motif_strings):\n blocklength = width\n mafsrc, mafstart, mafend = headers[0]\n mafchrom = mafsrc.split(\".\")[1]\n\n # lists of scores for each position in scoremax\n for mx_name, mx in scoremax.items():\n for offset in range(blocklength):\n # scan all species with threshold\n for i in range(len(species)):\n if mx[i][offset] > threshold:\n refstart = mafstart + offset - reftext.count(\"-\", 0, offset)\n refend = refstart + len(mx_name)\n\n data = \" \".join([\"%.2f\" % mx[x][offset] for x in range(len(species))])\n # quote the motif\n r = regions[mafchrom].find(refstart, refend)\n if mafchrom in regions and len(r) > 0:\n region_label = r[0].value\n else:\n # region_label = 0\n continue\n v_name = mx_name.replace(\" \", \"_\")\n print(mafchrom, refstart, refend, region_label, v_name, data)\n break\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"bxlab/bx-python","sub_path":"lib/bx/pwm/bed_score_aligned_string.py","file_name":"bed_score_aligned_string.py","file_ext":"py","file_size_in_byte":2949,"program_lang":"python","lang":"en","doc_type":"code","stars":138,"dataset":"github-code","pt":"3"}
+{"seq_id":"334052917","text":"import torch\nimport torch.optim as optim\nimport os\nimport time\nimport utils.evaluate as evaluate\nimport models.resnet as resnet\nfrom scipy.linalg import hadamard\nfrom loguru import logger\nfrom models.adsh_loss import ADSH_Loss\nfrom data.data_loader import sample_dataloader\nfrom utils import AverageMeter\nfrom utils.tools import compute_result, CalcTopMap\nfrom tqdm import tqdm\nimport random\nimport numpy as np\n\n\nclass CSQLoss(torch.nn.Module):\n def __init__(self, args, bit):\n super(CSQLoss, self).__init__()\n self.is_single_label = args.dataset not in {\"nuswide_21\", \"nus-widezs\", \"coco\"}\n print(\"is_single_label\", self.is_single_label)\n if bit == 16 or bit == 32 or bit == 64:\n print(\"use hadamard martrix to generate hash centers\")\n self.hash_targets = self.get_hash_targets(args.num_classes, bit).to(args.device)\n else:\n print(\"use bernoulli distribution to generate hash centers\")\n self.hash_targets = self.get_hash_targets_by_B(args.num_classes, bit).to(args.device)\n self.multi_label_random_center = torch.randint(2, (bit,)).float().to(args.device)\n self.criterion = torch.nn.BCELoss().to(args.device)\n self.lambd = args.lambd\n\n def forward(self, u, y, ind):\n # u = u.tanh()\n hash_center = self.label2center(y)\n center_loss = self.criterion(0.5 * (u + 1), 0.5 * (hash_center + 1))\n\n Q_loss = (u.abs() - 1).pow(2).mean()\n return center_loss + self.lambd * Q_loss\n\n def label2center(self, y):\n if self.is_single_label:\n hash_center = self.hash_targets[y.argmax(axis=1)]\n else:\n # to get sign no need to use mean, use sum here\n center_sum = y @ self.hash_targets\n random_center = self.multi_label_random_center.repeat(center_sum.shape[0], 1)\n center_sum[center_sum == 0] = random_center[center_sum == 0]\n hash_center = 2 * (center_sum > 0).float() - 1\n return hash_center\n\n #通过伯努利分布生成hash center\n def get_hash_targets_by_B(self, n_class, bit):\n\n hash_targets = torch.zeros((n_class, bit))\n for k in range(20):\n for index in range(n_class):\n\n ones = torch.ones(bit)\n # Bernouli distribution\n sa = random.sample(list(range(bit)), bit // 2)\n ones[sa] = -1\n hash_targets[index] = ones\n # to find average/min pairwise distance\n c = []\n for i in range(n_class):\n for j in range(n_class):\n if i < j:\n TF = sum(hash_targets[i] != hash_targets[j])\n c.append(TF)\n c = np.array(c)\n # choose min(c) in the range of K/4 to K/3\n # see in https://github.com/yuanli2333/Hadamard-Matrix-for-hashing/issues/1\n # but it is hard when bit is small\n if c.min() > bit / 4 and c.mean() >= bit / 2:\n print(c.min(), c.mean())\n break\n return hash_targets\n # use algorithm 1 to generate hash centers\n def get_hash_targets(self, n_class, bit):\n H_K = hadamard(bit)\n H_2K = np.concatenate((H_K, -H_K), 0)\n hash_targets = torch.from_numpy(H_2K[:n_class]).float()\n\n if H_2K.shape[0] < n_class:\n hash_targets.resize_(n_class, bit)\n for k in range(20):\n for index in range(H_2K.shape[0], n_class):\n #前2k个hash center通过hadmard matrix生成\n ones = torch.ones(bit)\n # Bernouli distribution\n sa = random.sample(list(range(bit)), bit // 2)\n ones[sa] = -1\n hash_targets[index] = ones\n # to find average/min pairwise distance\n c = []\n for i in range(n_class):\n for j in range(n_class):\n if i < j:\n TF = sum(hash_targets[i] != hash_targets[j])\n c.append(TF)\n c = np.array(c)\n\n # choose min(c) in the range of K/4 to K/3\n # see in https://github.com/yuanli2333/Hadamard-Matrix-for-hashing/issues/1\n # but it is hard when bit is small\n if c.min() > bit / 4 and c.mean() >= bit / 2:\n print(c.min(), c.mean())\n break\n return hash_targets\n\n\n\ndef train(\n test_loader,\n train_loader,\n database_loader,\n query_loader_zs,\n database_loader_zs,\n code_length,\n args,\n):\n \"\"\"\n Training model.\n\n Args\n test_loader, database_loader(torch.utils.data.dataloader.DataLoader): Data loader.\n code_length(int): Hashing code length.\n args.device(torch.args.device): GPU or CPU.\n lr(float): Learning rate.\n Returns\n mAP(float): Mean Average Precision.\n \"\"\"\n # Initialization\n # model = alexnet.load_model(code_length).to(args.device)\n\n\n device = args.device\n args.num_train = len(train_loader.dataset)\n # args.step_continuation = 20\n print(\"csq for zero shot\")\n\n model = resnet.resnet50(pretrained=args.pretrain, num_classes=code_length)\n print('backbone is resnet50')\n\n\n model.to(device)\n if args.optim == 'SGD':\n optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.wd, momentum=args.momen, nesterov=args.nesterov)\n elif args.optim == 'Adam':\n optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.wd)\n elif args.optim == 'AdamW':\n optimizer = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.wd)\n scheduler = optim.lr_scheduler.MultiStepLR(optimizer, args.lr_step)\n\n criterion = CSQLoss(args, code_length)\n\n losses = AverageMeter()\n start = time.time()\n best_mAP = 0\n corresponding_mAP_all = 0\n corresponding_zs_mAP = 0\n corresponding_zs_mAP_all = 0\n\n\n\n print(\"start training\")#*******************************************wp\n\n for it in range(args.max_iter):\n iter_start = time.time()\n # Sample training data for cnn learning\n train_dataloader, sample_index = sample_dataloader(train_loader, args.num_samples, args.batch_size, args.root, args.dataset)\n\n\n for epoch in range(args.max_epoch):\n # epoch_start = time.time()\n\n # criterion.scale = (epoch // args.step_continuation + 1) ** 0.5\n\n model.train()\n losses.reset()\n pbar = tqdm(enumerate(train_dataloader),total=len(train_dataloader),ncols = 50)\n # print((len(train_dataloader)))\n for batch, (data, targets, index) in pbar:\n # for batch, (data, targets, index) in enumerate(train_dataloader):\n # print(targets.shape)\n data, targets, index = data.to(device), targets.to(device), index.to(device)\n optimizer.zero_grad()\n u = model(data)\n loss = criterion(u, targets.float(), index)\n losses.update(loss.item())\n loss.backward()\n optimizer.step()\n logger.info('[epoch:{}/{}][loss:{:.6f}]'.format(epoch+1, args.max_epoch, losses.avg))\n\n scheduler.step()\n logger.info('[iter:{}/{}][iter_time:{:.2f}]'.format(it+1, args.max_iter,\n time.time()-iter_start))\n if (it < 35 and (it + 1) % args.val_freq == 0) or (it >= 35 and (it + 1) % 1 == 0):\n # if (it + 1) % 1 == 0 :\n\n query_code = generate_code(model, test_loader, code_length, args.device)\n query_targets = test_loader.dataset.get_onehot_targets()\n B = generate_code(model, database_loader, code_length, args.device)\n db_label= database_loader.dataset.get_onehot_targets()\n # if args.num_zs != 0:\n zs_test_binary = generate_code(model, query_loader_zs, code_length, args.device)\n zs_test_label = query_loader_zs.dataset.get_onehot_targets()\n\n zs_db_binary = generate_code(model, database_loader_zs, code_length, args.device)\n zs_db_label = database_loader_zs.dataset.get_onehot_targets()\n\n db_all_binary = torch.cat((B, zs_db_binary), 0)\n db_all_label = torch.cat((db_label, zs_db_label), 0)\n\n mAP = evaluate.mean_average_precision(\n query_code.to(args.device),\n B,\n query_targets[:,:args.num_classes].to(args.device),\n db_label[:,:args.num_classes].to(args.device),\n args.device,\n args.topk,\n )\n # if args.num_zs != 0:\n mAP_all = evaluate.mean_average_precision(\n query_code.to(args.device),\n db_all_binary.to(args.device),\n query_targets.to(args.device),\n db_all_label.to(args.device),\n args.device,\n args.topk,\n )\n zs_mAP_all = evaluate.mean_average_precision(\n zs_test_binary.to(args.device),\n db_all_binary.to(args.device),\n zs_test_label.to(args.device),\n db_all_label.to(args.device),\n args.device,\n args.topk,\n )\n zs_mAP = evaluate.mean_average_precision(\n zs_test_binary.to(args.device),\n zs_db_binary.to(args.device),\n zs_test_label.to(args.device),\n zs_db_label.to(args.device),\n args.device,\n args.topk,\n )\n\n\n if mAP > best_mAP:\n best_mAP = mAP\n corresponding_mAP_all = mAP_all\n corresponding_zs_mAP = zs_mAP\n corresponding_zs_mAP_all = zs_mAP_all\n ret_path = os.path.join('checkpoints', args.info, 'best_mAP',str(code_length))\n if not os.path.exists(ret_path):\n os.makedirs(ret_path)\n torch.save(query_code.cpu(), os.path.join(ret_path, 'query_code.t'))\n torch.save(B.cpu(), os.path.join(ret_path, 'database_code.t'))\n torch.save(query_targets.cpu(), os.path.join(ret_path, 'query_targets.t'))\n torch.save(db_label.cpu(), os.path.join(ret_path, 'database_targets.t'))\n torch.save(zs_test_binary.cpu(), os.path.join(ret_path, 'zs_test_binary.t'))\n torch.save(zs_db_binary.cpu(), os.path.join(ret_path, 'zs_db_binary.t'))\n torch.save(zs_test_label.cpu(), os.path.join(ret_path, 'zs_test_label.t'))\n torch.save(zs_db_label.cpu(), os.path.join(ret_path, 'zs_db_label.t'))\n torch.save(model.state_dict(), os.path.join(ret_path, 'model.pkl'))\n model = model.to(args.device)\n logger.info('[iter:{}/{}][code_length:{}][mAP:{:.5f}][mAP_all:{:.5f}][best_mAP:{:.5f}]'.format(it+1, args.max_iter, code_length, mAP,mAP_all ,best_mAP))\n logger.info('[iter:{}/{}][code_length:{}][zs_mAP:{:.5f}][zs_mAP_all:{:.5f}]'.format(it+1, args.max_iter, code_length, zs_mAP, zs_mAP_all))\n\n\n logger.info('[Training time:{:.2f}]'.format(time.time()-start))\n\n\n return best_mAP, corresponding_mAP_all, corresponding_zs_mAP, corresponding_zs_mAP_all\n\n\ndef generate_code(model, dataloader, code_length, device):\n # query_dataloader wp\n \"\"\"\n Generate hash code\n\n Args\n dataloader(torch.utils.data.DataLoader): Data loader.\n code_length(int): Hash code length.\n device(torch.device): Using gpu or cpu.\n\n Returns\n code(torch.Tensor): Hash code.\n \"\"\"\n\n model.eval()\n with torch.no_grad():\n N = len(dataloader.dataset)\n code = torch.zeros([N, code_length]).to(device)\n for data, _, index in dataloader:\n data = data.to(device)\n hash_code = model(data)\n code[index, :] = hash_code.sign()\n\n model.train()\n return code\n","repo_name":"Kso-Ayaka/DSH_Analysis","sub_path":"csq.py","file_name":"csq.py","file_ext":"py","file_size_in_byte":12082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14481655923","text":"import torch\nfrom torch import nn\nimport math\nimport matplotlib.pyplot as plt\n\n# for replication\ntorch.manual_seed(111)\n\n\nclass Discriminator(nn.Module):\n def __init__(self):\n super().__init__()\n self.model = nn.Sequential(\n nn.Linear(2, 256),\n nn.ReLU(),\n nn.Dropout(0.3),\n nn.Linear(256, 128),\n nn.ReLU(),\n nn.Dropout(0.3),\n nn.Linear(128, 64),\n nn.ReLU(),\n nn.Dropout(0.3),\n nn.Linear(64, 1),\n nn.Sigmoid(),\n )\n\n def forward(self, x):\n output = self.model(x)\n return output\n\n\nclass Generator(nn.Module):\n def __init__(self):\n super().__init__()\n self.model = nn.Sequential(\n nn.Linear(2, 16),\n nn.ReLU(),\n nn.Linear(16, 32),\n nn.ReLU(),\n nn.Linear(32, 2),\n )\n\n def forward(self, x):\n output = self.model(x)\n return output\n\n\n# create training data - pairs (x1, x2) so that x2 is the sine of x1\n# where x1 is in the interval from 0 to 2π\ntrain_data_length = 1024\ntrain_data = torch.zeros((train_data_length, 2))\ntrain_data[:, 0] = 2 * math.pi * torch.rand(train_data_length)\ntrain_data[:, 1] = torch.sin(train_data[:, 0])\ntrain_labels = torch.zeros(train_data_length)\ntrain_set = [\n (train_data[i], train_labels[i]) for i in range(train_data_length)\n]\n\n# plot training data\nplt.plot(train_data[:, 0], train_data[:, 1], \".\")\n\n# prepare model\nbatch_size = 32\ntrain_loader = torch.utils.data.DataLoader(\n train_set, batch_size=batch_size, shuffle=True\n)\ndiscriminator = Discriminator()\ngenerator = Generator()\nlr = 0.001\nnum_epochs = 300\nloss_function = nn.BCELoss()\n\noptimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=lr)\noptimizer_generator = torch.optim.Adam(generator.parameters(), lr=lr)\n\n# train model\nfor epoch in range(num_epochs):\n for n, (real_samples, _) in enumerate(train_loader):\n # Data for training the discriminator\n real_samples_labels = torch.ones((batch_size, 1))\n latent_space_samples = torch.randn((batch_size, 2))\n generated_samples = generator(latent_space_samples)\n generated_samples_labels = torch.zeros((batch_size, 1))\n all_samples = torch.cat((real_samples, generated_samples))\n all_samples_labels = torch.cat(\n (real_samples_labels, generated_samples_labels)\n )\n\n # Training the discriminator\n discriminator.zero_grad()\n output_discriminator = discriminator(all_samples)\n loss_discriminator = loss_function(\n output_discriminator, all_samples_labels)\n loss_discriminator.backward()\n optimizer_discriminator.step()\n\n # Data for training the generator\n latent_space_samples = torch.randn((batch_size, 2))\n\n # Training the generator\n generator.zero_grad()\n generated_samples = generator(latent_space_samples)\n output_discriminator_generated = discriminator(generated_samples)\n loss_generator = loss_function(\n output_discriminator_generated, real_samples_labels\n )\n loss_generator.backward()\n optimizer_generator.step()\n\n # Show loss\n if epoch % 10 == 0 and n == batch_size - 1:\n print(f\"Epoch: {epoch} Loss D.: {loss_discriminator}\")\n print(f\"Epoch: {epoch} Loss G.: {loss_generator}\")\n\n# generate sample\nlatent_space_samples = torch.randn(100, 2)\ngenerated_samples = generator(latent_space_samples)\ngenerated_samples = generated_samples.detach()\nplt.plot(generated_samples[:, 0], generated_samples[:, 1], \".\")\n\n\n\n# inspired by https://realpython.com/generative-adversarial-networks/","repo_name":"agnedil/Portfolio","sub_path":"01-Generative-AI-Prompt-Engineering/GANs/Simple_GAN_PyTorch.py","file_name":"Simple_GAN_PyTorch.py","file_ext":"py","file_size_in_byte":3759,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"12619239803","text":"\"\"\"\nExtension of XBlock Validation class to include information for presentation in Studio.\n\"\"\"\nfrom xblock.validation import Validation, ValidationMessage\n\n\nclass StudioValidationMessage(ValidationMessage):\n \"\"\"\n A message containing validation information about an xblock, extended to provide Studio-specific fields.\n \"\"\"\n\n # A special message type indicating that the xblock is not yet configured. This message may be rendered\n # in a different way within Studio.\n NOT_CONFIGURED = \"not-configured\"\n\n TYPES = [ValidationMessage.WARNING, ValidationMessage.ERROR, NOT_CONFIGURED]\n\n def __init__(self, message_type, message_text, action_label=None, action_class=None, action_runtime_event=None):\n \"\"\"\n Create a new message.\n\n Args:\n message_type (str): The type associated with this message. Most be `WARNING` or `ERROR`.\n message_text (unicode): The textual message.\n action_label (unicode): Text to show on a \"fix-up\" action (optional). If present, either `action_class`\n or `action_runtime_event` should be specified.\n action_class (str): A class to link to the \"fix-up\" action (optional). A click handler must be added\n for this class, unless it is \"edit-button\", \"duplicate-button\", or \"delete-button\" (which are all\n handled in general for xblock instances.\n action_runtime_event (str): An event name to be triggered on the xblock client-side runtime when\n the \"fix-up\" action is clicked (optional).\n \"\"\"\n super().__init__(message_type, message_text)\n if action_label is not None:\n if not isinstance(action_label, str):\n raise TypeError(\"Action label must be unicode.\")\n self.action_label = action_label\n if action_class is not None:\n if not isinstance(action_class, str):\n raise TypeError(\"Action class must be a string.\")\n self.action_class = action_class\n if action_runtime_event is not None:\n if not isinstance(action_runtime_event, str):\n raise TypeError(\"Action runtime event must be a string.\")\n self.action_runtime_event = action_runtime_event\n\n def to_json(self):\n \"\"\"\n Convert to a json-serializable representation.\n\n Returns:\n dict: A dict representation that is json-serializable.\n \"\"\"\n serialized = super().to_json()\n if hasattr(self, \"action_label\"):\n serialized[\"action_label\"] = self.action_label\n if hasattr(self, \"action_class\"):\n serialized[\"action_class\"] = self.action_class\n if hasattr(self, \"action_runtime_event\"):\n serialized[\"action_runtime_event\"] = self.action_runtime_event\n return serialized\n\n\nclass StudioValidation(Validation):\n \"\"\"\n Extends `Validation` to add Studio-specific summary message.\n \"\"\"\n\n @classmethod\n def copy(cls, validation):\n \"\"\"\n Copies the `Validation` object to a `StudioValidation` object. This is a shallow copy.\n\n Args:\n validation (Validation): A `Validation` object to be converted to a `StudioValidation` instance.\n\n Returns:\n StudioValidation: A `StudioValidation` instance populated with the messages from supplied\n `Validation` object\n \"\"\"\n if not isinstance(validation, Validation):\n raise TypeError(\"Copy must be called with a Validation instance\")\n studio_validation = cls(validation.xblock_id)\n studio_validation.messages = validation.messages\n return studio_validation\n\n def __init__(self, xblock_id):\n \"\"\"\n Create a `StudioValidation` instance.\n\n Args:\n xblock_id (object): An identification object that must support conversion to unicode.\n \"\"\"\n super().__init__(xblock_id)\n self.summary = None\n\n def set_summary(self, message):\n \"\"\"\n Sets a summary message on this instance. The summary is optional.\n\n Args:\n message (ValidationMessage): A validation message to set as this instance's summary.\n \"\"\"\n if not isinstance(message, ValidationMessage):\n raise TypeError(\"Argument must of type ValidationMessage\")\n self.summary = message\n\n @property\n def empty(self):\n \"\"\"\n Is this object empty (contains no messages and no summary)?\n\n Returns:\n bool: True iff this instance has no validation issues and therefore has no messages or summary.\n \"\"\"\n return super().empty and not self.summary\n\n def to_json(self):\n \"\"\"\n Convert to a json-serializable representation.\n\n Returns:\n dict: A dict representation that is json-serializable.\n \"\"\"\n serialized = super().to_json()\n if self.summary:\n serialized[\"summary\"] = self.summary.to_json()\n return serialized\n","repo_name":"openedx/edx-platform","sub_path":"xmodule/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":5012,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"25878879260","text":"from tkinter import *\nimport time\nfrom tkinter import messagebox\nimport random\nimport smtplib\nimport getpass\nimport pyaudio\nimport winsound\nimport sys\nimport wave\nimport pyglet\nfrom PIL import ImageTk, Image\n#all music and sound effects coded by Ian and Siddarth\n#gameOver coded by Jared\ndef gameOver():\n frame4.pack_forget()\n global frame5\n frame5 = Frame(newWindow)\n frame5.pack()\n global dragonHealth,playerHealth, turnNumber, entry\n win = \"Congratulations! You defeated the dragon!\"\n lose = \"The dragon burnt you to a crisp and ate you\"\n if (dragonHealth <= 0):\n gLabel = Label(frame5, text = win)\n if (playerHealth <= 0):\n gLabel = Label(frame5, text = lose)\n gLabel.grid(row = 1)\n emailLabel = Label(frame5, text = \"Enter your email if you want the results of your adventure\")\n emailLabel.grid(row = 2)\n myemail = StringVar()\n entry = Entry(frame5, textvariable = myemail)\n entry.grid(row = 2, column = 1)\n sendButton = Button(frame5, text = \"Send Email\", command = textEmail)\n sendButton.grid(row = 1, column =1)\n endLabel = Label(frame5, text = \"If not, thanks for playing!\")\n endLabel.grid(row = 3)\n endButton = Button(frame5, text = \"Exit\", command = Endgame)\n endButton.grid(row = 3, column = 1)\n#coded by Jared\ndef textEmail():\n global head, chest, legs, weapon, userEmail, turnNumber, playerHealth, dragonHealth, userEmail, entry, gold, emailText\n userEmail = entry.get()\n emailText = \"\"\n turnText= str(turnNumber)\n if(dragonHealth <= 0):\n emailText = \"Congratulations on beating the dragon! In order to defeat the dragon, you used your \" + weapon + \". You were wearing the \" + head + \", the \" + chest + \", and the \" + legs + \". You defeated the dragon in \" + turnText + \" turns. You were able to retire with \" + str(gold) + \" gold. Thanks for playing!\"\n if(playerHealth <= 0):\n emailText = \"The dragon bested you this time. Your \" + weapon + \" was unable to save you. You were wearing the \" + head + \", the \" + chest + \", and the \" + legs + \" before the dragon burnt them all to ash. The dragon defeated you in \" + turnText + \" turns. The dragon adds \" + str(gold) + \" gold to its hoard. Thanks for playing!\"\n if (userEmail != \"\"):\n passwordGet()\n#coded by Jared\ndef passwordGet():\n global frame5, passEntry\n frame5.destroy()\n frame6 = Frame(newWindow)\n frame6.pack()\n passText = StringVar()\n passEntry = Entry(frame6,textvariable = passText, show = \"*\")\n passEntry.grid(row = 1, column = 1)\n passLabel = Label(frame6, text = \"Enter your password: \")\n passLabel.grid(row = 1)\n passButton = Button(frame6, text = \"Send the email\", command = sendEmail)\n passButton.grid(row = 1, column = 2)\n#coded by Jared\ndef sendEmail():\n global passEntry, userEmail, emailText\n password = passEntry.get()\n try:\n gameserver = smtplib.SMTP('smtp.gmail.com',587)\n gameserver.ehlo()\n gameserver.starttls()\n gameserver.login( userEmail, password) # the user sends the results to themselves\n gameserver.sendmail( userEmail, userEmail, emailText)\n gameserver.quit()\n except:\n print(\"There was a problem\")\n Endgame()\n #coded by Jared\ndef Endgame():\n newWindow.destroy()\n player.pause()\n#coded by Jared\ndef bossFight():\n frame3.pack_forget()\n goldLabel.pack_forget()\n global frame4\n frame4 = Frame(newWindow)\n frame4.pack()\n dragonImage = Image.open(\"blueEyes.png\")\n dragonImage2 = ImageTk.PhotoImage(dragonImage)\n dragonLabel = Label(frame4, image = dragonImage2)\n dragonLabel.image = dragonImage2\n dragonLabel.grid(row = 0, columnspan = 4)\n global playerHealth, dragonHealth, actionPoints, turnNumber\n playerHealth = 100\n dragonHealth = 200\n actionPoints = 10\n turnNumber = 1\n global healthAmt, healthLabel\n healthAmt = StringVar()\n healthAmt.set(\"Health: \" + str(playerHealth))\n healthLabel = Label(frame4, textvariable = healthAmt)\n healthLabel.grid(row = 1)\n global apAmt, apLabel\n apAmt = StringVar()\n apAmt.set(\"Action Points: \" + str(actionPoints))\n apLabel = Label(frame4, textvariable = apAmt)\n apLabel.grid(row =1, column =1)\n global dHealthAmt, dHealthLabel\n dHealthAmt = StringVar()\n dHealthAmt.set(\"Dragon Health: \" + str(dragonHealth))\n dHealthLabel = Label(frame4, textvariable = dHealthAmt)\n dHealthLabel.grid(row=1, column = 3)\n global turnCount, turnLabel\n turnCount = StringVar()\n turnCount.set(\"Turn Number: \" + str(turnNumber))\n turnLabel = Label(frame4, textvariable = turnCount)\n turnLabel.grid(row = 1, column = 2)\n wAttack = Button(frame4, text = \"Weak Attack\", command = weakAttack)\n wAttack.grid(row = 2)\n sAttack = Button(frame4, text = \"Strong Attack\", command = strongAttack)\n sAttack.grid(row = 2, column = 1)\n spAttack = Button(frame4, text = \"Special Attack\", command = specialAttack)\n spAttack.grid(row = 2, column = 2)\n dodge = Button(frame4, text = \"Dodge\", command = dodging)\n dodge.grid(row = 2, column = 3)\n fightMusic = pyglet.media.load('bossFight.wav')\n global player\n player.queue(fightMusic)\n player.next_source()\n#coded by Jared\ndef dragonAttack():\n global head, chest, legs, playerHealth, healthAmt, healthLabel\n defenseBonus = 0\n if(head == \"Enchanted Hat\" or head == \"Enchanted Helm\" or head == \"Fire Hat\"):\n defenseBonus = defenseBonus + 1\n if(legs == \"Enchanted Pants\" or legs == \"Enchanted Leggings\" or legs == \"Fire Pants\"):\n defenseBonus = defenseBonus + 1\n if(chest == \"Enchanted Shirt\" or chest == \"Enchanted Chestplate\" or chest == \"FireShirt\"):\n defenseBonus = defenseBonus + 2\n dAttack = [1,2,3,4]\n randomAttack = random.choice(dAttack)\n if (randomAttack == 1 or randomAttack == 4):\n playerHealth = playerHealth - (10 - ( 2 * defenseBonus))\n messagebox.showinfo(\"Tail Attack!\", \"The dragon swipes at you with it's mighty tail!\")\n winsound.PlaySound('whipCrack', winsound.SND_ASYNC)\n if (randomAttack == 2):\n playerHealth = playerHealth - (14 - ( 3 * defenseBonus))\n messagebox.showinfo(\"Claw Attack!\", \"The dragon swipes you with its claws!\")\n winsound.PlaySound('dragonClaw', winsound.SND_ASYNC)\n if (randomAttack == 3):\n playerHealth = playerHealth - (20 - ( 4 * defenseBonus))\n messagebox.showinfo(\"Fire Attack!\", \"The dragon breathes its flames at you!\")\n winsound.PlaySound('fireBreath', winsound.SND_ASYNC)\n healthAmt.set(\"Health: \" + str(playerHealth))\n healthLabel = Label(frame4, textvariable = healthAmt)\n if(playerHealth <=0):\n gameOver()\n#coded by Jared\ndef weakAttack():\n global dragonHealth,frame4,dHealthLabel,dHealthAmt, weapon, turnNumber, turnCount, turnLabel\n attackBonus = 1\n if(weapon == \"Enchanted Bow\" or weapon == \"Enchanted Sword\" or weapon == \"waterstaff\"):\n attackBonus = 1.25\n dragonHealth = dragonHealth - (8 * attackBonus)\n messagebox.showinfo( \"Weak Attack\", \"You hit the dragon using your \" + weapon)\n if (weapon == \"Enchanted Bow\" or weapon == \"Compound Bow\"):\n winsound.PlaySound('rangerSp', winsound.SND_ASYNC)\n if (weapon == \"Enchanted Sword\" or weapon == \"Broadsword\"):\n winsound.PlaySound('warriorSp', winsound.SND_ASYNC)\n if (weapon == \"waterstaff\" or weapon == \"firestaff\"):\n winsound.PlaySound('wizardSp', winsound.SND_ASYNC)\n dHealthAmt.set(\"Dragon Health: \" + str(dragonHealth))\n dHealthLabel = Label(frame4, textvariable = dHealthAmt)\n if (dragonHealth <= 0):\n gameOver()\n else:\n dragonAttack()\n turnNumber = turnNumber + 1\n turnCount.set(\"Turn Number: \" + str(turnNumber))\n turnLabel = Label(frame4, textvariable = turnCount)\n frame4.update_idletasks()\n#coded by Jared\ndef strongAttack():\n global dragonHealth,frame4,dHealthLabel,dHealthAmt, weapon, actionPoints, apAmt, apLabel, turnNumber, turnCount, turnLabel\n attackBonus = 1\n if(weapon == \"Enchanted Bow\" or weapon == \"Enchanted Sword\" or weapon == \"waterstaff\"):\n attackBonus = 1.25\n if(actionPoints < 2.5):\n messagebox.showinfo(\"You missed!\",\"You used too much of your stamina and the dragon easily dodged\")\n if (actionPoints >= 2.5):\n dragonHealth = dragonHealth - (20 * attackBonus)\n actionPoints = actionPoints - 2.5\n messagebox.showinfo( \"Strong Attack\", \"You prepare yourself and hit the dragon with a mighty blow using your \" + weapon)\n if (weapon == \"Enchanted Bow\" or weapon == \"Compound Bow\"):\n winsound.PlaySound('rangerSp', winsound.SND_ASYNC)\n if (weapon == \"Enchanted Sword\" or weapon == \"Broadsword\"):\n winsound.PlaySound('warriorSp', winsound.SND_ASYNC)\n if (weapon == \"waterstaff\" or weapon == \"firestaff\"):\n winsound.PlaySound('wizardSp', winsound.SND_ASYNC)\n dHealthAmt.set(\"Dragon Health: \" + str(dragonHealth))\n dHealthLabel = Label(frame4, textvariable = dHealthAmt)\n apAmt.set(\"Action Points: \" + str(actionPoints))\n apLabel = Label(frame4, textvariable = apAmt)\n if (dragonHealth <= 0):\n gameOver()\n else:\n dragonAttack()\n turnNumber = turnNumber + 1\n turnCount.set(\"Turn Number: \" + str(turnNumber))\n turnLabel = Label(frame4, textvariable = turnCount)\n frame4.update_idletasks()\n#coded by Jared\ndef specialAttack():\n global dragonHealth,frame4,dHealthLabel,dHealthAmt, weapon, actionPoints, apAmt, apLabel, turnNumber, turnCount, turnLabel\n attackBonus = 1\n if(weapon == \"Enchanted Bow\" or weapon == \"Enchanted Sword\" or weapon == \"waterstaff\"):\n attackBonus = 1.25\n if(actionPoints <5):\n messagebox.showinfo(\"You missed!\",\"You used too much of your stamina and the dragon easily dodged\")\n if (actionPoints >= 5):\n dragonHealth = dragonHealth - (40 * attackBonus)\n actionPoints = actionPoints - 5\n if (weapon == \"Enchanted Bow\" or weapon == \"Compound Bow\"):\n messagebox.showinfo( \"Special Attack\", \"You shoot the dragon in the eye! It roars in agony!\")\n winsound.PlaySound('rangerSp', winsound.SND_ASYNC)\n if (weapon == \"Enchanted Sword\" or weapon == \"Broadsword\"):\n messagebox.showinfo(\"Special Attack\", \"You slice off one of the dragons claws! It roars in agony!\")\n winsound.PlaySound('warriorSp', winsound.SND_ASYNC)\n if (weapon == \"waterstaff\" or weapon == \"firestaff\"):\n messagebox.showinfo(\"Special Attack\", \"You charge up your power and blast the dragon with your magic! It roars in agony!\")\n winsound.PlaySound('wizardSp', winsound.SND_ASYNC)\n dHealthAmt.set(\"Dragon Health: \" + str(dragonHealth))\n dHealthLabel = Label(frame4, textvariable = dHealthAmt)\n apAmt.set(\"Action Points: \" + str(actionPoints))\n apLabel = Label(frame4, textvariable = apAmt)\n if (dragonHealth <= 0):\n gameOver()\n else:\n dragonAttack()\n turnNumber = turnNumber + 1\n turnCount.set(\"Turn Number: \" + str(turnNumber))\n turnLabel = Label(frame4, textvariable = turnCount)\n frame4.update_idletasks()\n#coded by Jared\ndef dodging():\n global dragonHealth,frame4,dHealthLabel,dHealthAmt, turnNumber, turnCount, turnLabel\n dodgeChance = [1,2]\n dodgePercent = random.choice(dodgeChance)\n if (dodgePercent == 1):\n dragonHealth = dragonHealth - 10\n dHealthAmt.set(\"Dragon Health: \" + str(dragonHealth))\n dHealthLabel = Label(frame4, textvariable = dHealthAmt)\n messagebox.showinfo(\"You dodged!\",\"You dodge out of the way of the dragon's attack and it injures itself\")\n if (dragonHealth <= 0):\n gameOver()\n if (dodgePercent == 2):\n messagebox.showinfo(\"You were hit!\",\"You tried dodging but the dragon hit you with its attack!\")\n dragonAttack()\n turnNumber = turnNumber + 1\n turnCount.set(\"Turn Number: \" + str(turnNumber))\n turnLabel = Label(frame4, textvariable = turnCount)\n frame4.update_idletasks()\n#showInventory designed by Jared\ndef showInventory():\n global frame3\n frame3 = Frame(newWindow)\n frame3.pack()\n global head, chest, legs, weapon\n hatBonus = \"\"\n chestBonus = \"\"\n legBonus = \"\"\n weaponBonus = \"\"\n if(head == \"Enchanted Hat\" or head == \"Enchanted Helm\" or head == \"Fire Hat\"):\n hatBonus = \" (+ 25 percent protection)\"\n if(chest == \"Enchanted Shirt\" or chest == \"Enchanted Chestplate\" or chest == \"FireShirt\"):\n chestBonus = \" (+ 50 percent protection)\"\n if(legs == \"Enchanted Pants\" or legs == \"Enchanted Leggings\" or legs == \"Fire Pants\"):\n legBonus = \" (+ 25 percent protection)\"\n if(weapon == \"Enchanted Bow\" or weapon == \"Enchanted Sword\" or weapon == \"waterstaff\"):\n weaponBonus = \" (+ 25 percent damage)\"\n hatLabel = Label(frame3, text = \"The headwear you chose was the \" + head + hatBonus)\n hatLabel.pack(side=TOP)\n chestLabel = Label(frame3, text = \"The chestwear you chose was the \" + chest + chestBonus)\n chestLabel.pack(side=TOP)\n legLabel = Label(frame3, text = \"The legwear you chose was the \" + legs + legBonus)\n legLabel.pack(side=TOP)\n weaponLabel = Label(frame3, text = \"The weapon you chose was the \" + weapon + weaponBonus)\n weaponLabel.pack(side=TOP)\n bossButton = Button(frame3, text = \"Ready to fight!\", command = bossFight)\n bossButton.pack(side = BOTTOM)\n#inventory designed by Devon\ndef inventory(item, choose):\n global head, chest, legs, weapon\n if(item == \"staff\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n weapon = \"waterstaff\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n weapon = \"firestaff\"\n if(item == \"wiztop\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n chest = \"FireShirt\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n chest = \"WaterShirt\"\n if(item == \"wizhat\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n head = \"Fire Hat\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n head = \"Water Hat\"\n if(item == \"wizlegs\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n legs = \"Fire Pants\"\n elif(choose ==2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n legs = \"Water Pants\"\n if(item == \"sword\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n weapon = \"Enchanted Sword\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n weapon = \"Broadsword\"\n if(item == \"boots\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n legs = \"Enchanted Leggings\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n legs = \"Armored Leggings\"\n if(item == \"chestplate\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n chest = \"Enchanted Chestplate\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n chest = \"Steel Chestplate\"\n if(item == \"helm\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n head = \"Enchanted Helm\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n head = \"Steel Helm\"\n if(item == \"bow\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n weapon = \"Enchanted Bow\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n weapon = \"Compound Bow\"\n if(item == \"archerhat\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n head = \"Enchanted Hat\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n head = \"Archer's Hat\"\n if(item == \"archerpants\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n legs = \"Enchanted Pants\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n legs = \"Archer's Pants\"\n if(item == \"archershirt\"):\n if(choose == 1):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n chest = \"Enchanted Shirt\"\n elif(choose == 2):\n winsound.PlaySound('equipitem.wav', winsound.SND_ASYNC)\n chest = \"Archer's Shirt\"\n#All gold functions designed by Jared\ndef resetGold(item, choose):\n spentGold()\n reset(item, choose)\ndef spentGold():\n global gold\n gold = gold - 250\n#reset designed by Siddarth\ndef ClearScreen():\n button1.destroy()\n button2.destroy()\n button3.destroy()\n#reset designed by Siddarth and Devon\ndef reset(itemchosen, choose):\n goldLabel.destroy()\n frame2.pack_forget()\n inventory(itemchosen, choose)\n if (numberOfDoors2 == 0):\n showInventory()\n if (choice == 1):\n WizardCreateDoors(numberOfDoors2)\n if (choice == 2):\n WarriorCreateDoors(numberOfDoors2)\n if (choice == 3):\n RangerCreateDoors(numberOfDoors2)\n#createRooms designed by Siddarth and Devon\ndef createRooms(item1, item2, itemChoice):\n global frame2\n frame2 = Frame(newWindow)\n frame2.pack()\n pathToImage1 = Image.open(item1)\n pathToImage2 = Image.open(item2)\n photoImage1 = ImageTk.PhotoImage(pathToImage1)\n photoImage2 = ImageTk.PhotoImage(pathToImage2)\n goldLabel.pack(side = TOP)\n selectedItem = itemChoice\n if gold >= 250:\n button1 = Button(frame2, image = photoImage1, text = \"Gold Price: 250\", compound = BOTTOM, command = lambda: resetGold(selectedItem, 1))\n button1.photoImage1 = photoImage1\n button1.pack(side = RIGHT)\n button2 = Button(frame2, image = photoImage2, text = \"Gold Price: 0\", compound = BOTTOM ,command = lambda: reset(selectedItem, 2))\n button2.photoImage2 = photoImage2\n button2.pack(side = RIGHT)\n\n#Rooms designed by Siddarth and Devon \ndef staffRoom():\n createRooms(\"waterstaff1.jpg\", \"fireStaff1.jpg\", \"staff\")\ndef topRoom():\n createRooms(\"firetop1.jpg\", \"waterRobe1.jpg\", \"wiztop\")\ndef wizardHatRoom():\n createRooms(\"fireHat1.jpg\", \"waterHat1.jpg\",\"wizhat\")\ndef bottomRoom():\n createRooms(\"fireRobe1.jpg\", \"waterBottom1.jpg\",\"wizlegs\")\n\ndef swordRoom():\n createRooms(\"ElementalSword.jpg\",\"MetalSword.jpg\",\"sword\")\ndef legsRoom():\n createRooms(\"ElementalLegs.jpg\",\"MetalLegs.jpg\",\"boots\")\ndef chestRoom():\n createRooms(\"ElementalPlate.jpg\", \"MetalBody.jpg\",\"chestplate\")\ndef helmRoom():\n createRooms(\"ElementalFullHelm.jpg\", \"MetalFullHelm.jpg\",\"helm\")\n\ndef bowRoom():\n createRooms(\"ElementalBow.jpg\", \"Bow.jpg\",\"bow\")\ndef RangerHatRoom():\n createRooms(\"elementalArcherHat.png\", \"robinHoodHat.jpg\",\"archerhat\")\ndef leggingsRoom():\n createRooms(\"elementalArcherLegs.png\",\"archerLegs.png\",\"archerpants\")\ndef torsoRoom():\n createRooms(\"elementalArcherTop.png\", \"ArcherTop.jpg\",\"archershirt\")\n\nrandomArray = [1,2,3,4]\nrandomGold = [1,2,3,4]\n#DoorUpdate Functions Designed by Siddarth\ndef WizardDoorUpdate():\n numberOfDoors1 = numberOfTimesDecremented\n global gold\n global numberOfDoors2\n numberOfDoors2 = numberOfDoors1 - 1\n\n randomNumber = random.choice(randomArray)\n goldFind = random.choice(randomGold)\n\n if randomNumber == 1:\n messagebox.showinfo(\"Staff\",\"You have entered the staff room\")\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n staffRoom()\n if randomNumber == 2:\n messagebox.showinfo(\"Top\",\"You have entered the top room\")\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n topRoom()\n if randomNumber == 3:\n messagebox.showinfo(\"Bottom\",\"You have entered the bottom room\")\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n bottomRoom()\n if randomNumber == 4:\n messagebox.showinfo(\"Hat\",\"You have entered the hat room\")\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n wizardHatRoom()\ndef WarriorDoorUpdate():\n numberOfDoors1 = numberOfTimesDecremented\n global gold\n global numberOfDoors2\n numberOfDoors2 = numberOfDoors1 - 1\n randomNumber = random.choice(randomArray)\n goldFind = random.choice(randomGold)\n if randomNumber == 1:\n messagebox.showinfo(\"Sword\",\"You have entered the sword room\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n swordRoom()\n if randomNumber == 2:\n messagebox.showinfo(\"Chest\",\"You have entered the chest room\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n chestRoom()\n if randomNumber == 3:\n messagebox.showinfo(\"Leggings\",\"You have entered the leggings room\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n legsRoom()\n if randomNumber == 4:\n messagebox.showinfo(\"Helm\",\"You have entered the helm room\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n helmRoom()\ndef RangerDoorUpdate():\n numberOfDoors1 = numberOfTimesDecremented\n global gold\n global numberOfDoors2\n numberOfDoors2 = numberOfDoors1 - 1\n randomNumber = random.choice(randomArray)\n goldFind = random.choice(randomGold)\n if randomNumber == 1:\n messagebox.showinfo(\"Bow\",\"You have entered the bow room\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n bowRoom()\n if randomNumber == 2:\n messagebox.showinfo(\"Torso\",\"You have entered the shirt room\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n torsoRoom()\n if randomNumber == 3:\n messagebox.showinfo(\"Leggings\",\"You have entered the legging's room\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n leggingsRoom()\n if randomNumber == 4:\n messagebox.showinfo(\"Hat\",\"You have entered the hat room\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n if goldFind == 1:\n messagebox.showinfo(\"Gold!!!\",\"You have also found gold in this room\")\n winsound.PlaySound('money.wav', winsound.SND_ASYNC)\n gold = gold + 250\n frame.pack_forget()\n randomArray.remove(randomNumber)\n RangerHatRoom()\n#Door functions designed by Siddarth\ndef WizardDoor():\n ClearScreen()\n global choice\n choice = 1\n messagebox.showinfo(\"wizard door\", \"You have entered the Wizard door! Choose another door and this will lead you to either the hat door, the top door, the robe door, or the staff door.\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n WizardCreateDoors(4)\ndef WarriorDoor():\n ClearScreen()\n global choice\n choice = 2\n messagebox.showinfo(\"warrior door\", \"You have entered the Warrior door! Choose another door and this will lead you to either the helm door, the chest door, the greaves door, or the sword door.\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n WarriorCreateDoors(4)\ndef RangerDoor():\n ClearScreen()\n global choice\n choice = 3\n messagebox.showinfo(\"ranger door\", \"You have entered the Ranger door! Choose another door and this will lead you to either the hat door, the torso door, the leggings door, or the bow door.\")\n winsound.PlaySound('door.wav', winsound.SND_ASYNC)\n RangerCreateDoors(4)\n#Create Doors Functions designed by Siddarth\ndef WizardCreateDoors(numberOfDoors):\n goldamount = StringVar()\n goldamount.set(\"Gold: \" + str(gold))\n global goldLabel\n goldLabel = Label(newWindow, textvariable = goldamount)\n goldLabel.pack(side = TOP)\n global numberOfTimesDecremented\n numberOfTimesDecremented = 0\n img5 = Image.open(\"door5.jpg\")\n img6 = ImageTk.PhotoImage(img1)\n global frame\n frame = Frame(newWindow)\n frame.pack()\n \n while numberOfDoors != 0:\n global labelx\n #labelx = Label(frame, image = img2)\n #labelx.pack(side = RIGHT)\n global wizardButton\n wizardButton = Button(frame, image = img6,command = WizardDoorUpdate)\n wizardButton.img6 = img6\n wizardButton.pack(side = RIGHT)\n numberOfDoors = numberOfDoors - 1\n numberOfTimesDecremented = numberOfTimesDecremented + 1\ndef WarriorCreateDoors(numberOfDoors):\n goldamount = StringVar()\n goldamount.set(\"Gold: \" + str(gold))\n global goldLabel\n goldLabel = Label(newWindow, textvariable = goldamount)\n goldLabel.pack(side = TOP)\n global numberOfTimesDecremented\n numberOfTimesDecremented = 0\n img5 = Image.open(\"door5.jpg\")\n img6 = ImageTk.PhotoImage(img1)\n global frame\n frame = Frame(newWindow)\n frame.pack()\n while numberOfDoors != 0:\n global labelx\n #labelx = Label(frame, image = img2)\n #labelx.pack(side = RIGHT)\n global warriorButton\n warriorButton = Button(frame, image = img6,command = WarriorDoorUpdate)\n warriorButton.img6 = img6\n warriorButton.pack(side = RIGHT)\n numberOfDoors = numberOfDoors - 1\n numberOfTimesDecremented = numberOfTimesDecremented + 1\ndef RangerCreateDoors(numberOfDoors):\n goldamount = StringVar()\n goldamount.set(\"Gold: \" + str(gold))\n global goldLabel\n goldLabel = Label(newWindow, textvariable = goldamount)\n goldLabel.pack(side = TOP)\n global numberOfTimesDecremented\n numberOfTimesDecremented = 0\n img5 = Image.open(\"door5.jpg\")\n img6 = ImageTk.PhotoImage(img1)\n global frame\n frame = Frame(newWindow)\n frame.pack()\n while numberOfDoors != 0:\n global labelx\n #labelx = Label(frame, image = img2)\n #labelx.pack(side = RIGHT)\n global rangerButton\n rangerButton = Button(frame, image = img6, command = RangerDoorUpdate)\n rangerButton.img6 = img6\n rangerButton.pack(side = RIGHT)\n numberOfDoors = numberOfDoors - 1\n numberOfTimesDecremented = numberOfTimesDecremented + 1\n#coded by Siddarth\ncasualBackground = pyglet.media.load('gamemusic.wav')\nglobal player\nplayer = pyglet.media.Player()\nplayer.queue(casualBackground)\nplayer.play()\ndef endMusic():\n newWindow.destroy()\n player.pause()\nnewWindow = Tk()\nnewWindow.protocol(\"WM_DELETE_WINDOW\",endMusic)\nmytext = StringVar()\ngold = 500\ngoldamount = StringVar()\ngoldamount.set(\"Gold: \" + str(gold))\nglobal goldLabel\nmessagebox.showinfo(\"Welcome Adventurer!\", \"There's a nasty dragon at the end of this dungeon that we need you to clear out. Here is 500 gold, you can buy equipment in the rooms beyond to help you with your battle. Better equipment costs more gold. Good luck and remember, we're all counting on you...\")\nwinsound.PlaySound('money.wav', winsound.SND_ASYNC)\ngoldLabel = Label(newWindow, textvariable = goldamount)\nimg1 = Image.open(\"door5.jpg\")\nimg2 = ImageTk.PhotoImage(img1)\nwarriorImg = Image.open(\"fighter.jpg\")\nwarriorImg2 = ImageTk.PhotoImage(warriorImg)\nwizardImg = Image.open(\"wizard.png\")\nwizardImg2 = ImageTk.PhotoImage(wizardImg)\nrangerImg = Image.open(\"ranger.jpg\")\nrangerImg2 = ImageTk.PhotoImage(rangerImg)\nwizardDoorCount = 4\nbutton1 = Button(newWindow, text = \"WIZARD\", image = wizardImg2, compound = BOTTOM ,command = WizardDoor)\nbutton1.wizardImg2 = wizardImg2\nbutton1.pack(side = RIGHT)\nbutton2 = Button(newWindow, text = \"WARRIOR\", image = warriorImg2, compound = BOTTOM, command = WarriorDoor)\nbutton2.warriorImg2 = warriorImg2\nbutton2.pack(side = RIGHT)\nbutton3 = Button(newWindow, text = \"RANGER\", image = rangerImg2, compound = BOTTOM, command = RangerDoor)\nbutton3.rangerImg2 = rangerImg2\nbutton3.pack(side = RIGHT)\n","repo_name":"Devonnair96/CST205P3","sub_path":"FinalTextGame.py","file_name":"FinalTextGame.py","file_ext":"py","file_size_in_byte":30791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38444124883","text":"'''\nProperty List Module\n====================\n\nThe Property List module reads and writes .plist files which are used\nheavily throughout OSX using the PyObjC bridge for NSPropertyListSerialization\n\nSeveral parts of this code have been taken/modified from gneagle's\nFoundationPlist class which is a part of the munki project.\n\nRemember that changes may not be effected immediately, and that you should try to avoid modifying the plist\nof any running process (If using NSPropertyListSerialization).\n\nTODO:\n\n- The write_key/read_key executions should support collections. Operations on selections via command line\nare always a bit clunky, so need to put some thought into this.\n\n:maintainer: Mosen \n:maturity: new\n:depends: objc,Foundation\n:platform: darwin\n'''\n\nimport logging\nimport salt.exceptions\n\nlog = logging.getLogger(__name__) # Start logging\n\nHAS_LIBS = False\ntry:\n import os\n\n from Foundation import NSData, \\\n NSPropertyListSerialization, \\\n NSPropertyListMutableContainers, \\\n NSPropertyListXMLFormat_v1_0, \\\n NSPropertyListBinaryFormat_v1_0, \\\n NSNumber, \\\n NSString, \\\n NSMutableDictionary, \\\n NSMutableArray, \\\n NSMutableData\n\n HAS_LIBS = True\nexcept ImportError:\n log.debug('Error importing dependencies for plist execution module.')\n\n__virtualname__ = 'plist'\n\n\ndef __virtual__():\n '''\n Only load if the platform is correct and we can use PyObjC libs\n '''\n if __grains__.get('kernel') != 'Darwin':\n return False\n\n if not HAS_LIBS:\n return False\n\n return __virtualname__\n\n\ndef _read_plist(filepath):\n \"\"\"\n Read a .plist file from filepath. Return the unpacked root object\n (which is usually a dictionary).\n\n If the file doesn't exist, this returns None\n \"\"\"\n if not os.path.isfile(filepath):\n log.debug('Tried to read non-existent property list at path: {0}'.format(filepath))\n return None\n\n plistData = NSData.dataWithContentsOfFile_(filepath)\n\n dataObject, plistFormat, error = \\\n NSPropertyListSerialization.propertyListFromData_mutabilityOption_format_errorDescription_(\n plistData, NSPropertyListMutableContainers, None, None)\n if error:\n error = error.encode('ascii', 'ignore')\n\n raise salt.exceptions.SaltInvocationError(\n 'Error decoding Property List : {}'.format(error)\n )\n else:\n return dataObject\n\n\ndef _write_plist(dataObject, filepath, format=NSPropertyListXMLFormat_v1_0):\n '''\n Write 'rootObject' as a plist to filepath.\n '''\n plistData, error = \\\n NSPropertyListSerialization.dataFromPropertyList_format_errorDescription_(\n dataObject, format, None)\n if error:\n error = error.encode('ascii', 'ignore')\n raise salt.exceptions.SaltInvocationError(\n 'Error encoding Property List: {}'.format(error)\n )\n else:\n if plistData.writeToFile_atomically_(filepath, True):\n return\n else:\n raise salt.exceptions.SaltInvocationError(\n 'Failed to write Property List at path: {}'.format(filepath)\n )\n\n\ndef _generate_plist_string(rootObject, format=NSPropertyListXMLFormat_v1_0):\n '''Return 'rootObject' as a plist-formatted string.'''\n plistData, error = \\\n NSPropertyListSerialization.dataFromPropertyList_format_errorDescription_(\n rootObject, format, None)\n if error:\n error = error.encode('ascii', 'ignore')\n raise salt.exceptions.SaltInvocationError(\n 'Error encoding Property List: {}'.format(error)\n )\n else:\n return str(plistData)\n\n\ndef _value_to_nsobject(value, nstype):\n '''Convert a string with a type specifier to a native Objective-C NSObject (serializable).'''\n return {\n 'string': lambda v: NSString.stringWithUTF8String_(v),\n 'int': lambda v: NSNumber.numberWithInt_(v),\n 'float': lambda v: NSNumber.numberWithFloat_(v),\n 'bool': lambda v: True if v == 'true' else False,\n 'data': lambda v: NSMutableData.dataWithLength_(len(value)).initWithBase64EncodedString_options_(value)\n }[nstype](value)\n\ndef _objects_for_dict(dict, keys, collector):\n \"\"\"Extract a section of a Property List by providing an existing structure (dict) of the keys.\n\n Recursive call traverses the specified keys in the NSDictionary, and retrieves the associated\n object at each 'leaf node', assigning a hierarchy of keys and the found value to the collector.\n\n Args:\n dict (NSDictionary): The current dictionary being operated on\n\n keys: The current dict describing a key or nested key in the dict parameter.\n\n collector: A reference to the current dict which can have value(s) set.\n \"\"\"\n # Stop collecting values if the specified key hierarchy doesn't exist.\n if dict is None:\n collector = None\n return\n\n for key, value in keys.items():\n if type(value) is dict:\n collector[key] = {}\n plist_value = dict.objectForKey_(key)\n _objects_for_dict(plist_value, value, collector[key])\n else:\n\n collector[key] = dict.objectForKey_(key)\n\ndef _set_objects_for_keys(dict, keys, changed=None):\n \"\"\"Set plist values using a given dict.\n\n Recursively finds or creates keys given and sets their values. This can be used to maintain a partial or\n complete override of any given property list file.\n\n Args:\n dict (NSMutableDictionary): The current dictionary being operated on. For a non existent file this will be\n blank.\n\n keys: A dict representing a hierarchy with leaf node values.\n \"\"\"\n if changed is None:\n changed = dict()\n\n for key, value in keys.items():\n existing_value = dict.objectForKey_(key)\n\n if type(value) is dict:\n # Value unavailable, so create structure\n if existing_value is None:\n child = NSMutableDictionary()\n dict.setObject_forKey_(child, key)\n\n changed[key] = {}\n _set_objects_for_keys(child, value, changed[key])\n else:\n if existing_value != value:\n dict.setObject_forKey_(value, key)\n changed[key] = value\n\n\ndef _remove_objects_for_keys(dict, keys, changed=None):\n \"\"\"Remove plist values using a given dict.\n\n Traverse each entry in the keys dict and remove the corresponding key (if it exists).\n If it doesn't exist then the function returns early.\n If the key was removed, the full path to that key is indicated in the changed dict.\n\n Args:\n dict (NSMutableDictionary): The current dictionary being operated on. For a non existent file this will be\n blank.\n\n keys: A dict representing a hierarchy pointing to keys to be removed\n\n changed: A dict used to record changes made\n \"\"\"\n if changed is None:\n changed = dict()\n\n for key, value in keys.items():\n existing_value = dict.objectForKey_(key)\n\n if not existing_value is None: # No need to process removal for non existent keys\n if type(value) is dict:\n changed[key] = {}\n _remove_objects_for_keys(existing_value, value, changed[key]) # Recurse deeper until not a dict\n else:\n dict.removeObjectForKey_(key)\n changed[key] = value\n\n\ndef _object_for_key_list(dict, keys, create=False):\n '''\n Get an object inside a nested NSDictionary structure, using a list of keys to traverse.\n\n If create is true, then missing elements are automatically created as NSMutableDictionary objects.\n '''\n key = keys.pop(0)\n\n # User accidentally supplies zero length key\n if len(key) == 0:\n return dict\n\n if len(keys) > 0:\n value = dict.objectForKey_(key)\n if value is None:\n if create:\n created = NSMutableDictionary()\n dict.setObject_forKey_(created, key)\n return _object_for_key_list(created, keys, create)\n else: # Just return None if path was not found\n log.debug('No key found in Property List: {0}'.format(key))\n return None\n else:\n return _object_for_key_list(dict.objectForKey_(key), keys, create)\n else:\n # TODO: special case for array index notation [x] if current object is NSArray\n # if dict.isKindOfClass_(NSArray.class_()):\n # return\n return dict.objectForKey_(key)\n\n\ndef _set_object_for_key_list(dict, keys, value, create=True, createNSType=NSMutableDictionary):\n '''\n Set the value of an object inside a nested NSDictionary structure, using a list of keys to traverse.\n\n If create is true, then missing elements are automatically created as NSMutableDictionary objects.\n createNSType can be passed a constructor to another possible collection type.\n '''\n key = keys.pop(0)\n\n # User accidentally supplies zero length key\n if len(key) == 0:\n return dict\n\n if len(keys) > 0:\n return _object_for_key_list(dict.objectForKey_(key), keys, create)\n else:\n dict.setObject_forKey_(value, key)\n\n\ndef _addObjectForKeyList(dict, keys, value, create=True):\n '''\n Add an object to an array inside a nested NSDictionary structure, using a list of keys.\n It is assumed that the last key points to an NSArray.\n\n If the create argument is true, non existent keys will be created as NSMutableDictionaries. The last item of the\n keys list will be created as an NSArray, and then the supplied value will be appended as an object\n '''\n key = keys.pop(0)\n\n # User accidentally supplies zero length key\n if len(key) == 0:\n return dict\n\n if len(keys) > 0:\n return _object_for_key_list(dict.objectForKey_(key), keys, create)\n else:\n dict.setObject_forKey_(value, key)\n\n\ndef _remove_object_for_key_list(dict, keys):\n '''\n Remove an object inside a nested NSDictionary structure, using a list of nested keys\n '''\n key = keys.pop(0)\n\n # User accidentally supplies zero length key\n if len(key) == 0:\n return dict\n\n if len(keys) > 0:\n return _object_for_key_list(dict.objectForKey_(key), keys)\n else:\n # TODO: special case for array index notation [x] if current object is NSArray\n # if dict.isKindOfClass_(NSArray.class_()):\n # return\n return dict.removeObjectForKey_(key)\n\n\ndef gen_string(data, format='xml'):\n '''\n Take a python struct (normally a dict) and generate a string representing a property list.\n\n data\n Python data structure (dict)\n\n format (default 'xml')\n Generate format, 'xml' or 'binary'\n '''\n serialization = NSPropertyListXMLFormat_v1_0 if format == 'xml' else NSPropertyListBinaryFormat_v1_0\n plistData, error = \\\n NSPropertyListSerialization.dataFromPropertyList_format_errorDescription_(\n data, serialization, None)\n if error:\n error = error.encode('ascii', 'ignore')\n log.debug('Error writing plist')\n log.debug(error)\n raise NSPropertyListSerializationException(error)\n else:\n return plistData\n\ndef parse_string(data):\n '''\n Take a property list as a string and return a python native representation.\n\n Used by other modules in salt-osx\n '''\n plistData = buffer(data)\n dataObject, plistFormat, error = \\\n NSPropertyListSerialization.propertyListFromData_mutabilityOption_format_errorDescription_(\n plistData, NSPropertyListMutableContainers, None, None)\n if error:\n error = error.encode('ascii', 'ignore')\n\n import traceback\n\n log.debug('Error parsing plist from string')\n log.debug(error)\n raise NSPropertyListSerializationException(error)\n else:\n return dataObject\n\ndef read_key(path, key=''):\n '''\n Read the value of a key contained within the Property List file specified\n\n path\n An absolute path to a property list (.plist) file, including the extension\n\n key\n The path specification for the key to modify. A list of keys separated by a colon. eg. 'path:to:name'\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' plist.read [key]\n '''\n dataObject = _read_plist(path)\n\n if dataObject is None:\n return None\n\n keys = key.split(':')\n if type(keys) is str:\n keys = list(keys)\n\n value = _object_for_key_list(dataObject, keys)\n return value\n\n\ndef write_key(path, key, nstype, value):\n '''\n Write the value of a key contained within the Property List file specified.\n If the property list file does not exist, the default behaviour is to create it with the keys/values given.\n\n path\n An absolute path to a property list (.plist) file, including the extension\n\n key\n The path specification for the key to modify. A list of keys separated by a colon.\n\n nstype\n The value type to write, one of 'string', 'int', 'float', 'bool', 'data'\n\n value\n The property value. If not specified it will be set to an empty value.\n\n CLI Example:\n\n .. code-block:: bash\n\n salt '*' plist.write [value]\n '''\n log.debug('Reading original plist for modification at path: %s' % path)\n dataObject = _read_plist(path)\n\n log.debug('Deriving key hierarchy from colon separated string')\n keys = key.split(':')\n if type(keys) is str:\n keys = list(keys)\n\n if dataObject is None:\n dataObject = NSMutableDictionary()\n\n log.debug('Performing string to NSObject conversion')\n nsval = _value_to_nsobject(value, nstype)\n log.debug('Setting object value in hierarchy')\n _set_object_for_key_list(dataObject, keys, nsval)\n log.debug('Writing out plist to original path')\n _write_plist(dataObject, path)\n\n\ndef delete_key(path, key):\n '''\n Delete the key from the property list at the specified path\n\n path\n An absolute path to a property list (.plist) file, including the extension\n\n key\n The specification of the key to modify. A list of keys separated by a colon.\n\n .. code-block:: bash\n\n salt '*' plist.delete [key]\n '''\n dataObject = _read_plist(path)\n\n if dataObject is None:\n return None # None indicating no action was taken.\n\n\n keys = key.split(':')\n if type(keys) is str:\n keys = list(keys)\n\n _remove_object_for_key_list(dataObject, keys)\n _write_plist(dataObject, path)\n\n\ndef append_key(path, key, nstype, value):\n '''\n Append an item to an array within a property list file.\n\n path\n An absolute path to a property list (.plist) file, including the extension\n\n key\n The specification of the key to append an element to. A list of keys separated by a colon.\n\n .. code-block:: bash\n\n salt '*' plist.append_key \n '''\n log.debug('Reading original plist for modification at path: %s' % path)\n root = _read_plist(path)\n\n log.debug('Deriving key hierarchy from colon separated string')\n keys = key.split(':')\n if type(keys) is str:\n keys = list(keys)\n\n if root is None:\n raise salt.exceptions.SaltInvocationError('Tried to append to non existing file, not currently supported.')\n\n log.debug('Performing string to NSObject conversion')\n nsval = _value_to_nsobject(value, nstype)\n log.debug('Setting object value in hierarchy')\n\n if len(keys) > 1:\n parent = _object_for_key_list(root, keys[:-1])\n else:\n parent = root\n\n log.debug('Updating or creating object at key: {}'.format(keys[-1]))\n collection = parent.objectForKey_(keys[-1])\n\n # This is destructive if the key value is already another type\n if collection is None or type(collection) is not NSMutableArray:\n collection = NSMutableArray()\n parent.setObject_forKey_(collection, keys[-1])\n\n collection.addObject_(nsval)\n\n log.debug('Writing out plist to original path')\n\n from pprint import pprint\n pprint(root)\n xml_plist = _generate_plist_string(root, NSPropertyListXMLFormat_v1_0)\n log.debug(xml_plist)\n _write_plist(root, path)\n\n\ndef read(path):\n '''\n Read the entire contents of the property list at the specified path\n\n path\n An absolute path to a property list (.plist) file, including the extension\n\n .. code-block:: bash\n\n salt '*' plist.read \n '''\n dataObject = _read_plist(path)\n return dataObject\n\ndef write(path, contents_dict):\n '''\n (over)write the entire contents of the property list at the specified path\n\n path\n An absolute path to a property list (.plist) file, including the extension\n\n contents_dict\n A python dict containing the objects to be encoded into the plist file.\n '''\n _write_plist(path, contents_dict)\n\n\ndef read_keys(path, keys):\n \"\"\"\n Read values of keys described by a dict.\n Each dict entry is traversed until it has no child dict.\n\n path\n An absolute path to a property list (.plist) file, including the extension\n\n keys\n A dict describing a key or nested keys, with any leaf values used to look up the\n corresponding plist value.\n \"\"\"\n dataObject = _read_plist(path)\n\n collector = {}\n _objects_for_dict(dataObject, keys, collector)\n\n return collector\n\n\ndef write_keys(path, keys, test=False):\n \"\"\"\n Write key structure and its values to the given property list.\n If a key does not exist in the target plist, it is created as a dictionary by default.\n\n path\n An absolute path to a property list (.plist) file, including the extension\n\n keys\n A dict describing a structure and value(s) that should exist inside the target plist\n\n test\n If test is true, no changes will be written, but you will receive a dict containing the changes that\n would have been performed.\n \"\"\"\n dataObject = _read_plist(path)\n\n if dataObject is None:\n dataObject = NSMutableDictionary()\n\n changed = {}\n _set_objects_for_keys(dataObject, keys, changed)\n\n if test == False:\n _write_plist(dataObject, path)\n\n return changed\n\n\ndef delete_keys(path, keys, test=False):\n \"\"\"\n Delete keys indicated by key dict structure.\n The deepest, or leaf node, of each dictionary entry will be removed from the plist.\n\n path\n An absolute path to a property list (.plist) file, including the extension\n\n keys\n A dict describing keys that should NOT exist inside the target plist\n\n test\n If test is true, no changes will be written, but you will receive a dict containing the keys that\n would have been removed.\n \"\"\"\n dataObject = _read_plist(path)\n changed = {}\n\n if dataObject is None:\n return changed # No need to remove anything from non existent property list\n\n _remove_objects_for_keys(dataObject, keys, changed)\n\n if test == False:\n _write_plist(dataObject, path)\n\n return changed\n","repo_name":"mosen/salt-osx","sub_path":"_modules/plist_serialization.py","file_name":"plist_serialization.py","file_ext":"py","file_size_in_byte":19147,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"3"}
+{"seq_id":"39048855712","text":"from pyesgf.search import SearchConnection\nconn = SearchConnection('http://esgf-index1.ceda.ac.uk/esg-search',\n distrib=False)\n\n#%%\nctx = conn.new_context(project='CORDEX', query='temperature')\nprint('Hits:', ctx.hit_count)\nprint('Institute:')\nctx.facet_counts['institute']\n#%%\nfrom pyesgf.logon import LogonManager\nlm = LogonManager()\nlm.logoff()\nlm.is_logged_on()","repo_name":"SebBub/misc_scripts","sub_path":"Download_Scripts/ESGF.py","file_name":"ESGF.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"18132609997","text":"# Дано число A (> 1).\n# Вывести наименьшее из целых чисел K, для которых сумма 1 + 1/2 + ... + 1/K будет больше A, и саму эту сумму.\n\nimport random\n\nA = random.randrange(2, 200)\nprint('A = ', A)\n\nK = 1\nS = 1\nwhile S <= A:\n K += 1\n S += K\nprint(\"K =\", K, \" , S =\", S)\n","repo_name":"galamyan01/Example","sub_path":"PZ_4/PZ_4_9_2.py","file_name":"PZ_4_9_2.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24498373949","text":"import sys\nfrom collections import defaultdict\nimport math\nimport random\nimport os\nimport os.path\n\"\"\"\nCOMS W4705 - Natural Language Processing - Fall 2022 \nProrgramming Homework 1 - Trigram Language Models\nDaniel Bauer\n\"\"\"\n\ndef corpus_reader(corpusfile, lexicon=None): \n with open(corpusfile,'r') as corpus: \n for line in corpus: \n if line.strip():\n sequence = line.lower().strip().split()\n if lexicon: \n yield [word if word in lexicon else \"UNK\" for word in sequence]\n else: \n yield sequence\n\ndef get_lexicon(corpus):\n word_counts = defaultdict(int)\n for sentence in corpus:\n for word in sentence: \n word_counts[word] += 1\n return set(word for word in word_counts if word_counts[word] > 1) \n\n\n\ndef get_ngrams(sequence, n):\n \"\"\"\n COMPLETE THIS FUNCTION (PART 1)\n Given a sequence, this function should return a list of n-grams, where each n-gram is a Python tuple.\n This should work for arbitrary values of 1 <= n < len(sequence).\n \"\"\"\n\n ngrams = []\n\n \n if n is 1:\n ngrams.append(('START',))\n\n \n RangeEnd = (len(sequence)+1)\n for index_word in range(0, RangeEnd):\n tuple_gram = ()\n for index_gram in range(index_word-n+1, index_word+1):\n word = None\n if index_gram < 0:\n word = 'START'\n elif index_gram >= len(sequence):\n word = 'STOP'\n else:\n word = sequence[index_gram]\n if word:\n tuple_gram = tuple_gram + (word,)\n \n ngrams.append(tuple_gram)\n\n return ngrams\n\n\nclass TrigramModel(object):\n \n def __init__(self, corpusfile):\n \n # Iterate through the corpus once to build a lexicon \n generator = corpus_reader(corpusfile)\n self.lexicon = get_lexicon(generator)\n self.lexicon.add(\"UNK\")\n self.lexicon.add(\"START\")\n self.lexicon.add(\"STOP\")\n \n # Now iterate through the corpus again and count ngrams\n generator = corpus_reader(corpusfile, self.lexicon)\n self.count_ngrams(generator)\n\n\n def count_ngrams(self, corpus):\n \"\"\"\n COMPLETE THIS METHOD (PART 2)\n Given a corpus iterator, populate dictionaries of unigram, bigram,\n and trigram counts. \n \"\"\"\n \n self.unigramcounts = {} # might want to use defaultdict or Counter instead\n self.bigramcounts = {} \n self.trigramcounts = {} \n self.totalcount = 0\n\n ##Your code here\n for sequence in corpus: \n for token in get_ngrams(sequence, 1): \n if not token in self.unigramcounts: \n self.unigramcounts[token] = 0 \n self.unigramcounts[token] += 1 \n\n for token in get_ngrams(sequence, 2): \n if not token in self.bigramcounts: \n self.bigramcounts[token] = 0 \n self.bigramcounts[token] += 1 \n\n for token in get_ngrams(sequence, 3): \n if not token in self.trigramcounts: \n self.trigramcounts[token] = 0 \n self.trigramcounts[token] += 1 \n \n self.totalcount += len(sequence) \n\n self.bigramcounts[(\"START\", \"START\")] = self.unigramcounts[(\"START\",)] \n\n return\n\n def raw_trigram_probability(self,trigram):\n \"\"\"\n COMPLETE THIS METHOD (PART 3)\n Returns the raw (unsmoothed) trigram probability\n \"\"\"\n tricount = self.trigramcounts.get(trigram, 0.0)\n bicount = float(self.bigramcounts.get(trigram[:-1], 0.0))\n \n return (tricount/bicount) if bicount != 0 else 0.0\n\n def raw_bigram_probability(self, bigram):\n \"\"\"\n COMPLETE THIS METHOD (PART 3)\n Returns the raw (unsmoothed) bigram probability\n \"\"\"\n bicount = self.bigramcounts.get(bigram, 0.0)\n unicount = float(self.unigramcounts.get(bigram[:-1], 0.0))\n\n return (bicount/unicount) if unicount != 0 else 0.0\n \n def raw_unigram_probability(self, unigram):\n \"\"\"\n COMPLETE THIS METHOD (PART 3)\n Returns the raw (unsmoothed) unigram probability.\n \"\"\"\n unicount = self.unigramcounts.get(unigram, 0.0)\n tricount = float(self.trigramcounts.get(unigram[:-1], 0.0))\n\n return (unicount/tricount) if tricount != 0 else 0.0\n #hint: recomputing the denominator every time the method is called\n # can be slow! You might want to compute the total number of words once, \n # store in the TrigramModel instance, and then re-use it. \n\n def generate_sentence(self,t=20): \n \"\"\"\n COMPLETE THIS METHOD (OPTIONAL)\n Generate a random sentence from the trigram model. t specifies the\n max length, but the sentence may be shorter if STOP is reached.\n \"\"\"\n return \n\n def smoothed_trigram_probability(self, trigram):\n \"\"\"\n COMPLETE THIS METHOD (PART 4)\n Returns the smoothed trigram probability (using linear interpolation). \n \"\"\"\n lambda1 = 1/3.0\n lambda2 = 1/3.0\n lambda3 = 1/3.0\n \n temp_unigram = [] \n temp_unigram.append(trigram[2]) \n p_unigram = self.raw_unigram_probability(tuple(temp_unigram)) \n\n temp_bigram = [] \n temp_bigram.append(trigram[1]) \n temp_bigram.append(trigram[2]) \n p_bigram = self.raw_bigram_probability(tuple(temp_bigram)) \n\n p_trigram = self.raw_trigram_probability(trigram) \n\n return lambda1 * p_trigram + lambda2 * p_bigram + lambda3 * p_unigram \n \n def sentence_logprob(self, sentence):\n \"\"\"\n COMPLETE THIS METHOD (PART 5)\n Returns the log probability of an entire sequence.\n \"\"\"\n trigrams = get_ngrams(sentence, 3)\n\n logprob = 0.0\n for gram in trigrams:\n smoothedprob = self.smoothed_trigram_probability(gram)\n if smoothedprob == 0.0:\n continue\n p = math.log(smoothedprob, 2)\n logprob = logprob + p\n\n\n return float(logprob)\n\n def perplexity(self, corpus):\n \"\"\"\n COMPLETE THIS METHOD (PART 6) \n Returns the log probability of an entire sequence.\n \"\"\"\n sum = 0 \n count = 0 \n for sentence in corpus: \n sum += self.sentence_logprob(sentence) \n count += len(sentence) \n\n sum /= count \n\n return float(2, -sum) \n\n\ndef essay_scoring_experiment(training_file1, training_file2, testdir1, testdir2):\n\n model1 = TrigramModel(training_file1)\n model2 = TrigramModel(training_file2)\n\n total = 0\n correct = 0 \n \n for f in os.listdir(testdir1):\n pp = model1.perplexity(corpus_reader(os.path.join(testdir1, f), model1.lexicon))\n # .. \n \n for f in os.listdir(testdir2):\n pp = model2.perplexity(corpus_reader(os.path.join(testdir2, f), model2.lexicon))\n # .. \n \n return 0.0\n\nif __name__ == \"__main__\":\n\n model = TrigramModel(sys.argv[1]) \n\n # put test code here...\n # or run the script from the command line with \n # $ python -i trigram_model.py [corpus_file]\n # >>> \n #\n # you can then call methods on the model instance in the interactive \n # Python prompt. \n\n # print(get_ngrams([\"natural\", \"language\", \"processing\"],1))\n # print(get_ngrams([\"natural\", \"language\", \"processing\"],2))\n # print(get_ngrams([\"natural\", \"language\", \"processing\"],3))\n #\n # print(len(model.unigramcounts))\n # print(len(model.bigramcounts))\n # print(len(model.trigramcounts))\n #\n # print(model.trigramcounts[('START','START','the')])\n # print(model.bigramcounts[('START','the')])\n # print(model.unigramcounts[('the',)])\n #\n # print(\"Unigram: \" + str(model.raw_unigram_probability(('department',))))\n # print(\"Bigram: \" + str(model.raw_bigram_probability(('highway', 'department'))))\n # print(\"Trigram: \" + str(model.raw_trigram_probability(('state','highway','department'))))\n #\n # print(\"Smoothed Trigram: \" + str(model.smoothed_trigram_probability(('state','highway','department'))))\n # print(\"Sentence Log Probability: \" + str(model.sentence_logprob('The State Highway department')))\n \n \n \n # Testing perplexity: \n # dev_corpus = corpus_reader(sys.argv[2], model.lexicon)\n # pp = model.perplexity(dev_corpus)\n # print(pp)\n\n\n # Essay scoring experiment: \n # acc = essay_scoring_experiment('./hw1_data/ets_toefl_data/train_high.txt', './hw1_data/ets_toefl_data/train_low.txt\", \"./hw1_data/ets_toefl_data/test_high\", \"./hw1_data/ets_toefl_data/test_low\")\n # print(acc)\n\n","repo_name":"ShengzhiLuo/NLP-ngram-Classification","sub_path":"trigram_model.py","file_name":"trigram_model.py","file_ext":"py","file_size_in_byte":8796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
| Name | \nPos | \nWeek | \nPts | \n
|---|---|---|---|
| Todd Gurley | \nRB | \n1 | \n22.6 | \n
| Christian McCaffrey | \nRB | \n1 | \n14.8 | \n
[0-9A-F]+)\\s*;\\s*[CS]\\s*;\\s*(?P[0-9A-F]+)\", re.IGNORECASE)\naliases = None\n\n\ndef openOrExit(path, mode):\n try:\n if sys.version_info.major >= 3:\n return open(path, mode, encoding=\"UTF-8\")\n else:\n return open(path, mode)\n except IOError as e:\n print(\"I/O error opening {0}, ({1}): {2}\".format(path, e.errno, e.strerror))\n exit(1)\n\n\ndef openUCDFileOrExit(path):\n if not UCDDirectoryPath:\n exit(1)\n\n return openOrExit(os.path.join(UCDDirectoryPath, path), 'r')\n\n\ndef verifyUCDFilesExist():\n if not UCDDirectoryPath:\n exit(1)\n\n missingFileCount = 0\n for file in RequiredUCDFiles:\n fullPath = os.path.join(UCDDirectoryPath, file)\n if not os.path.exists(fullPath):\n print(\"Couldn't find UCD file {0} at {1}\".format(file, fullPath))\n missingFileCount = missingFileCount + 1\n if missingFileCount:\n exit(1)\n\n\ndef ceilingToPowerOf2(size):\n powerOf2 = 1\n while size > powerOf2:\n powerOf2 = powerOf2 << 1\n\n return powerOf2\n\n\nclass Aliases:\n def __init__(self):\n self.globalNameToAliases = {}\n self.generalCategoryToAliases = {}\n self.aliasToGeneralCategory = {}\n self.scriptToAliases = {}\n self.aliasToScript = {}\n\n def parsePropertyAliasesFile(self, file):\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n aliases = [fields[0].strip()]\n fullName = fields[1].strip()\n for otherAlias in fields[2:]:\n aliases.append(otherAlias.strip())\n\n if fullName in self.globalNameToAliases:\n print(\"Error, already an alias for {}\".format(fullName))\n else:\n self.globalNameToAliases[fullName] = aliases\n\n def parsePropertyValueAliasesFile(self, file):\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n propertyType = fields[0].strip()\n\n if propertyType == \"gc\":\n mapToModify = self.generalCategoryToAliases\n reverseMapToModify = self.aliasToGeneralCategory\n elif propertyType == \"sc\":\n mapToModify = self.scriptToAliases\n reverseMapToModify = self.aliasToScript\n else:\n continue\n\n primaryAlias = fields[1].strip()\n fullName = fields[2].strip()\n aliases = [primaryAlias]\n for otherAlias in fields[3:]:\n aliases.append(otherAlias.strip())\n\n if fullName in mapToModify:\n print(\"Error, already an {} alias for {}\".format(propertyType, fullName))\n else:\n mapToModify[fullName] = aliases\n if reverseMapToModify != None:\n reverseMapToModify[primaryAlias] = fullName\n\n def globalAliasesFor(self, name):\n if name not in self.globalNameToAliases:\n return []\n return self.globalNameToAliases[name]\n\n def generalCategoryAliasesFor(self, name):\n if name not in self.generalCategoryToAliases:\n return \"\"\n return self.generalCategoryToAliases[name]\n\n def generalCategoryForAlias(self, name):\n if name not in self.aliasToGeneralCategory:\n return \"\"\n return self.aliasToGeneralCategory[name]\n\n def scriptAliasesFor(self, name):\n if name not in self.scriptToAliases:\n return \"\"\n return self.scriptToAliases[name]\n\n def scriptNameForAlias(self, name):\n if name not in self.aliasToScript:\n return \"\"\n return self.aliasToScript[name]\n\n\nclass PropertyData:\n allPropertyData = []\n\n def __init__(self, name):\n self.name = name\n self.aliases = []\n self.index = len(PropertyData.allPropertyData)\n self.hasBMPCharacters = False\n self.hasNonBMPCharacters = False\n self.hasStrings = False\n self.matchStrings = []\n self.matches = []\n self.ranges = []\n self.unicodeMatches = []\n self.unicodeRanges = []\n self.codePointCount = 0\n PropertyData.allPropertyData.append(self)\n\n def setAliases(self, aliases):\n self.aliases = aliases\n\n def makeCopy(self):\n result = copy.deepcopy(self)\n result.index = len(PropertyData.allPropertyData)\n PropertyData.allPropertyData.append(result)\n return result\n\n def getIndex(self):\n return self.index\n\n def getCreateFuncName(self):\n return \"createCharacterClass{}\".format(self.index)\n\n def addMatch(self, codePoint):\n if codePoint <= MaxBMP:\n self.hasBMPCharacters = True\n else:\n self.hasNonBMPCharacters = True\n if codePoint <= lastASCIICodePoint:\n if (len(self.matches) and self.matches[-1] > codePoint) or (len(self.ranges) and self.ranges[-1][1] > codePoint):\n self.addMatchUnordered(codePoint)\n return\n\n self.codePointCount = self.codePointCount + 1\n if len(self.matches) and self.matches[-1] == (codePoint - 1):\n lowCodePoint = self.matches.pop()\n self.ranges.append((lowCodePoint, codePoint))\n elif len(self.ranges) and self.ranges[-1][1] == (codePoint - 1):\n priorRange = self.ranges.pop()\n self.ranges.append((priorRange[0], codePoint))\n else:\n self.matches.append(codePoint)\n else:\n if (len(self.unicodeMatches) and self.unicodeMatches[-1] > codePoint) or (len(self.unicodeRanges) and self.unicodeRanges[-1][1] > codePoint):\n self.addMatchUnordered(codePoint)\n return\n\n self.codePointCount = self.codePointCount + 1\n if len(self.unicodeMatches) and self.unicodeMatches[-1] == (codePoint - 1):\n lowCodePoint = self.unicodeMatches.pop()\n self.unicodeRanges.append((lowCodePoint, codePoint))\n elif len(self.unicodeRanges) and self.unicodeRanges[-1][1] == (codePoint - 1):\n priorRange = self.unicodeRanges.pop()\n self.unicodeRanges.append((priorRange[0], codePoint))\n else:\n self.unicodeMatches.append(codePoint)\n\n def addMatchString(self, string):\n self.matchStrings.append(string)\n self.hasStrings = True\n\n def addRange(self, lowCodePoint, highCodePoint):\n if lowCodePoint <= MaxBMP:\n self.hasBMPCharacters = True\n if highCodePoint > MaxBMP:\n self.hasNonBMPCharacters = True\n if highCodePoint <= lastASCIICodePoint:\n if (len(self.matches) and self.matches[-1] > lowCodePoint) or (len(self.ranges) and self.ranges[-1][1] > lowCodePoint):\n self.addRangeUnordered(lowCodePoint, highCodePoint)\n return\n\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n if len(self.matches) and self.matches[-1] == (lowCodePoint - 1):\n lowCodePoint = self.matches.pop()\n elif len(self.ranges) and self.ranges[-1][1] == (lowCodePoint - 1):\n priorRange = self.ranges.pop()\n lowCodePoint = priorRange[0]\n self.ranges.append((lowCodePoint, highCodePoint))\n elif lowCodePoint <= lastASCIICodePoint:\n if lowCodePoint == lastASCIICodePoint:\n self.addMatch(lowCodePoint)\n else:\n self.addRange(lowCodePoint, lastASCIICodePoint)\n if highCodePoint == firstUnicodeCodePoint:\n self.addMatch(highCodePoint)\n else:\n self.addRange(firstUnicodeCodePoint, highCodePoint)\n else:\n if (len(self.unicodeMatches) and self.unicodeMatches[-1] > lowCodePoint) or (len(self.unicodeRanges) and self.unicodeRanges[-1][1] > lowCodePoint):\n self.addRangeUnordered(lowCodePoint, highCodePoint)\n return\n\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n if len(self.unicodeMatches) and self.unicodeMatches[-1] == (lowCodePoint - 1):\n lowCodePoint = self.unicodeMatches.pop()\n self.unicodeRanges.append((lowCodePoint, highCodePoint))\n elif len(self.unicodeRanges) and self.unicodeRanges[-1][1] == (lowCodePoint - 1):\n priorRange = self.unicodeRanges.pop()\n self.unicodeRanges.append((priorRange[0], highCodePoint))\n else:\n self.unicodeRanges.append((lowCodePoint, highCodePoint))\n\n def addMatchUnorderedForMatchesAndRanges(self, codePoint, matches, ranges):\n if codePoint in matches:\n return\n insertLocation = None\n lowCodePoint = None\n highCodePoint = None\n for idx in range(len(matches)):\n match = matches[idx]\n if codePoint == match + 1:\n lowCodePoint = match\n if idx < (len(matches) - 1) and codePoint == matches[idx + 1] - 1:\n highCodePoint = matches[idx + 1]\n del matches[idx + 1]\n self.codePointCount = self.codePointCount - 1\n else:\n highCodePoint = codePoint\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n break\n elif codePoint == match - 1:\n lowCodePoint = codePoint\n highCodePoint = match\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n break\n elif codePoint < match:\n insertLocation = idx\n break\n\n if insertLocation is None:\n insertLocation = len(matches)\n if lowCodePoint is None:\n lowCodePoint = codePoint\n highCodePoint = codePoint\n\n for idx in range(len(ranges)):\n cur_range = ranges[idx]\n if lowCodePoint >= cur_range[0] and highCodePoint <= cur_range[1]:\n return\n if lowCodePoint <= (cur_range[1] + 1) and highCodePoint >= (cur_range[0] - 1):\n while idx < len(ranges) and highCodePoint >= (ranges[idx][0] - 1):\n cur_range = ranges[idx]\n lowCodePoint = min(lowCodePoint, cur_range[0])\n highCodePoint = max(highCodePoint, cur_range[1])\n del ranges[idx]\n self.codePointCount = self.codePointCount - (cur_range[1] - cur_range[0]) - 1\n\n ranges.insert(idx, (lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n elif highCodePoint < cur_range[0]:\n if lowCodePoint != highCodePoint:\n ranges.insert(idx, (lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n break\n\n if lowCodePoint != highCodePoint:\n ranges.append((lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n\n if insertLocation is not None:\n matches.insert(insertLocation, codePoint)\n self.codePointCount = self.codePointCount + 1\n\n def addRangeUnorderedForMatchesAndRanges(self, lowCodePoint, highCodePoint, matches, ranges):\n if len(matches) and highCodePoint >= matches[0] and lowCodePoint <= matches[-1]:\n for idx in range(len(matches)):\n match = matches[idx]\n if lowCodePoint <= match and highCodePoint >= match:\n while idx < len(matches) and highCodePoint >= matches[idx]:\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n if idx < (len(matches) - 1) and highCodePoint == matches[idx + 1] - 1:\n highCodePoint = matches[idx + 1]\n del matches[idx + 1]\n self.codePointCount = self.codePointCount - 1\n break\n elif lowCodePoint == match + 1:\n lowCodePoint = match\n while idx < len(matches) and highCodePoint >= matches[idx]:\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n\n if idx < (len(matches) - 1) and highCodePoint == matches[idx + 1] - 1:\n highCodePoint = matches[idx + 1]\n del matches[idx + 1]\n self.codePointCount = self.codePointCount - 1\n break\n elif highCodePoint == match - 1:\n highCodePoint = match\n del matches[idx]\n self.codePointCount = self.codePointCount - 1\n break\n elif highCodePoint < match:\n break\n\n for idx in range(len(ranges)):\n cur_range = ranges[idx]\n if lowCodePoint >= cur_range[0] and highCodePoint <= cur_range[1]:\n return\n if lowCodePoint <= (cur_range[1] + 1) and highCodePoint >= (cur_range[0] - 1):\n while idx < len(ranges) and highCodePoint >= (ranges[idx][0] - 1):\n cur_range = ranges[idx]\n lowCodePoint = min(lowCodePoint, cur_range[0])\n highCodePoint = max(highCodePoint, cur_range[1])\n del ranges[idx]\n self.codePointCount = self.codePointCount - (cur_range[1] - cur_range[0]) - 1\n\n ranges.insert(idx, (lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n elif highCodePoint < cur_range[0]:\n ranges.insert(idx, (lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n return\n\n ranges.append((lowCodePoint, highCodePoint))\n self.codePointCount = self.codePointCount + (highCodePoint - lowCodePoint) + 1\n\n def addMatchUnordered(self, codePoint):\n if codePoint <= lastASCIICodePoint:\n self.addMatchUnorderedForMatchesAndRanges(codePoint, self.matches, self.ranges)\n else:\n self.addMatchUnorderedForMatchesAndRanges(codePoint, self.unicodeMatches, self.unicodeRanges)\n\n def addRangeUnordered(self, lowCodePoint, highCodePoint):\n if highCodePoint <= lastASCIICodePoint:\n self.addRangeUnorderedForMatchesAndRanges(lowCodePoint, highCodePoint, self.matches, self.ranges)\n elif lowCodePoint >= firstUnicodeCodePoint:\n self.addRangeUnorderedForMatchesAndRanges(lowCodePoint, highCodePoint, self.unicodeMatches, self.unicodeRanges)\n else:\n if lowCodePoint == lastASCIICodePoint:\n self.addMatchUnorderedForMatchesAndRanges(lowCodePoint, self.matches, self.ranges)\n else:\n self.addRangeUnorderedForMatchesAndRanges(lowCodePoint, lastASCIICodePoint, self.unicodeMatches, self.ranges)\n if highCodePoint == firstUnicodeCodePoint:\n self.addMatchUnorderedForMatchesAndRanges(highCodePoint, self.unicodeMatches, self.unicodeRanges)\n else:\n self.addRangeUnorderedForMatchesAndRanges(firstUnicodeCodePoint, highCodePoint, self.unicodeMatches, self.unicodeRanges)\n\n def removeMatchFromRanges(self, codePoint, ranges):\n for idx in range(len(ranges)):\n cur_range = ranges[idx]\n if cur_range[0] <= codePoint and codePoint <= cur_range[1]:\n ranges.pop(idx)\n if cur_range[0] < codePoint and codePoint < cur_range[1]:\n lowRange = (cur_range[0], codePoint - 1)\n highRange = (codePoint + 1, cur_range[1])\n # Since list.insert inserts before the index given, handle the high range first\n if highRange[0] == highRange[1]:\n self.addMatchUnordered(highRange[0])\n else:\n ranges.insert(idx, highRange)\n if lowRange[0] == lowRange[1]:\n self.addMatchUnordered(lowRange[0])\n else:\n ranges.insert(idx, lowRange)\n else:\n if cur_range[0] == codePoint:\n cur_range = (codePoint + 1, cur_range[1])\n else:\n cur_range = (cur_range[0], codePoint - 1)\n if cur_range[0] == cur_range[1]:\n self.addMatchUnordered(cur_range[0])\n else:\n ranges.insert(idx, cur_range)\n self.codePointCount = self.codePointCount - 1\n return\n\n def removeMatch(self, codePoint):\n if codePoint <= lastASCIICodePoint:\n if codePoint in self.matches:\n self.matches.remove(codePoint)\n self.codePointCount = self.codePointCount - 1\n else:\n self.removeMatchFromRanges(codePoint, self.ranges)\n else:\n if codePoint in self.unicodeMatches:\n self.unicodeMatches.remove(codePoint)\n self.codePointCount = self.codePointCount - 1\n else:\n self.removeMatchFromRanges(codePoint, self.unicodeRanges)\n\n def stringsCompare(self, bstr1, bstr2):\n # Longest strings first, then sort lexical.\n b1Len = len(bstr1)\n b2Len = len(bstr2)\n\n diff = b2Len - b1Len\n\n if diff != 0:\n return diff\n\n if bstr1 < bstr2:\n return -1\n\n if bstr1 > bstr2:\n return 1\n\n return 0\n\n def sortStrings(self):\n self.matchStrings.sort(key=cmp_to_key(self.stringsCompare))\n\n def dumpMatchData(self, file, valuesPerLine, dataList, formatter):\n valuesThisLine = 0\n firstValue = True\n\n file.write(\"{\")\n for elem in dataList:\n if firstValue:\n firstValue = False\n else:\n file.write(\", \")\n valuesThisLine = valuesThisLine + 1\n if valuesThisLine > valuesPerLine:\n file.write(\"\\n \")\n valuesThisLine = 1\n formatter(file, elem)\n file.write(\"}\")\n\n def convertStringToCppFormat(self, file, utf32String):\n output = \"\"\n for codePoint in utf32String:\n if codePoint <= '\\xff':\n output += codePoint\n else:\n output += \"\\\\x{:x}\".format(ord(codePoint))\n file.write(\"{{ reinterpret_cast(const_cast(U\\\"{}\\\")), {}}}\".format(output, len(utf32String)))\n\n def dump(self, file, commaAfter):\n file.write(\"static std::unique_ptr {}()\\n{{\\n\".format(self.getCreateFuncName()))\n file.write(\" // Name = {},\".format(self.name))\n if self.hasStrings:\n file.write(\" number of strings: {}\".format(len(self.matchStrings)))\n if self.codePointCount:\n file.write(\" number of codePoints: {}\".format(self.codePointCount))\n file.write(\"\\n\")\n file.write(\" auto characterClass = makeUnique(\\n\")\n if self.hasStrings:\n file.write(\" std::initializer_list>(\")\n self.dumpMatchData(file, 1, self.matchStrings, self.convertStringToCppFormat)\n file.write(\"),\\n\")\n file.write(\" std::initializer_list(\")\n self.dumpMatchData(file, 8, self.matches, lambda file, match: (file.write(\"{0:0=#4x}\".format(match))))\n file.write(\"),\\n\")\n file.write(\" std::initializer_list(\")\n self.dumpMatchData(file, 4, self.ranges, lambda file, range: (file.write(\"{{{0:0=#4x}, {1:0=#4x}}}\".format(range[0], range[1]))))\n file.write(\"),\\n\")\n file.write(\" std::initializer_list(\")\n self.dumpMatchData(file, 8, self.unicodeMatches, lambda file, match: (file.write(\"{0:0=#6x}\".format(match))))\n file.write(\"),\\n\")\n file.write(\" std::initializer_list(\")\n self.dumpMatchData(file, 4, self.unicodeRanges, lambda file, range: (file.write(\"{{{0:0=#6x}, {1:0=#6x}}}\".format(range[0], range[1]))))\n file.write(\"),\\n\")\n\n file.write(\" CharacterClassWidths::{}\".format((\"Unknown\", \"HasBMPChars\", \"HasNonBMPChars\", \"HasBothBMPAndNonBMP\")[int(self.hasNonBMPCharacters) * 2 + int(self.hasBMPCharacters)]))\n if self.hasStrings:\n file.write(\",\\n true\") # inCanonicalForm\n file.write(\");\\n\")\n file.write(\" return characterClass;\\n}\\n\\n\")\n\n @classmethod\n def dumpAll(cls, file):\n for propertyData in cls.allPropertyData:\n propertyData.dump(file, propertyData != cls.allPropertyData[-1])\n\n file.write(\"using CreateCharacterClass = std::unique_ptr (*)();\\n\")\n file.write(\"static CreateCharacterClass createCharacterClassFunctions[{}] = {{\\n \".format(len(cls.allPropertyData)))\n functionsOnThisLine = 0\n for propertyData in cls.allPropertyData:\n file.write(\" {},\".format(propertyData.getCreateFuncName()))\n functionsOnThisLine = functionsOnThisLine + 1\n if functionsOnThisLine == 4:\n file.write(\"\\n \")\n functionsOnThisLine = 0\n\n file.write(\"};\\n\\n\")\n\n @classmethod\n def createAndDumpHashTable(self, file, propertyDict, tablePrefix):\n def createAndDumpHashTableHelper(propertyDict, tablePrefix, isMac):\n propertyKeys = propertyDict.keys()\n numberOfKeys = len(propertyKeys)\n hashSize = ceilingToPowerOf2(numberOfKeys * 2)\n hashMask = hashSize - 1\n hashTable = [None] * hashSize\n valueTable = []\n tableSize = hashSize\n hashTableString = \"\"\n\n keyValuesToHash = []\n for propertyName in propertyKeys:\n propertyData = propertyDict[propertyName]\n keyValuesToHash.append((propertyName, propertyData.getIndex()))\n for alias in propertyData.aliases:\n keyValuesToHash.append((alias, propertyData.getIndex()))\n\n for keyValue in keyValuesToHash:\n key = keyValue[0]\n hash = stringHash(key, isMac) % hashSize\n while hashTable[hash] is not None:\n if hashTable[hash][1] is not None:\n hash = hashTable[hash][1]\n else:\n hashTable[hash] = (hashTable[hash][0], tableSize)\n hashTable.append(None)\n hash = tableSize\n tableSize = tableSize + 1\n\n hashTable[hash] = (len(valueTable), None)\n valueTable.append((key, keyValue[1]))\n\n hashTableString += \"static const struct HashIndex {}TableIndex[{}] = {{\\n\".format(tablePrefix, len(hashTable))\n\n for tableIndex in hashTable:\n value = -1\n next = -1\n if tableIndex is not None:\n value = tableIndex[0]\n if tableIndex[1] is not None:\n next = tableIndex[1]\n\n hashTableString += \" {{ {}, {} }},\\n\".format(value, next)\n\n hashTableString += \"};\\n\\n\"\n\n hashTableString += \"static const struct HashValue {}TableValue[{}] = {{\\n\".format(tablePrefix, len(valueTable))\n for value in valueTable:\n hashTableString += \" {{ \\\"{}\\\", {} }},\\n\".format(value[0], value[1])\n hashTableString += \"};\\n\\n\"\n\n hashTableString += \"static const struct HashTable {}HashTable = \\n\".format(tablePrefix)\n hashTableString += \" {{ {}, {}, {}TableValue, {}TableIndex }};\\n\\n\".format(len(valueTable), hashMask, tablePrefix, tablePrefix)\n return hashTableString\n\n hashTableForMacOS = createAndDumpHashTableHelper(propertyDict, tablePrefix, True)\n hashTableForIOS = createAndDumpHashTableHelper(propertyDict, tablePrefix, False)\n hashTableToWrite = hashTableForMacOS\n if hashTableForMacOS != hashTableForIOS:\n hashTableToWrite = \"#if PLATFORM(MAC)\\n{}#else\\n{}#endif\\n\".format(hashTableForMacOS, hashTableForIOS)\n file.write(hashTableToWrite)\n\n @classmethod\n def dumpMayContainStringFunc(cls, file):\n hasStringRanges = []\n currentRange = None\n for propertyData in cls.allPropertyData:\n if propertyData.hasStrings:\n propertyIndex = propertyData.index\n if not currentRange:\n currentRange = propertyIndex, propertyIndex\n elif currentRange[-1] == propertyIndex - 1:\n currentRange = currentRange[0], propertyIndex\n else:\n hasStringRanges.append(currentRange)\n currentRange = propertyIndex, propertyIndex\n\n if currentRange:\n hasStringRanges.append(currentRange)\n\n file.write(\"static bool unicodeCharacterClassMayContainStrings(unsigned unicodeClassId)\\n\")\n file.write(\"{\\n\")\n file.write(\" if (unicodeClassId >= {})\\n\".format(len(cls.allPropertyData)))\n file.write(\" return false;\\n\")\n file.write(\"\\n\")\n for range in hasStringRanges:\n if range[0] == range[1]:\n file.write(\" if (unicodeClassId == {})\\n\".format(range[0]))\n else:\n file.write(\" if (unicodeClassId >= {} && unicodeClassId <= {})\\n\".format(range[0], range[1]))\n file.write(\" return true;\\n\")\n file.write(\"\\n\")\n file.write(\" return false;\\n\")\n file.write(\"}\\n\\n\")\n\n\nclass Scripts:\n def __init__(self):\n self.allPropertyData = []\n self.scriptsByName = {}\n self.scriptExtensionsByName = {}\n self.unknownScript = PropertyData(\"Unknown\")\n self.unknownScript.setAliases(aliases.scriptAliasesFor(\"Unknown\"))\n self.allPropertyData.append(self.unknownScript)\n self.scriptsParsed = False\n\n def parseScriptsFile(self, file):\n currentScriptName = None\n currentPropertyData = None\n # To calculate the Unknown, we gather all the codePoints asigned to a scripts as ranges,\n # sort them, and then go the list to create the inverse of the assigned ranges.\n assignedCodePointRanges = []\n\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoints = fields[0].strip()\n scriptName = fields[1].strip()\n\n if scriptName != currentScriptName:\n currentScriptName = scriptName\n currentPropertyData = PropertyData(scriptName)\n currentPropertyData.setAliases(aliases.scriptAliasesFor(scriptName))\n self.allPropertyData.append(currentPropertyData)\n self.scriptsByName[scriptName] = currentPropertyData\n\n dotDot = codePoints.find(\"..\")\n if dotDot == -1:\n codePoint = int(codePoints, 16)\n currentPropertyData.addMatch(codePoint)\n assignedCodePointRanges.append((codePoint, codePoint))\n else:\n lowCodePoint = int(codePoints[:dotDot], 16)\n highCodePoint = int(codePoints[dotDot + 2:], 16)\n currentPropertyData.addRange(lowCodePoint, highCodePoint)\n assignedCodePointRanges.append((lowCodePoint, highCodePoint))\n\n assignedCodePointRanges.sort(key=lambda range: range[0])\n lastAssignedCodePoint = 0\n\n for range in assignedCodePointRanges:\n if range[0] - lastAssignedCodePoint > 1:\n if range[0] - lastAssignedCodePoint == 2:\n self.unknownScript.addMatch(lastAssignedCodePoint + 1)\n else:\n self.unknownScript.addRange(lastAssignedCodePoint + 1, range[0] - 1)\n lastAssignedCodePoint = range[1]\n\n if lastAssignedCodePoint < MaxUnicode:\n if MaxUnicode - lastAssignedCodePoint == 1:\n self.unknownScript.addMatch(MaxUnicode)\n else:\n self.unknownScript.addRange(lastAssignedCodePoint + 1, MaxUnicode)\n\n self.scriptsParsed = True\n\n def parseScriptExtensionsFile(self, file):\n currentPropertyData = None\n # To calculate the Unknown, we gather all the codePoints asigned to a scripts as ranges,\n # sort them, and then go the list to create the inverse of the assigned ranges.\n assignedCodePointRanges = []\n\n if not self.scriptsParsed:\n print(\"Error: parsing ScriptExtensions.txt before Scripts.txt\")\n exit(1)\n\n commonScriptExtenstionPropertyData = None\n inheritedScriptExtensionPropertyData = None\n\n scriptName = \"Common\"\n if scriptName in self.scriptsByName:\n commonScriptExtenstionPropertyData = self.scriptsByName[scriptName].makeCopy()\n else:\n commonScriptExtenstionPropertyData = PropertyData(scriptName)\n commonScriptExtenstionPropertyData.setAliases(aliases.scriptAliasesFor(scriptName))\n self.allPropertyData.append(commonScriptExtenstionPropertyData)\n self.scriptExtensionsByName[scriptName] = commonScriptExtenstionPropertyData\n\n scriptName = \"Inherited\"\n if scriptName in self.scriptsByName:\n inheritedScriptExtensionPropertyData = self.scriptsByName[scriptName].makeCopy()\n else:\n inheritedScriptExtensionPropertyData = PropertyData(scriptName)\n inheritedScriptExtensionPropertyData.setAliases(aliases.scriptAliasesFor(scriptName))\n self.allPropertyData.append(inheritedScriptExtensionPropertyData)\n self.scriptExtensionsByName[scriptName] = inheritedScriptExtensionPropertyData\n\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoints = fields[0].strip()\n scriptAliasList = fields[1].strip()\n\n for scriptAlias in scriptAliasList.split(' '):\n scriptName = aliases.scriptNameForAlias(scriptAlias)\n currentPropertyData = None\n\n if scriptName not in self.scriptExtensionsByName:\n currentPropertyData = self.scriptsByName[scriptName].makeCopy()\n self.allPropertyData.append(currentPropertyData)\n self.scriptExtensionsByName[scriptName] = currentPropertyData\n else:\n currentPropertyData = self.scriptExtensionsByName[scriptName]\n\n dotDot = codePoints.find(\"..\")\n if dotDot == -1:\n codePoint = int(codePoints, 16)\n currentPropertyData.addMatch(codePoint)\n commonScriptExtenstionPropertyData.removeMatch(codePoint)\n inheritedScriptExtensionPropertyData.removeMatch(codePoint)\n else:\n lowCodePoint = int(codePoints[:dotDot], 16)\n highCodePoint = int(codePoints[dotDot + 2:], 16)\n currentPropertyData.addRange(lowCodePoint, highCodePoint)\n for codePoint in range(lowCodePoint, highCodePoint + 1):\n commonScriptExtenstionPropertyData.removeMatch(codePoint)\n inheritedScriptExtensionPropertyData.removeMatch(codePoint)\n\n # For the scripts that don't have any additional extension codePoints, copy the script\n # data to the script extension with the same name\n for scriptName, propertyData in self.scriptsByName.items():\n if scriptName not in self.scriptExtensionsByName:\n self.scriptExtensionsByName[scriptName] = propertyData\n\n def dump(self, file):\n file.write(\"// Scripts:\\n\")\n PropertyData.createAndDumpHashTable(file, self.scriptsByName, \"script\")\n\n file.write(\"// Script_Extensions:\\n\")\n PropertyData.createAndDumpHashTable(file, self.scriptExtensionsByName, \"scriptExtension\")\n\n\nclass GeneralCategory:\n def __init__(self, file):\n self.file = file\n self.allPropertyData = []\n self.propertyDataByCategory = {}\n self.createSpecialPropertyData(\"Any\", (0, MaxUnicode))\n self.createSpecialPropertyData(\"ASCII\", (0, lastASCIICodePoint))\n self.assignedPropertyData = self.createSpecialPropertyData(\"Assigned\")\n self.unassignedProperyData = self.findPropertyGroupFor(\"Cn\")[1]\n self.casedLetterPropertyData = self.findPropertyGroupFor(\"LC\")[1]\n self.lastAddedCodePoint = 0\n\n def createSpecialPropertyData(self, name, range=None):\n propertyData = PropertyData(name)\n self.allPropertyData.append(propertyData)\n self.propertyDataByCategory[name] = propertyData\n if range:\n propertyData.addRange(range[0], range[1])\n\n return propertyData\n\n def findPropertyGroupFor(self, categoryAlias):\n category = aliases.generalCategoryForAlias(categoryAlias)\n allCategoryAliases = aliases.generalCategoryAliasesFor(category)\n categoryGroupAlias = categoryAlias[0]\n categoryGroup = aliases.generalCategoryForAlias(categoryGroupAlias)\n allCategoryGroupAlias = aliases.generalCategoryAliasesFor(categoryGroup)\n groupPropertyData = None\n propertyData = None\n\n if categoryGroup not in self.propertyDataByCategory:\n groupPropertyData = PropertyData(categoryGroup)\n groupPropertyData.setAliases(allCategoryGroupAlias)\n self.allPropertyData.append(groupPropertyData)\n self.propertyDataByCategory[categoryGroup] = groupPropertyData\n else:\n groupPropertyData = self.propertyDataByCategory[categoryGroup]\n\n if category not in self.propertyDataByCategory:\n propertyData = PropertyData(category)\n propertyData.setAliases(allCategoryAliases)\n self.allPropertyData.append(propertyData)\n self.propertyDataByCategory[category] = propertyData\n else:\n propertyData = self.propertyDataByCategory[category]\n\n return (groupPropertyData, propertyData)\n\n def addNextCodePoints(self, categoryAlias, codePoint, highCodePoint=None):\n if codePoint - self.lastAddedCodePoint > 1:\n propertyDatas = self.findPropertyGroupFor(\"Cn\")\n if codePoint - self.lastAddedCodePoint == 2:\n propertyDatas[0].addMatch(self.lastAddedCodePoint + 1)\n propertyDatas[1].addMatch(self.lastAddedCodePoint + 1)\n else:\n propertyDatas[0].addRange(self.lastAddedCodePoint + 1, codePoint - 1)\n propertyDatas[1].addRange(self.lastAddedCodePoint + 1, codePoint - 1)\n\n propertyDatas = self.findPropertyGroupFor(categoryAlias)\n if highCodePoint:\n propertyDatas[0].addRange(codePoint, highCodePoint)\n propertyDatas[1].addRange(codePoint, highCodePoint)\n if categoryAlias == \"Ll\" or categoryAlias == \"Lt\" or categoryAlias == \"Lu\":\n self.casedLetterPropertyData.addRange(codePoint, highCodePoint)\n self.assignedPropertyData.addRange(codePoint, highCodePoint)\n\n self.lastAddedCodePoint = highCodePoint\n else:\n propertyDatas[0].addMatch(codePoint)\n propertyDatas[1].addMatch(codePoint)\n if categoryAlias == \"Ll\" or categoryAlias == \"Lt\" or categoryAlias == \"Lu\":\n self.casedLetterPropertyData.addMatch(codePoint)\n self.assignedPropertyData.addMatch(codePoint)\n\n self.lastAddedCodePoint = codePoint\n\n def parse(self):\n lastLineFirstOfRange = None\n lastLineCodePoint = 0\n for line in self.file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoint = int(fields[0].strip(), 16)\n description = fields[1].strip()\n categoryAlias = fields[2].strip()\n\n if lastLineFirstOfRange:\n if description[-5:-1] == \"Last\":\n self.addNextCodePoints(categoryAlias, lastLineFirstOfRange, codePoint)\n lastLineFirstOfRange = None\n continue\n else:\n print(\"Malformed First..Last pair of lines\")\n\n if description[-6:-1] == \"First\":\n lastLineFirstOfRange = codePoint\n continue\n\n self.addNextCodePoints(categoryAlias, codePoint)\n\n if self.lastAddedCodePoint < MaxUnicode:\n propertyDatas = self.findPropertyGroupFor(\"Cn\")\n if MaxUnicode - self.lastAddedCodePoint == 1:\n propertyDatas[0].addMatch(MaxUnicode)\n propertyDatas[1].addMatch(MaxUnicode)\n else:\n propertyDatas[0].addRange(self.lastAddedCodePoint + 1, MaxUnicode)\n propertyDatas[1].addRange(self.lastAddedCodePoint + 1, MaxUnicode)\n\n def dump(self, file):\n file.write(\"// General_Category:\\n\")\n PropertyData.createAndDumpHashTable(file, self.propertyDataByCategory, \"generalCategory\")\n\n\nclass BinaryProperty:\n def __init__(self):\n self.allPropertyData = []\n self.propertyDataByProperty = {}\n\n def parsePropertyFile(self, file):\n currentPropertyName = None\n currentPropertyData = None\n\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoints = fields[0].strip()\n propertyName = fields[1].strip()\n\n if propertyName != currentPropertyName:\n if propertyName not in SupportedBinaryProperties:\n continue\n\n currentPropertyName = propertyName\n currentPropertyData = PropertyData(propertyName)\n currentPropertyData.setAliases(aliases.globalAliasesFor(propertyName))\n self.allPropertyData.append(currentPropertyData)\n self.propertyDataByProperty[propertyName] = currentPropertyData\n\n dotDot = codePoints.find(\"..\")\n if dotDot == -1:\n currentPropertyData.addMatch(int(codePoints, 16))\n else:\n currentPropertyData.addRange(int(codePoints[:dotDot], 16), int(codePoints[dotDot + 2:], 16))\n\n def dump(self, file):\n file.write(\"// binary properties:\\n\")\n PropertyData.createAndDumpHashTable(file, self.propertyDataByProperty, \"binaryProperty\")\n\n\nclass SequenceProperty:\n def __init__(self):\n self.allPropertyData = []\n self.propertyDataByProperty = {}\n self.RGIEmojiPropertyData = PropertyData(\"RGI_Emoji\")\n self.allPropertyData.append(self.RGIEmojiPropertyData)\n self.propertyDataByProperty[\"RGI_Emoji\"] = self.RGIEmojiPropertyData\n\n def parsePropertyFile(self, file):\n currentPropertyName = None\n currentPropertyData = None\n\n for line in file:\n line = line.split('#', 1)[0]\n line = line.rstrip()\n if (not len(line)):\n continue\n\n fields = line.split(';')\n if (not fields):\n continue\n\n codePoints = fields[0].strip()\n propertyName = fields[1].strip()\n\n if propertyName != currentPropertyName:\n if propertyName not in SupportedSequenceProperties:\n continue\n\n if currentPropertyData is not None:\n currentPropertyData.sortStrings()\n\n currentPropertyName = propertyName\n currentPropertyData = PropertyData(propertyName)\n self.allPropertyData.append(currentPropertyData)\n self.propertyDataByProperty[propertyName] = currentPropertyData\n\n dotDot = codePoints.find(\"..\")\n if dotDot == -1:\n space = codePoints.find(\" \")\n if space == -1:\n currentPropertyData.addMatch(int(codePoints, 16))\n self.RGIEmojiPropertyData.addMatch(int(codePoints, 16))\n else:\n # String, convert into UTF16 format with surrogate pairs as needed.\n utf32String = \"\"\n for hex in codePoints.split(' '):\n c = int(hex, 16)\n utf32String += \"\".join(chr(c))\n\n currentPropertyData.addMatchString(utf32String)\n self.RGIEmojiPropertyData.addMatchString(utf32String)\n\n else:\n currentPropertyData.addRange(int(codePoints[:dotDot], 16), int(codePoints[dotDot + 2:], 16))\n self.RGIEmojiPropertyData.addRange(int(codePoints[:dotDot], 16), int(codePoints[dotDot + 2:], 16))\n\n currentPropertyData.sortStrings()\n self.RGIEmojiPropertyData.sortStrings()\n\n def dump(self, file):\n file.write(\"// sequences properties:\\n\")\n PropertyData.createAndDumpHashTable(file, self.propertyDataByProperty, \"sequenceProperty\")\n\n\nif __name__ == \"__main__\":\n parser = optparse.OptionParser(usage=\"usage: %prog \")\n (options, args) = parser.parse_args()\n\n if len(args) != 2:\n parser.error(\" \")\n\n UCDDirectoryPath = args[0]\n unicodeProertyDataHPath = args[1]\n\n verifyUCDFilesExist()\n\n propertyAliasesFile = openUCDFileOrExit(\"PropertyAliases.txt\")\n propertyValueAliasesFile = openUCDFileOrExit(\"PropertyValueAliases.txt\")\n scriptsFile = openUCDFileOrExit(\"Scripts.txt\")\n scriptExtensionsFile = openUCDFileOrExit(\"ScriptExtensions.txt\")\n unicodeDataFile = openUCDFileOrExit(\"UnicodeData.txt\")\n derivedBinaryPropertiesFile = openUCDFileOrExit(\"DerivedBinaryProperties.txt\")\n derivedCorePropertiesFile = openUCDFileOrExit(\"DerivedCoreProperties.txt\")\n derivedNormalizationPropertiesFile = openUCDFileOrExit(\"DerivedNormalizationProps.txt\")\n propListFile = openUCDFileOrExit(\"PropList.txt\")\n emojiDataFile = openUCDFileOrExit(\"emoji-data.txt\")\n emojiSequencesFile = openUCDFileOrExit(\"emoji-sequences.txt\")\n emojiZWJSequencesFile = openUCDFileOrExit(\"emoji-zwj-sequences.txt\")\n\n aliases = Aliases()\n\n propertyDataHFile = openOrExit(unicodeProertyDataHPath, \"w\")\n\n propertyDataHFile.write(header)\n\n aliases.parsePropertyAliasesFile(propertyAliasesFile)\n aliases.parsePropertyValueAliasesFile(propertyValueAliasesFile)\n\n generalCategory = GeneralCategory(unicodeDataFile)\n generalCategory.parse()\n\n binaryProperty = BinaryProperty()\n binaryProperty.parsePropertyFile(derivedBinaryPropertiesFile)\n binaryProperty.parsePropertyFile(derivedCorePropertiesFile)\n binaryProperty.parsePropertyFile(derivedNormalizationPropertiesFile)\n binaryProperty.parsePropertyFile(propListFile)\n binaryProperty.parsePropertyFile(emojiDataFile)\n\n scripts = Scripts()\n scripts.parseScriptsFile(scriptsFile)\n scripts.parseScriptExtensionsFile(scriptExtensionsFile)\n\n sequenceProperty = SequenceProperty()\n sequenceProperty.parsePropertyFile(emojiSequencesFile)\n sequenceProperty.parsePropertyFile(emojiZWJSequencesFile)\n\n PropertyData.dumpAll(propertyDataHFile)\n generalCategory.dump(propertyDataHFile)\n binaryProperty.dump(propertyDataHFile)\n scripts.dump(propertyDataHFile)\n sequenceProperty.dump(propertyDataHFile)\n\n PropertyData.dumpMayContainStringFunc(propertyDataHFile)\n\n propertyDataHFile.write(footer)\n\n exit(0)\n","repo_name":"WebKit/WebKit","sub_path":"Source/JavaScriptCore/yarr/generateYarrUnicodePropertyTables.py","file_name":"generateYarrUnicodePropertyTables.py","file_ext":"py","file_size_in_byte":48324,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"16823917001","text":"s = 'full_string'\n\nif 'string' in s:\n print('Substring found')\n\nindex = s.find('string')\nif index != -1:\n print(f'Substring found at index {index}')\n\n #assert \"substring \" in full_string, \"expected {}, got {}\".format(full_string, substring)","repo_name":"alina-zakharenko/stepik_lessons","sub_path":"module_3/substring.py","file_name":"substring.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11786472455","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport operator\nfrom scipy import stats\nfrom math import log\n\ndef get_top_stems(model, num):\n\t\"\"\"Gets the most frequently appeared stems in a text model and returns them as a list of tuples (stem, frequency).\n\n\tKeyword arguments:\n\tmodel -- The model to be used.\n\tnum -- Number of results to be returned.\n\t\"\"\"\n\tsorted_stems = sorted(model.stems.iteritems(), key=operator.itemgetter(1), reverse=True)\n\ttop_ten = list(sorted_stems)[:num]\n\ts = \"\"\n\ti = 1\n\tfor line in top_ten:\n\t\ts += str(i) + \"\\t\" + str(line[1]) + \"\\t\\t\" + line[0] + \"\\n\"\n\t\ti += 1\n\treturn s\n\n\ndef t_test_wlen(model1, model2):\n\t\"\"\"Performs Student's t-test on word lengths between model1 and model2.\n\n\tThe test used is unpaired two-sample Student's t-test with Welch's correction.\n\n\tReturns a tuple (t_value, p_value).\"\"\"\n\n\tdef _populate_sample_list(model):\n\t\tsample = []\n\t\tfor (length, incidence) in model.wordlengths.iteritems():\n\t\t\tlength = float(length)\n\t\t\tfor i in range(incidence):\n\t\t\t\tsample.append(length)\n\t\treturn sample\n\n\tsample1 = _populate_sample_list(model1)\n\tsample2 = _populate_sample_list(model2)\n\tresult = stats.ttest_ind(sample1, sample2, equal_var=False)\n\treturn result\n\ndef t_test_slen(model1, model2):\n\t\"\"\"Performs Student's t-test on sentence lengths between model1 and model2.\n\n\tThe test used is unpaired two-sample Student's t-test with Welch's correction.\n\n\tReturns a tuple (t_value, p_value).\"\"\"\n\n\tdef _populate_sent_sample_list(model):\n\t\tsample = []\n\t\tfor (length, incidence) in model.sentencelengths.iteritems():\n\t\t\tlength = float(length)\n\t\t\tfor i in range(incidence):\n\t\t\t\tsample.append(length)\n\t\treturn sample\n\n\tsample1 = _populate_sent_sample_list(model1)\n\tsample2 = _populate_sent_sample_list(model2)\n\tresult = stats.ttest_ind(sample1, sample2, equal_var=False)\n\treturn result\n\ndef bayes_score(model1, model2, ignore_common=False):\n\t\"\"\"Calculates the Naive Bayes score between model1 and model2.\"\"\"\n\tCOMMON_WORDS = ['sum', 'possum', 'eo', 'ad', 'non', 'ne', 'cum', 'res', 'omnis']\n\tmodel2_stems = sum(model2.stems.values())\n\tbayes_score = 0\n\tfor stem in model1.stems.keys():\n\t\tif ignore_common and stem in COMMON_WORDS:\n\t\t\tcontinue\n\t\tif stem not in model2.stems:\n\t\t\tmodel2_prob = 1/float(model2_stems)\n\t\t\tbayes_score += log(model2_prob, 2) * model1.stems[stem]\n\t\telse:\n\t\t\tmodel2_prob = float(model2.stems[stem])/model2_stems\n\t\t\tbayes_score += log(model2_prob, 2) * model1.stems[stem]\n\treturn bayes_score\n\n","repo_name":"marczuo/LatinTextID","sub_path":"stats_tests.py","file_name":"stats_tests.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23677173601","text":"import asyncio\nfrom datetime import datetime as dt\nfrom unittest.mock import MagicMock\nimport tempfile\nimport os\nfrom types import SimpleNamespace\n\n\nfrom django.core import signing\n\nfrom channels.auth import get_user\n\nimport pytest\nimport skpy\n\nfrom core.tests.fixtures import *\nfrom core.models import Chat, Contact\n\n\nclass MessageEvent(skpy.event.SkypeNewMessageEvent):\n def __init__(self):\n self.__dict__['msg'] = SimpleNamespace(\n content='Priviet maniunia',\n userId='123',\n time=dt(2019, 5, 4, 10),\n )\n\n def __getattr__(self, name):\n return self.__dict__['msg']\n\n def __str__(self):\n return ''\n\n def __repr__(self):\n return ''\n\n\nclass SkypeContactsMock:\n def getMsgs(self):\n return [\n SimpleNamespace(\n content='whats up',\n userId='123',\n timestamp='1556834306289',\n ),\n SimpleNamespace(\n content='I am fine',\n userId='me',\n timestamp='1556834206289',\n ),\n ]\n\n contacts = [\n SimpleNamespace(\n id='123',\n name=SimpleNamespace(\n first='Tomasz',\n last='jD',\n ),\n )\n ]\n\n def __iter__(self):\n self.it = iter(self.contacts)\n return self.it\n\n def __next__(self):\n return next(self.it)\n\n def __getitem__(self, idx):\n for c in self.contacts:\n if c.id == idx:\n c.getMsgs = self.getMsgs\n return c\n\n\n@pytest.fixture(autouse=True)\n@pytest.mark.asyncio\ndef my_skype_chat(monkeypatch):\n monkeypatch.setattr(skpy, 'Skype', MagicMock())\n monkeypatch.setattr(skpy, 'SkypeEventLoop', MagicMock())\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_log_to(comm):\n await comm.send_json_to({\n 'action': 'provider_skype_am_i_logged',\n })\n\n resp = await comm.receive_json_from()\n\n assert resp == {'status': 'ok', 'action': 'provider_skype_am_i_logged', 'is_logged': False}\n\n await comm.send_json_to({\n 'action': 'provider_skype_login',\n 'username': '579631148',\n 'password': '12qwertyU',\n })\n f = asyncio.Future()\n f.set_result(None)\n\n resp = await comm.receive_json_from()\n\n tmpdir = tempfile.gettempdir()\n dirname = (await get_user(comm.instance.scope)).temp_dir\n\n skpy.Skype.return_value.conn.setTokenFile.assert_called_once_with(\n os.path.join(tmpdir, dirname, 'token-skype-app')\n )\n skpy.Skype.return_value.conn.readToken.assert_called_once_with()\n skpy.Skype.return_value.conn.setUserPwd.assert_not_called()\n skpy.Skype.return_value.conn.getSkypeToken.assert_not_called()\n skpy.Skype.return_value.conn.connected = True\n\n assert resp == {\n 'action': 'provider_skype_login',\n 'status': 'ok',\n 'msg': 'Successfully logged into Skype',\n }\n await comm.send_json_to({\n 'action': 'provider_skype_am_i_logged',\n })\n\n resp = await comm.receive_json_from()\n\n assert resp == {'status': 'ok', 'action': 'provider_skype_am_i_logged', 'is_logged': True}\n await comm.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_required_cred(comm):\n await comm.send_json_to({\n 'action': 'provider_skype_get_required_credentials',\n })\n\n resp = await comm.receive_json_from()\n\n assert resp == {\n 'status': 'ok',\n 'action': 'provider_skype_get_required_credentials',\n 'username': {'type': 'text', 'help': 'Nickname or phone number'},\n 'password': {'type': 'password', 'help': 'Password'}\n }\n await comm.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_list_chats(logged_skype):\n await logged_skype.send_json_to({\n 'action': 'get_chats',\n })\n f = [\n SimpleNamespace(\n name=SimpleNamespace(\n first='Andrzej',\n last='Pączek',\n ),\n id=1,\n ),\n SimpleNamespace(\n name=SimpleNamespace(\n first='Piotr',\n last='Czaja',\n ),\n id=3,\n ),\n ]\n\n skpy.Skype.return_value.contacts = f\n resp = await logged_skype.receive_json_from()\n chats = resp['chats']\n\n assert Chat.objects.all().count() == len(chats)\n assert 'Andrzej Pączek' in [c['name'] for c in resp['chats']]\n await logged_skype.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_store_creds(comm):\n await comm.send_json_to({\n 'action': 'provider_skype_login',\n 'username': '579631148',\n 'password': '12qwertyU',\n })\n\n await comm.receive_json_from()\n\n user = await get_user(comm.instance.scope)\n\n creds = signing.loads(user.credentials)\n\n assert creds['skype'] == {'username': '579631148', 'password': '12qwertyU'}\n\n await comm.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_send_messages(logged_skype):\n\n andrzej_uid = 'andriy5852'\n andrzej = await database_sync_to_async(Contact.objects.create)(\n provider='skype',\n uid=andrzej_uid,\n chat=await database_sync_to_async(Chat.objects.create)(name='Andrzej'),\n owner=await get_user(logged_skype.instance.scope),\n )\n await logged_skype.send_json_to({\n 'action': 'send_message',\n 'provider': 'skype',\n 'chat_id': andrzej.chat.id,\n 'content': 'Priviet maniunia'\n })\n f = asyncio.Future()\n f.set_result(None)\n contact_mock = MagicMock()\n skpy.Skype.return_value.contacts.__getitem__.return_value = contact_mock\n send_mock = contact_mock.chat.sendMsg\n send_mock.return_value = f\n\n resp = await logged_skype.receive_json_from()\n skpy.Skype.return_value.contacts.__getitem__.assert_called_once_with(andrzej_uid)\n send_mock.assert_called_once_with('Priviet maniunia')\n assert send_mock.call_args[0][0] == 'Priviet maniunia'\n\n assert resp == {\n 'action': 'send_message',\n 'chat_id': andrzej.chat.id,\n 'provider': 'skype',\n 'status': 'ok',\n 'content': 'Priviet maniunia'\n }\n await logged_skype.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_receive_messages(logged_skype):\n skpy.Skype.return_value.contacts = SkypeContactsMock()\n\n skpy.SkypeEventLoop.return_value.onEvent(\n MessageEvent(),\n )\n\n resp = await logged_skype.receive_json_from()\n\n chat = Contact.objects.get(chat__name='Tomasz jD')\n resp == {\n 'action': 'new_message',\n 'content': 'Priviet maniunia',\n 'userId': chat.id,\n 'provider': 'skype',\n }\n\n assert resp['content'] == 'Priviet maniunia'\n assert resp['chat_id'] == chat.id\n\n await logged_skype.disconnect()\n\n\n@pytest.mark.asyncio\n@pytest.mark.django_db(transaction=True)\nasync def test_can_get_chat_messages(logged_skype):\n skpy.Skype.return_value.chats = SkypeContactsMock()\n chat = Chat.objects.create(name='Tomasz Dul')\n Contact.objects.create(\n provider='skype',\n uid='123',\n chat=chat,\n owner=await get_user(logged_skype.instance.scope),\n\n )\n\n f = asyncio.Future()\n f.set_result([])\n\n skpy.Skype.return_value.getMsgs.return_value = f\n skpy.Skype.return_value.user.id = 'me'\n\n await logged_skype.send_json_to({\n 'action': 'get_messages',\n 'chat_ids': [chat.id],\n 'count': 50,\n })\n\n resp = await logged_skype.receive_json_from()\n\n assert resp == {\n 'action': 'get_messages',\n 'chats': [{\n 'id': chat.id,\n 'messages': [\n {\n 'provider': 'skype',\n 'content': 'whats up',\n 'me': False,\n 'time': '2019-05-02 21:58:26+00:00',\n },\n {\n 'provider': 'skype',\n 'content': 'I am fine',\n 'me': True,\n 'time': '2019-05-02 21:56:46+00:00',\n },\n ],\n }],\n 'status': 'ok',\n }\n\n await logged_skype.disconnect()\n","repo_name":"llusaq/kukuchat","sub_path":"backend/kukuchat/core/tests/test_skype.py","file_name":"test_skype.py","file_ext":"py","file_size_in_byte":8304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2847804118","text":"import pandas as pd\nimport math\nimport numpy as np\nfrom sklearn import preprocessing, model_selection, svm\nfrom sklearn.linear_model import LinearRegression\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nimport datetime\nimport pickle\n\nstyle.use('ggplot')\npd.options.mode.chained_assignment = None\n\ndef process_data(df):\n df = df[['CPU Cores','Memory','Bandwidth','Hour1','Hour2','Day Start','Day End','Month Start','Month End','Year Start','Year End','Instances']]\n df['HR_PCT'] = (df['Hour2'] - df['Hour1'])/df['Hour1']*100\n df['DY_PCT'] = (df['Day Start'] - df['Day End'])/df['Day Start']*100\n df['MN_PCT'] = (df['Month Start'] - df['Month End'])/df['Month Start']*100\n df['YR_PCT'] = (df['Year Start'] - df['Year End'])/df['Year Start']*100\n df = df[['CPU Cores','Memory','Bandwidth','Instances','HR_PCT','DY_PCT','MN_PCT','Month End']]\n return df\n\ndef train_test_splitting(X,y):\n return model_selection.train_test_split(X, y, test_size = 0.2)\n\ndef linear_training(X_train,X_test,y_train,y_test,X_lately):\n clf = LinearRegression(n_jobs=-1)\n clf.fit(X_train, y_train)\n accuracy = clf.score(X_test, y_test)\n forecast_set = clf.predict([[2,8,128,2,236.0,212.0,54.1,52]])\n df['Forecast'] = np.nan\n last_date = df.iloc[-1].name\n # print(last_date)\n last_unix = last_date\n one_day = 86400\n next_unix = last_unix + one_day\n\n for i in forecast_set:\n next_date = datetime.datetime.fromtimestamp(next_unix)\n next_unix += one_day\n df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)] + [i]\n\n df['Month End'].plot()\n df['Forecast'].plot()\n plt.legend(loc=4)\n plt.xlabel('Date')\n plt.ylabel('Price')\n plt.show()\n with open('azure_model.pkl','wb') as File:\n pickle.dump(clf,File)\n File.close()\n print(forecast_set, accuracy, forecast_out)\n\ndef svm_training(X_train,X_test,y_train,y_test,X_lately):\n clf = svm.SVR()\n clf.fit(X_train, y_train)\n accuracy = clf.score(X_test, y_test)\n forecast_set = clf.predict(X_lately)\n df['Forecast'] = np.nan\n last_date = df.iloc[-1].name\n # print(last_date)\n last_unix = last_date\n one_day = 86400\n next_unix = last_unix + one_day\n\n for i in forecast_set:\n next_date = datetime.datetime.fromtimestamp(next_unix)\n next_unix += one_day\n df.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)] + [i]\n\n df['Month End'].plot()\n df['Forecast'].plot()\n plt.legend(loc=4)\n plt.xlabel('Date')\n plt.ylabel('Price')\n plt.show()\n print(forecast_set, accuracy, forecast_out)\n \n\nif __name__ == '__main__':\n df = pd.read_csv(\"./data/AZURE_CSV.csv\")\n df = process_data(df) \n forecast_col = 'Month End' \n df.fillna(-99999, inplace = True)\n forecast_out = int(math.ceil(0.001*len(df)))\n # forecast_out = math.ceil(0)\n # forecast_out = math.ceil(-1)\n # forecast_out = math.ceil(-31.756)\n df['label'] = df[forecast_col].shift(-forecast_out)\n X = np.array(df.drop(['label'], 1))\n X = preprocessing.scale(X)\n X = X[:-forecast_out]\n X_lately = X[-forecast_out:]\n\n df.dropna(inplace = True)\n y = np.array(df['label'])\n\n X_train, X_test, y_train, y_test = train_test_splitting(X,y)\n print(X_lately)\n linear_training(X_train,X_test,y_train,y_test,X_lately)\n # svm_training(X_train,X_test,y_train,y_test,X_lately)\n\n\n","repo_name":"Lunaticsatoshi/BE-Final-Year-Project","sub_path":"Linearreg/linear.py","file_name":"linear.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"2544380276","text":"class Solution:\n def multiply(self, num1: str, num2: str) -> str:\n if num1 == '0' or num2 == '0':\n return '0'\n\n res = [0] * (len(num1) + len(num2))\n\n for i in range(len(num1) - 1, -1, -1):\n for j in range(len(num2) - 1, -1, -1):\n product = self.to_int(num1[i]) * self.to_int(num2[j])\n p1, p2 = i + j, i + j + 1\n total = product + res[p2]\n\n res[p1] += total // 10\n res[p2] = total % 10\n return ''.join(map(lambda x: str(x), res)).lstrip('0')\n\n def to_int(self, digit_str: str) -> int:\n return ord(digit_str) - ord('0')\n","repo_name":"xiaoyao2102/leetcode","sub_path":"0043_multiply_strings.py","file_name":"0043_multiply_strings.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39503524820","text":"# Exercise 1: Calculate the multiplication and sum of two numbers\n# Given two integer numbers return their product only if the product is equal to or lower than 1000, else return their sum.\n\na = int(input(\"Enter the first number! \"))\nb = int(input(\"Enter the second number! \"))\n\ndef product_or_sum(a, b):\n product = a * b\n sum = a + b\n\n if product <= 1000:\n return(product)\n else:\n return(sum)\n\nresult = product_or_sum(a, b)\nprint(result)","repo_name":"Naddalyy/Homework","sub_path":"FirstTask.py","file_name":"FirstTask.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31410251103","text":"from functools import wraps\nimport pyrogram\nfrom thebot import logging\nimport traceback\nimport sys\nfrom thebot import ALLOWED_USERS, LOG_CHANNEL\n\n\ndef split_limits(text):\n if len(text) < 2048:\n return [text]\n\n lines = text.splitlines(True)\n small_msg = \"\"\n result = []\n for line in lines:\n if len(small_msg) + len(line) < 2048:\n small_msg += line\n else:\n result.append(small_msg)\n small_msg = line\n else:\n result.append(small_msg)\n\n return result\n\n\ndef capture_err(func):\n @wraps(func)\n async def capture(client, message, *args, **kwargs):\n try:\n return await func(client, message, *args, **kwargs)\n except pyrogram.errors.exceptions.forbidden_403.ChatWriteForbidden as err:\n logging.info(\n \"Bot was muted in {} {}\".format(message.chat.title, message.chat.id)\n )\n await client.leave_chat(message.chat.id)\n except Exception as err:\n await message.reply(\"**Error:**\\n\" + str(err))\n exc_type, exc_obj, exc_tb = sys.exc_info()\n errors = traceback.format_exception(\n etype=exc_type, value=exc_obj, tb=exc_tb\n )\n error_feedback = split_limits(\n \"**EsseX ERROR** | `{}` | `{}`\\n\\n```{}```\\n\\n```{}```\\n\".format(\n 0 if not message.from_user else message.from_user.id,\n 0 if not message.chat else message.chat.id,\n message.text or message.caption,\n \"\".join(errors),\n )\n )\n for x in error_feedback:\n if LOG_CHANNEL:\n await client.send_message(LOG_CHANNEL, x)\n else:\n for m in ALLOWED_USERS:\n await client.send_message(m, x)\n raise err\n return capture","repo_name":"Dank-del/EsseX","sub_path":"thebot/utils/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"3"}
+{"seq_id":"17578190237","text":"from V1.imports import *\nfrom V1.model import create_model1, create_model2, create_features_vgg, create_features_resnet, create_features_xception, create_model3\nfrom keras.layers import concatenate\n\nphysical_devices = tf.config.experimental.list_physical_devices('GPU')\nif len(physical_devices) > 0:\n tf.config.experimental.set_memory_growth(physical_devices[0], True)\n\n# Taking images from dataset\npath = \"/home/prathamesh/Desktop/ML_Projects/Auto_Colorization/mirflickr25k\"\ntrain_datagen = ImageDataGenerator(rescale=1. / 255)\ntrain = train_datagen.flow_from_directory(path, target_size=(224, 224), batch_size=128, class_mode=None)\n\nX = []\nY = []\nfor img in train[0]:\n try:\n # converting rgb color space to lab\n lab = rgb2lab(img)\n # Appending L (Lightness in X)\n X.append(lab[:, :, 0])\n # Appending color in Y\n Y.append(lab[:, :, 1:] / 128)\n except:\n print('error')\nX = np.array(X)\nY = np.array(Y)\nX = X.reshape(X.shape + (1,))\nprint(X.shape)\nprint(Y.shape)\n\nmodel1 = create_model1()\nvgg_features = create_features_vgg(X, model1)\n\n#Encoder\nencoder_input = Input(shape=(7, 7, 512,))\n#Decoder\ndecoder_output = Conv2D(256, (3,3), activation='relu', padding='same')(encoder_input)\ndecoder_output = Conv2D(128, (3,3), activation='relu', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\ndecoder_output = Conv2D(64, (3,3), activation='relu', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\ndecoder_output = Conv2D(32, (3,3), activation='relu', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\ndecoder_output = Conv2D(16, (3,3), activation='relu', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\ndecoder_output = Conv2D(2, (3, 3), activation='tanh', padding='same')(decoder_output)\ndecoder_output = UpSampling2D((2, 2))(decoder_output)\nmodel = Model(inputs=encoder_input, outputs=decoder_output)\n\nmodel.compile(optimizer='Adam', loss='mse' , metrics=['accuracy'])\nadam = keras.optimizers.Adam(lr=1e-4)\nmodel.compile(optimizer=adam, loss='mse', metrics=['accuracy'])\nfilepath = \"/home/prathamesh/Desktop/ML_Projects/Auto_Colorization/V2/weights/weights-improvement-{epoch:02d}.hdf5\"\ncheckpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max')\ncallbacks_list = [checkpoint]\nmodel.fit(vgg_features, Y, verbose=1, validation_split=0.1,epochs=100, batch_size=128, callbacks=callbacks_list)\n\ntestpath = '/home/prathamesh/Desktop/ML_Projects/Auto_Colorization/mirflickr25k/mirflickr/im22.jpg'\n\ntest = img_to_array(load_img(testpath))\ntest = resize(test, (224,224), anti_aliasing=True)\ntest*= 1.0/255\nlab = rgb2lab(test)\nl = lab[:,:,0]\nL = gray2rgb(l)\nL = L.reshape((1,224,224,3))\n#print(L.shape)\nvggpred = model1.predict(L)\nab = model.predict(vggpred)\n#print(ab.shape)\nab = ab*128\ncur = np.zeros((224, 224, 3))\ncur[:,:,0] = l\ncur[:,:,1:] = ab\nimsave(\"result2.jpg\", lab2rgb(cur))","repo_name":"PrathameshDeshpande/Auto_Colorization","sub_path":"V2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3021,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37202372389","text":"# -*- coding:utf-8 -*-\n\nimport pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n# 打开911文件\nfilepath = './PM2.5/BeijingPM20100101_20151231.csv'\ndf = pd.read_csv(filepath)\n\n# 第一步: 设置时间序列索引\n# PeriodIndex用于格式化时间序列,按小时生成时间序列\n# 把分开的时间字符串转换为Pandas转换为Pandas的时间序列类型\nperiod = pd.PeriodIndex(year=df['year'], month=df['month'], day=df['day'], hour=df['hour'], freq='H')\n\n# 首先：将上面的时间序列加入到df数组中，设置为一个列索引\ndf['datetime'] = period\n\n# 然后：将上面的datetime列索引设置为index索引(行索引）\ndf.set_index('datetime', inplace=True)\nprint(df.head(3))\nprint(df.columns)\nprint('*' * 100)\n\n# 由于数据量太多，绘图数据可以只取部分数据，上面添加时间序列的数据进行降采样\n# 求每天的平均值，如果值为NaN,会自动排除掉\ndf = df.resample('7D').mean()\n\n\n# 第二步:处理缺失数据\n# 处理缺失数据，查看原始数据，有些监测点的PM是NaN\nprint(df['PM_US Post'])\nprint('*' * 100)\n# 删除缺失的数据,由于是只有一列，不需要指定轴向axis\ndata = df['PM_US Post'].dropna()\nprint(data)\nprint(type(data))\nprint('*' * 100)\n\n\n# 第三步：绘图\n_x = data.index\n# 格式化X轴坐标格式\n_x = [i.strftime('%Y-%m-%d') for i in _x]\n_y = data.values\n\nplt.figure(figsize=(20, 8), dpi=128)\nplt.plot(range(len(_x)), _y)\n\n# 原始画图数据太多，X轴每隔10个填入坐标数据\nplt.xticks(range(0, len(_x), 10), list(_x)[::10], rotation=45)\n\nplt.show()\n","repo_name":"FelixZFB/Python_data_analysis","sub_path":"002_Python_data_analysis_from_entry_to_master/ch11_Pandas/05_Pandas_时间序列_综合案例/009_时间序列_PeriodIndex_格式化时间序列_PM2.5_US统计的数据.py","file_name":"009_时间序列_PeriodIndex_格式化时间序列_PM2.5_US统计的数据.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40065594943","text":"# author: David Gessner \n\"\"\"\nThis module provides classes to define a simulated ethernet network.\n\n\"\"\"\n\nimport collections\n\nimport simpy\n\nimport ft4fttsim.ethernet as ethernet\nfrom ft4fttsim.exceptions import FT4FTTSimException\nfrom ft4fttsim.simlogging import log\n\n\nclass Port(object):\n \"\"\"\n Models physical Ethernet ports.\n\n Instances of class NetworkDevice can have several such ports, and to each\n one of them an instance of Link can be attached.\n\n Each port has an input queue and an output queue. The input queue is where\n messages that are received through the port are queued. The output queue is\n where messages that are to be transmitted through the port are queued.\n\n Input queues are modeled as simpy stores with infinite capacity. The\n waiting time in an input queue is therefore always zero for any message.\n That is, a message received in an input queue does not suffer any queuing\n delay. Output queues, on the other hand, are modeled by a simpy store with\n capacity 1. This means that messages transmitted through the output queue\n may suffer a queuing delay.\n\n \"\"\"\n\n def __init__(self, env, name):\n \"\"\"\n Constructor for Port instances.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the Port instance.\n\n \"\"\"\n self.in_queue = simpy.Store(env)\n self.out_queue = simpy.Store(env, capacity=1)\n # indicates whether the port is already connected to a link\n self.is_free = True\n self.name = name\n\n def __repr__(self):\n return self.name\n\n\nclass Link(object):\n \"\"\"\n Models links used in an Ethernet network.\n\n A Link object models a bidirectional physical link between exactly 2\n network device ports (objects of class Port). A Link object has a certain\n transmission speed (expressed in megabits per second) and propagation delay\n (expressed in microseconds).\n\n \"\"\"\n def __init__(\n self, env,\n port1, port2,\n megabits_per_second, propagation_delay_us):\n \"\"\"\n Create a new instance of class Link.\n\n Arguments:\n env: A simpy.Environment instance.\n port1: An instance of Port to which the link will be connected. It\n models the port at one extreme of the modeled link.\n port2: Another instance of Port to which the link will be\n connected. It models the port at the other extreme of the\n modeled link.\n megabits_per_second: Speed of the link in megabits per second.\n propagation_delay_us: Propagation delay of the link in\n microseconds.\n\n Raises:\n FT4FTTSimException: error if the arguments have invalid values,\n e.g., a negative value for the propagation delay.\n\n \"\"\"\n if megabits_per_second <= 0:\n raise FT4FTTSimException(\"Mbps must be a positive number.\")\n if propagation_delay_us < 0:\n raise FT4FTTSimException(\"Propagation delay cannot be negative.\")\n if not isinstance(port1, Port):\n raise FT4FTTSimException(\n \"port1 argument must be a port and not {}.\".format(\n type(port1)))\n if not isinstance(port2, Port):\n raise FT4FTTSimException(\n \"port2 argument must be a port and not {}.\".format(\n type(port2)))\n assert port1.is_free\n assert port2.is_free\n self.sublink = (\n _Sublink(env, self, port1, port2),\n _Sublink(env, self, port2, port1)\n )\n port1.is_free = False\n port2.is_free = False\n self.megabits_per_second = megabits_per_second\n self.propagation_delay_us = propagation_delay_us\n\n def transmission_time_us(self, num_bytes):\n \"\"\"\n Gives the time in microseconds to transmit num_bytes on the link.\n\n Arguments:\n num_bytes: The number of bytes we want to know the transmission\n time for.\n\n Returns:\n A floating-point number representing the number of microseconds\n that it would take a Port attached to the link to transmit\n num_bytes, counting from when the first bit until the last bit has\n left the Port.\n\n Example:\n\n Assuming a link of 100 Mbps, and 1526 bytes to transmit, results in a\n transmission time of 1526 * 8 / 10**8 = 122.08 microseconds:\n\n >>> env = simpy.Environment()\n >>> d = NetworkDevice(env, \"some device\", 1)\n >>> d2 = NetworkDevice(env, \"another device\", 1)\n >>> link = Link(env, d.ports[0], d2.ports[0], 100, 0)\n >>> link.transmission_time_us(1526)\n 122.08\n\n \"\"\"\n bits_to_transmit = num_bytes * 8\n transmission_time_us = (bits_to_transmit / self.megabits_per_second)\n return transmission_time_us\n\n\nclass _Sublink(object):\n \"\"\"\n Models a directional sublink of a Link.\n\n Link instances are comprised of 2 _Sublinks each, one for each direction.\n This means that _Sublinks are directional, i.e., they have a single\n transmitter and a single receiver port. Messages that are being modeled as\n being transmitted can only be transmitted from the transmitter to the\n receiver port, but not in the opposite direction. At any one time at most\n one message can be transmitted through a _Sublink. If the transmission of\n additional messages is ordered through the _Sublink when it already\n contains a message, then the additional messages will be queued in the\n output queue of the transmitter port of that _Sublink.\n\n \"\"\"\n def __init__(\n self, env, link,\n transmitter_port, receiver_port):\n \"\"\"\n Create a new instance of class _Sublink.\n\n Arguments:\n env: A simpy.Environment instance.\n link: The link that the _Sublink instance is a part of.\n transmitter_port: An instance of Port that will be attached to\n the link instance as the transmitter.\n receiver_port: An instance of Port that will be attached to\n the link instance as the receiver.\n\n \"\"\"\n self.env = env\n self.link = link\n self._transmitter_port = transmitter_port\n self._receiver_port = receiver_port\n env.process(self.run())\n\n @property\n def transmitter_port(self):\n \"\"\"\n The port that is at the transmitting end of the _Sublink.\n\n \"\"\"\n return self._transmitter_port\n\n @property\n def receiver_port(self):\n \"\"\"\n The port that is at the receiving end of the _Sublink.\n\n \"\"\"\n return self._receiver_port\n\n def run(self):\n \"\"\"\n Get a message from the transmitter port and simulate its transmission.\n\n \"\"\"\n while True:\n get_request = self.transmitter_port.out_queue.get()\n message = yield get_request\n log.debug(\"{} transmission of {} started\".format(self, message))\n bytes_to_transmit = (ethernet.PREAMBLE_SIZE_BYTES +\n ethernet.SFD_SIZE_BYTES +\n message.size_bytes)\n # wait for the transmission + propagation time to elapse\n yield self.env.timeout(\n self.link.transmission_time_us(bytes_to_transmit) +\n self.link.propagation_delay_us)\n log.debug(\"{} transmission of {} finished\".format(self, message))\n self.receiver_port.in_queue.put(message)\n # wait for the duration of the ethernet interframe gap to elapse\n yield self.env.timeout(\n self.link.transmission_time_us(ethernet.IFG_SIZE_BYTES))\n log.debug(\"{} inter frame gap finished\".format(self))\n\n def __repr__(self):\n return \"{}->{}\".format(self.transmitter_port, self.receiver_port)\n\n def __str__(self):\n return \"{}->{}\".format(self.transmitter_port, self.receiver_port)\n\n\nclass NetworkDevice(object):\n \"\"\"\n Models generic network devices.\n\n A NetworkDevice instance models a generic network device with a certain\n number of ethernet ports. Network devices can be interconnected by means of\n links attached to their ports:\n\n >>> env = simpy.Environment()\n >>> d = NetworkDevice(env, \"some device\", 1)\n >>> d2 = NetworkDevice(env, \"another device\", 1)\n >>> L = Link(env, d.ports[0], d2.ports[0], 100, 3)\n\n The main purpose of this class is to serve as the superclass for specific\n network devices such as Switch, MessagePlaybackDevice,\n MessageRecordingDevice, etc.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports):\n \"\"\"\n Constructor for NetworkDevice instances.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the NetworkDevice instance.\n num_ports: The number of ports that the NetworkDevice instance\n should have.\n\n \"\"\"\n self.env = env\n self.ports = [Port(self.env, \"{}-port{}\".format(name, i))\n for i in range(num_ports)]\n self.name = name\n\n def listen_for_messages(self, callback):\n \"\"\"\n Simpy process that calls callback when messages are received.\n\n Arguments:\n callback: The function to be called when one or more messages are\n received. The function should accept a single parameter that is\n the list of received messages.\n\n Note that a simpy process is a generator function (a.k.a., co-routine).\n It should therefore not be called directly. Instead, it should be\n registered with a simpy environment using simpy.Environment().process\n\n Example:\n\n >>> class MyNetworkDevice(NetworkDevice):\n ... def __init__(self, env, name, num_ports):\n ... NetworkDevice.__init__(self, env, name, num_ports)\n ... env.process(self.listen_for_messages(self.hello))\n ... def hello(self, messages):\n ... for msg in messages:\n ... print(\"Hello message {}\".format(msg))\n ...\n >>> env = simpy.Environment()\n >>> d = MyNetworkDevice(env, \"some device\", 1)\n\n \"\"\"\n # generate get requests for all input queues\n requests = [port.in_queue.get() for port in self.ports]\n while requests:\n # helper variable for the asserts\n queues_with_pending_requests = [req.resource for req in requests]\n # There is a request for each input queue.\n assert set(self.input_queues) == set(queues_with_pending_requests)\n # For each input queue there's exactly one request.\n assert (\n len(queues_with_pending_requests) ==\n len(set(queues_with_pending_requests)))\n\n log.debug(\"{} waiting for next reception\".format(self))\n completed_requests = (yield self.env.any_of(requests))\n received_messages = list(completed_requests.values())\n log.debug(\"{} received {}\".format(\n self, received_messages))\n\n callback(received_messages)\n\n # Only leave the requests which have not been completed yet\n remaining_requests = [\n req for req in requests if req not in completed_requests]\n # Input queues that have been emptied since the last wake up.\n emptied_queues = [req.resource for req in completed_requests]\n # Add new get requests for the input queues that have been emptied.\n new_requests = []\n for input_queue in emptied_queues:\n new_requests.append(input_queue.get())\n requests = remaining_requests + new_requests\n\n def instruct_transmission(self, message, port):\n \"\"\"\n Simpy process that transmits a given message through a given port.\n\n Arguments:\n message: Message instance to be transmitted.\n port: Port of self through which to transmit the message.\n\n Raises:\n FT4FTTSimException if port is not an element of self.ports.\n\n Note that instruct_transmission() is a generator function. It should\n not be called directly, but only passed as a parameter to the process()\n method of a simpy Environment instance.\n\n Example:\n\n >>> env = simpy.Environment()\n >>> d = NetworkDevice(env, \"some device\", 1)\n >>> d2 = NetworkDevice(env, \"another device\", 1)\n >>> L = Link(env, d.ports[0], d2.ports[0], 100, 3)\n >>> m = Message(env, d, d2, 1234, \"some message\")\n >>> env.process(d.instruct_transmission(m, d.ports[0]))\n \n\n \"\"\"\n log.debug(\"{} instructing transmission of {} on {}\".format(\n self, message, port))\n if port not in self.ports:\n raise FT4FTTSimException(\"{} is not a port of {}\".format(\n port, self))\n log.debug(\"{} queued for transmission\".format(message))\n yield port.out_queue.put(message)\n\n @property\n def input_queues(self):\n \"\"\"\n Returns a list of all the input queues of the NetworkDevice instance.\n\n \"\"\"\n return [port.in_queue for port in self.ports]\n\n def __str__(self):\n return self.name\n\n def __repr__(self):\n return self.name\n\n\nclass EchoDevice(NetworkDevice):\n \"\"\"\n Models a device that retransmits any message it receives.\n\n Instances of this class are mainly used for testing purposes.\n\n \"\"\"\n\n def __init__(self, env, name):\n \"\"\"\n Creates a new EchoDevice instance.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the EchoDevice instance.\n\n \"\"\"\n NetworkDevice.__init__(self, env, name, 1)\n self.env.process(self.listen_for_messages(self.echo))\n\n def echo(self, messages):\n \"\"\"\n Transmits messages through port number 0 of self.\n\n Arguments:\n messages: An iterable of the messages to transmit.\n\n \"\"\"\n for msg in messages:\n self.env.process(self.instruct_transmission(msg, self.ports[0]))\n\n\nclass MessageRecordingDevice(NetworkDevice):\n \"\"\"\n Models receivers that record and timestamp each received message.\n\n The main purpose of instances of this class is to make testing easier.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports):\n \"\"\"\n Create a new MessageRecordingDevice instance.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the MessageRecordingDevice\n instance.\n num_ports: The number of ports that the MessageRecordingDevice\n instance should have.\n\n \"\"\"\n NetworkDevice.__init__(self, env, name, num_ports)\n self.reception_records = {}\n self.env.process(self.listen_for_messages(self.do_timestamp_messages))\n\n def do_timestamp_messages(self, messages):\n \"\"\"\n Timestamp each message in messages with the current time.\n\n \"\"\"\n timestamp = self.env.now\n self.reception_records[timestamp] = messages\n log.debug(\"{} recorded {}\".format(self, self.reception_records))\n\n @property\n def recorded_messages(self):\n \"\"\"\n Return the messages so far received by self.\n\n \"\"\"\n messages = []\n for time in sorted(self.reception_records):\n messages.extend(self.reception_records[time])\n return messages\n\n @property\n def recorded_timestamps(self):\n \"\"\"\n Return list of instants of time when messages have been received.\n\n The list is sorted from earlier time instants to later time instants.\n\n \"\"\"\n return sorted(self.reception_records.keys())\n\n\nclass MessagePlaybackDevice(NetworkDevice):\n \"\"\"\n Instances of this class model devices that transmit prespecified messages.\n\n Instances of this class accept a series of transmission commands and then\n execute them. Each transmission command specifies what message to transmit,\n at what time, and through which port.\n\n The main purpose of instances of this class is to make testing easier.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports):\n NetworkDevice.__init__(self, env, name, num_ports)\n env.process(self.run())\n self.transmission_commands = {}\n\n def load_transmission_commands(self, transmission_commands):\n \"\"\"\n Load the transmission commands to execute once the run method is\n activated by simpy.\n\n Arguments:\n transmission_commands: The transmission commands to execute once\n the run method is activated by the simulator. This argument\n should be a dictionary whose keys are instants of time when\n message transmissions should be instructed. Each of the values\n of the dictionary should be another dictionary whose keys are\n the ports on which transmissions should be ordered and\n whose values are lists of messages to be transmitted through\n the corresponding port. Example:\n\n {\n 0.0: {port1: [message1, message2]},\n 12.0: {port1: [message3]},\n 13.5: {port3: [message4, message5, message6]}\n }\n\n \"\"\"\n self.transmission_commands = transmission_commands\n log.debug(\"{} loaded transmissions: {}\".format(\n self, self.transmission_commands))\n\n def run(self):\n \"\"\"\n Simpy process that executes previously loaded transmission commands.\n\n \"\"\"\n for time in sorted(self.transmission_commands):\n delay_before_next_tx_order = time - self.env.now\n log.debug(\"{} waiting for next transmission time\".format(self))\n # wait until next transmission time\n yield self.env.timeout(delay_before_next_tx_order)\n for port, messages_to_tx in \\\n self.transmission_commands[time].items():\n for message in messages_to_tx:\n self.env.process(\n self.instruct_transmission(message, port))\n\n @property\n def transmission_start_times(self):\n \"\"\"\n Returns a list of time instants when transmissions will be instructed.\n\n \"\"\"\n return sorted(self.transmission_commands.keys())\n\n\nclass MessagePlaybackAndRecordingDevice(\n MessagePlaybackDevice, MessageRecordingDevice):\n \"\"\"\n Models devices that transmit pre-specified messages and record messages.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports):\n MessagePlaybackDevice.__init__(self, env, name, num_ports)\n self.reception_records = {}\n self.env.process(self.listen_for_messages(self.do_timestamp_messages))\n\n\nclass Switch(NetworkDevice):\n \"\"\"\n Models standard Ethernet switches.\n\n \"\"\"\n\n def __init__(self, env, name, num_ports, forwarding_table=None):\n \"\"\"\n Creates a new Switch instance.\n\n Arguments:\n env: A simpy.Environment instance.\n name: A string used to identify the new switch instance.\n num_ports: The number of ports that the new switch instance should\n have.\n forwarding_table: Dictionary whose keys are network devices and\n whose values are ports of the Switch instance.\n\n \"\"\"\n NetworkDevice.__init__(self, env, name, num_ports)\n env.process(self.listen_for_messages(self.forward_messages))\n if forwarding_table is None:\n self.forwarding_table = {}\n else:\n self.forwarding_table = forwarding_table\n\n def forward_messages(self, message_list):\n \"\"\"\n Forward each message in 'message_list' through the appropriate port.\n\n Note that forwarding a message from one port to another port is\n implemented as creating a new message instance based on the message\n in the first port, and transmitting the new message instance on the\n second port.\n\n \"\"\"\n\n def find_ports(destination):\n \"\"\"\n Return a list of the ports that according to the forwarding table\n lead to 'destination'.\n\n Arguments:\n destination: an instance of class NetworkDevice or an iterable\n of NetworkDevice instances.\n\n Returns:\n A set of the ports that lead to the devices in 'destination'.\n\n \"\"\"\n output_ports = set()\n if isinstance(destination, collections.Iterable):\n for device in destination:\n # ports leading to device\n ports_towards_device = self.forwarding_table.get(\n device, self.ports)\n output_ports.update(ports_towards_device)\n else:\n output_ports.update(\n self.forwarding_table.get(destination, self.ports))\n return output_ports\n\n for message in message_list:\n destinations = message.destination\n output_ports = find_ports(destinations)\n for port in output_ports:\n new_message = Message.from_message(message)\n self.env.process(\n self.instruct_transmission(new_message, port))\n\n\nclass Message(object):\n \"\"\"\n Class for messages that model Ethernet frames.\n\n \"\"\"\n # next available identifier for message objects\n next_identifier = 0\n\n def __init__(\n self, env, source, destination, size_bytes, message_type,\n data=None):\n \"\"\"\n Create an instance of Message.\n\n Arguments:\n source: usually an instance of NetworkDevice. It models the\n MAC source address field of an Ethernet frame.\n destination: usually an instance of NetworkDevice or a list of\n instances of NetworkDevice. It models the MAC destination\n address field of an Ethernet frame. If it is iterable, then it\n models a multicast address, otherwise it models a unicast\n address.\n size_bytes: indicates the size in bytes of the Ethernet frame\n modeled by the Message instance created. The size does not\n include the Ethernet preamble, the start of frame delimiter, or\n an IEEE 802.1Q tag.\n message_type: models the Ethertype field.\n data: The data to be carried within the message. It models the\n Ethernet data field.\n\n \"\"\"\n if not isinstance(size_bytes, int):\n raise FT4FTTSimException(\"Message size must be integer\")\n if not (ethernet.MIN_FRAME_SIZE_BYTES <= size_bytes <=\n ethernet.MAX_FRAME_SIZE_BYTES):\n raise FT4FTTSimException(\n \"Message size must be between {} and {}, but is {}\".format(\n ethernet.MIN_FRAME_SIZE_BYTES,\n ethernet.MAX_FRAME_SIZE_BYTES,\n size_bytes))\n self.env = env\n self._identifier = Message.next_identifier\n Message.next_identifier += 1\n # source of the message. Models the source MAC address.\n self.source = source\n # destination of the message. It models the destination MAC address. It\n # is a list to allow multicast addressing.\n self.destination = destination\n self.size_bytes = size_bytes\n self.message_type = message_type\n self.data = data\n self.name = \"({:03d}, {}, {}, {:d}, {}, {})\".format(\n self.identifier, self.source, self.destination, self.size_bytes,\n self.message_type, self.data)\n log.debug(\"{} created\".format(self))\n\n @property\n def identifier(self):\n \"\"\"\n Integer that uniquely identifies the Message instance.\n\n \"\"\"\n return self._identifier\n\n @classmethod\n def from_message(cls, template_message):\n \"\"\"\n Creates a new message instance using template_message as a template.\n\n \"\"\"\n new_equivalent_message = cls(\n template_message.env,\n template_message.source,\n template_message.destination,\n template_message.size_bytes,\n template_message.message_type,\n template_message.data)\n return new_equivalent_message\n\n def __eq__(self, message):\n \"\"\"\n Returns true if self and message are identical except for the message\n identifier.\n\n \"\"\"\n return (self.source == message.source and\n self.destination == message.destination and\n self.size_bytes == message.size_bytes and\n self.message_type == message.message_type and\n self.data == message.data)\n\n def __str__(self):\n return self.name\n\n def __repr__(self):\n return self.name\n","repo_name":"davitenio/ft4fttsim","sub_path":"ft4fttsim/networking.py","file_name":"networking.py","file_ext":"py","file_size_in_byte":25265,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18105284977","text":"import asyncio\r\nimport schedule\r\nfrom db_connection import StudentDB\r\nfrom datetime import datetime\r\nfrom handlers import send_schedule\r\n\r\n# Время, когда нужно отправлять расписание\r\nschedule_times = [\"02:00-02:23\", \"14:30-15:30\"]\r\ndb = StudentDB\r\n\r\nasync def send_schedule_at_specific_times():\r\n # while True:\r\n # current_time = datetime.now().strftime(\"%H:%M\")\r\n\r\n # if current_time in schedule_times:\r\n list_of_chat_ids = db.get_all_user_ids()\r\n for chat_id in list_of_chat_ids:\r\n await send_schedule(chat_id)\r\n\r\n # Заснуть на 1 минуту перед проверкой времени снова\r\n # await asyncio.sleep(60)\r\n\r\nasync def task_send_schedule_at_specific_times():\r\n # Здесь вставьте инициализацию бота и другие настройки\r\n\r\n # Задача для отправки в 9:10\r\n schedule.every().day.at(\"09:10\").do(asyncio.run, send_schedule_at_specific_times)\r\n\r\n # Задача для отправки в 14:30\r\n schedule.every().day.at(\"14:30\").do(asyncio.run, send_schedule_at_specific_times)\r\n\r\n # Пример задачи для отправки за 10 минут до времени из переменной\r\n # Здесь переменная next_time должна содержать желаемое время в формате HH:MM\r\n next_time = \"02:46\"\r\n schedule.every().day.at(next_time).do(asyncio.run, send_schedule_at_specific_times)\r\n\r\n while True:\r\n schedule.run_pending()\r\n await asyncio.sleep(1)\r\n","repo_name":"Ch1kez/Ranh_bot","sub_path":"Ranh_bot_1_2/task_send.py","file_name":"task_send.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5234479806","text":"from __future__ import print_function\nfrom abc import abstractmethod\nimport smtplib\nimport sqlite3\nfrom twilio.rest import Client as TWClient\nfrom log import Log\nfrom utilities import DB_CORE\n\nclass Notification(object):\n \"\"\"Hold information about nofication that can be sent via SMS or email.\"\"\"\n\n def __init__(self):\n self.subject = \"Alert - {site}: {asset}.\"\n self.message = \"{time}: an alert was triggered at {site}, {asset}, value: {value}.\"\n self.sender = \"\"\n self.receiver = \"\"\n\n @abstractmethod\n def send(self):\n \"\"\"Send notification.\"\"\"\n\n @abstractmethod\n def load_settings(self):\n \"\"\"Load mail settings.\"\"\"\n\nclass Email(Notification):\n \"\"\"Email notification.\"\"\"\n\n def __init__(self):\n Notification.__init__(self)\n self.serveraddr = \"\"\n self.serverport = \"\"\n self.username = \"\"\n self.password = \"\"\n self.tls = False\n\n def load_settings(self):\n \"\"\"Load mail settings from the core database.\"\"\"\n try:\n field_names = '''\n serveraddr\n serverport\n username\n password\n sender\n receiver\n tls\n '''.split()\n sql = 'SELECT {fields} FROM mail_settings LIMIT 1;'.format(\n fields=', '.join(field_names))\n database = sqlite3.connect(DB_CORE)\n db_elements = database.cursor().execute(sql).fetchone()\n for field, value in zip(field_names, db_elements):\n setattr(self, field, value)\n Log.info(\"Mail settings loaded.\")\n except Exception as excpt:\n Log.exception(\"Trying to load mail settings: %s.\", excpt)\n finally:\n database.close()\n\n\n def build_message(self, subject, message):\n \"\"\"Build message body for e-mail.\"\"\"\n message = \"\\r\\n\".join([\n \"From: \" + self.sender,\n \"To: \" + self.receiver,\n \"Subject: \" + subject,\n \"\",\n message])\n\n return message\n\n def send(self, subject, message):\n \"\"\"Send email notification using paremeters from the database.\"\"\"\n try:\n Log.info(\"Sending email notification.\")\n self.load_settings()\n server = smtplib.SMTP(str(self.serveraddr), str(self.serverport))\n server.ehlo()\n if self.tls:\n server.starttls()\n server.login(self.username, self.password)\n server.sendmail(self.sender, self.receiver, self.build_message(subject, message))\n Log.info(\"Email notification sent.\")\n except Exception as excpt:\n Log.exception(\"Trying to send notificaton via e-mail: %s.\", excpt)\n finally:\n server.quit()\n\nclass SMS(Notification):\n \"\"\"SMS notification.\"\"\"\n\n def __init__(self):\n Notification.__init__(self)\n self.twilio_acct_sid = \"\"\n self.twilio_auth_token = \"\"\n\n def send(self, sender, receiver, message):\n \"\"\"Send SMS Notification via Twilio.\"\"\"\n try:\n Log.info(\"Sending SMS notification.\")\n self.load_settings()\n client = TWClient(self.twilio_acct_sid, self.twilio_auth_token)\n message = client.messages.create(to=receiver, from_=sender, body=message)\n Log.info(\"SMS notification sent: %s\", message.sid)\n except Exception as excpt:\n Log.exception(\"Trying to send notificaton via SMS: %s.\", excpt)\n\n def load_settings(self):\n \"\"\"Load SMS settings from the core database.\"\"\"\n try:\n field_names = '''\n sender\n receiver\n twilio_acct_sid\n twilio_auth_token\n '''.split()\n sql = 'SELECT {fields} FROM site LIMIT 1;'.format(\n fields=', '.join(field_names))\n database = sqlite3.connect(DB_CORE)\n db_elements = database.cursor().execute(sql).fetchone()\n for field, value in zip(field_names, db_elements):\n setattr(self, field, value)\n Log.info(\"SMS settings loaded.\")\n except Exception as excpt:\n Log.exception(\"Trying to load SMS settings: %s.\", excpt)\n finally:\n database.close()\n","repo_name":"mayaculpa/hapi","sub_path":"src/smart_module/notification.py","file_name":"notification.py","file_ext":"py","file_size_in_byte":4330,"program_lang":"python","lang":"en","doc_type":"code","stars":68,"dataset":"github-code","pt":"3"}
+{"seq_id":"29047336187","text":"from balance.actions.dcs.utils import nirvana as nirvana_utils\n\n\ndef test_get_nirvana_io_by_type():\n input_example = {\n 'key': {\n 'name': 'key',\n 'type': 'INPUT',\n 'items': [{'dataType': 'json'}],\n }\n }\n\n assert nirvana_utils.get_io_by_type({}, 'text') is None\n assert nirvana_utils.get_io_by_type(input_example, 'text') is None\n assert nirvana_utils.get_io_by_type(input_example, 'json') == 'key'\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/dcs/utils/test_nirvana.py","file_name":"test_nirvana.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13734239423","text":"#!/usr/bin/python3\n'''\nThe last exercise in this chapter adds a small feature to the other continuous exercise project, the notebook. In this exercise, add a feature which includes the date and time of the written note to the program. The program works as earlier, but saves data in the form \"[note]:::[date and time]\" meaning that there is a three-colon separator between the note and timestamp. The timestamp can be generated as follows:\n\n\n\n\n\t\n>>> import time\t\t\n\n>>> time.strftime(\"%X %x\")\n'19:01:34 01/03/09'\n>>> \n\nThis returns the date and time in a nice, compact string. When working correctly, the program prints the following:\n\n\n\n\n\t\t\t\n>>> \n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 2\nWrite a new note: Call mom.\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 1\nCall mom.:::11:44:41 04/25/11\n\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 4\nNotebook shutting down, thank you.\n>>> \n\n\nExample output:\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 2\nWrite a new note: Play football\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 1\nPlay football:::21:11:24 11/04/11\n\n(1) Read the notebook\n(2) Add note\n(3) Empty the notebook\n(4) Quit\n\nPlease select one: 4\nNotebook shutting down, thank you.\nThe verification of program output does not account for whitespace and is not case-sensitive (the least strict comparison level)\n'''\n\nimport time\n\ndef createNotebook(name):\n try:\n readfiles = open(name, 'r') \n readfiles.close()\n print(\"Now using file\", name)\n except IOError:\n if name == \"notebook.txt\":\n print(\"No default notebook was found, created one.\")\n else:\n print(\"No notebook with that name detected, created one.\")\n createFile = open(name, \"a\")\n print(\"Now using file \", name)\n createFile.close()\n \n\ndef readFile(name):\n try:\n readfile = open(name, 'r')\n content = readfile.read()\n readfile.close()\n print(content)\n except IOError:\n return False\n\n\n\ndef writeFile(name):\n stringuser = input(\"Write a new note: \")\n notebook = open(name, \"a\") \n check = time.strftime(\"%X %x\")\n all = stringuser + \":::\"+ check + \"\\n\"\n notebook.write(all)\n notebook.close()\ndef emptyFile():\n newFile = open(\"notebook.txt\", \"w+\")\n newFile.truncate(0)\n newFile.close()\n print(\"Notes deleted\")\n\nname = \"notebook.txt\"\noption = True\nwhile option == True:\n createNotebook(name)\n print(\"(1) Read the notebook \\n(2) Add note \\n(3) Empty the notebook\\n(4) Change the notebook \\n(5) Quit\")\n select = int(input(\"Please select one:\"))\n if (select == 1):\n readFile(name)\n elif (select == 2):\n writeFile(name)\n elif (select == 3):\n\t emptyFile()\n elif (select == 4):\n name = input(\"Give the name of the new file:\")\n elif (select == 5):\n option = False\n print(\"Notebook shutting down, thank you.\")\n\n else:\n print(\"Incorrect Selection.\")\n","repo_name":"sarakfouri/first_steps_python","sub_path":"task7_6.py","file_name":"task7_6.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6004266252","text":"from tkinter import *\n\njanela = Tk()\njanela.title('calculadora simples')\n\nentrd = Entry(janela, width=35, borderwidth=5)\nentrd.grid(row=0, column=0, columnspan=3, padx=10, pady=10)\n\n\n\n#entrd.insert(0, 'seu nome')\ndef Botao_clicado(numero):\n #entrd.delete(0, END)\n atual = entrd.get()\n\n entrd.delete(0, END)\n\n entrd.insert(0, str(atual) + str(numero))\n\ndef Botao_limpar():\n entrd.delete(0, END)\n \ndef Botao_mais():\n primeiro_numero = entrd.get()\n global p_num\n global matematica\n matematica = 'mais'\n p_num = int(primeiro_numero)\n entrd.delete(0, END)\n \ndef Botao_igual():\n segundo_numero = entrd.get()\n entrd.delete(0, END)\n \n if matematica == 'mais':\n entrd.insert(0, p_num + int(segundo_numero))\n \n if matematica == 'menos':\n entrd.insert(0, p_num - int(segundo_numero))\n \n if matematica == 'vezes':\n entrd.insert(0, p_num * int(segundo_numero))\n \n if matematica == 'divisao':\n entrd.insert(0, p_num / int(segundo_numero))\n \n \ndef Botao_menos():\n primeiro_numero = entrd.get()\n global p_num\n global matematica\n matematica = 'menos'\n p_num = int(primeiro_numero)\n entrd.delete(0, END)\n\ndef Botao_vezes():\n primeiro_numero = entrd.get()\n global p_num\n global matematica\n matematica = 'vezes'\n p_num = int(primeiro_numero)\n entrd.delete(0, END)\n \ndef Botao_dividir():\n primeiro_numero = entrd.get()\n global p_num\n global matematica\n matematica = 'divisao'\n p_num = int(primeiro_numero)\n entrd.delete(0, END)\n\nbt1 = Button(janela, text='1', padx=40, pady=20, command=lambda: Botao_clicado(1))\nbt2 = Button(janela, text='2', padx=40, pady=20, command=lambda: Botao_clicado(2))\nbt3 = Button(janela, text='3', padx=40, pady=20, command=lambda: Botao_clicado(3))\nbt4 = Button(janela, text='4', padx=40, pady=20, command=lambda: Botao_clicado(4))\nbt5 = Button(janela, text='5', padx=40, pady=20, command=lambda: Botao_clicado(5))\nbt6 = Button(janela, text='6', padx=40, pady=20, command=lambda: Botao_clicado(6))\nbt7 = Button(janela, text='7', padx=40, pady=20, command=lambda: Botao_clicado(7))\nbt8 = Button(janela, text='8', padx=40, pady=20, command=lambda: Botao_clicado(8))\nbt9 = Button(janela, text='9', padx=40, pady=20, command=lambda: Botao_clicado(9))\nbt0 = Button(janela, text='0', padx=40, pady=20, command=lambda: Botao_clicado(0))\n\nbt_mais = Button(janela, text='+', padx=39, pady=20, command=Botao_mais)\nbt_igual = Button(janela, text='=', padx=91, pady=20, command=Botao_igual)\nbt_menos = Button(janela, text='-', padx=40, pady=20, command=Botao_menos)\nbt_vezes = Button(janela, text='*', padx=40, pady=20, command=Botao_vezes)\nbt_dividir = Button(janela, text='/', padx=40, pady=20, command=Botao_dividir)\n\nbt_limpar = Button(janela, text='limpar', padx=79, pady=20, command= Botao_limpar)\n\n\n# colocando botoes em grid\n\nbt1.grid(row=3, column=0)\nbt2.grid(row=3, column=1)\nbt3.grid(row=3, column=2)\n\nbt4.grid(row=2, column=0)\nbt5.grid(row=2, column=1)\nbt6.grid(row=2, column=2)\n\nbt7.grid(row=1, column=0)\nbt8.grid(row=1, column=1)\nbt9.grid(row=1, column=2)\n\nbt0.grid(row=4, column=0)\n\nbt_limpar.grid(row=4, column=1, columnspan=2)\nbt_mais.grid(row=5, column=0)\nbt_igual.grid(row=5, column=1, columnspan=2)\n\nbt_menos.grid(row=6, column=0)\nbt_vezes.grid(row=6, column=1)\nbt_dividir.grid(row=6, column=2)\n\njanela.mainloop()","repo_name":"jonasht/tkinter-FreeCodeCamp.org","sub_path":"02-calculadoraSimples0.py","file_name":"02-calculadoraSimples0.py","file_ext":"py","file_size_in_byte":3409,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36388138976","text":"# Michael Rogers\nimport matplotlib.pyplot as plt\nimport random\n\nclear_board = [[0,0,0],\n [0,0,0],\n [0,0,0]]\n\nboard = clear_board\nX_wins = 0\nO_wins = 0\n\ndef play_games(games):\n X = 0\n O = 0\n for i in range(games+1):\n X1, O1 = play_game()\n X += X1\n O += O1\n print(\"X: \" + X + \" O: \" + O + \" Ties: \" + (100-X-O))\n\ndef play_game():\n for i in range(10):\n if i % 2 is 0:\n place_piece(1)\n else:\n place_piece(4)\n board = clear_board\n if check_winner() % 2 is 0:\n return 1, 0 \n else:\n return 0, 1\n\ndef check_winner():\n #Check one way\n if board[0][0] + board[0][1] + board[0][2] is 3:\n return 1\n elif board[0][0] + board[0][1] + board[0][2] is 12:\n return 2\n\n elif board[1][0] + board[1][1] + board[1][2] is 3:\n return 1\n elif board[1][0] + board[1][1] + board[1][2] is 12:\n return 2\n\n elif board[2][0] + board[2][1] + board[2][2] is 3:\n return 1\n elif board[2][0] + board[2][1] + board[2][2] is 12:\n return 2\n #Check other way\n elif board[0][0] + board[1][0] + board[2][0] is 3:\n return 1\n elif board[0][0] + board[1][0] + board[2][0] is 12:\n return 2\n\n elif board[0][1] + board[1][1] + board[2][1] is 3:\n return 1\n elif board[0][1] + board[1][1] + board[2][1] is 12:\n return 2\n \n elif board[0][2] + board[1][2] + board[2][2] is 3:\n return 1\n elif board[0][2] + board[1][2] + board[2][2] is 12:\n return 2\n #Check diagonals\n elif board[0][0] + board[1][1] + board[2][2] is 3:\n return 1\n elif board[0][0] + board[1][1] + board[2][2] is 12:\n return 2\n elif board[2][0] + board[1][1] + board[0][2] is 3:\n return 1\n elif board[2][0] + board[1][1] + board[0][2] is 12:\n return 2\n else:\n return 0\n\ndef place_piece(num):\n x = random.randint(0,2)\n y = random.randint(0,2)\n\n while board[x][y] is not 0:\n x = random.randint(0,2)\n y = random.randint(0,2)\n\n board[x][y] = num\n\nplay_games(100)","repo_name":"MRog40/Python","sub_path":"PyOther/TicTacToeOdds.py","file_name":"TicTacToeOdds.py","file_ext":"py","file_size_in_byte":2101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1414518242","text":"from __future__ import print_function # for calendar, must be at beginning of file\n\nprint(\"\\nLoading assistant...\")\n#################################\nimport datetime as DT\nimport random\nrandom.seed(DT.time.microsecond)\n# Google Calendar components\nimport httplib2\nimport os\nfrom apiclient import discovery\nfrom oauth2client import client\nfrom oauth2client import tools\nfrom oauth2client.file import Storage\ntry:\n\timport argparse\n\tflags = argparse.ArgumentParser(parents=[tools.argparser]).parse_args()\nexcept ImportError:\n\tflags = None\n# If modifying these scopes, delete your previously saved credentials\n# at ~/.credentials/calendar-python-quickstart.json\nSCOPES = 'https://www.googleapis.com/auth/calendar.readonly'\nCLIENT_SECRET_FILE = 'client_id.json'\nAPPLICATION_NAME = 'assistant'\n\n# Variables\nmyName = \"user\"\nNXTHOURS = DT.timedelta(hours=3) # in calendar, definition of an impending event in 3 hours\ntimeZone = DT.timedelta(hours=5) # shouldn't use this if possible\nstartUpTime = DT.datetime.now() # used for refreshing\n#################################\nprint(\"assistant has been loaded.\\n\")\n\ndef timeOfDay():\n\tprint(DT.time().hour,\"P\")\n\taroundTime = DT.datetime.now().hour + random.randint(-1,1)\n\tif(aroundTime <= 10):\n\t\treturn \"morning\"\n\telif(aroundTime <= 16):\n\t\treturn \"afternoon\"\n\telse:\n\t\treturn \"evening\"\n\n# for Google calendar\ndef get_credentials():\n\t\"\"\"Gets valid user credentials from storage.\n\tIf nothing has been stored, or if the stored credentials are invalid,\n\tthe OAuth2 flow is completed to obtain the new credentials.\n\tReturns:\n\t\tCredentials, the obtained credential.\n\t\"\"\"\n\thome_dir = os.path.expanduser('~')\n\tcredential_dir = os.path.join(home_dir, '.credentials')\n\tif not os.path.exists(credential_dir):\n\t\tos.makedirs(credential_dir)\n\tcredential_path = os.path.join(credential_dir,\n\t\t\t\t\t\t\t\t 'calendar-python-quickstart.json')\n\n\tstore = Storage(credential_path)\n\tcredentials = store.get()\n\tif not credentials or credentials.invalid:\n\t\tflow = client.flow_from_clientsecrets(CLIENT_SECRET_FILE, SCOPES)\n\t\tflow.user_agent = APPLICATION_NAME\n\t\tif flags:\n\t\t\tcredentials = tools.run_flow(flow, store, flags)\n\t\telse: # Needed only for compatibility with Python 2.6\n\t\t\tcredentials = tools.run(flow, store)\n\t\tprint('Storing credentials to ' + credential_path)\n\treturn credentials\ncredentials = get_credentials()\nhttp = credentials.authorize(httplib2.Http())\nservice = discovery.build('calendar', 'v3', http=http)\n\ndef formatGT(eventTime): # stands for format google thingy\n\teventTime = eventTime.split('T')\n\teventTime = ' '.join(eventTime)\n\teventTime = eventTime.split('-')\n\teventTime.pop(3)\n\teventTime = '-'.join(eventTime)\n\teventTime = DT.datetime.strptime(eventTime, \"%Y-%m-%d %H:%M:%S\")\n\treturn eventTime\n\ndef formatMinHr(numMinutes, fuzzy=0):\n\tif numMinutes == 1:\n\t\treturn \"1 minute\"\n\telif numMinutes < 60:\n\t\tif fuzzy == 1:\n\t\t\tnumMinutes = 10*round(numMinutes/10)\n\t\t\treturn \"about \"+str(numMinutes)+\" minutes\"\n\t\telse:\n\t\t\treturn str(numMinutes)+\" minutes\"\n\telse:\n\t\thrs = int(numMinutes/60) # floors it\n\t\tmins = numMinutes%60\n\t\tif hrs == 1:\n\t\t\thrs = \"1 hour\"\n\t\telse:\n\t\t\thrs = str(hrs)+\" hours\"\n\t\tif mins == 0:\n\t\t\treturn hrs # 0 minutes\n\t\telif mins == 1:\n\t\t\tif fuzzy == 1:\n\t\t\t\treturn \"about \"+hrs # 0 minutes\n\t\t\telse:\n\t\t\t\tmins = \"1 minute\"\n\t\telse:\n\t\t\tif fuzzy == 1:\n\t\t\t\tmins = 10*round(mins/10)\n\t\t\t\treturn \"about \"+hrs+\", \"+str(mins)+\" minutes\"\n\t\t\tmins = str(mins)+\" minutes\"\n\t\treturn hrs+\", \"+mins\n\t\ndef eventLen(myEvent):\n\ttimeT = formatGT(myEvent[\"end\"][\"dateTime\"])-formatGT(myEvent[\"start\"][\"dateTime\"])\n\treturn round(timeT.seconds/60)\n\t\ndef nextEvent(nowEvent = 0):\n\t# returns the next event to occur after the nowEvent, or if no next event, then 0; also returns time till\n\tif nowEvent == 0:\n\t\tmaxNow = DT.datetime.utcnow()\n\telse:\n\t\tmaxNow = formatGT(nowEvent[\"end\"][\"dateTime\"])\n\t# Getting the next 1 event\n\t# 'Z' indicates UTC time\n\teventsResult = service.events().list(calendarId='primary', timeMin=maxNow.isoformat()+\"-05:00\", maxResults=1, singleEvents=True,orderBy='startTime').execute()\n\tevents = eventsResult.get('items', [])\n\tif not events:\n\t\treturn 0\n\t# get time till\n\ttimeTill = formatGT(events[0][\"start\"][\"dateTime\"])-DT.datetime.now()\n\treturn (events[0], round(timeTill.seconds/60))\n\t\ndef imminEvent(nowEvent = 0, maxEvents = 20, maxLater = DT.datetime.now() + NXTHOURS):\n\t# returns all events within the next few hours, or a number representing the minutes of free time until the next event (-1 if no next)\n\tif nowEvent == 0:\n\t\tmaxNow = DT.datetime.now()\n\telse:\n\t\tmaxNow = formatGT(nowEvent[\"end\"][\"dateTime\"])\n\t# Getting the next 1 event\n\t# 'Z' indicates UTC time (taken off)\n\teventsResult = service.events().list(calendarId='primary', timeMin=maxNow.isoformat()+\"-05:00\", timeMax=maxLater.isoformat()+\"-05:00\", maxResults=maxEvents, singleEvents=True,orderBy='startTime').execute()\n\tevents = eventsResult.get('items', [])\n\tif not events:\n\t\t# return time till next event\n\t\t\tnxt, nxtTill = nextEvent(nowEvent)\n\t\t\tif nxt != 0:\n\t\t\t\treturn nxtTill\n\t\t\telse:\n\t\t\t\treturn -1\n\treturn events\n\t\ndef nowEvent():\n\t# returns the current event and number of other events, or (0,0) if none\n\tnow = DT.datetime.now()\n\t# Getting the \"current\" event\n\t# 'Z' indicates UTC time\n\teventsResult = service.events().list(calendarId='primary', timeMin=(now-2*NXTHOURS).isoformat()+\"-05:00\", timeMax=(now+2*NXTHOURS).isoformat()+\"-05:00\", maxResults=10, singleEvents=True,orderBy='startTime').execute()\n\tevents = eventsResult.get('items', [])\n\tevents = list(reversed(events))\n\tif not events:\n\t\treturn (0, now)\n\tfinalAns = []\n\tfor event in events:\n\t\tif formatGT(event[\"end\"][\"dateTime\"]) > now and formatGT(event[\"start\"][\"dateTime\"]) < now: # make sure is still running\n\t\t\tfinalAns.append(event)\n\tif len(finalAns) == 0:\n\t\treturn (0, 0)\n\treturn (finalAns[0], len(finalAns)-1)\n\nprint(\"Good %s, %s. It's %s.\" % (timeOfDay(), myName, DT.datetime.now().strftime('%A, %I:%M%p')))\nmyNowEvent, myNowEventEnd = nowEvent()\n\nuser = input()\nwhile user != \"thanks\":\n\tif user == \"refresh please\":\n\t\tmyNowEvent, myNowEventEnd = nowEvent()\n\t\tprint(\"Got it,\", myName)\n\t# check for conflicts XXXX\n\tif user == \"when's my next event?\":\n\t\tmyNextEvent, tillNextEvent = nextEvent(myNowEvent)\n\t\tprint(\"You've got\",myNextEvent[\"summary\"],\"scheduled for\",formatGT(myNextEvent[\"start\"][\"dateTime\"]).strftime(\"%I:%M\"))\n\tif user == \"how long until my next event?\":\n\t\tmyNextEvent, tillNextEvent = nextEvent(myNowEvent)\n\t\tprint(\"You've got\",formatMinHr(tillNextEvent,1))\n\tif user == \"how long is my next event?\":\n\t\tmyNextEvent, tillNextEvent = nextEvent(myNowEvent)\n\t\tprint(myNextEvent[\"summary\"],\"is for\",formatMinHr(eventLen(myNextEvent)))\n\tif user == \"anything coming up?\":\n\t\timEvents = imminEvent(myNowEvent)\n\t\tif isinstance(imEvents, int): # no events, just number\n\t\t\tif imEvents == -1:\n\t\t\t\tprint(\"You have no events coming up!\")\n\t\t\telse:\n\t\t\t\tprint(\"You've got\",imEvents,\"till your next event.\")\n\t\telse:\n\t\t\tprint(\"Yep. Next you have:\")\n\t\t\tfor imEv in imEvents:\n\t\t\t\tprint(imEv[\"summary\"])\n\tif user == \"can i move my next event up?\":\n\t\tmyNextEvent, tillNextEvent = nextEvent(myNowEvent)\n\t\tnxtEvLength = eventLen(myNextEvent)\n\t\tif myNextEvent == 0:\n\t\t\tprint(\"You don't have any events next.\")\n\t\telse:\n\t\t\t# get length of next event\n\t\t\tnxtImEvs = imminEvent(myNowEvent, maxLater=formatGT(myNextEvent[\"start\"][\"dateTime\"]))\n\t\t\tif isinstance(nxtImEvs,int) and nxtImEvs != -1:\n\t\t\t\tif nxtImEvs>nxtEvLength:\n\t\t\t\t\tprint(\"Sure, I can do that\")\n\t\t\t\telse:\n\t\t\t\t\tprint(\"You have\",formatMinHr(nxtImEvs),\"of time at X but it won't be enough\")\n\t\t\telse:\n\t\t\t\tprint(\"There's an event immediately after this so I can't do anything yet\")\n\tif user == \"add an event\":\n\t\tpass\n\tuser = input()\n\tuser = user.lower()\n\nprint(\"Closing assistant...\")\n","repo_name":"miya7090/calendar-assistant","sub_path":"sandbox.py","file_name":"sandbox.py","file_ext":"py","file_size_in_byte":7706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1002577016","text":"# -*- coding: utf-8 -*-\nimport sys\nimport click\nimport netaddr\nfrom ipamcli.cli import pass_context\nimport ipamcli.libs.phpipam.client as phpipam\n\n\n@click.command('search', short_help='search entry in phpIPAM')\n@click.option('--ip', help='ip address of entry to search')\n@click.option('--hostname', help='hostname of entry to search')\n@click.option('--first-empty', is_flag=True, help='search first empty IP address')\n@click.option('--last-empty', is_flag=True, help='search last empty IP address')\n@click.option('--network', help='network address for first-empty search')\n@click.option('--vlan-id', metavar='', help='vlan id for first-empty search')\n@click.option('--vlan-name', help='vlan name for first-empty search')\n@pass_context\ndef cli(ctx, ip, hostname, first_empty, last_empty, network, vlan_id, vlan_name):\n \"\"\"Search entry information in phpIPAM.\"\"\"\n if first_empty or last_empty:\n if not network and not vlan_id and not vlan_name:\n ctx.logerr('At least one of the --network / --vlan-id / --vlan-name option must be set when use --first-empty option.')\n sys.exit(1)\n\n if vlan_id:\n if vlan_id not in ctx.vlan_list:\n ctx.logerr('No such vlan id in list.')\n sys.exit(1)\n\n else:\n network = ctx.vlan_list[vlan_id]['prefix']\n\n elif vlan_name:\n for item in ctx.vlan_list:\n if ctx.vlan_list[item]['name'] == vlan_name:\n network = ctx.vlan_list[item]['prefix']\n\n if not network:\n ctx.logerr('No such vlan name in list.')\n sys.exit(1)\n\n try:\n network = netaddr.IPNetwork(network)\n\n except netaddr.core.AddrFormatError:\n ctx.logerr('Network address %s is invalid.', network)\n sys.exit(1)\n\n if first_empty:\n ip = phpipam.get_first_empty(ctx, network)\n elif last_empty:\n ip = phpipam.get_last_empty(ctx, network)\n\n if ip is None:\n sys.exit(1)\n else:\n ctx.log('Empty IP address in subnet %s(%s):\\n%s',\n str(network), phpipam.get_network_mask_by_subnet(str(network)), ip)\n\n else:\n if ip:\n if not phpipam.checkIP(ip):\n ctx.logerr('IP address %s is invalid.', ip)\n sys.exit(1)\n\n try:\n resp = phpipam.search_by_ip(ctx, ip)\n except Exception:\n ctx.logerr('Oops. HTTP API error occured.')\n sys.exit(1)\n\n elif hostname:\n try:\n resp = phpipam.search_by_hostname(ctx, hostname)\n except Exception:\n ctx.logerr('Oops. HTTP API error occured.')\n sys.exit(1)\n\n else:\n ctx.logerr('At least one of the search option must be set.')\n sys.exit(1)\n\n if resp:\n resp = sorted(resp, key=lambda k: netaddr.IPAddress(k['ip']))\n for item in resp:\n subnet = phpipam.get_subnet_by_id(ctx, item['subnetId'])\n ctx.log(u'ID: %s\\nSubnet prefix: %s\\nSubnet netmask: %s\\nSubnet description: %s\\nIP: %s\\nMAC: %s\\nFQDN: %s\\nDescription: %s\\n',\n item['id'],\n '{}/{}'.format(subnet['subnet'], subnet['mask']),\n phpipam.get_network_mask_by_subnet('{}/{}'.format(subnet['subnet'], subnet['mask'])),\n subnet['description'],\n item['ip'],\n item['mac'],\n item['hostname'],\n item['description'])\n\n else:\n if ip:\n ctx.log('There is no record for ip address %s.', ip)\n sys.exit(1)\n\n elif hostname:\n ctx.log('There is no record for hostname %s.', hostname)\n sys.exit(1)\n","repo_name":"verdel/ipamcli","sub_path":"ipamcli/commands/cmd_search.py","file_name":"cmd_search.py","file_ext":"py","file_size_in_byte":3918,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"16439771481","text":"import struct\nimport sys\nimport glob\nimport argparse\nimport datetime\n\n\nclass packet(object):\n \"\"\" Holds data for one 256-byte packet from the raw (numerical\n extension) RESmart data files. Also methods for parsing the data \"\"\"\n\n def __init__(self,start, pbuf):\n \"\"\" given pbuf list of bytes, parse out the data\"\"\"\n\n assert len(pbuf) >= 256\n\n # length of data fields (uint16_t), not counting zero pads and timestamp\n self.dlen = 106\n\n # extract timestamp\n self.parse_timestamp(pbuf)\n\n # extract data fields\n self.parse_data(pbuf)\n\n # generate human-readable labels\n self.setup_labels()\n\n if self.data[self.known_fields[\"spO2_pct\"]] > 0:\n self.has_pulse = True\n else:\n self.has_pulse = False\n \n\n def setup_labels(self):\n \"\"\" These are human-readable labels for the headers of .csv files\"\"\"\n # set up data structure for field labels\n self.timestamp_fields = [\"year\",\"month\",\"day\",\"hour\",\"minute\",\"second\",\"?\"]\n # default label is \"?\" for unknown data field\n self.data_fields = [\"?\" for i in range(self.dlen)]\n \n # first 85 fields are 25 Hz and 10 Hz measurements of pressure/flow\n for i in range(25):\n self.data_fields[4 + i ] = \"resA\".format(i)\n self.data_fields[4 + i + 25] = \"resB\".format(i)\n self.data_fields[4 + i + 50] = \"resC\".format(i)\n if i < 10:\n self.data_fields[4 + i + 75] = \"pulse\".format(i)\n \n # some data fields are known, label them\n self.known_fields = {\n \"Reslex\":1,\n \"IPAP\":2,\n \"EPAP\":3,\n \"tidal_vol\":99,\n \"spO2_pct\":102,\n \"HR_BPM\":103,\n \"rep_rate\":104}\n \n for key, val in self.known_fields.items():\n self.data_fields[val] = key\n \n def parse_timestamp(self,pbuf):\n \"\"\" the timestamp is the last 8 bytes of every packet\"\"\"\n self.timestamp = struct.unpack(\"HBBBBBB\",pbuf[256-8:256])\n\n # for readability and convenience, parse out individual fields.\n self.year = self.timestamp[0]\n self.month = self.timestamp[1]\n self.day = self.timestamp[2]\n self.hour = int(self.timestamp[3])\n self.minute = self.timestamp[4]\n self.second = self.timestamp[5]\n \n self.date = datetime.date(self.year,self.month,self.day)\n self.ordinal = self.date.toordinal()\n self.datestr = self.date.isoformat()\n\n def parse_data(self, pbuf):\n \"\"\" extract all 16-bit uint16_t data (dlen words) from the packet\"\"\"\n self.data = []\n assert 2*self.dlen < len(pbuf)\n for i in range(self.dlen):\n ptr = 2*i # uint16_t, 2-byte unsigned integers\n val = struct.unpack(\"H\",pbuf[ptr:ptr+2])\n self.data.append(val[0])\n\n def get_known_header_csv(self):\n # text descriptions of known values\n outstr = \"\"\n for key, val in self.known_fields.items():\n outstr += \"{}, \".format(key)\n return outstr\n\n def get_known_values_csv(self):\n # print only understood values\n outstr = \"\"\n for key, val in self.known_fields.items():\n outstr += \"{}, \".format(self.data[val])\n return outstr\n\n def get_all_values_csv(self):\n # print all data values whether we know what they are or not\n outstr = \"\"\n for i in range(1,3):\n outstr += \"{}, \".format(self.data[i])\n for i in range(89,self.dlen):\n outstr += \"{}, \".format(self.data[i])\n return outstr\n\n def get_all_values_header_csv(self):\n # print all data values whether we know what they are or not\n outstr = \"\"\n for i in range(1,3):\n outstr += \"{}, \".format(self.data_fields[i])\n for i in range(89,self.dlen):\n outstr += \"{}, \".format(self.data_fields[i])\n return outstr\n\n def fix_csv(self, csv_str):\n # remove trailing spaces & comma, add newline \n csv_str = csv_str.strip()\n if csv_str[-1] == ',':\n csv_str = csv_str[0:-1]\n return csv_str \n\n def get_time_ymd_header_csv(self):\n # return time string in year, month, day format\n return \",\".join(self.timestamp_fields)\n\n def get_time_ymd_csv(self):\n # return time string in year, month, day format\n outstr = \"\"\n for i in self.timestamp:\n outstr += \"{:d}, \".format(i)\n \n return outstr\n \n def get_time_seconds(self):\n return self.second + 60*self.minute + 3600*(self.hour + 24*(self.ordinal))\n def get_10hz_csv(self, i):\n return \"{}, \".format(self.data[4 + 75 + i])\n\n def get_25hz_csv(self, i):\n #These fields are arrays of 25 values/second, something to do with\n # respiration. Last one is cleanest -- filtered?\n outstr = \"{:d}, {:d}, {:d}, \".format(self.data[4 + i],\n self.data[4 + 25 + i],\n self.data[4 + 50 + i])\n return outstr\n\n \n\n#### methods for a collection of packets\ndef s2HMS(seconds):\n # should refactor this with datetime\n #return a string giving hours minutes seconds from seconds\n hours = int(seconds/3600.)\n minutes = int((seconds%3600)/60.)\n return\"{:02d}:{:02d}\".format(hours,minutes)\n\ndef get_day_info(packets):\n # given a list of packets, return a symbolic string of contents\n # one char per hour, '.' if no data, '+' if flow data, '0' if heartrate\n # should refactor this by date\n hstr = \"\"\n pptr = 0\n\n datestr = packets[0].datestr\n hour = -1\n # 24 chars, one for each hour, for graphical representation of data\n hstr = [\".\" for i in range(24)]\n infostr = \"\"\n daysecs = 0\n has_pulse = False\n for p in packets:\n\n daysecs += 1\n if p.has_pulse:\n has_pulse = True\n\n if p.hour > hour:\n hour = p.hour\n if has_pulse:\n hstr[hour] = 'O'\n has_pulse = False\n else: \n hstr[hour] = '+'\n\n if p.datestr != datestr:\n infostr += (datestr + \" \" + \"\".join(hstr) + \\\n \" {}\\n\".format(s2HMS(daysecs)))\n hstr = [\".\" for i in range(24)]\n daysecs = 0\n hour = -1\n datestr = p.datestr\n\n infostr += (datestr + \" \" + \"\".join(hstr) + \\\n \" {}\\n\".format(s2HMS(daysecs)))\n return infostr\n\n######################## main program starts here\n\nif sys.version_info.major < 3:\n print(\"sorry, requires Python 3.\")\n exit(1)\n\n\nparser = argparse.ArgumentParser(\n description='Extract data from BMC RESmart raw data files')\n\nparser.add_argument('--info','-i',\n action='store_true',\n help='Prints readable summary of data and dates to stdout.' )\n\nparser.add_argument('--f25_hz','-2',\n action='store_true',\n help='Print out all 25 Hz (flow) data. this will make output files 25x as big.' )\n\nparser.add_argument('--f10_hz','-1',\n action='store_true',\n help='Print out all 10 Hz (pulse) data. this will make output files 10x as big.' )\n\nparser.add_argument('--all_data','-a',\n action='store_true',\n help='Print out all 1Hz data fields known or unknown' )\n\nparser.add_argument('--time_ymd','-y',\n action='store_true',\n help='Print timestamp in Y, M, D, H, M, S format')\n\nparser.add_argument('--time_seconds','-s',\n action='store_true',\n help='Print timestamp in seconds since beginning of year')\n\nparser.add_argument('--quiet','-q',\n action='store_true',\n help='Do not print progress and info to stderr')\n\nparser.add_argument('output_file', nargs = '?', \n help='Output data CSV file, careful will overwrite existing data.',\n default=\"RESmart_data.csv\")\n\nparser.add_argument('--dates', '-d', nargs = '+', \n help='select date range in YYYY-MM-DD format. Single date is one day, two dates are start and end of time range.',\n default=[])\n\nargs = parser.parse_args()\n\n\n\nstart_date = None\nend_date = None\nif len(args.dates) > 0:\n try:\n start_date = datetime.datetime.strptime(args.dates[0], '%Y-%m-%d').date()\n except ValueError:\n raise ValueError(\"Incorrect -d date format, should be YYYY-MM-DD\")\n exit()\n\nif len(args.dates) > 1:\n try:\n end_date = datetime.datetime.strptime(args.dates[1], '%Y-%m-%d').date()\n except ValueError:\n raise ValueError(\"Incorrect -d date format, should be YYYY-MM-DD\")\n exit()\n\n\n# Should probably ensure these files all have the same root...\nfilesNNN = glob.glob('*.[0-9][0-9][0-9]')\nfilesNNN.sort()\n\npackets = []\nthispacket = None\npacketsize = 256\n\n\nfor datafile in filesNNN:\n with open(datafile, \"rb\") as f:\n databuff = f.read()\n\n oldday = -1\n p = 0 # pointer into byte array\n while p < (len(databuff) - packetsize):\n #print(i)\n\n val = int(struct.unpack(\"H\",databuff[p:p+2])[0])\n thispacket = packet(p, databuff[p:p+packetsize])\n p += packetsize\n packets.append(thispacket)\n\n if thispacket.day != oldday and not args.quiet:\n if thispacket.date == start_date:\n print(\"Found start date {}.\".format(start_date.isoformat()))\n oldday = thispacket.day\n print(\"reading data from {}\".format(thispacket.datestr))\n\nif not args.quiet:\n print(\"{:d} packets found in {} files\".format(len(packets), len(filesNNN)))\n \n\nif args.info:\n print(get_day_info(packets))\n\n\nif start_date is None:\n #default to beginning of data\n start_date = packets[0].date\nelse:\n if end_date is None:\n # if only start date is specified, use only that date\n end_date = start_date\n\nif end_date is None:\n #default to end of data\n end_date = packets[-1].date \n\n#print(start_date)\n#print(end_date)\n\nwith open(args.output_file, 'w') as outf:\n # read data \n \n day = -1\n for i, p in enumerate(packets):\n\n if p.date >= start_date and p.date <= end_date:\n\n\n if p.ordinal != day and not args.quiet:\n day = p.ordinal\n print(\"Writing {} data to {}\".format(p.datestr,\n args.output_file))\n\n outstr = \"\"\n if args.time_seconds:\n outstr += \"{}, \".format(p.get_time_seconds())\n\n if args.time_ymd:\n outstr += p.get_time_ymd_csv()\n\n if args.all_data:\n outstr += p.get_all_values_csv() \n else: \n outstr += p.get_known_values_csv()\n\n if args.f10_hz:\n for i in range(10):\n frac_sec = float(p.get_time_seconds()) + float(i)/10.\n tstr = \"{:.2f}, \".format(frac_sec)\n tstr += p.get_10hz_csv(i)\n outf.write(p.fix_csv(outstr + tstr) + \"\\n\") \n\n elif args.f25_hz:\n for i in range(25):\n frac_sec = float(p.get_time_seconds()) + float(i)/25.\n tstr = \"{:.2f}, \".format(frac_sec)\n tstr += p.get_25hz_csv(i)\n outf.write(p.fix_csv(outstr + tstr) + \"\\n\") \n else:\n outf.write(p.fix_csv(outstr) + \"\\n\") \n\n\n\n\n\n\n","repo_name":"headrotor/BMC_RESmart","sub_path":"resmart_parse.py","file_name":"resmart_parse.py","file_ext":"py","file_size_in_byte":11673,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"4708267385","text":"\"\"\"\n JOB SCRAPER for ARMANINO\n\n Created: 2020-12-03\n Modified: 2021-08-16\n Author: Israel Dryer\n\"\"\"\nfrom random import random\nimport wsl.webscraper as ws\nfrom wsl.datatools import DataTools, ConnectionString\n\nCONN_STRING = ConnectionString.build()\nINSERT_QUERY = \"INSERT INTO jobs.RawJobData VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?)\"\n\n\nclass JobScraper(ws.WebScraper):\n \"\"\"webscraper for Armanino jobs\"\"\"\n\n def __init__(self):\n super().__init__(name='Armanino')\n\n def extract_card_data(self, card):\n pass\n\n def extract_page_urls(self, _):\n url = \"https://careers-armaninollp.icims.com/jobs/search?ss=1&hashed=-435620445&in_iframe=1\"\n last_count = 0\n while True:\n soup = self.get_request(url, out_format='soup')\n for tag in soup.find_all('a', 'iCIMS_Anchor'):\n self.urls_to_scrape.add(tag['href'])\n url = soup.find('span', text='Next page of results').parent['href']\n if len(self.urls_to_scrape) == last_count:\n break\n last_count = len(self.urls_to_scrape)\n\n def extract_page_data(self, url):\n soup = self.get_request(url, out_format='soup', use_session=True)\n try:\n json_data = ws.json.loads(soup.find('script', {'type': 'application/ld+json'}).contents[0])\n except AttributeError:\n return # The job url has expired\n\n job_id = req_id = soup.find(\"span\", text=\"Job ID\").find_next_sibling().text.strip()\n title = json_data['title']\n city = json_data['jobLocation'][0]['address']['addressLocality']\n state = json_data['jobLocation'][0]['address']['addressRegion']\n country = json_data['jobLocation'][0]['address']['addressCountry']\n category = json_data['occupationalCategory']\n description = ws.BeautifulSoup(json_data['description'], 'lxml').text.replace('\\xa0', ' ')\n record_id = '170-' + self.today + str(job_id) + str(req_id)\n\n self.data_scraped.append([\n record_id, self.today, job_id, req_id, self.name, title,\n category, \"\", city, state, country, description, url])\n\n def run(self):\n self.create_session()\n self.extract_page_urls(None)\n\n for url in self.urls_to_scrape:\n self.extract_page_data(url)\n ws.sleep(random())\n\n if self.data_scraped:\n DataTools.save_to_database(self.data_scraped, CONN_STRING, INSERT_QUERY)\n print(f\"{self.name} >> {len(self.data_scraped)} records\")\n\n\nif __name__ == '__main__':\n print(\"Starting...\")\n scraper = JobScraper()\n scraper.run()\n print(\"Finished.\")\n","repo_name":"israel-dryer/PyJobScraper","sub_path":"acct_job_scrape/armanino.py","file_name":"armanino.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28950781320","text":"import re\nimport typing\n\n\ndef parse_input(data: str) -> typing.Tuple[typing.Dict[int, str], typing.List[str]]:\n rules, messages = data.strip().split(\"\\n\\n\")\n rules_dict = _parse_rules(rules)\n return rules_dict, messages.splitlines()\n\n\ndef _parse_rules(rules: str) -> typing.Dict[int, str]:\n rules_dict = {}\n\n pattern = r\"^(\\d+): ([0-9 |]+|\\\"[a-z]\\\")$\"\n for raw_rule in rules.splitlines():\n id_, rule_content = re.match(pattern, raw_rule.strip()).groups()\n rules_dict[int(id_)] = rule_content\n\n return rules_dict\n\n\ndef get_matching_patterns(\n key: int,\n raw_rules_dict: typing.Dict[int, str],\n current_matching_patterns: typing.Set[str] = None,\n) -> typing.Set[str]:\n value = raw_rules_dict[key]\n current_matching_patterns = current_matching_patterns or set()\n new_matching_patterns = set()\n\n if re.match(r\"^\\\"[a-z]\\\"$\", value):\n return set(value.strip('\"'))\n\n for or_section in value.split(\"|\"):\n for key in or_section.split():\n new_matching_patterns.update(get_matching_patterns(int(key), raw_rules_dict, current_matching_patterns))\n\n return new_matching_patterns\n","repo_name":"matthewelwell/adventofcode2020","sub_path":"utils/day19/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13425481386","text":"import random\nimport sys\nimport traceback\n\nimport cosim\nimport vfbdb\n\nWRITE_FIFO_PATH = sys.argv[1]\nREAD_FIFO_PATH = sys.argv[2]\n\ntry:\n iface = cosim.Iface(WRITE_FIFO_PATH, READ_FIFO_PATH)\n\n Main = vfbdb.Main(iface)\n\n subblocks = [Main.Blk0, Main.Blk1, Main.Blk1.Blk2]\n\n for i, sb in enumerate(subblocks):\n print(f\"Testing access to blk{i}\")\n r = random.randrange(0, 2 ** 32 - 1)\n print(f\"Writing value {r} to cfg register\")\n sb.Cfg.write(r)\n\n print(f\"Reading Cfg register\")\n read = sb.Cfg.read()\n assert read == r, f\"Read wrong value from Cfg register {read}\"\n\n print(f\"Reading St register\")\n read = sb.St.read()\n assert read == r, f\"Read wrong value from St register {read}\"\n\n iface.end(0)\n\nexcept Exception as E:\n iface.end(1)\n print(traceback.format_exc())\n","repo_name":"Functional-Bus-Description-Language/go-vfbdb","sub_path":"tests/co-simulations/subblock/0/python/tb.py","file_name":"tb.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21772521795","text":"import re\nimport unicodedata\nfrom urllib.parse import urlsplit, urlunsplit\nfrom urllib.parse import quote, unquote\n\n\ndef normalize_url(url, preferred_scheme='http', charset='utf-8'):\n def _clean(string):\n string = unquote(string)\n return unicodedata.normalize('NFC', string).encode('utf-8')\n\n default_port = {\n 'http': 80,\n 'https': 443,\n }\n\n if 'twitter' in url:\n url = url.replace('www.', '')\n\n if url[:4] == 'http' and preferred_scheme == 'https':\n url = url[7:]\n\n # if there is no scheme use http as default scheme\n if url[0] not in ['/', '-'] and ':' not in url[:7]:\n url = preferred_scheme + '://' + url\n\n # splitting url to useful parts\n scheme, auth, path, query, fragment = urlsplit(url.strip())\n (userinfo, host, port) = re.search('([^@]*@)?([^:]*):?(.*)', auth).groups()\n\n # Always provide the URI scheme in lowercase characters.\n scheme = scheme.lower()\n\n # Always provide the host, if any, in lowercase characters.\n host = host.lower()\n if host and host[-1] == '.':\n host = host[:-1]\n # take care about IDN domains\n host = host.encode(\"idna\").decode(\"utf-8\")\n\n # Only perform percent-encoding where it is essential.\n # Always use uppercase A-through-F characters when percent-encoding.\n # All portions of the URI must be utf-8 encoded NFC from Unicode strings\n path = quote(_clean(path), \"~:/?#[]@!$&'()*+,;=\")\n fragment = quote(_clean(fragment), \"~\")\n\n # note care must be taken to only encode & and = characters as values\n query = \"&\".join([\n \"=\".join([quote(_clean(t), \"~:/?#[]@!$'()*+,;=\") for t in q.split(\"=\", 1)]) for q in query.split(\"&\")\n ])\n\n # Prevent dot-segments appearing in non-relative URI paths.\n if scheme in [\"\", \"http\", \"https\", \"ftp\", \"file\"]:\n output = []\n for part in path.split('/'):\n if part == \"\":\n if not output:\n output.append(part)\n elif part == \".\":\n pass\n elif part == \"..\":\n if len(output) > 1:\n output.pop()\n else:\n output.append(part)\n if part in [\"\", \".\", \"..\"]:\n output.append(\"\")\n path = '/'.join(output)\n\n # For schemes that define a default authority, use an empty authority if\n # the default is desired.\n if userinfo in [\"@\", \":@\"]:\n userinfo = \"\"\n\n # For schemes that define an empty path to be equivalent to a path of \"/\",\n # use \"/\".\n if path == \"\" and scheme in [\"http\", \"https\", \"ftp\", \"file\"]:\n path = \"/\"\n\n # For schemes that define a port, use an empty port if the default is\n # desired\n if port and scheme in default_port.keys():\n if port.isdigit():\n port = str(int(port))\n if int(port) == default_port[scheme]:\n port = ''\n\n # Put it all back together again\n auth = (userinfo or \"\") + host\n if port:\n auth += \":\" + port\n if url.endswith(\"#\") and query == \"\" and fragment == \"\":\n path += \"#\"\n return urlunsplit((scheme, auth, path, query, fragment))\n","repo_name":"0x216/Hoop-Libaries","sub_path":"hoop/util/normalization.py","file_name":"normalization.py","file_ext":"py","file_size_in_byte":3150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74637300882","text":"from django.urls import path\n\nfrom . import views\n\napp_name = 'main'\n\nurlpatterns = [\n path('', views.allProdcat, name='allProdcat'),\n path('/', views.allProdcat, name='hackathonCategory'),\n path('//', views.hackathonList, name='hackathonList'),\n path('addcomment/', views.addcomment, name='addcomment'),\n]\n","repo_name":"it5prasoon/OrganiseHackathonsDjango","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"35398017304","text":"from typing import Any, List, Tuple\nimport numpy as np\nimport h5py\nimport tensorflow.keras as keras\nfrom ..encoder import get_encoder_by_name\nfrom .. import goboard\nfrom .. import utils\nfrom .base import Agent\nfrom .helpers import is_point_an_eye\nfrom ..rl.experience import ExperienceBuffer, ExperienceCollector\n\n__all__ = [\n 'ValueAgent',\n 'load_value_agent',\n]\n\n\nclass ValueAgent(Agent):\n\n def __init__(self,\n model: keras.Model,\n encoder: Any,\n policy: str = 'eps-greedy') -> None:\n Agent.__init__(self)\n \"\"\"\n 参数:\n model: 模型\n encoder: 编码器\n policy: eps-greedy | weighted\n eps-greedy epsilon-greedy的方式选择动作\n weighted 将values转换成概率, 然后抽样得到动作\n \"\"\"\n self.model = model\n self.encoder = encoder\n self.collector = None # ExperienceCollector\n self.temperature = 0.0 # Epsilon - Greedy的参数\n self.policy = policy\n self.last_move_value = 0\n\n def predict(self, game_state: goboard.GameState) -> np.ndarray:\n \"\"\"\n 预测一个状态的价值\n\n 返回:\n output: [1, ]\n \"\"\"\n encoded_state = self.encoder.encode(game_state)\n input_tensor = np.array([encoded_state])\n return self.model.predict(input_tensor)[0]\n\n def set_temperature(self, temperature: float) -> None:\n self.temperature = temperature\n\n def set_collector(self, collector: ExperienceCollector) -> None:\n \"\"\"\n 设置经验收集器\n \"\"\"\n self.collector = collector\n\n def set_policy(self, policy: str) -> None:\n if policy not in ('eps-greedy', 'weighted'):\n raise ValueError(policy)\n self.policy = policy\n\n def select_move(self, game_state: goboard.GameState) -> goboard.Move:\n \"\"\"\n 选择一个Move\n \n 参数:\n game_state: 游戏状态\n\n 返回:\n move: 动作\n \"\"\"\n\n moves = [] # list of int\n board_tensors = [\n ] # list of [num_planes, board_height, board_width], 每个元素是不同的\n # 遍历所有合法的动作\n for move in game_state.legal_moves():\n if not move.is_play:\n continue\n next_state = game_state.apply_move(move)\n # board_tensor.shape [num_planes, board_height, board_width]\n board_tensor = self.encoder.encode(next_state)\n moves.append(move)\n board_tensors.append(board_tensor)\n if not moves:\n return goboard.Move.pass_turn()\n\n num_moves = len(moves)\n # board_tensors.shape [num_moves, num_planes, board_height, board_width]\n board_tensors = np.array(board_tensors)\n\n # 要注意:\n # 这是对方视角的value, 并不是自己的value\n # opp_values.shape [num_moves, 1]\n opp_values = self.model.predict(board_tensors)\n # opp_values.shape [num_moves, ]\n opp_values = opp_values.reshape(num_moves)\n\n # 转换为自己的values\n # values.shape [num_moves, ]\n values = 1 - opp_values\n\n if self.policy == 'eps-greedy':\n ranked_moves = self.rank_moves_eps_greedy(values)\n elif self.policy == 'weighted':\n ranked_moves = self.rank_moves_weighted(values)\n else:\n ranked_moves = None\n\n for move_idx in ranked_moves:\n move = moves[move_idx]\n if not is_point_an_eye(game_state.board, move.point,\n game_state.next_player):\n # 选出来的动作不能是自己���眼\n if self.collector is not None:\n self.collector.record_decision(\n state=board_tensor,\n action=self.encoder.encode_point(move.point),\n )\n self.last_move_value = float(values[move_idx])\n return move\n return goboard.Move.pass_turn()\n\n def rank_moves_eps_greedy(self, values: List[float]) -> List[int]:\n \"\"\"\n epsilon-greedy的方式\n\n 参数:\n values: [num_moves, ]\n\n 返回:\n ranked_moves: [num_moves, ] 从大到小排序后的索引\n \"\"\"\n if np.random.random() < self.temperature:\n # 随机动作\n values = np.random.random(values.shape)\n # 挑选最优的动作\n ranked_moves = np.argsort(values)\n return ranked_moves[::-1]\n\n def rank_moves_weighted(self, values: List[float]) -> List[int]:\n \"\"\"\n 将values转换成概率, 然后根据概率大小采样\n\n 参数:\n values: [num_moves, ]\n\n 返回:\n ranked_moves: [num_moves, ] 从大到小排序后的索引\n \"\"\"\n # 将values转换成概率: p\n # p.shape [num_moves, ]\n p = values / np.sum(values)\n p = np.power(p, 1.0 / self.temperature)\n p = p / np.sum(p)\n return np.random.choice(np.arange(0, len(values)),\n size=len(values),\n p=p,\n replace=False)\n\n def train(self,\n experience: ExperienceBuffer,\n lr: float = 0.1,\n batch_size: int = 128) -> None:\n \"\"\"\n 用experience的数据来训练模型, 训练一个epoch\n\n 对experience的数据只迭代一个epoch, 不会重复利用experience的数据\n \"\"\"\n opt = keras.optimizers.SGD(lr=lr)\n # 直接使用MSE误差, 因为我们要学习的是一个连续值\n self.model.compile(loss='mse', optimizer=opt)\n\n n = experience.states.shape[0]\n # y.shape [n, ]\n y = np.zeros((n, ))\n for i in range(n):\n reward = experience.rewards[i]\n y[i] = 1 if reward > 0 else 0\n\n self.model.fit(experience.states, y, batch_size=batch_size, epochs=1)\n\n def serialize(self, h5file: h5py.File) -> None:\n \"\"\"\n 保存Agent(也就是保存Encoder信息 & model)到h5file\n\n h5file\n / \\\n encoder(group) model(group)\n |\n kerasmodel(group)\n\n >>> with h5py.File(output_file, 'w') as out_f:\n >>> agent.serialize(out_f)\n \"\"\"\n # 保存encoder信息(根据这些信息可以重构出encoder)\n h5file.create_group('encoder')\n h5file['encoder'].attrs['name'] = self.encoder.name()\n h5file['encoder'].attrs['board_width'] = self.encoder.board_width\n h5file['encoder'].attrs['board_height'] = self.encoder.board_height\n # 保存model\n h5file.create_group('model')\n utils.save_model_to_hdf5_group(self.model, h5file['model'])\n\n def diagnostics(self) -> Tuple[str, float]:\n return {'value': self.last_move_value}\n\n\ndef load_value_agent(h5file: h5py.File) -> ValueAgent:\n \"\"\"\n 从h5file中加载Agent\n\n h5file\n / \\\n encoder(group) model(group)\n |\n kerasmodel(group)\n \"\"\"\n # 加载model\n model = utils.load_model_from_hdf5_group(h5file['model'])\n encoder_name = h5file['encoder'].attrs['name']\n if not isinstance(encoder_name, str):\n encoder_name = encoder_name.decode('ascii')\n board_width = h5file['encoder'].attrs['board_width']\n board_height = h5file['encoder'].attrs['board_height']\n\n # 加载encoder\n encoder = get_encoder_by_name(encoder_name, (board_width, board_height))\n return ValueAgent(model, encoder)","repo_name":"relaxdl/relaxml","sub_path":"relaxml/relaxgo/agent/value.py","file_name":"value.py","file_ext":"py","file_size_in_byte":7659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15702399439","text":"## file: derivative_test.py\n##\n## Program to compare derivative approximation methods\n##\n## Programmer: Chrissy Schumm schummch@msu.edu\n##\n## Revision history:\n## 04/27/21 Original Code Created by Chrissy\n##\n## Notes:\n## * Based on the discussion of differentiation in Chap. 8\n## of \"Computational Physics\" by Landau and Paez.\n#*****************************************************************\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# define 3 derivative methods at the top, so h can be easily found and changed\ndef forward_diff(f,a,method='forward',h=10**-7.92):\n if method == 'forward':\n return (f(a + h) - f(a))/(h)\n\ndef central_diff(f,a,method='central',h=10**-4.91):\n if method == 'central':\n return (f(a + h/2) - f(a - h/2))/(h)\n\ndef extrap_diff(f,a,method='extrapolated',h= 10**-2.51):\n if method == 'extrapolated':\n return ( 8*(f(a + h/4) - f(a - h/4)) - (f(a + h/2) - f(a - h/2)) ) / (3*h)\n\n#************************** main program ***************************\n\nx = np.linspace(0,20,50) # use 50 evenly distributed x-values between 0 and 20\nf = lambda x: np.exp(-x) # This is our test function : e^-x (same one we used in class)\ny = f(x)\n\nfdydx = forward_diff(f,x) #find derivative of e^-x using forward difference method\ncdydx = central_diff(f, x) #find derivative of e^-x using central difference method\nedydx = extrap_diff(f, x) #find derivative of e^-x using extrapolated difference method\n\nexact = -np.exp(-x) #This is our exact value of the derivative we will use for comparison\n\n#************************** Plot the graph ***************************\nplt.figure(figsize=(12,5))\nplt.plot(x,y,label='y=f(x)')\nplt.plot(x,fdydx,'r.', label=\"Forward Difference y=f'(x)\")\nplt.plot(x,cdydx,'s', label=\"Central Difference y=f'(x)\")\nplt.plot(x,edydx,'p', label=\"Extrapolated Difference y=f'(x)\")\nplt.plot(x, exact, 'b', label= 'Exact')\nplt.title('Derivative Methods of y= e^-x')\nplt.xlabel('x value to take derivative')\nplt.ylabel('Derivative Value')\nplt.legend()\nplt.grid(True)\nplt.show()\n\n\n","repo_name":"schummch/PHY_905","sub_path":"Final Project/derivative_test.py","file_name":"derivative_test.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27985036280","text":"# 平方根取整问题\n# 输入一个整数, 返回其平方根的整数部分\n# 复杂度log(n)\ndef calculateSqrt(num):\n low = 0\n high = num\n # 0, 1, 2, 3, 4, 5, 6, 7, 8, 9\n while low < high:\n mid = (high - low) // 2 + low\n # [0, 1, 2, 3, 4] | 5, 6, 7, 8, 9\n if mid * mid > num:\n high = mid\n # 0, 1, 2, 3, 4 | [5, 6, 7, 8, 9]\n elif mid * mid < num:\n low = mid + 1\n else:\n return mid\n return high - 1\n\nprint(calculateSqrt(25))\n","repo_name":"Canadasunyan/codes","sub_path":"028-平方根取整.py","file_name":"028-平方根取整.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"24530745414","text":"#!/usr/bin/env python\nimport re\nimport os\nimport sys\nimport json\nimport time\nimport codecs\nimport signal\nimport requests\nfrom bs4 import BeautifulSoup\nfrom collections import deque\n\nqueue = deque([], 3)\nscript_name = \"Fast cli\"\n\n\nclass bcolors:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n FAIL = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\n\ndef display_title(title):\n print(title + \" by \" + bcolors.OKBLUE + \"@Kensai\" + bcolors.ENDC)\n\n\nclass ArgumentParser:\n def __init__(self, cmd_name, options):\n self.name = cmd_name\n self.options = options\n self.optional = {}\n self.args = []\n self.count = 0\n\n def parse(self):\n if len(sys.argv) == 1:\n return self.help(self.options)\n nextArg = \"\"\n for i in range(len(sys.argv)):\n elt = sys.argv[i]\n if elt[:2] == \"--\":\n self.optional[elt[2:]] = []\n nextArg = elt[2:]\n elif elt[0] == \"-\":\n self.optional[elt[1:]] = []\n nextArg = elt[1:]\n elif elt != sys.argv[0]:\n if nextArg == \"\":\n self.args.append(elt)\n else:\n self.optional[nextArg].append(elt)\n current = self.args[self.count]\n if current in self.options.keys():\n options = self.options[current]\n else:\n return self.help(self.options)\n while True:\n self.count += 1\n if type(options) is dict:\n if current in options.keys():\n current = self.args[self.count]\n options = options[current]\n else:\n if len(self.args) <= self.count:\n return self.help(options)\n else:\n current = self.args[self.count]\n if current in options.keys():\n options = options[current]\n else:\n return self.help(options)\n else:\n args = self.args[self.count:]\n if len(args) != 0 and args[0] == \"help\":\n return self.help(options)\n cmd = [script_name, self.name] + self.args[:self.count]\n if type(options[-1]) is str:\n return self.help(options)\n cmd_return = options[-1](*args, **self.optional)\n if cmd_return is None:\n return\n elif type(cmd_return) is str:\n print(\n bcolors.WARNING + \"Command {0} for {1} failed...\".format(\" \".join(cmd), \" \".join(args)))\n print(cmd_return)\n print(bcolors.ENDC)\n print(\"To get help: {0} help\".format(\" \".join(cmd)))\n return\n else:\n return self.help(options)\n\n def help(self, options):\n self.subs = self.args[:self.count]\n display_title(\"Fast cli\")\n if type(options) is dict:\n if not \"help\" in options.keys():\n options[\"help\"] = \"This message\"\n print(\"Usage: {0} {2}{{{1}}}\".format(\n self.name, \"|\".join(options.keys()), \" \".join(self.subs)))\n else:\n args = []\n for elt in options[1:]:\n if type(elt) != str:\n break\n if \"<\" == elt[0]:\n args.append(elt)\n if \"[--\" == elt[:3]:\n break\n print(\"Usage: {0} {1} {3} {2}\".format(script_name,\n self.name, \" \".join(args), \" \".join(self.subs)))\n print(\" {0}\".format(options[0]))\n if \" [--\" in \" \".join(options[:-1]):\n print()\n print(\"Options:\")\n length = 0\n for elt in options:\n if type(elt) != str:\n break\n if \"[--\" in elt:\n to_print = \" {0}\".format(\n elt.replace(\"[\", \"\").replace(\"]\", \"\"))\n length = len(to_print)+1\n print(to_print)\n elif length != 0:\n if elt[0] == \"<\" and elt[-1] == \">\":\n print(f\"\\033[F\\033[{length}G \"+elt)\n length += len(elt)+1\n else:\n print(\" {0}\".format(elt))\n return\n else:\n return\n print()\n print(\" Options\")\n maxsize = max([len(cmd) for cmd in options.keys()])\n if maxsize < 4:\n maxsize = 4\n for cmd, option in options.items():\n if type(option) is dict:\n print(\" {0}:{1}{2}\".format(\n cmd, (maxsize-len(cmd)+1) * \" \", option[\"help\"][0]))\n elif type(option) is list and cmd != \"help\":\n print(\" {0}:{1}{2}\".format(\n cmd, (maxsize-len(cmd)+1) * \" \", option[0]))\n print(\" help:{0}This message\".format((maxsize-3) * \" \"))\n print()\n print(\"For more usage info type: {0}{1} subcommand help\".format(\n self.name, \" \".join(self.subs)))\n\n\nclass Killer:\n now = False\n\n def __init__(self):\n signal.signal(signal.SIGINT, self.exit)\n\n def exit(self, signum, frame):\n self.now = True\n quit()\n\n\ndef quit():\n sys.stdout.write(\"\\x1b[2K\")\n sys.stdout.write(\"\\n\")\n sys.stdout.write(\"Program exits on user demand\\n\")\n sys.exit()\n\n\nkiller = Killer()\n\n\ndef get_path(path):\n return os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), \"../data\", path))\n\n\ndef printing(a, b, c):\n if killer.now:\n quit()\n\n for _ in range(len(queue)):\n sys.stdout.write(\"\\x1b[1A\\x1b[2K\")\n\n queue.append(a)\n queue.append(b)\n queue.append(c)\n\n for i in range(len(queue)):\n sys.stdout.write(queue[i] + \"\\n\")\n\n\ndef report_progress(job, current, total, name):\n\n progress_percent = current / total * 100\n total_percent = int((current / total) * 100)\n\n progress = \"Total progress: [{1:33}] {0:.2f}%\".format(progress_percent, \"#\" * int(progress_percent / 3),\n total_percent)\n job_spacing = len(progress) - len(str(total)) - \\\n len(str(current)) - len(job) - 1\n job_running = \"{0}{1}{2}/{3}\".format(job,\n \" \" * job_spacing, current, total)\n\n process = \"Processing:{0}{1}\"\n name_spacing = len(progress) - len(process) - len(name) + 6\n process_name = process.format(\" \" * name_spacing, name)\n printing(job_running, progress, process_name)\n\n\ndef get_text(url):\n return requests.get(url).text\n\n\ndef get_json(url):\n return requests.get(url).json()\n\n\ndef get_soup(url):\n return BeautifulSoup(get_text(url), 'html.parser')\n\n\ndef load_unicode(filename):\n f = codecs.open(filename, encoding='ISO-8859-1')\n return repr(f.readline())\n\n\ndef load_soup(filename):\n with open(get_path(filename), \"r\") as f:\n cnt = f.read()\n return BeautifulSoup(cnt, 'html.parser')\n\n\ndef load_json(filename):\n with open(get_path(filename), \"r\") as f:\n cnt = f.read()\n return json.loads(cnt)\n\n\ndef dump_json(filename, obj):\n json_string = json.dumps(obj, sort_keys=True, ensure_ascii=False)\n dump_file(filename, json_string)\n\n\ndef load_file(filename, strip=True):\n with open(get_path(filename), \"r\", encoding='utf-8') as f:\n cnt = f.readlines()\n if not strip:\n return [x.replace(\"\\n\", \"\") for x in cnt]\n return [x.strip() for x in cnt]\n\n\ndef dump_file(filename, string):\n with open(get_path(filename), \"w\") as f:\n f.write(string)\n\n\ndef arg_parse(cmd_name, options):\n ArgumentParser(cmd_name, options).parse()\n","repo_name":"Andrflor/fast_cli","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9377832844","text":"from turtle import Turtle\nimport random\n\nCOLORS = [\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"purple\"]\nSTARTING_MOVE_DISTANCE = 5\nMOVE_INCREMENT = 10\nLEVEL_UP_SPEED = 1\nCAR_SPAWN_RATE_CHANCE = 10\nCAR_START_POSITIONS = [-210, -200, -160, -120, -80, -40, 0, 40, 80, 120, 160, 200, 210]\n\n\nclass CarManager(Turtle):\n def __init__(self):\n super().__init__()\n self.hideturtle()\n self.all_cars = []\n self.car_speed = STARTING_MOVE_DISTANCE\n self.car_spawn_rate = CAR_SPAWN_RATE_CHANCE\n\n def create_car(self):\n random_chance = random.randint(1, self.car_spawn_rate)\n if random_chance == 1:\n new_car = Turtle(\"square\")\n new_car.penup()\n random_y_pos = random.choice(CAR_START_POSITIONS)\n new_car.color(random.choice(COLORS))\n new_car.shapesize(stretch_wid=1.25, stretch_len=3)\n new_car.goto(300, random_y_pos)\n self.all_cars.append(new_car)\n\n def car_movement(self):\n for car in self.all_cars:\n car.backward(self.car_speed)\n\n def level_up(self):\n self.car_speed += LEVEL_UP_SPEED\n if CAR_SPAWN_RATE_CHANCE > 3:\n self.car_spawn_rate -= 1\n","repo_name":"Reynbra/Portfolio","sub_path":"python/TurtleFrogger/car_manager.py","file_name":"car_manager.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6310205337","text":"dx = [0,1,0,-1]\ndy = [1,0,-1,0]\n\ndef check(i,j,cnt, s):\n global maxv, start\n for k in range(4):\n x, y = i + dx[k], j + dy[k]\n if 0 <= x < N and 0 <= y < N and room[x][y] == room[i][j] + 1:\n cnt += 1\n check(x,y, cnt, s)\n return\n if cnt > maxv:\n maxv = cnt\n start = s\n elif cnt == maxv and s < start:\n start = s\n\nT = int(input())\nfor tc in range(1, T+1):\n N = int(input())\n room = [list(map(int, input().split())) for _ in range(N)]\n maxv = 1\n start = (N * N) + 1\n for i in range(N):\n for j in range(N):\n if room[i][j] < start or room[i][j] > start + maxv - 1 :\n check(i,j,1, room[i][j])\n print(f'#{tc} {start} {maxv}')","repo_name":"judong93/TIL","sub_path":"algorithm/0824~/0826/정사각형 방.py","file_name":"정사각형 방.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43152996780","text":"import rospy\nimport rosbag\nimport argparse\nimport shutil\nimport os\n\ndef reorder_bag(bagfile):\n orig = os.path.splitext(bagfile)[0] + \".orig.bag\"\n shutil.move(bagfile, orig)\n with rosbag.Bag(bagfile, 'w') as outbag:\n for topic, msg, t in rosbag.Bag(orig).read_messages():\n if msg._has_header:\n outbag.write(topic, msg, msg.header.stamp)\n else:\n outbag.write(topic, msg, t)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Reorder a bagfile based on header timestamps.')\n parser.add_argument('bagfile', help='input bag file')\n args = parser.parse_args()\n reorder_bag(args.bagfile)\n\n","repo_name":"raphaelchang/omni_slam_eval","sub_path":"scripts/misc/reorder_bag.py","file_name":"reorder_bag.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"38772347925","text":"# -*- coding: utf-8 -*-\r\n\r\nclass Graaf:\r\n\r\n def __init__(self, maatriks):\r\n self.maatriks = maatriks\r\n \r\n def on_seos(self, i, j):\r\n return bool(self.maatriks[i][j])\r\n\r\n\r\n def kaal(self, i, j):\r\n if self.maatriks[i][j] != 0:\r\n return self.maatriks[i][j]\r\n else:\r\n return None\r\n\r\n def astak(self, tipp):\r\n \"\"\"\r\n Tagastab tipu astaku:\r\n sealt väjuvate seoste arvu\r\n \"\"\"\r\n return sum(self.maatriks[tipp])\r\n\r\n def tippude_arv(self):\r\n return len(self.maatriks)\r\n\r\n def teepikkus(self, marsruut):\r\n \"\"\"\r\n Tagastab tippude loendi 'marsruut' teepikkuse.\r\n Kui marsruut pole korrektne, siis None.\r\n \"\"\"\r\n if len(marsruut) < 2:\r\n return 0\r\n tulem = 0\r\n for alg in range(len(marsruut)-1):\r\n lopp = alg + 1\r\n k = self.kaal(marsruut[alg], marsruut[lopp])\r\n print(alg, lopp, marsruut[alg], marsruut[lopp], k)\r\n if k == None:\r\n return None\r\n else:\r\n tulem = tulem + k\r\n return tulem\r\n\r\n\r\n def seotud_tipud(self, tipp):\r\n \"\"\"\r\n Tagastab tipuga seotud tippude loendi\r\n \"\"\"\r\n tulem = []\r\n tippude_arv = len(self.maatriks)\r\n for i in range(tippude_arv):\r\n if self.on_seos(tipp, i):\r\n tulem.append(i)\r\n return tulem\r\n \r\n \r\n def tee(self, alg, lopp, senine_tee=[]):\r\n \"\"\"\r\n Tagastab mingi suvalise tee tipust alg tippu lopp\r\n \"\"\"\r\n seotud = self.seotud_tipud(alg)\r\n uus_tee = senine_tee + [alg]\r\n if lopp in seotud:\r\n return uus_tee + [lopp]\r\n else:\r\n tee_jargmisest = None\r\n for jargmine in seotud:\r\n if not jargmine in senine_tee:\r\n tee_jargmisest = self.tee(jargmine, lopp, uus_tee)\r\n if tee_jargmisest != None:\r\n return tee_jargmisest\r\n \r\n \r\n \r\n def uuenda_kaugused(self, valitu, kylastatud, kaugused):\r\n \"\"\"\r\n Uuenda sõnastikku kaugused nende teedega, mis lisavad valitud tipuni\r\n viivale teele ühe kaare(seose) külastamata tipuni, juhul kui leitud\r\n tee on lühem.\r\n \"\"\"\r\n valitu_kaugus, valitu_tee = kaugused[valitu]\r\n for tipp in self.seotud_tipud(valitu):\r\n if not tipp in kylastatud:\r\n kaugus_labi_valitu = valitu_kaugus + self.kaal(valitu, tipp)\r\n senine_kaugus, senine_tee = kaugused.get(tipp, (10**100, []))\r\n if kaugus_labi_valitu < senine_kaugus:\r\n kaugused[tipp] = (kaugus_labi_valitu, valitu_tee + [tipp])\r\n\r\n\r\n def lyhima_teega_tipp(self, kylastatud, kaugused):\r\n \"\"\"\r\n Lühima teega külastamata tipp sõnastikust kaugused\r\n None, kui ei ole külastamata tippu, milleni viib tee\r\n \"\"\"\r\n senine_lyhim = 10**100 # Lõpmatus\r\n tulem = None\r\n for tipp in kaugused:\r\n if not tipp in kylastatud:\r\n teepikkus, tee = kaugused[tipp]\r\n if teepikkus < senine_lyhim:\r\n senine_lyhim = teepikkus\r\n tulem = tipp\r\n return tulem\r\n\r\n \r\n def lyhim_tee(self, alg):\r\n \"\"\"\r\n Dijkstra algoritm\r\n Tagastab sõnastiku, mis sisaldab lühimaid teid graafi algtipust\r\n kõikidesse teistesse tippudesse, kuhu algtipust saab jõuda.\r\n Sõnastiku formaat: {tipu_indeks: (kaugus, [tee]),...}\r\n \"\"\"\r\n kaugused = {alg: (0, [alg])} # Tulem\r\n kylastatud = []\r\n valitu = alg\r\n # Kuni on veel teid, mis viivad külastamata tippudesse\r\n while valitu != None:\r\n self.uuenda_kaugused(valitu, kylastatud, kaugused)\r\n kylastatud.append(valitu)\r\n valitu = self.lyhima_teega_tipp(kylastatud, kaugused)\r\n return kaugused\r\n\r\n def __repr__(self):\r\n \"\"\"\r\n Objekti esitus stringina\r\n \"\"\"\r\n return \"\\n\".join([str(x) for x in self.maatriks])\r\n\r\n\r\ndef graaf_failist(fn):\r\n \"\"\"\r\n Loeb failist nimega fn graafi seosemaatriksi.\r\n Seosemaatriksi read peavad olema failis eraldatud reavahetustega.\r\n Elemendid peavad olema eraldatud komadega.\r\n \"\"\"\r\n f = open(fn)\r\n tulem = []\r\n for failirida in f.readlines():\r\n tulemirida = []\r\n for seoseStr in failirida.split(\",\"):\r\n tulemirida.append(int(seoseStr))\r\n tulem.append(tulemirida)\r\n return Graaf(tulem)\r\n\r\ndef suurim_astak(fn):\r\n \"\"\"\r\n Tagastab suurima astakuga tipu indeksi graafile, \r\n mille seosemaatriks on kirjeldatud failis nimega fn.\r\n \"\"\"\r\n graaf = graaf_failist(fn)\r\n seni_suurim_astak = -1\r\n for tipu_indeks in range(tippude_arv(graaf)):\r\n if astak(graaf, tipu_indeks) > seni_suurim_astak:\r\n seni_suurim_astak = astak(graaf, tipu_indeks)\r\n tulem = tipu_indeks\r\n return tulem\r\n \r\n\r\n\r\n\r\n \r\n \r\nif __name__ == \"__main__\":\r\n # Positiivne täisarv näitab seose olemasolu ja kaalu\r\n \r\n suhete_graaf = [[0, 10, 20, 0],\r\n [10, 0, 2, 0],\r\n [20, 2, 0, 0],\r\n [0, 0, 0, 0]]\r\n linnade_graaf = [[0, 200, 0, 200, 270],\r\n [200, 0, 100, 0, 0],\r\n [0, 100, 0, 0, 250],\r\n [200, 0, 0, 0 ,0],\r\n [270, 0, 250, 0, 0]]\r\n\r\n #print(tee(linnade_graaf, 3, 2))\r\n g = Graaf(suhete_graaf)\r\n print(g.lyhim_tee(1))\r\n print(g.seotud_tipud(1))\r\n linnaindeksid = {\"Tallinn\": 0,\r\n \"Kohtla-Jarve\": 1,\r\n \"Narva\": 2,\r\n \"Parnu\": 3,\r\n \"Tartu\": 4}\r\n\r\n def on_tee(linn1, linn2):\r\n return on_seos(linnade_graaf, linnaindeksid[linn1], linnaindeksid[linn2])\r\n\r\n #print(on_tee(\"Tallinn\", \"Kohtla-Jarve\"))\r\n #print(suurim_astak(\"linnadegraaf.txt\"))\r\n graaf2 = graaf_failist(\"linnadegraaf.txt\")\r\n print(graaf2)\r\n","repo_name":"NickOvt/Python-Projects","sub_path":"graaf_klassina.py","file_name":"graaf_klassina.py","file_ext":"py","file_size_in_byte":6128,"program_lang":"python","lang":"et","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73683379282","text":"import yaml\r\n\r\nfrom qgis.core import Qgis, QgsCoordinateReferenceSystem, QgsCoordinateTransform, QgsMessageLog, QgsPointXY, QgsProject\r\nfrom qgis.utils import iface\r\n\r\n\r\n# noinspection PyBroadException\r\ndef point2wgs84(point: QgsPointXY):\r\n \"\"\"\r\n Converts the coordinates of a point to WGS84\r\n\r\n :rtype: QgsPointXY\r\n :param point: A qgis.core.QgsPointXY object representing the point to convert\r\n :return: A qgis.core.QgsPointXY object representing the point in WGS84\r\n \"\"\"\r\n try:\r\n crs = iface.mapCanvas().mapRenderer().destinationCrs()\r\n except:\r\n crs = iface.mapCanvas().mapSettings().destinationCrs()\r\n\r\n t = QgsCoordinateReferenceSystem()\r\n t.createFromSrid(4326)\r\n f = crs\r\n\r\n try:\r\n transformer = QgsCoordinateTransform(f, t)\r\n except:\r\n transformer = QgsCoordinateTransform(f, t, QgsProject.instance())\r\n try:\r\n pt: QgsPointXY = transformer.transform(point)\r\n except:\r\n pt: QgsPointXY = transformer.transform(QgsPointXY(point))\r\n return pt\r\n\r\n\r\ndef dict2object(d: dict):\r\n \"\"\"\r\n Converts a dictionary to an object\r\n\r\n :rtype: new\r\n :param d: Dictionary to convert\r\n :return: The converted object\r\n \"\"\"\r\n top = type('new', (object,), d)\r\n seqs = tuple, list, set, frozenset\r\n for i, j in d.items():\r\n if isinstance(j, dict):\r\n setattr(top, i, dict2object(j))\r\n elif isinstance(j, seqs):\r\n setattr(top, i,\r\n type(j)(dict2object(sj) if isinstance(sj, dict) else sj for sj in j))\r\n else:\r\n setattr(top, i, j)\r\n return top\r\n\r\n\r\ndef load_config(config_file: str):\r\n \"\"\"\r\n Loads the yaml file as object\r\n\r\n :rtype: new\r\n :param config_file: The path to the yaml file\r\n :return: The object\r\n \"\"\"\r\n with open(config_file, 'r') as f:\r\n data_map = yaml.safe_load(f)\r\n return dict2object(data_map)\r\n\r\n\r\ndef save_config(config: dict, config_file: str):\r\n \"\"\"\r\n Saves the dict as a yaml file\r\n\r\n :rtype: None\r\n :param config: The dict to save\r\n :param config_file: The path to the yaml file\r\n \"\"\"\r\n with open(config_file, 'w') as f:\r\n yaml.dump(config, f)\r\n\r\n\r\ndef get_log_level(level):\r\n if level == 'info':\r\n lev = Qgis.Info\r\n elif level == 'warning':\r\n lev = Qgis.Warning\r\n elif level == 'critical':\r\n lev = Qgis.Critical\r\n elif level == 'success':\r\n lev = Qgis.Success\r\n else:\r\n lev = Qgis.Info\r\n return lev\r\n\r\n\r\ndef log_message(*msg, level: str = 'info'):\r\n \"\"\"\r\n Writes a message to the QGIS message log\r\n\r\n :rtype: None\r\n :param msg: The message / parts of the message to write\r\n :param level: The level that the message should have.
Can be 'info', 'warning', 'critical', 'success' or None\r\n \"\"\"\r\n message = ''\r\n for text in msg:\r\n message += '{0!s} '.format(text)\r\n QgsMessageLog.logMessage(message=message, level=get_log_level(level))\r\n\r\n\r\ndef message_bar(*msg, level: str = 'info'):\r\n \"\"\"\r\n Creates a new qgis message bar\r\n\r\n :rtype: None\r\n :param msg: The message / parts of the message to write\r\n :param level: The level that the message should have.
Can be 'info', 'warning', 'critical', 'success' or None\r\n \"\"\"\r\n message = ''\r\n for text in msg:\r\n message += '{0!s} '.format(text)\r\n iface.messageBar().pushMessage(text=message, level=get_log_level(level))\r\n\r\n\r\ndef rgb2abgr(rgb: str):\r\n \"\"\"\r\n Converts a rgb color (#rrggbb) to a google kml abgr (aabbggrr) color\r\n\r\n :rtype: str\r\n :param rgb: The rgb color code (with #)\r\n :return: The abgr color code (without #)\r\n \"\"\"\r\n abgr = 'ff'\r\n abgr += rgb[5:7]\r\n abgr += rgb[3:5]\r\n abgr += rgb[1:3]\r\n return abgr\r\n\r\n\r\nclass LayerNotFoundError(BaseException):\r\n \"\"\"An exception for a not-found layer\"\"\"\r\n pass\r\n\r\n\r\nclass FileNotFound(BaseException):\r\n \"\"\"An exception for a not-found file\"\"\"\r\n pass\r\n","repo_name":"bennetrr/graveyard","sub_path":"loeschwasser/loeschwasser-plugin/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4725217076","text":"import json\nimport FrontEnd.structure_alpha as Q\nimport time\nimport simple_functions_for_fit as sm\nimport copy\nimport numpy as np\nfrom simple_functions_for_fit import notice_positions\n\nscaleLimit = 5\nInf = 0.05\nwildInf = 0.015\n\n#rg = __import__(\"reel generator\")\n\ndef freq(obj, out):#генерируем начальную ленту. 1 аргумент - структура игры, 2 аргумент - структура с нужными параметрами hit rate\n average = out.total_length\n for i in obj.base.scatterlist:\n average -= out.scatter_index_with_frequency[i]\n average = average // (len(obj.base.symbol) - len(obj.base.scatterlist))\n tmp = [out.scatter_index_with_frequency[i] if i in obj.base.scatterlist else average for i in range(len(obj.base.symbol))]\n j = 0\n while sum(tmp) < out.total_length:\n if j not in obj.base.scatterlist:\n tmp[j] += 1\n j += 1\n frequency = []\n\n for i in range(obj.window[0]):\n frequency.append(copy.deepcopy(tmp))\n\n if sum(tmp) != out.total_length:\n print('ERROR')\n return -1\n\n return frequency\n\nclass point(object):\n def __init__(self, frequency, gametype):\n self.frequency = notice_positions(frequency, gametype)\n self.baseReel = '-1'\n self.freeReel = '-1'\n self.base_rtp = '-1'\n self.rtp = '-1'\n self.sdnew = '-1'\n self.hitrate = '-1'\n self.value = '-1'\n def fillVal(self, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew):\n self.value = F(base_rtp, rtp, sdnew, self.base_rtp, self.rtp, self.sdnew, err_base_rtp, err_rtp, err_sdnew)\n def scaling(self, scale=2):\n for i in range(len(self.frequency)):\n for j in range(len(self.frequency[i])):\n self.frequency[i][j] = scale*self.frequency[i][j]\n return self\n def fillPoint(self, obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew):\n\n n_symbols = len(obj.base.symbol)\n for symbol_id in range(n_symbols):\n for reel_id in range(obj.window[0]):\n #print(obj.base.symbol[symbol_id].position)\n if reel_id in obj.base.symbol[symbol_id].position:\n continue\n else:\n self.frequency[reel_id][symbol_id] = 0\n\n #print('frequency: ')\n #for i in range(obj.window[0]):\n # print(self.frequency[i])\n #print('\\n\\n\\n')\n\n obj.base.reel_generator(self.frequency, obj.window[0], obj.window[1])\n obj.free.reel_generator(self.frequency, obj.window[0], obj.window[1])\n obj.base.fill_frequency(self.frequency)\n obj.free.fill_frequency(self.frequency)\n\n obj.base.fill_count_killed(obj.window[0])\n #obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.base.fill_simple_num_comb(obj.window, obj.line)\n obj.base.fill_scatter_num_comb(obj.window)\n\n obj.free.fill_count_killed(obj.window[0])\n #obj.free.create_simple_num_comb(obj.window, obj.line)\n obj.free.fill_simple_num_comb(obj.window, obj.line)\n obj.free.fill_scatter_num_comb(obj.window)\n\n self.baseReel = obj.base.reels\n self.freeReel = obj.free.reels\n\n params = obj.count_parameters()\n\n self.base_rtp, self.rtp, self.sdnew, self.hitrate = params['base_rtp'], params['rtp'], params['sdnew'], params['hitrate']\n\n self.fillVal(base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew)\n\n def printBaseReel(self, file):\n max_length = 0\n file = file[:file.find('\\\\') + 1] + \"Base Reels\\\\\" + file[file.find('\\\\') + 1:]\n f = open(file, 'w')\n for l in range(len(self.baseReel)):\n if len(self.baseReel[l]) > max_length:\n max_length = len(self.baseReel[l])\n for i in range(max_length):\n s = ''\n for l in range(len(self.baseReel)):\n if i < len(self.baseReel[l]):\n t = (15 - len(self.baseReel[l][i].name))*' '\n s = s + self.baseReel[l][i].name + t\n else:\n s = 15*' '\n f.write(s + '\\n')\n f.write('\\n')\n #print('\\n')\n #print(s)\n f.close()\n\n\n\n#Класс окрестность. Аргументы: root - центр окрестности, obj - Юрина структура, base_rtp - параметр введенный пользователем, нужен для того, что бы\n#не обсчитывать всю окрестность, previous - окрестность на предыдущем шаге итерации, по дефолту предполагается фиктивной (для начальной точки).\n#Аргумент previous нужне для того, что бы на новой итерации не учитывать точки, отброшенные на предыдущей.\n\n\nclass neighbourhood(object):\n def __init__(self, root, obj, base_rtp, previous = 0):\n prev = []\n if previous != 0:\n prev = [previous.neighbour[i].frequency for i in range(len(previous.neighbour))]\n self.neighbour = []\n self.root = root\n if base_rtp > root.base_rtp:\n for i in range(len(obj.base.symbol) - 1):\n for j in range(i + 1, len(obj.base.symbol)):\n if not obj.base.symbol[i].scatter and not obj.base.symbol[j].scatter:\n if obj.base.symbol[i].payment[obj.window[0]] > obj.base.symbol[j].payment[obj.window[0]]:\n tmp = copy.deepcopy(root.frequency[0])\n tmp[i] += 1\n tmp[j] -= 1\n if tmp[i] > (1/obj.window[1])*sum(tmp) or tmp[j] < 0.05*sum(tmp):\n print('ABSOLUTELY DEGENERATE')\n break\n frequency = [copy.deepcopy(tmp) for i in range(obj.window[0])]\n if frequency not in prev:\n self.neighbour.append(point(frequency, obj.base))\n elif obj.base.symbol[i].payment[obj.window[0]] < obj.base.symbol[j].payment[obj.window[0]]:\n tmp = copy.deepcopy(root.frequency[0])\n tmp[j] += 1\n tmp[i] -= 1\n if tmp[j] > (1/obj.window[1])*sum(tmp) or tmp[i] < 0.05*sum(tmp):\n print('ABSOLUTELY DEGENERATE')\n break\n frequency = [copy.deepcopy(tmp) for i in range(obj.window[0])]\n if frequency not in prev:\n self.neighbour.append(point(frequency, obj.base))\n elif base_rtp < root.base_rtp:\n for i in range(len(obj.base.symbol) - 1):\n for j in range(i + 1, len(obj.base.symbol)):\n if not obj.base.symbol[i].scatter and not obj.base.symbol[j].scatter:\n if obj.base.symbol[i].payment[obj.window[0]] < obj.base.symbol[j].payment[obj.window[0]]:\n #print('C')\n tmp = copy.deepcopy(root.frequency[0])\n tmp[i] += 1\n tmp[j] -= 1\n if tmp[i] > (1/obj.window[1])*sum(tmp) or tmp[j] < 0.05*sum(tmp):\n print('ABSOLUTELY DEGENERATE')\n break\n frequency = [copy.deepcopy(tmp) for i in range(obj.window[0])]\n if frequency not in prev:\n self.neighbour.append(point(frequency, obj.base))\n #print('1st +1, sec -1', tmp, i, j)\n\n elif obj.base.symbol[i].payment[obj.window[0]] > obj.base.symbol[j].payment[obj.window[0]]:\n #print('D')\n tmp = copy.deepcopy(root.frequency[0])\n tmp[j] += 1\n tmp[i] -= 1\n if tmp[j] > (1/obj.window[1])*sum(tmp) or tmp[i] < 0.05*sum(tmp):\n print('ABSOLUTELY DEGENERATE')\n break\n frequency = [copy.deepcopy(tmp) for i in range(obj.window[0])]\n if frequency not in prev:\n self.neighbour.append(point(frequency, obj.base))\n #print('1st -1, sec +1', tmp, i, j)\n\n def fillParametrs(self, obj, base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew):\n for i in range(len(self.neighbour)):\n obj.base.reel_generator(self.neighbour[i].frequency, obj.window[0], obj.window[1])\n obj.free.reel_generator(self.neighbour[i].frequency, obj.window[0], obj.window[1])\n obj.base.fill_frequency(self.neighbour[i].frequency)\n obj.free.fill_frequency(self.neighbour[i].frequency)\n\n obj.base.fill_count_killed(obj.window[0])\n #obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.base.fill_simple_num_comb(obj.window, obj.line)\n obj.base.fill_scatter_num_comb(obj.window)\n\n obj.free.fill_count_killed(obj.window[0])\n #obj.free.create_simple_num_comb(obj.window, obj.line)\n obj.free.fill_simple_num_comb(obj.window, obj.line)\n obj.free.fill_scatter_num_comb(obj.window)\n\n self.neighbour[i].baseReel = obj.base.reels\n self.neighbour[i].freeReel = obj.free.reels\n\n\n params = obj.count_parameters()\n\n self.neighbour[i].base_rtp, self.neighbour[i].freemean, self.neighbour[i].rtp, self.neighbour[i].sdnew, self.neighbour[i].hitrate = params['base_rtp'], params['freemean'], params['rtp'], params['sdnew'], params['hitrate']\n\n self.neighbour[i].fillVal(base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n def neighbourVal(self):\n for i in range(len(self.neighbour)):\n print(self.neighbour[i].value, self.neighbour[i].frequency)\n def findMin(self):\n tmp_val = 100000\n tmp_ind = -1\n for i in range(len(self.neighbour)):\n if self.neighbour[i].value < tmp_val:\n tmp_val = self.neighbour[i].value\n tmp_ind = i\n print(tmp_val)\n return tmp_ind\n\n\ndef parametrs(file_name, out):\n file = open(file_name, 'r')\n j = file.read()\n\n interim = json.loads(j)\n\n obj = Q.Game(interim)\n\n distr = freq(obj, out)\n\n start_time = time.time()\n\n obj.base.reel_generator(distr, obj.window[0], obj.window[1])\n obj.free.reel_generator(distr, obj.window[0], obj.window[1])\n obj.base.fill_frequency(distr)\n obj.free.fill_frequency(distr)\n\n obj.base.fill_count_killed(obj.window[0])\n obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.base.fill_simple_num_comb(obj.window, obj.line)\n obj.base.fill_scatter_num_comb(obj.window)\n\n obj.free.fill_count_killed(obj.window[0])\n obj.free.create_simple_num_comb(obj.window, obj.line)\n obj.free.fill_simple_num_comb(obj.window, obj.line)\n obj.free.fill_scatter_num_comb(obj.window)\n\n root = point(distr, obj.base)\n root.baseReel = obj.base.reels\n root.freeReel = obj.free.reels\n\n params = obj.count_parameters()\n\n root.base_rtp, root.rtp, root.sdnew, root.hitrate = params['base_rtp'], params['rtp'], params['sdnew'], params['hitrate']\n\n roots = [root]\n\n roots = roots + initialDistributions(obj, out)\n print(time.time() - start_time)\n\n return distr, params['base_rtp'], params['rtp'], params['sdnew'], params['hitrate'], obj, roots\n\n\ndef initialDistributions(obj, out):\n t = [0 for j in range(len(obj.base.symbol))]\n numb_of_scatters = 0\n for j in range(len(obj.base.scatterlist)):\n t[obj.base.scatterlist[j]] = out.scatter_index_with_frequency[obj.base.scatterlist[j]]\n numb_of_scatters += t[obj.base.scatterlist[j]]\n num = (len(obj.base.symbol) - len(obj.base.scatterlist) - 1)#число доступных символов\n\n Sup = 1/obj.window[1]\n wildSup = 1/obj.window[1]\n\n\n initial = []\n for i in range(len(obj.base.symbol)):\n temp = copy.deepcopy(t)\n temp_ = copy.deepcopy(t)\n if not obj.base.symbol[i].scatter:\n if not obj.base.symbol[i].wild:\n temp[i] = int(Inf*out.total_length) + 1\n temp_[i] = int(Sup*out.total_length) - 1\n else:\n temp[i] = int(wildInf*out.total_length) + 1\n temp_[i] = int(wildSup*out.total_length) - 1\n total = out.total_length - sum(temp)\n total_ = out.total_length - sum(temp_)\n avg = total // num\n avg_ = total_ // num\n for j in range(len(obj.base.symbol)):\n if j not in obj.base.scatterlist and j != i:\n temp[j] = avg\n temp_[j] = avg_\n counter = 0\n for j in range(len(obj.base.symbol)):\n if j not in obj.base.scatterlist and j != i:\n temp[j] += 1\n counter += 1\n if counter >= total % num:\n break\n counter = 0\n for j in range(len(obj.base.symbol)):\n if j not in obj.base.scatterlist and j != i:\n temp_[j] += 1\n counter += 1\n if counter >= total_ % num:\n break\n temp2 = [temp for j in range(obj.window[0])]\n temp_2 = [temp_ for j in range(obj.window[0])]\n initial = initial + [point(temp2, obj.base),point(temp_2, obj.base)]\n\n for i in initial :\n i.baseReel = obj.base.reels\n i.freeReel = obj.free.reels\n\n params = obj.count_parameters()\n\n i.base_rtp, i.rtp, i.sdnew, i.hitrate = params['base_rtp'], params['rtp'], params['sdnew'], params['hitrate']\n #print(i.frequency[0], sum(i.frequency[0]))\n return initial\n\ndef F(base_rtp, rtp, sdnew, r_base_rtp, r_rtp, r_sdnew, err_base_rtp, err_rtp, err_sdnew):\n t1 = np.fabs(base_rtp - r_base_rtp)/err_base_rtp\n t2 = np.fabs(rtp - r_rtp)/err_rtp\n# t3 = np.fabs(sdnew - r_sdnew)/err_sdnew\n return max([t1])\n\n\n\n#1-ый метод. Работает в терминах окрестностей. Стартовой точкой является центральная - это та, в которой всех символов ~ поровну.\n#Минус метода в том, что при добавлении sd функционал F имеет множество локальных минимумов\n\ndef FirstMethod(hitrate, err_hitrate, file_name):\n\n #file_name = 'Games\\HappyBrauer.txt'\n #hitrate = 100\n\n out = sm.get_scatter_frequency(file_name, hitrate, err_hitrate)\n\n t_distr, r_base_rtp, r_freemean, r_rtp, r_sdnew, r_hitrate, obj, root = parametrs(file_name, out)\n\n print(root.frequency)\n\n\n\n #s = input()\n\n #s = s.split(', ')\n\n base_rtp, freemean, rtp, sdnew = 1.4, 1.5, 1.5, 6#float(s[0]), float(s[1]), float(s[2])\n err_base_rtp, err_freemean, err_rtp, err_sdnew = 0.1, 0.1, 0.1, 0.1\n\n print('assuming errors for base rtp, freemean, sd ', base_rtp, freemean, rtp, sdnew)\n print('assuming base rtp, freemean, sd ', err_base_rtp, err_freemean, err_rtp, err_sdnew)\n\n #s = input()\n\n #s = s.split(', ')\n\n #float(s[0]), float(s[1]), float(s[2])\n\n root.fill_point_metric(base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n print('root value ', root.value)\n\n print('assuming base_rtp, freemean, rtp, sdnew ', base_rtp, freemean, rtp, sdnew)\n\n print('assuming errors for base_rtp, freemean, rtp, sdnew ', err_base_rtp, err_freemean, err_rtp, err_sdnew)\n\n print(F(base_rtp, freemean, rtp, sdnew, r_base_rtp, r_freemean, r_rtp, r_sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew))\n\n #print(t_distr)\n\n\n\n neighbours = neighbourhood(root, obj, base_rtp)\n\n print('neigh', neighbours.neighbour[0].frequency)\n print(len(neighbours.neighbour))\n print(root.frequency)\n\n obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.free.create_simple_num_comb(obj.window, obj.line)\n\n neighbours.fillParametrs(obj, base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n neighbours.neighbourVal()\n\n counter = 0\n counter2 = 0\n\n while 1:\n print('AAA')\n tmp_neighbours = neighbours\n if tmp_neighbours.root.value < 1:\n print('000')\n print('minimum ', tmp_neighbours.root.value, tmp_neighbours.root.frequency)\n break\n tmp_min = neighbours.neighbour[neighbours.findMin()]\n\n while tmp_min.value > tmp_neighbours.root.value:\n print('Уперлись')\n tmp_root = tmp_neighbours.root.scaling()\n print('ы')\n tmp_neighbours = neighbourhood(tmp_root, obj, base_rtp)\n print('ыы')\n tmp_neighbours.fillParametrs(obj, base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n print('ыыы')\n tmp_min = tmp_neighbours.neighbour[neighbours.findMin()]\n print('scaling № ', counter2 , ' value ', tmp_min.value)\n counter2 += 1\n\n\n\n print('CHECKING ', tmp_min.frequency, tmp_min.value)\n if tmp_min.value < 1:\n print('111')\n print('minimum ', tmp_min.value, tmp_min.frequency)\n break\n neighbours = neighbourhood(tmp_min, obj, base_rtp, previous=tmp_neighbours)\n neighbours.fillParametrs(obj, base_rtp, freemean, rtp, sdnew, err_base_rtp, err_freemean, err_rtp, err_sdnew)\n neighbours.neighbourVal()\n print('iteration ', counter, ' value ', tmp_min.value, ' distribution ', tmp_min.frequency)\n if counter > 500:\n print('222')\n break\n\n counter += 1\n\n#FirstMethod(100, 1,'Games\\HappyBrauer.txt')\n\n\nclass group:\n def __init__(self, root, num, sortedSymbols, obj, blocked_scatters=[]):\n self.root = root\n self.total = [sum(i) for i in root.frequency]\n\n self.groups = []\n for i in range(len(obj.base.symbol)):\n if obj.base.symbol[i].wild != False:\n self.groups.append([i])\n if obj.base.symbol[i].scatter and i not in blocked_scatters:\n self.groups.append([i])\n c = 0\n t_num = num - len(self.groups)\n k = len(sortedSymbols)//t_num\n for i in range(t_num):\n g = []\n if c < len(sortedSymbols)%t_num:\n for j in range(c, k + c + 1):\n g.append(sortedSymbols[i*k + j])\n c += 1\n else:\n for j in range(c, k + c):\n g.append(sortedSymbols[i*k + j])\n\n self.groups.append(g)\n\n\n\n distributions = []\n for i in range(num - 1):\n for j in range(i + 1, num):\n tmp1 = [copy.deepcopy(f) for f in root.frequency]\n tmp2 = [copy.deepcopy(f) for f in root.frequency]\n for l in range(obj.window[0]):\n for k in range(min(len(self.groups[i]), len(self.groups[j]))):\n tmp1[l][self.groups[i][k]] += 1\n tmp2[l][self.groups[j][k]] += 1\n tmp1[l][self.groups[j][k]] -= 1\n tmp2[l][self.groups[i][k]] -= 1\n if tmp1[l][self.groups[j][k]] < 0:\n tmp1[l][self.groups[j][k]] += 1\n tmp1[l][self.groups[i][k]] -= 1\n if tmp2[l][self.groups[i][k]] < 0:\n tmp2[l][self.groups[i][k]] += 1\n tmp2[l][self.groups[j][k]] -= 1\n\n for l in range(obj.window[0]):\n total_i_1 = 0\n total_i_2 = 0\n total_j_1 = 0\n total_j_2 = 0\n for k in self.groups[i]:\n total_i_1 += tmp1[l][k]\n total_i_2 += tmp2[l][k]\n for k in self.groups[j]:\n total_j_1 += tmp1[l][k]\n total_j_2 += tmp2[l][k]\n\n\n for k in self.groups[i]:\n tmp1[l][k] = total_i_1 // len(self.groups[i])\n tmp2[l][k] = total_i_2 // len(self.groups[i])\n for k in self.groups[j]:\n tmp1[l][k] = total_j_1 // len(self.groups[j])\n tmp2[l][k] = total_j_2 // len(self.groups[j])\n\n\n for k in range(total_i_1 % len(self.groups[i])):\n tmp1[l][self.groups[i][k]] += 1\n for k in range(total_i_2 % len(self.groups[i])):\n tmp2[l][self.groups[i][k]] += 1\n\n for k in range(total_j_1 % len(self.groups[j])):\n tmp1[l][self.groups[j][k]] += 1\n for k in range(total_j_2 % len(self.groups[j])):\n tmp2[l][self.groups[j][k]] += 1\n tmp1 = notice_positions(tmp1, obj.base)\n tmp2 = notice_positions(tmp2, obj.base)\n #print('tmp1: ', tmp1)\n #print('tmp2: ', tmp2)\n tmp1_ok = True\n tmp2_ok = True\n '''if flag:\n print('tmp1: ', tmp1)\n print('tmp2: ', tmp2)'''\n for l in range(obj.window[0]):\n for s in range(len(tmp1[l])):\n if s not in obj.base.scatterlist:\n if s not in obj.base.wildlist and s not in obj.base.ewildlist:\n if tmp1[l][s] == 0 and l not in obj.base.symbol[s].position:\n continue\n if tmp1[l][s] > (1/obj.window[1])*self.total[l] or tmp1[l][s] < Inf*self.total[l]:\n tmp1_ok = False\n break\n else:\n if tmp1[l][s] == 0 and l not in obj.base.symbol[s].position:\n continue\n if tmp1[l][s] > (1/obj.window[1])*self.total[l] or tmp1[l][s] < wildInf*self.total[l]:\n tmp1_ok = False\n break\n else:\n if tmp1[l][s] == 0 and l in obj.base.symbol[s].position:\n tmp1_ok = False\n break\n for s in range(len(tmp2[l])):\n if s not in obj.base.scatterlist:\n if s not in obj.base.wildlist and s not in obj.base.ewildlist:\n if tmp2[l][s] == 0 and l not in obj.base.symbol[s].position:\n continue\n if tmp2[l][s] > (1/obj.window[0])*self.total[l] or tmp2[l][s] < Inf*self.total[l]:\n tmp2_ok = False\n break\n else:\n if tmp2[l][s] == 0 and l not in obj.base.symbol[s].position:\n continue\n if tmp2[l][s] > (1/obj.window[1])*self.total[l] or tmp2[l][s] < wildInf*self.total[l]:\n tmp2_ok = False\n break\n else:\n if tmp2[l][s] == 0 and l in obj.base.symbol[s].position:\n tmp2_ok = False\n break\n #print(tmp1_ok, 'tmp1: ', tmp1)\n #print(tmp2_ok, 'tmp1: ', tmp2)\n if tmp1_ok:\n distributions = distributions + [tmp1]\n if tmp2_ok:\n distributions = distributions + [tmp2]\n\n\n #print('AAAA')\n self.points = [point(d, obj.base) for d in distributions]\n\n def fillGroup(self, obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew):\n for p in self.points:\n p.fillPoint(obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew)\n def printGroup(self):\n print('root ', self.root.frequency, ' root value ', self.root.value, ' root base rtp = ', self.root.base_rtp, ' rtp = ', self.root.rtp , ' sdnew = ', self.root.sdnew, ' hitrate = ', self.root.hitrate)\n for i in range(len(self.points)):\n print(self.points[i].frequency, 'total = ', sum(self.points[i].frequency[0]),' value ', self.points[i].value)\n def findMin(self):\n index = -1\n current = copy.deepcopy(self.root.value)\n for i in range(len(self.points)):\n if self.points[i].value < current:\n current = self.points[i].value\n index = i\n if index == -1:\n print('no minimum found')\n return -1\n else:\n return copy.deepcopy(self.points[index])\n\ndef sortSymbols(obj):\n sortedSymbols = []\n for i in range(len(obj.base.symbol)):\n if not obj.base.symbol[i].scatter and not obj.base.symbol[i].wild:\n sortedSymbols.append(i)\n #пузырьковая сортировка, не думаю, что стоит мудрить с другой\n for i in range(len(sortedSymbols) - 1):\n for j in range(i + 1, len(sortedSymbols)):\n if obj.base.symbol[sortedSymbols[i]].payment[obj.window[0]] < obj.base.symbol[sortedSymbols[j]].payment[obj.window[0]]:\n sortedSymbols[i], sortedSymbols[j] = sortedSymbols[j], sortedSymbols[i]\n return sortedSymbols\n\n\ndef double_bouble(a, b):\n for i in range(len(a) - 1):\n for j in range(i + 1, len(a)):\n if a[i] > a[j]:\n a[i], a[j] = a[j], a[i]\n b[i], b[j] = b[j], b[i]\n\n#Здесь происходит некоторая херня с hitrate.\n\ndef SecondMethod(hitrate, err_hitrate, file_name, base_rtp, err_base_rtp):\n file = open(file_name, 'r')\n j = file.read()\n interim = json.loads(j)\n game = Q.Game(interim)\n file.close()\n\n out = sm.get_scatter_frequency(file_name, hitrate, err_hitrate)\n if out == -1 and hitrate != -1:\n exit('no free spins')\n elif out == -1:\n\n out = sm.OutResult(game.base.scatterlist)\n out.add_symbols(game.base.symbol)\n blocked_scatters = []\n for scatter_id in game.base.scatterlist:\n if max(game.base.symbol[scatter_id].scatter) > 0:\n blocked_scatters.append(scatter_id)\n\n\n t_distr, r_base_rtp, r_rtp, r_sdnew, r_hitrate, obj, roots = parametrs(file_name, out)\n\n\n\n rtp, sdnew, err_rtp, err_sdnew = 0.9, 14, 0.01, 0.01\n\n print('INITIAL POINTS, THEIR DISTRIBUTIONS, VALUES AND PARAMETRES:')\n a = []\n # TODO когда-нибудь убрать slice из roots\n for root in roots[0:1]:\n root.fillPoint(obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew)\n root.fillVal(base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew)\n print(root.frequency, ' value is ', root.value,'base_rtp, rtp, sdnew, hitrate :', root.base_rtp, root.rtp, root.sdnew, root.hitrate)\n a = a + [root.value]\n b = [i for i in range(len(a))]\n double_bouble(a, b) # здесь массив b - массив индексов начальных точек, упорядоченных по значению F\n\n\n print('assuming base rtp, rtp, sd ', base_rtp, rtp, sdnew)\n print('assuming errors for base rtp, rtp, sd ', err_base_rtp, err_rtp, err_sdnew)\n\n b = [0] + b\n\n for i in b:\n print('TRYING POINT', roots[i].frequency, ' value is ', roots[i].value,'base_rtp, rtp, sdnew, hitrate :', roots[i].base_rtp, roots[i].rtp, roots[i].sdnew, roots[i].hitrate)\n\n root = roots[i]\n\n obj.base.create_simple_num_comb(obj.window, obj.line)\n obj.free.create_simple_num_comb(obj.window, obj.line)\n sortedSymbols = sortSymbols(obj)\n check = False\n min_is_found = False\n currentScale = 0\n while not min_is_found and currentScale < scaleLimit:\n number_of_groups = len(obj.base.wildlist) + len(obj.base.ewildlist) + 1 + \\\n len(obj.base.scatterlist) - len(blocked_scatters)\n while number_of_groups < len(sortedSymbols) + len(obj.base.wildlist) + len(obj.base.ewildlist) + 1 + \\\n len(obj.base.scatterlist) - len(blocked_scatters):\n #print('is zis e los?')\n temp_group = group(root, number_of_groups, sortedSymbols, obj, blocked_scatters)\n print('группы ', temp_group.groups)\n temp_group.fillGroup(obj, base_rtp, rtp, sdnew, err_base_rtp, err_rtp, err_sdnew )\n print('точки групп и значения в них')\n temp_group.printGroup()\n if temp_group.findMin() != -1:\n if temp_group.findMin().value < 1:\n min_is_found = True\n print('ending with ', temp_group.findMin().value)\n print('base ', temp_group.findMin().base_rtp)\n print('rtp ', temp_group.findMin().rtp)\n print('sdnew ', temp_group.findMin().sdnew)\n print('hitrate ', temp_group.findMin().hitrate)\n root = copy.deepcopy(temp_group.findMin())\n print(root.frequency)\n check = True\n temp_group.findMin().printBaseReel(file_name)\n break\n else:\n print('path ', temp_group.findMin().value)\n root = copy.deepcopy(temp_group.findMin())\n else:\n print(number_of_groups)\n number_of_groups += 1\n if number_of_groups > (len(obj.base.wildlist) + len(obj.base.ewildlist) + len(sortedSymbols) + 1\n + len(obj.base.scatterlist) - len(blocked_scatters))/2: # and number_of_groups < len(sortedSymbols):\n number_of_groups = len(sortedSymbols) + len(obj.base.wildlist) + len(obj.base.ewildlist) + 1 \\\n + len(obj.base.scatterlist) - len(blocked_scatters)\n #print(min_is_found)\n if not min_is_found:\n\n root = root.scaling()\n currentScale += 1\n print('SCALING ', currentScale)\n else:\n break\n if check:\n print('Блеск')\n break\n\n\n\n#SecondMethod(100, 1,'Games\\HappyBrauer.txt')\n\n\n\n","repo_name":"DmitryBol/reel","sub_path":"neighborhood.py","file_name":"neighborhood.py","file_ext":"py","file_size_in_byte":31142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14428041603","text":"from Utils.FileUtils import resource_path\nfrom Utils.CachingUtils import read_from_config, add_to_config\nfrom configparser import NoSectionError, NoOptionError\nfrom datetime import datetime\nimport pygame\npygame.mixer.init(48000)\n\n\nclass NotificationManager:\n def __init__(self, parent):\n self.parent = parent\n self.time_since_last_warning_tone = None\n self.time_since_last_alarm_tone = None\n self.time_since_last_reminder_tone = None\n\n def reset(self):\n self.time_since_last_warning_tone = None\n self.time_since_last_alarm_tone = None\n self.time_since_last_reminder_tone = None\n pygame.mixer.music.stop()\n\n @staticmethod\n def get_time_between_alarms():\n try:\n return int(read_from_config(\"SETTINGS\", \"time_between_alarms\"))\n except (NoOptionError, NoSectionError):\n add_to_config(\"SETTINGS\", \"time_between_alarms\", 60)\n return 60\n\n def interacted(self, user):\n if not self.check_warning(user) and not self.check_alarm(user):\n if user.user_clock.state:\n play_started_tone()\n return True\n if not user.user_clock.state:\n play_stopped_tone()\n\n def remind(self, user):\n if not user.user_clock.state:\n if self.time_since_last_reminder_tone is None:\n play_reminder_tone()\n self.time_since_last_reminder_tone = datetime.now()\n elif (datetime.now() - self.time_since_last_reminder_tone).total_seconds() > 240:\n play_reminder_tone()\n self.time_since_last_reminder_tone = datetime.now()\n return True\n\n def user_conditions(self, user):\n return user == self.parent.current_user and user.user_clock.state\n\n def check_warning(self, user):\n if self.user_conditions(user) and user.is_warning_time():\n if self.time_since_last_warning_tone is None:\n play_warning_tone()\n self.time_since_last_warning_tone = datetime.now()\n return True\n\n def check_alarm(self, user):\n if self.user_conditions(user) and user.is_time_up():\n if self.time_since_last_alarm_tone is None:\n play_alarm_tone()\n self.time_since_last_alarm_tone = datetime.now()\n elif (datetime.now() - self.time_since_last_alarm_tone).total_seconds() > self.get_time_between_alarms():\n play_alarm_tone(1)\n self.time_since_last_alarm_tone = datetime.now()\n return True\n\n\ndef play_warning_tone(loops=2):\n pygame.mixer.music.load(resource_path(\"Sounds/warning.mp3\"))\n pygame.mixer.music.play(loops)\n\n\ndef play_alarm_tone(loops=2):\n pygame.mixer.music.load(resource_path(\"Sounds/alarm.mp3\"))\n pygame.mixer.music.play(loops)\n\n\ndef play_reminder_tone(loops=2):\n pygame.mixer.music.load(resource_path(\"Sounds/reminder.mp3\"))\n pygame.mixer.music.play(loops)\n\n\ndef play_started_tone(loops=1):\n pygame.mixer.music.load(resource_path(\"Sounds/started.mp3\"))\n pygame.mixer.music.play(loops)\n\n\ndef play_stopped_tone(loops=1):\n pygame.mixer.music.load(resource_path(\"Sounds/stopped.mp3\"))\n pygame.mixer.music.play(loops)\n","repo_name":"smithbro2115/GameTimer","sub_path":"Notifications.py","file_name":"Notifications.py","file_ext":"py","file_size_in_byte":3247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23437655840","text":"#!/usr/bin/python3\n#***********************************************************************\n# FILES:\n# 09_PyGObject_GTK_Glade_Clock.py\n#\t\t\t\t glade/09_PyGObject_GTK_Glade_Clock.glade\n# \n# USAGE: \n#\t\t\t\t ./09_PyGObject_GTK_Glade_Clock.py\n#\t\t\t\tor\n#\t\t\t\t python3 09_PyGObject_GTK_Glade_Clock.py\n# \n# DESCRIPTION: This code consist of the following:\n# 1- Designed GTK + UI with Glade\n# 2- creates a modular code using a class to creating window widgets\n# 3- Practical use of signals, events, callbacks, \n# and functions concepts.\n#\t\t\t\t 4- Using GLib.timeout_add function\n#\n#\n# OPTIONS: ---\n# REQUIREMENTS: Python, GTK+, Glade\n# BUGS: ---\n# NOTES: PyGObject tutorial\n# AUTHOR: Mahdi Bahmani (www.itstorage.co)\n# ORGANIZATION: merdasco\n# CREATED: 06.06.2020\n# LAST EDITED: \n# REVISION: 1.1\n#**********************************************************************/\nimport gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk, GLib, GObject\nfrom datetime import datetime\n\n\n# class is used to manipulate window widgets\nclass MyWindow(Gtk.Window):\n\t\n\ttimeout_id = None\n\tcounter = 0\n\t# Initializer function\n\tdef __init__(self):\n\t\t\n\t\t# A Gtk.Builder is an auxiliary object that reads textual \n\t\t# descriptions of a user interface and instantiates the \n\t\t# described objects. \n\t\tbuilder = Gtk.Builder()\n\t\t\n\t\t# Import UI designed via Glade and connect signals\n\t\tbuilder.add_from_file('glade/09_PyGObject_GTK_Glade_Clock.glade')\n\t\tbuilder.connect_signals(self)\n\t\t\n\t\t# The functions builder.get_object() can be used to access \n\t\t# the widgets in the interface by the names assigned to them \n\t\t# inside the UI description. \n\t\twindow = builder.get_object('window1')\n\t\tself.label1 = builder.get_object('label1')\n\t\tself.buttonStart = builder.get_object('button1')\n\n\t\t\n\t\t# Set attributes\n\t\twindow.set_border_width(10)\n\t\twindow.set_size_request(320, 100)\n\t\twindow.set_title(\"PyGObject - Show Date/Time\")\n\t\tself.label1.set_text(\"Date/Time: \")\n\t\t\n\t\t# Register callbacks\n\t\tself.buttonStart.connect(\"clicked\", self.on_buttonStart_clicked)\n\t\twindow.connect(\"destroy\", Gtk.main_quit)\n\t\twindow.connect(\"delete-event\", Gtk.main_quit)\n\t\t\n\t\twindow.show_all()\n\t\t\n\t# function definition\n\tdef on_buttonStart_clicked(self, widget, *args):\n\t\t\"\"\" Handles \"clicked\" event of buttonStart. \"\"\"\n\t\tself.buttonStart.set_sensitive(False)\n\t\tself.start_clock_timer()\n\t\t\n\t# Displays Timer\n\tdef display_clock(self, *args, **kwargs):\n\t\t# putting our datetime into a var and setting our label to the result. \n\t\t# we need to return \"True\" to ensure the timer continues to run, otherwise it will only run once.\n\t\tnow = datetime.now()\n\t\tdatetimenow = str(now.strftime(\"%Y-%M-%d %X\"))\n\t\tself.label1.set_label(datetimenow)\n\t\treturn True\n\n\t# Initialize Timer\n\tdef start_clock_timer(self):\n\t\tGLib.timeout_add(250, self.display_clock, None)\n\n\n#-----------------------------------------------------------------------\n# main function\ndef main():\n\t\n\twindow = MyWindow()\n\t\n\t# GTK main loop\n\tGtk.main()\n#-----------------------------------------------------------------------\n\n\t\t\nif __name__ == '__main__':\n main()\n\n","repo_name":"mbahmani80/Python_QT_PyGObject","sub_path":"PyGObject_GTK_Glade_python/09_PyGObject_GTK_Glade_Clock.py","file_name":"09_PyGObject_GTK_Glade_Clock.py","file_ext":"py","file_size_in_byte":3231,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"10241210026","text":"from mapa_streamlit.cleaning import (\n _delete_files_in_dir,\n _get_data_size_of_dir,\n _get_disk_usage,\n _get_number_of_files_in_dir,\n run_cleanup_job,\n)\n\n\ndef test__get_disk_usage():\n usage = _get_disk_usage()\n assert isinstance(usage, float)\n assert 0.0 < usage <= 100.0\n\n\ndef test__get_data_size_of_dir(tmp_path):\n # get size of empty dir\n size = _get_data_size_of_dir(tmp_path)\n assert size == 0\n\n # put dummy file into dir\n file_1_size = 1234\n file_1 = tmp_path / \"file_1.stl\"\n f = open(file_1, \"wb\")\n f.seek(file_1_size - 1)\n f.write(b\"\\0\")\n f.close()\n\n size = _get_data_size_of_dir(tmp_path)\n assert size == round(file_1_size / 1024**2, 4)\n\n # put another file into the dir\n file_2_size = 2345\n file_2 = tmp_path / \"file_2.stl\"\n f = open(file_2, \"wb\")\n f.seek(file_2_size - 1)\n f.write(b\"\\0\")\n f.close()\n\n size = _get_data_size_of_dir(tmp_path)\n assert size == round((file_1_size + file_2_size) / 1024**2, 4)\n\n # verify files in nested dirs are also taken into account\n file_3_size = 3456\n d = tmp_path / \"sub\"\n d.mkdir()\n file_3 = d / \"file_3.stl\"\n f = open(file_3, \"wb\")\n f.seek(file_3_size - 1)\n f.write(b\"\\0\")\n f.close()\n\n size = _get_data_size_of_dir(tmp_path)\n assert size == round((file_1_size + file_2_size + file_3_size) / 1024**2, 4)\n\n\ndef test__delete_files_in_dir(tmp_path) -> None:\n # verify function deletes stl file\n p = tmp_path / \"baa.stl\"\n p.write_text(\"foo\")\n assert p.is_file()\n _delete_files_in_dir(tmp_path, \".stl\")\n assert not p.is_file()\n\n # running it again does not fail\n _delete_files_in_dir(tmp_path, \".stl\")\n\n # ensure files beside stl files won't be deleted\n p = tmp_path / \"baa.tiff\"\n p.write_text(\"foo\")\n assert p.is_file()\n _delete_files_in_dir(tmp_path, \".stl\")\n assert p.is_file()\n\n\ndef test__get_number_of_files_in_dir(tmp_path) -> None:\n num = _get_number_of_files_in_dir(tmp_path, \".stl\")\n assert num == 0\n\n stl = tmp_path / \"baa.stl\"\n stl.write_text(\"foo\")\n num = _get_number_of_files_in_dir(tmp_path, \".stl\")\n assert num == 1\n num = _get_number_of_files_in_dir(tmp_path, \".tiff\")\n assert num == 0\n\n tiff = tmp_path / \"baa.tiff\"\n tiff.write_text(\"foo\")\n num = _get_number_of_files_in_dir(tmp_path, \".stl\")\n assert num == 1\n num = _get_number_of_files_in_dir(tmp_path, \".tiff\")\n assert num == 1\n\n\ndef test_run_cleanup_job(tmp_path) -> None:\n # chose very low threshold to ensure files will be delted\n stl = tmp_path / \"baa.stl\"\n stl.write_text(\"foo\")\n assert stl.is_file()\n tiff = tmp_path / \"baa.tiff\"\n tiff.write_text(\"foo\")\n assert tiff.is_file()\n zip = tmp_path / \"baz.zip\"\n zip.write_text(\"foo\")\n assert zip.is_file()\n\n run_cleanup_job(tmp_path, disk_cleaning_threshold=0.0)\n assert not stl.is_file()\n assert tiff.is_file()\n assert not zip.is_file()\n\n # chose very high threshold to check that files won't get deleted\n stl = tmp_path / \"baa.stl\"\n stl.write_text(\"foo\")\n assert stl.is_file()\n tiff = tmp_path / \"baa.tiff\"\n tiff.write_text(\"foo\")\n assert tiff.is_file()\n\n run_cleanup_job(tmp_path, disk_cleaning_threshold=100.0)\n assert stl.is_file()\n assert tiff.is_file()\n","repo_name":"fgebhart/mapa-streamlit","sub_path":"tests/test_cleaning.py","file_name":"test_cleaning.py","file_ext":"py","file_size_in_byte":3297,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"13262249327","text":"import rospy\nimport serial\nimport time\nfrom std_msgs.msg import Int32\n\npy_serial = serial.Serial(port='/dev/ttyUSB0', baudrate=9600,)\n\ndef talker():\n pub = rospy.Publisher('jetson', Int32, queue_size=1)\n rospy.init_node('talker', anonymous=True)\n rate = rospy.Rate(10) # 10hz\n timer10 = 0\n\n while(1):\n if py_serial.readable():\n sending = py_serial.readline()\n sending_int = int(sending[:len(sending)-1].decode())\n pub.publish(sending_int)\n rospy.loginfo(sending_int) \n\n # if (sending_int==100):\n # sending2_int = sending_int\n # rospy.loginfo(sending2_int)\n # start_time = time.time()\n\n # while (timer10 <= 10 and sending_int == 100):# or sending2_int!=99):\n # rospy.loginfo(\"in while2\")\n \n # if py_serial2.readable():\n # sending = py_serial2.readligitne()\n # sending_int = int(sending[:len(sending)-1].decode())\n \n # rospy.loginfo(sending_int)\n # end_time = time.time()\n # rospy.loginfo(end_time)\n # timer10 = end_time - start_time\n # rospy.loginfo(timer10)\n\n #sending_int = sending2_int\n # rospy.loginfo(\"escape!\")\n\n # if(timer10 > 10):\n # #시리얼로 아두이노에 cmd='Z' 먹이기\n # command = \"Z\"\n # py_serial.write(command.encode())\n # time.sleep(0.1)\n \n #time.sleep(3)\n #pub.publish(sending_int)\n #time.sleep(0.1)\n #rate.sleep()\n\nif __name__ == '__main__':\n talker()\n","repo_name":"1213tnals/CoffeeRobot","sub_path":"sumin-communication/beginner_tutorials/scripts/talker.py","file_name":"talker.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35165326921","text":"import pandas as pd\r\nsites_table = pd.read_csv(\"PTM_Table.csv\", low_memory=False)\r\ndict_sites_PTM = {}\r\nfor row, column in sites_table.iterrows():\r\n UID = column[2]\r\n site_pos = int(column[0].split(\"-\")[1]) # this is the variable that holds the site for each row\r\n if UID in dict_sites_PTM:\r\n dict_sites_PTM[UID].append(site_pos)\r\n if UID not in dict_sites_PTM:\r\n dict_sites_PTM[UID] = [site_pos] # dictionary of all PTM sites from the deconv_jmp table(renamed to PTM table here\r\n\r\n# the dict_sites_PTM, the key is the UID and the value is the list of the positions/PTM sites for each UID\r\n\r\ninterpro_result = pd.read_table(\"interproscan_results.tsv\", sep = \"\\t\")\r\n#interpro_result.columns = [\"Protein_accession\", \"Sequence_MD5_Digest\", \"Length\", \"Analysis\", \"Signature_accession\", \"Description\", \"Start_pos\", \"End_pos\", \"E-Val\", \"Status\", \"Date\", \"Interpro_anno\", \"Interpro_anno_2\"]\r\ninterpro_result_pos = {}\r\nfor row, column in interpro_result.iterrows():\r\n UID = column[0].split(\"|\")[1]\r\n start = int(column[6])\r\n stop = int(column[7])\r\n eval = column[8]\r\n if eval != \"-\":\r\n pos_list = (start, stop, eval)\r\n if UID in interpro_result_pos:\r\n interpro_result_pos[UID].append(pos_list)\r\n if UID not in interpro_result_pos:\r\n interpro_result_pos[UID] = [pos_list] # this dictionary holds the start, stop and evalue for each motif entry\r\n# the interpro_result dictionary is the UID as the key, and the start stop and evalue for each motif entry as the value\r\n\r\nresult_appended = []\r\nfor UID_PTM in interpro_result_pos: # the two dictionaries are looped through to find the PTM sites within the start and stop positions of the motifs\r\n if UID_PTM in dict_sites_PTM:\r\n list_pos = dict_sites_PTM[UID_PTM]\r\n for pos in list_pos:\r\n for result_pos in interpro_result_pos[UID_PTM]:\r\n if pos in range(result_pos[0], result_pos[1]):\r\n result = []\r\n result.append(UID_PTM)\r\n result.append(pos)\r\n result.append(result_pos[0])\r\n result.append(result_pos[1])\r\n result.append(result_pos[2])\r\n result_appended.append(result)\r\n# result appended is the list of the UID, pos and the start position, stop position and e-value of the motif the site falls into.\r\nresult_df = pd.DataFrame(result_appended)\r\nresult_df.columns = [\"UID\",\"Site_position\",\"Motif_start_pos\", \"Motif_stop_pos\",\"E-value\"]\r\nresult_df.to_csv(\"Result_table.csv\") # final .csv file with the above mentioned columns.\r\n","repo_name":"ManasaUpadhyaya/Logistic_regression_model_statistical_significance_of-PTM_sites","sub_path":"motif_score_finder.py","file_name":"motif_score_finder.py","file_ext":"py","file_size_in_byte":2624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74144272082","text":"\"\"\"\nTask (1.1): Create a function named evenOdd that takes an integer n as input and print whether n is an even number or an odd number.\nTask (1.2): Use evenOdd function from Task 1.1 to print even/odd for each element in the list. [10,9,12,5,7]\n\"\"\"\n\ndef even_odd(n):\n if n%2==0:\n return \"Even\"\n else:\n return \"Odd\"\n\nls = [10,9,12,5,7]\nln = len(ls)\nfor i in range(0,ln):\n e = ls[i]\n print(f\"{e}: {even_odd(e)}\")","repo_name":"enigmatic-cipher/PLAB-S3-Task-1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33362706811","text":"def clear():\r\n from os import system, name \r\n # for windows\r\n if name == 'nt':\r\n _ = system('cls') \r\n # for mac and linux(here, os.name is 'posix')\r\n else:\r\n _ = system('clear')\r\nclear()\r\n\r\nfrom turtle import Turtle, Screen\r\nimport heroes\r\nimport random\r\n\r\ntimmy = Turtle()\r\nscreen = Screen()\r\nscreen.colormode(255)\r\n\r\n# Functions\r\ndef random_color():\r\n '''Make Timmy a random color'''\r\n timmy.pencolor( random.randint(0, 255),\r\n random.randint(0, 255),\r\n random.randint(0, 255))\r\n\r\ndef timmy_square(size):\r\n '''Draw a square of size'''\r\n timmy.begin_fill()\r\n for _ in range(4):\r\n timmy.forward(size)\r\n timmy.right(90)\r\n timmy.end_fill()\r\n\r\ndef dash_line(dash, length):\r\n '''Draw a line of length, with adjustable dash size'''\r\n for _ in range(length):\r\n timmy.forward(dash)\r\n timmy.pu()\r\n timmy.forward(dash)\r\n timmy.pd() \r\n\r\ndef all_shapes(sides, size):\r\n '''Draw all polygons up to the number of sides'''\r\n for i in range(3, sides):\r\n # Change colour\r\n random_color()\r\n for _ in range(i):\r\n timmy.forward(size)\r\n timmy.right(360 / i)\r\n i += 1\r\n\r\ndef random_walk(size, steps):\r\n '''Randomly move in a direction for x amount of steps'''\r\n timmy.pensize(15)\r\n direction = [0, 90, 180, 270]\r\n for _ in range(steps):\r\n # Choose random direction\r\n timmy.setheading(random.choice(direction))\r\n # Colour and move\r\n random_color()\r\n timmy.forward(size)\r\n\r\ndef spirograph(size, steps):\r\n for _ in range(steps):\r\n random_color()\r\n timmy.left(360/steps)\r\n timmy.circle(size)\r\n\r\n# Change shape and settings\r\ntimmy.pensize(2)\r\ntimmy.speed(28)\r\n \r\n# Do something\r\n# random_walk(30, 150)\r\nspirograph(100, 40)\r\n\r\n# Exit on click at the end of the code\r\nscreen.exitonclick()","repo_name":"heySanj/100-PYTHON","sub_path":"exercises/hirst/turtle_practice.py","file_name":"turtle_practice.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72117896402","text":"import pandas as pd \nimport numpy as np \nimport pickle\nimport json\nfrom Models.databaru import Database\n\ndb = Database()\n\nmodel = pickle.load(open(r\"C:\\Users\\ROG\\Documents\\mobile_price_classification\\experiment\\model_xgb.pkl\", 'rb'))\n\n# function yang digunakan untuk melihat semua data training\ndef showDataTrain():\n try:\n df = pd.read_csv(r\"C:\\Users\\ROG\\Documents\\mobile_price_classification\\dataset\\train.csv\")\n return df.to_dict(orient=\"records\")\n except Exception as e:\n print(f\"kesalahan function showDataTrain: {e}\")\n\n# function yang digunakan untuk memprediksi Data\ndef showPrediction(**params):\n try:\n data = [[\n int(params[\"battery_power\"]),\n int(params[\"blue\"]),\n float(params[\"clock_speed\"]),\n int(params[\"dual_sim\"]),\n int(params[\"fc\"]),\n int(params[\"four_g\"]),\n int(params[\"int_memory\"]),\n float(params[\"m_dep\"]),\n int(params[\"mobile_wt\"]),\n int(params[\"n_cores\"]),\n int(params[\"pc\"]),\n int(params[\"px_height\"]),\n int(params[\"px_width\"]),\n int(params[\"ram\"]),\n int(params[\"sc_h\"]),\n int(params[\"sc_w\"]),\n int(params[\"talk_time\"]),\n int(params[\"three_g\"]),\n int(params[\"touch_screen\"]),\n int(params[\"wifi\"])\n ]]\n data_test = pd.DataFrame(data, index=[0], columns=['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g','touch_screen', 'wifi'])\n hasilPrediksi = model.predict(data_test)\n \n dataToInsert = {\n \"battery_power\": int(params[\"battery_power\"]),\n \"blue\": int(params[\"blue\"]),\n \"clock_speed\": float(params[\"clock_speed\"]),\n \"dual_sim\": int(params[\"dual_sim\"]),\n \"fc\": int(params[\"fc\"]),\n \"four_g\": int(params[\"four_g\"]),\n \"int_memory\": int(params[\"int_memory\"]),\n \"m_dep\": float(params[\"m_dep\"]),\n \"mobile_wt\": int(params[\"mobile_wt\"]),\n \"n_cores\": int(params[\"n_cores\"]),\n \"pc\": int(params[\"pc\"]),\n \"px_height\": int(params[\"px_height\"]),\n \"px_width\": int(params[\"px_width\"]),\n \"ram\": int(params[\"ram\"]),\n \"sc_h\": int(params[\"sc_h\"]),\n \"sc_w\": int(params[\"sc_w\"]),\n \"talk_time\": int(params[\"talk_time\"]),\n \"three_g\": int(params[\"three_g\"]),\n \"touch_screen\": int(params[\"touch_screen\"]),\n \"wifi\": int(params[\"wifi\"]),\n \"price_range\" : str(hasilPrediksi[0])\n }\n db.insertDataBaru(**dataToInsert)\n return str(hasilPrediksi[0])\n except Exception as e:\n print(f\"kesalahan function showPrediction: {e}\")\n\n# function yang digunakan untuk convert ObjectId ke string\ndef ObjToStr(obj):\n try:\n return str(obj[\"_id\"][\"$oid\"])\n except Exception as e:\n print(f\"kesalahan fucntion ObjToStr: {e}\")\n\n# function yang digunakan untuk melihat databaru dari database\ndef showAllDataBaru():\n try:\n dbhasil = db.showAllDataBaru()\n hasil = json.loads(dbhasil)\n data_list = []\n for data in hasil:\n data[\"id\"] = ObjToStr(data)\n data_list.append(data)\n return data_list\n except Exception as e:\n print(f\"kesalahan function showAllDatabaru: {e}\")\n\n# function yang digunakan untuk melihat data hasil_prediksi\ndef showHasilPrediksi():\n try:\n return pd.read_feather(r\"C:\\Users\\ROG\\Documents\\mobile_price_classification\\experiment\\hasil_prediksi.feather\").to_dict(orient=\"records\")\n except Exception as e:\n print(f\"kesalahan function showHasilPrediksi: {e}\")","repo_name":"rshby/mobile_price_classification","sub_path":"app/Controllers/mobile.py","file_name":"mobile.py","file_ext":"py","file_size_in_byte":3846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7658368776","text":"#!/usr/bin/env python\nfrom setuptools import setup\nimport os, subprocess\nimport argparse\n\n\ndef cpu_version_build(cur_path, sanitaser=False):\n print(\"Build CPU version\")\n cpp_files = ['/src/cpu/cpu_solver.cpp',\n '/src/python_package/py_wrapper_cpu.cpp',\n '/src/model.cpp']\n\n # Build only CPU version\n\n sanitaser_setup = ['-Wall', '-Werror']\n input_cpp_files = [cur_path + path for path in cpp_files]\n\n o_files = [cur_path + '/src/python_package/build/' + path\n for path in ['cpu_solver.o', 'py_wrapper_cpu.o', 'model_cpu.o']]\n print(\"Using cpp files: \", cpp_files)\n\n for cpp_file, o_file in zip(input_cpp_files, o_files):\n get_object_command = ['g++', '-c', '-std=c++14','-fPIC',\n cpp_file, '-o', o_file]\n if sanitaser:\n get_object_command += sanitaser_setup\n subprocess.call([*get_object_command])\n\n so_cpu_path = cur_path + '/src/python_package/build/python_package_cpu.so'\n subprocess.call(['g++', '-shared', '-std=c++14', '-o', so_cpu_path, *o_files])\n print(\"Success!\")\n\n\ndef gpu_version_build(cur_path):\n # Build only GPU version\n print(\"Build GPU version\")\n so_gpu_path = cur_path + '/src/python_package/build/python_package_gpu.so'\n \n subprocess.call(['g++', '-c', '-std=c++14', \n cur_path + '/src/python_package/py_wrapper_gpu.cpp', \n '-o', \n cur_path + '/src/python_package/build/py_wrapper_gpu.o',\n '-fPIC'])\n subprocess.call(['nvcc', '-c', '-std=c++14',\n cur_path + '/src/gpu/gpu_solver.cu',\n '-o',\n cur_path +'/src/python_package/build/gpu_solver.o',\n '-Xcompiler',\n '-fPIC'])\n\n subprocess.call(['nvcc', '-c', '-std=c++14',\n cur_path + '/src/model.cpp',\n '-o',\n cur_path +'/src/python_package/build/model_cuda.o',\n '-Xcompiler',\n '-fPIC'])\n\n subprocess.call(['nvcc', '-shared', '-std=c++14',\n '-o', so_gpu_path,\n cur_path + '/src/python_package/build/model_cuda.o',\n cur_path + '/src/python_package/build/py_wrapper_gpu.o',\n cur_path + '/src/python_package/build/gpu_solver.o'])\n print(\"GPU build success!\", so_gpu_path)\n\n\nSANITASER = False\ncur_path = os.path.join(os.path.dirname(os.path.abspath(__file__)))\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--GPU\", help=\"Build GPU module\", default = None)\nargs = parser.parse_args()\n\nif args.GPU:\n cpu_version_build(cur_path)\n gpu_version_build(cur_path)\n\nelse:\n cpu_version_build(cur_path)\n\n\n\n\n\n# setup(\n# name='space_junk_simulator',\n# version='0.1',\n# packages=[''],\n# package_dir={'': 'src/python_package'},\n# url='',\n# license='',\n# author='ivan_kharitonov',\n# author_email='',\n# description=''\n# )\n","repo_name":"neer201/space_junk_simulator","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72767623441","text":"import requests\nimport os\nimport telebot\nfrom decoder import *\n\n\nTOKEN = os.environ['TELEGRAM_TOKEN']\nCLOUD_API = os.environ['CLOUD_TOKEN']\n\nbot = telebot.TeleBot(TOKEN)\n\n@bot.message_handler(commands=['start', 'help'])\ndef send_welcome(message):\n\tbot.reply_to(message, \"Привет, епта, я Гарик!\")\n\n@bot.message_handler(regexp=\"Поиск\")\ndef handle_message(message):\n bot.send_message(message, '{} данных в городе {}, по количеству {} запросов'.format(message, 'Алматы', '10'))\n\n@bot.message_handler(content_types=[\"text\"])\ndef repeat_all_messages(message):\n bot.send_message(message.chat.id, message.text)\n\n@bot.message_handler(content_types=['document', 'audio'])\ndef handle_docs_audio(message):\n\tbot.send_message(message.chat.id, \"Обработка документов и звука, ага блять!\")\n\n@bot.message_handler(content_types=['voice'])\ndef voice_processing(message):\n file_info = bot.get_file(message.voice.file_id)\n file = requests.get('https://api.telegram.org/file/bot{0}/{1}'.format(TOKEN, file_info.file_path))\n try:\n # обращение к нашему новому модулю\n text = speech_to_text(bytes=file.content)\n bot.send_message(message.chat.id, 'Распознал команду ' + text)\n except SpeechException:\n bot.send_message(message.chat.id, 'Извините, не удалось распознать запрос')\n else:\n bot.send_message(message.chat.id, 'Почему-то выпал в блок else!')\n\nif __name__ == '__main__':\n bot.polling(none_stop=True)\n","repo_name":"Uarabei/herokutry","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1630,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44429727199","text":"# Web imports\nfrom flask import request\nfrom flask_restful import Resource, abort\n\n# Helper functions\nfrom models import (\n get_features,\n get_feature_index,\n get_feature_names,\n OrganismNotFoundError,\n MeasurementTypeNotFoundError,\n)\nfrom api.v1.utils import (\n clean_feature_string,\n)\n\n\nclass HasFeatures(Resource):\n \"\"\"Get list of features for an organism\"\"\"\n\n def get(self):\n \"\"\"Get list of features (genes)\"\"\"\n args = request.args\n measurement_type = args.get(\"measurement_type\", \"gene_expression\")\n organism = args.get(\"organism\", None)\n if organism is None:\n abort(400, message='The \"organism\" parameter is required.')\n features = args.get(\"features\", None)\n if features is None:\n abort(400, message='The \"features\" parameter is required.')\n try:\n features = clean_feature_string(features, organism, measurement_type)\n except FeatureStringFormatError:\n abort(400, message=f\"Feature string not recognised: {features}.\")\n\n try:\n features_lowercase = get_features(\n organism=organism,\n measurement_type=measurement_type,\n )\n except OrganismNotFoundError:\n abort(400, message=f\"Organism not found: {organism}.\")\n except MeasurementTypeNotFoundError:\n abort(\n 400,\n message=f\"Measurement type not found: {measurement_type}.\",\n )\n\n is_found = []\n features_corrected = []\n features_all = get_feature_names(\n organism=organism,\n measurement_type=measurement_type,\n )\n for fea in features:\n if fea.lower() not in features_lowercase:\n is_found.append(False)\n features_corrected.append(fea)\n else:\n is_found.append(True)\n idx = get_feature_index(organism, fea.lower(), measurement_type=measurement_type)\n features_corrected.append(features_all[idx])\n\n return {\n \"measurement_type\": measurement_type,\n \"organism\": organism,\n \"features\": features_corrected,\n \"found\": is_found,\n }\n\n","repo_name":"fabilab/cell_atlas_approximations_API","sub_path":"web/api/v1/objects/check_features.py","file_name":"check_features.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22554407949","text":"####################################################################\n# Script to do basic stats and regression on behavior data. Including transitional RT analysis\n####################################################################\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom scipy.stats import zscore\nimport statsmodels.formula.api as smf\nsns.set_context('paper', font_scale=1)\nsns.set_style('white')\nsns.set_palette(\"colorblind\")\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\nimport scipy.cluster.hierarchy as sch\n\nincluded_subjects = ['128', '112', '108', '110', '120', '98', '86', '82', '115', '94', '76', '91', '80', '95', '121', '114', '125', '70',\n'107', '111', '88', '113', '131', '130', '135', '140', '167', '145', '146', '138', '147', '176', '122', '118', '103', '142']\nclasses = ['dcb', 'dcr', 'dpb', 'dpr', 'fcb', 'fcr', 'fpb', 'fpr']\n\n#these are paths for Argon cluster at UIowa, so specific to where we stored our data.\ndata_path = '/Shared/lss_kahwang_hpc/ThalHi_data/RSA/'\nROOT = '/Shared/lss_kahwang_hpc/ThalHi_data/' \n\n\ndef compile_behavior():\n\t''' conmpile dataframe for behavior data'''\n\tall_df = pd.DataFrame()\n\tfor sub in included_subjects:\n\n\t\tmetadata = pd.read_csv((ROOT+'RSA/%s_metadata.csv' %sub))\n\t\tmetadata.loc[metadata.rt=='none', 'rt'] = np.nan #recode no responses into nans\n\t\tmetadata.loc[metadata.Subject_Respo=='none', 'Subject_Respo'] = np.nan #recode no responses into nans\n\t\tmetadata.rt = metadata.rt.astype('float') #convert string to float\n\t\tmetadata.Subject_Respo = metadata.Subject_Respo.astype('float') \n\t\tmetadata['RT_zscore'] = (metadata.rt - np.nanmean(metadata.rt)) / np.nanstd(metadata.rt) #zcore of RT, in case it is useful\n\t\tmetadata['Accuracy'] = metadata.trial_Corr\n\n\t\t#determine swap or not swap version of context-feature mapping\n\t\tif 'version' in metadata.columns: # only the swapped version has the version column\n\t\t\tmetadata['swapped'] = 1\n\t\t\t### Dillan didnt code the Task info for the swapped version\n\t\t\tmetadata.loc[metadata['cue'] == 'dcb', 'Task'] = 'Face'\n\t\t\tmetadata.loc[metadata['cue'] == 'dcr', 'Task'] = 'Face'\n\t\t\tmetadata.loc[metadata['cue'] == 'dpb', 'Task'] = 'Scene'\n\t\t\tmetadata.loc[metadata['cue'] == 'dpr', 'Task'] = 'Scene'\n\t\t\tmetadata.loc[metadata['cue'] == 'fcb', 'Task'] = 'Face'\n\t\t\tmetadata.loc[metadata['cue'] == 'fcr', 'Task'] = 'Scene'\n\t\t\tmetadata.loc[metadata['cue'] == 'fpb', 'Task'] = 'Face'\n\t\t\tmetadata.loc[metadata['cue'] == 'fpr', 'Task'] = 'Scene'\n\t\telse:\n\t\t\tmetadata['swapped'] = 0\n\n\t\t\tmetadata['version'] = \"Donut=Color,Filled=Shape\"\n\t\tall_df = all_df.append(metadata)\n\tall_df = all_df.drop(columns=['pic_stim', 'img_path', 'index', 'cue_stim', 'trigs', 'Unnamed: 0']) #clear book keeping vars.\n\n\treturn all_df\n\n#behavior_df = compile_behavior()\n#behavior_df.to_csv('Data/behavior_data.csv')\nbehavior_df = pd.read_csv('Data/behavior_data.csv')\n\nprint('mean')\nprint(behavior_df.groupby(['sub']).mean().mean())\nprint('std')\nprint(behavior_df.groupby(['sub']).mean().std())\n\n\ndef rt_matrix(inputdf):\n ''' calcuate transtional RT matrix'''\n Subjects = inputdf['sub'].astype('int').unique()\n mat = np.zeros((8,8))\n subN = np.zeros((8,8))\n amat = np.zeros((8,8))\n all_mat = np.zeros((8,8, len(Subjects)))\n for idx, s in enumerate(Subjects):\n df = inputdf.loc[inputdf['sub'] == s]\n df = df.reset_index()\n\n #orgaznie matrix by texture, shape, color, return indx. Make sure the ordre here matches the \"classes variable above\"\n def get_positions(x):\n return {\n ('Filled', 'Polygon', 'red'): 7,\n ('Filled', 'Polygon', 'blue'): 6,\n ('Filled', 'Circle', 'red'): 5,\n ('Filled', 'Circle', 'blue'): 4,\n ('Donut', 'Polygon', 'red'): 3,\n ('Donut', 'Polygon', 'blue'): 2,\n ('Donut', 'Circle', 'red'): 1,\n ('Donut', 'Circle', 'blue'): 0,\n }[x]\n\n transitionRTs = np.zeros((8,8))\n trialN = np.zeros((8,8))\n for i in df.index:\n if i == 0:\n continue\n # if df.loc[i, 'cue']== df.loc[i-1, 'cue']: #remove cue repetition effect....?\n # continue\n else:\n if df.loc[i, 'trial_Corr']==1: # only correct trials\n if df.loc[i-1, 'trial_Corr']!=0: # exclude post error slowing trials\n if df.loc[i, 'RT_zscore']<3: #no overly slow trials\n previous_condition = get_positions((df.loc[i-1, 'Texture'], df.loc[i-1, 'Shape'], df.loc[i-1, 'Color']))\n current_condition = get_positions((df.loc[i, 'Texture'], df.loc[i, 'Shape'], df.loc[i, 'Color']))\n diff_rt = df.loc[i,'rt'] #- df.loc[i-1, 'rt']\n transitionRTs[previous_condition, current_condition] = transitionRTs[previous_condition, current_condition] + diff_rt\n trialN[previous_condition, current_condition] = trialN[previous_condition, current_condition] + 1\n\n smat = transitionRTs/trialN\n smat[np.isnan(smat)] = np.nanmean(smat)\n \n #here calcuate switch cost\n bmat = smat.copy()\n for k in np.arange(0,8):\n for j in np.arange(0,8):\n smat[k,j] = bmat[k,j] - bmat[j,j] #switch trial minus repeat trial\n\n #normalize\n smat = (smat - np.nanmean(smat)) / np.nanstd(smat)\n subaM = np.ones((8,8))\n subaM[np.isnan(smat)]=0\n subN = subN + subaM\n amat = amat + smat\n #stack subjects\n all_mat[:,:,idx] = smat\n #mat = np.hstack((mat,smat))\n\n amat = amat / subN #average across subjects\n mat = np.reshape(all_mat,(8,all_mat.shape[1]*all_mat.shape[2]))\n return mat, amat, all_mat\n\n# swapdf = behavior_df.loc[behavior_df['swapped']==1]\n# noswapdf = behavior_df.loc[behavior_df['swapped']==0]\n# mat, amat, all_mat = rt_matrix(behavior_df)\n#mat is stacked version\n#amat is ave across subjects\n#all_mat is the full 3D matrices\n# for s in np.arange(np.shape(all_mat)[2]):\n# sns.heatmap(all_mat[:,:,s])\n# plt.show()\n\n\nmat, amat, all_mat = rt_matrix(behavior_df)\ndiag_idx = np.eye(8)==1 # to get rid of eyes in the transition matrix which are repetition effects\namat[diag_idx] = np.nan \nsns.heatmap(amat, cmap = \"bwr\")\nplt.savefig('Figures/behav.png')\n#plt.show()\n\n### create regression model\nfrom gen_RSA import create_RSA_models\ncontext_model, shape_model, color_model, identity_model, swapped_dimension_model, nonswapped_dimension_model, swapped_task_model, nonswapped_task_model, swapped_feature_model, nonswapped_feature_model, alternative_model = create_RSA_models()\ndiag_idx = np.eye(8)==0 # to get rid of eyes in the transition matrix which are repetition effects\nrt_rsa_df = pd.DataFrame()\nfor s, subject in enumerate(behavior_df['sub'].astype('int').unique()):\n sdf = pd.DataFrame()\n sdf['TransitionRT'] = all_mat[:,:,s][diag_idx].flatten()\n sdf['Context'] = 1-context_model[diag_idx].flatten()\n sdf['Identity'] = 1-identity_model[diag_idx].flatten()\n sdf['alternative'] = 1-alternative_model[diag_idx].flatten()\n if behavior_df.loc[behavior_df['sub']==subject]['swapped'].unique()[0]==1:\n sdf['Feature'] = 1-swapped_dimension_model[diag_idx].flatten()\n sdf['Task'] = 1-swapped_task_model[diag_idx].flatten()\n sdf['Swap'] = 1\n else:\n sdf['Feature'] = 1-nonswapped_dimension_model[diag_idx].flatten()\n sdf['Task'] = 1-nonswapped_task_model[diag_idx].flatten()\n sdf['Swap'] = 0\n sdf['Subject'] = subject\n sdf['Steps'] = (sdf['Context'] + sdf['Feature'])\n \n rt_rsa_df = rt_rsa_df.append(sdf)\n\nmodel = \"TransitionRT ~ 0 + Identity + Task\" # identity is effectively intercept\nbase_model = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(base_model.summary())\n\nmodel = \"TransitionRT ~ 0 + Identity + Task + Context + Feature\"\ntest_model = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model.summary())\n\nmodel = \"TransitionRT ~ 0 + Identity + Task + Feature\"\ntest_model2 = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model2.summary())\n\nmodel = \"TransitionRT ~ 0 + Identity + Task + Context + Feature + alternative\"\ntest_model3 = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model3.summary())\n\nmodel = \"TransitionRT ~ 1 + alternative\"\ntest_model4 = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model4.summary())\n\nmodel = \"TransitionRT ~ 1 + Task + alternative\"\ntest_model4 = smf.ols(formula = model, data = rt_rsa_df).fit() \nprint(test_model4.summary())\n\n### nested model comparison\nfrom statsmodels.stats.anova import anova_lm\nprint(anova_lm(base_model, test_model))\nprint(anova_lm(base_model, test_model2))\nprint(anova_lm(test_model, test_model2))\n\n\n\n# end of script","repo_name":"HwangLabNeuroCogDynamics/TaskRep","sub_path":"behav.py","file_name":"behav.py","file_ext":"py","file_size_in_byte":8920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32011718800","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nfrom wicketkeeping import *\n\ndef graph_wicketkeepers():\n wicketkeepers_name=list(wicket_keepers.NAME)\n\n mra_listforplot=[]\n avg_listforplot=[]\n stk_listforplot=[]\n brpi_listforplot=[]\n wka_listforplot=[]\n \n for i in wicketkeepers_name:\n mra_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].MRA_Rating))\n avg_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].BattingAverageRating))\n stk_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].StrikeRateRating))\n brpi_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].BRPI_Rating))\n wka_listforplot.append(float(wicket_keepers[wicket_keepers.NAME==i].WKA_Rating))\n bar3=[]\n bar4=[]\n bar5=[]\n for i in range(len(mra_listforplot)):\n \tbar3.append(avg_listforplot[i]+mra_listforplot[i])\n \tbar4.append(bar3[i]+stk_listforplot[i])\n \tbar5.append(bar4[i]+brpi_listforplot[i])\n\n plt.bar(wicketkeepers_name,wka_listforplot,label='WKA Rating',color=\"c\",bottom=bar5)\n plt.bar(wicketkeepers_name,brpi_listforplot,label='BRPI Rating',color='y',bottom=bar4)\n plt.bar(wicketkeepers_name,stk_listforplot,label='Strike Rate Rating',color='g',bottom=bar3)\n plt.bar(wicketkeepers_name,mra_listforplot,label='MRA_Rating',color='r',bottom=avg_listforplot)\n plt.bar(wicketkeepers_name,avg_listforplot,label='Average_Rating',color='b')\n plt.title('WicketKeepers Statistical Attributes Rating')\n plt.xticks(rotation=45)\n plt.legend()\n plt.show()\n\n\ngraph_wicketkeepers()\n\n","repo_name":"sushantsabnis218/CriketPlayerRatings","sub_path":"graph_wicketkeeper.py","file_name":"graph_wicketkeeper.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21743227690","text":"from sklearn.model_selection import ParameterGrid\nfrom skopt import gp_minimize\nfrom skopt.space import Categorical\nfrom skopt.utils import use_named_args \nfrom src import style\nfrom src.config import config_template\nfrom src.constants import Directories\nfrom src.io import read_image\nfrom src.utils import convert_to_serializable\nfrom src.utils import parse\nfrom src.utils import parse_search_string \nfrom src import search_io \nfrom tqdm import tqdm\nimport json\nimport numpy as np\nimport os\nimport subprocess\nimport urllib.request\n\n\n#TODO: merge parameter search and training, setup is similar\ndef start_training(config):\n experiment_root = f\"{Directories.EXPERIMENTS}/{config['experiment_name']}\"\n config[\"experiment_root\"] = experiment_root\n checkpoint_dir = f\"{experiment_root}/checkpoints\"\n config[\"checkpoint_dir\"] = checkpoint_dir\n os.makedirs(checkpoint_dir, exist_ok=True)\n with open(f\"{experiment_root}/config.json\", \"w\") as f:\n f.write(json.dumps(config))\n with open(f\"{experiment_root}/user.json\", \"w\") as f:\n f.write(json.dumps({}))\n\n style.run(config)\n\n\n\ndef __start_search_experiment(idx, config_setup, search_config):\n experiment_root = f\"{Directories.SEARCHES}/{search_config['experiment_name']}/experiments/{idx}\" \n checkpoint_dir = f\"{experiment_root}/checkpoints\"\n os.makedirs(checkpoint_dir, exist_ok=True)\n\n with open(f\"{experiment_root}/config.json\", \"w\") as f:\n f.write(json.dumps(config_setup))\n with open(f\"{experiment_root}/user.json\", \"w\") as f:\n f.write(json.dumps({\"rating\": \"unrated\"}))\n\n config_setup.update(search_config)\n\n config_setup[\"experiment_root\"] = experiment_root\n \n\n output_file = f\"{experiment_root}/output.jpg\"\n config_setup[\"output_image_name\"] = output_file\n config_setup[\"checkpoint_path\"] = f\"{checkpoint_dir}/\"\n\n style.run(config_setup)\n\ndef start_parameter_search(search_config, search_space):\n search_root = f\"{Directories.SEARCHES}/{search_config['experiment_name']}\"\n os.makedirs(search_root)\n os.makedirs(search_root + \"/experiments\")\n with open(f\"{search_root}/search_config.json\", \"w\") as f:\n f.write(json.dumps(search_config))\n with open(f\"{search_root}/search_space.json\", \"w\") as f:\n f.write(json.dumps(search_space))\n\n key_order = list(search_space.keys())\n search_bounds = []\n for parameter_name in key_order:\n parameter_space_str = search_space[parameter_name]\n bound = parse_search_string(parameter_space_str, parameter_name)\n search_bounds.append(bound)\n\n \n n_calls = search_config[\"initial_searches\"]\n progress_bar = tqdm(total=n_calls)\n @use_named_args(dimensions=search_bounds)\n def f(content_weight, history_size, iterations, learning_rate, max_iter, style_weight):\n config = {\"iterations\": int(iterations),\n \"learning_rate\": float(learning_rate),\n \"style_weight\": int(style_weight),\n \"content_weight\": int(content_weight),\n \"history_size\": int(history_size),\n \"max_iter\": int(max_iter)}\n\n idx = search_io.get_experiment_index(search_config)\n __start_search_experiment(idx, config, search_config)\n progress_bar.update()\n\n return 1.0\n \n res = gp_minimize(f, # the function to minimize\n\t\t search_bounds, # the bounds on each dimension of x\n\t\t n_calls=n_calls, # the number of evaluations of f\n n_random_starts=n_calls,\n n_initial_points=n_calls)\n\n\ndef download_image(image_type, image_url, image_name):\n\n with urllib.request.urlopen(image_url) as response, \\\n open(f\"{image_type}/{image_name}\", 'wb') as out_file:\n data = response.read()\n out_file.write(data)\n\ndef annotated_search(search_name, optimize_runs, random, new_width):\n search_config, search_space = search_io.read_search_config(search_name)\n key_order = list(search_space.keys())\n parameter_runs = search_io.list_parameter_runs(search_name)\n prior_x = []\n prior_y = []\n for parameter_run in parameter_runs:\n run_config = search_io.read_parameter_config(search_name, parameter_run)\n prior_x.append([run_config[key] for key in key_order])\n experiment_root = f\"{Directories.SEARCHES}/{search_name}/experiments/{parameter_run}\" \n run_user = search_io.read_user(experiment_root)\n prior_y.append( 0.0 if run_user[\"rating\"] == \"good\" else 1.0)\n\n\n search_bounds = []\n for parameter_name in key_order:\n parameter_space_str = search_space[parameter_name]\n bound = parse_search_string(parameter_space_str, parameter_name)\n search_bounds.append(bound)\n\n calls = optimize_runs + random\n progress_bar = tqdm(total=calls)\n @use_named_args(dimensions=search_bounds)\n def f(content_weight, history_size, iterations, learning_rate, max_iter, style_weight):\n config = {\"iterations\": int(iterations),\n \"learning_rate\": float(learning_rate),\n \"style_weight\": int(style_weight),\n \"content_weight\": int(content_weight),\n \"history_size\": int(history_size),\n \"max_iter\": int(max_iter)}\n search_config[\"image_width\"] = new_width\n\n idx = search_io.get_experiment_index(search_config)\n __start_search_experiment(idx, config, search_config)\n progress_bar.update()\n\n return 0.5\n \n\n res = gp_minimize(f, # the function to minimize\n\t\t search_bounds, # the bounds on each dimension of x\n\t\t n_calls=calls, # the number of evaluations of f\n n_random_starts=random,\n n_initial_points=calls,\n\t\t x0=prior_x,\n\t\t y0=prior_y) # the random seed\n\n f(res.x)\n\n\n","repo_name":"gozmo/neural_style","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29061275767","text":"from dataclasses import replace\nfrom uuid import uuid4\n\nimport psycopg2.errors\nimport pytest\n\nfrom hamcrest import assert_that, contains_inanyorder, equal_to\n\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage import IntegrationMapper\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage.entities.enums import IntegrationStatus\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage.entities.integration import Integration\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage.entities.merchant import Merchant\nfrom billing.yandex_pay_admin.yandex_pay_admin.storage.exceptions import (\n IntegrationDuplicateDeployedIntegrationError,\n IntegrationDuplicateMerchantPSPPerStage,\n)\nfrom billing.yandex_pay_admin.yandex_pay_admin.tests.unit.storage.mappers.base import BaseMapperTests\n\n\nclass TestIntegrationMapper(BaseMapperTests):\n @pytest.fixture\n def psp_id(self):\n return uuid4()\n\n @pytest.fixture\n def entity(self, psp_id, merchant: Merchant) -> Integration:\n assert merchant.merchant_id is not None\n\n return Integration(\n psp_id=psp_id,\n psp_external_id='payture',\n merchant_id=merchant.merchant_id,\n status=IntegrationStatus.DEPLOYED,\n )\n\n @pytest.fixture\n def another_entity(self, psp_id, merchant: Merchant) -> Integration:\n return Integration(\n psp_id=uuid4(),\n psp_external_id='uniteller',\n merchant_id=merchant.merchant_id,\n status=IntegrationStatus.READY,\n )\n\n @pytest.fixture\n def mapper(self, storage) -> IntegrationMapper:\n return storage.integration\n\n @pytest.mark.asyncio\n async def test_can_get_deployed_by_merchant_id(self, mapper, merchant, entity, another_entity):\n first_integration = await mapper.create(entity)\n await mapper.create(another_entity)\n\n found = await mapper.get_deployed_by_merchant_id(merchant.merchant_id, for_testing=True)\n\n assert_that(found, equal_to(first_integration))\n\n @pytest.mark.asyncio\n async def test_get_deployed_by_merchant_id_missing(self, mapper, entity, another_entity):\n integration = await mapper.create(entity)\n await mapper.create(another_entity)\n\n assert_that(integration.for_testing, equal_to(True))\n\n with pytest.raises(Integration.DoesNotExist):\n await mapper.get_deployed_by_merchant_id(integration.merchant_id, for_testing=False)\n\n @pytest.mark.asyncio\n async def test_list_by_merchant_id(self, mapper, merchant, entity, another_entity):\n first_integration = await mapper.create(entity)\n second_integration = await mapper.create(another_entity)\n\n found = await mapper.list_by_merchant_id(merchant.merchant_id)\n\n assert_that(found, contains_inanyorder(first_integration, second_integration))\n\n @pytest.mark.asyncio\n async def test_get_by_merchant_id_and_psp_id(self, mapper, merchant, entity, another_entity):\n first_integration = await mapper.create(entity)\n second_integration = await mapper.create(another_entity)\n\n loaded_first = await mapper.get_by_merchant_id_and_psp_id(\n merchant.merchant_id, first_integration.psp_id, for_testing=True\n )\n loaded_second = await mapper.get_by_merchant_id_and_psp_id(\n merchant.merchant_id, second_integration.psp_id, for_testing=True\n )\n\n assert_that(loaded_first, equal_to(first_integration))\n assert_that(loaded_second, equal_to(second_integration))\n\n @pytest.mark.asyncio\n async def test_get_by_merchant_id_and_psp_id__not_found(self, mapper, entity):\n integration = await mapper.create(entity)\n\n assert_that(integration.for_testing, equal_to(True))\n\n with pytest.raises(Integration.DoesNotExist):\n await mapper.get_by_merchant_id_and_psp_id(integration.merchant_id, integration.psp_id, for_testing=False)\n\n @pytest.mark.asyncio\n async def test_can_update(self, mapper, entity):\n created = await mapper.create(entity)\n created.status = IntegrationStatus.READY\n\n updated = await mapper.save(created)\n created.updated = updated.updated\n created.revision = updated.revision\n\n assert_that(\n updated,\n equal_to(created),\n )\n\n @pytest.mark.asyncio\n async def test_can_remove(self, mapper, entity, another_entity, merchant):\n created = await mapper.create(entity)\n second_integration = await mapper.create(another_entity)\n\n await mapper.delete(created)\n found = await mapper.list_by_merchant_id(merchant.merchant_id)\n\n assert_that(found, equal_to([second_integration]))\n\n @pytest.mark.asyncio\n async def test_update_bumps_revision(self, mapper, entity):\n created = await mapper.create(entity)\n assert_that(created.revision, equal_to(1))\n\n updated = await mapper.save(created)\n assert_that(updated.revision, equal_to(2))\n\n created.updated = updated.updated\n assert_that(updated, equal_to(created))\n\n @pytest.mark.asyncio\n async def test_has_foreign_key_on_merchant(self, mapper, entity):\n entity.merchant_id = uuid4()\n\n with pytest.raises(psycopg2.errors.ForeignKeyViolation):\n await mapper.create(entity)\n\n @pytest.mark.asyncio\n async def test_unique_merchant_psp_env_combination(self, rollback_after, mapper, entity, another_entity):\n first = await mapper.create(entity)\n second = await mapper.create(another_entity)\n\n async with rollback_after():\n with pytest.raises(IntegrationDuplicateMerchantPSPPerStage):\n await mapper.create(replace(entity, integration_id=uuid4()))\n\n second.psp_id = first.psp_id\n async with rollback_after():\n with pytest.raises(IntegrationDuplicateMerchantPSPPerStage):\n await mapper.save(second)\n\n second.for_testing = not second.for_testing\n await mapper.save(second)\n\n @pytest.mark.asyncio\n async def test_unique_deployed_integration_per_merchant(self, rollback_after, mapper, entity, another_entity):\n await mapper.create(entity)\n second = await mapper.create(another_entity)\n\n second.status = IntegrationStatus.DEPLOYED\n async with rollback_after():\n with pytest.raises(IntegrationDuplicateDeployedIntegrationError):\n await mapper.save(second)\n\n second.for_testing = not second.for_testing\n await mapper.save(second)\n\n @pytest.mark.parametrize('first_status', (IntegrationStatus.READY, IntegrationStatus.DEPLOYED))\n @pytest.mark.parametrize('second_status', (IntegrationStatus.READY, IntegrationStatus.DEPLOYED))\n @pytest.mark.parametrize('first_for_testing', (True, False))\n @pytest.mark.parametrize('second_for_testing', (True, False))\n @pytest.mark.asyncio\n async def test_list_by_partner_id(\n self,\n mapper,\n storage,\n entity,\n another_entity,\n partner,\n merchant,\n first_status,\n second_status,\n first_for_testing,\n second_for_testing,\n ):\n first = await mapper.create(replace(entity, for_testing=first_for_testing, status=first_status))\n second_merchant = await storage.merchant.create(replace(merchant, merchant_id=uuid4()))\n second = await mapper.create(\n replace(\n another_entity,\n for_testing=second_for_testing,\n status=second_status,\n merchant_id=second_merchant.merchant_id,\n )\n )\n # Extra integration\n outside_partner = await storage.partner.create(replace(partner, partner_id=uuid4()))\n outside_merchant = await storage.merchant.create(\n replace(merchant, partner_id=outside_partner.partner_id, merchant_id=uuid4())\n )\n await mapper.create(replace(entity, merchant_id=outside_merchant.merchant_id, for_testing=False))\n\n found = await mapper.list_by_partner_id(\n partner_id=merchant.partner_id, status=IntegrationStatus.DEPLOYED, for_testing=False\n )\n\n expected = []\n if first_status == IntegrationStatus.DEPLOYED and not first_for_testing:\n expected.append(first)\n if second_status == IntegrationStatus.DEPLOYED and not second_for_testing:\n expected.append(second)\n assert_that(found, contains_inanyorder(*expected))\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests/unit/storage/mappers/test_integration.py","file_name":"test_integration.py","file_ext":"py","file_size_in_byte":8477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14233057045","text":"import csv\nimport random\n\nnamefile = open(\"formatted_drinker_names.txt\", \"r\")\ncitystatefile = open(\"ddb+_city_state.txt\").read().splitlines()\naddrfile = open(\"ddb+_ppl_addr.txt\", \"r\")\nphonefile = open(\"formatted_phone_num.txt\", \"r\")\n\nwith open('drinkers.csv', 'w') as csvfile:\n filewriter = csv.writer(csvfile, delimiter=',')\n\n for nameline, phoneline, addrline in zip(namefile, phonefile, addrfile):\n cityline = random.choice(citystatefile)\n cstate = cityline.split(\", \")\n\n filewriter.writerow([nameline.strip(), \n cstate[0].strip(), cstate[1].strip(), \n phoneline.strip(), addrline.strip()])\n\nnamefile.close()\naddrfile.close()\nphonefile.close()\n\n ","repo_name":"nrf17/Restaurant_Database","sub_path":"table_data/Source Code/drinkerformat.py","file_name":"drinkerformat.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23848359739","text":"# %%\nimport sys, os\nimport torch\nimport numpy as np\nfrom torch.utils.data import random_split\nfrom torchvision.datasets import CIFAR10, CIFAR100\nimport torchvision.transforms as transforms\nfrom torch.utils.data import Dataset, DataLoader, SubsetRandomSampler\nfrom timm.data.transforms_factory import create_transform\n\n# %%\ndataset='cifar10'\ndata_path=f'/data1/lsj9862/data/{dataset}'\nbatch_size=256\nuse_validation=True\naug=True\nval_ratio=0.1\ndat_per_cls=1\nseed=1\n\n\n# %%\n## Set Seed\ndef set_seed(RANDOM_SEED=0):\n '''\n Set seed for reproduction\n '''\n torch.manual_seed(RANDOM_SEED)\n np.random.seed(RANDOM_SEED)\n torch.cuda.manual_seed(RANDOM_SEED)\n torch.cuda.manual_seed_all(RANDOM_SEED) # if use multi-GPU\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n import random\n random.seed(RANDOM_SEED)\n\nset_seed(seed)\n\n# %%\n## Get transform\nif aug:\n transform_train = create_transform(224, is_training=True)\n transform_test = create_transform(224)\nelse:\n transform_train = transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n ])\n\n transform_test = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),\n ]) \n\n# %%\n## Load Data\nif dataset=='cifar10':\n tr_data = CIFAR10(data_path, train=True, transform=transform_train,\n download=True)\n te_data = CIFAR10(data_path, train=False, transform = transform_test,\n download=True)\nelif dataset=='cifar100':\n tr_data = CIFAR100(data_path, train=True, transform=transform_train,\n download=True)\n te_data = CIFAR100(data_path, train=False, transform = transform_test,\n download=True)\n \nnum_classes = max(te_data.targets) + 1\nprint(f\"Number of classes : {num_classes} :: Data point per class : {dat_per_cls}\")\n\n\n# %%\n## Split Data\nval_len = int(len(tr_data) * val_ratio)\ntr_len = len(tr_data) - val_len\n\ntr_data, val_data = random_split(tr_data, [tr_len, val_len])\n\n# %%\n## Pre-Setting for Few-shot Setting\nclass_indices = [[] for _ in range(num_classes)]\nfor idx, (_, target) in enumerate(tr_data):\n class_indices[target].append(idx)\n\nfew_shot_indices = []\nfor indices in class_indices:\n few_shot_indices.extend(indices[:dat_per_cls])\n\nsampler = SubsetRandomSampler(few_shot_indices)\n\n# %%\n## Make Loader\ntr_loader = DataLoader(tr_data,\n batch_size=batch_size,\n num_workers=4,\n pin_memory=True,\n sampler=sampler)\n \n# %%\nos.makedirs(f'/mlainas/lsj9862/data/{dataset}/{dat_per_cls}shot', exist_ok=True)\ntorch.save(tr_loader, f'/mlainas/lsj9862/data/{dataset}/{dat_per_cls}shot/tr_loader_seed{seed}.pth')\n# %%\n","repo_name":"SungJun98/SA-BTL","sub_path":"utils/data/fewshot_loader.py","file_name":"fewshot_loader.py","file_ext":"py","file_size_in_byte":3149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20231692317","text":"import jwt\nimport psycopg2\nfrom fastapi import FastAPI, Depends, HTTPException, status\nfrom pydantic import BaseModel\nfrom config import DB_NAME, DB_HOST, DB_PORT, DB_PASS, DB_USER, SECRET_KEY\nfrom datetime import datetime\nfrom fastapi.security import HTTPBearer, HTTPAuthorizationCredentials\n\n\nclass NewSub(BaseModel):\n sub_name: str\n sub_price: int\n sub_currency: str\n sub_date: datetime\n sub_period: str\n fk_subs_users: int\n\n\napp = FastAPI()\nsecurity = HTTPBearer()\n\nconnection = psycopg2.connect(\n host=DB_HOST,\n user=DB_USER,\n password=DB_PASS,\n database=DB_NAME,\n port=DB_PORT,\n)\nconnection.autocommit = True\n\n\n@app.on_event(\"startup\")\nasync def startup_event():\n print(\"Сервер запущен\")\n\n\n@app.on_event(\"shutdown\")\nasync def shutdown_event():\n connection.close()\n print(\"Сервер остановлен\")\n\n\nasync def get_current_user_id(credentials: HTTPAuthorizationCredentials = Depends(security)):\n token = credentials.credentials\n\n try:\n secret_key = SECRET_KEY\n payload = jwt.decode(token, secret_key, algorithms=[\"HS256\"])\n user_id = payload.get(\"id\")\n return user_id\n except jwt.DecodeError:\n raise HTTPException(\n status_code=status.HTTP_401_UNAUTHORIZED,\n detail=\"Invalid token\",\n headers={\"WWW-Authenticate\": \"Bearer\"},\n )\n except jwt.ExpiredSignatureError:\n raise HTTPException(\n status_code=status.HTTP_401_UNAUTHORIZED,\n detail=\"Expired token\",\n headers={\"WWW-Authenticate\": \"Bearer\"}, )\n\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n@app.post(\"/subs\")\nasync def add_subscription(sub: NewSub, user_id: int = Depends(get_current_user_id)):\n try:\n with connection.cursor() as cursor:\n\n cursor.execute(\"SELECT * FROM users WHERE id = %s;\", (user_id,))\n user_data = cursor.fetchone()\n\n if user_data:\n\n cursor.execute(\n \"\"\"\n INSERT INTO subs (sub_name, sub_price, sub_currency, sub_date, sub_period, fk_subs_users)\n VALUES (%s, %s, %s, %s, %s, %s);\n \"\"\",\n (sub.sub_name, sub.sub_price, sub.sub_currency, sub.sub_date, sub.sub_period, user_id),\n )\n\n return {\"message\": \"Подписка успешно добавлена.\"}\n else:\n return {\"error\": \"Пользователь не найден.\"}\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n@app.put(\"/subs/{sub_id}\")\nasync def update_sub(sub_id: int, item: NewSub, user_id: int = Depends(get_current_user_id)):\n sub_name = item.sub_name\n sub_price = item.sub_price\n sub_currency = item.sub_currency\n sub_date = item.sub_date\n sub_period = item.sub_period\n\n try:\n with connection.cursor() as cursor:\n\n cursor.execute(\n \"SELECT * FROM subs WHERE id = %s AND fk_subs_users = %s;\",\n (sub_id, user_id),\n )\n sub_data = cursor.fetchone()\n\n if sub_data:\n cursor.execute(\n \"\"\"UPDATE subs SET sub_name = %s, sub_price = %s, sub_currency = %s, sub_date = %s,\n sub_period = %s, fk_subs_users = %s WHERE id = %s;\"\"\",\n (sub_name, sub_price, sub_currency, sub_date, sub_period, user_id, sub_id)\n )\n return {\"done\": f\"Подписка {sub_id} успешно обновлена!\"}\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n@app.get(\"/subs\")\nasync def get_user_subs(user_id: int = Depends(get_current_user_id)):\n try:\n with connection.cursor() as cursor:\n\n cursor.execute(\n \"\"\"SELECT * FROM subs WHERE fk_subs_users = %s;\"\"\",\n (user_id,)\n )\n\n subs = cursor.fetchall()\n\n if subs:\n\n subs_list = []\n\n for sub in subs:\n sub_data = {\n \"sub_name\": sub[1],\n \"sub_price\": sub[2],\n \"sub_currency\": sub[3],\n \"sub_date\": sub[4],\n \"sub_period\": sub[5],\n }\n subs_list.append(sub_data)\n\n return {\"subscriptions\": subs_list}\n else:\n return {\"subscriptions\": []}\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n@app.delete(\"/subs/{sub_id}\")\nasync def delete_sub(sub_id: int, user_id: int = Depends(get_current_user_id)):\n try:\n with connection.cursor() as cursor:\n\n cursor.execute(\n \"SELECT * FROM subs WHERE id = %s AND fk_subs_users = %s;\",\n (sub_id, user_id),\n )\n sub_data = cursor.fetchone()\n\n if sub_data:\n cursor.execute(\n \"DELETE FROM subs WHERE id = %s;\",\n (sub_id,),\n )\n\n return {\"message\": \"Подписка успешно удалена.\"}\n else:\n return {\"error\": \"Подписка не найдена или не принадлежит пользователю.\"}\n\n\n except Exception as ex:\n print(\"[INFO] Ошибка при работе с PostgreSQL:\", ex)\n\n\n\n","repo_name":"batyrio/fin-dev-subscriptionsAPI","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74853555920","text":"# -*- coding: utf-8 -*-\r\nimport binascii\r\nfrom urllib.parse import urlparse\r\n\r\n__author__ = 'gakki429'\r\n\r\nimport re\r\nimport os\r\nimport zlib\r\nimport hashlib\r\n\r\n\r\ndef _mkdir(path):\r\n path = os.path.dirname(path)\r\n if path and not os.path.exists(path):\r\n os.makedirs(path)\r\n\r\n\r\nclass GitPack(object):\r\n \"\"\"Git Parse Pack Format\"\"\"\r\n\r\n def __init__(self, git_path, pack_hash, git_leak_dict):\r\n self.git_path = git_path\r\n self.pack_hash = pack_hash\r\n self.objects = {} # hash: {type, length}\r\n self.objects_num = 0\r\n\r\n self.parsed = urlparse(self.git_path)\r\n self.base_path = './' + self.parsed.hostname + '.' + str(self.parsed.port) + self.parsed.path\r\n\r\n self.idx_path = self.base_path + 'objects/pack/pack-{}.idx'.format(self.pack_hash)\r\n self.pack_path = self.base_path + 'objects/pack/pack-{}.pack'.format(self.pack_hash)\r\n self.pack_data = ''\r\n self.git_leak_dict = git_leak_dict\r\n\r\n def pack_header(self):\r\n pack = open(self.pack_path, 'rb').read()\r\n signature = pack[:4]\r\n if signature == b'PACK':\r\n # print(22)\r\n version_number = pack[4:8]\r\n objects_number = pack[8:12]\r\n # print(int.from_bytes(objects_number,'big'))\r\n # print(binascii.b2a_hex(objects_number).decode())\r\n self.objects_num = int.from_bytes(objects_number, 'big')\r\n self.pack_data = pack\r\n\r\n def idx_header(self):\r\n pack_idx = open(self.idx_path, 'rb').read()\r\n magic_number = pack_idx[:4]\r\n if magic_number == b'\\xfftOc':\r\n version_number = pack_idx[4:8]\r\n fan_out_table = pack_idx[8:8 + 1024] # 网络字节序整数\r\n\r\n pack_hash = pack_idx[-40:-20]\r\n idx_hash = pack_idx[-20:]\r\n\r\n # Todo: 区分版本（v1，v2），区分idx大小，大于 2g 的 offset 为 8bytes\r\n idx_data = pack_idx[8 + 1024:-40]\r\n self.parse_idx(idx_data)\r\n\r\n def split_to_hex(self, length, data):\r\n length = length * 2\r\n data_hex_str = binascii.b2a_hex(data).decode()\r\n # print(data_hex_str[0:20])\r\n # print(data_hex_str)\r\n # print(len(data_hex_str))\r\n # print(length)\r\n # a7ab5d1025b971316024b84a1706a096b23e9c\r\n str_len = int(len(data_hex_str) / length)\r\n # print(len(data_hex_str))\r\n res_list = []\r\n # print(str_len)\r\n for i in range(str_len):\r\n res_list.append(data_hex_str[i * length:i * length + length])\r\n\r\n # print(len(res_list))\r\n return res_list\r\n\r\n def parse_idx(self, idx_data):\r\n num = self.objects_num\r\n # print(idx_data[:num*20])\r\n _hashs = self.split_to_hex(20, idx_data[:num * 20])\r\n # print(_hashs)\r\n\r\n _crcs = self.split_to_hex(4, idx_data[num * 20:num * 24])\r\n _offsets = self.split_to_hex(4, idx_data[num * 24:])\r\n for i in range(num):\r\n self.objects[_hashs[i]] = {'crc': _crcs[i], 'offset': int(_offsets[i], 16)}\r\n\r\n def extract_pack(self):\r\n # print(self.objects)\r\n sort_objects = sorted(self.objects.items(), key=lambda x: x[1]['offset'])\r\n for i in range(len(sort_objects)):\r\n _hash, _info = sort_objects[i]\r\n crc = _info['crc']\r\n offset = _info['offset']\r\n if i == len(sort_objects) - 1:\r\n next_offset = -20\r\n else:\r\n next_offset = sort_objects[i + 1][1]['offset']\r\n self.objects[_hash]['data'] = self.pack_data[offset:next_offset]\r\n self.parse_pack()\r\n\r\n def pack_type(self, num):\r\n _types = {\r\n '1': 'commit',\r\n '2': 'tree',\r\n '3': 'blob',\r\n '4': 'tag',\r\n '6': 'ofs_delta',\r\n '7': 'ref_delta',\r\n }\r\n return _types[str(int(num, 2))]\r\n\r\n def parse_pack(self):\r\n\r\n for _hash, _info in sorted(self.objects.items(), key=lambda x: x[1]['offset']):\r\n # print(_hash)\r\n # print(_info)\r\n _size = []\r\n try:\r\n flag, zlib_data = re.search(b'(.*?)(\\x78\\x9c.*)', _info['data'], re.S).groups()\r\n # print(flag)\r\n except AttributeError:\r\n return\r\n\r\n # for i in range(len(flag)):\r\n # # bin_info = bin(int(flag[i].encode('hex'), 16))[2:].rjust(8, '0')\r\n # # int.from_bytes(flag, 'big')\r\n # bin_info = bin(int.from_bytes(flag, 'big'))[2:].rjust(8, '0')\r\n # msb = bin_info[0]\r\n # if i == 0:\r\n # _type = bin_info[1:4]\r\n # _size.append(bin_info[-4:])\r\n # else:\r\n # _size.append(bin_info[-7:])\r\n # _length = int(''.join(_size[::-1]), 2) # 这里其实是小端，不是���端\r\n # _type = self.pack_type(_type)\r\n\r\n bin_info = bin(int.from_bytes(flag, 'big'))[2:].rjust(8, '0')\r\n # print('====')\r\n # print(bin_info)\r\n _length = bin_info[4:4 + (len(bin_info) // 8 - 1) * 7 + 4]\r\n # print(_length)\r\n # _type = int(bin_info[1:4],2)\r\n\r\n try:\r\n self.objects[_hash]['type'] = self.pack_type(bin_info[1:4])\r\n except:\r\n continue\r\n # self.objects[_hash]['length'] = int(_length,2) # 需要根据length和type来计算sha1 这里计算的不对 大端小端都试了 所以直接使用了解压后的data长度\r\n self.objects[_hash]['file'] = zlib.decompress(zlib_data)\r\n self.objects[_hash]['length'] = len(self.objects[_hash]['file'])\r\n self.objects[_hash]['bin_info'] = flag\r\n # print(self.objects[_hash]['file']) # 这个不知道文件名啊\r\n\r\n def pack_to_object_file(self):\r\n for _object in self.objects.values():\r\n try:\r\n if _object['type'] != 'blob':\r\n continue\r\n # object_format = '{} {}\\x00{}'.format(\r\n # _object['type'].encode(), _object['length'], _object['file'])\r\n object_format = _object['type'].encode() + b' ' + str(_object['length']).encode() + b'\\x00' + _object[\r\n 'file']\r\n # print(_object['type'], _object['length'], _object['bin_info'], _object['file'])\r\n # print(object_format)\r\n except KeyError:\r\n continue\r\n sha = hashlib.sha1(object_format).hexdigest()\r\n\r\n path = self.base_path+'objects/{}/{}'.format(sha[:2], sha[2:])\r\n zlib_object = zlib.compress(object_format)\r\n dir = path.rsplit('/', 1)[0]\r\n # print(dir)\r\n os.makedirs(dir, exist_ok=True)\r\n open(path, 'wb').write(zlib_object)\r\n\r\n if _object['type'] == 'blob' and sha in self.git_leak_dict:\r\n blob_path = self.git_leak_dict[sha]['path']\r\n dir=blob_path.rsplit('/', 1)[0]\r\n os.makedirs(dir, exist_ok=True)\r\n try:\r\n file = open(blob_path, 'rb').read()\r\n if file != _object['file']:\r\n filename = '{}.{}'.format(blob_path, sha[:6])\r\n with open(filename, 'wb') as f:\r\n f.write(_object['file'])\r\n f.close()\r\n # logger.info('find new file, save file to : {}'.format(filename))\r\n except FileNotFoundError:\r\n with open(blob_path, 'wb') as f:\r\n f.write(_object['file'])\r\n f.close()\r\n # logger.info('saving file to : {}'.format(blob_path))\r\n\r\n\r\n def pack_init(self):\r\n self.pack_header()\r\n self.idx_header()\r\n self.extract_pack()\r\n self.pack_to_object_file()\r\n # Todo: 重建 ofs_delta，ref_delta\r\n","repo_name":"Young873/gitdump","sub_path":"lib/git_packs_parse.py","file_name":"git_packs_parse.py","file_ext":"py","file_size_in_byte":7979,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"21394065529","text":"import os\r\nimport numpy as np\r\nfrom torch.utils.data import DataLoader, Dataset\r\nimport torch\r\nimport config\r\nfrom PIL import Image, ImageDraw2, ImageDraw\r\nimport logging\r\nfrom glob import glob\r\n\r\n\r\nclass xDataSet(Dataset):\r\n def __init__(self):\r\n self.OKroot = config.DataSetPath\r\n # self.NKroot = config.DataSetPath_\r\n self.images = [x.path for x in os.scandir(self.OKroot)]\r\n # self.images_ = [x.path for x in os.scandir(self.NKroot)]\r\n # print(self.images)\r\n # print(self.images)\r\n\r\n def __len__(self):\r\n return len(self.images)\r\n\r\n def __getitem__(self, index):\r\n img = Image.open(self.images[index]).convert('L')\r\n img1 = np.array(img) / 127.5 - 1\r\n\r\n img2 = img\r\n box, color = self.getbox()\r\n ImageDraw.Draw(img2).ellipse(box, fill=color)\r\n img2 = np.array(img2)\r\n img2 = img2 / 127.5 - 1\r\n\r\n return torch.from_numpy(img2).unsqueeze(0), torch.from_numpy(img1).unsqueeze(0)\r\n\r\n\r\n @staticmethod\r\n def getbox():\r\n # x1 = np.random.randint(1, 127)\r\n # y1 = np.random.randint(1, 127)\r\n # x2 = x1 + np.random.randint(1, 75)\r\n # y2 = y1 + np.random.randint(1, 75)\r\n x1 = torch.randint(low=1, high=127, size=[1]).squeeze(0)\r\n y1 = torch.randint(low=1, high=127, size=[1]).squeeze(0)\r\n x2 = x1 + torch.randint(low=1, high=75, size=[1]).squeeze(0)\r\n y2 = y1 + torch.randint(low=1, high=75, size=[1]).squeeze(0)\r\n color = np.random.randint(5, 250)\r\n if x2 > 127:\r\n x2 = 127\r\n if y2 > 127:\r\n y2 = 127\r\n\r\n return (x1, y1, x2, y2), color\r\n\r\nclass tDataSet(Dataset):\r\n def __init__(self):\r\n self.NKroot = config.DataSetPath_\r\n self.images_ = [x.path for x in os.scandir(self.NKroot)]\r\n\r\n def __len__(self):\r\n return len(self.images_)\r\n\r\n def __getitem__(self, index):\r\n img = Image.open(self.images_[index]).convert('L')\r\n img1 = np.array(img) / 127.5 - 1\r\n\r\n return torch.from_numpy(img1).unsqueeze(0)\r\n\r\nclass unetDataset(Dataset):\r\n def __init__(self, imgs_dir, scale = 1):\r\n self.imgs_dir = imgs_dir\r\n self.scale = scale\r\n assert 0 < scale <= 1, 'Scale must be between 0 and 1'\r\n\r\n self.ids = [os.path.splitext(file)[0] for file in os.listdir(imgs_dir)\r\n if not file.startswith('.')]\r\n logging.info(f'Creating dataset with {len(self.ids)} examples')\r\n\r\n def __len__(self):\r\n return len(self.ids)\r\n\r\n @classmethod\r\n def preprocess(cls, pil_img, scale):\r\n w, h = pil_img.size\r\n newW, newH = int(scale * w), int(scale * h)\r\n assert newW > 0 and newH > 0, 'Scale is too small'\r\n pil_img = pil_img.resize((newW, newH))\r\n\r\n x1 = np.random.randint(1, newW)\r\n y1 = np.random.randint(1, newH)\r\n x2 = x1 + np.random.randint(1, newW // 2)\r\n y2 = y1 + np.random.randint(1, newH // 2)\r\n color = np.random.randint(5, 250)\r\n if x2 > newW - 1:\r\n x2 = newW - 1\r\n if y2 > newH - 1:\r\n y2 = newH - 1\r\n box = (x1, y1, x2, y2)\r\n\r\n flaw = pil_img\r\n ImageDraw.Draw(flaw).ellipse(box, fill=color)\r\n img1 = np.array(flaw)\r\n\r\n mask = Image.new('L', (newW, newH), 0)\r\n ImageDraw.Draw(mask).ellipse(box, fill='white')\r\n img2 = np.array(mask)\r\n\r\n if len(img1.shape) == 2:\r\n img1 = np.expand_dims(img1, axis=2)\r\n if len(img2.shape) == 2:\r\n img2 = np.expand_dims(img2, axis=2)\r\n\r\n # HWC to CHW\r\n img_trans1 = img1.transpose((2, 0, 1))\r\n if img_trans1.max() > 1:\r\n img_trans1 = img_trans1 / 255\r\n img_trans2 = img2.transpose((2, 0, 1))\r\n if img_trans2.max() > 1:\r\n img_trans2 = img_trans2 / 255\r\n\r\n return img_trans1, img_trans2\r\n\r\n def __getitem__(self, i):\r\n idx = self.ids[i]\r\n img_file = glob(self.imgs_dir + idx + '.*')\r\n\r\n assert len(img_file) == 1, \\\r\n f'Either no image or multiple images found for the ID {idx}: {img_file}'\r\n img = Image.open(img_file[0]).convert('L')\r\n\r\n img, mask = self.preprocess(img, self.scale)\r\n\r\n return {\r\n 'image': torch.from_numpy(img).type(torch.FloatTensor),\r\n 'mask': torch.from_numpy(mask).type(torch.FloatTensor)\r\n }","repo_name":"lwshen/DCGAN","sub_path":"dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":4404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36332434538","text":"def main():\n X_train, Y_train = [], []\n with open('training_data.tsv', 'r') as f:\n for line in f:\n split = line.strip().split('\\t')\n # Get feature representation\n #embedding_1 = get_embedding(split[0], embeddings)\n #embedding_2 = get_embedding(split[1], embeddings)\n #X_train.append(embedding_1 + embedding_2)\n # Get label\n label = split[2]\n Y_train.append(label)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"leonwetzel/Learning-from-Data","sub_path":"Assignment 5/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38792864555","text":"import argparse\n\nfrom colcon_core.package_discovery import add_package_discovery_arguments\nfrom colcon_core.plugin_system import satisfies_version\nfrom colcon_core.verb import VerbExtensionPoint\nfrom colcon_ed.edit_tools import edit_target_file\nfrom colcon_ed.edit_tools import FileCompleter\nfrom colcon_ed.edit_tools import find_file\nfrom colcon_ed.edit_tools import get_package_path\n\n\nclass EditVerb(VerbExtensionPoint):\n \"\"\"Edit a file within a package.\"\"\"\n\n def __init__(self): # noqa: D107\n super().__init__()\n satisfies_version(VerbExtensionPoint.EXTENSION_POINT_VERSION, '^1.0')\n\n def add_arguments(self, *, parser): # noqa: D102\n parser.add_argument(\n 'package_name', metavar='PKG_NAME',\n type=argument_package_name,\n help='Package name')\n\n args = parser.add_argument(\n 'filename', metavar='FILE_NAME',\n type=argument_package_name,\n help='File name')\n args.completer = FileCompleter(package_name_key='package_name')\n\n add_package_discovery_arguments(parser)\n\n def main(self, *, context): # noqa: D102\n args = context.args\n package_path = get_package_path(args.package_name, args)\n if not package_path:\n return ((\"Package '{args.package_name}' \" +\n 'not found').format_map(locals()))\n\n file_paths = find_file(package_path, args.filename)\n if not file_paths:\n return ((\"File '{args.filename}' not found \" +\n 'in package {args.package_name}').format_map(locals()))\n\n # Ask the user to choose when there are more than one file found\n if len(file_paths) > 1:\n msg = 'Multiple files found:'\n for i in range(len(file_paths)):\n msg += '\\n ' + str(i + 1) + ') ' + file_paths[i]\n print(msg)\n try:\n index = input('Please enter a number: ')\n index = int(index) - 1\n except KeyboardInterrupt:\n return '\\n'\n except ValueError:\n return 'Not an integer'\n if index < 0 or index >= len(file_paths):\n return 'Index out of range'\n path = file_paths[index]\n else:\n path = file_paths[0]\n\n return edit_target_file(path)\n\n\ndef argument_package_name(value):\n \"\"\"\n Check if an argument is a valid package name.\n\n Used as a ``type`` callback in ``add_argument()`` calls.\n Package names starting with a dash must be prefixed with a space to avoid\n collisions with command line arguments.\n\n :param str value: The command line argument\n :returns: The package name\n :raises argparse.ArgumentTypeError: if the value starts with a dash\n \"\"\"\n if value.startswith('-'):\n raise argparse.ArgumentTypeError('unrecognized argument: ' + value)\n return value.lstrip()\n","repo_name":"colcon/colcon-ed","sub_path":"colcon_ed/verb/edit.py","file_name":"edit.py","file_ext":"py","file_size_in_byte":2912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15368333724","text":"import os\nimport requests\nimport re\n\nbase = 'http://sunlight.iis.sinica.edu.tw'\nfirst = '/cgi-bin/text.cgi'\nsecond = '/uwextract/show.php?type=tag&id='\n\ndef parseTerm(tag):\n split=tag.split('(')\n return {'term': split[0], 'tag': split[1][:-1]}\n\ndef getID(query):\n r = requests.post(base + first, data={'query': query.encode('big5')})\n regex = re.search('\\d+', r.text.split('URL=')[1])\n return regex.group(0)\n\ndef getTags(id):\n r = requests.get(base + second + id)\n r.encoding = 'Big5'\n regex = re.search('((.|\\n)*?)<\\/pre>', r.text)\n result = regex.group(1)\n\n pattern = re.compile('[^\\s!-]+')\n tags = pattern.findall(result)\n return map(parseTerm, tags)\n\ndef parse(query):\n id = getID(query)\n tags = getTags(id)\n return tags\n","repo_name":"renddiyeh/heroku-ckip-crawler","sub_path":"src/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74055391761","text":"# -*- encoding:utf-8 -*-\n_reload_all = True\n\nclass Record(object):\n\t__slots__ = ('ability_level','level_hp_rate','level_atk_rate','level_defence_rate','level_speed_rate',)\n\tdef __init__(self,k0,k1,k2,k3,k4):\n\t\tself.ability_level=k0\n\t\tself.level_hp_rate=k1\n\t\tself.level_atk_rate=k2\n\t\tself.level_defence_rate=k3\n\t\tself.level_speed_rate=k4\n\t\t\n\tdef get(self, key, default=None):\n\t\tret = getattr(self, key, None)\n\t\tif ret is None:\n\t\t\treturn default\n\t\telse:\n\t\t\treturn ret\n\t\t\t\n\t__getitem__ = object.__getattribute__\n\n\ndata = {\n\t1:Record(1,1.0,0.1,0.1,0.1),\n\t2:Record(2,2.0,0.2,0.2,0.2),\n\t3:Record(3,3.0,0.3,0.3,0.3),\n\t4:Record(4,4.0,0.4,0.4,0.4),\n\t5:Record(5,5.0,0.5,0.5,0.5),\n\t6:Record(6,6.0,0.6,0.6,0.6),\n\t7:Record(7,7.0,0.7,0.7,0.7),\n\t8:Record(8,8.0,0.8,0.8,0.8),\n\t9:Record(9,9.0,0.9,0.9,0.9),\n\t10:Record(10,10.0,1.0,1.0,1.0)\n}\n","repo_name":"shuitian/card_battle","sub_path":"datas/ability.py","file_name":"ability.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21537377965","text":"import tensorflow as tf\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\nfrom keras.layers import Dropout\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.utils import np_utils\n\nimport matplotlib.pyplot as pt\nimport numpy as np\n\nfrom keras.datasets import cifar10\nbatch_size = 20\nepochs = 4\nn_classes = 10\n\n(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n\nprint(x_train.shape)\n\nheight = x_train.shape[1]\nwidth = x_train.shape[2]\n\nx_val = x_train[:2000,:,:,:]\ny_val = y_train[:2000]\nx_train = x_train[2000:,:,:,:]\ny_train = y_train[2000:]\n\nprint('Train Dataset: ', x_train.shape, y_train.shape)\nprint('Validate dataset: ', x_val.shape, y_val.shape)\nprint('Test dataset: ', x_test.shape, y_test.shape)\n\nimage = x_train[4]\n\nlabel = y_train\nprint(label)\n\ny_train = np_utils.to_categorical(y_train, n_classes)\ny_val = np_utils.to_categorical(y_val, n_classes)\ny_test = np_utils.to_categorical(y_test, n_classes)\n\nprint(y_train)\n\nx_train = x_train.astype('float32')\nx_val = x_val.astype('float32')\nx_test = x_test.astype('float32')\n\nx_train /= 255\nx_val /= 255\nx_test /= 255\n\ndef create_model():\n model = Sequential()\n model.add(Conv2D(filters = 80, kernel_size = (4,4), input_shape=(height, width,4), strides=1, padding='same', activation='relu'))\n model.add(MaxPooling2D(pool_size=(2,2)))\n\n model.add(Conv2D(filters = 80, kernel_size=(4,4), strides=1, activation='relu'))\n model.add(MaxPooling2D(pool_size=(2,2)))\n model.add(Dropout(0.5))\n\n model.add(Flatten())\n model.add(Dense(40, activation='relu'))\n model.add(Dense(n_classes, activation='softmax'))\n\n return model\n\n model = create_model()\n\n model.compile(optimizer = 'adam',\n loss = 'categorical_crossentropy',\n metrics = ['accuracy'])\n \n model.fit(x_train, y_train, batch_size = 20, epochs = 4, validation_data=(x_val, y_val), verbose=2)\n\n model.summary()\n","repo_name":"GabiDuartte/Trabalho_final","sub_path":"rede.py","file_name":"rede.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13773360257","text":"from __future__ import absolute_import\n#Init has to be imported first because it has code to workaround the python bug where relative imports don't work if the module is imported as a main module.\nimport __init__\n\nfrom fabmetheus_utilities.fabmetheus_tools import fabmetheus_interpret\nfrom fabmetheus_utilities.vector3 import Vector3\nfrom fabmetheus_utilities import archive\nfrom fabmetheus_utilities import euclidean\nfrom fabmetheus_utilities import gcodec\nfrom fabmetheus_utilities import intercircle\nfrom fabmetheus_utilities import settings\nfrom skeinforge_application.skeinforge_utilities import skeinforge_craft\nfrom skeinforge_application.skeinforge_utilities import skeinforge_polyfile\nfrom skeinforge_application.skeinforge_utilities import skeinforge_profile\nimport math\nimport sys\n\n\n__author__ = 'Enrique Perez (perez_enrique@yahoo.com)'\n__date__ = '$Date: 2008/21/04 $'\n__license__ = 'GPL 3.0'\n\n\ndef getCraftedText( fileName, text = '', multiplyRepository = None ):\n\t\"Multiply the fill file or text.\"\n\treturn getCraftedTextFromText( archive.getTextIfEmpty( fileName, text ), multiplyRepository )\n\ndef getCraftedTextFromText( gcodeText, multiplyRepository = None ):\n\t\"Multiply the fill text.\"\n\tif gcodec.isProcedureDoneOrFileIsEmpty( gcodeText, 'multiply'):\n\t\treturn gcodeText\n\tif multiplyRepository == None:\n\t\tmultiplyRepository = settings.getReadRepository( MultiplyRepository() )\n\tif not multiplyRepository.activateMultiply.value:\n\t\treturn gcodeText\n\treturn MultiplySkein().getCraftedGcode( gcodeText, multiplyRepository )\n\ndef getNewRepository():\n\t\"Get the repository constructor.\"\n\treturn MultiplyRepository()\n\ndef writeOutput(fileName=''):\n\t\"Multiply a gcode linear move file.\"\n\tfileName = fabmetheus_interpret.getFirstTranslatorFileNameUnmodified(fileName)\n\tif fileName != '':\n\t\tskeinforge_craft.writeChainTextWithNounMessage( fileName, 'multiply')\n\n\nclass MultiplyRepository:\n\t\"A class to handle the multiply settings.\"\n\tdef __init__(self):\n\t\t\"Set the default settings, execute title & settings fileName.\"\n\t\tskeinforge_profile.addListsToCraftTypeRepository('skeinforge_application.skeinforge_plugins.craft_plugins.multiply.html', self )\n\t\tself.fileNameInput = settings.FileNameInput().getFromFileName( fabmetheus_interpret.getGNUTranslatorGcodeFileTypeTuples(), 'Open File for Multiply', self, '')\n\t\tself.openWikiManualHelpPage = settings.HelpPage().getOpenFromAbsolute('http://www.bitsfrombytes.com/wiki/index.php?title=Skeinforge_Multiply')\n\t\tself.activateMultiply = settings.BooleanSetting().getFromValue('Activate Multiply:', self, False )\n\t\tsettings.LabelSeparator().getFromRepository(self)\n\t\tsettings.LabelDisplay().getFromName('- Center -', self )\n\t\tself.centerX = settings.FloatSpin().getFromValue( - 100.0, 'Center X (mm):', self, 100.0, 0.0 )\n\t\tself.centerY = settings.FloatSpin().getFromValue( -100.0, 'Center Y (mm):', self, 100.0, 0.0 )\n\t\tsettings.LabelSeparator().getFromRepository(self)\n\t\tsettings.LabelDisplay().getFromName('- Number of Cells -', self )\n\t\tself.numberOfColumns = settings.IntSpin().getFromValue( 1, 'Number of Columns (integer):', self, 10, 1 )\n\t\tself.numberOfRows = settings.IntSpin().getFromValue( 1, 'Number of Rows (integer):', self, 10, 1 )\n\t\tsettings.LabelSeparator().getFromRepository(self)\n\t\tself.separationOverPerimeterWidth = settings.FloatSpin().getFromValue( 5.0, 'Separation over Perimeter Width (ratio):', self, 25.0, 15.0 )\n\t\tself.executeTitle = 'Multiply'\n\n\tdef execute(self):\n\t\t\"Multiply button has been clicked.\"\n\t\tfileNames = skeinforge_polyfile.getFileOrDirectoryTypesUnmodifiedGcode(self.fileNameInput.value, fabmetheus_interpret.getImportPluginFileNames(), self.fileNameInput.wasCancelled)\n\t\tfor fileName in fileNames:\n\t\t\twriteOutput(fileName)\n\n\nclass MultiplySkein:\n\t\"A class to multiply a skein of extrusions.\"\n\tdef __init__(self):\n\t\tself.distanceFeedRate = gcodec.DistanceFeedRate()\n\t\tself.isExtrusionActive = False\n\t\tself.layerCount = settings.LayerCount()\n\t\tself.layerIndex = 0\n\t\tself.layerLines = []\n\t\tself.lineIndex = 0\n\t\tself.lines = None\n\t\tself.oldLocation = None\n\t\tself.rowIndex = 0\n\t\tself.shouldAccumulate = True\n\n\tdef addElement( self, offset ):\n\t\t\"Add moved element to the output.\"\n\t\tfor line in self.layerLines:\n\t\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\t\tfirstWord = gcodec.getFirstWord(splitLine)\n\t\t\tif firstWord == 'G1':\n\t\t\t\tmovedLocation = self.getMovedLocationSetOldLocation( offset, splitLine )\n\t\t\t\tline = self.distanceFeedRate.getLinearGcodeMovement( movedLocation.dropAxis(2), movedLocation.z )\n\t\t\telif firstWord == '(':\n\t\t\t\tmovedLocation = self.getMovedLocationSetOldLocation( offset, splitLine )\n\t\t\t\tline = self.distanceFeedRate.getBoundaryLine( movedLocation )\n\t\t\tself.distanceFeedRate.addLine(line)\n\n\tdef addLayer(self):\n\t\t\"Add multiplied layer to the output.\"\n\t\tself.layerCount.printProgressIncrement('multiply')\n\t\tself.addRemoveThroughLayer()\n\t\toffset = self.centerOffset - self.arrayCenter - self.shapeCenter\n\t\tfor rowIndex in xrange( self.multiplyRepository.numberOfRows.value ):\n\t\t\tyRowOffset = float( rowIndex ) * self.extentPlusSeparation.imag\n\t\t\tif self.layerIndex % 2 == 1:\n\t\t\t\tyRowOffset = self.arrayExtent.imag - yRowOffset\n\t\t\tfor columnIndex in xrange( self.multiplyRepository.numberOfColumns.value ):\n\t\t\t\txColumnOffset = float( columnIndex ) * self.extentPlusSeparation.real\n\t\t\t\tif self.rowIndex % 2 == 1:\n\t\t\t\t\txColumnOffset = self.arrayExtent.real - xColumnOffset\n\t\t\t\telementOffset = complex( offset.real + xColumnOffset, offset.imag + yRowOffset )\n\t\t\t\tself.addElement( elementOffset )\n\t\t\tself.rowIndex += 1\n\t\tif len( self.layerLines ) > 1:\n\t\t\tself.layerIndex += 1\n\t\tself.layerLines = []\n\n\tdef addRemoveThroughLayer(self):\n\t\t'Parse gcode initialization and store the parameters.'\n\t\tfor layerLineIndex in xrange( len( self.layerLines ) ):\n\t\t\tline = self.layerLines[ layerLineIndex ]\n\t\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\t\tfirstWord = gcodec.getFirstWord(splitLine)\n\t\t\tself.distanceFeedRate.addLine(line)\n\t\t\tif firstWord == '(':\n\t\t\t\tself.layerLines = self.layerLines[ layerLineIndex + 1 : ]\n\t\t\t\treturn\n\n\tdef getCraftedGcode( self, gcodeText, multiplyRepository ):\n\t\t\"Parse gcode text and store the multiply gcode.\"\n\t\tself.centerOffset = complex( multiplyRepository.centerX.value, multiplyRepository.centerY.value )\n\t\tself.multiplyRepository = multiplyRepository\n\t\tself.numberOfColumns = multiplyRepository.numberOfColumns.value\n\t\tself.numberOfRows = multiplyRepository.numberOfRows.value\n\t\tself.lines = archive.getTextLines(gcodeText)\n\t\tself.parseInitialization()\n\t\tself.setCorners()\n\t\tfor line in self.lines[self.lineIndex :]:\n\t\t\tself.parseLine(line)\n\t\treturn self.distanceFeedRate.output.getvalue()\n\n\tdef getMovedLocationSetOldLocation( self, offset, splitLine ):\n\t\t\"Get the moved location and set the old location.\"\n\t\tlocation = gcodec.getLocationFromSplitLine(self.oldLocation, splitLine)\n\t\tself.oldLocation = location\n\t\treturn Vector3( location.x + offset.real, location.y + offset.imag, location.z )\n\n\tdef parseInitialization(self):\n\t\t'Parse gcode initialization and store the parameters.'\n\t\tfor self.lineIndex in xrange(len(self.lines)):\n\t\t\tline = self.lines[self.lineIndex]\n\t\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\t\tfirstWord = gcodec.getFirstWord(splitLine)\n\t\t\tself.distanceFeedRate.parseSplitLine(firstWord, splitLine)\n\t\t\tif firstWord == '()':\n\t\t\t\tself.distanceFeedRate.addLine('( multiply )')\n\t\t\t\tself.distanceFeedRate.addLine(line)\n\t\t\t\tself.lineIndex += 1\n\t\t\t\treturn\n\t\t\telif firstWord == '(':\n\t\t\t\tself.absolutePerimeterWidth = abs(float(splitLine[1]))\n\t\t\tself.distanceFeedRate.addLine(line)\n\n\tdef parseLine(self, line):\n\t\t\"Parse a gcode line and add it to the multiply skein.\"\n\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\tif len(splitLine) < 1:\n\t\t\treturn\n\t\tfirstWord = splitLine[0]\n\t\tif firstWord == '( )':\n\t\t\tself.addLayer()\n\t\t\tself.distanceFeedRate.addLine(line)\n\t\t\treturn\n\t\telif firstWord == '()':\n\t\t\tself.shouldAccumulate = False\n\t\tif self.shouldAccumulate:\n\t\t\tself.layerLines.append(line)\n\t\t\treturn\n\t\tself.distanceFeedRate.addLine(line)\n\n\tdef setCorners(self):\n\t\t\"Set maximum and minimum corners and z.\"\n\t\tcornerHighComplex = complex(-987654321.0, -987654321.0)\n\t\tcornerLowComplex = -cornerHighComplex\n\t\tfor line in self.lines[self.lineIndex :]:\n\t\t\tsplitLine = gcodec.getSplitLineBeforeBracketSemicolon(line)\n\t\t\tfirstWord = gcodec.getFirstWord(splitLine)\n\t\t\tif firstWord == 'G1':\n\t\t\t\tlocation = gcodec.getLocationFromSplitLine(self.oldLocation, splitLine)\n\t\t\t\tif self.isExtrusionActive:\n\t\t\t\t\tlocationComplex = location.dropAxis()\n\t\t\t\t\tcornerHighComplex = euclidean.getMaximum(locationComplex, cornerHighComplex)\n\t\t\t\t\tcornerLowComplex = euclidean.getMinimum(locationComplex, cornerLowComplex)\n\t\t\t\tself.oldLocation = location\n\t\t\telif firstWord == 'M101':\n\t\t\t\tself.isExtrusionActive = True\n\t\t\telif firstWord == 'M103':\n\t\t\t\tself.isExtrusionActive = False\n\t\tself.extent = cornerHighComplex - cornerLowComplex\n\t\tself.shapeCenter = 0.5 * ( cornerHighComplex + cornerLowComplex )\n\t\tself.separation = self.multiplyRepository.separationOverPerimeterWidth.value * self.absolutePerimeterWidth\n\t\tself.extentPlusSeparation = self.extent + complex( self.separation, self.separation )\n\t\tcolumnsMinusOne = self.numberOfColumns - 1\n\t\trowsMinusOne = self.numberOfRows - 1\n\t\tself.arrayExtent = complex( self.extentPlusSeparation.real * columnsMinusOne, self.extentPlusSeparation.imag * rowsMinusOne )\n\t\tself.arrayCenter = 0.5 * self.arrayExtent\n\n\ndef main():\n\t\"Display the multiply dialog.\"\n\tif len(sys.argv) > 1:\n\t\twriteOutput(' '.join(sys.argv[1 :]))\n\telse:\n\t\tsettings.startMainLoopFromConstructor( getNewRepository() )\n\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"makerbot/ReplicatorG","sub_path":"skein_engines/skeinforge-35/skeinforge_application/skeinforge_plugins/craft_plugins/multiply.py","file_name":"multiply.py","file_ext":"py","file_size_in_byte":9773,"program_lang":"python","lang":"en","doc_type":"code","stars":397,"dataset":"github-code","pt":"3"}
+{"seq_id":"23522218843","text":"import xgboost as xgb\nimport numpy as np\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, roc_auc_score\nimport matplotlib.pyplot as plt\n# Generate some example data\nX, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=42)\n\n# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Convert the data to a DMatrix format\ndtrain = xgb.DMatrix(X_train, label=y_train)\ndtest = xgb.DMatrix(X_test, label=y_test)\n\n# Set the hyperparameters\nparams = {\n \"max_depth\": 3,\n \"eta\": 0.1,\n \"objective\": \"binary:logistic\",\n \"eval_metric\": \"logloss\"\n}\n\n# Train the model\nnum_rounds = 100\nmodel = xgb.train(params, dtrain, num_rounds)\n\n# Make predictions on the test set\ny_pred = model.predict(dtest)\n\n# Evaluate the model\nprint(\"Accuracy:\", accuracy_score(y_test, np.round(y_pred)))\nprint(\"ROC AUC score:\", roc_auc_score(y_test, y_pred))\n\n# Print the feature importance\nimportances = model.get_score(importance_type='gain')\nimportances = sorted(importances.items(), key=lambda x: x[1], reverse=True)\nprint(\"Feature importance:\")\nfor i in importances:\n print(i)\n\n# visualize\n# Calculate ROC curve and AUC-ROC\nfpr, tpr, thresholds = roc_curve(y_test, y_pred)\nauc_roc = roc_auc_score(y_test, y_pred)\n\n# Visualize the ROC curve\nplt.plot(fpr, tpr, label=f\"AUC-ROC = {auc_roc:.3f}\")\nplt.plot([0, 1], [0, 1], linestyle=\"--\")\nplt.xlabel(\"False Positive Rate\")\nplt.ylabel(\"True Positive Rate\")\nplt.title(\"ROC Curve\")\nplt.legend()\nplt.show()\n","repo_name":"volom/statistics_tools","sub_path":"xgboost.py","file_name":"xgboost.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29652742201","text":"'''\n有一堆石头，用整数数组 stones 表示。其中 stones[i] 表示第 i 块石头的重量。\n每一回合，从中选出任意两块石头，然后将它们一起粉碎。\n假设石头的重量分别为 x 和 y，且 x <= y。那么粉碎的可能结果如下：\n如果 x == y，那么两块石头都会被完全粉碎；\n如果 x != y，那么重量为 x 的石头将会完全粉碎，而重量为 y 的石头新重量为 y-x。\n最后，最多只会剩下一块 石头。返回此石头 最小的可能重量 。如果没有石头剩下，就返回 0。\n\n思路：\n1.总重量Weight = sum(stones)\n 如果能完全粉碎，则Weight为偶数且两组石头的总量都是 Weight/2\n 就算不能完全粉碎，也应该使其中一组石头的重量尽可能接近 Weight/2\n2.令target = Weight//2\n 若stones中能选取一组石头，使其重量在其他选择中是最大的（在小于 target的情况下）\n 如果这组石头的重量为a，则所求的结果为(Weight-a) - a\n3.至此，转换成了背包问题\n dp[i+1][j]表示从stones[:i+1]中（即前i个石头）能否选出一组重量为j的石头组合\n 设stones长度为n，我们最终想要获得dp[n][target]（如果可以）\n 所以dp数组为n+1行，target+1列（不然取不到[n][target]下标）\n4.状态压缩\n i+1只和i有关，可以压缩。然后注意需要倒着遍历\n'''\nclass Solution:\n def lastStoneWeightII(self, stones: List[int]) -> int:\n total = sum(stones)\n n = len(stones)\n target = total // 2\n dp = [False] * (target+1)\n dp[0] = True\n\n for stone in stones:\n for j in range(target, stone-1, -1):\n dp[j] = dp[j] or dp[j-stone]\n \n ans = float('inf')\n for j in range(target, -1, -1):\n if dp[j]:\n ans = min(ans, total - 2*j)\n return int(ans)\n\n\n\n total = sum(stones)\n n = len(stones)\n target = total // 2\n dp = [[False]*(target+1) for _ in range(n+1)]\n dp[0][0] = True\n\n for i in range(n):\n for j in range(target + 1):\n if j < stones[i]:\n dp[i + 1][j] = dp[i][j]\n else:\n dp[i + 1][j] = dp[i][j] or dp[i][j - stones[i]]\n \n res= 0\n for j in range(target, -1, -1):\n if dp[-1][j] == True:\n res = total - 2*j\n break\n return res","repo_name":"miyagipipi/studying","sub_path":"bag/1049. 最后一块石头的重量 II.py","file_name":"1049. 最后一块石头的重量 II.py","file_ext":"py","file_size_in_byte":2466,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"3114665655","text":"\"\"\"\nTest estimators\n\"\"\"\n\nimport pytest\nfrom sklearn.datasets import load_iris, load_diabetes\n\nfrom discretefirstorder import DFORegressor\n\n\n@pytest.fixture\ndef iris_data():\n \"\"\"Load Iris dataset for testing\"\"\"\n return load_iris(return_X_y=True)\n\n\n@pytest.fixture\ndef diabetes_data():\n \"\"\"Load diabetes dataset for testing\"\"\"\n return load_diabetes(return_X_y=True)\n\n\ndef test_dfo_regressor_default():\n \"\"\"Test DFORegressor default params\"\"\"\n reg = DFORegressor()\n assert reg.loss == \"mse\"\n assert reg.learning_rate == \"auto\"\n assert reg.k == 1\n assert reg.polish is True\n assert reg.n_runs == 50\n assert reg.max_iter == 100\n assert reg.tol == 1e-3\n assert reg.fit_intercept is True\n assert reg.normalize is True\n\n\ndef test_dfo_regressor():\n \"\"\"Test DFORegressor params\"\"\"\n reg = DFORegressor(\n learning_rate=0.001,\n k=5,\n polish=False,\n n_runs=25,\n max_iter=50,\n tol=1e-4,\n fit_intercept=False,\n normalize=False,\n )\n assert reg.loss == \"mse\"\n assert reg.learning_rate == 0.001\n assert reg.k == 5\n assert reg.polish is False\n assert reg.n_runs == 25\n assert reg.max_iter == 50\n assert reg.tol == 1e-4\n assert reg.fit_intercept is False\n assert reg.normalize is False\n\n\ndef test_dfo_regressor_fit(iris_data):\n \"\"\"Test DFORegressor fit method\"\"\"\n reg = DFORegressor()\n reg.fit(*iris_data)\n assert hasattr(reg, \"is_fitted_\")\n\n\ndef test_invalid_loss():\n \"\"\"Test invalid loss\"\"\"\n with pytest.raises(NotImplementedError):\n _ = DFORegressor(loss=\"myloss\")\n\n\ndef test_invalid_value_learning_rate():\n \"\"\"Test invalid string value for learning rate\"\"\"\n with pytest.raises(ValueError):\n _ = DFORegressor(learning_rate=\"myrate\")\n\n\ndef test_invalid_k(iris_data):\n \"\"\"Test invalid k given data\"\"\"\n with pytest.raises(ValueError):\n reg = DFORegressor(k=10) # n_features = 4\n reg.fit(*iris_data)\n","repo_name":"miguelfmc/sklearn-discretefirstorder","sub_path":"discretefirstorder/tests/test_discrete_first_order.py","file_name":"test_discrete_first_order.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3837299096","text":"import calendar\nimport datetime\nimport unittest\nimport mock\nimport os\n\nfrom mip_convert_wrapper import TIME_UNIT, NEMO_SUBSTREAMS\nfrom mip_convert_wrapper.file_management import (\n _expected_ap, _expected_in, _expected_on,\n get_paths, copy_to_staging_dir, link_data,\n )\n\n\nclass TestMisc(unittest.TestCase):\n \"\"\"\n Test miscellaneous helper functions in the file_management.py module.\n \"\"\"\n\n def test_get_paths(self):\n \"\"\"\n Tests the file_management.get_paths function.\n \"\"\"\n suite_name = 'u-RUNID'\n stream = 'ap4'\n start_date = TIME_UNIT.num2date(0)\n length1 = 360 * 5\n end_date = TIME_UNIT.num2date(length1)\n step = 30\n input_dir = os.path.sep + os.path.join('path', 'to', 'input', 'dir')\n work_dir = os.path.sep + os.path.join('path', 'to', 'work', 'dir')\n\n (expected_files_test,\n old_input_location_test,\n new_input_location_test,) = get_paths(suite_name,\n stream,\n start_date,\n end_date,\n input_dir,\n work_dir)\n\n expected_files_exp = generate_expected(_ap_formatter_1,\n step,\n stream,\n length1,\n )\n\n old_input_exp = os.path.join(input_dir,\n suite_name,\n stream,\n )\n new_input_exp = os.path.join(work_dir,\n suite_name,\n stream,\n )\n\n self.assertEqual(old_input_location_test,\n old_input_exp,\n 'old_input_location not correct')\n\n self.assertEqual(new_input_location_test,\n new_input_exp,\n 'new_input_location not correct')\n\n self.assertEqual([l1 for l1 in expected_files_test],\n list(expected_files_exp))\n\n @mock.patch('os.path.exists')\n @mock.patch('shutil.copy')\n def test_copy_to_staging_dir(self,\n mock_shutil_copy2,\n mock_os_exists):\n \"\"\"\n Tests the file_management.copy_to_staging_dir function.\n \"\"\"\n src_dir = '/path/to/src/dir/'\n dest_dir = '/path/to/dest/dir/'\n start_dt = datetime.datetime(year=1850, month=1, day=1)\n end_dt = datetime.datetime(year=1854, month=12, day=30)\n expected_files = [(start_dt, end_dt, 'file1.nc'),\n (start_dt, end_dt, 'file2.nc'),\n (start_dt, end_dt, 'file3.nc')]\n\n mock_os_exists.return_value = True\n\n copy_to_staging_dir(expected_files, src_dir, dest_dir)\n\n copy2_calls_list = [mock.call(os.path.join(src_dir, f1),\n os.path.join(dest_dir, f1))\n for sdt, edt, f1 in expected_files]\n\n mock_shutil_copy2.assert_has_calls(copy2_calls_list)\n\n @mock.patch('glob.glob')\n @mock.patch('os.symlink')\n @mock.patch('os.path.exists')\n def test_link_data(self,\n mock_os_exists,\n mock_os_symlink,\n mock_glob_glob):\n \"\"\"\n Tests the file_management.link_data function.\n \"\"\"\n src_dir = '/path/to/src/dir/'\n dest_dir = '/path/to/dest/dir/'\n start_dt = datetime.datetime(year=1850, month=1, day=1)\n end_dt = datetime.datetime(year=1854, month=12, day=30)\n expected_files = [(start_dt, end_dt, 'file1.nc'),\n (start_dt, end_dt, 'file2.nc'),\n (start_dt, end_dt, 'file3.nc')]\n\n mock_glob_glob.return_value = []\n\n exists_returns = [True ,]\n for _ in expected_files:\n exists_returns += [False, True,]\n mock_os_exists.side_effect = exists_returns\n\n num_links, link_dir = link_data(expected_files, src_dir, dest_dir)\n\n self.assertEqual(3, num_links)\n symlink_calls = [mock.call(os.path.join(src_dir, f1),\n os.path.join(dest_dir, f1))\n for sdt, edt, f1 in expected_files]\n\n mock_os_symlink.assert_has_calls(symlink_calls)\n\n\nclass TestExpectedAP(unittest.TestCase):\n \"\"\"\n Test expected file names for UM output streams.\n \"\"\"\n def test_monthly(self):\n runid = 'RUNID'\n stream = 'ap4'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected(_ap_formatter_1, step, stream)\n actual = list(_expected_ap(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n def test_10day(self):\n runid = 'RUNID'\n stream = 'ap6'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 10\n expected = generate_expected(_ap_formatter_3, step, stream)\n actual = list(_expected_ap(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n\nclass TestExpectedIN(unittest.TestCase):\n \"\"\"\n Test expected file names for CICE output streams.\n \"\"\"\n def test_monthly(self):\n runid = 'RUNID'\n stream = 'inm'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected(_in_formatter, step, stream)\n actual = list(_expected_in(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n def test_1day(self):\n runid = 'RUNID'\n stream = 'ind'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected(_in_formatter, step, stream)\n\n actual = list(_expected_in(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n\nclass TestExpectedON(unittest.TestCase):\n \"\"\"\n Test expected files for NEMO output streams.\n \"\"\"\n def test_monthly(self):\n runid = 'RUNID'\n stream = 'onm'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected_ocean(step, stream)\n actual = list(_expected_on(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n def test_1day(self):\n runid = 'RUNID'\n stream = 'ond'\n start_date = TIME_UNIT.num2date(0)\n end_date = TIME_UNIT.num2date(360)\n step = 30\n expected = generate_expected_ocean(step, stream)\n actual = list(_expected_on(runid, stream, start_date, end_date))\n self.assertEqual(expected, actual)\n\n\ndef generate_expected(file_name_formatter, step, stream, length=360):\n \"\"\"\n Return a list of tuples representing the time bounds and name\n of each file expected.\n\n Parameters\n ----------\n file_name_formatter : function\n Function that takes the stream name (str) and the file start\n and end dates (datetimes) and returns a the file name expected\n as a string.\n step : int\n Number of days between files, e.g. 10\n stream : str\n Name of the stream, e.g. 'ap4'\n\n Returns\n -------\n : list\n start time, end time and file name for each expected file.\n \"\"\"\n expected = []\n for datenum in range(-step, length + step, step):\n file_start = TIME_UNIT.num2date(datenum)\n file_end = TIME_UNIT.num2date(datenum + step)\n file_name = file_name_formatter(stream, file_start, file_end)\n expected.append((file_start, file_end, file_name))\n return expected\n\n\ndef _ap_formatter_1(stream, file_start, file_end):\n month = calendar.month_abbr[file_start.month].lower()\n file_name = 'RUNIDa.p{}{}{}.pp'.format(\n stream[-1], file_start.year, month)\n return file_name\n\n\ndef _ap_formatter_3(stream, file_start, file_end):\n file_name = 'RUNIDa.p{}{}{:02d}{:02d}.pp'.format(\n stream[-1], file_start.year, file_start.month, file_start.day)\n return file_name\n\n\ndef _in_formatter(stream, file_start, file_end):\n file_name = 'cice_RUNIDi_1{}_{}-{}.nc'.format(\n stream[-1], file_start.strftime('%Y%m%d'),\n file_end.strftime('%Y%m%d'))\n return file_name\n\n\ndef generate_expected_ocean(step, stream):\n \"\"\"\n Return a list of tuples representing the time bounds and name\n of each file expected in ocean streams.\n\n Parameters\n ----------\n step : int\n Number of days between files, e.g. 10\n stream : str\n Name of the stream, e.g. 'ap4'\n\n Returns\n -------\n : list\n start time, end time and file name for each expected file.\n \"\"\"\n expected = []\n for substream in NEMO_SUBSTREAMS:\n for datenum in range(-step, 360 + step, step):\n file_start = TIME_UNIT.num2date(datenum)\n file_end = TIME_UNIT.num2date(datenum + step)\n file_name = 'nemo_RUNIDo_1{}_{}-{}_{}.nc'.format(\n stream[-1], file_start.strftime('%Y%m%d'),\n file_end.strftime('%Y%m%d'), substream\n )\n expected.append((file_start, file_end, file_name))\n return expected\n\n\ndef _on_formatter(stream, file_start, file_end, substream=None):\n file_name = 'nemo_RUNIDi_1{}_{}-{}_{}.nc'.format(\n stream[-1], file_start.strftime('%Y%m%d'),\n file_end.strftime('%Y%m%d'),\n substream)\n return file_name\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"NCAS-CMS/NCAS-Useful-Documentation","sub_path":"cdds_git_files/u-ak283_JSON_wip_first_run_23May2019/app/mip_convert/wrapper/mip_convert_wrapper/tests/test_file_management.py","file_name":"test_file_management.py","file_ext":"py","file_size_in_byte":9903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32389833342","text":"\"\"\"\nTo improve your defining function skills and get familiar with the recursion concept.\n\n -Define a function named my_fact to calculate factorial of the given number. Given a non-negative integer return the factorial of the integer.\n\n(Example: The factorial of 5 is: 5*4*3*2*1 = 120 and factorial of 0 is: 1)\n\"\"\"\n\ndef my_fact(n):\n multip = 1\n for i in range(1, n+1):\n if n == 0:\n return 1\n else:\n multip *= i\n return multip\n \n","repo_name":"msaidcevik/python_exercises","sub_path":"exercise-11-factorial.py","file_name":"exercise-11-factorial.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24742133678","text":"import numpy as np \n\n\ndef transform_data(data, values_key): \n import math\n def isNaN(num):\n return num != num\n def one_hot_embedding(labels, num_classes):\n \"\"\"Embedding labels to one-hot form.\n\n Args:\n labels: (LongTensor) class labels, sized [N,].\n num_classes: (int) number of classes.\n\n Returns:\n (tensor) encoded labels, sized [N, #classes].\n \"\"\"\n y = np.identity(num_classes) \n return y[labels] \n\n\n \n # split data into numeric and categoric\n\n data_num = data.select_dtypes(include=['float', 'int'])\n data_cat = data.select_dtypes(include='object')\n\n #replace values in dict by numeric \n for key, sub_dict in values_key.items():\n \n for sub_key, value in sub_dict.items():\n if isNaN(sub_key):\n data_cat.loc[data_cat[key].isna() , key] = value\n else:\n data_cat.loc[data_cat[key] == sub_key, key] = value\n\n # transform data to torch tensor\n numpy_num = data_num.values\n numpy_cat = data_cat.values\n\n\n\n def StandardizeData( data):\n \"\"\"Scales data to mean 0 and std 0\n\n Args:\n data\n\n Returns:\n (tensor) Normalised data\n \"\"\"\n \n return (data - np.mean( data, axis= 0))/np.std( data, axis = 0)\n \n \n\n print(np.sum(np.isnan(numpy_num) ))\n print(np.sum(np.isnan(numpy_cat) ))\n\n nr_data, num_features = np.shape(numpy_cat)\n print( nr_data, num_features)\n\n one_hot_data = [] \n for index,feat in enumerate( data_cat.columns):\n one_hot_data.append(one_hot_embedding( numpy_cat[:, index], len(values_key[feat])) )\n numpy_one_hot = np.concatenate( one_hot_data, axis= 1)\n numpy_num_standard = StandardizeData( numpy_num)\n\n\n all_data = np.concatenate(( numpy_num_standard , numpy_one_hot) , axis= 1)\n\n\n # fill torch num nan with -1000\n\n all_data[np.isnan( all_data)] = -1000\n\n print(all_data)\n return all_data\n\n","repo_name":"victor-armegioiu/Deep-Learning","sub_path":"Barebones Fully-Connected Nets/exercise_code/transform_house_data.py","file_name":"transform_house_data.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"72651791441","text":"import random, copy, sys, pygame, math\r\nfrom pygame.locals import *\r\n\r\nWIN_WIDTH = 640 # width of the program's window, in pixels\r\nWIN_HEIGHT = 480 # height in pixels\r\nCOLS = 7 # how many spaces wide the board is\r\nROWS = 6 # how many spaces tall the board is\r\nP_SIZE = 50 # size of the tokens and individual board spaces in pixels\r\nX_MARG = int((WIN_WIDTH - COLS * P_SIZE) / 2)\r\nY_MARG = int((WIN_HEIGHT - ROWS * P_SIZE) / 2)\r\n\r\nboard_width = COLS * P_SIZE\r\nboard_height = ROWS * P_SIZE\r\n\r\nNONE = None\r\nRED_TKN = 'R'\r\nYELLOW_TKN = 'Y'\r\nREQ_TO_WIN = 4\r\nassert COLS >= 4 and ROWS >= 4, 'Board must be at least 4x4.'\r\n\r\nFPS = 30 # frames per second to update the screen\r\n\r\n# Colours\r\nBRIGHTBLUE = (0, 50, 255)\r\nBLACK = (0, 0, 0)\r\nWHITE = (255, 255, 255)\r\nRED = (255, 0, 0)\r\nYELLOW = (255, 255, 0)\r\nBGCOLOR = BRIGHTBLUE\r\nTEXTCOLOR = WHITE\r\n\r\n\r\n\r\ndef main():\r\n '''\r\n Main function: calls user and computer turns\r\n '''\r\n global screen, clock, font\r\n pygame.init()\r\n clock = pygame.time.Clock()\r\n screen = pygame.display.set_mode((WIN_WIDTH, WIN_HEIGHT))\r\n font = pygame.font.SysFont('Calibri', 25, True, False)\r\n pygame.display.set_caption('C4')\r\n main_board = get_new_board()\r\n\r\n turn = \"player\"\r\n\r\n carry_on = True\r\n while carry_on:\r\n\r\n # --- Game logic ---\r\n\r\n for event in pygame.event.get():\r\n if event.type == QUIT or (event.type == KEYUP and event.key == K_ESCAPE):\r\n pygame.quit()\r\n sys.exit()\r\n # Draw board\r\n screen.fill(WHITE)\r\n draw_board(main_board)\r\n\r\n if turn == \"player\":\r\n # Human player's turn.\r\n # TEST FOR CORRECT COLOR TILE?\r\n xy_pos = get_player_move(main_board)\r\n \r\n # Check for win\r\n if is_position_winner(xy_pos[0], xy_pos[1], main_board):\r\n # Render some WInninG Text\r\n text = font.render(\"You WIN!\", True, BLACK)\r\n screen.blit(text, [WIN_WIDTH // 2, WIN_HEIGHT - P_SIZE])\r\n\r\n # break to outside Main Loop for reset or quit\r\n carry_on = False\r\n break\r\n # switch to other computers's turn\r\n turn = \"computer\"\r\n\r\n if turn == \"computer\":\r\n # index of poss_moves is COL, value is fitness\r\n computer_move = get_computer_move(main_board, YELLOW_TKN)\r\n comp_row = get_lowest_row(main_board, computer_move)\r\n main_board[computer_move][comp_row] = YELLOW_TKN\r\n\r\n if is_position_winner(computer_move, comp_row, main_board):\r\n text = font.render(\"You WIN!\", True, BLACK)\r\n screen.blit(text, [WIN_WIDTH // 2, WIN_HEIGHT - P_SIZE])\r\n # break to outside Main Loop for reset or quit\r\n carry_on = False\r\n break\r\n turn = \"player\"\r\n\r\n # --- Screen-clearing code goes here\r\n\r\n # Here, we clear the screen to white. Don't put other drawing commands\r\n # above this, or they will be erased with this command.\r\n\r\n # If you want a background image, replace this clear with blit'ing the\r\n # background image.\r\n screen.fill(WHITE)\r\n\r\n # --- Drawing code should go here\r\n draw_board(main_board)\r\n\r\n # --- Go ahead and update the screen with what we've drawn.\r\n\r\n pygame.display.flip()\r\n\r\n # --- Limit to 60 frames per second\r\n clock.tick(60)\r\n\r\n # Close the window and quit.\r\n pygame.quit()\r\n sys.exit()\r\n\r\n\r\ndef make_move(board, col, tile):\r\n '''\r\n takes duplicate/board, col, and tile color\r\n get's lowest row and sets board pos to color\r\n returns consecutive tiles with that board\r\n '''\r\n row = get_lowest_row(board, col)\r\n board[col][row] = tile\r\n return calc_consecutive(col, row, board)\r\n\r\n\r\n# basic algo to check each column move, and every counter move from that\r\ndef get_computer_move(board, color):\r\n # Set colors; irrelevant atm\r\n if color == YELLOW_TKN:\r\n other_color = RED_TKN\r\n else:\r\n other_color = YELLOW_TKN\r\n\r\n # possible_moves index position is col, value of index pos is strength\r\n possible_moves = [0] * COLS\r\n for col in range(COLS):\r\n dupe_board = copy.deepcopy(board)\r\n\r\n if not is_valid_move(board, col):\r\n possible_moves[col] = -1\r\n continue\r\n\r\n # Strength is consecutive tiles for that color\r\n lowest_row = get_lowest_row(dupe_board, col)\r\n dupe_board[col][lowest_row] = color\r\n initial_strength = calc_consecutive(col, lowest_row, dupe_board)\r\n initial_strength = make_move(dupe_board, col, color)\r\n\r\n # Check if winner\r\n if is_position_winner(col, lowest_row, dupe_board):\r\n possible_moves[col] = REQ_TO_WIN\r\n return col\r\n\r\n # Else, Check is winner for other color from that pos\r\n dupe_board2 = copy.deepcopy(dupe_board)\r\n dupe_board2[col][lowest_row] = other_color\r\n if is_position_winner(col, lowest_row, dupe_board2):\r\n return col\r\n\r\n # Check opponent's counter moves\r\n for counter_col in range(COLS):\r\n dupe_board2 = copy.deepcopy(dupe_board)\r\n\r\n if not is_valid_move(dupe_board2, counter_col):\r\n # counter move is worthless\r\n continue\r\n\r\n counter_row = get_lowest_row(dupe_board2, counter_col)\r\n\r\n # Test Play other_color tile for potential counter\r\n dupe_board2[counter_col][counter_row] = other_color\r\n counter_row = get_lowest_row(dupe_board2, counter_col)\r\n counter_strength = calc_consecutive(counter_col, counter_row, dupe_board2)\r\n\r\n # Check is_valid() for playing other_color\r\n if not is_valid_move(dupe_board2, counter_col):\r\n # Skip to next counter position\r\n continue\r\n\r\n # If counter is winner, then don't play init col\r\n # This can produce deviant results - where counter col is same as col\r\n if is_position_winner(counter_col, counter_row, dupe_board2):\r\n possible_moves[col] = -1\r\n # += poss_moves[col]?\r\n possible_moves[counter_col] = counter_strength\r\n else:\r\n if counter_strength >= initial_strength:\r\n # Defensive - Play counter col not init col\r\n possible_moves[counter_col] = counter_strength\r\n\r\n # After checking all counters\r\n if possible_moves[col] < initial_strength:\r\n possible_moves[col] = initial_strength\r\n\r\n best_moves = []\r\n max_str = max(possible_moves)\r\n\r\n for i in range(len(possible_moves)):\r\n if possible_moves[i] == max_str:\r\n # Append index value\r\n best_moves.append(i)\r\n\r\n return best_moves[random.randrange(len(best_moves))]\r\n\r\n\r\ndef get_player_move(board):\r\n '''\r\n Player move: click on column for tile placement\r\n Automatically drops tile to lowest row\r\n '''\r\n while True:\r\n # Check for exit, mouse click\r\n for event in pygame.event.get():\r\n if event.type == QUIT or (event.type == KEYUP and event.key == K_ESCAPE):\r\n pygame.quit()\r\n sys.exit()\r\n\r\n elif event.type == MOUSEBUTTONDOWN:\r\n pos_x, pos_y = event.pos\r\n\r\n elif event.type == MOUSEMOTION:\r\n pos_x, pos_y = event.pos\r\n\r\n elif event.type == MOUSEBUTTONUP:\r\n if X_MARG < pos_x < (WIN_WIDTH - X_MARG):\r\n\r\n column = int((pos_x - X_MARG) / P_SIZE)\r\n\r\n if is_valid_move(board, column):\r\n animate_falling_token(board, column, RED)\r\n row = get_lowest_row(board, column)\r\n\r\n # update board\r\n board[column][row] = RED_TKN\r\n draw_board(board)\r\n pygame.display.update()\r\n xy_human_pos = [column, row]\r\n return xy_human_pos\r\n draw_board(board)\r\n pygame.display.update()\r\n clock.tick()\r\n\r\n\r\n# returns max items in a line 1, 2, 3, or 4\r\n# use as strength of move\r\ndef calc_consecutive(pos_col, pos_row, board):\r\n # item = board[pos_col][pos_row]\r\n row_bound = len(board[0])\r\n col_bound = len(board)\r\n\r\n max_consecutive = 1\r\n temp_con = 1\r\n for delta_col, delta_row in [(0, 1), (1, 0), (1, 1), (-1, 1)]:\r\n consecutive_items = 1\r\n for delta in (1, -1):\r\n delta_col *= delta\r\n delta_row *= delta\r\n next_col = pos_col + delta_col\r\n next_row = pos_row + delta_row\r\n\r\n while 0 <= next_row < row_bound and 0 <= next_col < col_bound:\r\n if board[next_col][next_row] == board[pos_col][pos_row]:\r\n consecutive_items += 1\r\n temp_con = consecutive_items\r\n else:\r\n # if not consecutive, reset and break\r\n consecutive_items = 1\r\n break\r\n next_row += delta_row\r\n next_col += delta_col\r\n if consecutive_items == 4:\r\n return 4\r\n if temp_con > max_consecutive:\r\n max_consecutive = temp_con\r\n\r\n return max_consecutive\r\n\r\n# Almost duplicate code of consecutive_strength\r\ndef is_position_winner(pos_col, pos_row, board):\r\n # item = board[pos_col][pos_row]\r\n row_bound = len(board[0])\r\n col_bound = len(board)\r\n\r\n # pos_row = get_lowest_row(board, pos_col)\r\n\r\n for delta_col, delta_row in [(0, 1), (1, 0), (1, 1), (-1, 1)]:\r\n consecutive_items = 1\r\n\r\n for delta in (1, -1):\r\n delta_col *= delta\r\n delta_row *= delta\r\n next_col = pos_col + delta_col\r\n next_row = pos_row + delta_row\r\n\r\n while 0 <= next_row < row_bound and 0 <= next_col < col_bound:\r\n if board[next_col][next_row] == board[pos_col][pos_row]:\r\n consecutive_items += 1\r\n else:\r\n break\r\n if consecutive_items == 4:\r\n return True\r\n next_row += delta_row\r\n next_col += delta_col\r\n return False\r\n\r\n\r\ndef animate_falling_token(board, column, color):\r\n lowest_space = get_lowest_row(board, column)\r\n radius = 24\r\n # column += 1\r\n x = X_MARG + column * P_SIZE\r\n y = Y_MARG + radius\r\n drop_speed = 0.05\r\n # get_lowest already vetted in main\r\n\r\n while True:\r\n y += int(drop_speed)\r\n drop_speed += 0.01\r\n if int((y - Y_MARG) / P_SIZE) >= lowest_space:\r\n return\r\n draw_board(board)\r\n pygame.draw.circle(screen, color, (x + radius, y), radius)\r\n pygame.display.update()\r\n clock.tick()\r\n\r\n# Gravity call - will find lowest row for tile placement\r\ndef get_lowest_row(board, col):\r\n row = 5\r\n while board[col][row] != NONE and row > -1:\r\n row -= 1\r\n return row\r\n\r\n# Checks if tile can be placed\r\ndef is_valid_move(board, col):\r\n lowest_row = get_lowest_row(board, col)\r\n if lowest_row == -1:\r\n return False\r\n else:\r\n return True\r\n\r\n# Initialize board for new game\r\ndef get_new_board():\r\n board = [[NONE] * ROWS for _ in range(COLS)]\r\n return board\r\n\r\n\r\n# Board draw requires updated board\r\ndef draw_board(board):\r\n screen.fill(WHITE)\r\n # Start x, y\r\n x = int(X_MARG + (P_SIZE / 2))\r\n y = int(Y_MARG + (P_SIZE / 2))\r\n pygame.draw.rect(screen, BGCOLOR, [X_MARG, Y_MARG, COLS * P_SIZE, ROWS * P_SIZE])\r\n\r\n # Loop provides i, j of Board[i][j]\r\n for i in range(COLS): # i = 0,1,2,3...\r\n pygame.draw.circle(screen, WHITE, (x, y), 24)\r\n # init y coord\r\n y = int(Y_MARG + (P_SIZE / 2))\r\n\r\n for j in range(ROWS): # j = 0,1,2,3...\r\n # iterate rows downwards for first col\r\n # next centre at y += 52\r\n if board[i][j] == RED_TKN:\r\n pygame.draw.circle(screen, RED, (x, y), 24)\r\n elif board[i][j] == YELLOW_TKN:\r\n pygame.draw.circle(screen, YELLOW, (x, y), 24)\r\n else:\r\n pygame.draw.circle(screen, WHITE, (x, y), 24)\r\n y += 50\r\n\r\n # Render the text. \"True\" means anti-aliased text.\r\n\r\n text = font.render(str(i + 1), True, BLACK)\r\n\r\n # Put the image of the text on the screen at 250x250\r\n # screen.blit(text, [x, y])\r\n screen.blit(text, [x, y])\r\n x += 50\r\n\r\n\r\nmain()\r\n","repo_name":"Davvott/CP1401","sub_path":"C4_pygame.py","file_name":"C4_pygame.py","file_ext":"py","file_size_in_byte":12726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21175782950","text":"import re\nimport datetime\nimport inspect as ins\nfrom sys import stdout,stdin\nfrom time import sleep\nfrom debug import TDB,ploc\nfrom rig.ipython import embed\nfrom sqlalchemy.orm import object_session #FIXME vs database.imports?\nfrom database.decorators import Get_newest_id, datafile_maker, new_abf_DataFile, hardware_interface, is_mds, new_DataFile\nfrom threading import RLock\nfrom rig.gui import takeScreenCap #FIXME\n#from IPython import embed\ntry:\n import rpdb2\nexcept:\n pass\n\ntdb=TDB()\nprintD=tdb.printD\nprintFD=tdb.printFuncDict\ntdb.off()\n\n#file to consolidate all the different functions I want to execute using the xxx.Control classes\n#TODO this file needs a complete rework so that it can pass data to the database AND so that it can be used by keyboard AND so that it can be used by experiment scripts... means I may need to split stuff up? ;_;\n#TODO rig control vs experiment control... these are technically two different 'modes' one is keyboard controlled the other is keyboard initiated...\n#TODO ideally I want to do experiments the same way every time instead of allowing one part here and another there which is sloppy so those are highly ordered...\n#TODO BUT I need a way to fix things, for example if the slice moves and I need to recalibrate the slice position (FUCK, how is THAT going to work out in metadata)\n\n#TODO all of these are configured for terminal output only ATM, ideally they should be configged by whether they are called from keyboard or from experiment... that seems... reasonable??! not very orthogonal...\n#mostly because when I'm running an experiment I don't want to accientally hit something or cause an error\n\n#TODO split in to send and recieve?!?\n\n#TODO datasource/expected datasource mismatch\n\n#dec for telling the program to run the function in the same thread\ndef sameThread(function):\n def _sameThread(self,*args,**kwargs):\n out=function(self,*args,**kwargs)\n return out\n _sameThread.sameThread=True\n _sameThread.__name__=function.__name__\n return _sameThread\n\n\n#static method for adding a function as a key requester\ndef keyRequest(function): #FIXME sometimes we want to release a kr EARLY!\n def _keyRequest(self,*args,**kwargs):\n def wrapper():\n try:\n self.krdLock.acquire()\n self._kr_not_done+=1\n printD(self._kr_not_done)\n out=function(self,*args,**kwargs)\n except:\n #self._kr_not_done-=1\n raise\n #printD('an error has occured while calling func!')\n finally:\n self._kr_not_done-=1 #moved this here to prevent weird errors?\n printD(self._kr_not_done)\n self.krdLock.release()\n assert self._kr_not_done >= 0 ,'utoh! _kr_not_done < 0 !'\n printD('kr_not_done=',self._kr_not_done)\n if not self._kr_not_done: #TODO prevent multiple unlocks\n self._releaseKR()\n return out\n return wrapper() #FIXME this seems to be needed to prevent some other race condition >_<\n _keyRequest.keyRequester=True\n _keyRequest.__name__=function.__name__\n _keyRequest.__doc__=function.__doc__\n if hasattr(function,'is_mds'):\n _keyRequest.is_mds=True\n return _keyRequest\n\n###\n#class decorator\ndef hasKeyRequests(cls):\n cls.keyRequesters=[]\n cls._kr_not_done=0 #FIXME will we get nasty threading collisions here?\n def init_wrap(init):\n def __init__(self,modestate,*args):\n self._releaseKR=modestate.releaseKeyRequest\n for name in self.keyRequesters:\n modestate.registerKeyRequest(cls.__name__,name)\n init(self,modestate,*args)\n return __init__\n cls.__init__=init_wrap(cls.__init__)\n\n for name,method in cls.__dict__.items(): #register keyRequesters\n if hasattr(method,'keyRequester'):\n #printD('got one!',name)\n cls.keyRequesters.append(name)\n return cls\n\nclass kCtrlObj:\n \"\"\"key controller object\"\"\"\n def __init__(self, modestate):\n self.charBuffer=modestate.charBuffer\n #self.releaseKeyReq=modestate.releaseKeyRequest #FIXME don't even have to do this, @keyRequest can do it for me! :)\n #I probably do not need to pass key handler to thing outside of inputManager...\n #yep, not used anywhere, but I supose it could be used for submodes... we'll leave it in\n self.setMode=modestate.setMode\n self.__mode__=self.__class__.__name__\n self.keyThread=modestate.keyThread\n\n self._kr_not_done=0\n self._releaseKR=modestate.releaseKeyRequest\n self.krdLock=modestate.krdLock\n self.keyRequesters=[]\n for name,method in ins.getmembers(self):\n #printD(name,method)\n if ins.ismethod(method) and hasattr(method,'keyRequester'):\n self.keyRequesters.append(name)\n modestate.registerKeyRequest(self.__class__.__name__,name)\n\n #some way to pass the state along to other controllers XXX REQUIRED FOR CLASS DECORATORS TO WORK XXX\n def dat_get_write_targets():\n return modestate.ctrlDict['datFuncs'].getWriteTargets()\n def dat_get_df_subjects():\n return modestate.ctrlDict['datFuncs'].getDFSubjects()\n def dat_new_df():\n return modestate.ctrlDict['datFuncs'].newDataFileCallback()\n self._wt_getter=dat_get_write_targets\n self._sub_getter=dat_get_df_subjects #name needs to match for decorators to work >_<\n self._new_datafile=dat_new_df\n\n @keyRequest\n def __getChars__(self,prompt=''):\n \"\"\" replacement for input()\"\"\"\n\n class inputBuffer:\n char_list=[]\n pos=0\n _filler=0 #used to make the display update correct\n def put(self,char):\n self.char_list.insert(self.pos,char)\n #if self._filler > 0: #don't need it is ok to overfill\n #self._filler -= 1\n self.goR()\n def goR(self):\n if self.pos + 1 <= len(self.char_list):\n self.pos += 1\n def goL(self):\n if self.pos -1 >= 0:\n self.pos -= 1\n def home(self):\n self.pos = 0\n def end(self):\n self.pos = len(self.char_list)\n def backspace(self):\n if self.pos: #if we're already at zero dont go!\n self._filler+=1\n self.char_list.pop(self.pos-1)\n self.goL()\n def delete(self):\n try:\n self.char_list.pop(self.pos)\n self._filler+=1\n except IndexError:\n pass\n def __str__(self):\n return ''.join(self.char_list)\n @property\n def str_to_pos(self):\n return ''.join(self.char_list[:self.pos])\n def pop_filler(self):\n out=self._filler #ick this is terrible TODO FIXME\n self._filler=0\n return out\n\n ib=inputBuffer()\n\n cmddict={\n '[A':lambda:None,\n '[B':lambda:None,\n '[C':ib.goR,\n '[D':ib.goL,\n '\\x7f':ib.backspace,\n '[3~':ib.delete,\n '[7~':ib.home,\n '[8~':ib.end,\n 'esc':lambda:None,\n }\n\n def charHand(char):\n if cmddict.get(char):\n cmddict[char]()\n elif len(char) > 1:\n pass #prevents escape sequences from printing\n else:\n ib.put(char)\n\n #self.keyLock.acquire()\n stdout.write('\\r%s'%prompt)\n while self.keyThread.is_alive():\n char=self.charBuffer.get()\n #printD('got a key:', char) \n if char == '\\n' or char == '\\r':\n printD('done')\n stdout.write('\\n\\r')\n stdout.flush()\n break\n charHand(char)\n stdout.write('\\r%s'%prompt+str(ib)+' '*ib.pop_filler())\n stdout.write('\\r%s'%prompt+ib.str_to_pos)\n stdout.flush()\n if not self.keyThread.is_alive():\n raise IOError('Key thread is not alive!')\n else:\n #print(str(ib))\n return str(ib)\n\n @keyRequest\n def getString(self,prompt='string> '):\n print(\"Please enter a string.\",)\n return self.__getChars__(prompt)\n\n @keyRequest\n def getFloat(self,prompt='float> '):\n print('Please enter a floating point value.',)\n while self.keyThread.is_alive():\n string=self.__getChars__(prompt)\n try:\n out=float(string)\n return out\n except:\n print('Could not convert value to float, try again!')\n\n @keyRequest\n def getInt(self,prompt='int> '):\n print('please enter an integer',)\n while self.keyThread.is_alive():\n string=self.__getChars__(prompt)\n try:\n out=int(string)\n return out\n except ValueError as e:\n print(e,'Try again!')\n #print('could not convert value to int, try again!')\n\n @keyRequest\n def getBool(self,prompt='Boolean: hit space for True, anything else for False.'):\n print(prompt)\n true_key=' '\n #self.keyHandler(1) #requesting key passthrough\n out=self.charBuffer.get() == true_key\n return out\n\n def cleanup(self):\n pass\n\nfrom database.models import Person, Project, ExperimentType, Experiment, Cell, Slice, Mouse, DataFile\nfrom datetime import datetime\nfrom database.imports import NoResultFound\nclass datFuncs(kCtrlObj): \n #interface with the database TODO this should be able to run independently?\n \"\"\"Put ANYTHING permanent that might be data in here\"\"\"\n def __init__(self,modestate,*args,person_id=None,project_id=None):\n super().__init__(modestate)\n self.Session=modestate.Session #FIXME maybe THIS was the problem?\n session=self.Session()\n self.c_person=session.query(Person).filter_by(id=person_id).one()\n self.c_project=session.query(Project).filter_by(id=project_id).one() #FIXME the person could not be on the project\n self.getUnfinished(session)\n self.c_datafile=None\n self.session=session #FIXME\n self.sLock=RLock() #a lock for the session, see if we need it\n #session.close()\n\n def getUnfinished(self,session): #FIXME it is possible to get cells or slices w/o experiment...\n def queryUnf(obj):\n return session.query(obj).filter(obj.endDateTime==None)\n #experiemtns\n try:\n self.c_experiment=queryUnf(Experiment).one()\n self.c_target=self.c_experiment\n print('Got unfinished experiment ',repr(self.c_experiment))\n except NoResultFound:\n self.c_experiment=None\n self.c_target=None\n #slices\n try:\n self.c_slice=queryUnf(Slice).one()\n self.c_target=self.c_slice\n print('Got unfinished slice ',self.c_slice.strHelper())\n except NoResultFound:\n self.c_slice=None\n #cells\n try:\n cells=queryUnf(Cell).join(Experiment,Cell.experiments).filter(Experiment.id==self.c_experiment.id).join(Slice,Cell.parent).filter(Slice.id==self.c_slice.id).all()\n self.c_cells=cells\n if cells:\n print('Got unfinished cells ',[c.strHelper() for c in cells])\n self.c_target=self.c_cells[0]\n except AttributeError: #catch NoneType\n self.c_cells=[]\n #datafiles #TODO\n\n @keyRequest\n def setWriteTargets(self):\n print('slice','cell','exp','data')\n type_=self.__getChars__('target> ')\n if type_ == 'exp':\n self.c_target=self.c_experiment\n elif type_ == 'slice':\n self.c_target=self.c_slice\n elif type_ == 'cell':\n print('cell number 0-n amongst current cells n probably <=3')\n index=self.getInt('cell #> ')\n if index >= len(self.c_cells):\n index=len(self.c_cells)-1\n self.c_target=self.c_cells[index]\n elif type_ == 'data':\n self.c_target=self.c_datafile\n else:\n print('not a type, try fiddling with ipython if you really need to spec soemthing')\n\n\n def getDFSubjects(self):\n \"\"\" used to pass the current cells to the new_abf_DataFile decorator \"\"\"\n return self.c_cells\n \n def getWriteTargets(self): #XXX NOTE XXX there is only a SINGLE write target at a time now!\n targets=[] #FIXME change calling methods to match\n target=self.c_target\n #try:\n #iter(target)\n #targets.extend(target)\n #except TypeError:\n targets.append(target)\n\n return targets\n\n def get_df_statistics(self):\n \"\"\" calculate and rpint the test pulse statistics and hopefully save them to the database? \"\"\"\n from database.abf_analysis import print_tp_stats\n print_tp_stats(self.c_datafile.full_url[8:])\n \n def print_write_target(self):\n print(repr(self.c_target))\n try:\n iter(self.c_target)\n for t in self.c_target:\n self.session.refresh(t)\n print(t.metadata_)\n print(t.notes)\n try:\n print(t.datafiles)\n except:\n pass\n except:\n self.session.refresh(self.c_target)\n print(self.c_target.metadata_)\n print(self.c_target.notes)\n\n def set_slice_md(self,markDict): #FIXME seems like this might be related to our errors?\n self.c_slice.markDict=markDict\n self.session.add(self.c_slice)\n #print(self.c_slice.markDict)\n try:\n self.session.commit()\n except:\n session.rollback()\n raise\n\n def printAll(self):\n print(self.c_person.strHelper())\n print(repr(self.c_project))\n print(repr(self.c_experiment))\n print(repr(self.c_slice))\n print(self.c_cells)\n print(repr(self.c_datafile))\n print('target:',repr(self.c_target))\n\n def newDataFileCallback(self):\n new_df=self.session.query(DataFile).order_by(DataFile.creationDateTime.desc()).first()\n printD('c_datafile set to %s'%new_df)\n self.c_datafile=new_df\n self.c_target=new_df\n \n @keyRequest\n def newNote(self): #FIXME need a way to hit a single cell\n note=self.__getChars__('note> ')\n #try:\n #iter(self.c_target)\n #for t in self.c_target:\n #n=t.Note(note,t)\n #self.session.add(n)\n #except:\n n=self.c_target.Note(note,self.c_target)\n self.session.add(n)\n #finally:\n self.session.commit()\n\n @keyRequest\n def newExperiment(self): #FIXME this fails because of how dictMan works...\n session=self.session\n #session=self.Session()\n types=session.query(ExperimentType).all()\n print('please enter the type of experiment, available types are: %s'%[t.abbrev for t in types])\n type_=None\n type_string=self.__getChars__('type>')\n type_=[t for t in types if t.abbrev==type_string]\n if self.c_experiment: #FIXME this is here to prevent keyhandler from going haywire\n #if session.query(Experiment).get(self.c_experiment.id): #FIXME if an experiment is deled elsewhere\n print('[!] You already have an experiment, end the current one first!')\n return self\n #else:\n #self.c_experiment=None #FIXME actually noat an issue it would seem\n #print('[!] It would seem that you current expeirment got deleted...')\n if type_:\n if type_[0].abbrev=='patch':\n se=session.query(Experiment).join(ExperimentType,Experiment.type).filter(ExperimentType.abbrev=='slice').order_by(Experiment.id.desc()).first()\n if not se:\n print('[!] There is no slice experiment on record! Please add one!')\n return self\n experiment=Experiment(type_id=type_[0],project_id=self.c_project,person_id=self.c_person)\n session.add(experiment)\n try: \n session.commit()\n self.c_experiment=experiment\n self.c_target=experiment\n print('New experiment added = %s'%self.c_experiment.strHelper())\n except :\n print('[!] experiment NOT added')\n session.rollback() #FIXME could be dangerous? if others are in the session?\n finally:\n #session.close()\n pass\n else:\n print('Invalid type: experiment not created')\n return self\n #newExperiment.keyRequester=True\n\n def newSlice(self):\n if self.c_slice:\n #raise BaseException('You already have a slice on the rig!')\n print('[!] You already have a slice on the rig!')\n return self\n elif not self.c_experiment:\n print('[!] You have not added an experiment!')\n return self\n session=self.session\n #session=self.Session()\n ge=session.query(Experiment).join(ExperimentType,Experiment.type).filter(ExperimentType.abbrev=='slice').order_by(Experiment.id.desc()).first()\n if not ge:\n print('[!] There is no slice experiment on record! Please add one!')\n return self\n slice_=Slice(generating_experiment_id=ge)\n slice_.experiments.append(self.c_experiment)\n session.add(slice_)\n try:\n session.commit()\n self.c_slice=slice_\n self.c_target=slice_\n print('New slice added = %s'%repr(self.c_slice))\n except:\n session.rollback()\n finally:\n #session.close()\n pass\n\n def newCell(self):\n if not self.c_experiment:\n print('[!] Cells dont really belong in slice prep experiments')\n return self\n elif self.c_experiment.type.abbrev=='slice':\n print('[!] Cells dont really belong in slice prep experiments')\n return self\n if len(self.c_cells) >=2:\n print('[!] You already have 2 cells! New rig eh?')\n return self\n if not self.c_slice:\n print('You do not have a slice on the rig!')\n return self\n #raise BaseException('You do not have a slice on the rig!')\n #self.__getChars__('headstage>')\n cell=Cell(parent_id=self.c_slice.id,generating_experiment_id=self.c_experiment)\n cell.experiments.append(self.c_experiment)\n session=self.session\n #session=self.Session()\n session.add(cell)\n try:\n session.commit()\n self.c_cells.append(cell)\n self.c_target=cell #FIXME\n print('New cell added = %s'%repr(cell))\n except:\n session.rollback()\n finally:\n #session.close()\n pass\n #newCell.keyRequester=True\n \n @keyRequest #FIXME?\n def getBrokenIn(self):\n if self.getBool('Hit space if you broke in otherwise fail!'):\n #TODO metadata on breakin?\n print('Yay!')\n else:\n print('Your failure has been noted.')\n #self.c_target.notes.append('FAILURE MESSAGE') #TODO\n self.endCell(self.c_target) #FIXME make sure this works?\n return self\n\n def endDataFile(self):\n if not self.c_datafile:\n print('No datafile to end')\n return None\n self.c_datafile=None\n self.c_target=self.c_cells[0]\n\n def endCell(self,cell=None): #TODO I think it might be worth having a c_cell in addition??!?\n if not self.c_cells:\n print('No cell to end.')\n return self\n if not cell and type(self.c_target) is not Cell:\n print('Current target is not a cell!')\n return self\n elif not cell: #FIXME\n cell=self.c_target\n\n cell.endDateTime=datetime.now()\n self.session.commit()\n self.c_cells.remove(cell)\n print('Ended cell %s'%cell.strHelper())\n if self.c_cells:\n self.c_target=self.c_cells[0] #FIXME\n else:\n self.c_target=self.c_slice\n return self\n\n def endSlice(self): #TODO location in the pfa well?\n if not self.c_slice:\n print('No slice to end.')\n return self\n if self.c_cells:\n print('You still have cells! Please end them first!')\n return self\n self.c_slice.endDateTime=datetime.now()\n session=self.session\n #session=self.Session()\n #session.add(self.c_slice)\n session.commit()\n print('Ended slice %s'%self.c_slice.strHelper())\n self.c_slice=None\n self.c_target=self.c_experiment\n\n def endExperiment(self):\n if not self.c_experiment:\n print('No experiment to end.')\n return self\n if self.c_slice:\n print('[!] You still have slices! End them first!')\n return self\n self.c_experiment.endDateTime=datetime.now()\n session=self.session\n #session=self.Session()\n #session.add(self.c_experiment)\n session.commit()\n print('Ended experiment %s'%self.c_experiment.strHelper())\n self.c_experiment=None\n self.c_target=None\n \n@datafile_maker#@hardware_interface('Digidata 1322A')\nclass clxFuncs(kCtrlObj):\n def __init__(self, modestate, controller):\n try:\n if controller.__class__.__name__ is not 'clxControl':\n raise TypeError('wrong controller type')\n except:\n raise\n #from clx import clxControl\n super().__init__(modestate)\n self.clx=controller\n #self.initController(clxmsg)\n #printD('clx ctrl',self.clx)\n #self.clxCleanup=self.cleanup\n self.programDict={}\n self.current_program=''\n #self.wrapDoneCB()\n\n #class only\n def readProgDict(self,progDict):\n self.programDict=progDict\n return self\n\n def setProtocolPath(self,protoPath):\n print('PROTOCOL PATH SET %s'%protoPath)\n self.protocolPath=protoPath\n\n def cleanup(self):\n super().cleanup()\n try:\n self.clx.DestroyObject()\n print(self.clx.__class__,'handler destroyed')\n except:\n pass\n #print('this this works the way it is supposed to the we should never have to destory the object')\n\n\n #input with output\n def getStatus(self,outputs=None): #TODO outputs... should be able to output to as many things as I want... probably should be a callback to simplify things elsewhere? no?!?!\n status=self.clx.GetStatus()\n print(status)\n return self\n\n def wait_till_done(self):\n gs=self.clx.GetStatus\n while gs() != 'CLXMSG_ACQ_STATUS_IDLE' and self.keyThread.is_alive():\n sleep(.001) #FIXME\n print('Done recording')\n\n def loadfile(self,filename):\n try:\n filepath=self.protocolPath+filename\n self.clx.LoadProtocol(filepath.encode('ascii'))\n self.current_program=filepath\n print('%s loaded!'%filepath)\n except BaseException as e:\n print(e)\n\n #@keyRequest\n def load(self,key):\n #if not key:\n #print('Please enter the program to load')\n #key=self.charBuffer.get()\n try:\n filename=self.programDict[key]\n filepath=self.protocolPath+filename\n self.clx.LoadProtocol(filepath.encode('ascii'))\n self.current_program=filepath\n print('%s loaded!'%filepath)\n except BaseException as e:\n print(e)\n #print('Program not found')\n #raise\n return self\n\n @new_abf_DataFile()\n def record(self):\n #RECORD=109, RERECORD=110, VIEW=108\n try:\n self.clx.StartAcquisition(109)\n print('Running %s'%self.current_program)\n except:\n raise\n return self\n\n def view(self):\n #RECORD=109, RERECORD=110, VIEW=108\n try:\n self.clx.StartAcquisition(108)\n print('Viewing %s'%self.current_program)\n except:\n raise\n finally:\n return self\n \n def stop_rec(self):\n self.clx.StopAcquisition()\n print('Stopping curren acquistion')\n\n\n #input only\n def startMembTest(self):\n self.clx.StartMembTest(120)\n self.clx.StartMembTest(121)\n return self\n\n\n@hardware_interface('mc1') #FIXME or is it software interface?\nclass mccFuncs(kCtrlObj): #FIXME add a way to get the current V and I via... telegraph?\n def __init__(self, modestate, controller):\n try:\n if controller.__class__.__name__ is not 'mccControl':\n raise TypeError('wrong controller type')\n except:\n raise\n #from mcc import mccControl\n super().__init__(modestate) #better error messages?\n self.mcc=controller\n #self.initController(mccmsg)\n self.MCCstateDict={}\n #self.wrapDoneCB()\n \n #associated metadata sources\n #self.state1DataSource=None\n\n def setGain(self,value=1):\n self.mcc.SetPrimarySignalGain(value)\n print('Primary signal gain set to %s'%value)\n return self\n\n def allGain(self,value=1):\n self.mcc.selectMC(0)\n self.mcc.SetPrimarySignalGain(value)\n self.mcc.selectMC(1)\n self.mcc.SetPrimarySignalGain(value)\n return self\n\n\n def inpWait(self): #XXX depricated\n #wait for keypress to move to the next program, this may need to spawn its own thread?\n raise DeprecationWarning('please use trmFuncs.getKbdHit()')\n\n def getState(self): #FIXME this function and others like it should probably be called directly by dataman?\n printD('hMCCmsg outer',self.mcc.hMCCmsg)\n def base(state):\n state['HoldingEnable']=self.mcc.GetHoldingEnable()\n state['Holding']=self.mcc.GetHolding()\n state['PrimarySignal']=self.mcc.GetPrimarySignal()\n state['PrimarySignalGain']=self.mcc.GetPrimarySignalGain() #also in abf file and already handled by AxonIO\n state['PrimarySignalLPF']=self.mcc.GetPrimarySignalLPF() #also in abf file\n state['PipetteOffset']=self.mcc.GetPipetteOffset()\n\n #def vc(state):\n #base(state)\n #XXX ONLY RELEVANT FOR MODE 0\n state['FastCompCap']=self.mcc.GetFastCompCap()\n state['SlowCompCap']=self.mcc.GetSlowCompCap()\n state['FastCompTau']=self.mcc.GetFastCompTau()\n state['SlowCompTau']=self.mcc.GetSlowCompTau()\n state['SlowCTX20Enable']=self.mcc.GetSlowCompTauX20Enable()\n\n #def ic(state):\n #base(state)\n #XXX ONLY RELEVANT FOR MODE 1\n state['BridgeBalEnable']=self.mcc.GetBridgeBalEnable()\n state['BridgeBalResist']=self.mcc.GetBridgeBalResist()\n\n #def iez(state):\n #base(state)\n\n #modeDict={0:vc,1:ic,2:iez}\n stateList=[]\n for i in range(self.mcc.mcNum):\n self.mcc.selectMC(i)\n state={} #FIXME: make this a dict with keys as the name of the value? eh would probs complicate\n state['Channel']=i #might be suprflulous but it could simplify the code to read out stateList\n mode=self.mcc.GetMode()\n state['mode']=(mode)\n #modeDict[mode](state)\n base(state)\n stateList.append(state)\n print()\n print(state)\n\n self.MCCstateDict[datetime.utcnow()]=stateList\n #print(stateList)\n return stateList\n\n def stateToDataFile(self): #AAAAAAAAAAAHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH SO BAD FIXME\n datFunc=self.modestate.ctrlDict['datFuncs']\n df=datFunc.c_datafile\n stateList=self.getState()\n df.mddictlist=stateList\n datFunc.session.commit()\n return self #IF THIS WORKS I WILL BUY A HAT AND EAT IT WAIT I ALREADY HAVE A HAT\n\n\n def printMCCstate(self):\n print(re.sub('\\), ',')\\r\\n',str(self.MCCstateDict)))\n return self\n\n def setMCState(self,MC=None,Mode=None,Holding=None,HoldingEnable=None): #TODO\n #FIXME all of the experiment logic needs to be stored in one place instead of hidden in 10 files\n #selectMC,SetMode,SetHolding,SetHoldingEnable,\n #self.mcc.selectMC()\n return self\n\n def set_hs0(self):\n print('Setting headstage 0')\n self.mcc.selectMC(0)\n self.current_headstage=0\n return self\n def set_hs1(self):\n print('Setting headstage 1')\n self.mcc.selectMC(1)\n self.current_headstage=1 #TODO use this to link cells to the headstage!\n return self\n def set_hsAll(self): #FIXME\n self.ALL_HS=True\n\n def autoOffset(self):\n self.mcc.AutoPipetteOffset()\n def autoCap(self):\n self.mcc.AutoFastComp()\n self.mcc.AutoSlowComp()\n def setVCholdOFF(self):\n self.mcc.SetMode(0)\n self.mcc.SetHoldingEnable(0)\n def setVCholdON(self):\n self.mcc.SetMode(0)\n self.mcc.SetHoldingEnable(1)\n\n def setVChold(self,holding_volts):\n #print(holding_volts)\n self.mcc.SetMode(0)\n print(holding_volts)\n self.mcc.SetHolding(float(holding_volts))\n h=self.mcc.GetHolding()\n print(h)\n #sleep(.001) #FIXME trying to figure out why holding set to -1000mV\n #self.mcc.SetHoldingEnable(1)\n def setICnoHold(self):\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n\n def allVChold(self,holding_volts):\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(float(holding_volts))\n #self.mcc.SetHoldingEnable(1)\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(float(holding_volts))\n #self.mcc.SetHoldingEnable(1)\n return self\n\n def allIeZ(self):\n self.mcc.selectMC(0)\n self.mcc.SetMode(2)\n self.mcc.selectMC(1)\n self.mcc.SetMode(2)\n return self\n def allVCnoHold(self):\n #try:\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHoldingEnable(0)\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n self.mcc.SetHoldingEnable(0)\n #except:\n #raise BaseException\n return self\n def allVChold_60(self):\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(-.06)\n self.mcc.SetHoldingEnable(1)\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(-.06)\n self.mcc.SetHoldingEnable(1)\n return self\n def allICnoHold(self):\n self.mcc.selectMC(0)\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n self.mcc.selectMC(1)\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n return self\n def testZtO(self,holding_voltage):\n self.mcc.selectMC(0)\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(float(holding_voltage))\n self.mcc.SetHoldingEnable(1)\n return self\n def testOtZ(self,holding_voltage):\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(float(holding_voltage))\n self.mcc.SetHoldingEnable(1)\n self.mcc.selectMC(1)\n self.mcc.SetMode(1)\n self.mcc.SetHoldingEnable(0)\n return self\n def zeroVChold_60(self):\n self.mcc.selectMC(0)\n self.mcc.SetMode(0)\n self.mcc.SetHolding(-.06)\n self.mcc.SetHoldingEnable(1)\n return self\n def oneVChold_60(self):\n self.mcc.selectMC(1)\n self.mcc.SetMode(0)\n #self.mcc.poops(1) #awe, this is broken now due to something\n self.mcc.SetHolding(-.06)\n self.mcc.SetHoldingEnable(1)\n return self\n def cleanup(self):\n super().cleanup()\n try:\n self.mcc.DestroyObject()\n print(self.mcc.__class__,'handler destroyed')\n except:\n pass\n\n\n@hardware_interface('ESP300')\nclass espFuncs(kCtrlObj):\n def __init__(self, modestate, controller):\n try:\n if controller.__class__.__name__ is not 'espControl':\n raise TypeError('wrong controller type')\n except:\n raise\n #from esp import espControl\n\n\n super().__init__(modestate)\n self.esp=controller\n self.markDict={} #FIXME\n self.posDict={} #FIXME\n #self.initController(npControl)\n self.modestate=modestate\n self.setMoveDict()\n self.move_list=[]\n self.motor_on=False\n #self.event=modestate.event\n \n #associated metadatasources:\n #self.EspXDataSource=None\n #self.EspYDataSource=None\n\n @is_mds('m','m','ESP300',5)\n def getPos(self):\n \"\"\" get the (x,y) position of the eps300 \"\"\"\n #may want to demand a depth input (which can be bank)\n #try:\n pos=self.esp.getPos()\n #self.doneCB()\n self.posDict[datetime.now()]=pos #FIXME dat should handle ALL of this internally\n print(pos)\n #except:\n #printD('oops')\n #raise\n return list(pos)\n\n def motorToggle(self):\n self.esp.motorToggle()\n def printPosDict(self):\n #self.doneCB()\n print(re.sub('\\), ',')\\r\\n',str(self.posDict)))\n return self\n\n def set_move_list(self,move_list): #FIXME TODO should probably be saved at the... slice level?\n self.move_list=move_list\n self._current_move_list_pos=0\n\n def moveNext(self):\n if not self.move_list:\n print('No movelist! not moving')\n return None\n else:\n #stdout.write('\\rMoving to next position')\n #stdout.flush()\n self.esp.BsetPos(self.move_list[self._current_move_list_pos])\n if self._current_move_list_pos + 1 < len(self.move_list):\n self._current_move_list_pos+=1\n else:\n print('Move list is done! Resetting to the start!')\n self._current_move_list_pos=0\n\n @keyRequest\n def mark(self): #FIXME seems like this might be where the crashes are coming from?\n \"\"\"mark/store the position of a cell using a character sorta like vim\"\"\"\n try:\n slice_md=self.modestate.ctrlDict['datFuncs'].c_slice.markDict\n self.markDict=slice_md\n except AttributeError:\n pass\n stdout.write('\\rmark> ')\n stdout.flush()\n key=self.charBuffer.get()\n #printD('we got the key from charBuffer')\n if key in self.markDict:\n print('Mark %s is already being used, do you want to replace it? y/N'%(key))\n yeskey=self.charBuffer.get()\n #self._releaseKR() #XXX\n if yeskey=='y' or yeskey=='Y':\n self.markDict[key]=self.esp.getPos()\n print(key,'=',self.markDict[key])\n else:\n print('No mark set')\n elif key=='esc':\n self.unmark()\n else:\n #self._releaseKR() #XXX\n self.markDict[key]=self.esp.getPos()\n print(key,'=',self.markDict[key])\n #self.keyHandler(getMark) #fuck, this could be alot slower...\n try:\n self.modestate.ctrlDict['datFuncs'].set_slice_md(self.markDict)\n except AttributeError:\n pass\n #raise\n return self\n #mark.keyRequester=True\n\n @keyRequest\n def unmark(self):\n #self.doneCB()\n try:\n mark=self.charBuffer.get()\n pos=self.markDict.pop(mark)\n print(\"umarked '%s' at pos %s\"%(mark,pos))\n try:\n self.modestate.ctrlDict['datFuncs'].set_slice_md(self.markDict)\n except:\n raise\n except KeyError:\n stdout.write('\\n')\n stdout.flush()\n pass\n return self\n\n @keyRequest #FIXME we may actually want to use the manual version here due to BsetPos...\n def gotoMark(self): #FIXME\n #self.doneCB()\n stdout.write('\\rgoto:')\n stdout.flush()\n key=self.charBuffer.get()\n #self._releaseKR() #XXX\n if key in self.markDict:\n print('Moved to: ',self.esp.BsetPos(self.markDict[key])) #AH HA! THIS is what is printing stuff out as I mvoe FIXME I removed BsetPos at some point and now I need to add it... back? or what\n else:\n print('No position has been set for mark %s'%(key))\n return self\n #gotoMark.keyRequester=True\n\n @keyRequest\n def mark_to_movelist(self):\n names='origin','target'\n args=[]\n for i in range(2):\n stdout.write(names[i]+'> ')\n stdout.flush()\n key=self.charBuffer.get()\n stdout.write(key)\n stdout.flush()\n if key in self.markDict:\n args.extend(self.markDict[key]) #x,y,x,y\n stdout.write('\\n')\n stdout.flush()\n else:\n print('Mark not found exiting.')\n return None\n step=self.getFloat('step um>')\n number=self.getInt('number>')\n from rig.calcs import random_vector_points, vector_points, random_vector_ret_start\n #moves=vector_points(*args,number=number,spacing=step/1000) #.05 mm = 50um\n #moves=random_vector_points(*args,number=number,spacing=step/1000) #.05 mm = 50um\n moves=random_vector_ret_start(*args,number=number,spacing=step/1000)\n print(moves)\n self.set_move_list(moves)\n\n @keyRequest\n def mark_to_cardinal(self,step_um=25,number=8): #FIXME need a way to reload marks at startup!\n from numpy.random import shuffle\n from rig.calcs import vector_points, intersperse\n #step_um=self.getFloat('step um>') #FIXME stupid having to compile this at startup >_<\n #number=self.getInt('number>')\n\n start='origin'\n stdout.write(start+'> ')\n stdout.flush()\n key=self.charBuffer.get()\n stdout.write(key)\n stdout.flush()\n #base_args=[]\n if key in self.markDict:\n base_args=self.markDict[key] #x,y,x,y #XXX NOTE XXX this is the origin!\n stdout.write('\\n')\n stdout.flush()\n else:\n print('Mark not found!')\n return None\n\n dirs='dorsal','ventral','medial','lateral'\n base=[base_args] #include the origin somewhere in the list\n for i in range(len(dirs)):\n args=[]\n args.extend(base_args)\n stdout.write(dirs[i]+'> ')\n stdout.flush()\n key=self.charBuffer.get()\n stdout.write(key)\n stdout.flush()\n if key in self.markDict:\n args.extend(self.markDict[key]) #x,y,x,y\n stdout.write('\\n')\n stdout.flush()\n #print(base_args,args)\n #pts=vector_points(args[0],args[1],args[2],args[3],number=number,spacing=step_um/1000)\n pts=vector_points(*args,number=number,spacing=step_um/1000)[1:] #[1:] to prevent redundant origin calls\n #print(pts)\n base.extend(pts)\n else:\n print('Mark not found exiting.')\n return None\n shuffle(base)\n moves=[m for m in intersperse(base,base_args)]\n moves.append(base_args) #add the last validation origin point\n print(moves)\n print(len(moves))\n self.set_move_list(moves)\n return moves\n\n @keyRequest\n def mark_to_spline(self,number=10,step_um=100):\n from rig.calcs import get_moves_from_points\n from numpy.random import shuffle\n import pylab as plt\n\n marks='start','one','two','three','four','five'\n points=[]\n for i in range(len(marks)):\n stdout.write(marks[i]+'> ')\n stdout.flush()\n key=self.charBuffer.get()\n stdout.write(key)\n stdout.flush()\n if key in self.markDict:\n points.append(self.markDict[key]) #x,y,x,y\n stdout.write('\\n')\n stdout.flush()\n else:\n print('Mark not found exiting.')\n return None\n moves=get_moves_from_points(points,points[0],number,step_um/1000,switch_xy=True) #XXX NOTE THE SWITCH XXX\n plt.plot(-moves[0][0],moves[0][1]+.01,'yo')\n shuffle(moves)\n [plt.plot(-m[0],m[1],'ro') for m in points]\n [plt.plot(-m[0],m[1],'go') for m in moves]\n plt.axis('equal')\n plt.show()\n print(moves)\n print(len(moves))\n self.set_move_list(moves)\n\n\n def printMarks(self):\n \"\"\"print out all marks and their associated coordinates\"\"\"\n try:\n slice_md=self.modestate.ctrlDict['datFuncs'].c_slice.markDict\n self.markDict=slice_md\n except AttributeError:\n pass\n print(re.sub('\\), ','),\\r\\n ',str(self.markDict)))\n return self\n\n def fakeMove(self):\n #self.doneCB()\n if self.esp.sim:\n from numpy import random #only for testing remove later\n a,b=random.uniform(-10,10,2) #DO NOT SET cX or cY manually\n self.esp.setPos((a,b))\n #print(self.esp._cX,self.esp._cY)\n else:\n print('Not in fake mode! Not moving!')\n return self\n\n #callback to break out of display mode\n def _gd_break_callback(self): #XXX showDisp now\n print('leaving disp mode!')\n self._gd_exit=1\n self.modestate.keyActDict['esc']=self._gd_old_esc\n self._gd_old_esc=None\n del(self._gd_old_esc)\n self.modestate.keyActDict[self._gd_own_key]=self.showDisp\n self._gd_own_key=None\n del(self._gd_own_key)\n\n #@keyRequest #just added the getDisp.keyRequester=True at the bottom and we're good to go\n def showDisp(self):\n \"\"\"BLOCKING stream the displacement from a set point\"\"\"\n #self.doneCB()\n from numpy import sum as npsum #FIXME should these go here!?\n from numpy import array as arr\n print('entering disp mode')\n\n if not len(self.markDict):\n try:\n self.markDict.update(self.modestate.ctrlDict['datFuncs'].c_slice.markDict)\n except:\n pass #in the event there is no slice we don't really need to tell anyone\n if not len(self.markDict):\n self.mark()\n else:\n self._releaseKR() #XXX release whether we got it or not\n\n def get_key(_dict,func):\n for key,value in _dict.items():\n if value == func:\n return key\n raise KeyError('key not found!')\n\n\n self._gd_old_esc=self.modestate.keyActDict['esc']\n self._gd_own_key=get_key(self.modestate.keyActDict,self.showDisp)\n printD('get disp key:',self._gd_own_key)\n\n self._gd_exit=0\n self.modestate.keyActDict['esc']=self._gd_break_callback #FIXME probably going to need some try:finally:\n self.modestate.keyActDict[self._gd_own_key]=lambda:print('already in disp mode!')\n\n def format_str(string,width=9):\n missing=width-len(string)\n return string+' '*missing\n dist1=1\n print(list(self.markDict.keys()))\n while self.keyThread.is_alive() and not self._gd_exit: #FIXME may need a reentrant lock here to deal w/ keyboard control\n pos=self.markDict.values()\n dist=(npsum((arr(self.esp._BgetPos()-arr(list(pos))))**2,axis=1))**.5 #FIXME the problem here is w/ _BgetPos()\n if dist1!=dist[0]:\n #names='%s \\r'%(list(self.markDict.keys())) #FIXME sadly, it seems there is no way ;_;\n #stdout.write('\\r'+''.join(map('{:1.5f} '.format,dist)))\n stdout.write('\\r'+''.join([format_str('{:1.5f}'.format(d)) for d in dist]))\n stdout.flush()\n dist1=dist[0]\n\n #try:\n #key=self.charBuffer.get_nowait()\n ##FIXME this should go AFTER the other bit to allow proper mode changing\n #if key=='q' or key=='esc': #this should be dealt with through the key handler...\n #print('\\nleaving displacement mode')\n #break\n #elif key: #FIXME HOW DOES THIS EVEN WORK!?!??!\n ##printD('breaking shit')\n #try:\n #func=self.modestate.keyActDict[key] #FIXME WOW BAD\n #printD(func.__name__)\n #if func.__name__ !='getDisp': #FIXME this isnt catching it because lambda\n #from threading import Thread\n #a=Thread(target=func)\n #a.start()\n #except:\n #printD('should have keyHandled it')\n #except:\n #pass\n printD('done! loops broken!')\n return self\n showDisp.keyRequester=True\n\n def setSpeedDef(self):\n #self.doneCB()\n self.esp.setSpeedDefaults()\n return self\n\n def cleanup(self):\n super().cleanup()\n self.esp.cleanup()\n return self\n\n def setMoveDict(self,moveDict={'w':'up','s':'down','a':'left','d':'right'}):\n self.moveDict=moveDict\n return self\n\n def move(self): #FIXME this does not work in displacement mode because modestate.key is always i\n key=self.modestate.key\n #printD(key)\n if key.isupper(): #shit, this never triggers :(\n #printD('upper')\n self.esp.move(self.moveDict[key.lower()],.05) #FIXME\n else:\n #printD('lower')\n self.esp.move(self.moveDict[key.lower()],.1)\n return self\n\n def printError(self):\n print('Error:',self.esp.getErr().decode('utf-8'))\n return self\n def readProgram(self):\n #self.keyHandler(1)\n #num=self.charBuffer.get()\n print(self.esp.readProgram())\n return self\n\n\n\n@datafile_maker\nclass guiFuncs(kCtrlObj): #FIXME GOD DAMN IT now hardware interfaces and datafile makers are not sufficient because this gui could interface with a ton of stuff >_<\n @new_DataFile('jpg') #TODO integrate this with the device and stick it in a dedicated paths config file, could look it up by program but...\n def getCameraImage(self): #XXX: getSub_getCameraImage via df maker\n takeScreenCap()\n sleep(1) #ICK some day a callback FIXME\n\n\n\nclass keyFuncs(kCtrlObj):\n \"\"\"utility functions eg meta functions such as : cmd and the like\"\"\"\n def __init__(self, modestate):\n super().__init__(modestate)\n self.modestate=modestate\n def help(self):\n self.modestate.updateModeDict()\n tdb.on()\n printFD(self.modestate.helpDict)\n tdb.off()\n #self.doneCB()\n return self\n def esc(self):\n return 0\n\n\n@hardware_interface('keyboard')\nclass trmFuncs(kCtrlObj): #FIXME THIS NEEDS TO BE IN THE SAME THREAD\n def __init__(self, modestate):\n self._keyThread=modestate.keyThread\n self.keyLock=modestate.keyLock\n self.modestate=modestate #need this to det the current modestate\n super().__init__(modestate)\n #def printwrap(func):\n #def wrap(*args,**kwargs):\n #out=func()\n #printD(out)\n #return out\n #wrap.__name__=func.__name__\n #return wrap\n #for name in self.ctrl.__dir__():\n #if name[:3]=='get':\n #setattr(self,name,printwrap(getattr(self.ctrl,name)))\n #for name in self.__dir__():\n #if name[:3]=='get':\n #setattr(self,name,printwrap(getattr(self,name)))\n #setattr(getattr(self,name),'__name__',name)\n\n @is_mds('u','m','BX51WI',0)\n @keyRequest\n def getDistance_um(self):\n \"\"\" get a distance in um in this case read from the olympus bx51wi wheel \"\"\"\n value=self.getFloat('distance um> ')\n return value\n\n @keyRequest\n def getKbdHit(self,prompt='Hit any key to advance.'):\n print(prompt)\n self.charBuffer.get()\n return True\n\n def getDoneNB(self): #FIXME \n print('Hit space when you are done') #FIXME\n try:\n old_func=self.modestate.keyActDict[' ']\n except:\n old_func=None\n\n self._gdnb_cb_done=False\n def callback():\n self._gdnb_cb_done=True\n\n self.modestate.keyActDict[' ']=callback\n while not self._gdnb_cb_done and self.keyThread.is_alive(): \n sleep(.001) #FIXME\n printD('got it')\n \n if old_func:\n self.modestate.keyActDict[' ']=old_func #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(' ')\n return True\n \n def getDoneFailNB(self): #FIXME \n print('Hit space for success or \\ for failure') #FIXME use a reverse dict in keybinds\n #FIXME ick\n success=' '\n fail='\\\\'\n try:\n old_func=self.modestate.keyActDict[success]\n except:\n old_func=None\n try:\n old_fail=self.modestate.keyActDict[fail]\n except:\n old_fail=None\n\n self._gdnb_cb=0\n def callback():\n self._gdnb_cb=1 #'done'\n def fail_cb():\n self._gdnb_cb=2 #'fail'\n\n self.modestate.keyActDict[success]=callback\n self.modestate.keyActDict[fail]=fail_cb\n while not self._gdnb_cb and self.keyThread.is_alive():\n sleep(.001) #FIXME\n\n if self._gdnb_cb == 1:\n out=True\n printD('got success')\n else:\n out=False\n printD('got fail')\n \n if old_func:\n self.modestate.keyActDict[success]=old_func #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(success)\n if old_fail:\n self.modestate.keyActDict[fail]=old_fail #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(fail)\n return out\n\n def _getDoneFailNB(self): #FIXME \n print('Hit space for success or \\ for failure') #FIXME use a reverse dict in keybinds\n #FIXME ick\n succss=' '\n fail='\\\\'\n try:\n old_func=self.modestate.keyActDict[success]\n except:\n old_func=None\n try:\n old_fail=self.modestate.keyActDict[fail]\n except:\n old_fail=None\n\n self._gdnb_cb_done=False\n self._gdnb_cb_fail=False\n def callback():\n self._gdnb_cb_done=True\n def fail_cb():\n self._gdnb_cb_fail=True\n\n self.modestate.keyActDict[success]=callback\n while not self._gdnb_cb_done or not self._gdnb_cb_fail:\n sleep(.001) #FIXME\n printD('got it')\n\n if self._gdnb_cb_fail:\n out=False\n if self._gdnb_cb_done:\n out=True\n \n if old_func:\n self.modestate.keyActDict[success]=old_func #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(success)\n if old_fail:\n self.modestate.keyActDict[fail]=old_fail #FIXME danger in x thread?\n else:\n self.modestate.keyActDict.pop(fail)\n return out\n\n\n @keyRequest\n def command(self): #TODO\n \"\"\" vim : mode \"\"\"\n #TODO figure out where to move the command dict...\n #cmdDict={'ipython':'ipython'} #can't put ipython here due to race conditions\n cmdDict={}\n def parse_command(com_str): #TODO\n if com_str=='\\n':\n return None\n def match_key(com_str):\n printD(com_str)\n matches = [key for key in cmdDict.keys() if key[:len(com_str)]==com_str]\n matches.sort()\n return matches\n matches=match_key(com_str)\n printD(matches)\n try:\n if matches:\n func=getattr(self,matches[0],lambda:None)\n else:\n return None\n except:\n print('[!] no function found')\n return None\n func()\n #printD(com_str,'TODO FIXME')\n return com_str\n print('command opened')\n com_str=self.__getChars__(':')\n return parse_command(com_str)\n\n def ipython(self):\n #TODO how to deal with imports...\n try:\n self.keyLock.acquire() #XXX lock acquire so ipython can have input priority\n #embed()\n printD('Not Implemented here due to threading and needing to hit one extra key to break the lock in keyListener')\n finally:\n self.keyLock.release() #XXX lock release\n\n def test(self):\n print('testing testing 123')\n\ndef main():\n #esp=espFuncs()\n #clx=clxFuncs()\n #mcc=mccFuncs(None,None,None,None)\n pass\n\n__all__=(\n 'clxFuncs',\n 'mccFuncs',\n 'espFuncs',\n 'keyFuncs',\n 'trmFuncs',\n 'datFuncs',\n 'guiFuncs',\n)\nif __name__=='__main__':\n main()\n","repo_name":"tgbugs/mlab","sub_path":"rig/functions_seg.py","file_name":"functions_seg.py","file_ext":"py","file_size_in_byte":53699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29259095761","text":"'''\n494. 目标和\n给你一个整数数组 nums 和一个整数 target 。\n\n向数组中的每个整数前添加 '+' 或 '-' ，然后串联起所有整数，可以构造一个 表达式 ：\n\n例如，nums = [2, 1] ，可以在 2 之前添加 '+' ，在 1 之前添加 '-' ，然后串联起来得到表达式 \"+2-1\" 。\n返回可以通过上述方法构造的、运算结果等于 target 的不同 表达式 的数目。\n\n\n\n示例 1：\n\n输入：nums = [1,1,1,1,1], target = 3\n输出：5\n解释：一共有 5 种方法让最终目标和为 3 。\n-1 + 1 + 1 + 1 + 1 = 3\n+1 - 1 + 1 + 1 + 1 = 3\n+1 + 1 - 1 + 1 + 1 = 3\n+1 + 1 + 1 - 1 + 1 = 3\n+1 + 1 + 1 + 1 - 1 = 3\n示例 2：\n\n输入：nums = [1], target = 1\n输出：1\n\n\n提示：\n\n1 <= nums.length <= 20\n0 <= nums[i] <= 1000\n0 <= sum(nums[i]) <= 1000\n-1000 <= target <= 100\n'''\n\n\nclass Solution:\n def findTargetSumWays(self, nums, target: int) -> int:\n '''\n DFS全排列，搜索目标值，暴力解法O(2^n)，大用例会超时\n 考虑记忆化，减少重复运算 -> 类似场景都可以考虑DP\n '''\n self.res = 0\n # self.res_path = set()\n # self.visited = [0] * 10000\n\n def dfs(sum_in, i):\n print(sum_in, i)\n if i >= len(nums):\n if sum_in == target:\n self.res += 1\n return\n\n for sign in [-1, 1]:\n sum_in += (sign * nums[i])\n dfs(sum_in, i+1)\n sum_in -= (sign * nums[i])\n\n dfs(0, 0)\n\n return self.res\n\n def findTargetSumWays_dp(self, nums, S: int) -> int:\n '''设取＋的集合之和为x，取-的集合之和为y\n x+y = sum(nums)\n x-y = S\n 求得'x = (S+sum(nums))/2'\n 转换为标准01背包问题\n 目标值: 'x = (S+sum(nums))/2'\n dp[i][j]: i个数的组合能到j的方法数\n '''\n # 若目标大于集合总和返回0\n # 用例[1,2,7,9,981] 1000000000\n if S > sum(nums):\n return 0\n\n # 若为奇数返回0\n if (S+sum(nums)) % 2 == 1:\n return 0\n\n x = (S+sum(nums))//2\n\n dp = [0 for _ in range(x+1)]\n # base case，空集合有一种方法满足目标为0\n dp[0] = 1\n\n for i in range(1, len(nums)+1):\n for j in range(x, nums[i-1]-1, -1):\n # 状态转移方程：放->dp[j-cost_i]种方法+不放->dp[j]种方法\n dp[j] = dp[j] + dp[j - nums[i-1]]\n\n return dp[-1]\n# 示例 1：\n\n\n# 输入：\nnums = [1, 1, 1, 1, 1]\ntarget = 3\n# 输出：5\n# 解释：一共有 5 种方法让最终目标和为 3 。\n# -1 + 1 + 1 + 1 + 1 = 3\n# +1 - 1 + 1 + 1 + 1 = 3\n# +1 + 1 - 1 + 1 + 1 = 3\n# +1 + 1 + 1 - 1 + 1 = 3\n# +1 + 1 + 1 + 1 - 1 = 3\n# 示例 2：\nprint(Solution().findTargetSumWays(nums, target))\n\n# 输入：\nnums = [1]\ntarget = 1\n# 输出：1\nprint(Solution().findTargetSumWays(nums, target))\n","repo_name":"moshlwx/leetcode","sub_path":"CODE/494. 目标和.py","file_name":"494. 目标和.py","file_ext":"py","file_size_in_byte":2947,"program_lang":"python","lang":"zh","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"1858026608","text":"import pyrogue as pr\nimport rogue.interfaces.memory as rim\nimport threading\nfrom collections import OrderedDict as odict\n\n\nclass MemoryDevice(pr.Device):\n def __init__(self, *,\n name=None,\n description='',\n memBase=None,\n offset=0,\n size=0,\n hidden=False,\n expand=True,\n enabled=True,\n base=pr.UInt,\n wordBitSize=32,\n stride=4,\n verify=True):\n \n super().__init__(\n name=name,\n description=description,\n memBase=memBase,\n offset=offset,\n size=size,\n hidden=hidden,\n expand=expand,\n enabled=enabled,\n )\n\n self._lockCnt = 0\n self._base = base\n self._wordBitSize = wordBitSize\n self._stride = stride\n self._verify = verify\n\n self._setValues = odict()\n self._wrValues = odict() # parsed from yaml\n self._verValues = odict()\n self._wrData = odict() # byte arrays written\n self._verData = odict() # verify data wread back\n\n\n def _buildBlocks(self):\n pass\n\n def _setDict(self, d, writeEach, modes):\n # Parse comma separated values at each offset (key) in d\n with self._memLock:\n for offset, values in d.items():\n self._setValues[offset] = [self._base.fromString(s, self._wordBitSize) for s in values.split(',')]\n\n def _getDict(self,modes):\n return None\n\n def writeBlocks(self, force=False, recurse=True, variable=None, checkEach=False):\n if not self.enable.get(): return\n\n with self._memLock:\n self._wrValues = self._setValues\n \n for offset, values in self._setValues.items():\n wdata = self._rawTxnChunker(offset, values, self._base, self._stride, self._wordBitSize, rim.Write)\n if self._verify:\n self._wrData[offset] = wdata\n\n # clear out wrValues when done\n self._setValues = odict()\n \n\n def verifyBlocks(self, recurse=True, variable=None, checkEach=False):\n if not self.enable.get(): return\n\n with self._memLock:\n for offset, ba in self._wrData.items():\n self._verData[offset] = bytearray(len(ba))\n self._rawTxnChunker(offset, self._verData[offset], txnType=rim.Verify)\n\n self._wrData = odict()\n self._verValues = self._wrValues\n\n def checkBlocks(self, recurse=True, variable=None):\n with self._memLock:\n # Wait for all txns to complete\n self._waitTransaction(0)\n\n # Error check?\n error = self._getError()\n self._setError(0)\n\n # Convert the read verfiy data back to the natic type\n checkValues = odict()\n #print(self._verData.items())\n for offset, ba in self._verData.items():\n checkValues[offset] = [self._base.mask(self._base.fromBytes(ba[i:i+self._stride]), self._wordBitSize)\n for i in range(0, len(ba), self._stride)]\n\n # Do verify if necessary\n if len(self._verValues) > 0:\n # Compare wrData with verData\n if self._verValues != checkValues:\n msg = 'Verify error \\n'\n msg += f'Expected: \\n {self._verValues} \\n'\n msg += f'Got: \\n {checkValues}'\n print(msg)\n raise MemoryError(name=self.name, address=self.address, error=rim.VerifyError, msg=msg, size=self._size)\n\n\n # destroy the txn maps when done with verify\n self._verValues = odict()\n self._verData = odict()\n\n\n def readBlocks(self, recurse=True, variable=None, checkEach=False):\n pass\n\n","repo_name":"codacy-badger/rogue","sub_path":"python/pyrogue/_Memory.py","file_name":"_Memory.py","file_ext":"py","file_size_in_byte":3938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"12249321839","text":"from app.schemas import Build, GetTasksResponse\n\nfrom app.utils import get_order_tasks\nfrom app.errors import OperationError\nfrom redis_storage.connector import RedisConnector\nfrom config import SETTINGS\n\n\nclass Managers:\n @staticmethod\n def get_order_tasks(\n build: Build,\n connector: RedisConnector,\n ) -> GetTasksResponse:\n try:\n ordered_tasks: list[str] = get_order_tasks(\n build=build,\n connector=connector,\n )\n\n status = SETTINGS.STATUS_OK\n msg = {\"ordered_tasks\": ordered_tasks}\n except OperationError as error:\n status = error.status\n msg = error.msg\n\n response = {\"status\": status, \"msg\": msg}\n\n return GetTasksResponse(**response)\n","repo_name":"anonimneyshiy/TestTask","sub_path":"app/managers.py","file_name":"managers.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5745910671","text":"import codecs\n\nfor file_name in ['D_MEGA.HTM']:\n source_file = 'raw/{}'.format(file_name)\n dest_file = 'normalized/{}'.format(file_name)\n\n with codecs.open(source_file, \"r\",encoding='utf-8', errors='ignore') as fdata:\n html = fdata.read()\n\n with open(dest_file, 'w') as outfile:\n outfile.write(html)\n","repo_name":"samukasmk/bora_falar_de_python","sub_path":"2018-02-08/megasena/data/normalize.py","file_name":"normalize.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"21588184000","text":"#\n\n\n#\nimport numpy\nimport pandas\nfrom sklearn.metrics import matthews_corrcoef, confusion_matrix\n\n\n#\n\n\n#\ndef cm_adopted(data, clusters, categories):\n agg = pandas.crosstab(index=data[clusters], columns=data[categories])\n rates = agg / agg.sum(axis=0)\n\n clusters_re = {}\n categories_re = {}\n n = 0\n while rates.shape[0] > 0:\n n += 1\n ix_mx_clusters = numpy.where(rates.values == rates.values.max())[0][0]\n ix_mx_categories = numpy.where(rates.values == rates.values.max())[1][0]\n mx_cluster = rates.index[ix_mx_clusters]\n mx_category = rates.columns[ix_mx_categories]\n clusters_re[mx_cluster] = n\n categories_re[mx_category] = n\n\n rates = rates.drop(index=mx_cluster, columns=mx_category)\n\n def convert_clusters(x):\n return clusters_re[x]\n\n def convert_categories(x):\n return categories_re[x]\n\n data[clusters + '_RE'] = data[clusters].apply(func=convert_clusters)\n data[categories + '_RE'] = data[categories].apply(func=convert_categories)\n\n return data[clusters + '_RE'], data[categories + '_RE']\n\n\n# by https://en.wikipedia.org/wiki/Phi_coefficient\ndef mcc_final(c):\n ra = range(c.shape[0])\n num = sum([sum([sum([c[k, k] * c[l, m] - c[k, l] * c[m, k] for m in ra]) for l in ra]) for k in ra])\n den_p1 = sum([sum([c[k, l] for l in ra]) * sum([sum([c[k_, l_] for l_ in ra]) for k_ in ra if k_ != k]) for k in ra]) ** 0.5\n den_p2 = sum([sum([c[l, k] for l in ra]) * sum([sum([c[l_, k_] for l_ in ra]) for k_ in ra if k_ != k]) for k in ra]) ** 0.5\n return num / (den_p1 * den_p2)\n\n\ndef mcc_adopted(y_pred, y_true):\n data = pandas.DataFrame(data={'y_pred': y_pred, 'y_true': y_true})\n y_pred_new, y_true_new = cm_adopted(data=data, clusters='y_pred', categories='y_true')\n score = matthews_corrcoef(y_true=y_true_new, y_pred=y_pred_new)\n return score\n\n\na = [2, 1, 2, 2, 2]\nb = [1, 2, 2, 1, 1]\ndata = pandas.DataFrame(data={'a': a, 'b': b})\n\nr1 = mcc_final(confusion_matrix(y_true=a, y_pred=b))\nr2 = matthews_corrcoef(y_true=a, y_pred=b)\n\nr = cm_adopted(data=data.copy(), clusters='a', categories='b')\n\n","repo_name":"edazizovv/shock_pods","sub_path":"testing/mcc_adopt.py","file_name":"mcc_adopt.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27180938955","text":"import pygame\nimport random\nimport time\n\npygame.init()\npygame.display.set_caption(\"Inchaders\")\nScreenWidth = 800\nScreenHeight = 800\nscreen = pygame.display.set_mode((ScreenWidth,ScreenHeight))\nclock = pygame.time.Clock()\ngreen = (0, 255,0)\nblu = (0, 0, 255)\nplayer = pygame.image.load('I CAN1.gif')\n\n\ndoExit = False\nxpos = 0\nypos = 700\nVx = 0\n#constants\nLEFT = 0\nRIGHT = 1\nSPACE = 2\nkeys = [False, False, False]\ntimer = 0\nlives = 3\n\nmisslist = list()\njustShot = 0\n\n\nclass Wall:\n def __init__(self, xpos, ypos):\n self.xpos = xpos\n self.ypos = ypos\n self.numHits = 0\n def draw(self):\n if self.numHits == 0:\n pygame.draw.rect(screen, (250, 250, 20), (self.xpos, self.ypos, 30, 30))\n if self.numHits == 1:\n pygame.draw.rect(screen, (150, 150, 10), (self.xpos, self.ypos, 30, 30))\n if self.numHits == 2:\n pygame.draw.rect(screen, (50, 50, 0), (self.xpos, self.ypos, 30, 30))\n \n def collide(self, BulletX, BulletY):\n if self.numHits < 3:\n if BulletX > self.xpos:\n if BulletX < self.xpos + 30:\n if BulletY < self.ypos + 30:\n if BulletY > self.ypos:\n print(\"hit!\")\n self.numHits += 1\n return False\n return True\n \n\nwalls = []\nfor k in range(4):\n for i in range(2):\n for j in range(3):\n walls.append(Wall(j*30+200 * k + 50, i * 30 + 600))\n\nclass Alien:\n def __init__(self, xpos, ypos):\n self.xpos = xpos\n self.ypos = ypos\n self.isAlive = True\n self.direction = 1\n def draw(self):\n if self.isAlive == True:\n pygame.draw.rect(screen, (250, 250, 250), (self.xpos, self.ypos, 40, 40))\n \n def move(self, time):\n \n #reset what direction you're moving every 8 moves:\n if time % 800 == 0:\n self.ypos += 100\n self.direction *= -1\n return 0\n \n if timer%100 == 0:\n self.xpos += 50 * self.direction\n \n return time\n \n def collide(self, BulletX, BulletY):\n if self.isAlive:\n if BulletX > self.xpos:\n if BulletX < self.xpos + 50:\n if BulletY < self.ypos + 20:\n if BulletY > self.ypos:\n print(\"hit!\")\n self.isAlive = False\n return False\n return True\n\narmada = []\nfor i in range (4):\n for j in range(12):\n armada.append(Alien(j*60 + 70, i *50 + 70))\nclass Missile:\n def __init__(self):\n self.xpos = -10\n self.ypos = -10\n self.isAlive = False\n \n \n\nclass Bullet:\n def __init__(self, xpos, ypos):\n self.xpos = xpos\n self.ypos = ypos\n self.isAlive = False\n \n def move(self, xpos, ypos):\n if self.isAlive == True:\n self.ypos -=5\n if self.ypos < 0:\n self.isAlive = False\n self.xpos = xpos\n self.ypos = ypos\n \n def draw(self):\n if self.isAlive == True:\n pygame.draw.rect(screen, (250, 250, 250), (self.xpos, self.ypos, 3, 20))\n#instantiate bullet object\nbullet = Bullet(xpos+28, ypos)\n\nclass Missile:\n def __init__(self):\n self.xpos = -10\n self.ypos = -10\n self.isAlive = False\n def move(self):\n if self.isAlive == True:\n self.ypos +=5\n if self.ypos > 800:\n self.isAlive = False\n self.xpos = xpos\n self.ypos = ypos\n def draw(self):\n if self.isAlive == True:\n pygame.draw.rect(screen, (250, 250, 250), (self.xpos, self.ypos, 2, 10))\n\nmisslies = []\n\nfor i in range(10):\n misslies.append(Missile())\n\n \n\n\nwhile lives > 0:\n clock.tick(60)\n timer += 1;\n \n for event in pygame.event.get(): #quit game if x is pressed in top corner\n if event.type == pygame.QUIT:\n gameover = True\n \n if event.type == pygame.KEYDOWN: #keyboard input\n if event.key == pygame.K_LEFT:\n keys[LEFT]=True\n elif event.key == pygame.K_RIGHT:\n keys[RIGHT]=True\n if event.key == pygame.K_SPACE:\n keys[SPACE] = True\n\n \n if event.type == pygame.KEYUP: #keyboard input\n if event.key == pygame.K_LEFT:\n keys[LEFT]=False\n elif event.key == pygame.K_RIGHT:\n keys[RIGHT]=False\n if event.key == pygame.K_SPACE:\n keys[SPACE] = False\n \n if keys[SPACE] == True:\n bullet.isAlive = True\n \n if bullet.isAlive == True:\n bullet.move(xpos+28, ypos)\n if bullet.isAlive == True:\n #alien collision\n for i in range(len(armada)):\n bullet.isAlive = armada[i].collide(bullet.xpos, bullet.ypos)\n if bullet.isAlive == False:\n break\n if bullet.isAlive == True:\n for i in range(len(walls)):\n bullet.isAlive = walls[i].collide(bullet.xpos, bullet.ypos)\n if bullet.isAlive == False:\n break\n \n \n \n \n \n else:\n bullet.xpos = xpos+28\n bullet.ypos = ypos\n \n \n for i in range(len(walls)):\n for j in range(len(misslies)):\n if misslies[j].isAlive == True:\n if walls[i].collide(misslies[j].xpos, misslies[j].ypos) == False:\n misslies[j].isAlive = False\n break\n \n buffa = random.randrange(100)\n if buffa < 2:\n armbruh = random.randrange(len(armada))\n if armada[armbruh].isAlive == True:\n for i in range(len(misslies)):\n if misslies[i].isAlive == False:\n misslies[i].isAlive = True\n misslies[i].xpos = armada[i].xpos + 5\n misslies[i].ypos = armada[i].ypos\n break\n \n #player collision\n for i in range(len(misslies)):\n if misslies[i].isAlive:\n if misslies[i].xpos > xpos:\n if misslies[i].xpos < xpos + 40:\n if misslies[i].ypos < ypos + 40:\n if misslies[i].ypos > ypos:\n lives -= 1\n time.sleep(1)\n xpos = 0\n \n for i in range(len(armada)):\n if armada[i].ypos > ypos:\n lives -= 3\n print(\"Bruh how did they get this low\")\n time.sleep(1)\n break\n \n \n \n \n #left movement \n if keys[LEFT]==True:\n Vx = -5\n direction = LEFT\n \n #Right Movement\n elif keys[RIGHT]==True:\n Vx = 5\n direction = RIGHT\n \n else:\n Vx = 0\n #updating player\n xpos += Vx\n \n for i in range(len(armada)):\n armada[i].move(timer)\n for i in range(len(misslies)):\n misslies[i].move()\n\n \n screen.fill((0,0,0))\n \n \n my_font = pygame.font.SysFont('Comic Sans MS', 30)\n text_surface = my_font.render('LIVES:', False, (255, 0, 0))\n number = my_font.render(str(lives), False, (255,0,0))\n \n \n screen.blit(player, (xpos, ypos))\n for i in range(len(armada)):\n armada[i].draw()\n for i in range(len(walls)):\n walls[i].draw()\n bullet.draw()\n for i in range(len(misslies)):\n misslies[i].draw()\n \n screen.blit(text_surface, (0,0))\n screen.blit(number, (100,0))\n \n \n pygame.display.flip()\n \nprint(\"RIP\")\n\npygame.quit()\n","repo_name":"Shadowtheepic/I-M-GONNA-MAKE-ALL-OF-FORTNITE-irl","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39168635955","text":"\"\"\"holosongranker URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/4.0/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom django.conf.urls.i18n import i18n_patterns\nfrom django.views.generic.base import RedirectView\nfrom django.contrib.staticfiles.storage import staticfiles_storage\nfrom . import homepage\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path(\n \"ads.txt\",\n RedirectView.as_view(url=staticfiles_storage.url(\"ads.txt\")),\n ),\n]\n\nurlpatterns += i18n_patterns(\n path('', homepage.Index, name='homepage'),\n path('about', homepage.About, name='about'),\n path('songs/', include('songs.urls', namespace='songs')),\n path('singers/', include('singers.urls', namespace='singers')),\n)\n\nhandler404 = 'holosongranker.error_handler.view_404'\n\n# if settings.DEBUG:\n# import debug_toolbar\n# DEBUG_TOOLBAR_CONFIG = {\n# 'INTERCEPT_REDIRECTS': False,\n# }\n# urlpatterns += [\n# path('__debug__/', include(debug_toolbar.urls)),\n# ]\n","repo_name":"gift-li/holosongranker","sub_path":"holosongranker/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"2550806519","text":"# -*- coding: utf-8 -*-\n# purchasesDemo4\n\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier\n\n# 使用 Pandas 读取数据\ndf = pd.read_excel(\"user_fit.xlsx\", header=0)\n\n# 特征\nx = df.iloc[:, 0:8]\n\n# 目标\ny = df['用户是否为会员'.decode('utf-8')]\n\n# 安装 3:7 切分验证集和训练集\ntrain_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.3)\n\n# 构建神经网络分类器\n\nmodel_MLPClassifier= MLPClassifier(activation='logistic',max_iter=50,hidden_layer_sizes=(50,50,50))\n\n# 使用训练集训练模型\nmodel_MLPClassifier.fit(train_x, train_y)\n\n# 使用验证集评估准确度\nscore_trainset = model_MLPClassifier.score(train_x, train_y)*100\nscore_testset = model_MLPClassifier.score(test_x, test_y)*100\n\nprint(\"训练集预测准确率: %.2f%%\" % score_trainset)\nprint(\"测试集预测准确率: %.2f%%\" % score_testset)","repo_name":"sunshinelu/pythonDemo","sub_path":"shiyanlou/PythonDataAnalysisAndAdvanced/Purchases/purchasesDemo4.py","file_name":"purchasesDemo4.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37055323705","text":"from Predictions import *\r\n\r\nReactor_Shutdown = Linear_Combo()\r\nReactor_Shutdown.Wavelets += [Hubbert( x0=2035.000, s0=0.30586, s1=0.46300, peak=31.700)]\r\nReactor_Shutdown.Wavelets += [Sigmoid( x0=2053.000, s0=0.64954, left=0.000, right=10.000)]\r\nReactor_Shutdown.Wavelets += [Hubbert( x0=2050.000, s0=0.33832, s1=0.34093, peak=5.469)]\r\n\r\n# Needed for phenomenological model later\r\n_Nuclear_Functions = Linear_Combo()\r\n_Nuclear_Functions.Wavelets += [Weibull( 1968, .018, 2.2, 40000)]\r\n_Nuclear_Functions.Wavelets += [Hubbert( 2013, 0.3, 0.3, -100)]\r\n_Nuclear_Functions.Wavelets += [Hubbert( 2028, 0.4, 0.29, 70)]\r\n_Nuclear_Functions.Wavelets += [Hubbert( 2038, 0.4, 0.4, -70)]\r\n_Nuclear_Functions.Wavelets += [Hubbert( 2052, 0.4, 0.4, 40)]\r\n_Nuclear_Functions.Wavelets += [Sigmoid( 2063, .08, 0, 1550)]\r\n\r\n_Nuclear_Functions_2 = Linear_Combo()\r\n_Nuclear_Functions_2.Wavelets += [Weibull( 1968, .018, 2.2, 40000)]\r\n_Nuclear_Functions_2.Wavelets += [Hubbert( 2013, 0.3, 0.3, -100)]\r\n_Nuclear_Functions_2.Wavelets += [Hubbert( 2028, 0.4, 0.29, 70)]\r\n_Nuclear_Functions_2.Wavelets += [Hubbert( 2038, 0.4, 0.4, -70)]\r\n_Nuclear_Functions_2.Wavelets += [Hubbert( 2052, 0.4, 0.4, 40)]\r\n\r\nyears = np.linspace(1800, 2300, 501)\r\nMW_connected = np.zeros( len(years))\r\nf_start = open(\"./Data/Nuclear_Reactor_Commissioning.txt\")\r\nfor i in range(1000):\r\n s = f_start.readline()\r\n if len(s) <= 0: break\r\n ss = s.split(\"MW(e)\")\r\n n = int(s[0:4])\r\n ss1 = ss[0].split('(')\r\n w = float( ss1[-1])\r\n MW_connected[n-1800] += w/1000\r\nf_start.close()\r\nprd = 10\r\ngrowth = 1.02\r\nfor i in range(219,len(years)):\r\n MW_connected[i] += prd\r\n prd *= growth\r\n\r\nMW_disconnected = np.zeros( len(years))\r\nf_end = open(\"./Data/Nuclear_Reactor_Shutdown.txt\")\r\nfor i in range(1000):\r\n s = f_end.readline()\r\n if len(s) <= 0: break\r\n ss = s.split(\"MW(e)\")\r\n n = int(s[0:4])\r\n ss1 = ss[0].split('(')\r\n w = float( ss1[-1])\r\n MW_disconnected[n-1800] += w/1000\r\nf_end.close()\r\nfor i in range(219,270):\r\n MW_disconnected[i] += Reactor_Shutdown.Compute(years[i])\r\nfor i in range(270,len(years)):\r\n MW_disconnected[i] += MW_connected[i-50]\r\n\r\nw = 0\r\nMW_operating = np.zeros( len(years))\r\nfor i in range( len(years)):\r\n w += MW_connected[i]\r\n w -= MW_disconnected[i]\r\n if w < 0:\r\n w = 0\r\n MW_disconnected[i] = 0\r\n MW_operating[i] = w\r\n #print( \"{:g}\\t{:.0f}\\t{:.0f}\\t{:.0f}\".format(years[i],\r\n # MW_connected[i],MW_disconnected[i],w/1000))\r\n\r\nProduction = MW_operating * 0.77\r\nPop = Population().UN_Medium.GetVector( years)\r\n\r\ny, nuc = Load_Calibration(\"../Global Data/Energy_Calibration.csv\", [\"Year\", \"Nuclear\"])\r\nnuc *= 4419.2 / 24 / 365.25\r\nfor i in range(len(y)):\r\n j = int(y[i]-years[0])\r\n if j<0: continue\r\n Production[j] = nuc[i]\r\n\r\nx_start, x_end = 1950, 2150\r\n\r\nfig = plt.figure( figsize=(15,10))\r\nfig.suptitle( 'Гражданские генерирующие мощности ядерной энергетики', fontsize=22)\r\ngs = plt.GridSpec(2, 1, height_ratios=[2,1]) \r\nax1 = plt.subplot(gs[0])\r\nax2 = plt.subplot(gs[1])\r\n\r\nax1.set_title(\"Ввод и вывод мощностей\")\r\nax1.bar( years, MW_connected*10, 1, alpha=0.4, color=\"g\", label=\"Ввод (x10)\")\r\nax1.bar( years, -MW_disconnected*10, 1, alpha=0.4, color=\"r\", label=\"Вывод (x10)\")\r\nax1.bar( years[:219], MW_connected[:219]*10, 1, alpha=1, color=\"g\")\r\nax1.bar( years[:219], -MW_disconnected[:219]*10, 1, alpha=1, color=\"r\")\r\nax1.plot( years, MW_operating, \"-\", color=\"k\", label=\"Рабочих мощностей\")\r\nax1.errorbar( y, nuc, yerr=nuc*0.03, fmt=\".\", color=\"m\", label=\"Выработка АЭС по данным ВР\")\r\nax1.plot( years, Production, \"--\", color=\"m\", label=\"Выработка АЭС (модель)\")\r\n#ax1.plot( years, _Nuclear_Functions.GetVector(years)*0.39/31.6*42, \"--\", color=\"y\", label=\"Phenomenological Model 1\")\r\n#ax1.plot( years, _Nuclear_Functions_2.GetVector(years)*0.39/31.6*42, \"-.\", color=\"y\", label=\"Phenomenological Model 2\")\r\nax1.set_xlim(x_start, x_end)\r\nax1.set_ylim( -400, 1000)\r\nax1.text( 2030, 50, \"Рост/снижение по {:.1f}% в год\".format((growth-1)*100))\r\nax1.set_ylabel(\"Гигаватт\")\r\nax1.grid(True)\r\nax1.legend(loc=0)\r\n\r\nax2.set_title(\"Мощность на душу населения\")\r\nax2.plot( years, Production*1000/Pop, \"-\", color=\"m\")\r\nax2.set_xlim(x_start, x_end)\r\nax2.set_ylim( 0, 60)\r\nax2.set_xlabel(\"год\")\r\nax2.set_ylabel(\"Ватт(электрич.)\")\r\nax2.grid(True)\r\n\r\nplt.savefig( \"./Graphs/figure_12_11.png\")\r\nif InteractiveModeOn: plt.show(True)\r\n","repo_name":"myak555/LIMITS_TO_LIMITS","sub_path":"Chapter 12/Test_10_Nuclear_Reactors_Projection.py","file_name":"Test_10_Nuclear_Reactors_Projection.py","file_ext":"py","file_size_in_byte":4584,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"19754289952","text":"import os\r\nimport sys\r\nimport cv2\r\nimport PIL.Image\r\n\r\nchars = \" .:*+o0O\"\r\nnewWidth=100\r\n\r\ndef resize(image):\r\n width, height = image.size\r\n ratio = height/width\r\n newHeight = int(newWidth * ratio)\r\n resized_image = image.resize((newWidth, newHeight))\r\n return(resized_image)\r\n\r\ndef grayscale(image):\r\n grayscaleImage = image.convert(\"L\")\r\n return(grayscaleImage)\r\n \r\ndef ascii(image):\r\n pixels = image.getdata()\r\n characters = \"\".join([chars[pixel//25] for pixel in pixels])\r\n return(characters)\r\n\r\ndef framer(image):\r\n newImage = ascii(grayscale(resize(image)))\r\n pixelCount = len(newImage) \r\n asciiImage = \"\\n\".join([newImage[index:(index+newWidth)] for index in range(0, pixelCount, newWidth)])\r\n sys.stdout.write(asciiImage)\r\n os.system('cls' if os.name == 'nt' else 'clear') \r\n\r\ndef main(new_width=100):\r\n run = True\r\n while run:\r\n path = str(input(\"Paste the image path: \"))\r\n try:\r\n video = cv2.VideoCapture(path)\r\n except Exception:\r\n print(path, \" is not a valid pathname to an image.\")\r\n pass\r\n\r\n play = True \r\n while play:\r\n try:\r\n ret, frame = video.read()\r\n cv2.imshow(\"frame\",frame)\r\n framer(PIL.Image.fromarray(frame))\r\n cv2.waitKey(10)\r\n except:\r\n play = False\r\n\r\n redo = str(input(\"Have another file? (y/n): \"))\r\n if redo == 'y' or redo == 'Y':\r\n print(\"______________________________________________________________________________\")\r\n run = True\r\n else:\r\n run = False\r\n \r\nmain()\r\n","repo_name":"theeemanuel/ascii","sub_path":"video rendering in ascii/videoRendering.py","file_name":"videoRendering.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41687877453","text":"'''\nYou are climbing a staircase. It takes n steps to reach the top.\nEach time you can either climb 1 or 2 steps. In how many distinct ways can you climb to the top?\n\nn=2\n1+1\n2\n\nn=3\n1+1+1\n2+1\n1+2\n\nn=4\n1+1+1+1\n1+1+2\n2+1+1\n2+2\n\nn=5\n\n1+1+1+1+1\n1+1+1+2\n1+2+2\n2+2+1\n2+1+1+1\n2+1+2\n\nn=6\n1+1+1+1+1+1\n1+1+1+1+2\n1+1+2+2\n2+2+2\n2+1+1+1+1\n2+2+1+1\n1+2+1+1+1\n1+1+2+1+1\n\n\n\n# '''\n# #c = {}\n#\n# def steps(n):\n# c={}\n# if n ==1:\n# return 1\n#\n# if n ==2:\n# return 2\n#\n#\n# elif str(n) in c.keys():\n# return c[str(n)]\n# else:\n# c[str(n)]= steps(n-2)+steps(n-1)\n# return steps(n-2)+steps(n-1)\n#\n# step_count = steps(105)\n#\n# print (step_count)\n\n\nclass Solution:\n def climbStairs(self, n: int) -> int:\n if n == 1:\n return 1\n\n if n == 2:\n return 2\n\n else:\n # c[str(n)]= steps(n-2)+steps(n-1)\n return self.climbStairs(n - 2) + self.climbStairs(n - 1)\n\n\nS = Solution()\nt=S.climbStairs(10)\nprint(t)\n","repo_name":"pauyang/Interview_questions","sub_path":"steps.py","file_name":"steps.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70146171602","text":"from __future__ import print_function, division, absolute_import\n\n# Copyright (c) 2011 Red Hat, Inc.\n#\n# This software is licensed to you under the GNU General Public License,\n# version 2 (GPLv2). There is NO WARRANTY for this software, express or\n# implied, including the implied warranties of MERCHANTABILITY or FITNESS\n# FOR A PARTICULAR PURPOSE. You should have received a copy of GPLv2\n# along with this software; if not, see\n# http://www.gnu.org/licenses/old-licenses/gpl-2.0.txt.\n#\n# Red Hat trademarks are not licensed under GPLv2. No permission is\n# granted to use or replicate Red Hat trademarks that are incorporated\n# in this software or its documentation.\n#\ntry:\n import unittest2 as unittest\nexcept ImportError:\n import unittest\n\nfrom subscription_manager.gui.storage import MappedTreeStore, MappedListStore\n\n\nclass StorageTests(unittest.TestCase):\n\n def test_mapped_list_store(self):\n self._run_test(MappedListStore, lambda data, store: store.add_map(data))\n\n def test_mapped_tree_store(self):\n self._run_test(MappedTreeStore, lambda data, store: store.add_map(None, data))\n\n def _run_test(self, store_class, add_data_to_map_funct):\n expected_c1 = \"C1_VAL\"\n expected_c2 = True\n expected_c3 = \"C3_val\"\n store = self.create_store(store_class)\n data = self.create_data_map(expected_c1, expected_c2, expected_c3)\n add_data_to_map_funct(data, store)\n\n tree_iter = store.get_iter_first()\n self.assertEqual(expected_c1, store.get_value(tree_iter, store['c1']))\n self.assertEqual(expected_c2, store.get_value(tree_iter, store['c2']))\n self.assertEqual(expected_c3, store.get_value(tree_iter, store['c3']))\n\n def create_store(self, store_class):\n type_map = {\n 'c1': str,\n 'c2': bool,\n 'c3': str\n }\n\n return store_class(type_map)\n\n def create_data_map(self, c1_str, c2_bool, c3_str):\n return {\n 'c1': c1_str,\n 'c2': c2_bool,\n 'c3': c3_str\n }\n","repo_name":"Lorquas/rhsm","sub_path":"test/test_storage.py","file_name":"test_storage.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13406188537","text":"\"\"\"User model tests.\"\"\"\n\n# run these tests like:\n#\n# python -m unittest test_user_model.py\n\n\nimport os\nfrom unittest import TestCase\nfrom sqlalchemy.exc import IntegrityError\nfrom models import db, User, Message, Follows\n\n# BEFORE we import our app, let's set an environmental variable\n# to use a different database for tests (we need to do this\n# before we import our app, since that will have already\n# connected to the database\n\nos.environ['DATABASE_URL'] = \"postgresql:///warbler-test\"\n\n\n# Now we can import app\n\nfrom app import app\n\n# Create our tables (we do this here, so we only create the tables\n# once for all tests --- in each test, we'll delete the data\n# and create fresh new clean test data\n\ndb.create_all()\n\n\nclass UserModelTestCase(TestCase):\n \"\"\"Test views for messages.\"\"\"\n\n def setUp(self):\n \"\"\"Create test client, add sample data.\"\"\"\n\n User.query.delete()\n Message.query.delete()\n Follows.query.delete()\n with app.app_context():\n db.session.commit()\n self.u1 = User.signup(\"uniqueusername1\", \"uniqueemail1@email.com\", None)\n self.u2 = User.signup(\"uniqueusername2\", \"uniqueemail2@email.com\", None)\n\n with app.app_context():\n db.session.commit()\n\n self.client = app.test_client()\n\n def tearDown(self):\n db.session.rollback()\n\n def test_repr(self):\n self.u1.id = 1\n\n self.assertEqual(\n repr(self.u1), \"\")\n\n def test_user_model(self):\n \"\"\"Does basic model work?\"\"\"\n\n u = User(\n email=\"test@test.com\",\n username=\"testuser\",\n password=\"HASHED_PASSWORD\"\n )\n\n db.session.add(u)\n db.session.commit()\n\n # User should have no messages & no followers\n self.assertEqual(len(u.messages), 0)\n self.assertEqual(len(u.followers), 0)\n def test_is_following(self):\n \"\"\"Does is_following work as intended?\"\"\"\n\n self.u1.following.append(self.u2)\n self.u2.following.append(self.u3)\n\n self.assertEqual(self.u1.is_following(self.u2), True)\n self.assertEqual(self.u2.is_following(self.u3), True)\n self.assertEqual(self.u1.is_following(self.u3), False)\n\n def test_is_followed_by(self):\n \"\"\"Does is_followed_by work as intended?\"\"\"\n\n self.u2.followers.append(self.u1)\n self.u3.followers.append(self.u2)\n\n self.assertEqual(self.u2.is_followed_by(self.u1), True)\n self.assertEqual(self.u3.is_followed_by(self.u2), True)\n self.assertEqual(self.u3.is_followed_by(self.u1), False)\n\n def test_user_creation(self):\n\n count = User.query.count()\n self.u5 = User.signup(\n \"uniqueusername5\", \"uniqueemail5@email.com\", None)\n db.session.commit()\n\n self.assertEqual(User.query.count(), count+1)\n\n def test_user_creation_fail(self):\n # Duplicate username overlap with self.u1\n\n with self.assertRaises(IntegrityError):\n self.u6 = User.signup(\n \"uniqueusername1\", \"uniqueemail6@email.com\", None)\n db.session.commit()\n\n def test_authenticate(self):\n\n authentication = User.authenticate(self.u2.username)\n\n self.assertTrue(authentication)\n\n\n def test_authenticate_bad_username(self):\n\n authentication = User.authenticate(self.u3.username, \"wrongpass\")\n\n self.assertFalse(authentication)\n\n def test_authenticate_bad_pass(self):\n authentication = User.authenticate(\"wrongusername\")\n\n self.assertFalse(authentication)","repo_name":"ILikeeggs313/Twitter-clone-project","sub_path":"test_user_model.py","file_name":"test_user_model.py","file_ext":"py","file_size_in_byte":3603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23907052765","text":"# -*- coding: utf-8 -*-\nimport time\n\nclass TimeDetector():\n def __init__(self):\n self.times = []\n self.targetWords = ['시', '분', '반', '후', '뒤', ]\n self.timeWords = [\n '퇴근 후', '점심시간', '쉬는시간', '아침', '점심', '저녁', '밤', '새벽',\n '한시', '두시', '세시', '네시', '다섯시', '여섯시', '일곱시', '여덟시', '아홉시', '열시', '열한시', '열두시'\n ]\n\n def getTime(self):\n return self.times\n\n def addTime(self, word):\n if word: self.times.append(word)\n\n def isTimeFormat(self, input):\n try:\n time.strptime(input, '%H:%M')\n return True\n except ValueError:\n return False\n\n def hasNumbers(self, str):\n return any(char.isdigit() for char in str)\n\n # 단어에 포함된 특수문자 제거\n def deleteSpecialCharacters(self, word):\n word = ''.join(char for char in word if char.isalnum())\n return word\n\n # 시간과 관련된 단어 외의 글자들은 삭제\n def getOnlyTimeWord(self, _word):\n word = []\n def slice_list_by_targetWord(targetWord, word):\n idx = word.index(targetWord)\n return word[:idx+1]\n def slice_list_from_targetWord(targetWord, word):\n idx = word.index(targetWord)\n return word[idx:]\n\n #후, 뒤 -> 반 -> 분 -> 시 우선순위대로 자른다.\n targetWords = ['후', '뒤', '반', '분', '시']\n for targetWord in targetWords:\n if targetWord in _word:\n word = slice_list_by_targetWord(targetWord, _word)\n break\n # 첫번째 숫자 이전의 글자들 삭제\n for character in _word:\n if self.hasNumbers(character):\n word = slice_list_from_targetWord(character, _word)\n break\n return word\n\n # target이 포함된 word가 시간과 관련된 단어인지 유효성 검사\n def isValid(self, word, idx):\n if word == '':\n return False\n elif '시' in word or '분' in word:\n return self.hasNumbers(word)\n elif word in '반' or word in '후' or word in '뒤':\n if self.hasNumbers(word) :\n return True\n elif self.words[idx-1] in self.times:\n return True\n else: return False\n return False\n\n def time_detector(self, sentence):\n words = sentence.split(' ')\n\n # sentence 안에 시간과 관련된 단어만 times에 추가.\n for word in words:\n # 정규식 검사\n index = word.index('~') if '~' in word else word.index('-') if '-' in word else 0\n if index == 0:\n if self.isTimeFormat(word):\n self.addTime(word)\n continue\n start, end = word[:index], word[index+1:]\n if self.isTimeFormat(start) and self.isTimeFormat(end):\n self.addTime(start+\"-\"+end)\n continue\n\n #targetWords 단어 검사\n done = False\n for targetWord in self.targetWords:\n if targetWord in word:\n idx = words.index(word)\n if self.isValid(word,idx):\n # _word = self.deleteSpecialCharacters(word)\n _word = self.getOnlyTimeWord(word)\n self.addTime(_word)\n done = True\n if done: continue\n\n #timeWords 단어 검사\n for timeWord in self.timeWords:\n if timeWord in word:\n # _word = self.deleteSpecialCharacters(word)\n _word = self.getOnlyTimeWord(word)\n self.addTime(_word)\n\n return self.times\n\n def disaster_message_time_detector(self, times, message):\n time = ''\n\n # 불필요한 데이터 삭제\n _times = ''\n for _time in times:\n if _times: _times += ' '\n _times += _time\n _times = _times.replace('에서', '')\n _times = _times.replace('까지', '')\n _times = _times.replace('(', '')\n _times = _times.replace(')', '')\n # print(_times)\n\n # 첫번째 데이터가 구간인 경우 해당 데이터만 추출\n split_times = _times.split(' ')\n if '~' in split_times[0]: return split_times[0]\n\n # 첫번째 데이터가 정규표현식 형태의 시간일 경우 해당 데이터만 추출\n if self.isTimeFormat(split_times[0].split('-')[0]):\n return split_times[0]\n\n return time\n\nif __name__ == '__main__':\n timeDetector = TimeDetector()\n message = '[서산시청] 5.27.목 19시~21시까지 서산중앙병원 장례식장 별실 방문자는 6월 1일까지 보건소 선별진료소(09~18시까지)에서 검사 바랍니다.'\t\t\n message = '[서산시청] 5월 27일(목) 19시~21시까지 서산중앙병원 장례식장 별실 방문자는 6월 1일까지 보건소 선별진료소(09~18시까지)에서 검사 바랍니다.'\t\t\n message = '[서산시청] 5.27.목 19:30 서산중앙병원 장례식장 별실 방문자는 6월 1일까지 보건소 선별진료소(09~18시까지)에서 검사 바랍니다.'\t\t\n message = '[서산시청] 5.27.목 19:30~20:30 서산중앙병원 장례식장 별실 방문자는 6월 1일까지 보건소 선별진료소(09~18시까지)에서 검사 바랍니다.'\t\t\n times = timeDetector.time_detector(message)\n # print(times)\n disaster_times = timeDetector.disaster_message_time_detector(times, message)\n print(disaster_times)","repo_name":"gyuree-kim/auto-schedule-updater","sub_path":"server/sms_ner_pkg/sms_ner_pkg/time_detector.py","file_name":"time_detector.py","file_ext":"py","file_size_in_byte":5688,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"6229004917","text":"import io\nimport pathlib\n\nimport sentencepiece as spm\n\n\"\"\" This is just a demonstration code for the Medium article. Uncomment the piece of code you want to use. \"\"\"\n\nCURRENT_PATH = pathlib.Path(__file__).parent\nSPLITTED_ALICE_FILE_PATH = CURRENT_PATH.joinpath('dataset').joinpath('splitted_alice_in_wonderland.txt')\n\n\"\"\" 1) Train from a corpus file. \"\"\"\n\n# # Train from a corpus file\n# spm.SentencePieceTrainer.Train(input=SPLITTED_ALICE_FILE_PATH,\n# model_prefix='sp_alice',\n# vocab_size=1500,\n# pad_id=0, # Let's re-define the special tokens for TensorFlow!\n# unk_id=1,\n# bos_id=2,\n# eos_id=3\n# )\n\n\n\"\"\" 2) Train from a corpus in memory. \"\"\"\n\n# # Pretend we have the sentences in memory\n# with open(SPLITTED_ALICE_FILE_PATH, encoding='utf-8') as infile:\n# corpus = [line for line in infile]\n#\n# spm.SentencePieceTrainer.Train(sentence_iterator=iter(corpus),\n# model_prefix='sp_alice',\n# vocab_size=1500,\n# pad_id=0, # Let's re-define the special tokens for TensorFlow!\n# unk_id=1,\n# bos_id=2,\n# eos_id=3\n# )\n\n\n\"\"\" 3) Leave the model in memory. \"\"\"\n\nmodel = io.BytesIO()\n\n# Pretend we have the sentences in our memory\nwith open(SPLITTED_ALICE_FILE_PATH, encoding='utf-8') as infile:\n corpus = [line for line in infile]\n\nspm.SentencePieceTrainer.Train(sentence_iterator=iter(corpus),\n model_writer=model,\n vocab_size=1500,\n pad_id=0, # Let's re-define the special tokens for TensorFlow!\n unk_id=1,\n bos_id=2,\n eos_id=3\n )\n\n# Optionally, save it\nwith open(CURRENT_PATH.joinpath('sp_alice.model'), 'wb') as outfile:\n outfile.write(model.getvalue())\n\n# Optionally, visualize it\nsp = spm.SentencePieceProcessor(model_proto=model.getvalue())\nencoded_input = sp.Encode(\"'Yes, that's it,' said the Hatter with a sigh: 'it's always tea-time'\")\n\ntokenized_input = [sp.IdToPiece(id) for id in encoded_input]\nprint(*tokenized_input)\n\n","repo_name":"maxtlw/sp_tf2_medium","sub_path":"train_sentencepiece.py","file_name":"train_sentencepiece.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18832392146","text":"import json\nfrom django.views.generic import ListView\nfrom django.views.generic.detail import DetailView\nfrom catalog.models import Product, Gallery\nfrom card.forms import CartAddProductForm\nfrom django.http import JsonResponse\nfrom django.core import serializers\nfrom django.core.paginator import Paginator\nfrom django.db.models import Q\n\n\nclass ProductView(ListView):\n model = Product\n paginate_by = 1\n template_name = \"catalog/product.html\"\n context_object_name = 'product_list'\n queryset = Product.objects.all()\n\n\n\n # def sorting_price_text(self, text):\n # self.queryset = Product.objects.order_by(text)\n # data = {}\n # return JsonResponse(data, safe=False)\n #\n # def sorting_price_number(self, number):\n # self.queryset = Product.objects.filter(price__gt=number-50, price__lte=number)\n # data = {}\n # return JsonResponse(data, safe=False)\n\n # def get_context_data(self, **kwargs):\n # context = super().get_context_data(**kwargs)\n # product = Product.objects.get(id=1)\n # images = product.gallery_set.all()[:1]\n # context['images'] = images\n # return context\n\n\n# def sort(request):\n# print(1)\n# number_sort = request.POST['number']\n# print(number_sort)\n# if number_sort == 1:\n# queryset = Product.objects.order_by('-price')\n# data = serializers.serialize(\"json\", queryset)\n# print(queryset)\n# return JsonResponse(data, safe=False)\n# elif number_sort == -1:\n# queryset = Product.objects.order_by('price')\n# data = serializers.serialize(\"json\", queryset)\n# print(queryset)\n# return JsonResponse(data, safe=False)\n# elif number_sort is None:\n# queryset = Product.objects.all()\n# data = serializers.serialize(\"json\", queryset)\n# return JsonResponse(data, safe=False)\n# else:\n# min = int(number_sort)-50\n# max = int(number_sort)+50\n# queryset = Product.objects.filter(price__gt=min, price__lte=max)\n# data = serializers.serialize(\"json\", queryset)\n# return JsonResponse(data, safe=False)\n\n\nclass ProductDetailView(DetailView):\n model = Product\n pk_url_kwarg = 'product_id'\n template_name = \"catalog/product-detail.html\"\n context_object_name = 'product_detail'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n products = Product.objects.all()[:8]\n context['products'] = products\n context['selector_colors'] = Product.objects.filter(title=self.get_object().title)\n context['cart_product_form'] = CartAddProductForm()\n return context\n\n\nclass FilterProductView(ProductView):\n\n def get_queryset(self):\n data = self.request.GET\n print(data)\n queryset = Product.objects.all()\n if 'price_text_sort' in data:\n queryset = queryset.order_by(self.request.GET['price_text_sort'])\n\n if 'price_interval_sort' in data:\n queryset = queryset.filter(price__gt=int(self.request.GET['price_interval_sort'])-50,\n price__lte=int(self.request.GET['price_interval_sort']))\n\n if 'category_sort' in data:\n queryset = queryset.filter(categories__name__in=[self.request.GET['category_sort'], ])\n\n if 'between_sort_min' in data and 'between_sort_max' in data:\n queryset = queryset.filter(price__gt=int(self.request.GET['between_sort_min']),\n price__lte=int(self.request.GET['between_sort_max']))\n\n if 'title_sort' in data:\n queryset = queryset.filter(title__icontains=self.request.GET['title_sort'])\n print(self.request.GET['title_sort'])\n print(queryset)\n\n return queryset\n\n def get(self, request, *args, **kwargs):\n data = self.request.GET\n print(data)\n number_page = self.request.GET[\"number_page\"]\n change_sort = self.request.GET.get(\"change_sort\", False)\n queryset = self.get_queryset()\n p = Paginator(queryset, 1)\n num_pages = p.num_pages\n print(num_pages)\n if bool(change_sort) is True:\n number_page = 1\n page = p.page(number_page)\n page_queryset = page.object_list\n has_previous = page.has_previous()\n has_next = page.has_next()\n if has_next:\n next_page_number = page.next_page_number()\n previous_page_number = -1\n elif has_previous:\n next_page_number = -1\n previous_page_number = page.previous_page_number()\n elif has_next and has_previous:\n next_page_number = page.next_page_number()\n previous_page_number = page.previous_page_number()\n else:\n previous_page_number = -1\n next_page_number = -1\n json1 = serializers.serialize(\"json\", page_queryset)\n data = json.loads(json1)\n if len(queryset) > 0:\n k = 0\n else:\n k = 1\n\n for product in page_queryset:\n img = product.gallery.all()[:1]\n data[k]['fields']['img'] = str(img[0].image)\n data[k]['get_absolute_url'] = str(product.get_absolute_url())\n k += 1\n try:\n data[k-1]['num_pages'] = str(num_pages)\n data[k-1]['next_page_number'] = str(next_page_number)\n data[k-1]['previous_page_number'] = str(previous_page_number)\n data[k-1]['number_page'] = str(number_page)\n except:\n data={}\n return JsonResponse(data, safe=False)\n","repo_name":"Alexsandr007/djangoShop","sub_path":"djangoShop/catalog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72326868243","text":"# Join DataFrames together, using mappings that specify how\n#\n# N.B. join_with_mappings and join_with_mapping are two different functions\n\n# Python\nimport copy\n# Forklift\nfrom .joinable import are_dataframes_joinable\nfrom ..column_renamer import ColumnRenamer\n\n# This function takes in two DataFrames, and joins them using many mapping rules\n# It does this by recursing over each mapping, joining decorate_dataframe to with_dataframe for each mapping\ndef join_with_mappings(decorate_dataframe, with_dataframe, mappings):\n try:\n # Step 1: Get a mapping\n mapping = mappings.pop(0)\n # Step 2: Join the decorate_dataframe and with_dataframe DataFrames using the mapping\n decorated_dataframe = join_with_mapping(decorate_dataframe, with_dataframe, mapping)\n # Step 3: Call this function again, with the newly decorated_dataframe and the remaining mappings\n return join_with_mappings(decorated_dataframe, with_dataframe, mappings)\n except IndexError:\n # There are no more mappings left to join with, so we're done\n return decorate_dataframe\n\n# This function narrows down mappings to the ones that actually exist in the dataframe\ndef mappings_that_exist_on_dataframe(dataframe, mappings, mapping_key_for_dataframe):\n requested_columns_names = [column_mapping[mapping_key_for_dataframe] for column_mapping in mappings]\n requested_columns_that_actually_exist = list(set(requested_columns_names).intersection(set(dataframe.columns)))\n return [column_mapping for column_mapping in mappings if column_mapping[mapping_key_for_dataframe] in requested_columns_that_actually_exist]\n\n# This function narrows down mappings to the ones that don't exist in the dataframe\ndef mappings_that_dont_exist_on_dataframe(dataframe, mappings, mapping_key_for_dataframe):\n requested_columns_names = [column_mapping[mapping_key_for_dataframe] for column_mapping in mappings]\n requested_columns_that_dont_exist = [column_name for column_name in requested_columns_names if column_name not in dataframe.columns]\n return [column_mapping for column_mapping in mappings if column_mapping[mapping_key_for_dataframe] in requested_columns_that_dont_exist]\n\n# This function collects the values for one key in the mapping\ndef get_list_from_mappings(mappings, mapping_key):\n return [mapping[mapping_key] for mapping in mappings]\n\n# This function takes in two DataFrames, and joins them using a mapping rule\ndef join_with_mapping(decorate_dataframe, with_dataframe, mapping):\n # Step 1: Ensure we can even join these two DataFrames\n # Check that the decorate_dataframe and the with_dataframe both have the columns that the\n # mapping wishes to join on (otherwise we can't join, so return immediately)\n if not are_dataframes_joinable(decorate_dataframe, mapping[\"this_join_column_name\"], with_dataframe, mapping[\"with_join_column_name\"]):\n return decorate_dataframe\n # Step 2: Get the valid mappings\n # Make a deep copy of the mappings, since we edit the hash destructively\n mappings_dereferenced = copy.deepcopy(mapping[\"add_from_with_to_this\"])\n # Remove mappings that don't exist on with_dataframe\n compatible_mappings = mappings_that_exist_on_dataframe(with_dataframe, mappings_dereferenced, \"current_with_column_name\")\n # Remove mappings that would collide decorate_dataframe and with_dataframe\n compatible_mappings = mappings_that_dont_exist_on_dataframe(decorate_dataframe, compatible_mappings, \"becomes_this_column_name\")\n # Make sure there's at least 1 mapping left, otherwise joining would be pointless\n if len(compatible_mappings) < 1:\n return decorate_dataframe\n # Step 3: Narrow down with_dataframe to what is needed\n # Get the columns the user requested from with_dataframe\n with_dataframe = with_dataframe.select(get_list_from_mappings(compatible_mappings, \"current_with_column_name\") + [mapping[\"with_join_column_name\"]])\n # Rename the columns in with_dataframe_only_columns_needed\n column_renamer = ColumnRenamer({a_mapping[\"current_with_column_name\"]: a_mapping[\"becomes_this_column_name\"] for a_mapping in compatible_mappings})\n with_dataframe = column_renamer.rename_columns(with_dataframe)\n # Give the join column a predefined name, so that it doesn't collide with an existing column in decorate_dataframe\n with_dataframe = with_dataframe.withColumnRenamed(mapping[\"with_join_column_name\"], \"__temp_with_join_column\")\n # Step 4: Return the joined DataFrame,\n # getting rid of the uniquely-named join column along the way\n return decorate_dataframe.join(with_dataframe, decorate_dataframe[mapping[\"this_join_column_name\"]] == with_dataframe[\"__temp_with_join_column\"], \"left_outer\").drop(\"__temp_with_join_column\")\n","repo_name":"jerzygangi/forklift","sub_path":"forklift/join/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4653,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"3"}
+{"seq_id":"30289029515","text":"from typing import Callable, Iterable, List, Tuple, Union\n\nimport numpy as np\n\n\ndef vaidate_input_types_is_str(inputs: List[Union[str, np.ndarray]]) -> bool:\n \"\"\"Validates that the inputs are all the same type and one of str or np.ndarray.\n\n\n Args:\n inputs (List[Union[str, np.ndarray]]): List of inputs to validate\n Returns:\n true if the inputs are str and false if the inputs are np.ndarray\n Raises:\n ValueError if all inputs are not the same type\n ValueError if the types are other than np.ndarray or str\n\n \"\"\"\n types = set(map(type, inputs))\n if len(types) > 1:\n raise ValueError(\n \"Mixed input type usuage. Ensure all are numpy arrays or strings.\"\n )\n\n t = types.pop()\n\n if t in [str, np.ndarray]:\n return t == str\n else:\n raise ValueError(\"Input type must either be np.ndarray or string\")\n\n\ndef validate_parallel_params(\n gpus: List[int] = None, cpus: int = None, out_dir: str = None\n) -> Tuple[List[int], bool]:\n \"\"\"Validates that the parallel params.\n\n Args:\n gpus (List[int]): list GPU ids to use for parallel classification\n cpus (int): number of cpus to use for parallel classification\n Returns:\n A tuple where the first element is a List of integer id values for each\n worker. The second element is true if the ids are gpu ids and false if\n they are cpu ids\n Raises:\n ValueError if both gpus and cpus are given\n ValueError is cpus or gpus are given, but out_dir is not given\n ValueError if len(gpus)==1\n ValueError if cpus<2\n \"\"\"\n if (gpus is not None) and (cpus is not None):\n raise ValueError(\"Please only give a value cpus or gpus, not both.\")\n\n if (gpus is None) and (cpus is None):\n return [0], False\n\n if (gpus is not None or cpus is not None) and (out_dir is None):\n raise ValueError(\"Parallel classification requires an out_dir\")\n\n if gpus is not None:\n if len(gpus) == 1:\n err = \" \".join(\n [\n \"Only one gpu indicated. If you are trying to select\",\n \"a single gpu, then use the CUDA_VISIBLE_DEVICES environment\",\n \"variable. For more information visit:\",\n \"https://devblogs.nvidia.com/cuda-pro-tip-control-gpu-visibility-cuda_visible_devices/\",\n ]\n )\n\n raise ValueError(err)\n else:\n return gpus, True\n else:\n if cpus < 2:\n raise ValueError(\"If passing cpus please indicate a value greater than 1.\")\n\n return np.arange(cpus), False\n\n\ndef arrays_not_same_size(inputs: List[np.ndarray]) -> bool:\n \"\"\"Validates that all input arrays are the same size.\n\n Args:\n inputs (List[np.ndarray]): Input arrays to validate\n Returns:\n true if the arrays are the same size and false if they are not\n \"\"\"\n shapes = [i.shape for i in inputs]\n shp_first = shapes[0]\n shp_rest = shapes[1:]\n\n return not np.array_equiv(shp_first, shp_rest)\n\n\ndef apply(f: Callable, args: Iterable, kwargs: Iterable[dict] = None) -> None:\n \"\"\"Applies the function f to the args and kwargs.\n\n Args:\n f (Callable): fucntion to apply\n args (Iterable): iterable to apply f to\n kwargs (Iterable[dict]): iterable of a dict of kwargs to apply with\n each element in args\n Returns:\n None\n \"\"\"\n try:\n i_args = iter(args)\n i_kwargs = iter(kwargs) if kwargs else None\n while True:\n a = next(i_args)\n k = next(i_kwargs) if kwargs else {}\n if (type(a) is tuple) or (type(a) is list):\n f(*a, **k)\n else:\n f(a, **k)\n except StopIteration:\n pass\n","repo_name":"morpheus-project/morpheus-core","sub_path":"morpheus_core/helpers/misc_helper.py","file_name":"misc_helper.py","file_ext":"py","file_size_in_byte":3831,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29000220925","text":"\nfrom django.contrib import admin\nfrom django.urls import path,include\nfrom . import views\nfrom . import api_view\nurlpatterns = [\n path('invitedevents',api_view.invited_events_home),\n path('generalevents',api_view.general_events_home),\n path('generalevents/detail/',api_view.general_events_detail),\n path('generalevents/comment/',api_view.general_event_comment),\n path('inviteuser//',api_view.invite_users),\n path('invitedevent/details/',api_view.invited_event_details),\n path('invitedevent/comments/',api_view.invited_event_comment),\n \n path('signup',views.signup),\n\n \n path('login',views.login)\n \n \n]\n","repo_name":"utkarshparkhi/IMGSched","sub_path":"IMGbackend/IMGsched/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4249780930","text":"#string\ncamping_stuff = \"tent, sleeping bags, water, raspberry pi, coffee, knife, ethernet cable, flash drive, beard oil, marshmallows\"\n\n#list\n#ordered\n#zero-indexed\ncamping_list = [\"tent\", \"sleeping bags\", \"water\", \"raspberry pi\", \"coffee\", \"knife\", \"ethernet cable\", \"flash drive\", \"beard oil\", \"marshmallows\"]\n\n#who brings what?\nme = camping_list[4]\n\nyou = camping_list[3]\n\n#negative index\n#access last item in list\nthem = camping_list[-1]\n\nprint(me)\n\nprint(you)\n\nprint(them)\n\nprint(camping_list)\n\n#methods\nsupplies = [\"tent\", \"sleeping bags\", \"water\", \"raspberry pi\", \"coffee\", \"knife\", \"ethernet cable\", \"flash drive\", \"beard oil\", \"marshmallows\"]\n\ncamp_site = [\"Crystal Lake\", 404, 95.5, False]\n\n#add to list (one at a time) to end of list\nsupplies.append(\"toilet paper\")\n\nsupplies.append(\"bidet\")\n\n#add to list(multiple pieces of data) to end of list\nsupplies.extend([\"formula\", \"diapers\"])\n\nsupplies = supplies + [\"dog food\", \"treats\"]\n\n#add to list to beginning\nsupplies.insert(0,\"blankets\")\n\n#add to list second to last\nsupplies.insert(-1, \"leashes\")\n\n#delete from list \nsupplies.remove(\"tent\")\n\n#delete from list and return deleted item\nprint(\"This item was just deleted: \" + supplies.pop(0))\n\n#delete entire list\n#empty list\n#supplies.clear()\n\nprint(supplies)\n","repo_name":"paigejananne/NetworkChuck","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37125505714","text":"class Solution(object):\n def productExceptSelf(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[int]\n \"\"\"\n total = 1\n doesZeroExist = False\n # If 2 zeroes exist, then the nums list will be 0\n # If only 1 0 exist, then only the value with 0 will have the total value, and the rest of the variables will have 0\n doesTwoZeroExist = False\n for num in nums: \n if num == 0:\n if doesZeroExist == True:\n doesTwoZeroExist = True\n doesZeroExist = True\n continue\n total = total * num\n \n for count, num in enumerate(nums):\n if doesTwoZeroExist == True:\n nums[count] = 0\n continue\n\n if num == 0:\n nums[count] = total\n else:\n if doesZeroExist == True:\n nums[count] = 0\n continue\n nums[count] = total/num \n \n print(nums)\n return nums\n \ns = Solution()\ns.productExceptSelf([0, 0])\n","repo_name":"tanshiyu1999/LeetCode","sub_path":"238.Product_of_Array_Except_Self/productOfArrayExceptSelf.py","file_name":"productOfArrayExceptSelf.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2132506305","text":"import json\nimport re\nimport requests\nfrom app.models1 import Indicates\n\nclass Drugs(object):\n def __init__(self, indicate_name, client_snp):\n self.client_snp = client_snp\n self.indicate_name = indicate_name\n self.results = self.drug_get_results(self.indicate_name, self.client_snp)\n\n def split_job(self, indicate_info, clinet_SNP):\n \"\"\"\n module_dict:{ 2:{ATM:[{siteinfo1},{siteinfo2}}}\n \"\"\"\n conclusions = {}\n module_dict = {}\n gene_results = []\n advices = []\n level = 0\n conclusion = ''\n for siteinfo in indicate_info['site_result']:\n try:\n module_dict.setdefault(siteinfo['interpretation_label'], {}).\\\n setdefault(siteinfo['gene'], []).append(siteinfo)\n except:\n pass\n for module in module_dict:\n if int(module) == 2:\n ins = DrugModule2(drug_id=indicate_info['indicate_name'], client_SNP=clinet_SNP, sites_dict=module_dict[module])\n elif int(module) == 1:\n ins = DrugModule1(drug_id=indicate_info['indicate_name'], client_SNP=clinet_SNP, sites_dict=module_dict[module], conclusion_result=indicate_info['conclusion_result'])\n\n if ins.level > level:\n conclusion = ins.conclusion\n level = ins.level\n conclusions = {}\n conclusions[conclusion] = ins.level\n\n gene_results.extend(ins.gene_result)\n advices.extend(ins.advices)\n \n return conclusions, gene_results, advices\n\n def drug_get_results(self, indicate_name, client_SNP): \n indicates = Indicates.query.filter_by(name = indicate_name).first()\n if indicates:\n each_info = {}\n indicate_info = indicates.json_info\n indicate_info = json.loads(indicate_info)\n \n try:\n indicate_info['conclusion_result'] = json.loads(indicate_info['conclusion_result'])\n except:\n pass\n\n indicate_info['site_result'] = json.loads(indicate_info['site_result'])\n\n conclusions, gene_results, advices = self.split_job(indicate_info, client_SNP) \n each_info['name'] = indicate_name\n each_info['conclusion'] = list(conclusions.keys())[0]\n each_info['tableData'] = []\n each_info['explanation'] = []\n each_info['medication_advice'] = []\n\n for each in gene_results:\n table={}\n table['geneName'] = each[0]\n if type(each[1]) == list:\n table.setdefault('geneSite', each[1])\n else:\n table.setdefault('geneSite', []).append(each[1])\n table['GT'] = each[2]\n table['explain'] = each[3]\n table['tips'] = each[4]\n each_info['tableData'].append(table)\n \n \n for each in advices:\n tmp = {}\n tmp.setdefault('content',each)\n each_info['medication_advice'].append(tmp)\n\n each_info['explanation'].append({'content':indicate_info['explanation']})\n \n return each_info\n \n\nclass DrugModuleCommon(object):\n\n def __init__(self, drug_id, client_SNP, sites_dict):\n self.indicate_name = drug_id\n self.client_SNP = client_SNP\n self.sites_dict = sites_dict\n\n self.advices = []\n self.conclusion = None #\n self.level = None #\n self.gene_result = [] #表格中:基因 检测位点 基因型 表型 用药提示\n \n def mut_type(self, site, ref, alt, GT):\n \"\"\"\n 'rs_name': 'rs730012', 'ref': 'A', 'alt': 'C,T'\n GT: client SNP : C/C\n \"\"\"\n def len_base(alt):#判断Alt是否存在indel 缺失的情况。暂不考虑插入缺失\n len_base = 1\n for base in alt.split(','):\n if len(base) != 1:\n len_base = len(base)\n return len_base\n\n mutype = ''\n gt = set(tuple(GT)) # GG 变为（'G'）,AG变为（'A','G'）\n if (len(gt) == 1): #纯合\n if len(gt & set(['A', 'T', 'C' ,'G'])) == 1: #SNP，根据碱基来判断处于什么型别\n if ref in gt: #纯合野生型\n mutype = '野生型'\n elif len(gt & set(alt.split(','))) == 1: #纯合突变型\n mutype = '突变型,纯合'\n else:\n print (\"Error {} not exists\".format(GT))\n #print (siteinfo)\n elif len(gt & set(['I', 'D'])) == 1: #InDel，I表示野生型,D表示突变型\n if len(ref) != 1 or len_base(alt) != 1: #判断该位点在数据库中是否有InDel\n if list(gt)[0] == 'I':\n mutype = '野生型'\n elif list(gt)[0] == 'D':\n mutype = '缺失,纯合'\n else:\n print ('Error not exists InDel mutation, ref:{} alt{}'.format(ref, alt))\n elif (len(gt) == 2): #杂合\n if len(gt & set(['A', 'T', 'C' , 'G'])) == 2: #SNP，根据碱基来判断处于什么型别 \n if ref in gt or len(gt & set(alt.split(','))) == 1:\n mutype = '突变型,杂合'\n else:\n print (\"Error {} not exits\".format(GT))\n elif len(gt & set(['I', 'D'])) == 2: #InDel，I表示野生型,D表示突变型\n if len(ref) != 1 or len_base(alt) != 1: #判断该位点在数据库中是否有InDel\n mutype = '缺失,杂合'\n return mutype\n \n def get_user_muttype(self, siteinfo):\n '''\n get user and ref GT, and return mutype\n '''\n site = siteinfo['rs_name']\n ref = siteinfo['ref'] #碱基唯一\n #alt = siteinfo['alt_db'] #暂用alt_db替代\n alt = siteinfo['alt'] #可能存在多个碱基，用','分割\n try:\n GT = self.client_SNP[site].replace('/', '').replace('|', '') #提取用户的基因型，并去除分隔符号\n except:\n print (\"Error {} not exists Genotypes info\").format(site)\n return site, GT, self.mut_type(site, ref, alt, GT)\n\n\nclass DrugModule2(DrugModuleCommon):\n \"\"\"\n Site Depended Results\n \"\"\"\n def __init__(self, drug_id, client_SNP, sites_dict):\n super(DrugModule2, self).__init__(drug_id, client_SNP, sites_dict)\n self.get_result_model() #function，get genotype info\n\n def get_result_model(self):\n '''\n Get geneotype and add each SNP result to detail.\n :return: gene_types: Dict with gene, dbSNP and genotype. Example: {'SJDK9': {'rs2898': '突变，杂合'}}\n '''\n priority_gene_module = 0\n medication_tips_module = ''\n medication_advice_module = ''\n\n\n for gene, Gene in self.sites_dict.items():\n priority_gene = 0\n medication_tips_gene = ''\n medication_advice_gene = ''\n\n #判断用户的基因型对应数据位点数据库的哪一条数据 \n for siteinfo in Gene:\n priority_site = 0 #每一次位点的priority，初始值为0\n site, GT, mutype = self.get_user_muttype(siteinfo)\n if mutype == siteinfo['phenotype'].replace('，', ','):\n #ATM rs11212617 AC 突变型,杂合 提高疗效\n self.gene_result.append([gene, siteinfo['rs_name'], GT, siteinfo['phenotype'], siteinfo['medication_tips']])\n priority_site = int(siteinfo['priority'])\n if priority_gene < priority_site: #比较大小，site的priority高则用该site的值\n priority_gene = priority_site\n medication_advice_gene = siteinfo['medication_advice']\n medication_tips_gene = siteinfo['medication_tips']\n\n self.advices.append(medication_advice_gene)\n if priority_gene_module < priority_gene : #比较大小，gene的priority则用该gene的值\n priority_gene_module = priority_gene\n medication_tips_module = medication_tips_gene\n #medication_advice_module = medication_advice_gene\n\n self.level = priority_gene_module\n self.conclusion = medication_tips_module\n\n #self.advices.append(medication_advice_module)\n \n\nclass DrugModule14(DrugModuleCommon):\n def __init__(self, drug_id, client_SNP, sites_dict):\n super(DrugModule14, self).__init__(drug_id, client_SNP, sites_dict)\n \n def get_pheo_geno(self, gene, genotype):\n '''\n get IM, PM, PM from genotype \n '''\n ##json_data = requests.get(main_config['remote_api']['genotype_phenotype'], params=param).json() \n type_dict = json.load(open('app/rpt/config/{}.json'.format(gene), 'r'))\n\n type_m = type_dict[genotype[0]][genotype[1]]\n return type_m\n\n def _type2info(self, conclusion_result):\n type2info = {}\n for each in conclusion_result:\n type_m = each['phenotype'].replace(' ', '')\n type2info.setdefault(type_m, {}).setdefault('medication_tips', each['medication_tips'])\n type2info.setdefault(type_m, {}).setdefault('medication_advice', each['medication_advice'])\n type2info.setdefault(type_m, {}).setdefault('priority', each['priority'])\n return type2info\n\nclass DrugModule1(DrugModule14):\n def __init__(self, drug_id, client_SNP, sites_dict, conclusion_result):\n super(DrugModule1, self).__init__(drug_id, client_SNP, sites_dict)\n self.__conclusion_result = conclusion_result\n self.get_result_model() #function，get genotype info\n\n def bubble_sort(self, lst, priority):\n '''\n 根据priority进行排序, 冒泡排序法\n '''\n n = len(lst)\n if n <= 1:\n return lst\n for i in range(0, n):\n for j in range(0, n-i-1):\n if priority[lst[j]] < priority[lst[j+1]]:\n lst[j], lst[j+1] = lst[j+1], lst[j]\n return lst\n\n def get_result_model(self):\n '''\n Get gene subtypes.\n :return: gene_types: python dict with gene name and subtypes. {gene, subtypes},\n example {'CYP2D6', ('CYP2D6*1', 'CYP2D6*2')}\n '''\n try:\n type_info = self._type2info(self.__conclusion_result) # get infomation for each coclusion type\n except:\n print ('Error: conclusion result something error {}'.format(self.__conclusion_result))\n\n priority_gene_module = 0\n medication_tips_module = ''\n medication_advice_module = ''\n \n for gene, Gene in self.sites_dict.items():\n priority_gene = 0\n medication_tips_gene = ''\n medication_advice_gene = ''\n priority_type = {} # this is gene type priority, for each type 'CYP2D6*1', 'CYP2D6*2'\n\n wild_type = gene + '_1' # each genetype, _1 set to wild type\n genotype = [wild_type, wild_type] # \n priority_type[wild_type] = 0\n sites = []\n \n for siteinfo in Gene:\n site, GT, mutype = self.get_user_muttype(siteinfo)\n sites.append(site)\n priority_type.setdefault(siteinfo['phenotype'],int(siteinfo['priority']))\n if re.search(r'杂合', mutype):\n genotype.append(siteinfo['phenotype'])\n elif re.search(r'纯合',mutype):\n genotype.append(siteinfo['phenotype'])\n genotype.append(siteinfo['phenotype'])\n\n genotype = self.bubble_sort(genotype, priority_type)[0:2] #降序排序，取前两位\n type_m = self.get_pheo_geno(gene, genotype)\n genotype_simple = '*' + genotype[0].split('_')[1] + '/' + '*' + genotype[1].split('_')[1]\n try:\n self.gene_result.append([gene, sites, genotype_simple, type_m, type_info[type_m]['medication_tips']])\n except:\n self.gene_result.append([gene, sites, genotype_simple, type_m, \"\"]) \n try:\n priority_gene_module < type_info[type_m]['priority'] #this is medication tips priority\n priority_gene_module = type_info[type_m]['priority']\n medication_tips_module = type_info[type_m]['medication_tips']\n medication_advice_module = type_info[type_m]['medication_advice']\n except:\n pass\n\n #如果结论中有priority且priority大于1,那么就按照priority的方式，取prority为大的结论。\n #如果priority为0，则表示priority失效，则按照基因与基因之间(2个)的矩阵取值\n ##针对与两个基因决定一个结论的情况\n if priority_gene_module == 0:\n if len(self.gene_result) == 2:\n #\n\n gene1 = self.gene_result[0][0]\n gene2 = self.gene_result[1][0]\n con1 = self.gene_result[0][3]\n con2 = self.gene_result[1][3]\n genename = gene1 + '_' + gene2 #CYP2D6_CYP2C19\n genotype = [gene1 + '_' + con1, gene2 + '_' + con2] # CYP2D6_IM，CYP2C19_PM\n type_m = self.get_pheo_geno(genename, genotype) # 指定conclusions\n self.gene_result[0][4] = type_info[type_m]['medication_tips']\n self.gene_result[1][4] = type_info[type_m]['medication_tips']\n self.level = 1 #为了避免后续错误，如果level为0，则是初始化值，不会替换初始化值\n self.conclusion = type_info[type_m]['medication_tips']\n self.advices.append(type_info[type_m]['medication_advice'])\n\n else:\n self.level = priority_gene_module\n self.conclusion = medication_tips_module\n self.advices.append(medication_advice_module)","repo_name":"afei2357/bird_strike","sub_path":"backend/app/rpt/Drugs.py","file_name":"Drugs.py","file_ext":"py","file_size_in_byte":14215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22120387047","text":"#! /usr/bin/python\n# -*- coding: utf-8 -*-\n'''\nImplementing watchdogs for others fsm.\n\n@date: Oct 2016\n@author: Davide Marcato\n@email: davide.marcato@lnl.infn.it\n'''\n\nfrom random import uniform\nfrom . import fsmBase\n\n\nclass fsmWatchdog(fsmBase):\n '''A FSM that implements watchdogs for others fsm.'''\n\n def __init__(self, name: str, fsms: list, **args) -> None:\n super(fsmWatchdog, self).__init__(name, **args)\n\n # a list of timers with timer name and input linked\n self.timers = {}\n # connect input for each fsm\n # the input is the pv where to write each time to say the fsm is alive\n for fsm in fsms:\n wd = fsm.getWatchdogInput()\n if wd is not None:\n inp = wd[0]\n self.timers[inp.ioname()] = fsm\n\n statesWithIOs = {\n \"run\": []\n }\n self.setSensLists(statesWithIOs)\n self.gotoState('run')\n\n def run_entry(self):\n for tmrName, fsm in self.timers.items():\n # start with a random delay not to write all pvs at the same instant\n interval = fsm.getWatchdogInput()[2]\n randDelay = uniform(0, interval)\n self.tmrSet(tmrName, randDelay)\n self.logD(\"set %d watchdogs\" % len(self.timers))\n\n def run_eval(self):\n for tmrName, fsm in self.timers.items():\n if self.tmrExpired(tmrName):\n inp, mode, interval = fsm.getWatchdogInput()\n self.tmrSet(tmrName, interval)\n if inp.connected():\n if mode == \"on\":\n inp.put(int(fsm.is_alive()))\n elif mode == \"off\":\n inp.put(int(not fsm.is_alive()))\n elif mode == \"on-off\":\n if fsm.is_alive():\n inp.put(int(not inp.val()))\n else:\n self.logE(\"Unknown watchdog mode\")\n","repo_name":"darcato/pysmlib","sub_path":"smlib/fsmWatchdog.py","file_name":"fsmWatchdog.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"34320518904","text":"import os\n\nimport stat_attributes as attributes\nfrom build_tools_crawler import BuildToolsCrawler\nfrom msvs_tools import MsvsTools\nfrom ide_writer import IdeXmlWriter, IdeCompositeWriter\nfrom services import locateResource, toNativePath\n\n\nclass MsvsSolutionWriter(object):\n _TEMPLATE_FILENAME = 'vs_solution.tsln'\n _SOLUTION_FILENAME = \"vs_{name}.sln\"\n\n def __init__(self, contents, tools, projectGuid, projectFilePath):\n \"\"\"\n :type contents: StatMakefileProject\n :type tools: MsvsTools\n \"\"\"\n self.__contents = contents\n self.__tools = tools\n self.__projectFilePath = projectFilePath\n self.__projectGuid = projectGuid\n pass\n\n def write(self):\n fileContents = self.__readSolutionFileTemplate()\n fileContents = fileContents.format(\n format=self.__tools.solutionFormat, version=self.__tools.versionId, year=self.__tools.year,\n name=self.__contents.name, project_file=self.__projectFilePath, guid=self.__projectGuid)\n filename = self._SOLUTION_FILENAME.format(name=self.__contents.name)\n filePath = \"./{0}/{1}\".format(attributes.IDE_DIRECTORY, filename)\n _file = open(filePath, \"w\")\n _file.write(fileContents)\n _file.flush()\n _file.close()\n print('\\nVisual-Studio solution \"{0}\" has been created successfully.'.format(os.path.abspath(filePath)))\n\n def __readSolutionFileTemplate(self):\n filePath = locateResource(self._TEMPLATE_FILENAME)\n _file = open(filePath)\n _template = _file.read()\n _file.close()\n return _template\n\n\nclass MsvsLegacyWriter(IdeXmlWriter):\n _PROJECT_GUID = '86FC28D1-F4DE-4209-B544-10B5415D0C20'\n _PROJECT_FILENAME = \"vs_{0}.vcproj\"\n\n def __init__(self, contents, tools):\n \"\"\"\n :type contents: StatMakefileProject\n :type tools: MsvsTools\n \"\"\"\n super(MsvsLegacyWriter, self).__init__(contents)\n self._filename = self._PROJECT_FILENAME.format(self._contents.name)\n self.__tools = tools\n output = os.path.join(\"..\", attributes.OUTPUT_DIRECTORY, self._contents.outputName,\n \"ide_{0}\".format(self._contents.name))\n self.__includePath = os.path.join(output, \"inc\")\n self.__executable = os.path.join(output, \"bin\", \"{0}.exe\".format(self._contents.outputName))\n root = self.__composeBodyBase()\n for component in self.__composeComponents():\n root.appendChild(component)\n\n def __composeComponents(self):\n self.__rootDirectoryTag = self.composeElement(\"Files\")\n return (\n self.__composePlatforms(),\n self.composeElement(\"ToolFiles\"),\n self.__composeConfigurations(),\n self.composeElement(\"References\"),\n self.__rootDirectoryTag,\n self.composeElement(\"Globals\")\n )\n\n def __composeConfigurations(self):\n tool = self.composeElement(\n \"Tool\",\n Name=\"VCNMakeTool\",\n BuildCommandLine=self.formatCommandLine(\"build\"),\n ReBuildCommandLine=self.formatCommandLine(\"rebuild\"),\n CleanCommandLine=self.formatCommandLine(\"clean\"),\n Output=self.__executable,\n PreprocessorDefinitions=\"WIN32;_DEBUG;{0}\".format(';'.join(self._contents.definitions)),\n IncludeSearchPath=self.__includePath,\n ForcedIncludes=\"\",\n AssemblySearchPath=\"\",\n ForcedUsingAssemblies=\"\",\n CompileAsManaged=\"\"\n )\n configuration = self.composeElement(\n \"Configuration\", Name=\"Debug|Win32\", OutputDirectory=\".\", IntermediateDirectory=\".\", ConfigurationType=\"0\")\n configuration.appendChild(tool)\n configurations = self.composeElement(\"Configurations\")\n configurations.appendChild(configuration)\n return configurations\n\n def __composePlatforms(self):\n platforms = self.composeElement(\"Platforms\")\n platforms.appendChild(self.composeElement(\"Platform\", Name=\"Win32\"))\n return platforms\n\n def __composeBodyBase(self):\n body = self.composeElement(\n \"VisualStudioProject\",\n ProjectType=\"Visual C++\",\n Version=\"{0:.2f}\".format(self.__tools.versionId),\n Name=self._contents.name,\n ProjectGUID=\"{{{0}}}\".format(self._PROJECT_GUID),\n Keyword=\"MakeFileProj\",\n RootNamespace=self._contents.name\n )\n self._doc.appendChild(body)\n return body\n\n def createRootToken(self):\n return self.__rootDirectoryTag\n\n def createDirectoryToken(self, name, parentDirectoryToken):\n childNodes = parentDirectoryToken.childNodes if parentDirectoryToken.hasChildNodes() else ()\n for element in [node for node in childNodes if node.tagName == \"Filter\"]:\n if element.getAttribute(\"Name\") == name:\n break\n else:\n element = self._doc.createElement(\"Filter\")\n element.setAttribute(\"Name\", name)\n parentDirectoryToken.appendChild(element)\n return element\n\n def addFile(self, filePath, parentDirectoryToken):\n element = self._doc.createElement(\"File\")\n element.setAttribute(\"RelativePath\", os.path.join(\"..\", toNativePath(filePath)))\n parentDirectoryToken.appendChild(element)\n\n def write(self):\n super(MsvsLegacyWriter, self).write()\n solution = MsvsSolutionWriter(self._contents, self.__tools, self._PROJECT_GUID, self._filename)\n solution.write()\n\n\nclass Msvs2010ProjectWriter(IdeXmlWriter):\n _PROJECT_GUID = '86FC28D1-F4DE-4209-B544-10B5415D0C20'\n _PROJECT_FILENAME = \"vs_{0}.vcxproj\"\n\n def __init__(self, contents, tools):\n \"\"\"\n :type contents: StatMakefileProject\n :type tools: MsvsTools\n \"\"\"\n super(Msvs2010ProjectWriter, self).__init__(contents)\n self._filename = self._PROJECT_FILENAME.format(self._contents.name)\n self.__tools = tools\n output = os.path.join(\"..\", attributes.OUTPUT_DIRECTORY, self._contents.outputName,\n \"ide_{0}\".format(self._contents.name))\n self.__executable = os.path.join(output, \"bin\", \"{0}.exe\".format(self._contents.outputName))\n self.__includePath = os.path.join(output, \"inc\")\n self.__sources = self.composeElement(\"ItemGroup\")\n self.__headers = self.composeElement(\"ItemGroup\")\n body = self.__composeBodyBase()\n for element in self.__composeBodyElements():\n body.appendChild(element)\n\n def __composeBodyElements(self):\n return [\n self.___composeConfigGroup(),\n self.composeElement(\"PropertyGroup\",\n context=dict(ProjectName=self._contents.name,\n ProjectGuid=\"{{{0}}}\".format(self._PROJECT_GUID),\n Keyword=\"MakeFileProj\",\n RootNamespace=self._contents.name),\n Label=\"Globals\"),\n self.composeElement(\"Import\", Project=r\"$(VCTargetsPath)\\Microsoft.Cpp.Default.props\"),\n self.composeElement(\"PropertyGroup\",\n context=dict(ConfigurationType=\"Makefile\", ),\n Condition=\"'$(Configuration)|$(Platform)'=='Debug|Win32'\",\n Label=\"Configuration\"),\n self.composeElement(\"Import\", Project=r\"$(VCTargetsPath)\\Microsoft.Cpp.props\"),\n self.composeElement(\"ImportGroup\", Label=\"ExtensionSettings\"),\n self.__composePropertySheets(),\n self.composeElement(\"PropertyGroup\", Label=\"UserMacros\"),\n self.composeElement(\"PropertyGroup\",\n context=dict(_ProjectFileVersion=\"{0:.2f}\".format(self.__tools.versionId))),\n self.__composeCommandLineGroup(),\n self.composeElement(\"ItemDefinitionGroup\"),\n self.__sources,\n self.__headers,\n self.composeElement(\"Import\", Project=r\"$(VCTargetsPath)\\Microsoft.Cpp.targets\"),\n self.composeElement(\"ImportGroup\", Label=\"ExtensionTargets\"),\n ]\n\n def __composeCommandLineGroup(self):\n includePath = \"{0};$(NMakeIncludeSearchPath)\".format(self.__includePath)\n definitions = \"WIN32;_DEBUG;{0};$(NMakePreprocessorDefinitions)\".format(';'.join(self._contents.definitions))\n commandLineGroup = self.composeElement(\n \"PropertyGroup\",\n dict(OutDir=\".\",\n IntDir=\".\",\n NMakeBuildCommandLine=self.formatCommandLine(\"build\"),\n NMakeReBuildCommandLine=self.formatCommandLine(\"rebuild\"),\n NMakeCleanCommandLine=self.formatCommandLine(\"clean\"),\n NMakeOutput=self.__executable,\n NMakePreprocessorDefinitions=definitions,\n NMakeIncludeSearchPath=includePath\n ),\n Condition=\"'$(Configuration)|$(Platform)'=='Debug|Win32'\")\n return commandLineGroup\n\n def __composePropertySheets(self):\n propertySheets = self.composeElement(\"ImportGroup\",\n Condition=\"'$(Configuration)|$(Platform)'=='Debug|Win32'\",\n Label=\"PropertySheets\")\n propertySheets.appendChild(self.composeElement(\n \"Import\",\n Project=r\"$(UserRootDir)\\Microsoft.Cpp.$(Platform).user.props\",\n Condition=r\"exists('$(UserRootDir)\\Microsoft.Cpp.$(Platform).user.props')\",\n Label=\"LocalAppDataPlatform\"))\n return propertySheets\n\n def ___composeConfigGroup(self):\n configGroup = self.composeElement(\"ItemGroup\", Label='ProjectConfigurations')\n projectConfig = self.composeElement(\"ProjectConfiguration\",\n context=dict(Configuration=\"Debug\", Platform=\"Win32\"),\n Include=\"Debug|Win32\")\n configGroup.appendChild(projectConfig)\n return configGroup\n\n def __composeBodyBase(self):\n body = self.composeElement(\n \"Project\", DefaultTargets=\"Build\",\n ToolsVersion=\"{0:.2f}\".format(self.__tools.versionId),\n xmlns=\"http://schemas.microsoft.com/developer/msbuild/2003\"\n )\n self._doc.appendChild(body)\n return body\n\n def createRootToken(self):\n pass\n\n def createDirectoryToken(self, name, parentDirectoryToken):\n pass\n\n def addFile(self, filePath, parentDirectoryToken):\n if os.path.splitext(filePath)[1] == \".c\":\n self.__sources.appendChild(self.composeElement(\"ClCompile\", Include=os.path.join(\"..\", filePath)))\n if os.path.splitext(filePath)[1] == \".h\":\n self.__headers.appendChild(self.composeElement(\"ClInclude\", Include=os.path.join(\"..\", filePath)))\n\n def write(self):\n super(Msvs2010ProjectWriter, self).write()\n solution = MsvsSolutionWriter(self._contents, self.__tools, self._PROJECT_GUID, self._filename)\n solution.write()\n\n\nclass MsvsWriter(IdeCompositeWriter):\n IDE = 'MSVS'\n writers = []\n\n def __init__(self, contents, *args):\n \"\"\"\n :type contents: StatMakefileProject\n \"\"\"\n super(MsvsWriter, self).__init__(contents, args)\n tools = BuildToolsCrawler().retrieveMsvs()\n if isinstance(tools, MsvsTools):\n writer = Msvs2010ProjectWriter if tools.year >= 2010 else MsvsLegacyWriter\n self._instances.append(writer(contents, tools))\n else:\n raise MsvsIdeWriterException(\"No workspace was created due to lack of MSVS.\")\n\n\nclass MsvsIdeWriterException(Exception):\n \"\"\"\n Custom exception for STAT MSVS IDE Writer\n \"\"\"","repo_name":"westerndigitalcorporation/stat","sub_path":"msvs_ide_writer.py","file_name":"msvs_ide_writer.py","file_ext":"py","file_size_in_byte":11863,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"40605886815","text":"import os\nimport streamlit as st\nfrom streamlit_router import StreamlitRouter\nfrom streamlit_option_menu import option_menu\nfrom supabase import create_client, Client\nimport streamlit as st\n\nimport datetime\nimport cv2\nimport csv\nimport tempfile\nfrom tensorflow.keras.models import load_model\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom collections import Counter\nimport mediapipe as mp\nfrom mediapipe.python.solutions.drawing_utils import DrawingSpec\nfrom mediapipe.framework.formats import landmark_pb2\nfrom FaceDetection import FaceDetector\nimport datetime,time\nimport pandas as pd\n\nurl = \"https://fzpvefiyomazymkowund.supabase.co\"\nkey = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6ImZ6cHZlZml5b21henlta293dW5kIiwicm9sZSI6ImFub24iLCJpYXQiOjE2ODI4NTYyNjQsImV4cCI6MTk5ODQzMjI2NH0.qxmzEFCys_1i1Auln9xO1GRMpMYlznSo-qNjbLYK6cI\"\nsupabase = create_client(url, key)\n\n@st.cache_resource\ndef load_face_detection():\n return FaceDetector()\n\nfd = load_face_detection()\n\n@st.cache_resource\ndef load_classifier_model():\n model = load_model(\"model/model.h5\")\n model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n return model\n\nmodel = load_classifier_model()\n\nmp_drawing = mp.solutions.drawing_utils\ndrawing_spec = DrawingSpec(color=(255, 255, 255), thickness=1, circle_radius=0)\nmp_face_mesh = mp.solutions.face_mesh\n\nface_mesh = mp_face_mesh.FaceMesh(\n max_num_faces=1,\n refine_landmarks=True,\n min_detection_confidence=0.5,\n min_tracking_confidence=0.5)\n\n\n\n\n@st.cache_data\ndef detect_face(frame, _faceNet,threshold=0.5):\n\t# grab the dimensions of the frame and then construct a blob\n\t# from it\n\tglobal detections \n\t(h, w) = frame.shape[:2]\n\tblob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),(104.0, 177.0, 123.0))\n\n\t# pass the blob through the network and obtain the face detections\n\t_faceNet.setInput(blob)\n\tdetections = _faceNet.forward()\n\n\n\tlocs = []\n\n\tfor i in range(0, detections.shape[2]):\n\t\t# extract the confidence (i.e., probability) associated with\n\t\tconfidence = detections[0, 0, i, 2]\n\n\t\t# filter out weak detections by ensuring the confidence is\n\t\t# greater than the minimum confidence\n\t\tif confidence >threshold:\n\t\t\t# compute the (x, y)-coordinates of the bounding box for\n\t\t\t# the object\n\t\t\tbox = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n\t\t\t(startX, startY, endX, endY) = box.astype(\"int\")\n\n\t\t\t# ensure the bounding boxes fall within the dimensions of\n\t\t\t# the frame\n\t\t\t(startX, startY) = (max(0, startX), max(0, startY))\n\t\t\t(endX, endY) = (min(w - 1, endX), min(h - 1, endY))\n \n\t\t\t# add the face and bounding boxes to their respective\n\t\t\t# lists\n\t\t\tlocs.append((startX, startY, endX, endY))\n\n\treturn (locs)\n\n\nfacenet = cv2.dnn.readNet(\"model/deploy.prototxt\",\"model/res10_300x300_ssd_iter_140000.caffemodel\")\n\ntry:\n user = supabase.auth.get_user()\nexcept:\n user = None\npage = None\ndef index(router):\n \n if user is not None:\n user_email = user.user.dict()['email']\n user_data = supabase.table('users').select(\"*\").eq(\"email\",user_email).execute()\n if len(user_data.data) == 0:\n supabase.auth.sign_out()\n router.redirect(*router.build(\"index\"))\n\n if user_data.data[0]['role'] == 0:\n with st.sidebar:\n page = option_menu(None, [\"Home\", \"Detect From Video\",\"Detect From Webcam\", \"Logout\"],\n icons=['house',\"film\",\"webcam-fill\",\"box-arrow-right\"],key=\"menu\")\n elif user_data.data[0]['role'] == 1:\n with st.sidebar:\n page = option_menu(None, [\"Admin Panel\",\"Manage Users\", \"Logout\"],icons=[\"speedometer2\",\"people-fill\",\"box-arrow-right\"],key=\"menu\")\n \n else:\n with st.sidebar:\n page = option_menu(None, [\"Home\", \"Login\", \"Signup\"],icons=[\"house\",\"door-open\",\"person-plus-fill\"],key=\"menu\")\n\n\n if page == \"Home\":\n st.write(\"# Human Behaviour Detection\")\n st.write(\"\"\"\n \n #### The Human Behaviour Recognition System is an application that uses deep learning classifier models to detect and classify human facial expressions in real-time\n\n \"\"\")\n\n st.video(\"video\\Media1.mp4\",start_time=0)\n elif page == \"Login\":\n router.redirect(*router.build(\"login\"))\n\n elif page == \"Signup\":\n router.redirect(*router.build(\"signup\"))\n\n elif page == \"Detect From Video\":\n router.redirect(*router.build(\"detect_from_video\"))\n\n elif page == \"Logout\":\n supabase.auth.sign_out()\n router.redirect(*router.build(\"index\"))\n \n elif page == \"Detect From Webcam\":\n router.redirect(*router.build(\"detect_from_webcam\"))\n\n elif page == \"Manage Users\":\n router.redirect(*router.build(\"manage_users\"))\n\n elif page == \"Admin Panel\":\n st.write(\"# Admin Panel\")\n st.write(f\"\"\"\n #### Welcome {user_data.data[0]['name'].capitalize()} !!\n \"\"\")\n users = supabase.table(\"users\").select(\"*\").eq('role',0).execute()\n df= pd.DataFrame(users.data)\n df['date_joined'] = pd.to_datetime(df['created_at']).dt.date\n st.area_chart(df['date_joined'].value_counts())\n\n\n\n\n\n\ndef signup(router):\n with st.form(\"Signup\"):\n st.write(\"# Signup\")\n name = st.text_input(\"Name\")\n email = st.text_input(\"Email\")\n password = st.text_input(\"Password\", type=\"password\")\n submit_button = st.form_submit_button(label=\"Signup\")\n\n if submit_button:\n try:\n res = supabase.auth.sign_up({\n \"email\": email,\n \"password\": password,\n })\n user = supabase.auth.sign_in_with_password({\n \"email\": email,\n \"password\": password,\n })\n supabase.table('users').insert({\"name\": name,\"email\":email}).execute()\n st.success(\"Signup successful\")\n time.sleep(1)\n router.redirect(*router.build(\"index\"))\n except Exception as e:\n st.error(\"Signup failed\")\n st.write(e)\n if st.button(\"Back to Homepage\"):\n router.redirect(*router.build(\"index\"))\n\n\ndef login(router):\n \n with st.form(\"Login\"):\n st.write(\"# Login\")\n email = st.text_input(\"Email\")\n password = st.text_input(\"Password\", type=\"password\")\n submit_button = st.form_submit_button(label=\"Login\")\n\n if submit_button:\n try:\n supabase.auth.sign_in_with_password({\"email\": email, \"password\": password}) \n user = supabase.table('users').select(\"*\").eq(\"email\",email).execute() \n if len(user.data) ==0:\n raise Exception(\"User not found\")\n router.redirect(*router.build(\"index\"))\n except Exception as e: \n st.error(e.__str__())\n \n if st.button(\"Back to Homepage\"):\n router.redirect(*router.build(\"index\"))\n\ndef show_pie_chart():\n\n try:\n with open(\"results/data.csv\", 'r') as f:\n reader = csv.reader(f)\n data = list(reader)\n df = pd.DataFrame(data, columns = ['Emotion', 'data/time'])\n df = df['Emotion']\n count = Counter(df)\n fig, ax = plt.subplots()\n ax.pie(count.values(), labels=count.keys(), autopct='%1.1f%%', shadow=True, startangle=90)\n ax.axis('equal')\n st.pyplot(fig)\n os.remove(\"results/data.csv\")\n except:\n pass\n\n\n\n \n\ndef detect_from_video(router):\n global emotions_detected\n emotion_labels = ['Angry','Disgust','Fear','Happy','Neutral', 'Sad', 'Surprise']\n\n with st.sidebar:\n st.subheader(\"Settings\")\n video_file = st.file_uploader(\"Upload a video\",type=[\"mp4\",\"avi\"])\n st.sidebar.markdown(\"## Settings\")\n show_face_mesh = st.sidebar.checkbox(\"Show Face Mesh\",True)\n\n st.divider()\n if st.button(\"Go to Homepage\"):\n router.redirect(*router.build(\"index\"))\n\n\n if video_file is not None:\n tffile = tempfile.NamedTemporaryFile(delete=False)\n tffile.write(video_file.read())\n cap = cv2.VideoCapture(tffile.name)\n emotions_detected = []\n st.write(\"## Emotion Detection From Video\")\n stframe = st.empty()\n stop_detection = False\n\n col = st.columns(1)\n col[0] = st.empty()\n with col[0]:\n if st.button(\"STOP\"):\n stop_detection = True \n col[0].empty()\n\n \n ret=True\n while stop_detection != True and ret==True:\n ret,frame = cap.read()\n \n if not ret:\n \n break\n\n faces = detect_face(frame,facenet,0.5)\n for (x,y,x2,y2) in faces:\n cv2.rectangle(frame,(x,y),(x2,y2),(255,255,255),2)\n roi = frame[y:x2,x:y2]\n roi = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n roi = cv2.resize(roi,(48,48),interpolation=cv2.INTER_AREA)\n roi = np.array(roi)\n roi = roi/255.0\n roi = np.expand_dims(roi, axis=0)\n prediction = np.argmax(model.predict(roi))\n emotions_detected.append(emotion_labels[prediction])\n cv2.putText(frame,f\"{emotion_labels[prediction]}\",(x,y-10),0,0.5,(255,255,255),2)\n\n with open(\"results/data.csv\",\"a\", newline=\"\\n\") as f:\n csvwriter = csv.writer(f)\n csvwriter.writerow([emotion_labels[prediction], datetime.datetime.now()])\n\n if show_face_mesh:\n results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n if results.multi_face_landmarks:\n for face_landmarks in results.multi_face_landmarks:\n mp_drawing.draw_landmarks(\n image=frame,\n landmark_list=face_landmarks,\n connections=mp_face_mesh.FACEMESH_TESSELATION,\n landmark_drawing_spec=None,\n connection_drawing_spec=drawing_spec ) \n \n \n stframe.image(frame,channels=\"BGR\")\n\n show_pie_chart()\n\ndef detect_from_webcam(router):\n \n emotion_labels = ['Angry','Disgust','Fear','Happy','Neutral', 'Sad', 'Surprise']\n\n with st.sidebar:\n st.subheader(\"Settings\")\n st.sidebar.markdown(\"## Settings\")\n show_face_mesh = st.sidebar.checkbox(\"Show Face Mesh\",True)\n \n\n st.divider()\n if st.button(\"Go to Homepage\"):\n router.redirect(*router.build(\"index\"))\n\n st.write(\"## Emotion Detection From Webcam\")\n if st.button(\"Start\"):\n cap = cv2.VideoCapture(0)\n emotions_detected = []\n stframe = st.empty()\n stop_detection = False\n\n ret,frame = cap.read()\n\n if ret == False:\n st.error(\"Can't Access Webcam\")\n st.stop()\n\n \n col = st.columns(1)\n col[0] = st.empty()\n with col[0]:\n if st.button(\"STOP\"):\n stop_detection = True\n \n col[0].empty()\n\n\n while stop_detection != True:\n \n if not ret:\n break\n\n faces = detect_face(frame,facenet,0.5)\n for (x,y,x2,y2) in faces:\n cv2.rectangle(frame,(x,y),(x2,y2),(255,255,255),2)\n roi = frame[y:x2,x:y2]\n roi = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n roi = cv2.resize(roi,(48,48),interpolation=cv2.INTER_AREA)\n roi = np.array(roi)\n roi = roi/255.0\n roi = np.expand_dims(roi, axis=0)\n prediction = np.argmax(model.predict(roi))\n emotions_detected.append(emotion_labels[prediction])\n cv2.putText(frame,f\"{emotion_labels[prediction]}\",(x,y-10),0,0.5,(255,255,255),2)\n\n with open(\"results/data.csv\",\"+a\", newline=\"\\n\") as f:\n csvwriter = csv.writer(f)\n csvwriter.writerow([emotion_labels[prediction], datetime.datetime.now()])\n\n if show_face_mesh:\n results = face_mesh.process(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n if results.multi_face_landmarks:\n for face_landmarks in results.multi_face_landmarks:\n mp_drawing.draw_landmarks(\n image=frame,\n landmark_list=face_landmarks,\n connections=mp_face_mesh.FACEMESH_TESSELATION,\n landmark_drawing_spec=None,\n connection_drawing_spec=drawing_spec ) \n\n ret,frame = cap.read()\n \n \n stframe.image(frame,channels=\"BGR\")\n \n show_pie_chart()\n \n\n \ndef manage_users(router):\n st.write(\"## Manage Users\")\n \n users = supabase.table(\"users\").select(\"*\").eq('role',0).execute()\n df= pd.DataFrame(users.data)\n cols = st.columns(4)\n cols[0].write(\"Name\")\n cols[1].write(\"Email\")\n cols[2].write(\"Action\")\n\n notification = st.empty()\n for user in users.data:\n cols = st.columns(4)\n cols[0].write(f\"{user['name']}\")\n cols[1].write(f\"{user['email']}\")\n cols[2].button(\"Delete\",key=f\"del{user['id']}\", on_click=delete_user, args=(user['id'],notification))\n cols[3].button(\"Make Admin\",key=f\"adm{user['id']}\", on_click=make_admin, args=(user['id'],notification))\n\n if st.button(\"Go to Homepage\"):\n supabase.auth.sign_out()\n router.redirect(*router.build(\"index\"))\n\ndef make_admin(userid,notification):\n try:\n supabase.table(\"users\").update({\"role\":1}).eq('id',userid).execute()\n with notification:\n st.success(\"User made admin successfully\")\n time.sleep(1)\n st.experimental_rerun()\n except:\n pass\n\ndef delete_user(userid,notification):\n try:\n supabase.table(\"users\").delete().eq('id',userid).execute()\n with notification:\n st.success(\"User deleted successfully\")\n time.sleep(1)\n st.experimental_rerun()\n except:\n pass\n\n\n \n\nrouter = StreamlitRouter()\nrouter.register(index, '/')\nrouter.register(login, '/login')\nrouter.register(signup, '/signup')\nrouter.register(detect_from_video, '/detect-from-video')\nrouter.register(detect_from_webcam, '/detect-from-webcam')\nrouter.register(manage_users, '/manage-users')\n\nrouter.serve()","repo_name":"meetxvii/HumanBehaviourRecognition","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":14784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26885410005","text":"\"\"\"\nClass for Pikachu\n\"\"\"\nimport pygame\nfrom spritesheet import SpriteSheet\nfrom player import Player\n\nvec = pygame.math.Vector2\n\n\nclass Pikachu(Player):\n \"\"\"\n Class for Pikachu character\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Creates Player object with Pikachu's attributes\n\n Room for expansion with custom functions/attacks\n \"\"\"\n pygame.init()\n self.spritesheet = SpriteSheet(\"resources/pikachu_sheet.png\")\n right = pygame.transform.scale(\n self.spritesheet.image_at((5, 40, 35, 28), (134, 255, 104)),\n (70, 56),\n )\n left = pygame.transform.flip(right, flip_x=True, flip_y=False)\n tilt_r = pygame.transform.scale(\n self.spritesheet.image_at((14, 3067, 55, 28), (134, 255, 104)),\n (120, 52),\n )\n tilt_l = pygame.transform.flip(tilt_r, flip_x=True, flip_y=False)\n smash_r = self.spritesheet.image_at(\n (316, 1093, 120, 309), (255, 255, 255)\n )\n smash_l = pygame.transform.flip(smash_r, flip_x=True, flip_y=False)\n self.images = {\n \"left\": left,\n \"right\": right,\n \"tilt_r\": tilt_r,\n \"tilt_l\": tilt_l,\n \"smash_r\": smash_r,\n \"smash_l\": smash_l,\n }\n super().__init__()\n\n self.name = \"pikachu\"\n self.weight = 3\n self.speed = 4.5\n self.smash_cooldown=60\n self.hurtbox = pygame.Rect(self.rect.x, self.rect.y, 70, 56)\n self.attacks = {\n \"tilt\": {\"damage\": 5, \"base\": 2, \"ratio\": 1 / 2},\n \"smash\": {\"damage\": 20, \"base\": 8, \"ratio\": 1},\n }\n\n def set_boxes(self):\n \"\"\"\n Update Pikachu's hitboxes and hurtboxes\n \"\"\"\n if self.attack_cooldown > 0:\n if self.attack == \"tilt\":\n if self.direction == \"left\":\n self.hurtbox = pygame.Rect(\n self.rect.x + 58, self.rect.y, 62, 56\n )\n self.hitbox = pygame.Rect(\n self.rect.x, self.rect.y + 10, 68, 42\n )\n else:\n self.hurtbox = pygame.Rect(self.rect.x, self.rect.y, 62, 56)\n self.hitbox = pygame.Rect(\n self.rect.x + 52, self.rect.y + 10, 68, 42\n )\n if self.attack == \"smash\":\n self.hurtbox = pygame.Rect(\n self.rect.x + 50, self.rect.y + 236, 68, 52\n )\n self.hitbox = pygame.Rect(\n self.rect.x + 37, self.rect.y - 15, 49, 322\n )\n self.rect = self.image.get_rect()\n else:\n self.hurtbox = pygame.Rect(self.rect.x, self.rect.y, 70, 56)\n self.hitbox = pygame.Rect(0, 0, 0, 0)\n self.rect = self.image.get_rect()\n\n def tilt(self):\n \"\"\"\n Perform a tilt attack\n \"\"\"\n self.attack = \"tilt\"\n self.attack_cooldown = 15\n if self.direction == \"left\":\n self.pos.x -= 30\n\n def smash(self):\n \"\"\"\n Perform a smash attack\n \"\"\"\n self.attack = \"smash\"\n self.attack_cooldown = 60\n if self.direction == \"left\":\n self.pos.x -= 30\n\n def gravity(self):\n \"\"\"\n Apply acceleration due to gravity to player object except if on a\n platform\n Changes jump count to 3\n \"\"\"\n self.acc = vec(0, 0.5)\n if 185 < self.pos.x < (185 + 861) and 405 < (self.pos.y - 70) < 430:\n self.vel.y = 1\n if self.knockback_counter <= 0 and 405 > (self.pos.y - 70):\n hits = pygame.sprite.spritecollide(self, self.platforms, False)\n if hits:\n self.pos.y = hits[0].rect.top + 1\n self.vel.y = 0\n self.jump_count = 3\n","repo_name":"olincollege/pysmash","sub_path":"characters/pikachu.py","file_name":"pikachu.py","file_ext":"py","file_size_in_byte":3897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39303620230","text":"#P3.py\n#Continuous Assesment Test main file\n\nimport random\nimport analyzer\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom deap import creator,base,tools,algorithms\nfrom functools import reduce\n\ndef fulfill_constraints(max_imagery=1000, min_imagery_type=10):\n def decorator(func):\n def wrapper(*args, **kargs):\n offspring = func(*args, **kargs)\n\n for child in offspring:\n\n decoded_child = list(analyzer.decode(child))\n #decoded_child = list(analyzer.decode_trivial(child))\n\n\n ####################\n ####Exercise 2c#####\n ####################\n\n # TO DO: here 'child' is an individual generated by DEAP (child[i] is a 'gene'\n # of this individual). You need to force\n # it to fulfill the problem requirements, taking care of the coding you chose\n # At the end, every child must contain a coded individual that fulfills\n # the requirements of max_imagery and min_imagery_type\n\n # HINT: use the decoded_child to work with it with ease.\n # First, focus on the min_imagery_type constraint\n # Second, focus on max_imagery constraint\n # Finally, you will need to encode back the individual before modifying the child array\n # You can use as many vars as you want, of course\n new_child = []\n \n\n #firstly, minimum 10 images per image type\n for i in range(0,len(decoded_child)):\n # remove the 'pass' instruction and complete this loop as you need\n if decoded_child[i] < 10:\n #print(\"MinDectedted: \",decoded_child[i])\n \n #print(\"Decoded_child. N_Images[\"\n #,sum(decoded_child),\"] \"\n #,decoded_child[0],\" \"\n #,decoded_child[1],\" \"\n #,decoded_child[2],\" \"\n #,decoded_child[3],\" \"\n #,decoded_child[4],\" \")\n \n decoded_child[i] = random.randint(10, 255)\n \n # print(\"Decoded_child_revised. N_Images[\"\n #,sum(decoded_child),\"] \"\n #,decoded_child[0],\" \"\n #,decoded_child[1],\" \"\n #,decoded_child[2],\" \"\n #,decoded_child[3],\" \"\n #,decoded_child[4],\" \")\n \n\n #secondly, max amount of images: 1000\n while sum(decoded_child) > max_imagery:\n # print(\"MaxDectedted: \",sum(decoded_child))\n \n #print(\"Decoded_child. N_Images[\"\n # ,sum(decoded_child),\"] \"\n # ,decoded_child[0],\" \"\n # ,decoded_child[1],\" \"\n # ,decoded_child[2],\" \"\n # ,decoded_child[3],\" \"\n # ,decoded_child[4],\" \")\n \n # remove the 'pass' instruction and complete this loop as you need\n #Recorre els diferents números d'imatges i assignar valors aleatoris entre 10 i 255\n for i in range(0,len(decoded_child)):\n decoded_child[i] = random.randint(10, 255)\n\n #print(\"Decoded_child_revised. N_Images[\"\n # ,sum(decoded_child),\"] \"\n # ,decoded_child[0],\" \"\n # ,decoded_child[1],\" \"\n # ,decoded_child[2],\" \"\n # ,decoded_child[3],\" \"\n # ,decoded_child[4],\" \")\n \n #for i in range(0, len(child)):\n # remove the 'pass' instruction and\n # do the necessary actions (inside and/or outside this loop) to get the new_child\n # as the encoded representation of the new individual, which fulfills the requirements\n \n \n new_child = analyzer.encode(decoded_child) \n\n # offspring's child modification\n # encode back your decoded individual before entering this loop!!!\n for i in range(0, len(child)):\n child[i] = new_child[i]\n \n\n return offspring\n return wrapper\n return decorator\n\n\n#Implement GA. Prepare deap: initialization, definition of strategies\n\n#GA initialization\n\n# Create the item dictionary: item name is an integer, and value is\n# a (weight, value) 2-uple.\n#ITEMS_NAME = \"RGB\", \"term\", \"IR\", \"RADAR\", \"LiDAR\"\n#ITEMS_NAME2 = 1,2,3,4,5\n\n#items = {}\n#Create the item dictionary: item name is an integer an value is the number of images\n#for i in ITEMS_NAME2:\n# items[i] = (ITEMS_NAME2[i], random.randint(0, 255))\n \n\nITEMS_NAME = \"RGB\", \"term\", \"IR\", \"RADAR\", \"LiDAR\"\n#IMAGES = (1, 2, 3, 3, 4)\n#ITEMS = dict(\n# (name, (images, random.uniform(1, 5))) \n# for name, images in zip(ITEMS_NAME, IMAGES)\n# ) \n\ncreator.create('FitnessMin', base.Fitness, weights=(-1.0,))\ncreator.create('Individual', list, fitness=creator.FitnessMin)\n#creator.create('Individual', set, fitness=creator.FitnessMin)\n#creator.create('Individual', np.ndarray, fitness=creator.FitnessMin)\n\ntoolbox = base.Toolbox()\n\n####################\n####Exercise 2a#####\n####################\n\n#TO DO: Complete the following sentences to prepare your GA algorithm\n#Register the attributes, individuals, population... according to your individual coding\n#toolbox.register('attrImageType', random.randint, 0, 4)\ntoolbox.register('attr_numberImages', random.randint, 0, 255)\n#toolbox.register(, , , )\ntoolbox.register('individual', tools.initRepeat, creator.Individual, \n #(toolbox.attr_typeImage,toolbox.attr_numberImages),\n toolbox.attr_numberImages,\n n=10)\ntoolbox.register('population', tools.initRepeat, list, toolbox.individual)\n\n#GA strategies definition\n#Objective function is analyzer.analyze_performance\nobjective_function = analyzer.analyze_performance\n\n#TO DO: Complete the following sentences to prepare your GA algorithm\n#Register the GA operations\n\ntoolbox.register('evaluate', objective_function)\ntoolbox.register('mate', tools.cxTwoPoint)\ntoolbox.register('mutate',tools.mutShuffleIndexes , indpb=0.1)\ntoolbox.register('select', tools.selTournament, tournsize=3)\n\n\n#GA decoration\n\n###############################\n####Exercise 2 - final step####\n###############################\n# TO DO: uncomment the following 3 lines\ntoolbox.decorate('population', fulfill_constraints())\ntoolbox.decorate('mate', fulfill_constraints())\ntoolbox.decorate('mutate', fulfill_constraints())\n\n#run the GA\npopulation = toolbox.population(25)\nprint(population)\n\nNGEN = 20\ncx_prob = 0.5\nmut_prob = 0.2\n\nfor gen in range(NGEN):\n\n offspring = algorithms.varAnd(population, toolbox, cxpb=cx_prob, mutpb=mut_prob)\n fits = toolbox.map(toolbox.evaluate, offspring)\n\n for fit,ind in zip(fits, offspring):\n ind.fitness.values = fit\n\n population = toolbox.select(offspring, k=len(population))\n top = tools.selBest(population, k=1)\n fitness = objective_function(top[0])\n ####################\n ####Exercise 2######\n ####################\n # TO DO: replace the call to 'decode_trivial' with a call to the function 'decode' that you just implemented\n #print(\"Decode_Trivial: \", gen, fitness, analyzer.decode_trivial(top[0]), top[0])\n print(\"Decode: \", gen, fitness, analyzer.decode(top[0]), top[0])\n ####################\n ####Exercise 4######\n ####################\n # TO DO: Save the best fitness somehow, so that you can plot them later\n\n# TO DO: plot some charts to assess how the error behaves over generations\n# you can use x as the x axis list (this may help you plot)\nx = list(range(NGEN))\n#...\n\n\n\n\n","repo_name":"manelzz/uoc-exercises-iaa","sub_path":"PAC3/P3.py","file_name":"P3.py","file_ext":"py","file_size_in_byte":8151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4258865133","text":"class stack():\n def __init__(self) -> None:\n self.st=list()\n def push(self,value:int)->None:\n self.st.append(value)\n def top(self)->int:\n return self.st[-1] if self.st else None\n def pop(self)->None:\n if self.st:\n self.st.pop()\n def is_empty(self)->bool:\n return len(self.st)==0\nclass solution():\n def is_equal(self,char1:str,char2:str)->bool:\n if char1 =='(' and char2==')':\n return True\n elif char1=='{' and char2=='}':\n return True\n elif char1=='[' and char2==']':\n return True\n else:\n return False\n\n def valid_parenthis(self,string:str)->bool:\n st=stack()\n for chr in string:\n curr=st.top()\n if self.is_equal(curr,chr):\n st.pop()\n continue\n st.push(chr)\n return st.is_empty()\n\nif __name__=='__main__':\n string=input().strip()\n sol=solution()\n print(sol.valid_parenthis(string))\n ","repo_name":"farhan1503001/Hackerrank-Python-Language-Problems","sub_path":"Python datastructuresleetcode/validparenthesis.py","file_name":"validparenthesis.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"10633799292","text":"import os\nimport glob\nimport csv\ntry:\n import junction_quality\n import svg_arclengths_statistics\n import files\nexcept:\n from tools import junction_quality, files\n from tools import svg_arclengths_statistics\n\nfrom numpy import mean, median, percentile\n\ndef get_gt_by_sketch(sketch, gt_list):\n '''\n Given:\n sketch, name of a single sketch\n gt_list, full list of all gt sketches\n Return:\n a dict as retrieves that contains all gt sketch with same base name of that given sketch\n all gts are also grouped by author name, such as {author1: [gt1, gt2,...], ...}\n '''\n \n gt_dict_by_test = {}\n name, _ = splitext(sketch)\n base_sketch_name = '_'.join(name.split('_')[0:4])\n for gt in gt_list:\n gt_name, _ = splitext(gt)\n author = gt_name.split('_')[4]\n if base_sketch_name in gt:\n if author in gt_dict_by_test:\n gt_dict_by_test[author].append(gt)\n else:\n gt_dict_by_test[author] = [gt]\n return gt_dict_by_test\n\ndef generate_stroke_analyze_csv(config, path_to_table = None, alg = None, p = None, \n path_to_csv = None, path_to_svg_folder = None):\n '''\n '''\n # initialize path information\n if path_to_table != None and alg != None and p != None:\n path_to_csv = os.path.join(path_to_table, \"Stroke_Analysis\", \"SA_%s_%s.csv\"%(alg, p[0]))\n path_to_svg_folder = p[1]\n elif path_to_csv != None and path_to_svg_folder != None:\n print(\"Log:\\tworking in command line mode\")\n else:\n print(\"Error:\\tincorrect parameter\")\n\n # initalize header\n # let's use messiness instead of number strokes!\n header = [\"sketch\", \"arc_len_min\", \"arc_len_25-th\", \"arc_len_median\", \n \"arc_len_75-th\", \"arc_len_max\", \"arc_len_mean\", \"junc_min_dist_sum\", \"junc_dist_count_0.1\", \"used time\"]\n\n if os.path.exists(path_to_csv):\n print(\"Log:\\tfind %s exists, skip stroke analyze\"%path_to_csv)\n return None\n else:\n print(\"Log:\\tcreating stroke analysis table: %s\"%path_to_csv)\n\n row_list = []\n\n log = os.path.join(config['log_dir'], \"%s_%s.csv\"%(alg, p[0]))\n\n alg_input_full_tag = files.open_sketch_list(log, basemode = True)\n h = files.get_header(row = alg_input_full_tag[0])\n \n \n\n for i in range(len(alg_input_full_tag)):\n \n \n file = alg_input_full_tag[i][h[0]]\n name, extension = os.path.splitext(file)\n file = name + \".svg\"\n path_to_svg = os.path.join(path_to_svg_folder, file)\n running_time = alg_input_full_tag[i][h[1]]\n \n \n if os.path.exists(path_to_svg):\n print(\"Log:\\topen %s\"%path_to_svg)\n \n # evaluate svg paths, there may have some exception case that interrupt analysis\n # so any expection will be caught and recorded\n try:\n lengths = svg_arclengths_statistics.arc_length_statistics(path_to_svg)\n arc_num = len(lengths)\n arc_mean = mean( lengths )\n arc_min = min( lengths )\n arc_25 = percentile( lengths, 25 )\n arc_50 = median( lengths )\n arc_75 = percentile( lengths, 75 )\n arc_max = max( lengths )\n \n except ValueError as e:\n print(\"Log:\\t\" + str(e))\n arc_num = \"svgpathtool open error\"\n arc_mean = \"svgpathtool open error\"\n arc_min = \"svgpathtool open error\"\n arc_25 = \"svgpathtool open error\"\n arc_50 = \"svgpathtool open error\"\n arc_75 = \"svgpathtool open error\"\n arc_max = \"svgpathtool open error\"\n except Exception as e:\n print(\"Log:\\t\" + str(e))\n arc_num = \"unknown error\"\n arc_mean = \"unknown error\"\n arc_min = \"unknown error\"\n arc_25 = \"unknown error\"\n arc_50 = \"unknown error\"\n arc_75 = \"unknown error\"\n arc_max = \"unknown error\"\n \n \n try:\n minimum_distances = junction_quality.endpoint_statistics(path_to_svg)\n junc_sum = minimum_distances.sum()\n junc_count = ( minimum_distances > 0.0001 ).sum()\n except ValueError as e:\n print(\"Log:\\t\" + str(e))\n junc_sum = \"svgpathtool open error\"\n junc_count = \"svgpathtool open error\"\n except RecursionError as e:\n print(\"Log:\\t\" + str(e))\n junc_sum = \"excess of points\"\n junc_count = \"excess of points\"\n except Exception as e:\n print(\"Log:\\t\" + str(e))\n if \"has not yet been implemented\" in str(e):\n junc_sum = \"svgpathtool func error\"\n junc_count = \"svgpathtool func error\"\n else:\n junc_sum = \"unknown error\"\n junc_count = \"unknown error\"\n \n \n value = [file, arc_min, arc_25, arc_50, arc_75, arc_max, arc_mean,\n junc_sum, junc_count, running_time]\n row = {}\n for i in range(len(header)):\n row[header[i]] = value[i]\n row_list.append(row)\n # write results\n \n # write csv\n folder, _ = os.path.split(path_to_csv)\n if os.path.isdir(folder) is False:\n os.makedirs(folder)\n \n with open(path_to_csv, 'w', newline = '') as f:\n dict_writer = csv.DictWriter(f, fieldnames = header)\n dict_writer.writeheader()\n for row in row_list:\n dict_writer.writerow(row)\n\nif __name__ == \"__main__\":\n\n import argparse\n parser = argparse.ArgumentParser( description = \"Compute svg stroke statistics and write to csv\" )\n parser.add_argument( \"svg\", help = \"The root folder of algorithm output (SVG file) to analyze.\" )\n parser.add_argument( \"csv\", help = \"Path to save the analyze csv results\" )\n args = parser.parse_args()\n\n generate_stroke_analyze_csv(path_to_csv = args.csv, path_to_svg_folder = args.svg)\n","repo_name":"Nauhcnay/A-Benchmark-for-Rough-Sketch-Cleanup","sub_path":"tools/metrics_stroke.py","file_name":"metrics_stroke.py","file_ext":"py","file_size_in_byte":6237,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"3"}
+{"seq_id":"23168177180","text":"import os\nfrom torch_geometric.data import InMemoryDataset, DataLoader, Batch\nfrom torch_geometric import data as DATA\nimport torch\n\n\n# initialize the dataset\nclass DTADataset(InMemoryDataset):\n def __init__(self, root='/tmp', dataset='davis',\n xd=None, y=None, transform=None,\n pre_transform=None, smile_graph=None, target_key=None, target_graph=None):\n\n super(DTADataset, self).__init__(root, transform, pre_transform)\n self.dataset = dataset\n self.process(xd, target_key, y, smile_graph, target_graph)\n\n @property\n def raw_file_names(self):\n pass\n # return ['some_file_1', 'some_file_2', ...]\n\n @property\n def processed_file_names(self):\n return [self.dataset + '_data_mol.pt', self.dataset + '_data_pro.pt']\n\n def download(self):\n # Download to `self.raw_dir`.\n pass\n\n def _download(self):\n pass\n\n def _process(self):\n if not os.path.exists(self.processed_dir):\n os.makedirs(self.processed_dir)\n\n def process(self, xd, target_key, y, smile_graph, target_graph):\n assert (len(xd) == len(target_key) and len(xd) == len(y)), 'The three lists must be the same length!'\n data_list_mol = []\n data_list_pro = []\n data_len = len(xd)\n for i in range(data_len):\n smiles = xd[i]\n tar_key = target_key[i]\n labels = y[i]\n # convert SMILES to molecular representation using rdkit\n c_size, features, edge_index = smile_graph[smiles]\n target_size, target_features, target_edge_index = target_graph[tar_key]\n # print(np.array(features).shape, np.array(edge_index).shape)\n # print(target_features.shape, target_edge_index.shape)\n # make the graph ready for PyTorch Geometrics GCN algorithms:\n GCNData_mol = DATA.Data(x=torch.Tensor(features),\n edge_index=torch.LongTensor(edge_index).transpose(1, 0),\n y=torch.FloatTensor([labels]))\n GCNData_mol.__setitem__('c_size', torch.LongTensor([c_size]))\n\n GCNData_pro = DATA.Data(x=torch.Tensor(target_features),\n edge_index=torch.LongTensor(target_edge_index).transpose(1, 0),\n y=torch.FloatTensor([labels]))\n GCNData_pro.__setitem__('target_size', torch.LongTensor([target_size]))\n # print(GCNData.target.size(), GCNData.target_edge_index.size(), GCNData.target_x.size())\n data_list_mol.append(GCNData_mol)\n data_list_pro.append(GCNData_pro)\n\n if self.pre_filter is not None:\n data_list_mol = [data for data in data_list_mol if self.pre_filter(data)]\n data_list_pro = [data for data in data_list_pro if self.pre_filter(data)]\n if self.pre_transform is not None:\n data_list_mol = [self.pre_transform(data) for data in data_list_mol]\n data_list_pro = [self.pre_transform(data) for data in data_list_pro]\n self.data_mol = data_list_mol\n self.data_pro = data_list_pro\n\n def __len__(self):\n return len(self.data_mol)\n\n def __getitem__(self, idx):\n return self.data_mol[idx], self.data_pro[idx]\n\n\n# training function at each epoch\ndef train(model, device, train_loader, optimizer, epoch):\n print('Training on {} samples...'.format(len(train_loader.dataset)))\n model.train()\n LOG_INTERVAL = 10\n TRAIN_BATCH_SIZE = 512\n loss_fn = torch.nn.MSELoss()\n for batch_idx, data in enumerate(train_loader):\n data_mol = data[0].to(device)\n data_pro = data[1].to(device)\n optimizer.zero_grad()\n output = model(data_mol, data_pro)\n loss = loss_fn(output, data_mol.y.view(-1, 1).float().to(device))\n loss.backward()\n optimizer.step()\n if batch_idx % LOG_INTERVAL == 0:\n print('Train epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(epoch,\n batch_idx * TRAIN_BATCH_SIZE,\n len(train_loader.dataset),\n 100. * batch_idx / len(train_loader),\n loss.item()))\n\n# predict\ndef predicting(model, device, loader):\n model.eval()\n total_preds = torch.Tensor()\n total_labels = torch.Tensor()\n print('Make prediction for {} samples...'.format(len(loader.dataset)))\n with torch.no_grad():\n for data in loader:\n data_mol = data[0].to(device)\n data_pro = data[1].to(device)\n output = model(data_mol, data_pro)\n total_preds = torch.cat((total_preds, output.cpu()), 0)\n total_labels = torch.cat((total_labels, data_mol.y.view(-1, 1).cpu()), 0)\n return total_labels.numpy().flatten(), total_preds.numpy().flatten()\n\n\n#prepare the protein and drug pairs\ndef collate(data_list):\n batchA = Batch.from_data_list([data[0] for data in data_list])\n batchB = Batch.from_data_list([data[1] for data in data_list])\n return batchA, batchB\n","repo_name":"595693085/DGraphDTA","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5269,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"3"}
+{"seq_id":"33417464455","text":"from django.db import models\n\n\nclass Appointment(models.Model):\n date_appointment = models.DateField(verbose_name=\"Дата записи\")\n time_appointment = models.TimeField(verbose_name=\"Время записи\")\n last_name = models.CharField(max_length=255, verbose_name=\"Фамилия, имя, отчество\", blank=True, null=True)\n date_of_birth = models.CharField(max_length=20, verbose_name=\"Дата рождения\", blank=True, null=True)\n email = models.EmailField(verbose_name=\"E-mail\", blank=True, null=True)\n date_of_write = models.DateTimeField(verbose_name=\"Дата записи\", blank=True, null=True, auto_now=True)\n is_booked = models.BooleanField(verbose_name=\"Забронировано\", default=False)\n comment = models.TextField(verbose_name=\"Комментарий к записи\", blank=True, null=True)\n\n def __str__(self):\n return str(self.date_appointment) + ' ' + str(self.time_appointment) + ' ' + str(self.last_name)\n\n class Meta:\n ordering = ('pk',)\n verbose_name = 'Запись на тестирование'\n verbose_name_plural = 'Записи на тестирование'\n","repo_name":"levenkoevgeny/amia_queue","sub_path":"amia/appointment/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9931187527","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nimport csv\nimport formz.models.db as db\nfrom datetime import timedelta\nfrom sqlobject import AND, IN\nfrom mx.DateTime.mxDateTime.mxDateTime import now\nfrom collections import Counter\n\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\nos.chdir(os.getcwd() + '/tools/stats/temp/')\n\nHEADERS = [\n 'Логин',\n 'Эл.почта',\n 'Дата регистрации',\n 'Бизнес Плюс/Лайт',\n 'Дата окончания подписки',\n 'Последний вход в систему',\n 'Общее количество документов',\n 'Товарный чек',\n 'Счет на оплату',\n 'Расходная накладная',\n 'УПД',\n 'ТТН (форма 1-Т)',\n 'Транспортная накладная',\n 'Акт',\n 'Счет-фактура',\n 'Ценники',\n 'ПКО',\n 'РКО',\n 'Платежное поручение',\n 'Дата создания последнего документа',\n 'Часовой пояс',\n 'Организация/ ИП',\n 'Наименование',\n 'ИНН',\n 'Адрес',\n 'Эл.почта',\n 'Телефон',\n 'Веб-сайт',\n 'Название банка',\n 'БИК банка',\n 'Местонахождение банка',\n 'ФИО руководителя',\n 'Тип учета НДС'\n]\n\n\ndef timed(fn):\n def decorated(*args):\n start = now()\n result = fn(*args)\n print(\"Executing %s took %d sec\" % (fn.__name__, (now() - start)))\n return result\n\n return decorated\n\n\nclass ReportConstructor:\n \"\"\"Отчёт по всем пользователям Formz\"\"\"\n\n def __init__(self):\n self.data = ReportDataAggregator(\n loginCount=1,\n loginPeriod=30,\n docsCount=2,\n docsPeriod=60\n ).data\n\n @staticmethod\n def validateSubscribed(row):\n \"\"\"Валидация поля subscribed\"\"\"\n\n subs = row.get('subscribed', '')\n subsUntil = row.get('subscribedUntil', '')\n\n if subs and subsUntil:\n if subsUntil > now():\n if row.get('createdOn') > now() - timedelta(days=30):\n subs = u'Бизнес-плюс'\n else:\n subs = u'Триал'\n else:\n subs = u'Лайт'\n else:\n subs = u'Лайт'\n\n row['subscribed'] = subs\n return row\n\n @staticmethod\n def timeZone(row):\n \"\"\"Больше читабельности часовому поясу\"\"\"\n\n timezone = row.get('timezone', '')\n\n row['timezone'] = u'МСК {0:+d}'.format(timezone)\n return row\n\n @staticmethod\n def nds(row):\n \"\"\"Валидируем значения поля НДС\"\"\"\n\n nds = row.get('doc_nds_type', '')\n\n enumDict = {\n '': u'не задано',\n '0': u'без НДС',\n '1': u'НДС в сумме',\n '2': u'НДС сверху'\n }\n\n row['doc_nds_type'] = enumDict.get(nds)\n return row\n\n @staticmethod\n def companyPhone(row):\n \"\"\"Убираем лишние символы в строке телефон что бы не сбивались колонки\"\"\"\n\n phone = row.get('company_phone', '')\n\n if ',' in phone:\n phone = '; '.join(phone.split(','))\n\n row['company_phone'] = phone\n return row\n\n @staticmethod\n def statusCompany(row):\n \"\"\"Валидируем статус\"\"\"\n\n status = row.get('company_status')\n\n if status:\n if not int(status):\n status = u'Организация'\n else:\n status = u'ИП'\n else:\n status = u'Физ. лицо'\n\n row['company_status'] = status\n\n return row\n\n @staticmethod\n def extends(row):\n \"\"\"Обновляем словарь из вложенного словаря\"\"\"\n\n data = row.pop('data', {})\n row.update(data)\n\n return row\n\n @staticmethod\n def calculateDocsCount(userDocs, docTypes=None):\n \"\"\"Возвращает количество документов переданного типа или общее количество\"\"\"\n\n count = 0\n if docTypes:\n for docType in docTypes:\n count += userDocs.get(docType, 0)\n else:\n count = sum(userDocs.values())\n\n return count\n\n @staticmethod\n def calculationLastLogin(logs, userId):\n \"\"\"Вычисляем последний логин в систему\"\"\"\n\n sortedLogs = [i.createdOn for i in logs if i.userID == userId]\n\n return max(sortedLogs)\n\n @staticmethod\n def convertDictToList(dictRow):\n \"\"\"Преобразуем словарь в лист перед записью\"\"\"\n\n listRow = list()\n\n sequence = (\n 'login', 'email', 'createdOn', 'subscribed', 'subscribedUntil', 'lastLogin', 'documentCount',\n 'check', 'invoice', 'torg12', 'upd', 'ttn', 'ttn-2011', 'act', 'sfv_2012', 'cen', 'pko', 'rko', 'platezhka',\n 'lastDocCreate', 'timezone', 'company_status', 'company_name', 'company_inn', 'company_address',\n 'company_email',\n 'company_phone', 'company_website', 'company_bank_name', 'company_bank_bik', 'company_bank_address',\n 'company_director', 'doc_nds_type'\n )\n\n for key in sequence:\n listRow.append(dictRow.get(key, ''))\n\n return listRow\n\n @staticmethod\n def sortedUniqueUsers(logs):\n \"\"\"Из записей логов фильтрует данные пользователей возвращает уникальных\"\"\"\n\n userList = [log.user for log in logs]\n\n uniqIds = list()\n uniqUsers = list()\n\n for user in userList:\n if user.id not in uniqIds:\n uniqIds.append(user.id)\n uniqUsers.append(user)\n\n return uniqUsers\n\n @timed\n def prepareData(self):\n \"\"\"Готовит стуктуру данных перед валидацией\"\"\"\n\n logs = self.data.get('logs')\n docs = self.data.get('documents')\n rawListData = list()\n\n validFunc = (\n self.validateSubscribed,\n self.timeZone,\n self.extends,\n self.nds,\n self.companyPhone,\n self.statusCompany,\n self.convertDictToList\n )\n\n for user in self.sortedUniqueUsers(logs):\n\n row = {\n 'login': user.login,\n 'email': user.email,\n 'createdOn': user.createdOn,\n 'subscribed': user.subscribed,\n 'subscribedUntil': user.subscribedUntil,\n 'lastLogin': self.calculationLastLogin(logs, user.id),\n 'documentCount': self.calculateDocsCount(docs[user.id]),\n\n 'check': self.calculateDocsCount(docs[user.id], ['check']),\n 'invoice': self.calculateDocsCount(docs[user.id], ['invoice']),\n 'torg12': self.calculateDocsCount(docs[user.id], ['torg12']),\n 'upd': self.calculateDocsCount(docs[user.id], ['upd']),\n 'ttn': self.calculateDocsCount(docs[user.id], ['ttn']),\n 'ttn-2011': self.calculateDocsCount(docs[user.id], ['ttn-2011']),\n 'act': self.calculateDocsCount(docs[user.id], ['act', 'ks2', 'act_sverki', 'mx1', 'torg16', 'mx3']),\n 'sfv_2012': self.calculateDocsCount(docs[user.id], ['sfv_2012', 'sfv']),\n 'cen': self.calculateDocsCount(docs[user.id], ['cen']),\n 'pko': self.calculateDocsCount(docs[user.id], ['pko']),\n 'rko': self.calculateDocsCount(docs[user.id], ['rko']),\n 'platezhka': self.calculateDocsCount(docs[user.id], ['platezhka']),\n\n 'lastDocCreate': self.calculationLastLogin(logs, user.id),\n 'timezone': user.timezone,\n 'data': user.data\n }\n\n for func in validFunc:\n row = func(row)\n\n rawListData.append(row)\n\n return rawListData\n\n def generate(self):\n \"\"\"Подгатавливаем и записываем данные в файл\"\"\"\n\n rawData = self.prepareData()\n\n # пишем построчно в файл с заголовками\n with open('stats' + '.csv', \"w\") as f:\n writer = csv.writer(f)\n writer.writerow(HEADERS)\n writer.writerows(rawData)\n\n\nclass ReportDataAggregator:\n \"\"\"Класс для формирования данных по заданным измирениям\"\"\"\n\n listDocuments = [\n 'check',\n 'invoice',\n 'torg12',\n 'upd',\n 'ttn',\n 'ttn_2011',\n 'act',\n 'ks2',\n 'act_sverki',\n 'mx1',\n 'torg16',\n 'mx3',\n 'sfv_2012',\n 'sfv',\n 'cen',\n 'pko',\n 'rko',\n 'platezhka'\n ]\n\n def __init__(self, loginCount=None, loginPeriod=None, dateStart=None, dateEnd=None, docsCount=None, docsPeriod=None,\n listDocuments=None):\n self.logins = loginCount\n self.docsCount = docsCount\n self.loginPeriod = loginPeriod\n self.docsPeriod = docsPeriod\n self.dateStart = dateStart\n self.dateEnd = dateEnd\n\n self.logs = list()\n self.docs = list()\n\n if listDocuments:\n self.listDocuments = listDocuments\n\n @property\n def data(self):\n \"\"\"Фомирует сагрегированные данные по заданным параметрам\"\"\"\n validUserIds = self.__getUsers()\n dictDocsCount = self.__getCountDocuments(validUserIds)\n logs = [i for i in self.logs if i.userID in validUserIds]\n\n return {\n 'logs': logs,\n 'documents': dictDocsCount\n }\n\n @staticmethod\n def __selectedItems(items, sortedField, minValue):\n \"\"\"Отбирает значения по заданному полю и порогу вхождения\"\"\"\n\n countedDict = Counter([i.sqlmeta.asDict().get(sortedField) for i in items])\n\n return [i for i in countedDict.keys() if countedDict[i] >= minValue]\n\n @timed\n def __getCountDocuments(self, validUserIds):\n \"\"\"Список документов c их количеством для каждого пользователя\"\"\"\n\n dictDocsCount = dict()\n\n for userId in validUserIds:\n forms = \", \".join(\"'{0}'\".format(doc) for doc in self.listDocuments)\n\n query = \"\"\"\n SELECT form, count(form)\n FROM document\n WHERE user_id = '{0}' AND form IN ({1})\n GROUP BY form\n \"\"\".format(userId, forms)\n\n query_result = db.db_conn.queryAll(query)\n\n dictDocsCount[userId] = {i[0]: i[1] for i in query_result}\n\n return dictDocsCount\n\n @timed\n def __getUsers(self):\n \"\"\"Список пользователей для отчёта\"\"\"\n\n if self.loginPeriod:\n userQuery = AND(\n db.UsersLog.q.createdOn >= (now() - timedelta(days=self.loginPeriod))\n )\n self.logs = list(db.UsersLog.select(userQuery))\n else:\n self.logs = list(db.UsersLog.select())\n\n if self.docsPeriod:\n users = [i.userID for i in self.logs]\n docsQuery = AND(\n db.Document.q.createdOn >= (now() - timedelta(days=self.docsPeriod)),\n IN(db.Document.q.user, users)\n )\n self.docs = list(db.Document.select(docsQuery))\n\n userIds = self.__selectedItems(self.logs, 'userID', self.logins)\n userDocIds = self.__selectedItems(self.docs, 'userID', self.docsCount)\n\n if userIds and userDocIds:\n validUserId = list(set(userIds) & set(userDocIds))\n else:\n validUserId = userIds\n\n return validUserId\n\n\nif __name__ == '__main__':\n ReportConstructor().generate()\n","repo_name":"Artem-Ivanov/python_scripts","sub_path":"stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":12274,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72801567760","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport time\nfrom collections import defaultdict\nfrom .base import BaseCache\n\n\nclass DictCache(BaseCache):\n\n def __init__(self, config=None):\n super(DictCache, self).__init__(config)\n self._data = None\n\n @property\n def data(self):\n if self._data is None:\n self._data = defaultdict(lambda: ('', 0)) \\\n if self.config.get('max_age') else defaultdict(str)\n return self._data\n\n def get(self, key):\n if self.config.get('max_age'):\n value, expiry_time = self.data[key]\n if time.time() > expiry_time:\n self.delete(key)\n raise KeyError(key)\n else:\n value = self.data[key]\n return value\n\n def set(self, key, value):\n max_age = self.config.get('max_age')\n if max_age:\n _, expiry_time = self.data[key]\n if expiry_time == 0:\n # ie: if we got the default value\n expiry_time = time.time() + max_age\n self.data[key] = (value, expiry_time)\n else:\n self.data[key] = value\n\n def delete(self, key):\n del(self.data[key])\n\n def clear(self):\n return self.data.clear()\n","repo_name":"lonetwin/supycache","sub_path":"supycache/backends/dict_cache.py","file_name":"dict_cache.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":160,"dataset":"github-code","pt":"3"}
+{"seq_id":"71506242962","text":"\"\"\"Unittests for propagation of linker inputs through Rust libraries\"\"\"\n\nload(\"@bazel_skylib//lib:unittest.bzl\", \"analysistest\", \"asserts\")\n\ndef _shared_lib_is_propagated_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n link_action = [action for action in tut.actions if action.mnemonic == \"CppLink\"][0]\n\n lib_name = _get_lib_name(ctx, name = \"foo_shared\")\n asserts.true(env, _contains_input(link_action.inputs, lib_name))\n\n return analysistest.end(env)\n\ndef _static_lib_is_not_propagated_test_impl(ctx):\n env = analysistest.begin(ctx)\n tut = analysistest.target_under_test(env)\n link_action = [action for action in tut.actions if action.mnemonic == \"CppLink\"][0]\n\n lib_name = _get_lib_name(ctx, name = \"foo\")\n asserts.false(env, _contains_input(link_action.inputs, lib_name))\n\n return analysistest.end(env)\n\ndef _contains_input(inputs, name):\n for input in inputs.to_list():\n # We cannot check for name equality because rlib outputs contain\n # a hash in their name.\n if input.basename.startswith(name):\n return True\n return False\n\ndef _get_lib_name(ctx, name):\n if ctx.target_platform_has_constraint(ctx.attr._windows_constraint[platform_common.ConstraintValueInfo]):\n return name\n else:\n return \"lib{}\".format(name)\n\nstatic_lib_is_not_propagated_test = analysistest.make(\n _static_lib_is_not_propagated_test_impl,\n attrs = {\n \"_windows_constraint\": attr.label(default = Label(\"@platforms//os:windows\")),\n },\n)\n\nshared_lib_is_propagated_test = analysistest.make(\n _shared_lib_is_propagated_test_impl,\n attrs = {\n \"_macos_constraint\": attr.label(default = Label(\"@platforms//os:macos\")),\n \"_windows_constraint\": attr.label(default = Label(\"@platforms//os:windows\")),\n },\n)\n\ndef _linker_inputs_propagation_test():\n static_lib_is_not_propagated_test(\n name = \"depends_on_foo_via_staticlib\",\n target_under_test = \"//test/linker_inputs_propagation:depends_on_foo_via_staticlib\",\n )\n\n shared_lib_is_propagated_test(\n name = \"depends_on_shared_foo_via_staticlib\",\n target_under_test = \"//test/linker_inputs_propagation:depends_on_shared_foo_via_staticlib\",\n )\n\n static_lib_is_not_propagated_test(\n name = \"depends_on_foo_via_sharedlib\",\n target_under_test = \"//test/linker_inputs_propagation:depends_on_foo_via_sharedlib\",\n )\n\n shared_lib_is_propagated_test(\n name = \"depends_on_shared_foo_via_sharedlib\",\n target_under_test = \"//test/linker_inputs_propagation:depends_on_shared_foo_via_sharedlib\",\n )\n\ndef linker_inputs_propagation_test_suite(name):\n \"\"\"Entry-point macro called from the BUILD file.\n\n Args:\n name: Name of the macro.\n \"\"\"\n _linker_inputs_propagation_test()\n\n native.test_suite(\n name = name,\n tests = [\n \":depends_on_foo_via_staticlib\",\n \":depends_on_shared_foo_via_staticlib\",\n \":depends_on_foo_via_sharedlib\",\n \":depends_on_shared_foo_via_sharedlib\",\n ],\n )\n","repo_name":"bazelbuild/rules_rust","sub_path":"test/unit/linker_inputs_propagation/linker_inputs_propagation_test.bzl","file_name":"linker_inputs_propagation_test.bzl","file_ext":"bzl","file_size_in_byte":3117,"program_lang":"python","lang":"en","doc_type":"code","stars":568,"dataset":"github-code","pt":"3"}
+{"seq_id":"25815710063","text":"#!/usr/bin/py\n\nimport argparse\nfrom Bio import SeqIO\nfrom collections import Counter\nimport glob\nimport gzip\nimport logging\n#import numpy as np\nimport os\nimport pandas as pd\nfrom plotnine import ggplot, aes, geom_bar, theme, element_text, xlab, ggtitle\nimport psutil\nimport subprocess\nimport sys\nimport time\nimport xlsxwriter\n\n\ndef parseArgs(argv):\n '''\n Parsing the arguments\n '''\n parser = argparse.ArgumentParser(description='Takes the output from taxprofiler and parses it')\n parser.add_argument(\"--Tools\", dest = 'toolfolders', required=True, help =\"All tools you want to parse together from the nextflow parser (Required), could be [Diamond, Kraken2, KrakenUniq, Metaphlan4], presented in a space seperatated string\", nargs=\"+\")\n arguments = parser.parse_args(argv)\n return arguments\n\n\n\n\ndef SummarizeInExcel(toolfolders):\n \"\"\"\n One sheet per sample\n \"\"\"\n\n OutExcel=\"Out_ParsedExcel.xlsx\"\n writer = pd.ExcelWriter(OutExcel, engine='xlsxwriter')\n\n Countfiles={}\n\n # Generate 1 dataframe per sample! \n for t in toolfolders:\n Countfiles[t]=[]\n countstoplot=glob.glob(t+\"/*_CountsForplotting.txt\")\n for c in countstoplot:\n samplename=c.split(\"/\")[-1]\n Countfiles[t].append(samplename)\n \n uniqueCountFiles=list(set(sum(list(Countfiles.values()),[]))) # Get the unique counts for plotting name to be able to loop through them!\n\n for c in uniqueCountFiles:\n #df=pd.DataFrame() # For every sample generate a dataframe \n counter=0\n for tool, reports in Countfiles.items():\n counter+=1\n if c in reports:\n samplename=c.split(\"_CountsForplotting.txt\")[0]\n d={}\n fullpath=tool+\"/\"+c\n \n with open(fullpath, \"r\") as inf: # Check amount of rows to make sure you can loop through it! \n nrowsCounts=len(inf.readlines())\n if nrowsCounts > 1:\n with open(fullpath, \"r\") as inf:\n next(inf) # Skip headerline\n for l in inf:\n l=l.strip()\n taxnr=l.split(\"\\t\")[0]\n taxname=l.split(\"\\t\")[1]\n counts=int(l.split(\"\\t\")[2])\n d[taxname]=counts\n if counter == 1: # First tool for the sample generate the dataframe\n df=pd.DataFrame.from_dict(d, orient='index', columns=[tool])\n else: # If we already had a tool append to the existing dataframe\n df2=pd.DataFrame.from_dict(d, orient='index', columns=[tool])\n df=pd.merge(df,df2, how='outer', left_index=True, right_index=True)\n\n df=df.astype('Int64') # To convert columns with NAN to int we use int64\n # Print calculate the detection rate, if 1 in 4 the detection is 25%\n df[\"DetectionRate\"]=(len(df.columns)-df.isna().sum(axis=1))/len(df.columns)\n df=df.sort_values('DetectionRate', ascending=False)\n\n # We cannot have sheet name longer than 30 characters in excel\n if len([*samplename]) > 30:\n new_samplename=\"\".join([*samplename][0:30])\n\n else:\n new_samplename=samplename\n\n # Create a Pandas Excel writer using XlsxWriter as the engine.\n df.to_excel(writer, sheet_name=new_samplename)\n writer.close()\n \ndef main(toolfolders):\n SummarizeInExcel(toolfolders)\n \nif __name__ == '__main__':\n args=parseArgs(sys.argv[1:])\n main(args.toolfolders)\n\n \n","repo_name":"ClinicalGenomicsGBG/Taxprofiler-Metagenomics","sub_path":"Scripts/ParserScripts/WriteExcel.py","file_name":"WriteExcel.py","file_ext":"py","file_size_in_byte":3666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32934041950","text":"import sys\nimport pandas as pd\n\n\ndef vowel_count(text):\n text = text.lower()\n count = 0\n vowels = set(\"aeiou\")\n for char in text:\n if char in vowels:\n count += 1\n return count\n\n\ndef consonant_count(text):\n text = text.lower()\n count = 0\n vowels = set(\"bcdfghjklmnpqrstvxyz\")\n for char in text:\n if char in vowels:\n count += 1\n return count\n\n\ndef fileToArray(path):\n file = open(path, 'r')\n lines = file.read().split('\\n')\n file.close()\n return lines\n\n\ndef commonFactor(num1, num2):\n small = min(num1, num2)\n for i in range(2, small+1):\n if (num1 % i == 0 and num2 % i == 0):\n return True\n return False\n\n\ndef topSecretAlgoritm(destinationArry, driverArr):\n\n table_destination = []\n table_drivers = []\n table_ss = []\n for destination in destinationArry:\n driver_dest_ss = []\n for driver in driverArr[:]:\n destinationLength = len(destination.replace(\" \", \"\"))\n driverLength = len(driver.replace(\" \", \"\"))\n if destinationLength % 2 == 0:\n vcount = vowel_count(driver)\n ss = vcount*1.5\n else:\n ccount = consonant_count(driver)\n ss = ccount\n\n _commonFactor = commonFactor(destinationLength, driverLength)\n if _commonFactor:\n ss = ss+ss*.5\n\n driver_dest_ss.append(ss)\n # GETTING THE MAX SS VALUE\n max_value = max(driver_dest_ss)\n max_index = driver_dest_ss.index(max_value)\n\n # ADDING DESTINATION TO THE FINAL DESTINATION ARRAY\n table_destination.append(destination)\n # ADDING DRIVER TO THE FINAL DRIVERS ARRAY\n table_drivers.append(driverArr[max_index])\n # ADDING SS VALUE TO THE FINAL SS ARRAY\n table_ss.append(max_value)\n del driverArr[max_index]\n\n data = {'Destination': table_destination,\n 'Driver': table_drivers,\n 'SS': table_ss}\n return data\n\n\ndef main():\n try:\n destination_path = sys.argv[1]\n drivers_path = sys.argv[2]\n destination_array = fileToArray(destination_path)\n drivers_array = fileToArray(drivers_path)\n data = topSecretAlgoritm(destination_array, drivers_array)\n\n table = pd.DataFrame(data)\n print(table)\n except Exception as e:\n print(\n \"An error occurred please check that you have entered the file paths correctly\")\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"javiervar/SDE-Code-Exercise","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33896050421","text":"# 공유기 설치\n# https://www.acmicpc.net/problem/2110\n\n# 5 3\n# 1\n# 2\n# 8\n# 4\n# 9\n\nimport sys\n\nn,c=map(int, sys.stdin.readline().split())\nnums=[]\nfor _ in range(n):\n nums.append(int(sys.stdin.readline()))\nnums.sort()\n\nstart=1\nend=nums[-1]-nums[0]\nresult=0\nwhile start<=end:\n center=(start+end)//2\n cnt=1\n temp=nums[0]\n for i in range(1, n):\n if temp+center<=nums[i]:\n temp=nums[i]\n cnt+=1\n if cnt>=c:\n result=center\n start=center+1\n else:\n end=center-1\nprint(result)\n","repo_name":"Greek-and-Roman-God/Athena","sub_path":"codingtest/week19/install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38944174450","text":"from datetime import datetime\n\nclass Pessoas:\n def __init__(self,nome: str,cpf: str,idade: int,endereco: str,telefone: int):\n self.nome = nome\n self.cpf = cpf\n self.idade = idade\n self.endereco = endereco\n self.telefone = telefone\n\nclass Funcionarios(Pessoas):\n def __init__(self, nome: str, cpf: str, idade: int, endereco: str, telefone: int, salario: float,data_de_contratacao: datetime , numero_de_alugueis: int, status:bool = True ):\n super().__init__(nome, cpf, idade, endereco, telefone)\n self.salario = salario\n self.data_de_contratacao = data_de_contratacao\n self.numero_de_alugueis = numero_de_alugueis\n self._status = status\n\n @property\n def status(self):\n if self._status == True:\n return 'Ativado'\n return 'Desativado'\n\n @property\n def ativar(self):\n if self._status == False:\n self._status = True\n \n @property\n def desativar(self):\n if self._status == True:\n self._status = False\n \n @property\n def tempo_de_servico(self):\n return datetime.now() - self.data_de_contratacao \n \nclass Clientes(Pessoas):\n def __init__(self, nome: str, cpf: str, idade: int, endereco: str, telefone: int, numero_carteira_motorista: int,ano_vencimento_carteira_motorista: int):\n super().__init__(nome, cpf, idade, endereco, telefone)\n self.numero_carteira_motorista = numero_carteira_motorista\n self.ano_vencimento_carteira_motorista = ano_vencimento_carteira_motorista ","repo_name":"Thiagomrad/Locadora_de_carros","sub_path":"pessoas.py","file_name":"pessoas.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35197889303","text":"from proglog import ProgressBarLogger\nimport moviepy.editor as mp\nimport os\nimport random\nimport string\n\n\nclass MyBarLogger(ProgressBarLogger):\n progress_message = None\n parametr_message = None\n sticker_message = None\n total_message = None\n prev_percent = 0\n\n def __init__(self, user_id, bot):\n super().__init__()\n self.user_id = user_id\n self.bot = bot\n\n # калбак каждого вызова logger message\n def callback(self, **changes):\n if self.total_message is None:\n self.showSticker()\n self.total_message = self.bot.send_message(self.user_id, f'Кол-во параметров: {len(changes.items())}')\n\n for (_, value) in changes.items():\n print(\"> bot work status:\", value)\n if self.parametr_message is None:\n self.parametr_message = self.bot.send_message(self.user_id, 'Текущий: %s' % (value))\n else:\n self.bot.edit_message_text(chat_id=self.user_id, message_id=self.parametr_message.message_id,\n text='Текущий: %s' % (value))\n self.prev_percent = 0\n\n def showSticker(self):\n self.sticker_message = self.bot.send_animation(self.user_id,\n r'https://media.tenor.com/-2me41xDUsIAAAAC/working-cat-cat.gif')\n\n def hideSticker(self):\n self.bot.delete_message(chat_id=self.user_id, message_id=self.sticker_message.message_id)\n self.progress_message = None\n self.parametr_message = None\n self.sticker_message = None\n self.total_message = None\n self.prev_percent = 0\n\n # калбак каждого обновления logger progress\n def bars_callback(self, bar, _, value, old_value=None):\n percent = (value / self.bars[bar]['total']) * 100\n if self.progress_message is None:\n self.progress_message = self.bot.send_message(self.user_id, 'Загрузка: 0%')\n else:\n if round(percent - self.prev_percent) >= 30:\n self.bot.edit_message_text(chat_id=self.user_id, message_id=self.progress_message.message_id,\n text=f'Загрузка: {percent:.2f}%')\n self.prev_percent = percent\n\n\nclass Error:\n error_gif_url = 'https://goo.su/t8mNi'\n\n def __init__(self, bot, markup):\n super().__init__()\n self.bot = bot\n self.markup = markup\n\n def sendError(self, user_id):\n self.bot.send_animation(user_id, self.error_gif_url)\n self.bot.send_message(user_id, 'Что-то пошло не так..❌\\nПовтори пожалуйста попытку', reply_markup=self.markup)\n\n\nclass Loader:\n loading_message = None\n isLoading = False\n loader_gif_url = r'https://media.tenor.com/fjdydcAjFo8AAAAi/capoo-blue.gif'\n\n # todo сделать выбор оформления темы типо (котики, строгий вуз, универсальное)\n\n def __init__(self, user_id, bot):\n super().__init__()\n self.user_id = user_id\n self.bot = bot\n\n def showLoader(self):\n if not self.isLoading:\n self.isLoading = True\n self.loading_message = self.bot.send_animation(self.user_id, self.loader_gif_url)\n\n def hideLoader(self):\n if self.isLoading:\n self.isLoading = False\n self.bot.delete_message(chat_id=self.user_id, message_id=self.loading_message.message_id)\n self.bot.send_message(self.user_id, \"✅\")\n\n\ndef downloadVideo(bot, message):\n user_id = message.chat.id\n loader = Loader(user_id, bot)\n loader.showLoader()\n file_info = bot.get_file(message.video.file_id)\n downloaded_file = bot.download_file(file_info.file_path)\n local_file_name = f'{user_id}_{getUniqID()}_video.mp4'\n with open(local_file_name, 'wb') as new_file:\n new_file.write(downloaded_file)\n loader.hideLoader()\n return local_file_name\n\n\ndef getUniqID():\n # генерирует случайную строку из букв и цифр длиной 5 символов\n letters_and_digits = string.ascii_letters + string.digits\n random_string = ''.join(random.choice(letters_and_digits) for _ in range(5))\n return random_string\n\n\ndef extractAudio(bot, message, local_file_name):\n logger = MyBarLogger(message.chat.id, bot)\n try:\n video = mp.VideoFileClip(local_file_name)\n audio = video.audio\n local_audio_file_name = f'{message.chat.id}_{getUniqID()}_audio.mp3'\n audio.write_audiofile(local_audio_file_name, logger=logger)\n logger.hideSticker()\n return local_audio_file_name\n except:\n return ''\n finally:\n audio.close()\n video.close()\n\n\ndef downloadConvertVoice(bot, message):\n user_id = message.chat.id\n loader = Loader(user_id, bot)\n loader.showLoader()\n file_info = bot.get_file(message.voice.file_id)\n downloaded_file = bot.download_file(file_info.file_path)\n local_ogg_file_name = f'{user_id}_{getUniqID()}_voice-audio.ogg'\n with open(local_ogg_file_name, 'wb') as new_file:\n new_file.write(downloaded_file)\n audio = mp.AudioFileClip(local_ogg_file_name)\n local_mp3_file_name = f'{user_id}_{getUniqID()}_audio.mp3'\n audio.write_audiofile(local_mp3_file_name)\n audio.close()\n loader.hideLoader()\n return local_mp3_file_name\n\n\ndef downloadAudio(bot, message):\n user_id = message.chat.id\n loader = Loader(user_id, bot)\n loader.showLoader()\n file_info = bot.get_file(message.audio.file_id)\n downloaded_file = bot.download_file(file_info.file_path)\n local_file_name = f'{user_id}_{getUniqID()}_audio.mp3'\n with open(local_file_name, 'wb') as new_file:\n new_file.write(downloaded_file)\n\n loader.hideLoader()\n return local_file_name\n\n\ndef overlayMedia(bot, user_id, local_file_name, local_mp3_file_name):\n logger = MyBarLogger(user_id, bot)\n try:\n video = mp.VideoFileClip(local_file_name)\n audio = mp.AudioFileClip(local_mp3_file_name)\n final_video = video.set_audio(audio)\n local_final_video_name = f'{user_id}_{getUniqID()}_final.mp4'\n final_video.write_videofile(local_final_video_name, codec='libx264', logger=logger)\n logger.hideSticker()\n return local_final_video_name\n except:\n return ''\n finally:\n audio.close()\n video.close()\n\n\ndef concatenateAudio(bot, user_id, local_file_name, local_file_name2):\n logger = MyBarLogger(user_id, bot)\n try:\n audio_clip1 = mp.AudioFileClip(local_file_name)\n audio_clip2 = mp.AudioFileClip(local_file_name2)\n final_audio = mp.concatenate_audioclips([audio_clip1, audio_clip2])\n local_final_audio = f'{user_id}_{getUniqID()}_final_audio.mp3'\n final_audio.write_audiofile(local_final_audio, logger=logger)\n logger.hideSticker()\n return local_final_audio\n except:\n return ''\n finally:\n audio_clip1.close()\n audio_clip2.close()\n\n\ndef deleteUserFiles(user_id):\n files = os.listdir()\n for file_name in files:\n if str(user_id) in file_name:\n os.remove(file_name)\n print(\"> files deleted\")\n","repo_name":"psevdo-progger/audio_bot","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":7289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30910233816","text":"from bs4 import BeautifulSoup\nimport urllib.request\n\nurl = \"https://en.wikipedia.org/wiki/Colt_Python\"\n\nresponse = urllib.request.urlopen(url)\nsoup = BeautifulSoup(response,\"html.parser\")\n\nprint(response.geturl())\nprint(\"*********************************\")\ndef getSoupTitle(soup):\n return soup.title\n\ndef printList(list,number):\n print(\"length of the list=\",len(list))\n for i in list[:number]:\n print (i,\"\\n\")\n\ndef getUrls(soup):\n urls=soup.find_all(\"a\")\n url_list=[]\n for a in urls:\n #excluding tel ,mailto, and #(same page) links\n if (a.get(\"href\"))and(\"tel\" not in a.get(\"href\"))and(\"mailto\" not in a.get(\"href\")) and (\"#\" not in a.get(\"href\")):\n # part of URLs on wiki presented as \\\\url without http start and part of the URLs presented as \\w\\index or \\wiki\\,\n # so we need to check and to \"repair\" those links\n if str(a.get(\"href\"))[:2]==\"//\":\n url_list.append(\"https:\"+a.get(\"href\"))\n elif str(a.get(\"href\"))[:2]==\"/w\":\n url_list.append(\"https://en.wikipedia.org\" + a.get(\"href\"))\n else:\n url_list.append(a.get(\"href\"))\n return url_list\n\n\n\n\n\n\n\n\n\ndef main():\n\n\n printList((getUrls(soup)),280)\nif __name__ == \"__main__\":\n main()","repo_name":"StasLi/src","sub_path":"002.py","file_name":"002.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"69873640402","text":"import sys\nfrom collections import deque\nN = int(sys.stdin.readline().strip())\nboard = [[0]*N for _ in range(N)]\nbsx, bsy = 0, 0 # 아기상어 좌표\nbs = 2 # 아기상어 크기 = 2\nfor i in range(N):\n a = list(map(int, sys.stdin.readline().strip().split()))\n for j in range(N):\n if a[j] == 9:\n bsx, bsy = i, j\n board[i][j] = 9\n elif 1 <= a[j] <= 6:\n board[i][j] = a[j]\n\ndx = (0, 1, 0, -1)\ndy = (1, 0, -1, 0)\n\ndef bfs(bsx, bsy):\n dq = deque()\n dq.append((bsx, bsy, 0))\n dist = []\n chk = [[False]*N for _ in range(N)]\n chk[bsx][bsy] = True\n while dq:\n x, y, n = dq.popleft()\n if 1 <= board[x][y] <= 6 and bs > board[x][y]: # 먹을 수 있는 물고기\n dist.append((n, x, y))\n for i in range(4):\n nx = x + dx[i]\n ny = y + dy[i]\n if 0 <= nx < N and 0 <= ny < N and not chk[nx][ny] and board[nx][ny] <= bs: # 지나갈 수 있는지\n dq.append((nx, ny, n+1))\n chk[nx][ny] = True\n\n dist.sort(key = lambda x : (x[0], x[1], x[2]))\n # print(dist)\n return dist\n\ntime = 0\ncnt = 0 # 같은 크기 물고기 잡아먹는 경우 카운트\nwhile True:\n fish = bfs(bsx, bsy)\n if len(fish) == 0:\n print(time)\n break\n\n board[bsx][bsy] = 0\n bsx, bsy = fish[0][1], fish[0][2]\n\n board[fish[0][1]][fish[0][2]] = 0\n time += fish[0][0]\n cnt += 1\n if cnt == bs: # 크기 갱신\n bs += 1\n cnt = 0\n\n\n\n\n\n\n\n","repo_name":"auddus16/Algorithm_python","sub_path":"백준_문제집_삼성기출/16236_아기상어.py","file_name":"16236_아기상어.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20956999785","text":"import math\nimport threading\nimport time\n\nfrom selenium.common.exceptions import TimeoutException\nfrom bs4 import BeautifulSoup as bs4\nimport requests as rq\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.chrome.options import Options\n\n\n# noinspection PyMethodMayBeStatic,PyBroadException\nclass Heart:\n def __init__(self):\n self.website_url = 'https://dramacool.cr'\n self.browser_exe_path = 'C:\\\\Program Files\\\\BraveSoftware\\\\Brave-Browser\\\\Application\\\\brave.exe'\n\n def search(self, name: str):\n user_looking_for = name.replace(' ', '+')\n response = rq.get(f'{self.website_url}/search?type=movies&keyword={user_looking_for}')\n soup = bs4(response.text, 'html.parser')\n names = [i.string for i in soup.select('ul li a h3')]\n img_url = [i.get('data-original') for i in soup.select('ul li a img')[1:]]\n urls = [self.website_url + ((i.get('onclick')).replace('window.location = \\'', '')).replace('\\'', '') for i in\n soup.select('ul li a h3')]\n result = [{'name': names[i], 'img': img_url[i], 'url': urls[i]} for i in range(len(names))]\n\n return result\n\n def home_pg(self):\n chrome_driver_path = '.\\\\chromedriver.exe'\n options = Options()\n # browser exe file\n options.binary_location = self.browser_exe_path\n\n options.add_argument('headless')\n driver = webdriver.Chrome(options=options, service=Service(chrome_driver_path))\n driver.get(self.website_url)\n a = driver.find_elements(By.CSS_SELECTOR, '.views1 ul li')\n b = [{'name': i.find_element(By.CSS_SELECTOR, 'a').get_attribute('title'),\n 'url': i.find_element(By.CSS_SELECTOR, 'a').get_attribute('href'),\n 'img': i.find_element(By.CSS_SELECTOR, 'a div').get_attribute('style')[23:-3]} for i in a]\n return b\n\n def get_all_ep_pg(self, media_page: str):\n response = rq.get(media_page)\n soup = bs4(response.text, 'html.parser')\n some_useless_data = soup.select('.all-episode li a h3')\n ep_pg = [self.website_url + ((ep.get('onclick')).replace('window.location = \\'', '').replace('\\'', '')) for ep\n in some_useless_data]\n ep_pg.reverse()\n return ep_pg\n\n def get_ep_download_links(self, ep_to_download):\n ep_download = []\n for ep in ep_to_download:\n response = rq.get(ep)\n soup = bs4(response.text, 'html.parser')\n some_useless_data = soup.select('.download a')\n for i in some_useless_data:\n ep_download.append(\"https://\" + str(i).split('\\n')[0][11:-18])\n return ep_download\n\n # noinspection PyShadowingNames,PyBroadException\n def downloading(self, ep):\n chrome_driver_path = '.\\\\chromedriver.exe'\n options = Options()\n options.binary_location = self.browser_exe_path\n # fdm\n options.add_extension('.\\\\fdm.crx')\n # buster\n options.add_extension('.\\\\buster.crx')\n options.add_experimental_option('detach', True)\n\n driver = webdriver.Chrome(options=options, service=Service(chrome_driver_path))\n driver.minimize_window()\n driver.get(ep)\n if self.check_captcha(driver):\n driver.maximize_window()\n\n WebDriverWait(driver, 100).until(EC.frame_to_be_available_and_switch_to_it(\n (By.CSS_SELECTOR, \"iframe[name^='a-'][src^='https://www.google.com/recaptcha/api2/anchor?']\")))\n WebDriverWait(driver, 100).until(\n EC.element_to_be_clickable((By.XPATH, \"//span[@id='recaptcha-anchor']\"))).click()\n try:\n WebDriverWait(driver, 5).until(\n EC.presence_of_element_located(\n (By.XPATH, \"//span[@id='recaptcha-anchor' and @aria-checked='true']\")))\n except TimeoutException:\n self.solve_captcha(driver)\n\n driver.switch_to.default_content()\n driver.find_element(By.ID, 'btn-submit').click()\n driver.minimize_window()\n time.sleep(5)\n\n for i in range(4, 0, -1):\n try:\n btn = driver.find_element(By.XPATH, f'/html/body/section/div/div[2]/div/div[4]/div[1]/div[{i}]')\n except Exception:\n pass\n else:\n btn.click()\n driver.minimize_window()\n break\n\n time.sleep(1)\n driver.quit()\n return\n\n # noinspection PyBroadException\n def solve_captcha(self, driver):\n driver.switch_to.default_content()\n WebDriverWait(driver, 50).until(EC.frame_to_be_available_and_switch_to_it(\n (By.CSS_SELECTOR, \"iframe[name^='c-'][src^='https://www.google.com/recaptcha/api2/bframe?']\")))\n\n WebDriverWait(driver, 10).until(\n EC.element_to_be_clickable((By.CSS_SELECTOR, '.help-button-holder'))).click()\n time.sleep(3)\n\n driver.switch_to.default_content()\n try:\n WebDriverWait(driver, 5).until(EC.frame_to_be_available_and_switch_to_it(\n (By.CSS_SELECTOR, \"iframe[name^='a-'][src^='https://www.google.com/recaptcha/api2/anchor?']\")))\n WebDriverWait(driver, 4).until(EC.presence_of_element_located(\n (By.XPATH, \"//span[@id='recaptcha-anchor' and @aria-checked='true']\")))\n return\n except Exception:\n driver.switch_to.default_content()\n WebDriverWait(driver, 50).until(EC.frame_to_be_available_and_switch_to_it(\n (By.CSS_SELECTOR, \"iframe[name^='c-'][src^='https://www.google.com/recaptcha/api2/bframe?']\")))\n WebDriverWait(driver, 10).until(\n EC.element_to_be_clickable((By.CSS_SELECTOR, '.image-button-holder'))).click()\n time.sleep(2)\n self.solve_captcha(driver)\n\n def check_captcha(self, driver):\n \"\"\"Returns true if captcha found on page\"\"\"\n try:\n WebDriverWait(driver, 5).until(\n EC.presence_of_element_located((By.ID, \"captcha-v2\"))\n )\n except Exception:\n return False\n else:\n return True\n\n\ndef partition(ls: list):\n x = math.ceil(len(ls) / 6) if 6 < len(ls) else 1\n n = 0\n ls_for_challenge = []\n for i in range(x):\n if n + 6 > len(ls):\n ls_for_challenge.append(ls[n:len(ls)])\n else:\n ls_for_challenge.append(ls[n:n + 6])\n n += 6\n\n return ls_for_challenge\n\n\ndef handle_download(download_links: list, func):\n thread_list, ls = [], []\n for a in partition(download_links):\n for link in a:\n t = threading.Thread(target=func, kwargs={'ep': link})\n t.start()\n time.sleep(1)\n thread_list.append(t)\n # Wait for all threads to complete\n for thread in thread_list:\n thread.join()\n return\n","repo_name":"IMFinallyHere/Dramacool_download_GUI","sub_path":"heart.py","file_name":"heart.py","file_ext":"py","file_size_in_byte":7124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31829635467","text":"from selenium import webdriver\nimport os\nimport time\nimport argparse\n\n\ndef file_is_downloaded():\n \"\"\"Check if video is being downloaded\n\n Returns\n ---------\n True\n \"\"\"\n time.sleep(3)\n downloaded = False\n\n while not downloaded:\n files = os.listdir(args.location)\n downloaded = True\n for file in files:\n if \".crdownload\" in file:\n downloaded = False\n print(\"STILL DOWNLOADING\")\n time.sleep(1)\n\n return True\n\n\nif __name__ == \"__main__\":\n \"\"\"Downloads videos from the sentence_urls.csv list\n\n Returns\n ---------\n Saves videos to downloads path\n \"\"\"\n\n # Arguments\n parser = argparse.ArgumentParser(description=\"\")\n parser.add_argument(\n \"-l\", \"--location\", help=\"Location to where the videos will be downloaded to\", type=str, required=True\n )\n args = parser.parse_args()\n\n with open(\"sentence_urls.csv\") as f:\n data = f.readlines()[1:]\n\n urls = []\n for item in data:\n erf, mov = item.split(\",\")\n urls.append(erf)\n urls.append(mov)\n\n options = webdriver.ChromeOptions()\n options.add_argument(\"--start-maximized\")\n prefs = {\"download.default_directory\": args.location}\n options.add_experimental_option(\"prefs\", prefs)\n driver = webdriver.Chrome(options=options)\n i = 0\n\n for url in urls:\n driver.get(url)\n driver.switch_to.frame(driver.find_element_by_name(\"MainFrame\"))\n driver.switch_to.frame(driver.find_element_by_name(\"SideMain\"))\n driver.find_element_by_xpath('//*[text()=\"Continue >>\"]').click()\n\n assert file_is_downloaded()\n print(i)\n i += 1\n\n driver.close()\n","repo_name":"KristinaG5/AI-Sign-Language-Translator","sub_path":"dataset/webscraper/sentence_clip_fetcher.py","file_name":"sentence_clip_fetcher.py","file_ext":"py","file_size_in_byte":1712,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"29026531727","text":"# coding: utf-8\n\n\nimport pytest\n\nfrom idm.users.models import Department\nfrom idm.tests.utils import create_user\n\npytestmark = [pytest.mark.django_db, pytest.mark.robotless]\n\n\ndef test_subdepartments():\n assert Department.objects.count() == 0\n\n yandex = Department.objects.create(\n id=3,\n name='Яндекс',\n )\n akadem = Department.objects.create(\n id=392,\n name='Отдел по работе с академическими программами',\n parent=yandex,\n )\n Department.objects.create(\n id=450,\n name='Группа практики школы анализа данных',\n parent=akadem,\n )\n\n assert Department.objects.count() == 3\n\n\ndef test_bind_user():\n user = create_user('art')\n yandex = Department.objects.create(name='Яндекс')\n\n assert yandex.users2.count() == 0\n yandex.users2.add(user)\n assert yandex.users2.count() == 1\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/models/users/test_departments.py","file_name":"test_departments.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19039425301","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\" calculate estimate xg values for a range if weightening parameters and dumps the best estimates into json files \"\"\"\nimport os\nimport sys\nimport numpy as np\nsys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))\nsys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)))\n\n# pylint: disable=E0401, C0413\nfrom rest.functions.corsi import goals5v5_get, gameshots5v5_get\nfrom rest.functions.helper import config_load, logger_setup, json_load, json_store, uts_now\nfrom rest.functions.match import match_info_get, match_list_get\nfrom rest.functions.periodevent import periodevent_get\nfrom rest.functions.shift import shift_get\nfrom rest.functions.shot import shot_list_get\nfrom rest.functions.xg import xgmodel_get, shotlist_process, xgf_calculate, xgscore_get\n\ndef _config_load(logger, cfg_file=os.path.dirname(__file__)+'/'+'hockeygraphs.cfg'):\n \"\"\"\" load config from file \"\"\"\n logger.debug('_config_load()')\n\n rebound_interval = 0\n break_interval = 0\n config_dic = config_load(cfg_file=cfg_file)\n if 'Shots' in config_dic:\n if 'rebound_interval' in config_dic['Shots']:\n rebound_interval = int(config_dic['Shots']['rebound_interval'])\n if 'break_interval' in config_dic['Shots']:\n break_interval = int(config_dic['Shots']['break_interval'])\n logger.debug('_config_load() ended.')\n\n return (rebound_interval, break_interval)\n\ndef dump_it(g_result_dic_, filename='result.json', limit=20):\n \"\"\" dump and shrink result dictionary \"\"\"\n ele_list = ['home_correct', 'visitor_correct', 'winloss_correct', 'result_correct']\n\n # new_result_dic = {'home_correct': {}, 'visitor_correct': {}, 'winloss_correct': {}, 'result_correct': {}}\n new_result_dic = {}\n\n for ele in ele_list:\n new_result_dic[ele] = []\n cnt = 0\n # pylint: disable=W0640\n for result in sorted(g_result_dic_[ele], key=lambda i: (i[ele], i['result_correct'], i['winloss_correct'], i['g_cnt']), reverse=True):\n cnt += 1\n new_result_dic[ele].append(result)\n if cnt >= limit:\n break\n\n # dump dictionary to store data\n # print('########## dump-disabled ##############')\n json_store(filename, new_result_dic)\n\n return new_result_dic\n\ndef _inputparameters_get(logger, parameter_file, start_val, max_val, step):\n \"\"\"\" load config from file \"\"\"\n logger.debug('inputparameters_get()')\n\n # quantifier dictionary\n input_parameters_ = {\n 'shots_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'handness_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'handness_shots_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'rb_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'rb_shots_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'br_pctg': {'start': start_val, 'max': max_val, 'step': step},\n 'br_shots_pctg': {'start': start_val, 'max': max_val, 'step': step}\n }\n\n # process parameters file if exists\n if os.path.exists(parameter_file):\n # load start values from file\n parameter_dic = json_load(parameter_file)\n\n # overwrite start_value we have it in parameter file\n for parameter in input_parameters_:\n if parameter in parameter_dic:\n input_parameters_[parameter]['start'] = parameter_dic[parameter]\n\n return input_parameters_\n\ndef shotstats_get(logger, match_list):\n \"\"\"\" gets shots per match \"\"\"\n logger.debug('stats_get()')\n\n g_shotstat_dict = {}\n g_goal_dic_ = {}\n\n for match_id_ in match_list:\n\n # get match information\n match_dic = match_info_get(LOGGER, match_id_, None, ['date', 'home_team_id', 'home_team__shortcut', 'visitor_team_id', 'visitor_team__shortcut', 'result'])\n\n # get list of shots\n vlist = ['shot_id', 'match_id', 'match_shot_resutl_id', 'player_id', 'player__first_name', 'player__last_name', 'player__jersey', 'player__stick', 'team_id', 'coordinate_x', 'coordinate_y', 'match__home_team_id', 'match__visitor_team_id', 'timestamp', 'real_date']\n shot_list = shot_list_get(LOGGER, 'match_id', match_id_, vlist)\n shift_list = shift_get(logger, 'match_id', match_id, ['shift'])\n periodevent_list = periodevent_get(logger, 'match_id', match_id, ['period_event'])\n\n # get corsi statistics\n (shots_for_5v5, shots_against_5v5, shots_ongoal_for_5v5, shots_ongoal_against_5v5, shot_list_5v5) = gameshots5v5_get(logger, match_id_, match_dic, 'home', shot_list, shift_list, periodevent_list)\n\n # 5v5 goals from periodevents\n goals5v5_dic = goals5v5_get(logger, match_id_, match_dic)\n\n # convert shots and goals into structure we can process later on\n # we also need the XGMODEL_DIC to check if we have the shotcoordinates in our structure\n (shotstat_dic, goal_dic) = shotlist_process(LOGGER, shot_list_5v5, XGMODEL_DIC, REBOUND_INTERVAL, BREAK_INTERVAL)\n g_shotstat_dict[match_id_] = shotstat_dic\n\n # g_goal_dic_[match_id_] = {'home': len(goal_dic['home']), 'visitor': len(goal_dic['visitor'])}\n # we need to compare against 5v5 goals\n g_goal_dic_[match_id_] = {'home': goals5v5_dic['home'], 'visitor': goals5v5_dic['visitor']}\n\n return(g_shotstat_dict, g_goal_dic_)\n\nif __name__ == \"__main__\":\n\n # DEBUG mode on/off\n DEBUG = False\n\n MODEL_FILE = 'model_data.json'\n PARAMETER_FILE = 'model_parameters.json'\n RESULT_FILE = 'result.json'\n\n # initialize logger\n LOGGER = logger_setup(DEBUG)\n\n # load rebound and break interval from config file\n (REBOUND_INTERVAL, BREAK_INTERVAL) = _config_load(LOGGER)\n\n # get model from database\n # XGMODEL_DIC = xgmodel_get(LOGGER)\n XGMODEL_DIC = json_load(MODEL_FILE)\n\n # get list of matches to investigate\n MATCH_LIST = match_list_get(LOGGER, 'season_id', 1, ['match_id'])\n # MATCH_LIST = [1820]\n\n START_VAL = 1.0\n MAX_VAL = 3.5\n STEP = 0.5\n\n # START_VAL = 1.0\n # MAX_VAL = 1.1\n # STEP = 0.1\n\n # quantifier dictionary\n input_parameters = _inputparameters_get(LOGGER, PARAMETER_FILE, START_VAL, MAX_VAL, STEP)\n\n from pprint import pprint\n pprint(input_parameters)\n\n # get global shot and goal stats\n (g_shotstat_dic, g_goal_dic) = shotstats_get(LOGGER, MATCH_LIST)\n\n # load result dic from file to continue on a certain point\n if os.path.exists(RESULT_FILE):\n g_result_dic = json_load(RESULT_FILE)\n else:\n g_result_dic = {'home_correct': [], 'visitor_correct': [], 'winloss_correct': [], 'result_correct': []}\n\n # set startpoint for measurement\n uts_start = uts_now()\n\n # initialze counter\n G_CNT = 0\n for shots_pctg in np.arange(START_VAL, input_parameters['shots_pctg']['max'] + input_parameters['shots_pctg']['step'], input_parameters['shots_pctg']['step']):\n if shots_pctg < input_parameters['shots_pctg']['start']:\n continue\n else:\n input_parameters['shots_pctg']['start'] = START_VAL\n\n for handness_pctg in np.arange(START_VAL, input_parameters['handness_pctg']['max'] + input_parameters['handness_pctg']['step'], input_parameters['handness_pctg']['step']):\n if handness_pctg < input_parameters['handness_pctg']['start']:\n continue\n else:\n input_parameters['handness_pctg']['start'] = START_VAL\n\n for handness_shots_pctg in np.arange(START_VAL, input_parameters['handness_shots_pctg']['max'] + input_parameters['handness_shots_pctg']['step'], input_parameters['handness_shots_pctg']['step']):\n if handness_shots_pctg < input_parameters['handness_shots_pctg']['start']:\n continue\n else:\n input_parameters['handness_shots_pctg']['start'] = START_VAL\n\n for rb_pctg in np.arange(START_VAL, input_parameters['rb_pctg']['max'] + input_parameters['rb_pctg']['step'], input_parameters['rb_pctg']['step']):\n if rb_pctg < input_parameters['rb_pctg']['start']:\n continue\n else:\n input_parameters['rb_pctg']['start'] = START_VAL\n\n for rb_shots_pctg in np.arange(START_VAL, input_parameters['rb_shots_pctg']['max'] + input_parameters['rb_shots_pctg']['step'], input_parameters['rb_shots_pctg']['step']):\n if rb_shots_pctg < input_parameters['rb_shots_pctg']['start']:\n continue\n else:\n input_parameters['rb_shots_pctg']['start'] = START_VAL\n\n for br_pctg in np.arange(START_VAL, input_parameters['br_pctg']['max'] + input_parameters['br_pctg']['step'], input_parameters['br_pctg']['step']):\n if br_pctg < input_parameters['br_pctg']['start']:\n continue\n else:\n input_parameters['br_pctg']['start'] = START_VAL\n\n for br_shots_pctg in np.arange(START_VAL, input_parameters['br_shots_pctg']['max'] + input_parameters['br_shots_pctg']['step'], input_parameters['br_shots_pctg']['step']):\n if br_shots_pctg < input_parameters['br_shots_pctg']['start']:\n continue\n else:\n input_parameters['br_shots_pctg']['start'] = START_VAL\n\n G_CNT += 1\n # print(round(shots_pctg, 1), round(handness_pctg, 1), round(handness_shots_pctg, 1), round(rb_pctg, 1), round(rb_shots_pctg, 1), round(br_pctg, 1), round(br_shots_pctg, 1))\n quantifier_dic = {\n 'home': {\n 'shots_pctg': round(shots_pctg, 1),\n 'handness_pctg': round(handness_pctg, 1),\n 'handness_shots_pctg': round(handness_shots_pctg, 1),\n 'rb_pctg': round(rb_pctg, 1),\n 'rb_shots_pctg': round(rb_shots_pctg, 1),\n 'br_pctg': round(br_pctg, 1),\n 'br_shots_pctg': round(br_shots_pctg, 1),\n },\n 'visitor': {\n 'shots_pctg': round(shots_pctg, 1),\n 'handness_pctg': round(handness_pctg, 1),\n 'handness_shots_pctg': round(handness_shots_pctg, 1),\n 'rb_pctg': round(rb_pctg, 1),\n 'rb_shots_pctg': round(rb_shots_pctg, 1),\n 'br_pctg': round(br_pctg, 1),\n 'br_shots_pctg': round(br_shots_pctg, 1),\n }\n }\n\n MATCH_CNT = 0\n HOME_CORRECT = 0\n VISITOR_CORRECT = 0\n WINLOSS_CORRECT = 0\n RESULT_CORRECT = 0\n\n for match_id in g_shotstat_dic:\n MATCH_CNT += 1\n\n playerxgf_dic = xgf_calculate(LOGGER, g_shotstat_dic[match_id], quantifier_dic)\n xgf_dic = xgscore_get(LOGGER, playerxgf_dic)\n # convert xg to make them comparable with 5v5 goals\n xgf_dic['home'] = int(round(xgf_dic['home'], 0))\n xgf_dic['visitor'] = int(round(xgf_dic['visitor'], 0))\n # print(xgf_dic['home'], xgf_dic['visitor'], g_goal_dic[match_id]['home'], g_goal_dic[match_id]['visitor'])\n\n # check homescore\n if xgf_dic['home'] == g_goal_dic[match_id]['home']:\n HOME_CORRECT += 1\n # check visitor score\n if xgf_dic['visitor'] == g_goal_dic[match_id]['visitor']:\n VISITOR_CORRECT += 1\n\n # check full score\n if xgf_dic['home'] == g_goal_dic[match_id]['home'] and xgf_dic['visitor'] == g_goal_dic[match_id]['visitor']:\n RESULT_CORRECT += 1\n\n # check for winloss\n if xgf_dic['home'] < xgf_dic['visitor'] and g_goal_dic[match_id]['home'] < g_goal_dic[match_id]['visitor']:\n WINLOSS_CORRECT += 1\n elif xgf_dic['home'] > xgf_dic['visitor'] and g_goal_dic[match_id]['home'] > g_goal_dic[match_id]['visitor']:\n WINLOSS_CORRECT += 1\n elif xgf_dic['home'] == xgf_dic['visitor'] and g_goal_dic[match_id]['home'] == g_goal_dic[match_id]['visitor']:\n WINLOSS_CORRECT += 1\n\n tmp_dic = {'g_cnt': G_CNT, 'match_cnt': MATCH_CNT, 'home_correct': HOME_CORRECT, 'visitor_correct': VISITOR_CORRECT, 'winloss_correct': WINLOSS_CORRECT, 'result_correct': RESULT_CORRECT, 'quantifier_dic': quantifier_dic}\n # store results in different trees\n g_result_dic['home_correct'].append(tmp_dic)\n g_result_dic['visitor_correct'].append(tmp_dic)\n g_result_dic['winloss_correct'].append(tmp_dic)\n g_result_dic['result_correct'].append(tmp_dic)\n\n print('{0}/{4}: dump shots_pctg: {1}, handness_pctg: {2}, handness_shots_pctg: {3}'.format(uts_now()-uts_start, round(shots_pctg, 1), round(handness_pctg, 1), round(handness_shots_pctg, 1), G_CNT))\n # dump here\n g_result_dic = dump_it(g_result_dic, RESULT_FILE)\n # store quantifier dic\n json_store(PARAMETER_FILE, quantifier_dic['home'])\n\n # print status\n print('g_cnt', G_CNT, 'match_cnt', MATCH_CNT, 'home_correct', HOME_CORRECT, 'visitor_correct', VISITOR_CORRECT, 'winloss_correct', WINLOSS_CORRECT, 'result_correct', RESULT_CORRECT)\n # dump with uts at the end\n uts = uts_now()\n g_result_dic = dump_it(g_result_dic, 'result_handness_{0}.json'.format(uts))\n","repo_name":"grindsa/hockey_graphs","sub_path":"rest/tools/xg_weightener.py","file_name":"xg_weightener.py","file_ext":"py","file_size_in_byte":15044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29933602502","text":"import numpy as np\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.datasets import fetch_openml\n\n\n# Network fcn class.\nclass NeuralNetwork:\n # config data.\n TOTAL_EPOCH = 15\n BATCH_SIZE = 1000\n LEARNING_RATE = 0.001\n SEED = 42\n TRAIN_DATASET_SIZE = 60000\n TEST_DATASET_SIZE = 10000\n\n def __init__(self, learning_rate):\n # for batch count.\n self._i = 0\n\n # weight initialize\n self.W1 = np.random.randn(784, 100)\n self.W2 = np.random.randn(100, 10)\n self.learningRate = learning_rate\n\n # load data using scikit-learn.\n self.X_train, self.X_test, self.y_train, self.y_test = self.load_data(one_hot=True)\n\n def sigmoid(self, x):\n return 1.0 / (1.0 + np.exp(-x))\n\n def back_sigmoid(self, x):\n return x * (1. - x)\n\n def softmax(self, x):\n if x.ndim == 1:\n x = x.reshape([1, x.size])\n modifiedX = x - np.max(x, 1).reshape([x.shape[0], 1])\n sigmoid = np.exp(modifiedX)\n return sigmoid / np.sum(sigmoid, axis=1).reshape([sigmoid.shape[0], 1])\n\n def getCrossEntropy(self, predictY, labelY):\n return np.mean(-np.sum(labelY * np.log(self.softmax(predictY)), axis=1))\n\n def feedForward(self, x):\n y1 = np.dot(x, self.W1)\n sigmoidY1 = self.sigmoid(y1)\n\n y2 = np.dot(sigmoidY1, self.W2)\n softmaxY2 = self.softmax(y2)\n\n return sigmoidY1, softmaxY2\n\n def backpropagation(self, x, labelY, predictY1, predictY2):\n dW2 = predictY1.T.dot((predictY2 - labelY) / NeuralNetwork.BATCH_SIZE)\n dW1 = x.T.dot(np.matmul(predictY2 - labelY, self.W2.T) * self.back_sigmoid(predictY1))\n\n return dW1, dW2\n\n def update_weight(self, dW1, dW2):\n self.W1 -= self.learningRate * dW1\n self.W2 -= self.learningRate * dW2\n\n def load_data(self, one_hot=True):\n\n print(\"loading data...\")\n # only use scikit-learn when load MNIST data for convenience\n mnist = fetch_openml('mnist_784')\n X, y = mnist[\"data\"], mnist[\"target\"]\n\n if one_hot:\n one_hot = OneHotEncoder()\n y = one_hot.fit_transform(y.reshape(-1, 1))\n y = y.toarray()\n print(\"y are one-hot encoded..\")\n\n return X[:NeuralNetwork.TRAIN_DATASET_SIZE], X[NeuralNetwork.TRAIN_DATASET_SIZE:], y[\n :NeuralNetwork.TRAIN_DATASET_SIZE], y[\n NeuralNetwork.TRAIN_DATASET_SIZE:]\n\n # print next batch.\n def next_batch(self, batch_size):\n x, y = self.X_train[self._i:self._i + batch_size], self.y_train[self._i: self._i + batch_size]\n self._i = (self._i + batch_size) % len(self.X_train)\n return x, y\n\n def shake_data(self):\n rnd_index = np.random.permutation(len(self.X_train))\n self.X_train = self.X_train[rnd_index]\n self.y_train = self.y_train[rnd_index]\n\n\n# fix the random value.\nnp.random.seed(NeuralNetwork.SEED)\n\n# define network fcn.\nnetwork_model = NeuralNetwork(learning_rate=NeuralNetwork.LEARNING_RATE)\n\n# using mini-batch\nfor i in range(NeuralNetwork.TOTAL_EPOCH):\n print(\"============== EPOCH {} START ==============\".format(i + 1))\n for j in range(NeuralNetwork.TRAIN_DATASET_SIZE // NeuralNetwork.BATCH_SIZE):\n print(\"-------------- batch {} training...\".format(j))\n batch_x, batch_y = network_model.next_batch(NeuralNetwork.BATCH_SIZE)\n\n # TODO: feed-forward term.\n y1, y2 = network_model.feedForward(batch_x)\n\n # TODO: back-propagation term.\n dW1, dW2 = network_model.backpropagation(batch_x, batch_y, y1, y2)\n network_model.update_weight(dW1, dW2)\n print(\"============== EPOCH {} END ================\".format(i + 1))\n\n # shake data.\n network_model.shake_data()\n\n y1, y2 = network_model.feedForward(network_model.X_test)\n correct_prediction = np.equal(np.argmax(y2, axis=1), np.argmax(network_model.y_test, axis=1))\n accuracy = np.mean(correct_prediction)\n print(accuracy)\n","repo_name":"hololee/Implement-neural-network-from-scratch","sub_path":"test/question_2layer_sc.py","file_name":"question_2layer_sc.py","file_ext":"py","file_size_in_byte":4169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12612337263","text":"\"\"\"Tests covering utilities for integrating with the catalog service.\"\"\"\n# pylint: disable=missing-docstring\n\n\nfrom collections import defaultdict\nfrom datetime import timedelta\nfrom unittest import mock\n\nfrom django.contrib.auth import get_user_model\nfrom django.core.cache import cache\nfrom django.test import TestCase, override_settings\nfrom django.test.client import RequestFactory\nfrom django.utils.timezone import now\nfrom opaque_keys.edx.keys import CourseKey\n\nfrom common.djangoapps.course_modes.helpers import CourseMode\nfrom common.djangoapps.course_modes.tests.factories import CourseModeFactory\nfrom common.djangoapps.entitlements.tests.factories import CourseEntitlementFactory\nfrom openedx.core.constants import COURSE_UNPUBLISHED\nfrom openedx.core.djangoapps.catalog.cache import (\n CATALOG_COURSE_PROGRAMS_CACHE_KEY_TPL,\n COURSE_PROGRAMS_CACHE_KEY_TPL,\n PATHWAY_CACHE_KEY_TPL,\n PROGRAM_CACHE_KEY_TPL,\n PROGRAMS_BY_TYPE_CACHE_KEY_TPL,\n PROGRAMS_BY_TYPE_SLUG_CACHE_KEY_TPL,\n SITE_PATHWAY_IDS_CACHE_KEY_TPL,\n SITE_PROGRAM_UUIDS_CACHE_KEY_TPL\n)\nfrom openedx.core.djangoapps.catalog.models import CatalogIntegration\nfrom openedx.core.djangoapps.catalog.tests.factories import (\n CourseFactory,\n CourseRunFactory,\n PathwayFactory,\n ProgramFactory,\n ProgramTypeFactory,\n ProgramTypeAttrsFactory\n)\nfrom openedx.core.djangoapps.catalog.tests.mixins import CatalogIntegrationMixin\nfrom openedx.core.djangoapps.catalog.utils import (\n child_programs,\n course_run_keys_for_program,\n is_course_run_in_program,\n get_course_run_details,\n get_course_runs,\n get_course_runs_for_course,\n get_currency_data,\n get_localized_price_text,\n get_owners_for_course,\n get_pathways,\n get_program_types,\n get_programs,\n get_programs_by_type,\n get_programs_by_type_slug,\n get_visible_sessions_for_entitlement,\n normalize_program_type,\n)\nfrom openedx.core.djangoapps.content.course_overviews.tests.factories import CourseOverviewFactory\nfrom openedx.core.djangoapps.site_configuration.tests.factories import SiteFactory\nfrom openedx.core.djangolib.testing.utils import CacheIsolationTestCase, skip_unless_lms\nfrom common.djangoapps.student.tests.factories import CourseEnrollmentFactory, UserFactory\nfrom openedx.core.djangoapps.site_configuration.tests.test_util import with_site_configuration_context\n\nUTILS_MODULE = 'openedx.core.djangoapps.catalog.utils'\nUser = get_user_model() # pylint: disable=invalid-name\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.logger.info')\n@mock.patch(UTILS_MODULE + '.logger.warning')\nclass TestGetPrograms(CacheIsolationTestCase):\n ENABLED_CACHES = ['default']\n\n def setUp(self):\n super().setUp()\n self.site = SiteFactory()\n\n def test_get_many(self, mock_warning, mock_info):\n programs = ProgramFactory.create_batch(3)\n\n # Cache details for 2 of 3 programs.\n partial_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in programs[:2]\n }\n cache.set_many(partial_programs, None)\n\n # When called before UUIDs are cached, the function should return an\n # empty list and log a warning.\n with with_site_configuration_context(domain=self.site.name, configuration={'COURSE_CATALOG_API_URL': 'foo'}):\n assert get_programs(site=self.site) == []\n mock_warning.assert_called_once_with(\n f'Failed to get program UUIDs from the cache for site {self.site.domain}.'\n )\n mock_warning.reset_mock()\n\n # Cache UUIDs for all 3 programs.\n cache.set(\n SITE_PROGRAM_UUIDS_CACHE_KEY_TPL.format(domain=self.site.domain),\n [program['uuid'] for program in programs],\n None\n )\n\n actual_programs = get_programs(site=self.site)\n\n # The 2 cached programs should be returned while info and warning\n # messages should be logged for the missing one.\n assert {program['uuid'] for program in actual_programs} == \\\n {program['uuid'] for program in partial_programs.values()}\n mock_info.assert_called_with('Failed to get details for 1 programs. Retrying.')\n mock_warning.assert_called_with(\n 'Failed to get details for program {uuid} from the cache.'.format(uuid=programs[2]['uuid'])\n )\n mock_warning.reset_mock()\n\n # We can't use a set comparison here because these values are dictionaries\n # and aren't hashable. We've already verified that all programs came out\n # of the cache above, so all we need to do here is verify the accuracy of\n # the data itself.\n for program in actual_programs:\n key = PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid'])\n assert program == partial_programs[key]\n\n # Cache details for all 3 programs.\n all_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in programs\n }\n cache.set_many(all_programs, None)\n\n actual_programs = get_programs(site=self.site)\n\n # All 3 programs should be returned.\n assert {program['uuid'] for program in actual_programs} ==\\\n {program['uuid'] for program in all_programs.values()}\n assert not mock_warning.called\n\n for program in actual_programs:\n key = PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid'])\n assert program == all_programs[key]\n\n @mock.patch(UTILS_MODULE + '.cache')\n def test_get_many_with_missing(self, mock_cache, mock_warning, mock_info):\n programs = ProgramFactory.create_batch(3)\n\n all_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in programs\n }\n\n partial_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in programs[:2]\n }\n\n def fake_get_many(keys):\n if len(keys) == 1:\n return {PROGRAM_CACHE_KEY_TPL.format(uuid=programs[-1]['uuid']): programs[-1]}\n else:\n return partial_programs\n\n mock_cache.get.return_value = [program['uuid'] for program in programs]\n mock_cache.get_many.side_effect = fake_get_many\n\n with with_site_configuration_context(domain=self.site.name, configuration={'COURSE_CATALOG_API_URL': 'foo'}):\n actual_programs = get_programs(site=self.site)\n\n # All 3 cached programs should be returned. An info message should be\n # logged about the one that was initially missing, but the code should\n # be able to stitch together all the details.\n assert {program['uuid'] for program in actual_programs} ==\\\n {program['uuid'] for program in all_programs.values()}\n assert not mock_warning.called\n mock_info.assert_called_with('Failed to get details for 1 programs. Retrying.')\n\n for program in actual_programs:\n key = PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid'])\n assert program == all_programs[key]\n\n def test_get_one(self, mock_warning, _mock_info):\n expected_program = ProgramFactory()\n expected_uuid = expected_program['uuid']\n\n assert get_programs(uuid=expected_uuid) is None\n mock_warning.assert_called_once_with(\n f'Failed to get details for program {expected_uuid} from the cache.'\n )\n mock_warning.reset_mock()\n\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=expected_uuid),\n expected_program,\n None\n )\n\n actual_program = get_programs(uuid=expected_uuid)\n assert actual_program == expected_program\n assert not mock_warning.called\n\n def test_get_from_course(self, mock_warning, _mock_info):\n expected_program = ProgramFactory()\n expected_course = expected_program['courses'][0]['course_runs'][0]['key']\n\n assert get_programs(course=expected_course) == []\n\n cache.set(\n COURSE_PROGRAMS_CACHE_KEY_TPL.format(course_run_id=expected_course),\n [expected_program['uuid']],\n None\n )\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=expected_program['uuid']),\n expected_program,\n None\n )\n\n actual_program = get_programs(course=expected_course)\n assert actual_program == [expected_program]\n assert not mock_warning.called\n\n def test_get_via_uuids(self, mock_warning, _mock_info):\n first_program = ProgramFactory()\n second_program = ProgramFactory()\n\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=first_program['uuid']),\n first_program,\n None\n )\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=second_program['uuid']),\n second_program,\n None\n )\n\n results = get_programs(uuids=[first_program['uuid'], second_program['uuid']])\n\n assert first_program in results\n assert second_program in results\n assert not mock_warning.called\n\n def test_get_from_catalog_course(self, mock_warning, _mock_info):\n expected_program = ProgramFactory()\n expected_catalog_course = expected_program['courses'][0]\n\n assert get_programs(catalog_course_uuid=expected_catalog_course['uuid']) == []\n\n cache.set(\n CATALOG_COURSE_PROGRAMS_CACHE_KEY_TPL.format(course_uuid=expected_catalog_course['uuid']),\n [expected_program['uuid']],\n None\n )\n cache.set(\n PROGRAM_CACHE_KEY_TPL.format(uuid=expected_program['uuid']),\n expected_program,\n None\n )\n\n actual_program = get_programs(catalog_course_uuid=expected_catalog_course['uuid'])\n\n assert actual_program == [expected_program]\n assert not mock_warning.called\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.logger.info')\n@mock.patch(UTILS_MODULE + '.logger.warning')\nclass TestGetPathways(CacheIsolationTestCase):\n ENABLED_CACHES = ['default']\n\n def setUp(self):\n super().setUp()\n self.site = SiteFactory()\n\n def test_get_many(self, mock_warning, mock_info):\n pathways = PathwayFactory.create_batch(3)\n\n # Cache details for 2 of 3 programs.\n partial_pathways = {\n PATHWAY_CACHE_KEY_TPL.format(id=pathway['id']): pathway for pathway in pathways[:2]\n }\n cache.set_many(partial_pathways, None)\n\n # When called before pathways are cached, the function should return an\n # empty list and log a warning.\n assert get_pathways(self.site) == []\n mock_warning.assert_called_once_with('Failed to get credit pathway ids from the cache.')\n mock_warning.reset_mock()\n\n # Cache all 3 pathways\n cache.set(\n SITE_PATHWAY_IDS_CACHE_KEY_TPL.format(domain=self.site.domain),\n [pathway['id'] for pathway in pathways],\n None\n )\n\n actual_pathways = get_pathways(self.site)\n\n # The 2 cached pathways should be returned while info and warning\n # messages should be logged for the missing one.\n assert {pathway['id'] for pathway in actual_pathways} ==\\\n {pathway['id'] for pathway in partial_pathways.values()}\n mock_info.assert_called_with('Failed to get details for 1 pathways. Retrying.')\n mock_warning.assert_called_with(\n 'Failed to get details for credit pathway {id} from the cache.'.format(id=pathways[2]['id'])\n )\n mock_warning.reset_mock()\n\n # We can't use a set comparison here because these values are dictionaries\n # and aren't hashable. We've already verified that all pathways came out\n # of the cache above, so all we need to do here is verify the accuracy of\n # the data itself.\n for pathway in actual_pathways:\n key = PATHWAY_CACHE_KEY_TPL.format(id=pathway['id'])\n assert pathway == partial_pathways[key]\n\n # Cache details for all 3 pathways.\n all_pathways = {\n PATHWAY_CACHE_KEY_TPL.format(id=pathway['id']): pathway for pathway in pathways\n }\n cache.set_many(all_pathways, None)\n\n actual_pathways = get_pathways(self.site)\n\n # All 3 pathways should be returned.\n assert {pathway['id'] for pathway in actual_pathways} ==\\\n {pathway['id'] for pathway in all_pathways.values()}\n assert not mock_warning.called\n\n for pathway in actual_pathways:\n key = PATHWAY_CACHE_KEY_TPL.format(id=pathway['id'])\n assert pathway == all_pathways[key]\n\n @mock.patch(UTILS_MODULE + '.cache')\n def test_get_many_with_missing(self, mock_cache, mock_warning, mock_info):\n pathways = PathwayFactory.create_batch(3)\n\n all_pathways = {\n PATHWAY_CACHE_KEY_TPL.format(id=pathway['id']): pathway for pathway in pathways\n }\n\n partial_pathways = {\n PATHWAY_CACHE_KEY_TPL.format(id=pathway['id']): pathway for pathway in pathways[:2]\n }\n\n def fake_get_many(keys):\n if len(keys) == 1:\n return {PATHWAY_CACHE_KEY_TPL.format(id=pathways[-1]['id']): pathways[-1]}\n else:\n return partial_pathways\n\n mock_cache.get.return_value = [pathway['id'] for pathway in pathways]\n mock_cache.get_many.side_effect = fake_get_many\n\n actual_pathways = get_pathways(self.site)\n\n # All 3 cached pathways should be returned. An info message should be\n # logged about the one that was initially missing, but the code should\n # be able to stitch together all the details.\n assert {pathway['id'] for pathway in actual_pathways} ==\\\n {pathway['id'] for pathway in all_pathways.values()}\n assert not mock_warning.called\n mock_info.assert_called_with('Failed to get details for 1 pathways. Retrying.')\n\n for pathway in actual_pathways:\n key = PATHWAY_CACHE_KEY_TPL.format(id=pathway['id'])\n assert pathway == all_pathways[key]\n\n def test_get_one(self, mock_warning, _mock_info):\n expected_pathway = PathwayFactory()\n expected_id = expected_pathway['id']\n\n assert get_pathways(self.site, pathway_id=expected_id) is None\n mock_warning.assert_called_once_with(\n f'Failed to get details for credit pathway {expected_id} from the cache.'\n )\n mock_warning.reset_mock()\n\n cache.set(\n PATHWAY_CACHE_KEY_TPL.format(id=expected_id),\n expected_pathway,\n None\n )\n\n actual_pathway = get_pathways(self.site, pathway_id=expected_id)\n assert actual_pathway == expected_pathway\n assert not mock_warning.called\n\n\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetProgramTypes(CatalogIntegrationMixin, TestCase):\n \"\"\"Tests covering retrieval of program types from the catalog service.\"\"\"\n @override_settings(COURSE_CATALOG_API_URL='https://api.example.com/v1/')\n def test_get_program_types(self, mock_get_edx_api_data):\n \"\"\"Verify get_program_types returns the expected list of program types.\"\"\"\n program_types = ProgramTypeFactory.create_batch(3)\n mock_get_edx_api_data.return_value = program_types\n\n # Catalog integration is disabled.\n data = get_program_types()\n assert data == []\n\n catalog_integration = self.create_catalog_integration()\n UserFactory(username=catalog_integration.service_username)\n data = get_program_types()\n assert data == program_types\n\n program = program_types[0]\n data = get_program_types(name=program['name'])\n assert data == program\n\n\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetCurrency(CatalogIntegrationMixin, TestCase):\n \"\"\"Tests covering retrieval of currency data from the catalog service.\"\"\"\n @override_settings(COURSE_CATALOG_API_URL='https://api.example.com/v1/')\n def test_get_currency_data(self, mock_get_edx_api_data):\n \"\"\"Verify get_currency_data returns the currency data.\"\"\"\n currency_data = {\n \"code\": \"CAD\",\n \"rate\": 1.257237,\n \"symbol\": \"$\"\n }\n mock_get_edx_api_data.return_value = currency_data\n\n # Catalog integration is disabled.\n data = get_currency_data()\n assert data == []\n\n catalog_integration = self.create_catalog_integration()\n UserFactory(username=catalog_integration.service_username)\n data = get_currency_data()\n assert data == currency_data\n\n\n@mock.patch(UTILS_MODULE + '.get_currency_data')\nclass TestGetLocalizedPriceText(TestCase):\n \"\"\"\n Tests covering converting prices to a localized currency\n \"\"\"\n def test_localized_string(self, mock_get_currency_data):\n currency_data = {\n \"BEL\": {\"rate\": 0.835621, \"code\": \"EUR\", \"symbol\": \"\\u20ac\"},\n \"GBR\": {\"rate\": 0.737822, \"code\": \"GBP\", \"symbol\": \"\\u00a3\"},\n \"CAN\": {\"rate\": 2, \"code\": \"CAD\", \"symbol\": \"$\"},\n }\n mock_get_currency_data.return_value = currency_data\n\n request = RequestFactory().get('/dummy-url')\n request.session = {\n 'country_code': 'CA'\n }\n expected_result = '$20 CAD'\n assert get_localized_price_text(10, request) == expected_result\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetCourseRuns(CatalogIntegrationMixin, CacheIsolationTestCase):\n \"\"\"\n Tests covering retrieval of course runs from the catalog service.\n \"\"\"\n def setUp(self):\n super().setUp()\n\n self.catalog_integration = self.create_catalog_integration(cache_ttl=1)\n self.user = UserFactory(username=self.catalog_integration.service_username)\n\n def assert_contract(self, call_args):\n \"\"\"\n Verify that API data retrieval utility is used correctly.\n \"\"\"\n args, kwargs = call_args\n\n for arg in (self.catalog_integration, 'course_runs'):\n assert arg in args\n\n assert kwargs['base_api_url'] == self.catalog_integration.get_internal_api_url() # pylint: disable=protected-access, line-too-long\n\n querystring = {\n 'page_size': 20,\n 'exclude_utm': 1,\n }\n\n assert kwargs['querystring'] == querystring\n\n return args, kwargs\n\n def test_config_missing(self, mock_get_edx_api_data):\n \"\"\"\n Verify that no errors occur when catalog config is missing.\n \"\"\"\n CatalogIntegration.objects.all().delete()\n self.clear_caches()\n\n data = get_course_runs()\n assert not mock_get_edx_api_data.called\n assert data == []\n\n @mock.patch(UTILS_MODULE + '.logger.error')\n def test_service_user_missing(self, mock_log_error, mock_get_edx_api_data):\n \"\"\"\n Verify that no errors occur when the catalog service user is missing.\n \"\"\"\n catalog_integration = self.create_catalog_integration(service_username='nonexistent-user')\n\n data = get_course_runs()\n mock_log_error.any_call(\n 'Catalog service user with username [%s] does not exist. Course runs will not be retrieved.',\n catalog_integration.service_username,\n )\n assert not mock_get_edx_api_data.called\n assert data == []\n\n def test_get_course_runs(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of course runs.\n \"\"\"\n catalog_course_runs = CourseRunFactory.create_batch(10)\n mock_get_edx_api_data.return_value = catalog_course_runs\n\n data = get_course_runs()\n assert mock_get_edx_api_data.called\n self.assert_contract(mock_get_edx_api_data.call_args)\n assert data == catalog_course_runs\n\n def test_get_course_runs_by_course(self, mock_get_edx_api_data):\n \"\"\"\n Test retrievals of run from a Course.\n \"\"\"\n catalog_course_runs = CourseRunFactory.create_batch(10)\n catalog_course = CourseFactory(course_runs=catalog_course_runs)\n mock_get_edx_api_data.return_value = catalog_course\n\n data = get_course_runs_for_course(course_uuid=str(catalog_course['uuid']))\n assert mock_get_edx_api_data.called\n assert data == catalog_course_runs\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetCourseOwners(CatalogIntegrationMixin, TestCase):\n \"\"\"\n Tests covering retrieval of course runs from the catalog service.\n \"\"\"\n def setUp(self):\n super().setUp()\n\n self.catalog_integration = self.create_catalog_integration(cache_ttl=1)\n self.user = UserFactory(username=self.catalog_integration.service_username)\n\n def test_get_course_owners_by_course(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of course runs.\n \"\"\"\n catalog_course_runs = CourseRunFactory.create_batch(10)\n catalog_course = CourseFactory(course_runs=catalog_course_runs)\n mock_get_edx_api_data.return_value = catalog_course\n\n data = get_owners_for_course(course_uuid=str(catalog_course['uuid']))\n assert mock_get_edx_api_data.called\n assert data == catalog_course['owners']\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestSessionEntitlement(CatalogIntegrationMixin, TestCase):\n \"\"\"\n Test Covering data related Entitlements.\n \"\"\"\n def setUp(self):\n super().setUp()\n\n self.catalog_integration = self.create_catalog_integration(cache_ttl=1)\n self.user = UserFactory(username=self.catalog_integration.service_username)\n self.tomorrow = now() + timedelta(days=1)\n\n def test_get_visible_sessions_for_entitlement(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of visible session entitlements.\n \"\"\"\n catalog_course_run = CourseRunFactory.create()\n catalog_course = CourseFactory(course_runs=[catalog_course_run])\n mock_get_edx_api_data.return_value = catalog_course\n course_key = CourseKey.from_string(catalog_course_run.get('key'))\n course_overview = CourseOverviewFactory.create(id=course_key, start=self.tomorrow)\n CourseModeFactory.create(mode_slug=CourseMode.VERIFIED, min_price=100, course_id=course_overview.id)\n course_enrollment = CourseEnrollmentFactory(\n user=self.user, course=course_overview, mode=CourseMode.VERIFIED\n )\n entitlement = CourseEntitlementFactory(\n user=self.user, enrollment_course_run=course_enrollment, mode=CourseMode.VERIFIED\n )\n\n session_entitlements = get_visible_sessions_for_entitlement(entitlement)\n assert session_entitlements == [catalog_course_run]\n\n def test_get_visible_sessions_for_entitlement_expired_mode(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of visible session entitlements.\n \"\"\"\n catalog_course_run = CourseRunFactory.create()\n catalog_course = CourseFactory(course_runs=[catalog_course_run])\n mock_get_edx_api_data.return_value = catalog_course\n course_key = CourseKey.from_string(catalog_course_run.get('key'))\n course_overview = CourseOverviewFactory.create(id=course_key, start=self.tomorrow)\n CourseModeFactory.create(\n mode_slug=CourseMode.VERIFIED,\n min_price=100,\n course_id=course_overview.id,\n expiration_datetime=now() - timedelta(days=1)\n )\n course_enrollment = CourseEnrollmentFactory(\n user=self.user, course=course_overview, mode=CourseMode.VERIFIED\n )\n entitlement = CourseEntitlementFactory(\n user=self.user, enrollment_course_run=course_enrollment, mode=CourseMode.VERIFIED\n )\n\n session_entitlements = get_visible_sessions_for_entitlement(entitlement)\n assert session_entitlements == [catalog_course_run]\n\n def test_unpublished_sessions_for_entitlement_when_enrolled(self, mock_get_edx_api_data):\n \"\"\"\n Test unpublished course runs are part of visible session entitlements when the user\n is enrolled.\n \"\"\"\n catalog_course_run = CourseRunFactory.create(status=COURSE_UNPUBLISHED)\n catalog_course = CourseFactory(course_runs=[catalog_course_run])\n mock_get_edx_api_data.return_value = catalog_course\n course_key = CourseKey.from_string(catalog_course_run.get('key'))\n course_overview = CourseOverviewFactory.create(id=course_key, start=self.tomorrow)\n CourseModeFactory.create(\n mode_slug=CourseMode.VERIFIED,\n min_price=100,\n course_id=course_overview.id,\n expiration_datetime=now() - timedelta(days=1)\n )\n course_enrollment = CourseEnrollmentFactory(\n user=self.user, course=course_overview, mode=CourseMode.VERIFIED\n )\n entitlement = CourseEntitlementFactory(\n user=self.user, enrollment_course_run=course_enrollment, mode=CourseMode.VERIFIED\n )\n\n session_entitlements = get_visible_sessions_for_entitlement(entitlement)\n assert session_entitlements == [catalog_course_run]\n\n def test_unpublished_sessions_for_entitlement(self, mock_get_edx_api_data):\n \"\"\"\n Test unpublished course runs are not part of visible session entitlements when the user\n is not enrolled and upgrade deadline is passed.\n \"\"\"\n catalog_course_run = CourseRunFactory.create(status=COURSE_UNPUBLISHED)\n catalog_course = CourseFactory(course_runs=[catalog_course_run])\n mock_get_edx_api_data.return_value = catalog_course\n course_key = CourseKey.from_string(catalog_course_run.get('key'))\n course_overview = CourseOverviewFactory.create(id=course_key, start=self.tomorrow)\n CourseModeFactory.create(\n mode_slug=CourseMode.VERIFIED,\n min_price=100,\n course_id=course_overview.id,\n expiration_datetime=now() - timedelta(days=1)\n )\n entitlement = CourseEntitlementFactory(\n user=self.user, mode=CourseMode.VERIFIED\n )\n\n session_entitlements = get_visible_sessions_for_entitlement(entitlement)\n assert not session_entitlements\n\n\n@skip_unless_lms\n@mock.patch(UTILS_MODULE + '.get_api_data')\nclass TestGetCourseRunDetails(CatalogIntegrationMixin, TestCase):\n \"\"\"\n Tests covering retrieval of information about a specific course run from the catalog service.\n \"\"\"\n def setUp(self):\n super().setUp()\n self.catalog_integration = self.create_catalog_integration(cache_ttl=1)\n self.user = UserFactory(username=self.catalog_integration.service_username)\n\n def test_get_course_run_details(self, mock_get_edx_api_data):\n \"\"\"\n Test retrieval of details about a specific course run\n \"\"\"\n course_run = CourseRunFactory()\n course_run_details = {\n 'content_language': course_run['content_language'],\n 'weeks_to_complete': course_run['weeks_to_complete'],\n 'max_effort': course_run['max_effort']\n }\n mock_get_edx_api_data.return_value = course_run_details\n data = get_course_run_details(course_run['key'], ['content_language', 'weeks_to_complete', 'max_effort'])\n assert mock_get_edx_api_data.called\n assert data == course_run_details\n\n\nclass TestProgramCourseRunCrawling(TestCase):\n @classmethod\n def setUpClass(cls):\n super().setUpClass()\n cls.grandchild_1 = {\n 'title': 'grandchild 1',\n 'curricula': [{'is_active': True, 'courses': [], 'programs': []}],\n }\n cls.grandchild_2 = {\n 'title': 'grandchild 2',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [{\n 'course_runs': [\n {'key': 'course-run-4'},\n ],\n }],\n 'programs': [],\n },\n ],\n }\n cls.grandchild_3 = {\n 'title': 'grandchild 3',\n 'curricula': [{'is_active': False}],\n }\n cls.child_1 = {\n 'title': 'child 1',\n 'curricula': [{'is_active': True, 'courses': [], 'programs': [cls.grandchild_1]}],\n }\n cls.child_2 = {\n 'title': 'child 2',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [{\n 'course_runs': [\n {'key': 'course-run-3'},\n ],\n }],\n 'programs': [cls.grandchild_2, cls.grandchild_3],\n },\n ],\n }\n cls.complex_program = {\n 'title': 'complex program',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [{\n 'course_runs': [\n {'key': 'course-run-2'},\n ],\n }],\n 'programs': [cls.child_1, cls.child_2],\n },\n ],\n }\n cls.simple_program = {\n 'title': 'simple program',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [{\n 'course_runs': [\n {'key': 'course-run-1'},\n ],\n }],\n 'programs': [cls.grandchild_1]\n },\n ],\n }\n cls.empty_program = {\n 'title': 'notice that I have a curriculum, but no programs inside it',\n 'curricula': [\n {\n 'is_active': True,\n 'courses': [],\n 'programs': [],\n },\n ],\n }\n\n def test_child_programs_no_curriculum(self):\n program = {\n 'title': 'notice that I do not have a curriculum',\n }\n assert not child_programs(program)\n\n def test_child_programs_no_children(self):\n assert not child_programs(self.empty_program)\n\n def test_child_programs_one_child(self):\n assert [self.grandchild_1] == child_programs(self.simple_program)\n\n def test_child_programs_many_children(self):\n expected_children = [\n self.child_1,\n self.grandchild_1,\n self.child_2,\n self.grandchild_2,\n self.grandchild_3,\n ]\n assert expected_children == child_programs(self.complex_program)\n\n def test_course_run_keys_for_program_no_courses(self):\n assert set() == course_run_keys_for_program(self.empty_program)\n\n def test_course_run_keys_for_program_one_course(self):\n assert {'course-run-1'} == course_run_keys_for_program(self.simple_program)\n\n def test_course_run_keys_for_program_many_courses(self):\n expected_course_runs = {\n 'course-run-2',\n 'course-run-3',\n 'course-run-4',\n }\n assert expected_course_runs == course_run_keys_for_program(self.complex_program)\n\n def test_is_course_run_in_program(self):\n assert is_course_run_in_program('course-run-4', self.complex_program)\n assert not is_course_run_in_program('course-run-5', self.complex_program)\n assert not is_course_run_in_program('course-run-4', self.simple_program)\n\n\n@skip_unless_lms\nclass TestGetProgramsByType(CacheIsolationTestCase):\n \"\"\" Test for the ``get_programs_by_type()`` and the ``get_programs_by_type_slug()`` functions. \"\"\"\n ENABLED_CACHES = ['default']\n\n @classmethod\n def setUpClass(cls):\n \"\"\" Sets up program data. \"\"\"\n super().setUpClass()\n cls.site = SiteFactory()\n cls.other_site = SiteFactory()\n cls.masters_program_1 = ProgramFactory.create(\n type='Masters',\n type_attrs=ProgramTypeAttrsFactory.create(slug=\"masters\")\n )\n cls.masters_program_2 = ProgramFactory.create(\n type='Masters',\n type_attrs=ProgramTypeAttrsFactory.create(slug=\"masters\")\n )\n cls.masters_program_other_site = ProgramFactory.create(\n type='Masters',\n type_attrs=ProgramTypeAttrsFactory.create(slug=\"masters\")\n )\n cls.bachelors_program = ProgramFactory.create(\n type='Bachelors',\n type_attrs=ProgramTypeAttrsFactory.create(slug=\"bachelors\")\n )\n cls.no_type_program = ProgramFactory.create(\n type=None,\n type_attrs=None\n )\n\n def setUp(self):\n \"\"\" Loads program data into the cache before each test function. \"\"\"\n super().setUp()\n self.init_cache()\n\n def init_cache(self):\n \"\"\" This function plays the role of the ``cache_programs`` management command. \"\"\"\n all_programs = [\n self.masters_program_1,\n self.masters_program_2,\n self.bachelors_program,\n self.no_type_program,\n self.masters_program_other_site\n ]\n cached_programs = {\n PROGRAM_CACHE_KEY_TPL.format(uuid=program['uuid']): program for program in all_programs\n }\n cache.set_many(cached_programs, None)\n\n programs_by_type = defaultdict(list)\n programs_by_type_slug = defaultdict(list)\n for program in all_programs:\n program_type = normalize_program_type(program.get('type'))\n program_type_slug = (program.get('type_attrs') or {}).get('slug')\n site_id = self.site.id\n\n if program == self.masters_program_other_site:\n site_id = self.other_site.id\n\n program_type_cache_key = PROGRAMS_BY_TYPE_CACHE_KEY_TPL.format(\n site_id=site_id,\n program_type=program_type\n )\n program_type_slug_cache_key = PROGRAMS_BY_TYPE_SLUG_CACHE_KEY_TPL.format(\n site_id=site_id,\n program_slug=program_type_slug\n )\n programs_by_type[program_type_cache_key].append(program['uuid'])\n programs_by_type_slug[program_type_slug_cache_key].append(program['uuid'])\n\n cache.set_many(programs_by_type, None)\n cache.set_many(programs_by_type_slug, None)\n\n def test_get_masters_programs(self):\n expected_programs = [self.masters_program_1, self.masters_program_2]\n self.assertCountEqual(expected_programs, get_programs_by_type(self.site, 'masters'))\n self.assertCountEqual(expected_programs, get_programs_by_type_slug(self.site, 'masters'))\n\n def test_get_bachelors_programs(self):\n expected_programs = [self.bachelors_program]\n assert expected_programs == get_programs_by_type(self.site, 'bachelors')\n assert expected_programs == get_programs_by_type_slug(self.site, 'bachelors')\n\n def test_get_no_such_type_programs(self):\n expected_programs = []\n assert expected_programs == get_programs_by_type(self.site, 'doctorate')\n assert expected_programs == get_programs_by_type_slug(self.site, 'doctorate')\n\n def test_get_masters_programs_other_site(self):\n expected_programs = [self.masters_program_other_site]\n assert expected_programs == get_programs_by_type(self.other_site, 'masters')\n assert expected_programs == get_programs_by_type_slug(self.other_site, 'masters')\n\n def test_get_programs_null_type(self):\n expected_programs = [self.no_type_program]\n assert expected_programs == get_programs_by_type(self.site, None)\n assert expected_programs == get_programs_by_type_slug(self.site, None)\n\n def test_get_programs_false_type(self):\n expected_programs = []\n assert expected_programs == get_programs_by_type(self.site, False)\n assert expected_programs == get_programs_by_type_slug(self.site, False)\n\n def test_normalize_program_type(self):\n assert 'none' == normalize_program_type(None)\n assert 'false' == normalize_program_type(False)\n assert 'true' == normalize_program_type(True)\n assert '' == normalize_program_type('')\n assert 'masters' == normalize_program_type('Masters')\n assert 'masters' == normalize_program_type('masters')\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/catalog/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":36458,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"15747812182","text":"from collections import deque\n\n\ndef dfs(graph, v, visited):\n #현재노드 방문처리\n visited[v] = 1\n print(v, end=' ')\n for i in graph[v]:\n if visited[i] != 1:\n dfs(graph, i, visited)\n\n\ndef bfs(graph, start, visited):\n #시작 방문노드 큐 추가 및 방문처리\n queue = deque([start])\n visited[start] = 1\n while queue:\n v = queue.popleft()\n print(v, end=' ')\n for i in graph[v]:\n if visited[i] != 1:\n queue.append(i)\n visited[i] = 1\n\n\nn, m, v = map(int, input().split())\ngraph = [[] for _ in range(n + 1)]\nfor i in range(m):\n a, b = map(int, input().split())\n graph[a].append(b)\n graph[b].append(a)\n\n#번호가 작은것 번저 방문하기 위한 정렬\nfor i in range(n+1):\n graph[i].sort()\n\nvisited = [0] * (n + 1)\ndfs(graph, v, visited)\nprint('')\nvisited = [0] * (n + 1)\nbfs(graph, v, visited)\n\n\n#https://www.acmicpc.net/problem/1260\n\n\n\n\n\n\n\n\n\n\n","repo_name":"agmon5959/TS_Study","sub_path":"BOJ_STUDY/DFS & BFS/박도현/BOJ_1260.py","file_name":"BOJ_1260.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"45074991112","text":"import os\n\nimport filetype\n\nfrom apps.data_source.enums.type import DataTypeChoices\nfrom apps.data_source.models import AIServices\nfrom apps.users.models import UserRoles\n\n\nclass RequestHandler:\n @classmethod\n def get_user_id(cls, request):\n user_id = None\n try:\n user_id = request.user.id\n except:\n user_id = None\n finally:\n return user_id\n\n @classmethod\n def get_user(cls, request):\n user = None\n try:\n user = request.user\n except:\n user = None\n finally:\n return user\n\n @classmethod\n def get_user_role(cls, request):\n role = None\n try:\n role_key = cls.get_request_data(request=request, key=\"role_key\")\n role = UserRoles.get_role(role_key=role_key)\n except:\n role = None\n finally:\n return role\n\n @classmethod\n def get_files(cls, request):\n file_list = None\n try:\n file_list = request.FILES.getlist(\"file\")\n except:\n file_list = None\n finally:\n return file_list\n\n\n\n @classmethod\n def get_request_data(cls, request, key):\n file_type = None\n try:\n file_type = request.data.get(key)\n except:\n file_type = None\n finally:\n return file_type\n\n @classmethod\n def get_service_type(cls, request):\n service = None\n try:\n service_type_key = cls.get_request_data(request=request, key=\"service_type\")\n service_obj = AIServices.get_service(service_key=service_type_key)\n if service_obj:\n service = service_obj.pk\n\n except:\n service = None\n finally:\n return service\n\n\ndef get_file_name_type(file):\n filename = None\n file_type = None\n try:\n filename, file_extension = os.path.splitext(file.name)\n\n kind = filetype.guess(file).mime.split(\"/\")[0]\n if kind == \"image\":\n if file_extension in [\".tiff\", \".tif\"]:\n file_type = DataTypeChoices.SATELLITE\n else:\n file_type = DataTypeChoices.IMAGE\n elif kind == \"video\":\n file_type = DataTypeChoices.VIDEO\n elif kind == \"audio\":\n file_type = DataTypeChoices.AUDIO\n else:\n file_type = None\n except:\n filename = None\n file_type = None\n finally:\n if file_type is None:\n raise Exception\n else:\n return filename, file_type\n","repo_name":"AliCheema77/aix_project","sub_path":"customutils/request_handlers.py","file_name":"request_handlers.py","file_ext":"py","file_size_in_byte":2583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74377588562","text":"import randominfo as ri\nimport random\nimport string\nimport randomtimestamp as rt\n\nnum = random.random()\nprint('hello')\noutput = open(\"sampledata.txt\", 'w')\n\n# for i in range(220):\n# user1 = random.randrange(0,110)\n# user2 = random.randrange(0,110)\n# while user2 == user1:\n# user2 = random.randrange(0,110)\n# Jdate = ri.get_birthdate(startAge = 0, endAge = 2, _format = \"%Y-%m-%d\")\n# output.write(\"INSERT INTO friend VALUES (\" + str(user1) + \",\" + str(user2) + \",'\" + Jdate + \"',\" + \"\\'Lets be friends!\\'\" +\");\\n\")\n\n# for i in range(10):\n# letters = string.ascii_lowercase\n# gId = i\n# gName = ''.join(random.choice(letters) for i in range(10))\n# size = 20\n# description = ''.join(random.choice(letters) for i in range(75))\n# output.write(\"INSERT INTO groupInfo VALUES (\" + str(gId) + \",\" + gName + \",\" + str(size) + \",\\'\" + description +\"\\');\\n\")\n\n\n# for i in range(10):\n# member_list = random.sample(range(0,110), 20)\n# for j in member_list:\n# output.write(\"INSERT INTO groupMember VALUES (\" + str(i) + \",\" + str(j) + \",\" + \"user\" + \",\\'\" + str(rt.randomtimestamp()) + \"\\');\\n\" )\n\nfor i in range(1000):\n user1 = random.randrange(0,110)\n user2 = random.randrange(0,110)\n while user2 == user1:\n user2 = random.randrange(0,110)\n\n Jdate = ri.get_birthdate(startAge = 0, endAge = 2, _format = \"%Y-%m-%d\")\n output.write(\"INSERT INTO message VALUES (\" + str(i) + \",\" + str(user1) + \",'\" + \"Bing bong\" + \"',\" + str(user2) + \",\"+ str(0) +\",'\" + str(rt.randomtimestamp()) +\"');\\n\")\n","repo_name":"rhn5/1555-project","sub_path":"Sample Data Maker/sampleData.py","file_name":"sampleData.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34541260299","text":"#!/usr/bin/env python\r\n# -*- coding: UTF-8 -*-\r\n\r\n# block_retriever.py\r\n\r\n\"\"\"\r\n%prog <.align_file> \r\n\r\nretrieve blocks from MCscan output .aligns file\r\n\r\noutput format:\r\nblock_ID qstart_gene qend_gene sstart_gene send_gene block_size score\r\n\r\n\"\"\"\r\n\r\n\r\nimport os,sys\r\nfrom optparse import OptionParser\r\nfrom itertools import groupby\r\n\r\n\r\ndef retrieve_blocks(fi):\r\n '''function to parse MCSCAN .align file for geenrating block summary.'''\r\n with open(fi,'r') as f:\r\n for block_id, block in groupby(f, lambda x: x.split()[2].rstrip(':') if \"Alignment\" in x else x.split('-')[0].strip()): # hard coded\r\n block=list(block)\r\n try: size = block[0].split(\"N=\")[1].split()[0]\r\n except IndexError: continue\r\n score = block[0].split(\"score=\")[1].split()[0]\r\n flag=0 # initialize\r\n for align in block[1:]:\r\n align = align.strip('\\n').split('\\t')\r\n end1, end2 = align[1:3]\r\n if (not flag):\r\n flag=1\r\n start1, start2 = align[1:3]\r\n if start1 > end1:\r\n start1,end1=end1,start1\r\n if start2 > end2:\r\n start2,end2=end2,start2\r\n yield (block_id, start1, end1, start2, end2, size, score)\r\n \r\n \r\nif __name__ == \"__main__\":\r\n \r\n usage = \"usage: %prog <.align_file> \"\r\n parser = OptionParser(usage)\r\n args=parser.parse_args()[1]\r\n try:\r\n fi = args[0]\r\n fo = args[1]\r\n except:\r\n sys.exit(parser.print_help())\r\n\r\n fo = open(fo,'w')\r\n for block in retrieve_blocks(fi):\r\n fo.write('\\t'.join(block)+'\\n')\r\n fo.close()\r\n \r\n \r\n","repo_name":"Jingping/BiteTools","sub_path":"mcscan_block_getter.py","file_name":"mcscan_block_getter.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"873529653","text":"from lib2to3.pgen2 import token\nfrom turtle import pos\nimport pandas as pd\nimport numpy as np\nimport torch.nn.functional as F\nfrom torch.utils import data\nimport os\nimport torch\nfrom torch.utils.data import DataLoader,Dataset\nimport matplotlib.pyplot as plt\nimport jieba\nfrom transformers import BertTokenizer\nfrom bert.make_data import cut_sent\n\nclass ChSentiDataSet(Dataset):\n def __init__(self, data_path, embedding) -> None:\n # 由于数据比较少, 我们直接一次加载进来就好.\n # label, review\n self.data_all = pd.read_csv(data_path, keep_default_na=False, header=0)\n self.embedding = embedding\n super().__init__()\n pass\n\n def __len__(self):\n # 返回数据集大小\n return self.data_all.shape[0]\n\n def __getitem__(self, index):\n # 返回下标为index的数据项\n review = self.data_all.iloc[index,1]\n review = self.embedding.toTensor(review)\n label =int(self.data_all.iloc[index,0])\n return review, label\n\nclass embedding:\n # 不太了解embedding, 先使用所有训练数据中的句子拆出的词表, 把出现次数少(<100)的词语视为other \n # 拆出的词表需要去掉停止词\n def __init__(self, all_sentence_path,minfr=100) -> None:\n # 读取所有句子\n all_sentence = pd.read_csv(all_sentence_path,header=0,names=['label','sentence'],keep_default_na=False)\n #读取停止词\n self.stop_words = pd.read_csv(\"data/stop_words.csv\",header=None,sep='!')\n\n # 计算词频\n # FIXME: 性能有待提升\n self.word_dict = {}\n for sentence in all_sentence['sentence']:\n words = jieba.lcut(sentence)\n for wd in words:\n self.word_dict[wd]= self.word_dict[wd]+1 if self.word_dict.__contains__(wd) else 1\n # 得到所有单词\n self.words = list(self.word_dict.keys())\n\n # 删除停止词和低频词\n for wd in self.word_dict:\n if self.word_dict[wd]<=minfr:\n self.words.remove(wd)\n\n # 添加other\n self.words.append(\"@other\")\n self.words.append(\"@pad\")\n self.words.append(\"@cls\")\n self.words.append(\"@sep\")\n\n def __len__(self):\n return len(self.words)\n\n def toTensor(self, sentence):\n # 得到一个句子的向量化结果\n # 词袋\n try:\n st_words = jieba.lcut(sentence)\n except:\n # print(sentence)\n st_words = [' ']\n wd_dict = dict(zip(self.words, np.zeros(len(self.words))))\n for wd in st_words:\n # if wd_dict.__contains__(wd):\n if wd in self.stop_words:\n continue\n if wd in self.words:\n wd_dict[wd] = wd_dict[wd]+1\n else:\n wd_dict[\"@other\"]+=1\n \n ret = torch.tensor(list(wd_dict.values()),dtype=float)\n ret = F.softmax(ret,dim=-1)\n return ret\n\nclass oneHotEmbedding(embedding):\n def __init__(self, all_sentence_path, length, minfr=100) -> None:\n super().__init__(all_sentence_path, minfr)\n # 得到词典\n self.wordsindex = dict((_,i) for i,_ in enumerate(self.words))\n\n self.length = length\n\n def toTensor(self, sentence):\n sent_words = jieba.lcut(sentence)\n sent_index = []\n # 把词语转换维字典对应的序号\n for word in sent_words:\n if self.wordsindex.__contains__(word):\n sent_index.append(self.wordsindex[word]) # 已存在的\n else:\n sent_index.append(self.wordsindex['@other']) # unk\n while len(sent_index) < self.length: # padding\n sent_index.append(self.wordsindex['@pad'])\n\n # 将id转换为独热码\n id = torch.tensor(sent_index[:self.length]).reshape(self.length,1) \n sent_tensor = torch.zeros((self.length,len(self.words)), dtype=torch.float).scatter_(dim=1, index=id, value=1)\n\n return sent_tensor.unsqueeze(0) # 为了后面卷积方便调用接口, 因此加一层channel维\n\nclass indexDictEmbedding(oneHotEmbedding):\n def __init__(self, all_sentence_path, length, minfr=100) -> None:\n super().__init__(all_sentence_path, length, minfr)\n \n def toTensor(self, sentence):\n sent_words = jieba.lcut(sentence)\n sent_index = []\n # 把词语转换维字典对应的序号\n for word in sent_words:\n if self.wordsindex.__contains__(word):\n sent_index.append(self.wordsindex[word]) # 已存在的\n else:\n sent_index.append(self.wordsindex['@other']) # unk\n while len(sent_index) < self.length: # padding\n sent_index.append(self.wordsindex['@pad'])\n\n # 得到句子中每个单词的id\n id = torch.tensor(sent_index[:self.length])\n\n return id.unsqueeze(0) # 为了后面卷积方便调用接口, 因此加一层channel维\n\nclass bertEmbedding(embedding):\n def __init__(self, all_sentence_path, length, minfr=100) -> None:\n super().__init__(all_sentence_path, minfr)\n self.length = length\n # 使用的预训练模型：https://github.com/ymcui/Chinese-BERT-wwm\n # self.tokenizer = BertTokenizer.from_pretrained('hfl/chinese-bert-wwm',padding=True, truncation=True, return_tensors=\"pt\")\n self.tokenizer = BertTokenizer('data/bert_min/vocab.txt')\n\n def __len__(self):\n return self.tokenizer.vocab_size\n\n def toTensor(self, sentence):\n token_words = self.tokenizer.encode_plus(sentence, max_length=self.length, padding='max_length',truncation=True)\n token_words['attention_mask'] = (np.array(token_words['attention_mask'])-1)*([-1]) # if my bert\n return torch.tensor([token_words['input_ids'], token_words['token_type_ids'], token_words['attention_mask']])\n # return torch.tensor(token_words['input_ids'])\n\nclass bertEmbeddingv1(embedding):\n def __init__(self, all_sentence_path, length, minfr=100) -> None:\n super().__init__(all_sentence_path, minfr)\n # 得到词典\n self.wordsindex = dict((_,i) for i,_ in enumerate(self.words))\n self.length = length\n \n def toTensor(self, sentence):\n sent_words = jieba.lcut(sentence)\n sent_index = []\n # 把词语转换维字典对应的序号\n sent_index.append(self.wordsindex['@cls'])\n for word in sent_words:\n if self.wordsindex.__contains__(word):\n sent_index.append(self.wordsindex[word]) # 已存在的\n else:\n sent_index.append(self.wordsindex['@other']) # unk\n sent_index.append(self.wordsindex['@sep'])\n\n while len(sent_index) < self.length: # padding\n sent_index.append(self.wordsindex['@pad'])\n\n # 得到句子中每个单词的id\n input_ids = torch.tensor(sent_index[:self.length])\n attention_mask = torch.tensor([i!=self.wordsindex['@pad'] for i in sent_index])\n token_type_ids = torch.tensor([0]*self.length)\n position_ids = torch.tensor([i for i in range(self.length)])\n return torch.tensor(input_ids, attention_mask, token_type_ids, position_ids)\n\nclass HANembedding(embedding):\n def __init__(self, all_sentence_path, doc_len, sent_len, minfr=100) -> None:\n super().__init__(all_sentence_path, minfr)\n self.doc_len = doc_len\n self.sent_len = sent_len\n self.wordsindex = dict((_,i) for i,_ in enumerate(self.words))\n \n def toTensor(self, sentence):\n doc = sentence\n ret = []\n for sent in cut_sent(doc):\n sent_words = jieba.lcut(sent)\n sent_index = []\n # 把词语转换维字典对应的序号\n for word in sent_words:\n if self.wordsindex.__contains__(word):\n sent_index.append(self.wordsindex[word]) # 已存在的\n else:\n sent_index.append(self.wordsindex['@other']) # unk\n while len(sent_index) < self.sent_len: # padding\n sent_index.append(self.wordsindex['@pad'])\n\n ret.append(sent_index[:self.sent_len])\n while len(ret) < self.doc_len:\n ret.append([self.wordsindex['@pad']]*self.sent_len) \n ret = ret[:self.doc_len]\n\n return torch.tensor(ret) # [doc_len, sent_len]的二维数组\n \nclass corpusInfo:\n def __init__(self, path) -> None:\n self.data = pd.read_csv(path, keep_default_na=False, header=0, names=['label','sentence'])\n\n def maxWords(self):\n max_cnt = 0\n max_sent =''\n for sent in self.data['sentence']:\n word_sent = jieba.lcut(sent)\n if max_cnt < len(word_sent):\n max_cnt = len(word_sent)\n max_sent = sent\n return (max_sent, max_cnt)\n \n def minWords(self):\n min_cnt = 1000000\n min_sent =''\n for sent in self.data['sentence']:\n word_sent = jieba.lcut(sent)\n if min_cnt > len(word_sent) and len(word_sent) != 0:\n min_cnt = len(word_sent)\n min_sent = sent\n return (min_sent, min_cnt)\n\n def meanWords(self):\n mean_cnt = 0.0\n for sent in self.data['sentence']:\n word_sent = jieba.lcut(sent)\n mean_cnt += len(word_sent)\n return mean_cnt/3200.0\n\n def histWords(self):\n cnt = []\n for sent in self.data['sentence']:\n cnt.append(len(jieba.lcut(sent)))\n cnt.sort()\n plt.hist(cnt,bins=300)\n plt.show()\n print(cnt[round(len(cnt)*0.9)])\n\n def HANstatics(self):\n doc_all = self.data['sentence']\n sent_len = []\n word_len = []\n sent = []\n word = []\n for doc in doc_all:\n sents = cut_sent(doc)\n sent.append(sents)\n sent_len.append(len(sents))\n words = []\n for st in sents:\n token = jieba.lcut(st)\n words.append(token)\n word_len.append(len(token))\n word.append(words)\n \n print(f\"mean length of the doc:{np.mean(sent_len)}\")\n print(f\"mean length of the sentence:{np.mean(word_len)}\")\n print(f\"max length of the doc:{np.max(sent_len)}\")\n print(f\"max length of the sentence:{np.max(word_len)}\")\n print(f\"min length of the doc:{np.min(sent_len)}\")\n print(f\"min length of the sentence:{np.min(word_len)}\")\n sent_len.sort()\n word_len.sort()\n plt.subplot(2, 1, 1)\n plt.hist(sent_len,bins=300)\n plt.subplot(2, 1, 2)\n plt.hist(word_len,bins=300)\n plt.show()\n\nif __name__ == \"__main__\":\n info = corpusInfo(\"data/ChnSentiCorp_htl_all/ChnSentiCorp_htl_all.csv\")\n info.HANstatics()\n # embedding = bertEmbedding(\"data/ChnSentiCorp_htl_all/ChnSentiCorp_htl_all.csv\", 128)\n # train_data = ChSentiDataSet(\"data/ChnSentiCorp_htl_all/train_1600+1600.csv\", embedding)\n # print(train_data.__getitem__(2))\n","repo_name":"Hsyy04/NLP_elementary","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":11039,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"33149726067","text":"from argparse import ArgumentParser\nfrom pathlib import Path\n\n\ndef solve_example(parser: ArgumentParser) -> None:\n \"\"\"Adds arguments to parser for solving a single example\"\"\"\n parser.add_argument(\"--word\", type=str, help=\"Word representing the one relator\", required=True)\n\n\ndef solve_examples(parser: ArgumentParser) -> None:\n \"\"\"adds arguments to parse for solving multiple examples\"\"\"\n parser.add_argument(\n \"--word-size-range\",\n type=int,\n nargs=2,\n help=\"range of word sizes to run examples on\",\n required=True\n )\n\n parser.add_argument(\n \"--sample-size\",\n type=int,\n help=\"Size of sample to run on each word size\",\n )\n\n\ndef get_polytope(parser: ArgumentParser) -> None:\n \"\"\"Adds arguments to parser for outputting polytope of single example\"\"\"\n parser.add_argument(\"--word\", type=str, help=\"Word representing the one relator\", required=True)\n \n\ndef get_polytopes(parser: ArgumentParser) -> None:\n \"\"\"\n Adds arguments to parse for outputting poltyopes for multiple examples\n \"\"\"\n parser.add_argument(\n \"--words\", type=str, nargs=\"+\", help=\"List of words to get polytopes for\", required=True\n )\n\n\ndef get_all_cycle_data(parser: ArgumentParser) -> None:\n \"\"\"Adds arguments for reading database files\"\"\"\n parser.add_argument(\n \"--input-dir\", type=Path, help=\"Directory containing database files\"\n )\n","repo_name":"antonydellavecchia/one_relator_curvature","sub_path":"one_relator_curvature/parsers.py","file_name":"parsers.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37681665834","text":"import os\r\nimport os.path as osp\r\nimport argparse\r\nimport utils\r\nimport face_alignment\r\nfrom facenet_pytorch import MTCNN\r\nimport numpy as np\r\nimport mmcv, cv2\r\nfrom mmengine import ProgressBar\r\nfrom PIL import Image, ImageDraw\r\n\r\nimport torch\r\n\r\n\r\ndef face2head(boxes, scale=1.5):\r\n new_boxes = []\r\n for box in boxes:\r\n width = box[2] - box[0]\r\n height = box[3] - box[1]\r\n width_center = (box[2] + box[0]) / 2\r\n height_center = (box[3] + box[1]) / 2\r\n square_width = int(max(width, height) * scale)\r\n new_box = [width_center - square_width / 2, height_center - square_width / 2, width_center + square_width / 2,\r\n height_center + square_width / 2]\r\n new_boxes.append(new_box)\r\n return new_boxes\r\n\r\n\r\ndef bb_intersection_over_union(boxA, boxB):\r\n # determine the (x, y)-coordinates of the intersection rectangle\r\n xA = max(boxA[0], boxB[0])\r\n yA = max(boxA[1], boxB[1])\r\n xB = min(boxA[2], boxB[2])\r\n yB = min(boxA[3], boxB[3])\r\n # compute the area of intersection rectangle\r\n interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)\r\n # compute the area of both the prediction and ground-truth\r\n # rectangles\r\n boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)\r\n boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)\r\n # compute the intersection over union by taking the intersection\r\n # area and dividing it by the sum of prediction + ground-truth\r\n # areas - the interesection area\r\n iou = interArea / float(boxAArea + boxBArea - interArea)\r\n # return the intersection over union value\r\n return iou\r\n\r\n\r\ndef main():\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument('--video_root', type=str, required=True, help=\"The root directory of the videos' path\")\r\n parser.add_argument('--faces', type=str, required=True, help=\"The root directory of output face videos' path\")\r\n parser.add_argument('--csv_root', type=str, required=True, help=\"The root directory of output csv file for each video\")\r\n parser.add_argument('--landmark', type=str, required=True, help=\"The root directory of output videos' path\")\r\n parser.add_argument('--tracked_video', type=str, required=True, help=\"The root directory of output tracked videos' path\")\r\n\r\n parser.add_argument('--detect_every_N_frame', type=int, default=8, help=\"detect face for every N frames\")\r\n parser.add_argument('--scalar_face_detection', type=float, default=1.5)\r\n parser.add_argument('--number_of_speakers', type=int, default=1, help=\"number of speakers in videos\")\r\n args = parser.parse_args()\r\n\r\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\r\n print('Running on device: {}'.format(device))\r\n os.makedirs(args.faces, exist_ok=True)\r\n os.makedirs(args.csv_root, exist_ok=True)\r\n os.makedirs(args.landmark, exist_ok=True)\r\n os.makedirs(args.tracked_video, exist_ok=True)\r\n\r\n videos = os.listdir(args.video_root)\r\n\r\n pb = ProgressBar(len(videos))\r\n pb.start()\r\n\r\n for video_input in videos:\r\n landmarks_dic = {}\r\n faces_dic = {}\r\n boxes_dic = {}\r\n for i in range(args.number_of_speakers):\r\n landmarks_dic[i] = []\r\n faces_dic[i] = []\r\n boxes_dic[i] = []\r\n\r\n mtcnn = MTCNN(keep_all=True, device=device)\r\n\r\n video = mmcv.VideoReader(osp.join(args.video_root, video_input))\r\n print(\"Video statistics: \", video.width, video.height, video.resolution, video.fps)\r\n frames = [Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) for frame in video]\r\n print('Number of frames in video: ', len(frames))\r\n fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False)\r\n\r\n try:\r\n for i, frame in enumerate(frames):\r\n print('\\rTracking frame: {}'.format(i + 1), end='')\r\n\r\n # Detect faces\r\n if i % args.detect_every_N_frame == 0:\r\n boxes, _ = mtcnn.detect(frame)\r\n boxes = boxes[:args.number_of_speakers]\r\n boxes = face2head(boxes, args.scalar_face_detection)\r\n else:\r\n boxes = [boxes_dic[j][-1] for j in range(args.number_of_speakers)]\r\n\r\n # Crop faces and save landmarks for each speaker\r\n if len(boxes) != args.number_of_speakers:\r\n boxes = [boxes_dic[j][-1] for j in range(args.number_of_speakers)]\r\n\r\n for j, box in enumerate(boxes):\r\n face = frame.crop((box[0], box[1], box[2], box[3])).resize((224, 224))\r\n preds = fa.get_landmarks(np.array(face))\r\n if i == 0:\r\n faces_dic[j].append(face)\r\n landmarks_dic[j].append(preds)\r\n boxes_dic[j].append(box)\r\n else:\r\n iou_scores = []\r\n for b_index in range(args.number_of_speakers):\r\n last_box = boxes_dic[b_index][-1]\r\n iou_score = bb_intersection_over_union(box, last_box)\r\n iou_scores.append(iou_score)\r\n box_index = iou_scores.index(max(iou_scores))\r\n faces_dic[box_index].append(face)\r\n landmarks_dic[box_index].append(preds)\r\n boxes_dic[box_index].append(box)\r\n\r\n for s in range(args.number_of_speakers):\r\n frames_tracked = []\r\n for i, frame in enumerate(frames):\r\n # Draw faces\r\n frame_draw = frame.copy()\r\n draw = ImageDraw.Draw(frame_draw)\r\n draw.rectangle(boxes_dic[s][i], outline=(255, 0, 0), width=6)\r\n # Add to frame list\r\n frames_tracked.append(frame_draw)\r\n dim = frames_tracked[0].size\r\n fourcc = cv2.VideoWriter_fourcc(*'FMP4')\r\n\r\n video_tracked = cv2.VideoWriter(osp.join(args.tracked_video, video_input), fourcc, 25.0, dim)\r\n for frame in frames_tracked:\r\n video_tracked.write(cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))\r\n video_tracked.release()\r\n\r\n # Save landmarks\r\n for i in range(args.number_of_speakers):\r\n utils.save2npz(osp.join(args.landmark, osp.splitext(video_input)[0] + '.npz'), data=landmarks_dic[i])\r\n dim = face.size\r\n fourcc = cv2.VideoWriter_fourcc(*'FMP4')\r\n speaker_video = cv2.VideoWriter(osp.join(args.faces, video_input), fourcc, 25.0, dim)\r\n for frame in faces_dic[i]:\r\n speaker_video.write(cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR))\r\n speaker_video.release()\r\n\r\n csvfile = open(osp.join(args.csv_root, osp.splitext(video_input)[0] + '.csv'), 'w')\r\n for i in range(args.number_of_speakers):\r\n csvfile.write('speaker' + str(i + 1) + ',0\\n')\r\n csvfile.close()\r\n except:\r\n continue\r\n\r\n pb.update()\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","repo_name":"MCG-NJU/BFRNet","sub_path":"utils/detect_faces.py","file_name":"detect_faces.py","file_ext":"py","file_size_in_byte":7281,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"23145852713","text":"#!/usr/bin/python3\ndef list_division(my_list_1, my_list_2, list_length):\n '''\n Divides the elements of 2 lists with the same index\n Parameters:\n my_list_1 (list): The first list\n my_list_2 (list): The second list\n list_length (int): The length of the items to divide\n Returns:\n A new list consisting of the result of the divisions\n '''\n new_list = []\n for i in range(list_length):\n result = 0\n try:\n a, b = (my_list_1[i], my_list_2[i])\n result = a / b\n except TypeError:\n print(\"wrong type\")\n except ZeroDivisionError:\n print(\"division by 0\")\n except IndexError:\n print(\"out of range\")\n finally:\n new_list.append(result)\n return new_list\n","repo_name":"OBigVee/alx-higher_level_programming","sub_path":"0x05-python-exceptions/4-list_division.py","file_name":"4-list_division.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"11225174361","text":"def parse_geology():\n\twith open('input.txt', 'r') as f:\n\t\tgeology_matrix = f.read().splitlines()\n\n\treturn geology_matrix\n\ndef calculate_num_trees(num_right, num_down, geology_matrix):\n\tmatrix_width = len(geology_matrix[0])\n\n\tx_pos = 0\n\ty_pos = 0\n\ttrees_count = 0\n\n\twhile y_pos < len(geology_matrix):\n\t\tif geology_matrix[y_pos][x_pos] == \"#\":\n\t\t\ttrees_count += 1\n\t\tx_pos += num_right\n\t\tx_pos = x_pos % matrix_width\n\t\t\n\t\ty_pos += num_down\n\n\treturn trees_count \n\nif __name__ == \"__main__\":\n\tparsed_board = parse_geology()\n\ttree_count_3_1 = calculate_num_trees(3, 1, parsed_board)\n\tprint('The answer to day 3 part 1 is {}.'.format(tree_count_3_1))\n\n\ttree_count_1_1 = calculate_num_trees(1, 1, parsed_board)\n\ttree_count_5_1 = calculate_num_trees(5, 1, parsed_board)\n\ttree_count_7_1 = calculate_num_trees(7, 1, parsed_board)\n\ttree_count_1_2 = calculate_num_trees(1, 2, parsed_board)\n\n\ttree_product = tree_count_3_1 * tree_count_1_1 * tree_count_5_1 * \\\n\t\ttree_count_7_1 * tree_count_1_2\n\tprint('The answer to day 3 part 2 is {}.'.format(tree_product))","repo_name":"keing1/advent-of-code-2020","sub_path":"day_03/toboggan_trajectory.py","file_name":"toboggan_trajectory.py","file_ext":"py","file_size_in_byte":1045,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31569117120","text":"from turbogears.widgets.base import JSSource, CoreWD, RenderOnlyWD\n\nclass RPyJSSource(JSSource):\n def __init__(self, src, location=None):\n #print 'RPyJSSource: python:', src\n mod = 'RPyJSSourceTmp.py'\n f = open(mod, 'w')\n f.write(src)\n f.close()\n function_names = []\n from rpython2javascript.pypy.translator.js.main import rpython2javascript_main\n jssrc = rpython2javascript_main([mod] + function_names)\n #print 'RPyJSSource: javascript:', jssrc\n super(RPyJSSource, self).__init__(jssrc)\n \nclass RPyJSSourceDesc(CoreWD, RenderOnlyWD):\n name = \"RPyJSSource\"\n for_widget = RPyJSSource(\"def main(): return 42\")\n","repo_name":"camillobruni/pygirl","sub_path":"pypy/translator/js/turbogears/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"23587534646","text":"import os\nimport csv\n\ncsvpath = os.path.join(\"..\", \"Resources\", \"budget_data.csv\")\n\nmonthly_change = []\ndate = []\n\nmonth = 0\nfirst_mo_profit = 0\ntotal_profit = 0\nprevious_profit = 0\n\nwith open(csvpath,'r', newline=\"\") as csvfile:\n csvreader = csv.reader(csvfile, delimiter=\",\")\n next(csvreader)\n\n for row in csvreader:\n date.append(row[0])\n month = month + 1\n total_profit += int(row[1])\n profit_change = int(row[1])-previous_profit\n previous_profit = int(row[1])\n monthly_change_profit = profit_change\n monthly_change.append(profit_change)\n \n monthly_change.pop(0)\n \n max_increase_profit = max(monthly_change)\n max_increase_date = date[monthly_change.index(max_increase_profit)]\n max_decrease_profit = min(monthly_change)\n max_decrease_date = date[monthly_change.index(max_decrease_profit)]\n\naverage_change_profit = round(sum(monthly_change)/(month-1),2)\n \nprint(\"Financial Analysis\")\nprint(\"--------------------\")\nprint(f\"Total # of Months: {month}\")\nprint(f\"Total Profits: ${total_profit}\")\nprint(f\"Average Monthly Change in Profits: ${average_change_profit}\")\nprint(f\"Greatest Increase in Profits: {max_increase_date} ${max_increase_profit}\")\nprint(f\"Greatest Decrease in Profits: {max_decrease_date} ${max_decrease_profit}\")\n\ntextpath = os.path.join(\"..\", \"Resources\", \"main.txt\")\n\nwith open(textpath,'w') as text:\n\n text.write(\"Financial Analysis\" + \"\\n\")\n text.write(\"--------------------\\n\")\n text.write(f\"Total # of Months: {month} \\n\")\n text.write(f\"Total Profits: ${total_profit} \\n\")\n text.write(f\"Average Monthly Change in Profits: ${average_change_profit} \\n\")\n text.write(f\"Greatest Increase in Profits: {max_increase_date} ${max_increase_profit} \\n\")\n text.write(f\"Greatest Decrease in Profits: {max_decrease_date} ${max_decrease_profit} \\n\") ","repo_name":"captainjamesmisson/Homework","sub_path":"3-Python/PyBank/Resources/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11577230801","text":"\"\"\"Groups are a named collection of series and tests. They can be manipulated\nwith the `pav group` command.\"\"\"\n\nfrom pathlib import Path\nimport re\nimport shutil\nfrom typing import NewType, List, Tuple, Union, Dict\nimport uuid\n\nfrom pavilion import config\nfrom pavilion.errors import TestGroupError\nfrom pavilion.series import TestSeries, list_series_tests, SeriesInfo\nfrom pavilion.test_run import TestRun, TestAttributes\nfrom pavilion.utils import is_int\n\nGroupMemberDescr = NewType('GroupMemberDescr', Union[TestRun, TestSeries, \"TestGroup\", str])\nFlexDescr = NewType('FlexDescr', Union[List[GroupMemberDescr], GroupMemberDescr])\n\n\nclass TestGroup:\n \"\"\"A named collection tests and series.\"\"\"\n\n GROUPS_DIR = 'groups'\n TESTS_DIR = 'tests'\n SERIES_DIR = 'series'\n EXCLUDED_DIR = 'excluded'\n\n TEST_ITYPE = 'test'\n SERIES_ITYPE = 'series'\n GROUP_ITYPE = 'group'\n EXCL_ITYPE = 'test*'\n\n group_name_re = re.compile(r'^[a-zA-Z][a-zA-Z0-9_-]+$')\n\n def __init__(self, pav_cfg: config.PavConfig, name: str):\n\n self.pav_cfg = pav_cfg\n\n self._check_name(name)\n\n self.name = name\n\n self.path = self.pav_cfg.working_dir/self.GROUPS_DIR/self.name\n\n if self.path.exists():\n self.created = True\n else:\n self.created = False\n\n def create(self):\n \"\"\"Actually create the group.\"\"\"\n\n try:\n self.path.mkdir(parents=True, exist_ok=True)\n except OSError as err:\n raise TestGroupError(\"Could not create group dir at '{}'\"\n .format(self.path), prior_error=err)\n\n for category in self.TESTS_DIR, self.SERIES_DIR, self.GROUPS_DIR, self.EXCLUDED_DIR:\n cat_dir = self.path/category\n try:\n cat_dir.mkdir(exist_ok=True)\n except OSError as err:\n raise TestGroupError(\"Could not create group category directory '{}'\"\n .format(cat_dir.as_posix()),\n prior_error=err)\n\n self.created = True\n\n def exists(self) -> bool:\n \"\"\"Whether this group exists.\"\"\"\n\n return self.path.exists()\n\n def info(self) -> Dict:\n \"\"\"Return some basic group info. Number of tests, series, sub-groups, creation time.\"\"\"\n\n info = {\n 'name': self.name,\n 'created': self.path.stat().st_mtime,\n }\n for cat_type, cat_dir in (\n (self.TEST_ITYPE, self.TESTS_DIR),\n (self.SERIES_ITYPE, self.SERIES_DIR),\n (self.GROUP_ITYPE, self.GROUPS_DIR),):\n cat_path = self.path/cat_dir\n\n if not cat_path.exists():\n info[cat_dir] = 0\n continue\n info[cat_dir] = len(list(cat_path.iterdir()))\n\n return info\n\n def tests(self, seen_groups=None) -> List[Path]:\n \"\"\"Returns a list of paths to all tests in this group. Use with\n cmd_utils.get_tests_by_paths to convert to real test objects. Bad links are ignored.\n Groups are recursively examined (loops are allowed, but not followed).\n \"\"\"\n\n seen_groups = seen_groups if seen_groups is not None else []\n seen_groups.append(self.name)\n\n tests = []\n\n if not self.exists():\n return []\n\n # Get all the tests directly added to the group.\n try:\n if (self.path/self.TESTS_DIR).exists():\n for test_dir in (self.path/self.TESTS_DIR).iterdir():\n try:\n if not test_dir.exists():\n continue\n except OSError as err:\n raise TestGroupError(\n \"Error getting test '{}' from group '{}'\"\n .format(test_dir.name, self.name),\n prior_error=err)\n\n tests.append(test_dir)\n\n except OSError as err:\n raise TestGroupError(\"Error getting tests for group '{}'\"\n .format(self.name), prior_error=err)\n\n # Get all the tests from each series\n try:\n if (self.path/self.SERIES_DIR).exists():\n for series_dir in (self.path/self.SERIES_DIR).iterdir():\n try:\n if not series_dir.exists():\n continue\n except OSError as err:\n raise TestGroupError(\n \"Error getting test series '{}' from group '{}'\"\n .format(series_dir.name, self.name),\n prior_error=err)\n\n tests.extend(list_series_tests(self.pav_cfg, series_dir.name))\n\n except OSError as err:\n raise TestGroupError(\n \"Error getting series for group '{}'\"\n .format(self.name), prior_error=err)\n\n # Finally, recursively get the tests for each group that's under this group.\n try:\n if (self.path/self.GROUPS_DIR).exists():\n for group_file in (self.path/self.GROUPS_DIR).iterdir():\n group_name = group_file.name\n sub_group = TestGroup(self.pav_cfg, group_name)\n if group_name not in seen_groups:\n tests.extend(sub_group.tests(seen_groups=seen_groups))\n\n except OSError as err:\n raise TestGroupError(\n \"Error getting sub groups for group '{}'\"\n .format(self.name), prior_error=err)\n\n # Filter out any excluded tests.\n excluded_paths = list(self._excluded().values())\n tests = [test_path.resolve() for test_path in tests]\n return [test_path for test_path in tests if test_path not in excluded_paths]\n\n def _has_test(self, test_path: Path) -> bool:\n \"\"\"Return True if the given test path is included anywhere in the group.\"\"\"\n\n return test_path in self.tests()\n\n def add(self, items: FlexDescr) -> Tuple[List[str], List[TestGroupError]]:\n \"\"\"Add each of the given items to the group. Accepts TestRun, TestSeries, and TestGroup\n objects, as well as just the test/series(sid)/group names as strings.\n\n :returns: A list of the item names added, and a list of errors\n \"\"\"\n\n if not isinstance(items, (list, tuple)):\n items = [items]\n\n if not self.created:\n self.create()\n\n warnings = []\n added = []\n for item in items:\n # Get the type of item we're dealing with, and where it will be put in the group.\n try:\n itype, item_path = self._get_member_info(item)\n except TestGroupError as err:\n warnings.append(\n TestGroupError(\"Could not add unknown item to test group '{}': '{}' '{}'\"\n .format(self.name, type(item), item), prior_error=err))\n continue\n\n if item_path.exists():\n # Don't try to add items that are already in the group.\n continue\n\n # Get a string a name for the item, and the path to the actual item.\n try:\n if itype == self.TEST_ITYPE:\n iname, dest_path = self._get_test_info(item)\n elif itype == self.SERIES_ITYPE:\n iname, dest_path = self._get_series_info(item)\n elif itype == self.GROUP_ITYPE:\n if isinstance(item, TestGroup):\n agroup = item\n else:\n agroup = TestGroup(self.pav_cfg, item)\n\n # Don't add a test group to itself.\n if agroup.name == self.name:\n continue\n\n if not agroup.exists():\n warnings.append(\n TestGroupError(\"Group '{}' does not exist.\".format(agroup.name)))\n continue\n\n iname = agroup.name\n\n except TestGroupError as err:\n warnings.append(\n TestGroupError(\"Could not add to test group '{}'\".format(self.name),\n prior_error=err))\n continue\n\n if itype == self.TEST_ITYPE:\n if iname in self._excluded():\n try:\n self._remove_excluded(iname)\n except TestGroupError as err:\n warnings.append(\n TestGroupError(\n \"Could not remove exclusion for test {}.\".format(iname),\n prior_error=err))\n\n if self._has_test(dest_path):\n added.append((self.EXCL_ITYPE, iname))\n continue\n\n try:\n # For tests and series, symlink to their directories.\n if itype in (self.TEST_ITYPE, self.SERIES_ITYPE):\n # Add the item, unless it just needed to be un-excluded.\n item_path.symlink_to(dest_path)\n # For groups, just touch a file of that name (prevents symlink loops).\n else:\n item_path.touch()\n added.append((itype, iname))\n except OSError as err:\n warnings.append(\n TestGroupError(\"Could not add {} '{}' to test group '{}'\"\n .format(itype, iname, self.name), prior_error=err))\n continue\n\n return added, warnings\n\n def remove(self, items: FlexDescr) -> Tuple[List[str], List[TestGroupError]]:\n \"\"\"Remove all of the given items from the group. Returns a list of warnings.\"\"\"\n\n removed = []\n warnings = []\n\n if not isinstance(items, list):\n items = [items]\n\n all_tests = None\n\n for item in items:\n if isinstance(item, int):\n item = str(item)\n\n itype, rmpath = self._get_member_info(item)\n\n if not rmpath.exists():\n if itype == self.TEST_ITYPE:\n try:\n t_full_id, t_path = self._get_test_info(rmpath.name)\n except TestGroupError as err:\n warnings.append(\n TestGroupError(\n \"Could not exclude item {}.\".format(item),\n prior_error=err))\n continue\n\n # Check to see if this is in our list of tests at all.\n if all_tests is None:\n all_tests = self.tests()\n\n if t_path in all_tests:\n self._add_excluded(t_full_id, t_path)\n removed.append((self.EXCL_ITYPE, t_full_id))\n continue\n\n warnings.append(\n \tTestGroupError(\"Given {} '{}' to remove, but it is not in group '{}'.\"\n .format(itype, item, self.name)))\n continue\n\n try:\n rmpath.unlink()\n removed.append((itype, rmpath.name))\n except OSError:\n warnings.append(\n TestGroupError(\"Could not remove {} '{}' from group '{}'.\"\n .format(itype, item, self.name)))\n continue\n\n return removed, warnings\n\n def members(self, recursive=False, seen_groups=None) -> List[Dict]:\n \"\"\"Return a list of dicts of member info, keys 'itype', 'name'.\"\"\"\n\n seen_groups = seen_groups if seen_groups is not None else []\n seen_groups.append(self.name)\n\n if not self.exists():\n return []\n\n members = []\n\n for itype, type_dir in (\n (self.TEST_ITYPE, self.TESTS_DIR),\n (self.SERIES_ITYPE, self.SERIES_DIR),\n (self.GROUP_ITYPE, self.GROUPS_DIR)):\n\n try:\n for path in (self.path/type_dir).iterdir():\n abs_path = None\n try:\n if path.exists():\n abs_path = path.resolve()\n except OSError:\n pass\n\n members.append({\n 'group': self.name,\n 'itype': itype,\n 'path': abs_path,\n 'id': path.name,})\n\n if recursive and itype == self.GROUP_ITYPE and path.name not in seen_groups:\n try:\n subgroup = self.__class__(self.pav_cfg, path.name)\n except TestGroupError:\n continue\n\n members.extend(subgroup.members(recursive=True, seen_groups=seen_groups))\n\n except OSError as err:\n raise TestGroupError(\n \"Could not list {} for group '{}'\".format(type_dir, self.name),\n prior_error=err)\n\n for mem_info in members:\n path = mem_info['path']\n if path is None:\n continue\n\n if mem_info['itype'] == self.TEST_ITYPE:\n test_attrs = TestAttributes(mem_info['path'])\n mem_info['name'] = test_attrs.name\n mem_info['created'] = test_attrs.created\n elif mem_info['itype'] == self.SERIES_ITYPE:\n series_info = SeriesInfo(self.pav_cfg, path)\n mem_info['name'] = series_info.name\n mem_info['created'] = series_info.created\n else: # Groups\n path = self.path.parents[1]/path.name\n if path.exists():\n mem_info['created'] = path.stat().st_mtime\n return members\n\n def member_tuples(self) -> List[Tuple[str,str]]:\n \"\"\"As per 'members', but return a list of (item_type, item_id) tuples.\"\"\"\n\n tups = []\n for item in self.members():\n tups.append((item['itype'], item['id']))\n return tups\n\n def clean(self) -> List[TestGroupError]:\n \"\"\"Remove all dead links and group files, then delete the group if it's empty.\n Returns a list of errors/warnings.\"\"\"\n\n keepers = False\n warnings = []\n\n # Cleanup items for each item type (tests, series, groups)\n for itype, type_dir in (\n (self.TEST_ITYPE, self.TESTS_DIR),\n (self.SERIES_ITYPE, self.SERIES_DIR),\n (self.GROUP_ITYPE, self.GROUPS_DIR)):\n\n try:\n for item_path in (self.path/type_dir).iterdir():\n # Skip that items that still exist.\n if itype == self.GROUP_ITYPE:\n if (self.path.parent/item_path.name).exists():\n keepers = True\n continue\n elif item_path.exists():\n # Note - this tests both if the target of the symlink and the symlink\n # itself exit. (Absent a race condition of some sort, the\n # symlink will exist.)\n keepers = False\n continue\n\n try:\n item_path.unlink()\n except OSError as err:\n warnings.append(\n TestGroupError(\n \"Could not remove test '{}' from group '{}'.\"\n .format(test_link.name, self.name),\n prior_error=err))\n except OSError as err:\n warnings.append(\n TestGroupError(\n \"Could not cleanup {} for group '{}'\"\n .format(self.GROUPS_DIR, self.name),\n prior_error=err))\n\n if not keepers:\n try:\n self.delete()\n except TestGroupError as err:\n warnings.append(err)\n\n return warnings\n\n def delete(self):\n \"\"\"Delete this group.\"\"\"\n\n try:\n # Symlinks are just removed, not followed.\n shutil.rmtree(self.path.as_posix())\n except OSError as err:\n raise TestGroupError(\n \"Could not delete group '{}'\".format(self.name),\n prior_error=err)\n\n self.created = False\n\n def rename(self, new_name, redirect_parents=True):\n \"\"\"Rename this group.\n\n :param redirect_parents: Search other test groups for inclusion of this group,\n and point them at the new name.\n \"\"\"\n\n self._check_name(new_name)\n\n new_path = self.path.parent/new_name\n\n if new_path.exists():\n raise TestGroupError(\"Renaming group '{}' to '{}' but a group already exists \"\n \"under that name.\".format(self.name, new_name))\n\n try:\n self.path.rename(new_path)\n except OSError as err:\n raise TestGroupError(\n \"Could not rename group '{}' to '{}'\".format(self.name, new_name),\n prior_error=err)\n\n if redirect_parents:\n try:\n for group_path in self.path.parent.iterdir():\n for sub_group in (group_path/self.GROUPS_DIR).iterdir():\n if sub_group.name == self.name:\n new_sub_path = sub_group.parent/new_name\n sub_group.rename(new_sub_path)\n except OSError as err:\n raise TestGroupError(\"Failed to redirect parents of group '{}' to the new name.\"\n .format(self.name), prior_error=err)\n\n self.name = new_name\n self.path = new_path\n\n def _check_name(self, name: str):\n \"\"\"Make sure the given test group name complies with the naming standard.\"\"\"\n\n if self.group_name_re.match(name) is None:\n raise TestGroupError(\n \"Invalid group name '{}'\\n\"\n \"Group names must start with a letter, but can otherwise have any \"\n \"combination of letters, numbers, underscores and dashes.\"\n .format(name))\n if name[0] in ('s', 'S') and is_int(name[1:]):\n raise TestGroupError(\n \"Invalid group name '{}'\\n\"\n \"Group name looks too much like a series ID.\"\n .format(name))\n\n def _get_test_info(self, test: Union[TestRun, str]) -> Tuple[str, Path]:\n \"\"\"Find the test full id and path from the given test information.\"\"\"\n\n if isinstance(test, TestRun):\n if not test.path.exists():\n raise TestGroupError(\"Test '{}' does not exist.\".format(test.full_id))\n return test.full_id, test.path\n\n if isinstance(test, str):\n if '.' in test:\n cfg_label, test_id = test.split('.', maxsplit=1)\n else:\n cfg_label = config.DEFAULT_CONFIG_LABEL\n test_id = test\n\n elif isinstance(test, int):\n cfg_label = config.DEFAULT_CONFIG_LABEL\n test_id = str(int)\n # We'll use this as our full_id too.\n test = test_id\n\n if not is_int(test_id):\n raise TestGroupError(\n \"Invalid test id '{}' from test id '{}'.\\n\"\n \"Test id's must be a number, like 27.\"\n .format(test_id, test))\n if cfg_label not in self.pav_cfg.configs:\n raise TestGroupError(\n \"Invalid config label '{}' from test id '{}'.\\n\"\n \"No such Pavilion configuration directory exists. Valid config \"\n \"labels are:\\n {}\"\n .format(cfg_label, test,\n '\\n'.join([' - {}'.format(lbl for lbl in self.pav_cfg.configs)])))\n\n rel_cfg = self.pav_cfg.configs[cfg_label]\n tpath = rel_cfg.working_dir/'test_runs'/test_id\n\n if not tpath.is_dir():\n raise TestGroupError(\n \t\"Could not add test '{}' to group, test directory could not be found.\\n\"\n \"Looked at '{}'\".format(test, tpath))\n\n return test, tpath\n\n def _get_series_info(self, series: Union[TestSeries, str]) -> Tuple[str, Path]:\n \"\"\"Get the sid and path for a series, given a flexible description.\"\"\"\n\n if isinstance(series, TestSeries):\n if not series.path.exists():\n raise TestGroupError(\"Series '{}' at '{}' does not exist.\"\n .format(series.sid, series.path))\n return series.sid, series.path\n\n series = str(series)\n if series.startswith(\"s\"):\n series_id = series[1:]\n sid = series\n else:\n sid = 's{}'.format(series)\n\n if not is_int(series_id):\n raise TestGroupError(\"Invalid series id '{}', not numeric id.\"\n .format(series))\n\n series_dir = self.pav_cfg.working_dir/'series'/series_id\n\n if not series_dir.is_dir():\n raise TestGroupError(\"Series directory for sid '{}' does not exist.\\n\"\n \"Looked at '{}'\".format(sid, series_dir))\n\n return sid, series_dir\n\n def _get_member_info(self, item: GroupMemberDescr) -> Tuple[str, Path]:\n \"\"\"Figure out what type of item 'item' is, and return its type name and path in\n the group.\"\"\"\n\n if isinstance(item, TestRun):\n return self.TEST_ITYPE, self.path/self.TESTS_DIR/item.full_id\n elif isinstance(item, TestSeries):\n return self.SERIES_ITYPE, self.path/self.SERIES_DIR/item.sid\n elif isinstance(item, self.__class__):\n return self.GROUP_ITYPE, self.path/self.GROUPS_DIR/item.name\n elif isinstance(item, str):\n if is_int(item) or '.' in item:\n # Looks like a test id\n return self.TEST_ITYPE, self.path/self.TESTS_DIR/item\n elif item[0] == 's' and is_int(item[1:]):\n # Looks like a sid\n return self.SERIES_ITYPE, self.path/self.SERIES_DIR/item\n else:\n # Anything can only be a group\n return self.GROUP_ITYPE, self.path/self.GROUPS_DIR/item\n else:\n raise TestGroupError(\"Invalid group item '{}' given for removal.\".format(item))\n\n def _excluded(self) -> Dict[str, Path]:\n \"\"\"Excluded items are items that are in series or sub-groups that were deleted\n from this group.\n\n Returns a dict of excluded test id's and the test directories they point to.\n \"\"\"\n\n excluded = {}\n try:\n for test_path in (self.path/self.EXCLUDED_DIR).iterdir():\n full_id = test_path.name\n test_path = test_path.resolve()\n if test_path.exists():\n excluded[full_id] = test_path\n except (OSError, FileNotFoundError):\n pass\n\n return excluded\n\n def _add_excluded(self, full_id: str, test_path: Path):\n \"\"\"Add the given test path to the excluded directory.\"\"\"\n\n path = self.path/self.EXCLUDED_DIR/full_id\n\n try:\n if not path.exists():\n path.symlink_to(test_path)\n except (OSError, FileNotFoundError) as err:\n raise TestGroupError(\n \"Could not create test exclusion record at {}\".format(path),\n prior_error=err)\n\n def _remove_excluded(self, full_id: str):\n \"\"\"Remove the test from the exclusion records.\"\"\"\n\n path = self.path/self.EXCLUDED_DIR/full_id\n\n try:\n if path.exists():\n path.unlink()\n except (OSError, FileNotFoundError) as err:\n raise TestGroupError(\n \"Could not remove test exclusion record at {}\".format(path),\n prior_error=err)\n\n def _clean_excluded(self):\n \"\"\"Remove any dead links from the exclusion records.\"\"\"\n\n root_path = self.path/self.EXCLUDED_DIR\n\n for full_id, path in self._excluded().items():\n if not path.exists():\n ex_path = root_path/full_id\n try:\n ex_path.unlink()\n except (OSError, FileNotFoundError) as err:\n pass\n","repo_name":"hpc/pavilion2","sub_path":"lib/pavilion/groups.py","file_name":"groups.py","file_ext":"py","file_size_in_byte":24554,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"3"}
+{"seq_id":"71382521040","text":"import autodynatrace\nfrom ddtrace import tracer\nfrom fastapi import APIRouter, Depends, Header\nfrom fastapi.security import HTTPAuthorizationCredentials, HTTPBearer\nfrom shared.infrastructure import HttpResponse\nfrom shared.infrastructure.settings import get_settings\nfrom worker.domain import (responses_layaway, responses_liveness,\n responses_readiness)\nfrom worker.domain.entities import Filter, Layaway\nfrom worker.infrastructure import WorkerController\n\n# layaway: /orders/api/v1/layaway/docs\n\nsettings = get_settings()\n\nversion = settings.API_VERSION\nnamespace = settings.NAMESPACE\nresource = settings.RESOURCE\nprefix = f'/{namespace}/api/{version}/{resource}'\n\ndescriptions = {\n 'liveness': \"Verifica que el servicio se encuentre disponible.\",\n 'readiness': \"Verifica que existan conexiones activas a MONGO/REDIS/FIREBASE.\",\n 'set_layaway': \"Agrega un cómic al apartado.\",\n 'get_layaway': \"Obtiene el apartado de cómics del usuario.\"\n}\n\nrouter = APIRouter(prefix=prefix)\n\n\n@router.get('/liveness', tags=['Health Checks'], responses=responses_liveness,\n summary=descriptions['liveness'])\n@autodynatrace.trace(f'{prefix}/liveness')\n@tracer.wrap(service='userauth', resource=f'GET {prefix}/liveness')\ndef liveness() -> dict:\n return {'status': 'Success'}\n\n\n@router.get('/readiness', tags=[\"Health Checks\"], responses=responses_readiness,\n summary=descriptions['readiness'])\n@autodynatrace.trace(f'{prefix}/readiness')\n@tracer.wrap(service='userauth', resource=f'GET {prefix}/liveness')\ndef readiness() -> HttpResponse:\n return WorkerController.readiness()\n\n\n@router.post(f'', tags=[\"Layaway\"], responses=responses_layaway,\n summary=descriptions['set_layaway'])\n@autodynatrace.trace(f'{prefix}')\n@tracer.wrap(service='userauth', resource=f'POST {prefix}/{resource}')\ndef set_layaway(body: Layaway,\n authorization: HTTPAuthorizationCredentials = Depends(HTTPBearer())) -> HttpResponse:\n \"\"\" Agrega un cómic al apartado. \"\"\"\n token = authorization.credentials\n return WorkerController.set_layaway(token, body)\n\n\n@router.get(f'', tags=[\"Layaway\"], responses=responses_layaway,\n summary=descriptions['get_layaway'])\n@autodynatrace.trace(f'{prefix}')\n@tracer.wrap(service='userauth', resource=f'GET {prefix}/{resource}')\ndef get_layaway(filter: Filter = Depends(),\n authorization: HTTPAuthorizationCredentials = Depends(HTTPBearer())) -> HttpResponse:\n \"\"\" Obtiene el apartado de cómics del usuario. \"\"\"\n token = authorization.credentials\n return WorkerController.get_layaway(token, filter)\n","repo_name":"LawlietJH/ComicShop","sub_path":"layaway/src/worker/infrastructure/worker_routes.py","file_name":"worker_routes.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38583838100","text":"#Author: Guido S. Nickels \n#Based on the utorrent maraschino module\n\nfrom flask import render_template\nfrom datetime import timedelta\nfrom maraschino import app, logger\nfrom maraschino.tools import *\nfrom rtorrent import RTorrent\n\ndef log_error(ex):\n\tlogger.log('RTORRENTDL :: EXCEPTION - %s' % ex, 'DEBUG')\n\n@app.route('/xhr/rtorrentdl/')\n@requires_auth\n\ndef xhr_rtorrentdl():\n\n\t# url qualification\n\tdef url_qualify(url_proto,url_host,url_port):\n\n\t\turl_host_part=str(url_host).partition('/')\n\n\t\t# validate host (kinda... just make sure it's not empty)\n\t\tif url_host_part[0]:\n\n\t\t\t# validate port\n\t\t\tif not url_port:\n\n\t\t\t\t# for http and https we can assume default service ports\n\t\t\t\tif url_proto == 'http':\n\t\t\t\t\turl_port = 80\n\t\t\t\telif url_proto == 'https':\n\t\t\t\t\turl_port = 443\n\t\t\t\telse:\n\t\t\t\t\traise Exception('port must be defined for protocol %s' % (url_proto))\n\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\turl_port=int(url_port)\n\t\t\t\texcept ValueError:\n\t\t\t\t\traise Exception('port must be a numeric value')\n\n\t\t\turl_qualified='%s://%s:%i%s%s' % (\n\t\t\t\turl_proto,\n\t\t\t\turl_host_part[0],\n\t\t\t\turl_port,\n\t\t\t\turl_host_part[1],\n\t\t\t\turl_host_part[2]\n\t\t\t)\n\n\t\t\treturn url_qualified\n\n\t\telse:\n\t\t\traise Exception('invalid host: %s' % (url_host[0]))\n\n\t# initialize empty list, which will be later populated with listing\n\t# of active torrents\n\ttorrentlist = list()\n\n\t# connection flag\n\tconnected = False\n\n\t# global rates\n\tdown_rate = 0.0\n\tup_rate = 0.0\n\n\trtorrent_url = None\n\trtorrent_user = None\n\trtorrent_password = None\n\n\ttry:\n\t\tif get_setting_value('rtorrent_host') is not None:\n\t\t\trtorrent_url = url_qualify(\n\t\t\t\tget_setting_value('rtorrent_proto'),\n\t\t\t\tget_setting_value('rtorrent_host'),\n\t\t\t\tget_setting_value('rtorrent_port')\n\t\t\t)\n\texcept Exception as ex:\n\t\tlog_error(ex)\n\n\ttry:\n\t\tif rtorrent_url:\n\t\t\t# user/password login is not implemented for scgi\n\t\t\tif get_setting_value('rtorrent_proto') != 'scgi':\n\t\t\t\trtorrent_user = get_setting_value('rtorrent_user')\n\t\t\t\trtorrent_password = get_setting_value('rtorrent_password')\n\n\t\t\tclient = RTorrent(rtorrent_url,rtorrent_user,rtorrent_password,True)\n\n\t\t\tif client is not None:\n\t\t\t\tconnected = True\n\t\t\t\tdown_rate = client.get_down_rate()\n\t\t\t\tup_rate = client.get_up_rate()\n\n\t\t\t# loop through each job and add all torrents to torrentlist()\n\t\t\tfor torrent in client.get_torrents():\n\t\t\t\t# friendly status and time left\n\t\t\t\ttime_left = -1\n\t\t\t\tif torrent.complete:\n\t\t\t\t\tif torrent.active:\n\t\t\t\t\t\tstatus = 'seeding'\n\t\t\t\t\telse:\n\t\t\t\t\t\tstatus = 'done'\n\t\t\t\telse:\n\t\t\t\t\tif torrent.active:\n\t\t\t\t\t\tif torrent.down_rate > 0:\n\t\t\t\t\t\t\ttime_left = str(timedelta(seconds = round(float(torrent.left_bytes) / torrent.down_rate)))\n\t\t\t\t\t\t\tstatus = 'leeching'\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tstatus = 'waiting'\n\t\t\t\t\telse:\n\t\t\t\t\t\tstatus = 'inactive'\n\n\t\t\t\t# get torrent file list\n\t\t\t\t# FIXME takes too much time and is not used anyway for now\n\t\t\t\t#torrent_filelist = []\n\t\t\t\t#for file_current in torrent.get_files():\n\t\t\t\t#\ttorrent_filelist.append(os.path.join(torrent.directory,file_current.path))\n\n\t\t\t\t# what's left?\n\t\t\t\tprogress = float(100.0 / torrent.size_bytes * (torrent.size_bytes-torrent.left_bytes))\n\n\t\t\t\t# append to list\n\t\t\t\ttorrentlist.append({\n\t\t\t\t\t'name': torrent.name,\n\t\t\t\t\t'info_hash': torrent.info_hash,\n\t\t\t\t\t'status': status,\n\t\t\t\t\t'state': torrent.state,\n\t\t\t\t\t'progress': progress,\n\t\t\t\t\t'time_left': time_left,\n\t\t\t\t\t'down_rate': torrent.down_rate,\n\t\t\t\t\t'up_rate': torrent.up_rate,\n\t\t\t\t\t'ratio': torrent.ratio\n\t\t\t\t#\t'folder': torrent.directory,\n\t\t\t\t#\t'files': '|'.join(torrent_filelist)\n\t\t\t\t})\n\n\t\t\t# no torrents -> empty list\n\t\t\tif not torrentlist.__len__():\n\t\t\t\ttorrentlist = None\n\n\texcept Exception as ex:\n\t\tlog_error(ex)\n\t\ttorrentlist = None\n\n\treturn render_template('rtorrentdl.html',\n\t\tconnected = connected,\n\t\ttorrentlist_scroll = get_setting_value('rtorrent_list_scroll'),\n\t\ttorrentlist = torrentlist,\n\t\tdown_rate = down_rate,\n\t\tup_rate = up_rate\n\t)\n","repo_name":"mrkipling/maraschino","sub_path":"modules/rtorrentdl.py","file_name":"rtorrentdl.py","file_ext":"py","file_size_in_byte":3861,"program_lang":"python","lang":"en","doc_type":"code","stars":426,"dataset":"github-code","pt":"3"}
+{"seq_id":"34070260296","text":"import requests\n\nfrom licenseware.common.constants import envs\nfrom licenseware.utils.dramatiq_redis_broker import broker\nfrom licenseware.utils.logger import log\n\n\nclass RegistrationFailed(Exception):\n ...\n\n\ndef registration_failed(retries_so_far: int, exception):\n return isinstance(exception, RegistrationFailed)\n\n\n@broker.actor(retry_when=registration_failed, queue_name=envs.QUEUE_NAME)\ndef register_all(payload: dict):\n\n if envs.DESKTOP_ENVIRONMENT:\n return\n\n log.info(\"Sending payload to registry-service\")\n\n registration = requests.post(\n url=envs.REGISTER_ALL_URL,\n json=payload,\n headers={\n \"Authorization\": envs.get_auth_token(),\n \"Content-type\": \"application/json\",\n \"Accept\": \"application/json\",\n },\n )\n\n if registration.status_code != 200:\n log.warning(registration.content)\n raise RegistrationFailed(\"Registration failed\")\n\n log.success(\"Registration successful!\")\n","repo_name":"licenseware/licenseware-sdk-v2","sub_path":"licenseware/registry_service/register_all.py","file_name":"register_all.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"26544875004","text":"# myAgents.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).\nfrom tracemalloc import start\nfrom game import Agent\nfrom util import PriorityQueue\nfrom searchProblems import PositionSearchProblem\nfrom collections import namedtuple\nimport util\nimport time\nimport search\nimport random\nfrom functools import partial\n\n\ndef find_shortest_food(position, gridList):\n val_list = []; least_element = 0; min_val = 99999; result = 0; \n for element in gridList:\n val = 0;\n val += abs(position[0] - element[0])\n val += abs(position[1] - element[1])\n val_list.append(val)\n if min_val > val:\n min_val = val\n least_element = element\n if min_val != 99999:\n return least_element, min_val\n else: \n return 0\n\ndef foodHeuristic(state, problem):\n\n position = state\n val = 0\n val += abs(position[0] - problem.goal[0])\n val += abs(position[1] - problem.goal[1])\n\n return val\n\ndef weirdHeuristic(state, problem):\n position = state\n val = 100\n for i in range(problem.Num):\n pac = problem.gameState.getPacmanPosition(i)\n val += 0.5 ** abs(position[0] - pac[0])\n val += 0.5 ** abs(position[1] - pac[1])\n return val\n\ndef manhattanHeuristic(goal, position, info={}):\n \"The Manhattan distance heuristic for a PositionSearchProblem\"\n xy1 = position\n xy2 = goal\n return abs(xy1[0] - xy2[0]) + abs(xy1[1] - xy2[1])\n\n\n\n\n\n\n\"\"\"\nIMPORTANT\n`agent` defines which agent you will use. By default, it is set to ClosestDotAgent,\nbut when you're ready to test your own agent, replace it with MyAgent\n\"\"\"\ndef createAgents(num_pacmen, agent_list= ['Pioneer']):\n ret_list = []\n for i in range(num_pacmen):\n index = i%len(agent_list)\n ret_list.append(eval(agent_list[index])(index=i) )\n return ret_list\n\n\nclass Pioneer(Agent):\n \"\"\"\n Implementation of your agent.\n \"\"\"\n def findPathToClosestDot(self, gameState):\n \"\"\"\n Returns a path (a list of actions) to the closest dot, starting from\n gameState.\n \"\"\"\n # Here are some useful elements of the startState\n numAgent = gameState.getNumAgents()\n pacPosList = []\n \n for i in range(numAgent):\n pacPosList.append(gameState.getPacmanPosition(i))\n startPosition = pacPosList[self.index]\n pacPosList[self.index] = (999,999)\n \n global food\n gridList = food.asList()\n length = len(gridList)\n\n \"*** YOUR CODE HERE ***\"\n \n if length > 0:\n least_element, min_val = find_shortest_food(startPosition, gridList)\n\n\n self.destination[0] = least_element\n\n # for i in range(numAgent):\n # pacPosList[i] = manhattanHeuristic(pacPosList[i], startPosition)\n # min_pac = min(pacPosList)\n # if min_pac < 7:\n # decision_goAway = random.randint(0,2)\n # if decision_goAway == 2:\n # ran = random.randint(0, length-1)\n # least_element = gridList[ran]\n # self.destination[0] = least_element\n # problem = AnyFoodSearchProblem(gameState = gameState, food = gridList, agentIndex = self.index, goal = self.destination, goal_val = min_val, agentLocation=pacPosList)\n # result = search.astar(problem, foodHeuristic)\n # return result\n\n problem = AnyFoodSearchProblem(gameState = gameState, food = gridList, agentIndex = self.index, goal = self.destination, goal_val = min_val, agentLocation=pacPosList)\n\n before = time.time()\n result = search.astar(problem, foodHeuristic)\n after = time.time()\n return result\n \n\n\n def getAction(self, state):\n global food\n food = state.getFood()\n if len(self.actionList) == 0:\n self.actionList = self.findPathToClosestDot(state)\n if food[self.destination[0][0]][self.destination[0][1]] == False:\n self.actionList = self.findPathToClosestDot(state)\n ret_val = self.actionList[0]\n self.actionList = self.actionList[1:]\n return ret_val\n\n\n\n def initialize(self):\n self.actionList = []\n self.destination = [(999,999)]\n self.dest_list = []\n \"\"\"\n Intialize anything you want to here. This function is called\n when the agent is first created. If you don't need to use it, then\n leave it blank\n \"\"\"\n\n\n\n\"\"\"\nPut any other SearchProblems or search methods below. You may also import classes/methods in\nsearch.py and searchProblems.py. (ClosestDotAgent as an example below)\n\"\"\"\n\nclass ClosestDotAgent(Agent):\n\n def findPathToClosestDot(self, gameState):\n \"\"\"\n Returns a path (a list of actions) to the closest dot, starting from\n gameState.\n \"\"\"\n # Here are some useful elements of the startState\n startPosition = gameState.getPacmanPosition(self.index)\n food = gameState.getFood()\n gridList = food.asList()\n \n \"*** YOUR CODE HERE ***\"\n if len(gridList) > 0:\n least_element, min_val = find_shortest_food(startPosition, gridList)\n problem = AnyFoodSearchProblem(gameState = gameState, food = gridList, agentIndex = self.index, goal = least_element)\n startPosition = least_element\n result = search.ucs(problem)\n\n return result\n\n def getAction(self, state):\n return self.findPathToClosestDot(state)[0]\n\n\nclass AnyFoodSearchProblem(PositionSearchProblem):\n \"\"\"\n A search problem for finding a path to any food.\n This search problem is just like the PositionSearchProblem, but has a\n different goal test, which you need to fill in below. The state space and\n successor function do not need to be changed.\n The class definition above, AnyFoodSearchProblem(PositionSearchProblem),\n inherits the methods of the PositionSearchProblem.\n You can use this search problem to help you fill in the findPathToClosestDot\n method.\n \"\"\"\n\n def __init__(self, food, gameState, goal, agentIndex, goal_val, agentLocation):\n \"Stores information from the gameState. You don't need to change this.\"\n # Store the food for later reference\n self.food = food\n self.gameState = gameState\n self.goal = goal[0]\n self.goal_list = goal\n self.goal_val = goal_val\n self.Num = self.gameState.getNumAgents()\n # Store info for the PositionSearchProblem (no need to change this)\n self.walls = gameState.getWalls()\n self.agentIndex = agentIndex\n self.agentLocation = agentLocation\n self.startState = gameState.getPacmanPosition(agentIndex)\n self.foundPacman = False\n self.costFn = lambda pos: 1\n # if self.agentIndex == 2:\n # self.costFn = lambda pos: .8 ** pos[1]\n # if self.agentIndex == 3:\n # self.costFn = lambda pos: 1.2 ** pos[1]\n # if self.agentIndex == 6:\n # self.costFn = lambda pos: .8 ** pos[0]\n # if self.agentIndex == 7:\n # self.costFn = lambda pos: 1.2 ** pos[0]\n self._visited, self._visitedlist, self._expanded = {}, [], 0 # DO NOT CHANGE\n self.counter = 0\n\n def isGoalState(self, state):\n \"\"\"\n The state is Pacman's position. Fill this in with a goal test that will\n complete the problem definition.\n \"\"\"\n\n (x,y) = state\n \n \n self.counter += 1\n if (x,y) in self.food:\n\n self.goal_list[0] = (x,y)\n return True\n else: \n return False\n \n # if self.foundPacman == True:\n # return False\n \n \n\n # if (x,y) in self.agentLocation:\n # self.foundPacman == True\n\n \"*** YOUR CODE HERE ***\"","repo_name":"xlhuang37/midterm-contest","sub_path":"myAgents.py","file_name":"myAgents.py","file_ext":"py","file_size_in_byte":8518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12520255945","text":"\"\"\"\n Created by Dhaif BEKHA, (dhaif.bekha@cea.fr).\n\n \"\"\"\nimport pandas as pd\nfrom subprocess import Popen, PIPE\nimport os\nimport numpy as np\n\"\"\"\n\nGoal\n----\nCreate a Fractional Anisotropy (FA) template image based \non the NAIS controls children group. After that, we compute\nthe white matter skeleton based on the FA template. The FA template and\nthe corresponding white matter skeleton can be directly use with\nthe TBSS methodology.\n\nThis code use python wrapper's code around FSL and DTI-TK,\nsome of A. Grigis (antoine.grigis@cea.fr), and some of mine. Please visit :\nhttps://github.com/neurospin/pyconnectome/tree/master/pyconnectome.\n\n\nAll the steps for the creation of the template are done with the help\nof DTI-TK and can be found at:\nhttp://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.HomePage\n\nAll the DTI-TK template preparation steps for the TBSS pipeline can be found at:\nhttp://dti-tk.sourceforge.net/pmwiki/pmwiki.php?n=Documentation.TBSS\n\nAll the steps of the TBSS pipeline can be found at:\nhttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/TBSS/UserGuide\n----\n\nStep 1\n------\nConvert the DTIFIT FSL output to the DTI-TK tensors\nfile format.\n\nStep 2\n------\nCompute a tensors template from a subset group of \nsubjects. This step increase in time with the number\nof subject.\n\nStep 3\n------\nWarp all subject in the tensor template space \npreviously computed\n\nStep 4\n------\nGenerate a final refine template by taking\nthe mean of all normalized tensors image \npreviously computed. \n\nStep 5\n------\nOn the final template, compute the 1mm3\nFA template map.\n\nStep 6\n------\nOn the previously computed FA map, compute\nthe skeleton of major tracks.\n\n\"\"\"\n# read subjects list\nsubjects = list(pd.read_csv('/neurospin/grip/protocols/MRI/Ines_2018/controls_list.txt',\n header=None)[0])\n# main TBSS analysis directory\ntbss_directory = '/media/db242421/db242421_data/DTI_TBSS_M2Ines/controls_dtitk/dtitk_native_tensors'\n\n# PyConnectome script directory: Change according to you're installation !!\npyconnectome_script = '/media/db242421/db242421_data/pyconnectome/pyconnectome/scripts'\n\n# Number of controls used for the template creation\nsubset_of_controls = 12\n\n# Template output directory\ntemplate_outdir = '/media/db242421/db242421_data/DTI_TBSS_M2Ines/controls_dtitk/' \\\n 'controls_dti_template'\n\n# Normalized tensors output directory\nnormalized_tensors_outdir = '/media/db242421/db242421_data/DTI_TBSS_M2Ines/controls_dtitk/normalized_tensors'\n\n# Final template output directory for the TBSS analysis\nfinal_high_res_template_outdir = '/media/db242421/db242421_data/DTI_TBSS_M2Ines/controls_dtitk/final_template'\n\n\"\"\"\nConvert DTIFIT fsl output to DTI-TK tensors file format.\n\"\"\"\nfor subject in subjects:\n print('Converting FSL tensor to DTI-TK tensor file format for {}'.format(subject))\n\n # Build the tensor file format requested for dti-tk\n fsl_to_dtitk = [\"python\",\n \"{}\".format(os.path.join(pyconnectome_script,\n 'pyconnectome_dtitk_import_tensors')),\n \"-b\", \"{}\".format(os.path.join(tbss_directory,\n subject, \"dtifit\", \"dtifit_\" + subject)),\n \"-s\", \"{}\".format(subject),\n \"-o\", \"{}\".format(os.path.join(tbss_directory, subject))]\n fsl_to_dtitk_process = Popen(fsl_to_dtitk, stdout=PIPE)\n fsl_to_dtitk_output, fsl_to_dtitk_error = fsl_to_dtitk_process.communicate()\n\n\n\"\"\"\nRandomly choose control subject to create a bootstrapped sample.\n\"\"\"\n# Extract a subset of controls for the creation of the template\nsubset_of_controls_nip = list(np.random.choice(a=subjects, size=subset_of_controls))\n# build full path to the dti-tk tensors file of the subset\nsubset_controls_tensors_path = [os.path.join(tbss_directory, s_subset,\n s_subset, 'dtifit_' + s_subset + '_dtitk.nii.gz')\n for s_subset in subset_of_controls_nip]\n\n\"\"\"\nBuild the DTI template based on the bootstrapped sample.\n\"\"\"\n# For formatting purposes, I have to write down the command in a text\nwith open(os.path.join(template_outdir, \"cmd_create_template.txt\"), 'w') as command_txt:\n command_txt.writelines('python {}/pyconnectome_dtitk_create_templates '.format(pyconnectome_script))\n command_txt.writelines('-t ')\n for tensor_path in range(len(subset_controls_tensors_path)):\n command_txt.writelines('{} '.format(subset_controls_tensors_path[tensor_path]))\n command_txt.writelines('-o {} '.format(template_outdir))\n command_txt.writelines('-V 2')\n\nwith open(os.path.join(template_outdir, \"cmd_create_template.txt\"), 'r') as command_txt:\n dtitk_create_template = command_txt.read()\n\ndtitk_create_template_process = Popen(dtitk_create_template.split(), stdout=PIPE)\ndtitk_create_template_output, dtitk_create_template_error = \\\n dtitk_create_template_process.communicate()\n\n# Write command output\nwith open(os.path.join(template_outdir, \"output_create_template.txt\"), 'w') as create_template_out:\n create_template_out.write(dtitk_create_template_output.decode('utf-8'))\n\n\"\"\"\nWarp all controls tensors in the template space, with final resolution \nof 1mm3 \n\"\"\"\n# Loop over all controls subject\nfor subject in subjects:\n dtitk_register = [\"python\", os.path.join(pyconnectome_script,\n \"pyconnectome_dtitk_register\"),\n \"-t\", os.path.join(tbss_directory, subject, subject,\n \"dtifit_\" + subject + \"_dtitk.nii.gz\"),\n \"-s\", subject,\n \"-e\", os.path.join(template_outdir,\n \"mean_diffeomorphic_initial6.nii.gz\"),\n \"-o\", normalized_tensors_outdir,\n \"-b\", os.path.join(template_outdir, \"mask.nii.gz\"),\n \"-V 2\"]\n dtitk_register_process = Popen(dtitk_register, stdout=PIPE)\n dtitk_register_output, dtitk_register_error = dtitk_register_process.communicate()\n\n\"\"\"\nGenerate the population specific DTI template with\nisotropic 1mm3 spacing\n\"\"\"\n# Write down in a text file the path to the normalized\n# tensors for all controls\nwith open(os.path.join(final_high_res_template_outdir,\n \"subjects_normalized.txt\"), \"w\") as tensors_path_file:\n for subject in subjects:\n tensors_path_file.write(os.path.join(normalized_tensors_outdir, subject,\n \"dtifit_\" + subject + \"_dtitk_diffeo.nii.gz\"))\n tensors_path_file.write(\"\\n\")\n# Full path to final tensor template to be written\nmean_final_high_res = os.path.join(final_high_res_template_outdir,\n \"mean_final_high_res.nii.gz\")\n# Compute the final template with the TVMean command\ncompute_final_template = [\"TVMean\",\n \"-in\", os.path.join(final_high_res_template_outdir,\n \"subjects_normalized.txt\"),\n \"-out\", mean_final_high_res,\n \"-type\", \"ORIGINAL\",\n \"-interp\", \"LEI\"\n ]\nprint(\"Computing the final tensor template with isotropic resolution...\")\ncompute_final_template_process = Popen(compute_final_template, stdout=PIPE)\ncompute_final_template_output, compute_final_template_error = \\\n compute_final_template_process.communicate()\n\n# Write command output\nwith open(os.path.join(final_high_res_template_outdir,\n \"TVMean_output.txt\"), \"w\") as TVMean_output:\n TVMean_output.write(compute_final_template_output.decode('utf-8'))\n\n\"\"\"\nCompute the high resolution FA template map from\nthe previously computed tensor template\n\"\"\"\ngenerate_FA_template = [\"TVtool\",\n \"-in\", mean_final_high_res,\n \"-fa\"]\ngenerate_FA_template_process = Popen(generate_FA_template, stdout=PIPE)\ngenerate_FA_template_output, generate_FA_template_error = \\\n generate_FA_template_process.communicate()\n# Write command output\nwith open(os.path.join(final_high_res_template_outdir,\n \"TVtool_FA_output.txt\"), \"w\") as TVtool_output:\n TVtool_output.write(compute_final_template_output.decode('utf-8'))\n\n\n\"\"\"\nOptional: Compute the RGB image from\nthe final tensor template. Very convenient\nwith ITK-SNAP viewer for example.\n\n\"\"\"\ngenerate_RGB_template = [\"TVtool\",\n \"-in\", mean_final_high_res,\n \"-rgb\"]\ngenerate_RGB_template_process = Popen(generate_RGB_template, stdout=PIPE)\ngenerate_RGB_template_output, generate_RGB_template_error = \\\n generate_RGB_template_process.communicate()\n# Write command output\nwith open(os.path.join(final_high_res_template_outdir,\n \"TVtool_RGB_output.txt\"), \"w\") as TVtool_RGB_output:\n TVtool_RGB_output.write(generate_RGB_template_output.decode('utf-8'))\n\n\"\"\"\nCompute the skeleton from\nthe FA map template\n\n\"\"\"\nskeletonize_FA = [\"tbss_skeleton\",\n \"-i\", os.path.join(final_high_res_template_outdir,\n \"mean_final_high_res_fa.nii.gz\"),\n \"-o\", os.path.join(final_high_res_template_outdir,\n \"mean_final_high_res_fa_skeleton.nii.gz\")\n ]\nskeletonize_FA_process = Popen(skeletonize_FA, stdout=PIPE)\nskeletonize_FA_output, skeletonize_FA_error = \\\n skeletonize_FA_process.communicate()\n# Write command output\nwith open(os.path.join(final_high_res_template_outdir,\n \"tbss_skeleton_RGB_output.txt\"), \"w\") as skeleton_output:\n skeleton_output.write(skeletonize_FA_output.decode('utf-8'))\n\n\"\"\"\nEnd of the creation of the FA and corresponding skeleton template.\nThe output of this script can now replace the standard templates provide by \nthe FSL software for the TBSS analysis.\n\"\"\"\n","repo_name":"ConPagnon/conpagnon","sub_path":"conpagnon/examples_and_test/tbss_create_controls_template.py","file_name":"tbss_create_controls_template.py","file_ext":"py","file_size_in_byte":9948,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"71626089682","text":"from access_token import TOKEN\nimport requests\nfrom time import sleep\n\n\ndef write_csv(list_groups: list) -> None:\n \"\"\"Записывает id групп в файл (они могут повторяться)\"\"\"\n with open('groups_id.csv', 'a', encoding='utf-8') as file:\n for group in list_groups:\n if not group['is_closed']:\n file.write(f\"{group['id']}\\n\")\n\n\ndef get_a_list_of_groups(key_word: str) -> list:\n \"\"\"по ключевому слову возвращает список групп\"\"\"\n params = {\n 'q': key_word,\n 'access_token': TOKEN,\n 'v': 5.103,\n 'type': 'group',\n 'sort': 0,\n \"count\": 1000,\n 'offset': 0\n }\n response = requests.get(f'https://api.vk.com/method/groups.search', params)\n print(response.status_code)\n list_groups = response.json()['response']['items']\n return list_groups\n\n\ndef main():\n key_words = [\n \"Путин\",\n \"политика\",\n \"протест\",\n \"революция\",\n \"россия\",\n \"полика новости\",\n \"путин новости\",\n \"путин бог\",\n \"путин госсовет\",\n \"политика новости\",\n \"политика россии\",\n \"политика сегодня\",\n \"политика в россии\",\n 'Единая Россия',\n 'КПРФ',\n 'ЛДПР',\n 'Справедливая Россия'\n 'Коммунисты России',\n 'Яблоко',\n 'Российская партия пенсионеров за социальную справедливость',\n 'Родина',\n 'Партия Роста',\n 'Зеленые',\n 'ПАРНАС',\n 'Патриоты России',\n 'Гражданская платформа и Гражданская сила',\n \"навальный митинг\",\n \"навальный новости\"\n ]\n for key_word in key_words:\n sleep(1)\n write_csv(get_a_list_of_groups(key_word))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"DenZaytsev/vk_cluster","sub_path":"search_groups.py","file_name":"search_groups.py","file_ext":"py","file_size_in_byte":2173,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22512645702","text":"import config\nfrom distexprunner import *\n\n\nparameter_grid = ParameterGrid(\n dramGB=[150],\n factor_mem_ssd=[10],\n # numberNodes= range(1,6),\n numberNodes= [5],\n pp=[2],\n fp=[1],\n workers = range(1,25),\n partitioned= [\"OLAP_partitioned\", \"noOLAP_partitioned\"],\n # partitioned= [\"OLAP_partitioned\"], \n # partitioned= [\"noOLAP_partitioned\"],\n)\n\n\n@reg_exp(servers=config.server_list)\ndef compile(servers):\n servers.cd(\"/home/tziegler/scalestore/build\")\n cmake_cmd = f'cmake -DSANI=OFF -DCMAKE_BUILD_TYPE=Release ..'\n procs = [s.run_cmd(cmake_cmd) for s in servers]\n assert(all(p.wait() == 0 for p in procs))\n\n make_cmd = f'make -j'\n procs = [s.run_cmd(make_cmd) for s in servers]\n assert(all(p.wait() == 0 for p in procs))\n\n \n\n@reg_exp(servers=config.server_list, params=parameter_grid, raise_on_rc=False)\ndef ycsbBenchmark(servers, dramGB, factor_mem_ssd, numberNodes ,pp,fp,workers, partitioned):\n servers.cd(\"/home/tziegler/scalestore/build/frontend\")\n\n cmds = []\n for i in range(0,numberNodes):\n print(i)\n cmd = f'blkdiscard {servers[i].ssdPath}'\n cmds += [servers[i].run_cmd(cmd)]\n \n if not all(cmd.wait() == 0 for cmd in cmds):\n return Action.RESTART\n \n cmds = []\n for i in range(0, numberNodes):\n cmd = f'numactl --membind=0 --cpunodebind=0 ./OLAP -worker={workers} -dramGB={dramGB} -nodes={numberNodes} -messageHandlerThreads=2 -ownIp={servers[i].ibIp} -pageProviderThreads={pp} -coolingPercentage=10 -freePercentage={fp} -csvFile=olap_scale_server.csv -OLAP_run_for_seconds=30 --ssd_path={servers[i].ssdPath} --ssd_gib=2000 --OLAP_factor_mem_ssd={factor_mem_ssd} -{partitioned} -tag={partitioned} -evictCoolestEpochs=0.5'\n cmds += [servers[i].run_cmd(cmd)]\n \n if not all(cmd.wait() == 0 for cmd in cmds):\n return Action.RESTART\n","repo_name":"DataManagementLab/ScaleStore","sub_path":"distexperiments/experiments/olap_scale_servers.py","file_name":"olap_scale_servers.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"3"}
+{"seq_id":"43179614330","text":"def play():\n for i in range(1, N + 1):\n if li[1][i] == 1:\n stack.append(i)\n li[1][i] = 0\n li[i][1] = 0\n\n for j in range(len(stack)):\n for i in range(1, N + 1):\n if li[stack[j]][i] == 1 and i not in stack:\n stack.append(i)\n li[j][i] = 0\n li[i][j] = 0\n\n\nT = int(input())\n\nfor TC in range(1, T + 1):\n N, M = map(int, input().split())\n li = [[0] * (N + 1) for _ in range(N + 1)]\n v = [0] * (N + 1)\n for i in range(M):\n a, b = map(int, input().split())\n li[a][b] = 1\n li[b][a] = 1\n stack = []\n play()\n print('#%d %d' %(TC, len(stack)))\n","repo_name":"sondongmin0419/study","sub_path":"python/s_5521 상원이의 생일파티.py","file_name":"s_5521 상원이의 생일파티.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35691353823","text":"import psycopg2\r\nimport os\r\nfrom os import environ\r\n\r\n#Example python code to connect to a PG Database, pull back the table and add new records\r\n#really basic\r\n\r\n#execute using account equinox on RHEL8 VM\r\n\r\ndef insert_record(cur,last_id):\r\n cur.execute(\"INSERT INTO event (id, app, ac3,ac4,submit,staffid) VALUES \\\r\n (%s, %s, %s, %s, %s, %s)\", ((str(int(last_id)+1)),\"Jira\",\"BigData\",\"MSS\",\"2021-01-31\",\"04992466\"))\r\n\r\n\r\ndef print_table(results):\r\n\r\n for row in results:\r\n print('id: %s , app: %s , ac3: %s , ac4: %s, submit: %s, staffid %s' % (row[0],row[1],row[2],row[3],row[4],row[5]))\r\n last_id=row[0]\r\n return last_id \r\n\r\n\r\ndef main():\r\n \r\n #Connect\r\n conn = psycopg2.connect(\"dbname=equinox_db user=equinox\")\r\n cur = conn.cursor()\r\n \r\n \r\n #extract data\r\n cur.execute(\"SELECT * FROM event;\")\r\n results=cur.fetchall() \r\n \r\n \r\n #print table and get last ID\r\n last_id=print_table(results)\r\n \r\n #insert a new record\r\n insert_record(cur,last_id)\r\n\r\n #Close connection\r\n conn.commit() \r\n cur.close()\r\n conn.close()\r\n\r\nif __name__ == \"__main__\":\r\n\r\n main()","repo_name":"lewchenko/pgconnector","sub_path":"pgconnector.py","file_name":"pgconnector.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70940832403","text":"import netCDF4 as nc\nimport numpy as np\nimport os\nimport scipy.stats as st\nimport operator\nfrom collections import OrderedDict\nimport constants as CC\nimport scipy as sp\nfrom scipy import ndimage as ndi\nimport time\nfrom skimage import measure\nfrom scipy.spatial.distance import pdist\nfrom matplotlib import pyplot as plt\n\n\ndef stats(tab):\n return [np.mean(tab),np.min(tab),np.max(tab),np.std(tab),np.median(tab),np.percentile(tab,5),np.percentile(tab,95)]\n\ndef do_cyclic(label, choice=(-2,-1), rename=True) :\n if isinstance(choice,int): choice=(choice,)\n if not isinstance(choice,tuple): raise NameError(choice+\" is not a valid choice for cyclic conditions.\\nUse None or a tuple\")\n objects = label\n shape = objects.shape\n listObj = np.unique(objects[objects>0]).tolist()\n nbr0 = len(listObj)\n tmp = np.moveaxis(objects, list(choice), range(-len(choice),0)) # swap cyclic axes to last axes\n dim = len(shape)\n # reshape to 4D if needed\n if dim==4 :\n n0,n1,n2,n3 = shape\n elif dim==3 :\n n1,n2,n3 = shape\n n0 = 1\n tmp = tmp[None,:,:,:]\n elif dim==2 :\n n2,n3 = shape\n n0 = 1 ; n1 = 1\n tmp = tmp[None,None,:,:]\n for i0 in range(n0) :\n for i1 in range(n1) :\n for i2 in range(n2) :\n # last dim is cyclic\n if (tmp[i0,i1,i2,n3-1] and tmp[i0,i1,i2,0] and (tmp[i0,i1,i2,n3-1]!=tmp[i0,i1,i2,0])) :\n num = tmp[i0,i1,i2,n3-1]\n tmp[tmp==num] = tmp[i0,i1,i2,0]\n if rename : tmp[tmp==listObj.pop(-1)]=num\n if len(choice)==2 :\n for i3 in range(n3) :\n # also one before last dim is cyclic\n if (tmp[i0,i1,n2-1,i3] and tmp[i0,i1,0,i3] and (tmp[i0,i1,n2-1,i3]!=tmp[i0,i1,0,i3])) :\n num = tmp[i0,i1,n2-1,i3]\n tmp[tmp==num] = tmp[i0,i1,0,i3]\n if rename : tmp[tmp==listObj.pop(-1)]=num\n tmp = tmp.reshape(shape)\n objects = np.moveaxis(tmp, range(-len(choice),0), list(choice))\n nbr = len(np.unique(objects[objects>0]))\n #print \"\\t', nbr0 - nbr, 'objects on the borders'\n #self.nbr=nbr\n return objects\n\n\"\"\" Statistical functions for binary cloud masks. \"\"\"\n\"\"\" Extracted from Typhon website \"\"\"\ndef get_cloudproperties(cloudmask, connectivity=1):\n \"\"\"Calculate basic cloud properties from binary cloudmask.\n\n Note:\n All parameters are calculated in pixels!!\n\n See also:\n :func:`skimage.measure.label`:\n Used to find different clouds. \n :func:`skimage.measure.regionprops`:\n Used to calculate cloud properties.\n\n Parameters:\n cloudmask (ndarray): 2d binary cloud mask.\n connectivity (int): Maximum number of orthogonal hops to consider\n a pixel/voxel as a neighbor (see :func:`skimage.measure.label`).\n\n Returns:\n list:\n List of :class:`RegionProperties`\n (see :func:`skimage.measure.regionprops`)\n \"\"\"\n cloudmask[np.isnan(cloudmask)] = 0\n\n labels = measure.label(cloudmask, connectivity=connectivity)\n #print labels.shape\n #plt.contourf(cloudmask);plt.colorbar();plt.show()\n #plt.contourf(labels);plt.title('cloud properties');plt.colorbar();plt.show()\n return measure.regionprops(labels)\n\n\ndef neighbor_distance(cloudproperties, mindist=0):\n \"\"\"Calculate nearest neighbor distance for each cloud.\n periodic boundaries\n\n Note: \n Distance is given in pixels.\n\n See also: \n :class:`scipy.spatial.cKDTree`:\n Used to calculate nearest neighbor distances. \n\n Parameters: \n cloudproperties (list[:class:`RegionProperties`]):\n List of :class:`RegionProperties`\n (see :func:`skimage.measure.regionprops` or\n :func:`get_cloudproperties`).\n mindist\n Minimum distance to consider between centroids.\n If dist < mindist: centroids are considered the same object\n\n Returns: \n ndarray: Nearest neighbor distances in pixels.\n \"\"\"\n centroids = [prop.centroid for prop in cloudproperties]\n indices = np.arange(len(centroids))\n neighbor_distance = np.zeros(len(centroids))\n centroids_array = np.asarray(centroids)\n\n for n, point in enumerate(centroids):\n #print n, point\n # use all center of mass coordinates, but the one from the point\n mytree = sp.spatial.cKDTree(centroids_array[indices != n])\n dist, indexes = mytree.query(point,k=len(centroids)-1)\n #print n,dist\n #print n,point,indexes,dist\n #ball = mytree.query_ball_point(point,mindist)\n distsave=dist[dist>mindist] \n #print distsave\n\n #if abs(centroids_array[indexes[0]][0]-point[0])>100.:\n # print centroids_array[indexes[0]]\n # print n,point#,[centroids_array[ij] for ij in indexes]\n\n neighbor_distance[n] = distsave[0]\n\n return neighbor_distance\n\ndef iorg(neighbor_distance, cloudmask):\n \"\"\"Calculate the cloud cluster index 'I_org'.\n\n See also: \n :func:`scipy.integrate.trapz`:\n Used to calculate the integral along the given axis using\n the composite trapezoidal rule.\n\n Parameters: \n neighbor_distance (list or ndarray): Nearest neighbor distances. \n Output of :func:`neighbor_distance`. \n cloudmask (ndarray): 2d binary cloud mask.\n\n Returns:\n float: cloud cluster index I_org.\n\n References: \n Tompkins, A. M., and A. G. Semie (2017), Organization of tropical \n convection in low vertical wind shears: Role of updraft entrainment, \n J. Adv. Model. Earth Syst., 9, 1046-1068, doi:10.1002/2016MS000802.\n \"\"\"\n nn_sorted = np.sort(neighbor_distance)\n NL = len(neighbor_distance)\n \n nncdf = np.array(range(NL)) / float(NL)\n #print len(neighbor_distance),nncdf,nn_sorted\n \n # theoretical nearest neighbor cumulative frequency\n # distribution (nncdf) of a random point process (Poisson)\n lamb = (NL /\n float(cloudmask.shape[0] * cloudmask.shape[1]))\n nncdf_poisson = 1 - np.exp(-lamb * np.pi * nn_sorted**2)\n\n return sp.integrate.trapz(y=nncdf, x=nncdf_poisson)\n\ndef scai(cloudproperties, cloudmask, connectivity=1):\n \"\"\"Calculate the 'Simple Convective Aggregation Index (SCAI)'. \n\n The SCAI is defined as the ratio of convective disaggregation\n to a potential maximal disaggregation.\n\n See also: \n :func:`scipy.spatial.distance.pdist`:\n Used to calculate pairwise distances between cloud entities. \n :func:`scipy.stats.mstats.gmean`:\n Used to calculate the geometric mean of all clouds in pairs. \n\n Parameters:\n cloudproperties (list[:class:`RegionProperties`]):\n Output of function :func:`get_cloudproperties`. \n cloudmask (ndarray): 2d binary cloud mask.\n connectivity (int): Maximum number of orthogonal hops to consider\n a pixel/voxel as a neighbor (see :func:`skimage.measure.label`).\n mask (ndarray): 2d mask of non valid pixels.\n\n Returns:\n float: SCAI.\n\n References: \n Tobin, I., S. Bony, and R. Roca, 2012: Observational Evidence for \n Relationships between the Degree of Aggregation of Deep Convection, \n Water Vapor, Surface Fluxes, and Radiation. J. Climate, 25, 6885-6904,\n https://doi.org/10.1175/JCLI-D-11-00258.1\n\n \"\"\"\n centroids = [prop.centroid for prop in cloudproperties]\n\n # number of cloud clusters\n N = len(centroids)\n\n # potential maximum of N depending on cloud connectivity\n if connectivity == 1:\n chessboard = np.ones(cloudmask.shape).flatten()\n # assign every second element with \"0\"\n chessboard[np.arange(1, len(chessboard), 2)] = 0\n # reshape to original cloudmask.shape\n chessboard = np.reshape(chessboard, cloudmask.shape)\n # inlcude NaNmask\n chessboard[np.isnan(cloudmask)] = np.nan\n N_max = np.nansum(chessboard)\n elif connectivity == 2: # it doesn't work\n #chessboard = np.ones(cloudmask.shape).flatten() # add by Florent\n chessboard[np.arange(1, cloudmask.shape[0], 2), :] = 0\n chessboard = np.reshape(chessboard, cloudmask.shape)\n chessboard[np.isnan(cloudmask)] = np.nan\n N_max = np.sum(chessboard)\n else:\n raise ValueError('Connectivity argument should be `1` or `2`.')\n\n # distance between points (center of mass of clouds) in pairs\n di = pdist(centroids, 'euclidean')\n # order-zero diameter\n D0 = sp.stats.mstats.gmean(di)\n\n # characteristic length of the domain (in pixels): diagonal of box\n L = np.sqrt(cloudmask.shape[0]**2 + cloudmask.shape[1]**2)\n\n return N / N_max * D0 / L * 1000\n\ndef adjust_spines(ax, spines):\n for loc, spine in ax.spines.items():\n if loc in spines:\n spine.set_position(('outward', 0)) # outward by 10 points\n spine.set_smart_bounds(True)\n else:\n spine.set_color('none') # don't draw spine\n\n # turn off ticks where there is no spine\n if 'left' in spines:\n ax.yaxis.set_ticks_position('left')\n else:\n # no yaxis ticks\n ax.yaxis.set_ticks([])\n\n if 'bottom' in spines:\n ax.xaxis.set_ticks_position('bottom')\n else:\n # no xaxis ticks\n ax.xaxis.set_ticks([])\n\ndef timing(f):\n def wrap(*args):\n time1 = time.time()\n ret = f(*args)\n time2 = time.time()\n #print '%s function took %0.3f ms' % (f.func_name, (time2-time1)*1000.0)\n return ret\n return wrap\n\ndef nc_dataset_list(files):\n data = {}\n for ss in files.keys():\n #print files\n #print ss\n #print files[ss]\n data[ss]=nc.Dataset(files[ss],'r')\n return data\n\ndef mkdir(path):\n try:\n os.mkdir(path)\n except:\n pass\n\ndef mask999(data):\n NPV = 999.\n data = np.ma.masked_values(data, NPV)\n return data\n\ndef mask0(data):\n NPV = 0.\n data = np.ma.masked_values(data, NPV)\n return data\n\ndef createoffset(offset):\n offset['LWP']=1000.\n rho0=1.14; RLVTT=2.5e6;RCP=1004.\n offset['E0']=rho0*RLVTT\n offset['Q0']=rho0*RCP\n offset['RC']=1000.\n offset['RV']=1000.\n offset['RT']=1000.\n offset['RCT']=1000.\n offset['RVT']=1000.\n offset['DTHRAD']=86400.\n offset['RNPM']=1000.\n return offset\n\ndef readvalues(file,typ=None,over=None):\n if typ is None:\n typ = '\\r'\n if over is None:\n over= 1\n f = open(file, 'r'); data = f.read().split(typ)\n dataout = [ij.split('\\t') for ij in data[over:]];f.close()\n return dataout\n\ndef resiz(tmp): # should be removed by pre-treatment\n tmp = np.squeeze(tmp)\n return tmp\n\ndef removebounds(tmp):\n if len(tmp.shape) == 1.:\n tmp = tmp[1:-1]\n elif len(tmp.shape) == 3.:\n tmp = tmp[1:-1,1:-1,1:-1]\n else:\n #print 'Problem removebounds'\n pass\n return tmp\n\ndef cond2plot(case,prefix,boxch=False):\n boxes = None\n if boxch:\n offbox = 15\n boxes = [0,200,0,200] # x1,x2,y1,y2\n boxes = [ij+offbox for ij in boxes]\n\n xycas = {}\n xycas['IHOP'] = {'008':([50,250],[410,410])} #[[x1,x2],[y1,y2]]\n try:\n xy = xy[case][prefix]\n except:\n #print 'Error in xy for ',case,prefix\n xy = None\n return boxes,xy\n\n\ndef selboxes(tmp,boxes,idx=None):\n if len(tmp.shape) == 1. and idx is not None:\n tmp = tmp[idx[0]:idx[1]]\n elif len(tmp.shape) == 3.:\n idxy=(boxes[2],boxes[3])\n idxx=(boxes[0],boxes[1])\n tmp = tmp[:,idxy[0]:idxy[1],idxx[0]:idxx[1]]\n else:\n #print 'Problem selboxes ',tmp.shape\n pass\n return tmp\n\n\ndef anomcalc(tmp,ip=0):\n mean = np.mean(np.mean(tmp,axis=ip+2),axis=ip+1)\n if ip == 0:\n mean = np.repeat(np.repeat(mean[ :,np.newaxis, np.newaxis],tmp.shape[ip+1],axis=ip+1),tmp.shape[ip+2],axis=ip+2)\n data = tmp-mean\n return data\n \ndef giveminmaxflux(vars):\n # vars need to be 2D at least\n levels,vmin,vmax,vdiff = None,None,None,None\n if vars[1] == \"WT\" or vars[1] == \"W\":\n if vars[0] == \"THLM\":\n #vmin,vmax,vdiff = -0.04,0.01,0.01 # old\n vmin,vmax,vdiff = -0.02,0.005,0.005\n if vars[0] == \"RNPM\":\n vmin,vmax,vdiff = -0.005,0.02,0.005\n\n if vmin is not None:\n nb = abs(vmax-vmin)/vdiff+1\n levels = [vmin + float(ij)*vdiff for ij in range(int(nb))]\n return levels\n\ndef giveminmax(var,aa,offset):\n levels,vmin,vmax,vdiff,cmap = None,None,None,None,None\n if var == \"WT\" or var == \"W\":\n if aa=='Anom' or aa=='Mean':\n vmin,vmax,vdiff = -0.8,0.8,0.2 #0.05\n elif var == \"WTMEAN\":\n if aa=='Anom' or aa=='Mean':\n vmin,vmax,vdiff = -0.3,0.3,0.05\n elif var[0:2] == \"TH\": # or var == \"THLM\" or var == \"THV\":\n if aa=='Anom':\n #vmin,vmax,vdiff = -3.0,3.0,0.25\n vmin,vmax,vdiff = -0.5,0.5,0.1\n elif var == \"LWP\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0.00,0.14,0.002\n if aa=='Anom':\n vmin,vmax,vdiff = -0.08,0.08,0.005\n elif var == \"SVT004\" or var == \"SVT001\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0,26,2\n if aa=='Anom':\n vmin,vmax,vdiff = -12,12,4\n elif var == \"SVT005\" or var == \"SVT002\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0,800,100\n if aa=='Anom':\n vmin,vmax,vdiff = -400,400,50\n elif var == \"SVT006\" or var == \"SVT003\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0,900,100\n if aa=='Anom':\n vmin,vmax,vdiff = -200,200,50\n elif var == \"RVT\" or var == \"RNPM\":\n if aa=='Anom':\n vmin,vmax,vdiff = -6e-4,6e-4,1e-4\n elif var == \"RCT\":\n if aa=='Mean':\n vmin,vmax,vdiff = 0,5e-4,5e-5 #0,5e-4,1e-5 (used for 3D)\n if aa=='Anom':\n cmap = 'RdBu'\n vmin,vmax,vdiff = -4e-4,4e-4,1e-4\n elif var == \"DTHRAD\":\n if aa=='Anom':\n vmin,vmax,vdiff = -10,10,1\n vmin,vmax,vdiff = [ij/offset[var] for ij in (vmin,vmax,vdiff)]\n elif var == \"DIVUV\":\n if aa=='Anom' or aa=='Mean':\n vmin,vmax,vdiff = -0.02,0.02,0.005 #0.05\n elif var == \"UT\" or var == \"VT\":\n if aa=='Anom':\n vmin,vmax,vdiff = -2.,2.,0.5 #0.05\n\n\n if vmin is not None:\n if var in offset.keys():\n vmin,vmax,vdiff = [ij*offset[var] for ij in (vmin,vmax,vdiff)]\n nb = abs(vmax-vmin)/vdiff+1\n levels = [vmin + float(ij)*vdiff for ij in range(int(nb))]\n #print var,aa,levels\n\n \n return levels,vmin,vmax,vdiff,cmap\n\n\ndef selectdata(data,zyx,x,y,z,xy=None):\n tmpdata = [x,y,z]\n nbsum = sum([ij==None for ij in tmpdata])\n title = ''\n\n if len(data.shape)==3 and (nbsum != 3 or nbsum != 0): # number of None\n if xy is not None:\n axis = [zyx[0],np.arange(xy.shape[1])]\n tmp = np.zeros((len(axis[0]),len(axis[1])))\n for ij in range(xy.shape[1]):\n tmp[:,ij]=data[:,xy[1,ij],xy[0,ij]]\n data = tmp\n elif x is not None:\n data=data[:,:,x];axis=[zyx[0],zyx[1]];title+='_x'+str(x)\n if y is not None:\n data=data[:,y];axis=[zyx[0]];title+='_y'+str(y)\n if z is not None:\n data=data[z,:];axis=[zyx[1]];title+='_z'+str(z)\n elif y is not None:\n data=data[:,y,:];axis=[zyx[0],zyx[2]];title+='_y'+str(y)\n if z is not None:\n data=data[z,:];axis=[zyx[2]];title+='_z'+str(z)\n elif z is not None:\n data=data[z,:,:];axis=[zyx[1],zyx[2]];title+='_z'+str(z)\n elif len(data.shape)==2:\n axis=[zyx[1],zyx[2]];title+='_z'+str(z) # 2D is x,y data\n elif len(data.shape)==1:\n axis=[zyx[0]] # 1D is z data\n else:\n data = None; axis = None\n #print data.shape,axis\n return data,axis,title\n\ndef selectdiag(zyx,xy1,xy2):\n # diagonal that start on (x1,y1) and ends on (x2,y2)\n # xy1 : [x1,y1]\n # make diagonal\n #print xy2[0]==xy1[0],xy2[0],xy1[0],xy2[1],xy1[1]\n xx = np.mgrid[xy1[0]:xy2[0]:len(zyx[-1])*2*1j] # a lot of points\n if xy2[0]==xy1[0]:\n aa = 0\n yy = np.mgrid[xy1[1]:xy2[1]:len(zyx[-2])*2*1j] # a lot of points\n else:\n aa = float(xy2[1]-xy1[1])/float(xy2[0]-xy1[0])\n bb = xy1[1]-aa*xy1[0]\n yy = aa*xx+bb\n #print aa\n xy = np.zeros((2,len(xx)))*np.nan\n xorig= np.arange(len(zyx[2]))\n yorig= np.arange(len(zyx[1]))\n ik = -1\n for ij in range(len(xx)):\n ik += 1\n xy[0,ik] = xorig[near(xorig,xx[ij])] \n xy[1,ik] = yorig[near(yorig,yy[ij])]\n if ik>0:\n if (xy[0,ik]==xy[0,ik-1]) and (xy[1,ik]==xy[1,ik-1]):\n ik = ik-1\n xy = xy[:,:ik+1]\n return xy\n \n\n# Find nearrest point \ndef near(array,value):\n idx=(abs(array-value)).argmin()\n return idx+1 # +1 because it starts at index=0\n\n# sign character\ndef signch(data):\n signch='p'\n if np.sign(data)<0:\n signch='m'\n return signch\n \ndef unitch(case):\n switcher = {\n 'WT': \"m/s\",\n 'RCT': \"kg/kg\",\n 'RVT': \"kg/kg\",\n 'THT': \"K\",\n 'TKET': \"m2/s2\",\n 'SVT004': \"-\",\n }\n return switcher.get(case, \"Invalid units\")\n\n\ndef findbasetop(data,z,epsilon):\n # Find cloud base and cloud top\n # Define as the first layer where RCT (ql) > epsilon\n cloudbase = np.nan; cloudtop = np.nan;\n for ij in range(len(z)):\n if np.isnan(cloudbase) and data[ij] > epsilon:\n cloudbase = ij\n if ~np.isnan(cloudbase) and np.isnan(cloudtop) and data[ij] < epsilon:\n cloudtop = ij\n #print 'base,top : ',cloudbase,cloudtop,data*1000.\n return cloudbase,cloudtop\n\ndef cloudinfo(nx,ny,z,rct,epsilon):\n # Find cloud base and cloud top and middle of cloud\n\n # First computation : Use the mean \n meanql = np.mean(np.mean(rct,axis=2),axis=1)\n cloudbase,cloudtop = findbasetop(meanql,z,epsilon)\n \"\"\"\n print cloudbase,cloudtop\n print 'cloudbase : ij='+str(cloudbase)+' for z='+str(z[cloudbase])\n print 'cloudtop : ij='+str(cloudtop)+' for z='+str(z[cloudtop]) \n \"\"\"\n # Second computation : Use all data and compute its mean\n cloudbase0 = np.zeros((ny,nx))\n cloudtop0 = np.zeros((ny,nx))\n for ix in range(nx):\n for iy in range(ny):\n data = rct[:,iy,ix]\n cloudbase0[iy,ix],cloudtop0[iy,ix] = findbasetop(data,z,epsilon) \n \n \"\"\"\n print np.nanmean(cloudbase0[:]) \n print np.nanmean(cloudtop0[:])\n \"\"\"\n cloudbase2 = np.nanmean(cloudbase0[:]) # real\n cloudtop2 = np.nanmean(cloudtop0[:]) # real\n # Make integer\n cloudbase2 = int(cloudbase2)\n cloudtop2 = int(cloudtop2)+1\n \n # Choice the second computation\n baseandtop = 1 # hard choice\n #baseandtop = 2 # hard choice\n if baseandtop == 2:\n cloudbase = cloudbase2\n cloudtop = cloudtop2\n elif baseandtop == 3:\n cloudbase = min(cloudbase,cloudbase2)\n cloudtop = min(cloudtop,cloudtop2) # not sure, max or min\n\n cloudmiddle = int(round((cloudbase+cloudtop)/2))\n \"\"\"\n print 'Compute final :'\n print 'cloudbase : ij='+str(cloudbase)+' for z='+str(z[cloudbase])\n print 'cloudtop : ij='+str(cloudtop)+' for z='+str(z[cloudtop])\n \"\"\"\n return cloudbase,cloudmiddle,cloudtop,cloudbase0,cloudtop0\n\n\ndef qs(temp,pres,order) :\n esat = CC.es0 * np.exp(-(CC.RLVTT*CC.mv/CC.RU)*(1./temp - 1./CC.T0) )\n qstmp = CC.epsilon * esat/ (pres - esat)\n if order >= 1 :\n dqsP = -1*qstmp/pres # change of qsat for a change of P, with fixed T\n dqsT = CC.RLVTT*CC.epsilon*qstmp/ (CC.RD*temp*temp)# change of qsat for a change of T, with fixed P\n else :\n dqsP = 0; dqsT =0\n if order >=2 :\n d2qsP = 2*qstmp/(pres*pres) # change of dqsat/dP for a change of P, with fixed T\n d2qsT = CC.RLVTT *CC.epsilon*CC.epsilon*qstmp*(2/temp -1 )/(CC.RD*temp*temp*temp ) # change of dqsat/dT for a change of T, with fixed P\n else :\n d2qsP = 0; d2qsT =0\n\n return qstmp,dqsP,dqsT,d2qsP,d2qsT\n\n\ndef findlcl(q0,t0,temp,rvt,pres,z):\n # Find Lifting Condensation Level\n # surface humidity q0 (2D)\n # temp 3D, pres 3D\n # z 3D\n gradsec = -0.0098 # K/m z in meter\n gradhum = -0.0065 # K/m z in meter\n order = 0\n ss = pres.shape\n q2D = np.repeat(q0[np.newaxis,:,:],ss[0],axis=0)\n lcl = np.ones((ss[1],ss[2]))*np.nan\n lfc = np.ones((ss[1],ss[2]))*np.nanmax(z,axis=0) #*np.nan\n for i1 in range(ss[1]):\n for i2 in range(ss[2]):\n conditionlcl = True\n conditionlfc = True\n i0 = -1\n while(conditionlfc and i0 temp[i0,i1,i2]:\n conditionlfc=False\n lfc[i1,i2] = z[i0,i1,i2]\n #print i1,i2,i0,lcl[i1,i2],lfc[i1,i2]\n if conditionlcl and qsat1 < q0[i1,i2]:\n conditionlcl=False\n lcl[i1,i2] = z[i0,i1,i2]\n tlcl = t1\n #print i1,i2,i0,lcl[i1,i2]\n #print i1,i2,i0,t1,temp[i0,i1,i2]\n return lcl,lfc\n\ndef makethv(theta,qv,ql):\n return theta*(1.0+0.61*qv/1000.-ql/1000.)\n\ndef makethl(theta,ql):\n return theta-(CC.RLVTT/CC.RCP)*ql/1000.\n\ndef makeths1(thlm,rnpm):\n a1 = 5.87\n QT = rnpm/(np.ones((rnpm.shape))+rnpm)\n return thlm * np.exp(a1 * QT)\n\ndef makeths2(thlm,rv,rnpm):\n a1 = 5.87; a2 = -0.46\n rstar = np.ones((rv.shape))*0.0124\n QT = rnpm/(np.ones((rnpm.shape))+rnpm)\n QV = rv/(np.ones((rnpm.shape))+rnpm)\n tmp = thlm * np.exp( a1 * QT)\\\n * np.exp( a2 * np.log(QV/rstar) * QT )\\\n * np.exp( a2 * (QT-QV) )\n return tmp\n \n\n\ndef group_variables(data, files, varread, varwrite, offset, resizb=0):\n # Create the variable vars with all information in files\n vars = {}\n extraname = []; minusname =[]\n for ss in files.keys():\n vars[ss] = {}\n for v in np.arange(len(varread)):\n #print ss,v,varread[v]\n try:\n tmp = data[ss][varread[v]][:]\n except:\n tmp = None\n #if 'SV' in varread[v]: \n # print 'SV1 ',tmp.shape,data[ss][varread[v]].shape\n if resizb and tmp is not None:\n tmp = resiz(tmp)\n\n tmpsave = None\n varwriteall = [varwrite[v]]\n if tmp is not None:\n if len(tmp.shape)==3: # traceurs\n varwriteall=[]\n minusname += [varwrite[v]]\n for ij in range(tmp.shape[0]):\n aa = varwrite[v].split('_')\n #print 'aa ',aa \n varwriteall += [aa[0]+'_'+aa[1]+str(ij+1)+'_'+aa[2]]\n extraname += varwriteall\n tmpsave = tmp\n\n ik = -1\n for namevar in varwriteall:\n ik+=1\n if tmpsave is not None:\n tmp = tmpsave[ik,:,:]\n offch = [ij in namevar for ij in offset.keys()]\n if sum(offch) and tmp is not None: # namevar in offset.keys() and tmp is not None:\n #print offch,np.where(offch)[0][0],offset.keys()[np.where(offch)[0][0]]\n tmp = tmp*offset[offset.keys()[np.where(offch)[0][0]]]\n vars[ss][namevar] = tmp\n return vars,extraname,minusname\n\n\ndef quadrants(data,data2=None,data3=None,threshold=0.0,threshold2=0.0,threshold3=0.0):\n # Make quadrants or octants\n # data should be one arrow (e.g. 40000 points)\n # By default\n # data is WT\n # data2 is SVT004\n # data3 is SVT006\n # Outputs : slct(4D index), frac0,frac1 (fraction of quadrants), colors (index), colorall (each color info)\n thr = [-1*abs(threshold),abs(threshold)]\n thr2 = [-1*abs(threshold2),abs(threshold2)]\n thr3 = [-1*abs(threshold3),abs(threshold3)]\n\n idx1a = (data >= thr[1]);idx1b = (data <= thr[0]) #updraft/downdraft\n idx1 = [idx1a,idx1b]\n idx2a,idx2b,idx3a,idx3b = None,None,None,None\n if data2 is not None:\n idx2a = (data2 >= thr2[1]);idx2b = (data2 <= thr2[0])\n if data3 is not None:\n idx3a = (data3 >= thr3[1]);idx3b = (data3 <= thr3[0])\n \n idx1 = [idx1a,idx1b]; idx2 = [idx2a,idx2b]; idx3 = [idx3a,idx3b];\n if data2 is not None:\n slct = quadindex(idx1,idx2)\n else:\n slct = idx1\n if data3 is not None:\n slct = octaindex(idx1,idx2,idx3)\n\n #print 'sum ',quad.shape,sum(quad)\n #print 'Sum : ',[100*sum(ij)/len(data) for ij in quad],len(data)\n\n frac0,frac1 = percentquad(data,slct)\n colors,colorall = makecolors(data,slct)\n return slct,frac0,frac1,colors,colorall\n\ndef quadindex(idx1,idx2):\n # updraft that bring SVT004 anomaly\n idxA = idx1[0][:] & idx2[0][:]\n # updraft that has lower than average SVT004\n idxB = idx1[0][:] & idx2[1][:]\n # downdraft that has higher than average SVT004\n idxC = idx1[1][:] & idx2[0][:]\n # downdraft that bring clear air downwards\n idxD = idx1[1][:] & idx2[1][:]\n # Quadrant index\n quad = [idxA,idxB,idxC,idxD]\n return quad\n\ndef octaindex(idx1,idx2,idx3):\n\n # updraft that bring SVT004 anomaly + positive cloud-top tracer anomaly\n idxA = idx1[0][:] & idx2[0][:] & idx3[0][:]\n # updraft that has lower than average SVT004 + positive cloud-top tracer anomaly\n idxB = idx1[0][:] & idx2[1][:] & idx3[0][:]\n # downdraft that has higher than average SVT004 + positive cloud-top tracer anomaly\n idxC = idx1[1][:] & idx2[0][:] & idx3[0][:]\n # downdraft that bring clear air downwards + positive cloud-top tracer anomaly\n idxD = idx1[1][:] & idx2[1][:] & idx3[0][:]\n\n # updraft that bring SVT004 anomaly + negative cloud-top tracer anomaly\n idxE = idx1[0][:] & idx2[0][:] & idx3[1][:]\n # updraft that has lower than average SVT004 + negative cloud-top tracer anomaly\n idxF = idx1[0][:] & idx2[1][:] & idx3[1][:]\n # downdraft that has higher than average SVT004 + negative cloud-top tracer anomaly\n idxG = idx1[1][:] & idx2[0][:] & idx3[1][:]\n # downdraft that bring clear air downwards + negative cloud-top tracer anomaly\n idxH = idx1[1][:] & idx2[1][:] & idx3[1][:]\n\n # Octant index\n #print 'idxA ',idxA.shape # nx*ny\n octa = [idxA,idxB,idxC,idxD,idxE,idxF,idxG,idxH]\n return octa\n\ndef percentquad(data,quad):\n # Give relative fraction of quadrant\n # frac0 : relative to the grid-box\n # frac1 : relative the selection\n # size of data?\n ss = len(data)\n tmp = [sum(ij) for ij in quad]\n frac0 = [100.0*ij/ss for ij in tmp] \n if sum(tmp)!=0:\n frac1 = [100.0*ij/sum(tmp) for ij in tmp]\n else:\n frac1 = None \n\n return frac0,frac1\n\n\ndef makecolors(data,quad): #data4=None,threshold=0.0):\n # Make colors bar for coloring scatter plot in plotdistrib\n # By default\n # data is Wt\n\n colorblack = (0.6,0.6,0.6) # grey\n color1,color2 = (0.,0.,1.),(1.,0.,0.) # blue if negative and red if positive\n colorall = [color2,color1]\n colors = np.array([colorblack]*len(data))\n if len(quad) == 2:\n colors[quad[0][:]] = colorall[0]\n colors[quad[1][:]] = colorall[1]\n elif len(quad) == 4:\n #print 'Lengths ',len(data)\n color3,color4 = (0.,0.8,1.),(1.,0.5,0.) # bleu ciel : (-,+) ; orange (+,-)\n colorall = [color2,color4,color3,color1]\n # updraft that bring SVT004 anomaly\n colors[quad[0][:]] = colorall[0]\n # updraft that has lower than average SVT004\n colors[quad[1][:]] = colorall[1]\n # downdraft that has higher than average SVT004\n colors[quad[2][:]] = colorall[2]\n # downdraft that bring clear air downwards\n colors[quad[3][:]] = colorall[3]\n elif len(quad) == 8:\n # As before, but with negative cloud-top tracer anomaly\n # (w+,s1+,s2+),(w+,s1-,s2+),(w-,s1+,s2+),(w-,s1-,s2+),(w+,s1+,s2-),(w+,s1-,s2-),(w-,s1+,s2-),(w-,s1-,s2-)\n # the closer to Park or Davini\n color1,color2,color3,color4 = (1.,1.,0.),(1.,0.7,0.7),(0.2,0.4,0.1),(0.6,1.0,0.6) # yellow, pink, dark green, light green\n color5,color6,color7,color8 = (1.,0.,0.),(1.,0.5,0.),(0.,0.8,1.),(0.,0.,1.) # red,orange,light blue,blue\n # new\n #color1,color2,color3,color4 = (1.,1.,0.),(0.9,0.9,0.9),(0.2,1.0,0.),(0.2,0.6,0) # light yellow, white, light green, green\n #color5,color6,color7,color8 = (1.,0.,0.),(0.9,0.4,1.),(0.,0.6,1.),(0.,0.,1.) # red,light red/purple,light blue,blue\n colorall = [color1,color2,color3,color4,color5,color6,color7,color8]\n for ij in range(len(quad)):\n colors[quad[ij][:]] = colorall[ij]\n \n return colors,colorall\n \n\ndef infos(case,sens,prefix='000',vtype='V0301',concat=''):\n #files={}\n files = OrderedDict() #dict([(sens[i],'i') for i in range(len(sens))])\n # local path with simulations\n path0 = '/cnrm/tropics/user/brientf/MESONH/'\n path0 = path0 + case +'/'\n # list with the names of files\n for ss in sens:\n #print 'ss ',ss, case\n # path of the selected experience\n path=path0+ss+'/'\n # name of the file (e.g. sel_HTFIR.1.V0301.000.nc4)\n #vtype = 'V0301'\n \n #prefix1 = '000'\n #if prefix is not 'None':\n # prefix1 = prefix\n prefix += concat+\".nc4\"\n\n # Too heavy file, pre-selection before\n # e.g. ncks -O -v XHAT,YHAT,ZHAT,RHOREFZ,RCT,THLM HTFIR.1.V0301.012.nc4 HTFIR.1.V0301.012.nc4.out\n prefix2 = prefix\n if ss == 'L25.6km':\n prefix2 = prefix #+ '.out'\n\n if ss == 'SMALL':\n prefix2 = prefix #+ '.MNH' #'.save' #'.test2'\n\n if ss == 'SMALR':\n prefix2 = prefix + '.MOCR.FIRE' #'.T150' \n\n if (ss == 'REF' or ss == 'L25.6km') and case == 'FIRE':\n name = 'HTFIR'\n else:\n name = ss\n\n filename = name+\".1.\"+vtype+\".\"+prefix2\n\n files[ss]=path+filename\n #print 'files ',files\n\n return path0,files\n\ndef makepdf(tmprh,tmpcf,ltsrange):\n # Binning information\n ltstable = np.arange(ltsrange[0],ltsrange[1]+ltsrange[2],ltsrange[2])+ltsrange[2]/2\n\n kde = st.gaussian_kde(tmprh)\n pdftmp = kde(ltstable)\n pdftmp = 100.*pdftmp/sum(pdftmp)\n\n statstmp, bin_edges, binnumber = st.binned_statistic(tmprh, tmpcf, 'mean', bins=len(ltstable),range=(ltsrange[0],ltsrange[1]))\n stdtmp = [ np.std(tmpcf[binnumber==ij]) for ij in np.arange(1,len(ltstable)+1) ]\n #print ' binnumber ',binnumber\n #for ij in np.arange(1,len(ltstable)+1):\n # print ij,np.std(tmpcf[binnumber==ij]),tmpcf[binnumber==ij]\n \n return pdftmp,statstmp,stdtmp\n\ndef do_unique(tmp):\n tmp[tmp>0]=1\n return tmp\n\ndef do_delete(objects,nbmin,rename=True) :\n listObj = np.unique(objects[objects>0]).tolist()\n if len(listObj):\n nbmax = max(listObj)\n #print 'nbmax ', nbmax\n while (len(listObj)):\n num = listObj.pop(0) \n nbcels = len(objects[objects==num])\n if (nbcels < nbmin):\n objects[objects==num] = 0\n if rename and len(listObj) : \n lastnum = listObj.pop(-1)\n while (len(objects[objects==lastnum]) 0:\n mems_loco[0].update(self._anchor_position_signal)\n \n self.lock_drones.release()\n\n #la seconda callback avviene quando i dati provenienti dai droni sono pronti\n def _anchor_position_signal(self,mem):\n \n self.lock_anchor_writing_data.acquire()\n for anchor_number, anchor_data in enumerate(mem.anchor_data):\n if anchor_data.is_valid:\n self.anchor_pos[anchor_number]=anchor_data.position\n self.lock_anchor_data_are_ready.release()\n\n\n #questa funzione permette di creare i percorsi per i droni\n def add_route(self, data):\n\n eventi=EventEmitter()\n \n self.lock_drones.acquire()\n lista_drones=self.drones.items()\n self.lock_drones.release()\n\n self.lock_route.acquire()\n\n for url,drone in lista_drones: \n self.route_collection[drone.uri]=Route_class(data[url],self.drones,url,eventi)\n\n self.lock_route.release()\n\n return \"ok\"\n \n\n def start_all_drones(self):\n\n self.lock_drones.acquire()\n\n for url,drone in self.drones.items():\n if drone.mc!=None:\n self.launchthread(drone)\n \n else:\n print(\"drone:\",drone.uri,\"not ready\")\n \n self.lock_drones.release()\n\n def movement(self,uav: UAV):\n print(\"mi muovo\")\n time.sleep(1)\n \n percorso=self.route_collection[uav.uri]\n \n punto=percorso.pop()\n percorso.passed(self.route_collection)\n \n punto=percorso.pop()\n \n \n if punto[1]==\"takeoff\":\n print(\"decollo\")\n uav.take_off(float(punto[0][1]))\n percorso.passed(self.route_collection)\n else:\n print(\"errore posizione di decollo\")\n return 0\n \n while True:\n punto=percorso.pop()\n \n if punto[1]==\"landing\":\n \n print(\"atteraggio\")\n uav.land()\n return\n \n elif(punto[1]==\"base\"):\n print(\"vado al punto:\",punto[0][0],punto[0][2],punto[0][1])\n uav.go_to(float(punto[0][0]),float(punto[0][2]),float(punto[0][1]))\n else:\n print(punto[1])\n print(\"vado al punto:\",punto[0][0],punto[0][2],punto[0][1])\n uav.go_to(float(punto[0][0]),float(punto[0][2]),float(punto[0][1]))\n \n \n percorso.passed(self.route_collection)\n \n def launchthread(self,uav: UAV):\n th = threading.Thread(target=self.movement, args=(uav,))\n th.start()\n\n def set_anchor_position(self, data):\n \n self.lock_drones.acquire()\n \n json={}\n if len(self.drones)==0:\n self.lock_drones.release()\n json[\"status\"]=\"no drone connected\"\n return json\n\n \n ancore = LoPoAnchor(list(self.drones.values())[0].scf.cf)\n \n for id, pos in data.items():\n ancore.set_position(int(id), (float(pos[\"x\"]), float(pos[\"z\"]), float(pos[\"y\"])))\n\n self.lock_drones.release()\n json[\"status\"]=\"successfully anchor setted\"\n return json\n\n\n def reset_estimation_for_all_drone_connected(self):\n for url,drone in self.drones.items():\n drone.reset_estimator() ","repo_name":"UniCT-ARSLab/Twinflie","sub_path":"python server/classe refactoring/classes/drones_collection.py","file_name":"drones_collection.py","file_ext":"py","file_size_in_byte":8345,"program_lang":"python","lang":"it","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"29497351586","text":"'''\n给定一个非空的整数数组，返回其中出现频率前 k 高的元素。\n\n示例 1:\n\n输入: nums = [1,1,1,2,2,3], k = 2\n输出: [1,2]\n示例 2:\n\n输入: nums = [1], k = 1\n输出: [1]\n \n\n提示：\n\n你可以假设给定的 k 总是合理的，且 1 ≤ k ≤ 数组中不相同的元素的个数。\n你的算法的时间复杂度必须优于 O(n log n) , n 是数组的大小。\n题目数据保证答案唯一，换句话说，数组中前 k 个高频元素的集合是唯一的。\n你可以按任意顺序返回答案。\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/top-k-frequent-elements\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n'''\n\n'''\n思路：1.字典统计次数\n 2.建大顶堆\n 要点：两个函数percUp和percDown\n percUp————对每一个非叶节点进行percDown\n percDown————将某一个节点向下调整到“合适”位置\n 3.弹出k个堆顶值：（每弹一个就要维护堆）\n'''\nclass Solution:\n def topKFrequent(self, nums: [int], k: int) -> [int]:\n\n #从末位的第一个非叶节点开始，逐个进行percDown\n def percUp(lst):\n new_lst = [-1] + lst\n n = len(new_lst)-1\n for i in range(n//2,0,-1):\n percDown(new_lst,i)\n\n return new_lst\n #将某一位置的节点向下调整到合适的位置\n def percDown(lst, i):\n n = len(lst)-1\n j = 2*i\n while j <= n:\n if j+1 <= n and lst[j+1][1] > lst[j][1]:\n j = j+1\n if lst[i][1] < lst[j][1]:\n lst[i],lst[j] = lst[j],lst[i]\n i = j\n j = 2*i\n else:\n break\n # 用字典统计数字的次数\n d_cnt = {}\n for a in nums:\n if a not in d_cnt:\n d_cnt[a] = 1\n else:\n d_cnt[a] += 1\n lst_cnt = list(d_cnt.items())\n print(lst_cnt)\n\n #建大顶堆\n heap = percUp(lst_cnt)\n #取前K个元素\n res = []\n for _ in range(k):\n heap[1],heap[-1] = heap[-1],heap[1]\n res.append(heap.pop()[0])\n percDown(heap,1)\n return res\n\n#测试\nnums = [1]\nk = 1\nres = Solution().topKFrequent(nums, k)\nprint(res)\n","repo_name":"Da1anna/Data-Structed-and-Algorithm_python","sub_path":"leetcode/其它题型/哈希表/common/前K个高频元素.py","file_name":"前K个高频元素.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2720203890","text":"class TweetData(object):\n def __init__(self, tweet_id, created, text, geo, coordinates, place, retweet_count, favorite_count, lang,\n user_location, user_description):\n self.tweet_id = tweet_id\n self.created = created\n self.text = text\n self.geo = geo\n self.coordinates = coordinates\n self.place = place\n self.retweet_count = retweet_count\n self.favorite_count = favorite_count\n self.lang = lang\n self.user_location = user_location\n self.user_description = user_description\n\n def __repr__(self):\n return str(self.__dict__)\n","repo_name":"ShawonAshraf/text-tech-project-winter20","sub_path":"view/tweet_data.py","file_name":"tweet_data.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"4616126724","text":"from django.http import HttpResponse\nfrom django.template import loader\nfrom .models import Member\nimport requests\nfrom django.shortcuts import render\nimport pandas as pd\n\n#Lấy ip public\nip = requests.get('https://api.ipify.org').text\n\n#API lấy data thời tiết\nGetLocal = \"https://weatherapi-com.p.rapidapi.com/ip.json\"\nGetMappoint = \"https://weatherapi-com.p.rapidapi.com/astronomy.json\"\nGetWeather = \"https://weatherapi-com.p.rapidapi.com/current.json\"\nGetForecast = \"https://weatherapi-com.p.rapidapi.com/forecast.json\"\n\n# Query của api\nquerystring = {\"q\":ip}\n\n# Key sử dụng api\nheaders = {\n \"X-RapidAPI-Key\": \"5761b749b1msh63b3d30b91858ddp173a64jsna65a320effd1\",\n \"X-RapidAPI-Host\": \"weatherapi-com.p.rapidapi.com\"\n }\n\n#Lấy data vị trí từ ip public\nlocals = requests.get(GetLocal, headers=headers, params=querystring)\n\nquerystringMappoint = {\"q\":locals.json()['city']}\n\n#Lấy vị trí cụ thể trên map\nMappoint = requests.get(GetMappoint, headers=headers, params=querystring)\n\nStringmappoint = str(Mappoint.json()['location']['lat']) +',' +str(Mappoint.json()['location']['lon'])\n\nquerystringWeather = {\"q\": Stringmappoint}\n\n#Lấy data thời tiết trong thời gian thực\nWeather = requests.get(GetWeather, headers=headers, params=querystringWeather)\n\n#Lấy data thời gian\ndate=pd.Timestamp(Weather.json()['location']['localtime'])\n\nquerystringForecast = {\"q\":locals.json()['city'],\"days\":\"3\"}\n\n#Lấy data dự báo thời tiết\nForecast = requests.get(GetForecast, headers=headers, params=querystringForecast)\n\n#Hiển thị thời tiết trang chủ, auto chọn vị trí từ IP\ndef weather(request): \n info = {\n 'forecastday' : [],\n 'avgtemp_c' : [],\n 'maxwind_kph' :[],\n 'date' :[],\n }\n\n #Tại api xài free nên chỉ có cho xem dự báo thời tiết trong 3 ngày :'(\n for i in range(3):\n info['forecastday'].append(Forecast.json()['forecast']['forecastday'][i]['day']['condition']['icon']) \n info['avgtemp_c'].append(Forecast.json()['forecast']['forecastday'][i]['day']['avgtemp_c'])\n info['maxwind_kph'].append(Forecast.json()['forecast']['forecastday'][i]['day']['maxwind_kph'])\n info['date'].append(pd.Timestamp(Forecast.json()['forecast']['forecastday'][i]['date']).day_name())\n \n return render(request,'template.html',{'city':locals.json()['city'],\n 'temp_c':int(Weather.json()['current']['temp_c']),\n 'humidity':int(Weather.json()['current']['humidity']),\n 'wind_kph':int(Weather.json()['current']['wind_kph']),\n 'icon':Weather.json()['current']['condition']['icon'],\n 'time':date.time(),\n 'date':date.day_name(),'month':date.month_name(),'datenumber':date.strftime(\"%d\"),\n 'info':info,'n':range(3)})\n\n# Dự báo thời tiết trong 24 giờ của ngày cụ thể nào đó\ndef weather24(request):\n weather24 ={\n 'date':[],\n 'time':[],\n 'temp_c':[],\n 'icon' :[],\n 'wind_kph':[],\n 'chance_of_rain':[],\n 'humidity':[]\n }\n for i in range(3):\n #Lưu data trong 24 giờ vào direction\n for x in range(24):\n weather24['date'].append(pd.Timestamp(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['time']).day_name())\n weather24['time'].append(pd.Timestamp(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['time']).time())\n weather24['temp_c'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['temp_c'])\n weather24['icon'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['condition']['icon'])\n weather24['wind_kph'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['wind_kph'])\n weather24['chance_of_rain'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['chance_of_rain'])\n weather24['humidity'].append(Forecast.json()['forecast']['forecastday'][i]['hour'][x]['humidity'])\n\n return render(request,'weather24.html',{'weather24':weather24,\n 'n':range(12)})\n\n# Tìm theo tên thành phố\ndef search(request):\n #Lấy data từ form input của HTML\n city = request.POST['city']\n querystringForecast1 = {\"q\":city,\"days\":\"3\"}\n\n #Lấy data dự báo thời tiết dựa theo tên thành phố lấy từ form\n ForecastSearch = requests.get(GetForecast, headers=headers, params=querystringForecast1)\n \n info = {\n 'forecastday' : [],\n 'avgtemp_c' : [],\n 'maxwind_kph' :[],\n 'date' :[],\n }\n\n for i in range(3):\n info['forecastday'].append(ForecastSearch.json()['forecast']['forecastday'][i]['day']['condition']['icon']) \n info['avgtemp_c'].append(ForecastSearch.json()['forecast']['forecastday'][i]['day']['avgtemp_c'])\n info['maxwind_kph'].append(ForecastSearch.json()['forecast']['forecastday'][i]['day']['maxwind_kph'])\n info['date'].append(pd.Timestamp(ForecastSearch.json()['forecast']['forecastday'][i]['date']).day_name())\n \n return render(request, 'search.html',{'info':info,'n':range(3),'city':ForecastSearch.json()['location']['name'],\n 'temp_c':int(ForecastSearch.json()['current']['temp_c']),\n 'humidity':int(ForecastSearch.json()['current']['humidity']),\n 'wind_kph':int(ForecastSearch.json()['current']['wind_kph']),\n 'date':pd.Timestamp(ForecastSearch.json()['location']['localtime']).day_name(),\n 'time':pd.Timestamp(ForecastSearch.json()['location']['localtime']).time(),\n 'month':pd.Timestamp(ForecastSearch.json()['location']['localtime']).month_name(),\n 'datenumber':pd.Timestamp(ForecastSearch.json()['location']['localtime']).strftime(\"%d\")})\n\n","repo_name":"PnThinh/weather","sub_path":"members/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6217,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5415661973","text":"import itertools\nfrom copy import deepcopy\nfrom dataclasses import dataclass\nfrom typing import Optional, AnyStr\n\nfrom lxml.etree import _Element as Element\n\nfrom .find_utils import uses, flatten_list\nfrom .grade_code_found import GradeCodeFound, get_node_source\nfrom .ast_to_xml import xml\nfrom .xml_utils import (\n compare_xml_nodes, literal, expr_xml_node\n)\n\n@dataclass\nclass Arg:\n \"\"\"A dataclass to represent a function's positional argument\"\"\"\n name: Optional[str] = ''\n code: AnyStr = None\n xml_node: AnyStr = None\n\n def __str__(self) -> str:\n if self.name != '':\n return f\"positional argument: {self.name} = {self.code}\"\n else:\n return f\"positional argument: {self.code}\"\n\n@dataclass\nclass KWArg:\n \"\"\"A dataclass to represent a function's keyword argument\"\"\"\n name: Optional[str] = ''\n code: AnyStr = None\n value_xml_node: AnyStr = None\n # this field is so we can refer to originating keyword XML Element\n # when it comes to match and store keyword arguments for `find_arguments()`\n keyword_xml_node: AnyStr = None\n \n def __str__(self) -> str:\n return f\"keyword argument: {self.name} = {self.code}\"\n\n@dataclass\nclass ArgList:\n \"\"\"A dataclass to represent a function's argument or keyword argument\"\"\"\n args: list[Arg]\n\n def __init__(self, args: list[Arg] = None):\n self.args = [] if args is None else args\n\ndef args(*args: AnyStr, **kwargs: AnyStr) -> ArgList:\n \"\"\"Returns a list of Arg objects for positional (unnamed) argument values \n or keyword arguments to facilitate finding arguments in function calls.\n\n Returns\n -------\n ArgList\n an ArgList object that holds a list of Arg(s)\n \"\"\"\n all_args = []\n\n # first add the positional arguments\n if len(args) > 0:\n positional = list(args)\n all_args.extend([\n # because Python eagerly evaluates all arguments passed in to args()\n # we have to specially handle the quoting of them: \n # - if the argument was meant to be a `str` we double quote `\"` it\n # - anything else we just wrap into a normal string\n Arg(\n code = arg.source if isinstance(arg, literal) else arg,\n xml_node = expr_xml_node(arg)\n )\n for arg in positional\n ])\n\n # then, add the keyword arguments\n if len(kwargs) > 0:\n all_kwargs = list(kwargs.items())\n # this is extracting the name = value\n all_args.extend(\n # similar logic to positional arguments here, where kwargs value strings\n # are quoted if it's meant to be a `str`\n KWArg(\n name = kwarg[0],\n code = kwarg[1].source if isinstance(kwarg[1], literal) else kwarg[1],\n value_xml_node = expr_xml_node(kwarg[1])\n )\n for kwarg in all_kwargs\n )\n\n return ArgList(all_args)\n\ndef make_kwargs(code: str, kwarg_xml_nodes: list[Element]) -> list[KWArg]:\n \"\"\"Return KWArg(s) based on XML Element(s)\n\n Parameters\n ----------\n code : str\n the source code text\n kwarg_xml_nodes : list[Element]\n list of XML Elements representing keyword\n\n Returns\n -------\n list[KWArg]\n a list of KWArgs representing those keywords\n \"\"\"\n code_kwargs_list = []\n for kwarg_node in kwarg_xml_nodes:\n # keyword argument name\n kwarg_names = [\n x.text for x in kwarg_node.xpath(\"./arg\")\n ]\n\n # get the textual form for values\n kwarg_values = [v for v in kwarg_node.xpath(\"./value/*\")]\n kwargs_values_strings = [\n get_node_source(code, kw)\n for kw in kwarg_values\n ]\n\n # combine them into pairs\n kwargs_pairs = list(zip(kwarg_names, kwargs_values_strings))\n\n # construct the list of Arg(s) from the keyword pairs\n code_kwargs_list.append([\n KWArg(\n name = k,\n code = v,\n value_xml_node = expr_xml_node(v),\n keyword_xml_node = kwarg_node\n )\n for k, v in kwargs_pairs\n ])\n return flatten_list(code_kwargs_list)\n\ndef find_arguments(\n code: str | GradeCodeFound,\n match: ArgList = ArgList(),\n recurse: bool = True\n) -> GradeCodeFound:\n \"\"\"Find arguments of function calls in the code.\n\n Parameters\n ----------\n code : str | GradeCodeFound\n a string for the source code, or a GradeCodeFound for chaining queries\n match : ArgList\n an ArgList representing the arguments to match against\n recurse : bool\n a flag that requests whether or not to look for arguments\n within function call arguments for a previous 'function' request, by default False\n\n Returns\n -------\n GradeCodeFound\n a GradeCoundFound object that holds the list of previous results\n and the current query results if any, by default True\n \n Examples\n --------\n >>> code = \"sum([1, round(2.5), 3])\\nprint('Hello', 'World!', sep=', ')\"\n >>> find_arguments(code)\n ── pygradecode found ──\n print(1, \"2\", sep=\", \", end=foo(x = [\"\\n\"]))\n\n ── Request ──\n arguments \n Found 5 results.\n\n ── Result 1 ──\n 1\n\n ── Result 2 ──\n \"2\"\n\n ── Result 3 ──\n sep=\", \"\n\n ── Result 4 ──\n end=foo(x = [\"\\n\"]))\n\n ── Result 5 ──\n x = [\"\\n\"])\n \"\"\"\n if not isinstance(code, str) and not isinstance(code, GradeCodeFound):\n raise Exception(\"`code` should be a `str` or `GradeCodeFound`\")\n \n gcf = deepcopy(code) if isinstance(code, GradeCodeFound) else GradeCodeFound(code)\n xml_tree = xml(gcf.source) if isinstance(code, GradeCodeFound) else xml(code)\n\n request_type = 'arguments'\n # TODO create a __str__ representation for ArgList\n request = ''\n results = []\n\n # Note: we have to encode the code to escape it properly\n match_args = match.args\n\n # get previous function calls in results if any, otherwise get all function calls\n all_call_xml_nodes = []\n if gcf.has_previous_request():\n for node in gcf.last_result:\n if recurse:\n all_call_xml_nodes.append(node.xpath('//Call'))\n else:\n all_call_xml_nodes.append(node)\n all_call_xml_nodes = list(itertools.chain(*all_call_xml_nodes))\n else:\n all_call_xml_nodes = xml_tree.xpath(\"//Call\")\n\n # if there are no arguments to look for in particular, look for\n # all of the arguments in code\n if len(match_args) == 0:\n results = [\n node.xpath(\"./args/*|./keywords/*\")\n for node in all_call_xml_nodes\n ]\n results = list(itertools.chain(*results))\n else:\n # Extracting each keyword pair is a bit tricky because a value\n # could be any ast node so there is no way to have one query\n # satisfy all types of nodes. So, instead we look at the\n # and its node source text to reconstruct them.\n # TODO if a gcf was passed when there was a previous request\n # we need to iterate through gcf.results to repeat this process\n # so we can make all of the Arg/KWArg\n\n # extract the positional args\n all_arg_xml_nodes = flatten_list([\n call.xpath(\"./args/*\")\n for call in all_call_xml_nodes\n ])\n # extract the kwargs args\n all_kwarg_xml_nodes = flatten_list([\n call.xpath(\"./keywords/*\")\n for call in all_call_xml_nodes\n ])\n\n # construct Arg() for positional arguments\n code_args_list = [\n Arg(\n code = get_node_source(code, arg),\n xml_node = deepcopy(arg)\n )\n for arg in all_arg_xml_nodes\n ]\n\n # extract the keyword args\n code_kwargs_list = make_kwargs(code, all_kwarg_xml_nodes)\n # combine the args with the kwargs\n all_code_args = code_args_list + code_kwargs_list\n \n # check that each match Arg is in code's [Arg]\n results = get_matched_arguments(match_args, all_code_args)\n\n # TODO make the chaining work well\n return gcf.push(request_type=request_type, request=request, result=results)\n\ndef get_matched_arguments(\n match_args: list[Arg | KWArg],\n all_code_args: list[Arg | KWArg]\n ) -> list[Element]:\n \"\"\"Return all code XML elements that match given the list of match and code arguments.\n\n Parameters\n ----------\n match_args : list[Arg | KWArg]\n a list with Arg and KWArgs representing positional and keyword arguments\n to match against\n all_code_args : list[Arg | KWArg]\n a list with Arg and KWArgs representing positional and keyword arguments\n of the code\n\n Returns\n -------\n list[Element]\n a list of XML Element(s)\n \"\"\"\n results = []\n for match_arg in match_args:\n # for argument, element in args_spec:\n for argument in all_code_args:\n # if match Arg Element is the same as the code Arg Element\n # add the code Arg Element as a result\n if type(match_arg) != type(argument):\n continue\n elif isinstance(match_arg, KWArg) and isinstance(argument, KWArg):\n # if we're comparing keyword arguments \n # check value nodes match\n value_nodes_match = compare_xml_nodes(\n match_arg.value_xml_node,\n argument.value_xml_node\n )\n # check the kwarg name too\n arg_names_match = match_arg.name == argument.name\n if arg_names_match and value_nodes_match:\n results.append(argument.value_xml_node)\n elif compare_xml_nodes(match_arg.xml_node, argument.xml_node):\n results.append(argument.xml_node)\n return results\n\ndef uses_argument(code: str, match: str = \"\") -> bool:\n \"\"\"Check if the code has arguments.\n\n Parameters\n ----------\n code : str\n the source code\n match : str, optional\n argument name(s), by default None\n\n Returns\n -------\n bool\n True if found, False otherwise\n\n Examples\n --------\n >>> code = \"print('Hello', 'World!', sep=', ')\"\n >>> uses_argument(code, \"sum\")\n True\n >>> uses_argument(code, \"round\")\n False\n \"\"\"\n return uses(find_arguments, code, match)\n","repo_name":"rstudio/pygradethis","sub_path":"python/pygradecode/find_arguments.py","file_name":"find_arguments.py","file_ext":"py","file_size_in_byte":9587,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"18281746363","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[9]:\n\n\n#由于基于原图每个像素生成锚框，数量级过大，计算量太大\n#将原图输入卷积层后得到特征图，基于特征图生成锚框，在不同的特征图中生成的锚框对应原图中不同尺寸的感受野\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport d2lzh as d2l\nfrom mxnet import contrib, image, nd\n\nimg = image.imread('../img/catdog.jpg')\nh, w = img.shape[0: 2]\nh, w\n\n\n# In[17]:\n\n\n#定义display_anchors函数\nd2l.set_figsize()\n\ndef display_anchors(fmap_w, fmap_h, s):\n fmap = nd.zeros((1, 10, fmap_w, fmap_h))\n anchors = contrib.nd.MultiBoxPrior(fmap, sizes=s, ratios=[1, 2, 0.5])\n bbox_scale = nd.array((w, h, w, h))\n d2l.show_bboxes(d2l.plt.imshow(img.asnumpy()).axes,\n anchors[0] * bbox_scale)\n\n\n# In[18]:\n\n\ndisplay_anchors(fmap_w=4, fmap_h=4, s=[0.15])\n\n\n# In[20]:\n\n\ndisplay_anchors(fmap_w=2, fmap_h=2, s=[0.4])\n\n\n# In[21]:\n\n\ndisplay_anchors(fmap_w=1, fmap_h=1, s=[0.8])\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"HaitaoWang97/training7","sub_path":"multi_scale.py","file_name":"multi_scale.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12616119403","text":"\"\"\"\nCustom routers used by both lms and cms when routing tasks to worker queues.\n\nFor more, see https://celery.readthedocs.io/en/latest/userguide/routing.html#routers\n\"\"\"\n\nimport logging\n\nfrom django.conf import settings\n\n\nlog = logging.getLogger(__name__)\n\n\ndef route_task(name, args, kwargs, options, task=None, **kw): # pylint: disable=unused-argument\n \"\"\"\n Celery-defined method allowing for custom routing logic.\n\n If None is returned from this method, default routing logic is used.\n \"\"\"\n if name in settings.EXPLICIT_QUEUES:\n return settings.EXPLICIT_QUEUES[name]\n\n alternate_env = settings.ALTERNATE_ENV_TASKS.get(name, None)\n if alternate_env:\n return ensure_queue_env(alternate_env)\n\n\ndef ensure_queue_env(desired_env):\n \"\"\"\n Helper method to get the desired type of queue.\n\n If no such queue is defined, default routing logic is used.\n \"\"\"\n queues = getattr(settings, 'CELERY_QUEUES', None)\n return next(\n (\n queue\n for queue in queues\n if f'.{desired_env}.' in queue\n ),\n None\n )\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/lib/celery/routers.py","file_name":"routers.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"38110607849","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Apr 5 15:05:35 2019\n\n@author: david\n\"\"\"\n\n\n# Chromatic Scale\nCH_SCALE = {\n \"fr\" : [\"Do\", \"Do#\", \"Re\", \"Re#\", \"Mi\", \"Fa\", \"Fa#\", \"Sol\", \"Sol#\", \"La\", \"La#\", \"Si\"],\n \"en\" : [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"],\n \"id\" : [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], # Sur gamme chromatique de Do\n }\n\nclass Accord:\n \n def __init__(self):\n \n self._ind = []\n \n @property\n def fr(self):\n return self.get_chord('fr')\n \n @fr.setter\n def fr(self, chord):\n self.set_chord(chord, 'fr')\n \n @property\n def en(self):\n return self.get_chord('en')\n \n @en.setter\n def en(self, chord):\n self.set_chord(chord, 'en')\n \n @property\n def id(self):\n return self.get_chord('id')\n \n @id.setter\n def id(self, chord):\n self.set_chord(chord, 'id')\n \n def get_chord(self, lang):\n chord = [CH_SCALE[lang][x] for x in self._ind] \n return chord\n \n def set_chord(self, chord, lang):\n \n self._ind = [CH_SCALE[lang].index(note) for note in chord] \n \n\nif __name__ == \"__main__\" :\n \n accord = Accord()\n print(\"accord = Accord()\")\n accord.fr = ['Mi', 'Sol', 'Do', 'Si']\n print(\"accord.fr = ['Mi', 'Sol', 'Do', 'Si']\")\n print(\" \")\n \n print(\"accord.fr : \" + str(accord.fr))\n print(\"accord.en : \" + str(accord.en))\n print(\"accord.id : \" + str(accord.id))\n print(\" \")\n accord.en = ['C','B','G']\n print(\"accord.en = ['C','B','G']\")\n print(\" \")\n print(\"accord.fr : \" + str(accord.fr))\n print(\"accord.en : \" + str(accord.en))\n print(\"accord.id : \" + str(accord.id))\n \n \n \n \n \n \n \n \n ","repo_name":"Daguhh/GuitarChordGeneratorVrac","sub_path":"class_accord.py","file_name":"class_accord.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71560796243","text":"import collections\nn = int(input())\nlst = [int(i) for i in input().split()]\nL = collections.Counter(lst)\nx = n\nans = 0\n\nfor k in sorted(L.keys()):\n x -= L[k]\n ans += x*L[k]\nprint(ans)","repo_name":"dakkenkd/AtCoder","sub_path":"ABC/ABC206/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24816980241","text":"from bael import project\nfrom baelfire.core import Core\nfrom baelfire.filedict import FileDict\nfrom os.path import abspath\nfrom os.path import dirname\nfrom os.path import exists\nfrom os.path import join\n\n\nclass ProjectCore(Core):\n\n def phase_settings(self):\n super().phase_settings()\n data = self.get_data_settings()\n\n self.settings['project_name'] = data['name']\n self.settings['package_name'] = data['package name']\n\n self.paths.set('cwd', dirname(self.find_pyproject_file()))\n self.paths.set('report', '.bael.yml', 'cwd')\n self.paths.set('pyproject', dirname(project.__file__))\n self.paths.set('pyptemplates', 'templates', 'pyproject')\n\n self.paths.set('package:src', 'src', 'cwd')\n self.paths.set_generator('package:main', lambda paths: self.settings['package_name'], 'package:src')\n self.paths.set(\n 'template_setuppy',\n 'setuppy.jinja2',\n 'pyptemplates',\n )\n self.paths.set('setuppy', 'setup.py', 'cwd')\n\n self.paths.set_generator(\n 'virtualenv:base',\n lambda paths: 'venv_{0}'.format(self.settings['package_name']),\n 'cwd',\n )\n self.paths.set('virtualenv:bin', 'bin', 'virtualenv:base')\n self.paths.set('exe:python', 'python', 'virtualenv:bin')\n self.paths.set('exe:pip', 'pip', 'virtualenv:bin')\n\n def get_data_settings(self):\n path = self.find_pyproject_file()\n data = FileDict(path)\n data.load(True)\n data.ensure_key_exists('name', 'Project name')\n data.ensure_key_exists('package name', 'Package name')\n data.save()\n return data\n\n def find_pyproject_file(self, path=None):\n if not path:\n path = abspath('./')\n file_path = join(path, '.pyproject.yaml')\n if exists(file_path):\n return file_path\n else:\n toppath = dirname(path)\n if toppath == path:\n return '.pyproject.yaml'\n return self.find_pyproject_file(toppath)\n","repo_name":"socek/bael.project","sub_path":"src/bael/project/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":2073,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"43227595955","text":"import errno\nimport time\nfrom time import sleep\nimport json\nimport logging\nimport os\nimport re\nimport requests\nimport selenium\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nfrom banned_exception import BannedException\nfrom constants import AMAZON_BASE_URL\nimport pandas\n\nOUTPUT_DIR = 'comments'\n\nif not os.path.exists(OUTPUT_DIR):\n os.makedirs(OUTPUT_DIR)\n\n\ndef get_reviews_filename(product_id):\n filename = os.path.join(OUTPUT_DIR, '{}.csv'.format(product_id))\n exist = os.path.isfile(filename)\n return filename, exist\n\n\ndef mkdir_p(path):\n try:\n os.makedirs(path)\n except OSError as exc:\n if exc.errno == errno.EEXIST and os.path.isdir(path):\n pass\n else:\n raise\n\n\ndef persist_comment_to_disk(reviews):\n if len(reviews) == 0:\n return False\n product_id_set = set([r['product_id'] for r in reviews])\n assert len(product_id_set) == 1, 'all product ids should be the same in the reviews list.'\n product_id = next(iter(product_id_set))\n output_filename, exist = get_reviews_filename(product_id)\n if exist:\n print(\"File with same pid is there...\")\n return False\n mkdir_p(OUTPUT_DIR)\n # https://stackoverflow.com/questions/18337407/saving-utf-8-texts-in-json-dumps-as-utf8-not-as-u-escape-sequence/18337754\n with open(output_filename, 'w', encoding='utf-8') as fp:\n json.dump(reviews, fp, sort_keys=True, indent=4, ensure_ascii=False)\n\n pandas.read_json(output_filename).to_csv(output_filename)\n #return True\n return output_filename\n\n\ndef extract_product_id(link_from_main_page):\n # e.g. B01H8A7Q42\n p_id = -1\n tags = ['/dp/', '/gp/product/']\n for tag in tags:\n try:\n p_id = link_from_main_page[link_from_main_page.index(tag) + len(tag):].split('/')[0]\n except:\n pass\n m = re.match('[A-Z0-9]{10}', p_id)\n if m:\n return m.group()\n else:\n return None\n\n\n'''\nfrom lxml.html import fromstring\nfrom itertools import cycle\nimport traceback\n\ndef get_proxies():\n url = 'https://free-proxy-list.net/'\n response = requests.get(url)\n parser = fromstring(response.text)\n proxie = list()\n for i in parser.xpath('//tbody/tr')[:100]:\n if i.xpath('.//td[7][contains(text(),\"yes\")]'):\n proxy = \":\".join([i.xpath('.//td[1]/text()')[0], i.xpath('.//td[2]/text()')[0]])\n proxie.append(proxy)\n return proxie\n\n\n#If you are copy pasting proxy ips, put in the list below\n#proxies = ['121.129.127.209:80', '124.41.215.238:45169', '185.93.3.123:8080', '194.182.64.67:3128', '106.0.38.174:8080', '163.172.175.210:3128', '13.92.196.150:8080']\nproxie = get_proxies()\nprint(proxie)\n#proxy_pool = cycle(proxies)\n\n\nclass myclass:\n c = 7\n'''\ndriver = webdriver.Chrome('D:/chromedriver')\n\ndef get_soup(url):\n if AMAZON_BASE_URL not in url:\n url = AMAZON_BASE_URL + url\n #nap_time_sec = 1\n #logging.debug('Script is going to sleep for {} (Amazon throttling). ZZZzzzZZZzz.'.format(nap_time_sec))\n #sleep(nap_time_sec)\n #url = \"www.google.com\"\n driver.get('https://www.proxysite.com/')\n url_input_box = driver.find_element_by_name('d')\n url_input_box.send_keys(url)\n #time.sleep(10)\n #go_submit_button = driver.find_element_by_xpath('//*[@id=\"url-form-wrap\"]/form/div[2]/button').click()\n try:\n go_submit_button = WebDriverWait(driver, 20).until(EC.element_to_be_clickable((By.XPATH, '//*[@id=\"url-form-wrap\"]/form/div[2]/button')))\n time.sleep(1)\n go_submit_button.click()\n print(\"YOU link found and returned\")\n except TimeoutException:\n print(\"YOU link not found ... breaking out\")\n \n time.sleep(1)\n html = driver.page_source\n soup = BeautifulSoup(html, \"lxml\")\n return soup\n\n\n'''\ndef get_soup(url):\n if AMAZON_BASE_URL not in url:\n url = AMAZON_BASE_URL + url\n nap_time_sec = 1\n logging.debug('Script is going to sleep for {} (Amazon throttling). ZZZzzzZZZzz.'.format(nap_time_sec))\n sleep(nap_time_sec)\n #header = requests.utils.default_headers()\n header = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36'\n }\n logging.debug('-> to Amazon : {}'.format(url))\n # Get a proxy from the pool\n #proxy = next(proxy_pool)\n m = myclass()\n m.c += 1\n proxy = proxie[m.c]\n print(proxy)\n #proxy = next(i)\n try:\n print('\\na')\n sleep(2)\n out = requests.get(url, headers=header ,proxies={'https': 'http://213.32.5.165:443' ,'http': 'http://213.32.5.165:443'})\n assert out.status_code == 200\n soup = BeautifulSoup(out.content, 'lxml')\n if 'captcha' in str(soup):\n raise BannedException('Your bot has been detected. Please wait a while.')\n return soup\n\n except requests.exceptions.ConnectionError as error:\n print(error)\n\n \n except BaseException as error:\n print(error)\n # Most free proxies will often get connection errors. You will have retry the entire request using another proxy to work.\n # We will just skip retries as its beyond the scope of this tutorial and we are only downloading a single url\n print(\"Skipping. Connnection error\")\n'''\n","repo_name":"sherlvick/sentimental-analysis_SVM","sub_path":"amazon-reviews-scraper-master/core_utils.py","file_name":"core_utils.py","file_ext":"py","file_size_in_byte":5513,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"8731789061","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('', views.IndexView.as_view(), name='index'),\n path(\"contacts\", views.ContactsView.as_view(), name=\"contacts\"),\n path(\"about\", views.AboutView.as_view(), name=\"about\"),\n path('kurzy/', views.CourseListView.as_view(), name='course_list'),\n path('pobocky/', views.BranchListView.as_view(), name='branch_list'),\n path('tym/', views.PersonListView.as_view(), name='branch_list'),\n # Verze z první poloviny\n #path('prihlaska/', views.ApplicationCreateView.as_view(), name='application_create'),\n path('prihlaska//', views.ApplicationCreateView.as_view(), name='application_create'),\n path('prihlaska/potvrzeni/', views.ApplicationConfirmation.as_view(), name='application_confirmation'),\n path('prihlaska-clena-tymu/', views.PersonRegisterView.as_view(), name='person_register'),\n path('kurz/', views.CourseDetailView.as_view(), name='course_detail'),\n path('pobocka/', views.BranchDetailView.as_view(), name='course_detail'),\n]\n","repo_name":"pesikj/progr2-python","sub_path":"python-pro-web/reseni/formulare/projekt/czechitas/courses/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"22419579775","text":"class Solution:\n def coinChange(self, coins: List[int], amount: int) -> int:\n if not coins:\n return -1\n numCoins = [float('inf')]*(amount+1)\n numCoins[0] = 0\n for i in range(1,len(numCoins)):\n for j in range(len(coins)):\n if coins[j] <= i:\n numCoins[i] = min(numCoins[i], 1+numCoins[i-coins[j]])\n if numCoins[-1] < float('inf'):\n return numCoins[-1]\n return -1\n","repo_name":"abhinay-b/Leetcode-Submissions","sub_path":"accepted_codes/Coin_Change/Coin Change_292911169.py","file_name":"Coin Change_292911169.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"27276447399","text":"def sum_power(a, b):\n num = a ** b\n total = 0\n for c in str(num):\n total += int(c)\n return total\n\n\nif __name__ == '__main__':\n maximum = 0\n for a in range(100):\n for b in range(100):\n s = sum_power(a, b)\n if s > maximum:\n maximum = s\n print(maximum)\n","repo_name":"Rylan12/Project-Euler","sub_path":"solved/p56.py","file_name":"p56.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25511511500","text":"import osmnx as ox\nimport math\nimport networkx as nx\nimport geopy\nimport json\nfrom shapely import geometry\nimport sys\n\n\nlchs_lat = 39.12\nlchs_long = -77.58\ndouglass_lat = 39.11\ndouglass_long = -77.55\n\n# G = ox.graph_from_place(\"Leesburg, Virginia, USA\", network_type='drive')\n# G2 = ox.graph_from_bbox(lchs_lat, douglass_lat, douglass_long, lchs_long, network_type='drive')\n# G is a multidigraph created by the networkx python package\n\n# for (u, v, l) in G.edges(data='length'):\n# print(u, v, l)\n\n# lchs = ox.get_nearest_node(G, (lchs_lat, lchs_long))\n# douglass = ox.get_nearest_node(G, (douglass_lat, douglass_long))\n\n\ndef generate_int(n):\n for i in range(n):\n yield i\n\n\ndef get_travel_time(edge):\n \"\"\"\n :param edge: dictionary containing edge data\n :return: approximate time in minutes to travel along the edge\n \"\"\"\n\n # dictionary of default speed limits to be used when no speed limit is given\n # based off of descriptions found on https://wiki.openstreetmap.org/wiki/Key:highway\n default_speed_limits = {\n 'motorway': 55,\n 'trunk': 55,\n 'primary': 45,\n 'secondary': 45,\n 'tertiary': 35,\n 'unclassified': 30,\n 'residential': 25,\n 'motorway_link': 45,\n 'trunk_link': 45,\n 'primary_link': 35,\n 'secondary_link': 35,\n 'tertiary_link': 25,\n 'living_street': 15,\n }\n\n distance_mi = edge['length'] / 1609.0 # convert from meters to miles\n try:\n edge['maxspeed']\n if type(edge['maxspeed']) == str:\n maxspeed = int(edge['maxspeed'][:-3])\n elif type(edge['maxspeed']) == list:\n maxspeed = int(edge['maxspeed'][-1][:-3]) # get highest speed, remove last three characters (mpg)\n except KeyError:\n road_type = edge['highway']\n try:\n maxspeed = default_speed_limits[road_type]\n except KeyError:\n maxspeed = 25\n\n travel_time = (distance_mi / maxspeed) * 60\n return travel_time\n\n\ndef dijkstra(start, graph):\n print(\"Dijkstra\", type(graph))\n infinity = math.inf\n distances = {start: (0, None)}\n seen = []\n\n for node in list(graph.nodes()):\n if node != start:\n distances[node] = (infinity, None)\n\n q = list(graph.nodes())\n while q:\n u = min((distances[i][0], i) for i in (distances.keys() - seen))[1]\n seen.append(u)\n try:\n q.remove(u)\n except ValueError:\n print(\"{} not in list: {}\".format(u, u not in q))\n break\n neighbors = graph.neighbors(u)\n for n in neighbors:\n travel_time = get_travel_time(graph.get_edge_data(u, n)[0])\n if distances[n][0] > distances[u][0] + travel_time:\n distances[n] = (distances[u][0] + travel_time, u)\n\n return distances\n\n\ntest_graph = nx.Graph()\ntest_graph.add_nodes_from(\"abcdefg\")\ntest_graph.add_edges_from([(\"a\", \"b\", {0: {'length': 1600, 'maxspeed': '55 mph'}}),\n (\"a\", \"c\", {0: {'length': 1810, 'maxspeed': '45 mph'}}),\n (\"a\", \"g\", {0: {'length': 300, 'maxspeed': '25 mpg'}}),\n (\"b\", \"d\", {0: {'length': 800, 'maxspeed': '45 mpg'}}),\n (\"c\", \"d\", {0: {'length': 1650, 'maxspeed': '45 mpg'}}),\n (\"c\", \"e\", {0: {'length': 510, 'maxspeed': '35 mpg'}}),\n (\"d\", \"e\", {0: {'length': 743, 'maxspeed': '25 mpg'}}),\n (\"e\", \"f\", {0: {'length': 621, 'maxspeed': '25 mpg'}}),\n (\"f\", \"g\", {0: {'length': 750, 'maxspeed': '25 mpg'}})])\n\n\ndef dijkstra_brute_force(graph, start_nodes):\n print(\"BF\",type(graph))\n dijkstra_results = []\n for node in start_nodes:\n dijkstra_results.append(dijkstra(node, graph))\n\n node_sums = {}\n for node in graph.nodes():\n node_sum = 0\n for result in dijkstra_results:\n node_sum += result[node][0]\n node_sums[node] = node_sum\n\n meeting_node = min((node_sums[i], i) for i in node_sums.keys())[1]\n\n paths = {}\n for i in range(len(start_nodes)):\n path = [meeting_node]\n res = dijkstra_results[i]\n current = meeting_node\n\n while res[current][1] is not None:\n path.insert(0, res[current][1])\n current = res[current][1]\n paths[start_nodes[i]] = path\n\n return meeting_node, paths\n\n\ndef to_coords(locations):\n \"\"\"\n Get coordinates of given locations\n :param locations: List of strings representing geographical locations\n :return: list of tuples containing coordinates of locations\n \"\"\"\n coordinates = []\n locator = geopy.Nominatim(user_agent=\"myGeocoder\", scheme='http')\n\n for location in locations:\n try:\n location_coords = locator.geocode(location)[1]\n except TypeError:\n print(\"Invalid location: {}\".format(location), file=sys.stderr)\n return None\n coordinates.append(location_coords)\n return coordinates\n\n\ndef create_graph(initial_locations):\n \"\"\"\n Creates a graph to perform meeting place finding algorithm\n :param initial_locations: List of strings containing initial locations\n :return: Networkx graph and list of coordinate tuples\n \"\"\"\n coordinates = to_coords(initial_locations)\n lat = []\n long = []\n\n for c in coordinates:\n lat.append(c[0])\n long.append(c[1])\n\n north = max(lat)\n south = min(lat)\n east = max(long)\n west = min(long)\n\n return ox.graph_from_bbox(north, south, east, west, network_type='drive')\n\n\ndef find_meeting_place(initial_locations, algorithm=\"bf\"):\n \"\"\"\n Finds meeting place on graph between initial locations\n :param initial_locations: List of strings containing initial locations\n :param algorithm: String describing which algorithm to use\n :return: Returns dictionary of relevant data of meeting place\n \"\"\"\n\n G = create_graph(initial_locations)\n print(\"init\",type(G))\n coordinates = to_coords(initial_locations)\n\n if G is None:\n print(\"Invalid location was specified\", file=sys.stderr)\n return None\n\n if algorithm == \"bf\":\n initial_nodes = []\n for location in coordinates:\n print(\"Adding node for {}\".format(location))\n initial_nodes.append(ox.get_nearest_node(G, location))\n meeting_place, _ = dijkstra_brute_force(G, initial_nodes)\n else:\n return None\n\n return meeting_place\n\n\nlocations = [\n \"Loudoun County High School, Leesburg, VA\",\n \"407 E Market St, Leesburg, VA\",\n \"18 E Market St, Leesburg, VA\"\n]\n\nprint(find_meeting_place(locations))\n\n\n# locator = geopy.Nominatim(user_agent=\"myGeocoder\", scheme='http')\n# lchs_coords = locator.geocode(\"Loudoun County High School, Leesburg, VA\")[1]\n# douglass_coords = locator.geocode(\"407 E Market St, Leesburg, VA\")[1]\n# court_coords = locator.geocode(\"18 E Market St, Leesburg, VA\")[1]\n#\n# large_test_graph = ox.graph_from_place(\"Leesburg, Virginia, USA\", network_type='drive')\n#\n# lchs = ox.get_nearest_node(large_test_graph, (lchs_coords[0], lchs_coords[1]))\n# douglass = ox.get_nearest_node(large_test_graph, (douglass_coords[0], douglass_coords[1]))\n# courthouse = ox.get_nearest_node(large_test_graph, (court_coords[0], court_coords[1]))\n#\n#\n# meeting_place, paths = dijkstra_brute_force([lchs, douglass, courthouse], large_test_graph)\n# node_data = dict(large_test_graph.nodes(data=True))\n# print(node_data[meeting_place])\n\n\n\n#meeting_node, paths = dijkstra_brute_force(['a', 'd', 'f'], test_graph)\n#print(meeting_node)\n#print(paths)\n\n\ndef floyd_warshall(graph):\n infinity = math.inf\n distances = {}\n q = list(graph.nodes())\n print(q)\n\n num_nodes = graph.number_of_nodes()\n for i in generate_int(num_nodes):\n for j in generate_int(num_nodes):\n if (q[i], q[j]) in graph.edges():\n road_length = graph.get_edge_data(q[i], q[j])[0]['length']\n try:\n max_speed = graph.get_edge_data(q[i], q[j])[0]['max_speed']\n except KeyError:\n road_type = graph.get_edge_data(q[i], q[j])[0]['highway']\n if road_type == 'residential':\n max_speed = 25\n else:\n print(road_type)\n\n travel_time = get_travel_time(road_length, max_speed)\n distances[(q[i], q[j])] = travel_time\n else:\n distances[(q[i], q[j])] = infinity\n\n for k in generate_int(num_nodes):\n for i in generate_int(num_nodes):\n for j in generate_int(num_nodes):\n if (q[i], q[k]) in graph.edges():\n length_i_k = graph.get_edge_data(q[i], q[k])[0]['length']\n try:\n max_speed_i_k = graph.get_edge_data(q[i], q[k])[0]['max_speed']\n except KeyError:\n road_type = graph.get_edge_data(q[i], q[k])[0]['highway']\n if road_type == 'residential':\n max_speed_i_k = 25\n else:\n print(road_type)\n travel_time_i_k = get_travel_time(length_i_k, max_speed_i_k)\n\n if (q[k], q[j]) in graph.edges():\n length_k_j = graph.get_edge_data(q[k], q[j])[0]['length']\n try:\n max_speed_k_j = graph.get_edge_data(q[k], q[j])[0]['max_speed']\n except KeyError:\n road_type = graph.get_edge_data(q[k], q[j])[0]['highway']\n if road_type == 'residential':\n max_speed_k_j = 25\n else:\n print(road_type)\n travel_time_k_j = get_travel_time(length_k_j, max_speed_k_j)\n\n new_dist = travel_time_i_k + travel_time_k_j\n if distances[(q[i], q[j])] > new_dist:\n distances[(q[i], q[j])] = new_dist\n\n for item in distances:\n print(\"{} --> {}\".format(item, distances[item]))\n\n for edge in graph.edges():\n print(graph.get_edge_data(edge[0], edge[1]))\n\n\n\n\n","repo_name":"APettit98/Senior-IS","sub_path":"PracticeCode/practice2.py","file_name":"practice2.py","file_ext":"py","file_size_in_byte":10313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21904893837","text":"#Django imports\nfrom django import urls\nfrom django.urls import path, include\n\n\n\n# Standard Package Imports\n\n# Project Imports\nfrom . import views\n\n\n# Third Party Imports\n\n\napp_name = 'event'\n\nurlpatterns = [\n path('official_dashboard', views.dashboard, name='dashboard'),\n path('official_dashboard/add_social', views.dashboard_add_social, name='dashboard_add_social'),\n path('official_dashboard/view_social/(?P[0-9a-f-]+)', views.dashboard_view_social, name='dashboard_view_social'),\n path('official_dashboard/del_social/(?P[0-9a-f-]+)', views.dashboard_del_social, name='dashboard_del_social'),\n path('official_dashboard/add_sports', views.dashboard_add_sports, name='dashboard_add_sports'),\n path('official_dashboard/view_sports/(?P[0-9a-f-]+)', views.dashboard_view_sports, name='dashboard_view_sports'),\n path('official_dashboard/del_sports/(?P[0-9a-f-]+)', views.dashboard_del_sports, name='dashboard_del_sports'), \n path('official_dashboard/add_studentwelfare', views.dashboard_add_studentwelfare, name='dashboard_add_studentwelfare'),\n path('official_dashboard/view_studentwelfare/(?P[0-9a-f-]+)', views.dashboard_view_studentwelfare, name='dashboard_view_studentwelfare'),\n path('official_dashboard/del_studentwelfare/(?P[0-9a-f-]+)', views.dashboard_del_studentwelfare, name='dashboard_del_studentwelfare'),\n path('official_dashboard/add_technology', views.dashboard_add_technology, name='dashboard_add_technology'),\n path('official_dashboard/view_technology/(?P[0-9a-f-]+)', views.dashboard_view_technology, name='dashboard_view_technology'),\n path('official_dashboard/del_technology/(?P[0-9a-f-]+)', views.dashboard_del_technology, name='dashboard_del_technology'),\n path('technology', views.technology, name='technology'),\n path('SocialandCulture', views.social, name='social'),\n path('SportsandGames', views.sports, name='SportsAndGames'),\n path('Studentswelfare', views.studentWelfare, name='studentWelfare'),\n path('technology/', views.technology_details, name='technology'),\n path('studentWelfare/', views.studentWelfare_details, name='studentWelfare'),\n path('SocialandCulture/', views.social_details, name='social'),\n path('SportsandGames/', views.sports_details, name='sports'),\n path('Results', views.result, name='result'),\n path('interIIT', views.interIIT, name='interIIT'),\n path('interIIT-Technology', views.interIITtechnology,name='interIIT-Technology'),\n path('interIITSocult', views.interIITsocult, name='interIIT-socult'),\n path('GCTechnology', views.GCTechnology, name='GCTechnology'),\n path('GCSocult', views.GCSocult, name='GCSocult'),\n]\n","repo_name":"bhagyashree201/TSGhackathon","sub_path":"event/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37013999836","text":"from random import randint\nfrom time import sleep\n\ndef sorteia(sorte):\n for c in range(1, 6):\n sorte.append(randint(1, 10))\n print(f'Sorteando os valores da lista:', end=' ')\n for v in sorte:\n print(f'{v}', end=' ')\n sleep(0.3)\n print()\n\n\ndef somaPar(n2):\n soma = 0\n for valor in numeros:\n if valor % 2 ==0:\n soma += valor\n print(f'Somando os valores pares de {numeros}, temos {soma}.')\n\n#Programa principal\nnumeros = []\nsorteia(numeros)\nsomaPar(numeros)\n","repo_name":"maisalobao/Python","sub_path":"pythonProject/desafio100.py","file_name":"desafio100.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"42057623406","text":"import os\nfrom glob import glob\n\nimport numpy as np\n\nfrom yt.utilities.grid_data_format.conversion.conversion_abc import Converter\nfrom yt.utilities.on_demand_imports import _h5py as h5py\n\ntranslation_dict = {}\ntranslation_dict[\"density\"] = \"density\"\ntranslation_dict[\"total_energy\"] = \"specific_energy\"\ntranslation_dict[\"velocity_x\"] = \"velocity_x\"\ntranslation_dict[\"velocity_y\"] = \"velocity_y\"\ntranslation_dict[\"velocity_z\"] = \"velocity_z\"\ntranslation_dict[\"cell_centered_B_x\"] = \"mag_field_x\"\ntranslation_dict[\"cell_centered_B_y\"] = \"mag_field_y\"\ntranslation_dict[\"cell_centered_B_z\"] = \"mag_field_z\"\n\n\nclass AthenaDistributedConverter(Converter):\n def __init__(self, basename, outname=None, source_dir=None, field_conversions=None):\n self.fields = []\n self.current_time = 0.0\n name = basename.split(\".\")\n self.ddn = int(name[1])\n if source_dir is None:\n source_dir = \"./\"\n self.source_dir = source_dir\n self.basename = name[0]\n if outname is None:\n outname = self.basename + \".%04i\" % self.ddn + \".gdf\"\n self.outname = outname\n if field_conversions is None:\n field_conversions = {}\n self.field_conversions = field_conversions\n self.handle = None\n\n def parse_line(self, line, grid):\n # grid is a dictionary\n splitup = line.strip().split()\n if \"vtk\" in splitup:\n grid[\"vtk_version\"] = splitup[-1]\n elif \"Really\" in splitup:\n grid[\"time\"] = splitup[-1]\n self.current_time = grid[\"time\"]\n elif \"PRIMITIVE\" in splitup:\n grid[\"time\"] = float(splitup[4].rstrip(\",\"))\n grid[\"level\"] = int(splitup[6].rstrip(\",\"))\n grid[\"domain\"] = int(splitup[8].rstrip(\",\"))\n self.current_time = grid[\"time\"]\n elif \"DIMENSIONS\" in splitup:\n grid[\"dimensions\"] = np.array(splitup[-3:], dtype=\"int64\")\n elif \"ORIGIN\" in splitup:\n grid[\"left_edge\"] = np.array(splitup[-3:], dtype=\"float64\")\n elif \"SPACING\" in splitup:\n grid[\"dds\"] = np.array(splitup[-3:], dtype=\"float64\")\n elif \"CELL_DATA\" in splitup:\n grid[\"ncells\"] = int(splitup[-1])\n elif \"SCALARS\" in splitup:\n field = splitup[1]\n grid[\"read_field\"] = field\n grid[\"read_type\"] = \"scalar\"\n elif \"VECTORS\" in splitup:\n field = splitup[1]\n grid[\"read_field\"] = field\n grid[\"read_type\"] = \"vector\"\n\n def write_gdf_field(self, fn, grid_number, field, data):\n f = self.handle\n ## --------- Store Grid Data --------- ##\n if \"grid_%010i\" % grid_number not in f[\"data\"].keys():\n g = f[\"data\"].create_group(\"grid_%010i\" % grid_number)\n else:\n g = f[\"data\"][\"grid_%010i\" % grid_number]\n name = field\n try:\n name = translation_dict[name]\n except Exception:\n pass\n # print 'Writing %s' % name\n if name not in g.keys():\n g.create_dataset(name, data=data)\n\n def read_and_write_index(self, basename, ddn, gdf_name):\n \"\"\"Read Athena legacy vtk file from multiple cpus\"\"\"\n proc_names = glob(os.path.join(self.source_dir, \"id*\"))\n # print('Reading a dataset from %i Processor Files' % len(proc_names))\n N = len(proc_names)\n grid_dims = np.empty([N, 3], dtype=\"int64\")\n grid_left_edges = np.empty([N, 3], dtype=\"float64\")\n grid_dds = np.empty([N, 3], dtype=\"float64\")\n grid_levels = np.zeros(N, dtype=\"int64\")\n grid_parent_ids = -1 * np.ones(N, dtype=\"int64\")\n grid_particle_counts = np.zeros([N, 1], dtype=\"int64\")\n\n for i in range(N):\n if i == 0:\n fn = os.path.join(\n self.source_dir, f\"id{i}\", basename + f\".{ddn:04d}.vtk\"\n )\n else:\n fn = os.path.join(\n self.source_dir, f\"id{i}\", basename + f\"-id{i}.{ddn:04d}.vtk\"\n )\n\n print(f\"Reading file {fn}\")\n f = open(fn, \"rb\")\n grid = {}\n grid[\"read_field\"] = None\n grid[\"read_type\"] = None\n line = f.readline()\n while grid[\"read_field\"] is None:\n self.parse_line(line, grid)\n if \"SCALAR\" in line.strip().split():\n break\n if \"VECTOR\" in line.strip().split():\n break\n if \"TABLE\" in line.strip().split():\n break\n if len(line) == 0:\n break\n del line\n line = f.readline()\n\n if len(line) == 0:\n break\n\n if np.prod(grid[\"dimensions\"]) != grid[\"ncells\"]:\n grid[\"dimensions\"] -= 1\n grid[\"dimensions\"][grid[\"dimensions\"] == 0] = 1\n if np.prod(grid[\"dimensions\"]) != grid[\"ncells\"]:\n print(\n \"product of dimensions %i not equal to number of cells %i\"\n % (np.prod(grid[\"dimensions\"]), grid[\"ncells\"])\n )\n raise TypeError\n\n # Append all hierarchy info before reading this grid's data\n grid_dims[i] = grid[\"dimensions\"]\n grid_left_edges[i] = grid[\"left_edge\"]\n grid_dds[i] = grid[\"dds\"]\n # grid_ncells[i]=grid['ncells']\n del grid\n\n f.close()\n del f\n f = self.handle\n\n ## --------- Begin level nodes --------- ##\n g = f.create_group(\"gridded_data_format\")\n g.attrs[\"format_version\"] = np.float32(1.0)\n g.attrs[\"data_software\"] = \"athena\"\n f.create_group(\"data\")\n f.create_group(\"field_types\")\n f.create_group(\"particle_types\")\n pars_g = f.create_group(\"simulation_parameters\")\n\n gles = grid_left_edges\n gdims = grid_dims\n dle = np.min(gles, axis=0)\n dre = np.max(gles + grid_dims * grid_dds, axis=0)\n glis = ((gles - dle) / grid_dds).astype(\"int64\")\n\n ddims = (dre - dle) / grid_dds[0]\n\n # grid_left_index\n f.create_dataset(\"grid_left_index\", data=glis)\n # grid_dimensions\n f.create_dataset(\"grid_dimensions\", data=gdims)\n\n # grid_level\n f.create_dataset(\"grid_level\", data=grid_levels)\n\n ## ----------QUESTIONABLE NEXT LINE--------- ##\n # This data needs two dimensions for now.\n f.create_dataset(\"grid_particle_count\", data=grid_particle_counts)\n\n # grid_parent_id\n f.create_dataset(\"grid_parent_id\", data=grid_parent_ids)\n\n ## --------- Done with top level nodes --------- ##\n\n pars_g.attrs[\"refine_by\"] = np.int64(1)\n pars_g.attrs[\"dimensionality\"] = np.int64(3)\n pars_g.attrs[\"domain_dimensions\"] = ddims\n pars_g.attrs[\"current_time\"] = self.current_time\n pars_g.attrs[\"domain_left_edge\"] = dle\n pars_g.attrs[\"domain_right_edge\"] = dre\n pars_g.attrs[\"unique_identifier\"] = \"athenatest\"\n pars_g.attrs[\"cosmological_simulation\"] = np.int64(0)\n pars_g.attrs[\"num_ghost_zones\"] = np.int64(0)\n pars_g.attrs[\"field_ordering\"] = np.int64(1)\n pars_g.attrs[\"boundary_conditions\"] = np.int64([0] * 6) # For Now\n\n # Extra pars:\n # pars_g.attrs['n_cells'] = grid['ncells']\n pars_g.attrs[\"vtk_version\"] = 1.0\n\n # Add particle types\n # Nothing to do here\n\n # Add particle field attributes\n # f.close()\n\n def read_and_write_data(self, basename, ddn, gdf_name):\n proc_names = glob(os.path.join(self.source_dir, \"id*\"))\n # print('Reading a dataset from %i Processor Files' % len(proc_names))\n N = len(proc_names)\n for i in range(N):\n if i == 0:\n fn = os.path.join(\n self.source_dir, f\"id{i}\", basename + f\".{ddn:04d}.vtk\"\n )\n else:\n fn = os.path.join(\n self.source_dir, +f\"id{i}\", basename + f\"-id{i}.{ddn:04d}.vtk\"\n )\n f = open(fn, \"rb\")\n # print('Reading data from %s' % fn)\n line = f.readline()\n while line != \"\":\n if len(line) == 0:\n break\n splitup = line.strip().split()\n\n if \"DIMENSIONS\" in splitup:\n grid_dims = np.array(splitup[-3:], dtype=\"int64\")\n line = f.readline()\n continue\n elif \"CELL_DATA\" in splitup:\n grid_ncells = int(splitup[-1])\n line = f.readline()\n if np.prod(grid_dims) != grid_ncells:\n grid_dims -= 1\n grid_dims[grid_dims == 0] = 1\n if np.prod(grid_dims) != grid_ncells:\n print(\n \"product of dimensions %i not equal to number of cells %i\"\n % (np.prod(grid_dims), grid_ncells)\n )\n raise TypeError\n break\n else:\n del line\n line = f.readline()\n read_table = False\n while line != \"\":\n if len(line) == 0:\n break\n splitup = line.strip().split()\n if \"SCALARS\" in splitup:\n field = splitup[1]\n if not read_table:\n line = f.readline() # Read the lookup table line\n read_table = True\n data = np.fromfile(f, dtype=\">f4\", count=grid_ncells).reshape(\n grid_dims, order=\"F\"\n )\n if i == 0:\n self.fields.append(field)\n # print('writing field %s' % field)\n self.write_gdf_field(gdf_name, i, field, data)\n read_table = False\n\n elif \"VECTORS\" in splitup:\n field = splitup[1]\n data = np.fromfile(f, dtype=\">f4\", count=3 * grid_ncells)\n data_x = data[0::3].reshape(grid_dims, order=\"F\")\n data_y = data[1::3].reshape(grid_dims, order=\"F\")\n data_z = data[2::3].reshape(grid_dims, order=\"F\")\n if i == 0:\n self.fields.append(field + \"_x\")\n self.fields.append(field + \"_y\")\n self.fields.append(field + \"_z\")\n\n # print('writing field %s' % field)\n self.write_gdf_field(gdf_name, i, field + \"_x\", data_x)\n self.write_gdf_field(gdf_name, i, field + \"_y\", data_y)\n self.write_gdf_field(gdf_name, i, field + \"_z\", data_z)\n del data, data_x, data_y, data_z\n del line\n line = f.readline()\n f.close()\n del f\n\n f = self.handle\n field_g = f[\"field_types\"]\n # Add Field Attributes\n for name in self.fields:\n tname = name\n try:\n tname = translation_dict[name]\n except Exception:\n pass\n this_field = field_g.create_group(tname)\n if name in self.field_conversions.keys():\n this_field.attrs[\"field_to_cgs\"] = self.field_conversions[name]\n else:\n this_field.attrs[\"field_to_cgs\"] = np.float64(\"1.0\") # For Now\n\n def convert(self, index=True, data=True):\n self.handle = h5py.File(self.outname, mode=\"a\")\n if index:\n self.read_and_write_index(self.basename, self.ddn, self.outname)\n if data:\n self.read_and_write_data(self.basename, self.ddn, self.outname)\n self.handle.close()\n\n\nclass AthenaConverter(Converter):\n def __init__(self, basename, outname=None, field_conversions=None):\n self.fields = []\n self.basename = basename\n name = basename.split(\".\")\n fn = \"%s.%04i\" % (name[0], int(name[1]))\n self.ddn = int(name[1])\n self.basename = fn\n if outname is None:\n outname = fn + \".gdf\"\n self.outname = outname\n if field_conversions is None:\n field_conversions = {}\n self.field_conversions = field_conversions\n\n def parse_line(self, line, grid):\n # grid is a dictionary\n splitup = line.strip().split()\n if \"vtk\" in splitup:\n grid[\"vtk_version\"] = splitup[-1]\n elif \"Really\" in splitup:\n grid[\"time\"] = splitup[-1]\n elif \"DIMENSIONS\" in splitup:\n grid[\"dimensions\"] = np.array(splitup[-3:], dtype=\"int64\")\n elif \"ORIGIN\" in splitup:\n grid[\"left_edge\"] = np.array(splitup[-3:], dtype=\"float64\")\n elif \"SPACING\" in splitup:\n grid[\"dds\"] = np.array(splitup[-3:], dtype=\"float64\")\n elif \"CELL_DATA\" in splitup:\n grid[\"ncells\"] = int(splitup[-1])\n elif \"SCALARS\" in splitup:\n field = splitup[1]\n grid[\"read_field\"] = field\n grid[\"read_type\"] = \"scalar\"\n elif \"VECTORS\" in splitup:\n field = splitup[1]\n grid[\"read_field\"] = field\n grid[\"read_type\"] = \"vector\"\n\n def read_grid(self, filename):\n \"\"\"Read Athena legacy vtk file from single cpu\"\"\"\n f = open(filename, \"rb\")\n # print('Reading from %s'%filename)\n grid = {}\n grid[\"read_field\"] = None\n grid[\"read_type\"] = None\n table_read = False\n line = f.readline()\n while line != \"\":\n while grid[\"read_field\"] is None:\n self.parse_line(line, grid)\n if grid[\"read_type\"] == \"vector\":\n break\n if not table_read:\n line = f.readline()\n if \"TABLE\" in line.strip().split():\n table_read = True\n if len(line) == 0:\n break\n\n if len(line) == 0:\n break\n if np.prod(grid[\"dimensions\"]) != grid[\"ncells\"]:\n grid[\"dimensions\"] -= 1\n if np.prod(grid[\"dimensions\"]) != grid[\"ncells\"]:\n print(\n \"product of dimensions %i not equal to number of cells %i\"\n % (np.prod(grid[\"dimensions\"]), grid[\"ncells\"])\n )\n raise TypeError\n\n if grid[\"read_type\"] == \"scalar\":\n grid[grid[\"read_field\"]] = np.fromfile(\n f, dtype=\">f4\", count=grid[\"ncells\"]\n ).reshape(grid[\"dimensions\"], order=\"F\")\n self.fields.append(grid[\"read_field\"])\n elif grid[\"read_type\"] == \"vector\":\n data = np.fromfile(f, dtype=\">f4\", count=3 * grid[\"ncells\"])\n grid[grid[\"read_field\"] + \"_x\"] = data[0::3].reshape(\n grid[\"dimensions\"], order=\"F\"\n )\n grid[grid[\"read_field\"] + \"_y\"] = data[1::3].reshape(\n grid[\"dimensions\"], order=\"F\"\n )\n grid[grid[\"read_field\"] + \"_z\"] = data[2::3].reshape(\n grid[\"dimensions\"], order=\"F\"\n )\n self.fields.append(grid[\"read_field\"] + \"_x\")\n self.fields.append(grid[\"read_field\"] + \"_y\")\n self.fields.append(grid[\"read_field\"] + \"_z\")\n else:\n raise TypeError\n grid[\"read_field\"] = None\n grid[\"read_type\"] = None\n line = f.readline()\n if len(line) == 0:\n break\n grid[\"right_edge\"] = grid[\"left_edge\"] + grid[\"dds\"] * (grid[\"dimensions\"])\n return grid\n\n def write_to_gdf(self, fn, grid):\n f = h5py.File(fn, mode=\"a\")\n\n ## --------- Begin level nodes --------- ##\n g = f.create_group(\"gridded_data_format\")\n g.attrs[\"format_version\"] = np.float32(1.0)\n g.attrs[\"data_software\"] = \"athena\"\n data_g = f.create_group(\"data\")\n field_g = f.create_group(\"field_types\")\n f.create_group(\"particle_types\")\n pars_g = f.create_group(\"simulation_parameters\")\n\n dle = grid[\"left_edge\"] # True only in this case of one grid for the domain\n gles = np.array([grid[\"left_edge\"]])\n gdims = np.array([grid[\"dimensions\"]])\n glis = ((gles - dle) / grid[\"dds\"]).astype(\"int64\")\n\n # grid_left_index\n f.create_dataset(\"grid_left_index\", data=glis)\n # grid_dimensions\n f.create_dataset(\"grid_dimensions\", data=gdims)\n\n levels = np.array([0], dtype=\"int64\") # unigrid example\n # grid_level\n f.create_dataset(\"grid_level\", data=levels)\n\n ## ----------QUESTIONABLE NEXT LINE--------- ##\n # This data needs two dimensions for now.\n n_particles = np.array([[0]], dtype=\"int64\")\n # grid_particle_count\n f.create_dataset(\"grid_particle_count\", data=n_particles)\n\n # Assume -1 means no parent.\n parent_ids = np.array([-1], dtype=\"int64\")\n # grid_parent_id\n f.create_dataset(\"grid_parent_id\", data=parent_ids)\n\n ## --------- Done with top level nodes --------- ##\n\n f.create_group(\"index\")\n\n ## --------- Store Grid Data --------- ##\n\n g0 = data_g.create_group(\"grid_%010i\" % 0)\n for field in self.fields:\n name = field\n if field in translation_dict.keys():\n name = translation_dict[name]\n if name not in g0.keys():\n g0.create_dataset(name, data=grid[field])\n\n ## --------- Store Particle Data --------- ##\n\n # Nothing to do\n\n ## --------- Attribute Tables --------- ##\n\n pars_g.attrs[\"refine_by\"] = np.int64(1)\n pars_g.attrs[\"dimensionality\"] = np.int64(3)\n pars_g.attrs[\"domain_dimensions\"] = grid[\"dimensions\"]\n try:\n pars_g.attrs[\"current_time\"] = grid[\"time\"]\n except Exception:\n pars_g.attrs[\"current_time\"] = 0.0\n pars_g.attrs[\"domain_left_edge\"] = grid[\"left_edge\"] # For Now\n pars_g.attrs[\"domain_right_edge\"] = grid[\"right_edge\"] # For Now\n pars_g.attrs[\"unique_identifier\"] = \"athenatest\"\n pars_g.attrs[\"cosmological_simulation\"] = np.int64(0)\n pars_g.attrs[\"num_ghost_zones\"] = np.int64(0)\n pars_g.attrs[\"field_ordering\"] = np.int64(0)\n pars_g.attrs[\"boundary_conditions\"] = np.int64([0] * 6) # For Now\n\n # Extra pars:\n pars_g.attrs[\"n_cells\"] = grid[\"ncells\"]\n pars_g.attrs[\"vtk_version\"] = grid[\"vtk_version\"]\n\n # Add Field Attributes\n for name in g0.keys():\n tname = name\n try:\n tname = translation_dict[name]\n except Exception:\n pass\n this_field = field_g.create_group(tname)\n if name in self.field_conversions.keys():\n this_field.attrs[\"field_to_cgs\"] = self.field_conversions[name]\n else:\n this_field.attrs[\"field_to_cgs\"] = np.float64(\"1.0\") # For Now\n\n # Add particle types\n # Nothing to do here\n\n # Add particle field attributes\n f.close()\n\n def convert(self):\n grid = self.read_grid(self.basename + \".vtk\")\n self.write_to_gdf(self.outname, grid)\n\n\n# import sys\n# if __name__ == '__main__':\n# n = sys.argv[-1]\n# n = n.split('.')\n# fn = '%s.%04i'%(n[0],int(n[1]))\n# grid = read_grid(fn+'.vtk')\n# write_to_hdf5(fn+'.gdf',grid)\n","repo_name":"yt-project/yt","sub_path":"yt/utilities/grid_data_format/conversion/conversion_athena.py","file_name":"conversion_athena.py","file_ext":"py","file_size_in_byte":19713,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"3"}
+{"seq_id":"71483738000","text":"\r\nfrom OpenGL.GL import *\r\nfrom OpenGL.GLU import *\r\nfrom OpenGL.GLUT import *\r\nimport sys\r\n\r\n\r\n\r\nimport math\r\n\r\nsys.setrecursionlimit(8000)\r\n\r\n\r\ndef init():\r\n glClearColor(1.0, 1.0, 1.0, 1.0)\r\n glPointSize(1.0)\r\n glMatrixMode(GL_PROJECTION)\r\n glLoadIdentity()\r\n gluOrtho2D(-250, 250.0, -250.0, 250.0)\r\n\r\n\r\ndef Display():\r\n\r\n glClear(GL_COLOR_BUFFER_BIT)\r\n glColor3f(0, 1, 1)\r\n glWindowPos2d(250,250)\r\n for ch in \"Hello World\":\r\n glutBitmapCharacter(GLUT_BITMAP_9_BY_15, ctypes.c_int(ord(ch)))\r\n print(ch)\r\n\r\n\r\n\r\n glBegin(GL_POINTS)\r\n glVertex2f(0,0)\r\n glEnd()\r\n glFlush()\r\n glFlush()\r\n\r\n\r\n\r\ndef main():\r\n glutInit(sys.argv)\r\n glutInitDisplayMode(GLUT_SINGLE | GLUT_RGB )\r\n glutInitWindowSize(500, 500)\r\n glutInitWindowPosition(50, 50)\r\n glutCreateWindow(\"Hello World\")\r\n glutDisplayFunc(Display)\r\n init()\r\n glutMainLoop()\r\n\r\n\r\nmain()\r\n","repo_name":"akulsanthosh/randomAlgos","sub_path":"CG LAB/Hello.py","file_name":"Hello.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"70745806803","text":"import os\n\nfrom flask import (\n current_app,\n render_template,\n Blueprint,\n request,\n jsonify,\n send_from_directory,\n)\n\nfrom werkzeug.utils import secure_filename\n\nassignment_form = Blueprint(\"assignment_form\", __name__, template_folder=\"templates\")\n\n\n@assignment_form.route(\"/\")\ndef hello_world():\n return jsonify(hello=\"from the other side\")\n\n\n@assignment_form.route(\"/static/\")\ndef staticfiles(filename):\n return send_from_directory(current_app.config[\"STATIC_FOLDER\"], filename)\n\n\n@assignment_form.route(\"/media/\")\ndef mediafiles(filename):\n return send_from_directory(current_app.config[\"MEDIA_FOLDER\"], filename)\n\n\n@assignment_form.route(\"/upload\", methods=[\"GET\", \"POST\"])\ndef upload_file():\n if request.method == \"POST\":\n file = request.files[\"file\"]\n filename = secure_filename(file.filename)\n file.save(os.path.join(current_app.config[\"MEDIA_FOLDER\"], filename))\n return \"\"\"\n \n upload new File \n \n \"\"\"\n","repo_name":"danhagg/flask-on-docker","sub_path":"services/web/project/assignment_form/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28195101143","text":"import os\n\ndef load_input(file):\n with open(file, 'r') as lines:\n return [str(line).split() for line in lines]\n\ndef count_easy(outputs):\n count = 0\n for line in outputs:\n for digit in line:\n if len(digit) == 7 or (len(digit)>1 and len(digit)<5):\n count += 1\n return count\n\ndef decode_easy(digits, decoded):\n for digit in digits:\n if len(digit) == 2:\n decoded[1] = digit\n elif len(digit) == 3:\n decoded[7] = digit\n elif len(digit) == 4:\n decoded[4] = digit\n elif len(digit) == 7:\n decoded[8] = digit\n return decoded\n\ndef decode_remaining(digits, decoded):\n decoded[3] = find_3(digits, decoded)\n decoded[5] = find_5(digits, decoded)\n decoded[2] = find_2(digits, decoded)\n decoded[0] = find_0(digits, decoded)\n decoded[9] = find_9(digits, decoded)\n decoded[6] = find_6(digits, decoded)\n return decoded\n\ndef find_0(digits, decoded):\n for digit in digits:\n if len(digit) == 6:\n for x in decoded[5]:\n if not x in digit:\n return digit\n\ndef find_2(digits, decoded):\n for digit in digits:\n if len(digit) == 5:\n if not (digit==decoded[3] or digit==decoded[5]):\n return digit\n\ndef find_3(digits, decoded):\n for digit in digits:\n if len(digit) == 5:\n is3 = True\n for x in decoded[1]:\n if not x in digit:\n is3 = False\n break\n if is3 == True:\n return digit\n\ndef find_5(digits, decoded):\n for digit in digits:\n if len(digit) == 5 and not digit==decoded[3]:\n common = 0\n for x in decoded[4]:\n if x in digit:\n common += 1 \n if common == 3:\n return digit\n\ndef find_6(digits, decoded):\n for digit in digits:\n if len(digit) == 6:\n if not (digit==decoded[0] or digit==decoded[9]):\n return digit\n\ndef find_9(digits, decoded):\n for digit in digits:\n if len(digit) == 6 and not digit==decoded[0]:\n is9 = True\n for x in decoded[1]:\n if not x in digit:\n is9 = False\n break\n if is9 == True:\n return digit\n\ndef read_output(decoded, output):\n output_digits = [0]*4\n for i in range(4):\n for j in range(10):\n if len(decoded[j]) == len(output[i]):\n same = True\n for x in decoded[j]:\n if not x in output[i]:\n same = False\n break\n if same == True:\n output_digits[i] = j\n return output_digits[0]*1000 + output_digits[1]*100 + output_digits[2]*10 + output_digits[3]\n\ndef main():\n # load file\n file_name = os.path.join(os.path.dirname(__file__), \"input.txt\")\n lines = load_input(file_name) \n\n #part 1\n outputs = [line[11:] for line in lines]\n print(\"Part 1: \" + str(count_easy(outputs)))\n\n #part 2\n patterns = [line[:10] for line in lines]\n sum = 0\n for i in range(len(patterns)):\n decoded = [\"\"]*10\n decoded = decode_easy(patterns[i], decoded)\n decoded = decode_remaining(patterns[i], decoded)\n sum += read_output(decoded, outputs[i])\n print(\"Part 2: \" + str(sum))\n\n\nif __name__ == '__main__':\n main()","repo_name":"MartynaBorak/AOC_2021","sub_path":"Day 08/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":3483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10969707663","text":"from datetime import datetime\n\nfrom src.db.db import DataBaseService\nfrom src.entities.compositor import CompositorEntity\nfrom src.service.per_musical_service import PerMusicalService\nfrom src.entities.per_musical import PerMusicalEntity\n\n\nclass CompositorService:\n def __init__(self):\n pass\n\n # relational = true: estamos criando um periodo musical em conjunto\n # relational = false: o periodo musical do compositor ja existe\n def add_to_db(self, relational=False):\n compositor = CompositorEntity()\n\n if relational:\n per_music = PerMusicalEntity()\n PerMusicalService().add_to_db_relational(per_music.id, per_music.descr, per_music.start_date, per_music.end_date)\n if compositor.death_date != None:\n sql_query_compositor = f\"compositor (id_compositor, id_periodo_musical, nome, local_nasc, data_nasc, data_morte) VALUES ('{compositor.id}', '{per_music.id}', '{compositor.name}', '{compositor.birth_place}', '{compositor.birthday}', '{compositor.death_date}')\"\n else:\n sql_query_compositor = f\"compositor (id_compositor, id_periodo_musical, nome, local_nasc, data_nasc) VALUES ('{compositor.id}', '{per_music.id}', '{compositor.name}', '{compositor.birth_place}', '{compositor.birthday}')\" \n DataBaseService().insert(sql_query_compositor)\n else:\n print(\"\\n\")\n PerMusicalService().show_per_music()\n\n per_music_name = str(input(\"Digite o nome do periodo musical do compositor: \"))\n id_per_music = PerMusicalService().find_by_name(per_music_name)\n\n if compositor.death_date != None:\n sql_query_compositor = f\"compositor (id_compositor, id_periodo_musical, nome, local_nasc, data_nasc, data_morte) VALUES ('{compositor.id}', '{id_per_music}', '{compositor.name}', '{compositor.birth_place}', '{compositor.birthday}', '{compositor.death_date}')\"\n else:\n sql_query_compositor = f\"compositor (id_compositor, id_periodo_musical, nome, local_nasc, data_nasc) VALUES ('{compositor.id}', '{id_per_music}', '{compositor.name}', '{compositor.birth_place}', '{compositor.birthday}')\"\n DataBaseService().insert(sql_query_compositor)\n return compositor.id\n\n def show_compositors(self):\n sql_query = f\"nome FROM compositor\"\n rows = DataBaseService().search(sql_query)\n print(\"\\n--------- COMPOSITORES ---------\")\n for row in rows:\n print(row[0])\n \n def find_by_name(self, name):\n db_service = DataBaseService()\n sql_query = f\"id_compositor FROM compositor WHERE nome = '{name}'\"\n return db_service.search(sql_query)[0][0]\n\n def update(self):\n CompositorService().show_compositors()\n comp_name = str(input(\"\\n\\nIdentifique o nome do compositor: \"))\n\n new_death_date = str(input(\"\\nDigite a nova data de morte: \"))\n death_date = datetime.strptime(new_death_date, \"%Y-%m-%d\")\n\n sql_query = f\"compositor SET data_morte = '{death_date}' WHERE nome = '{comp_name}'\"\n DataBaseService().update(sql_query)\n\n def view_compositor_q3(self):\n sql_query = f\"comp.nome, COUNT(fp.id_faixa) AS Contagem FROM compositor comp INNER JOIN faixa_compositor fc ON comp.id_compositor = fc.id_compositor INNER JOIN faixa f ON fc.id_faixa = f.id_faixa INNER JOIN faixa_playlist fp ON f.id_faixa = fp.id_faixa GROUP BY comp.nome ORDER BY Contagem DESC LIMIT 1\"\n rows = DataBaseService().search(sql_query)\n print(\"\\n------------- Questao 3 -------------\")\n for row in rows:\n print(\"NOME | CONTAGEM\")\n print(f\"{row[0]} - {row[1]}\")\n","repo_name":"erikbayerlein/fbd-spotify","sub_path":"src/service/compositor_service.py","file_name":"compositor_service.py","file_ext":"py","file_size_in_byte":3691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25065162326","text":"# remove the dups without allocating another array in the memory\n# return the length of the value and also the fixed array that does not have any duplicates\n\nnums = [0, 4, 4]\n\nclass Solution:\n def removeDuplicates(self, nums):\n if len(nums) in [0, 1]:\n return len(nums)\n\n j = 0\n for i in range(len(nums)-1):\n if nums[i] != nums[i+1]:\n nums[j] = nums[i]\n j += 1\n nums[j] = nums[-1]\n nums = nums[:j+1]\n\n return j+1\n\n\nL = Solution()\nx = L.removeDuplicates(nums)\nprint(x)\n","repo_name":"jkfer/LeetCode","sub_path":"Remove_dups_from_sorted_array.py","file_name":"Remove_dups_from_sorted_array.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18622106681","text":"from flask import request\n\n\ndef get_safe_max_id(v):\n if not v:\n return None\n\n try:\n rv = int(v)\n if rv <= 0:\n return None\n\n except ValueError:\n rv = None\n\n return rv\n\n\ndef get_safe_count(v, default=10, max=100):\n if not v:\n return default\n\n try:\n rv = int(v)\n if not (1 <= rv <= max):\n rv = default\n except ValueError:\n rv = default\n\n return rv","repo_name":"yoophi/flaskygram","sub_path":"flaskygram/library/get_params.py","file_name":"get_params.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71071338643","text":"# Uses python3\nimport sys\n\ndef fibonacci_sum(n):\n if n > 2:\n fibonacci = [0, 1, 1]\n sequence = [0, 1, 1]\n\n m = 10\n\n for i in range(3, n + 1):\n fibonacci.append(((fibonacci[-2] ) + (fibonacci[-1] )) % m)\n\n sequence[0] = fibonacci[-3]\n sequence[1] = fibonacci[-2]\n sequence[2] = fibonacci[-1]\n\n if sequence == [0, 1, 1]:\n n_mod = n // (i - 2)\n sequence_sum = sum(fibonacci[0:(i - 2)])\n ramainder_sum = sum(fibonacci[0:(n + 1 - ((i - 2) * n_mod))])\n _sum = (sequence_sum * n_mod) + ramainder_sum\n return _sum % m\n\n return sum(fibonacci) % m\n else:\n return n\n\nif __name__ == '__main__':\n # input = input()\n input = sys.stdin.read()\n n = int(input)\n print(fibonacci_sum(n))\n","repo_name":"HPaulowicz/algorithmic-toolbox","sub_path":"introduction_starter/fibonacci_sum_last_digit/fibonacci_sum_last_digit.py","file_name":"fibonacci_sum_last_digit.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23309883609","text":"import heapq\n\ndef minimum_classrooms(N, schedule):\n if N == 0:\n return 0\n\n # 수업을 시작 시간(Si)을 기준으로 오름차순 정렬\n schedule.sort(key=lambda x: x[0])\n\n # 우선순위 큐를 사용하여 강의실 관리\n classrooms = []\n\n # 첫 번째 수업을 첫 번째 강의실에 배정\n heapq.heappush(classrooms, schedule[0][1])\n\n for i in range(1, N):\n if schedule[i][0] >= classrooms[0]:\n # 현재 강의실의 종료 시간(Ti)가 다음 수업의 시작 시간(Si)보다 작거나 같으면\n # 같은 강의실에서 이어서 들을 수 있으므로 우선순위 큐를 업데이트\n heapq.heappop(classrooms)\n # 다음 수업을 해당 강의실로 배정\n heapq.heappush(classrooms, schedule[i][1])\n\n # 필요한 강의실의 개수 반환\n return len(classrooms)\n\n# 입력 받기\nN = int(input())\nschedule = []\nfor _ in range(N):\n Si, Ti = map(int, input().split())\n schedule.append((Si, Ti))\n\n# 결과 출력\nresult = minimum_classrooms(N, schedule)\nprint(result)","repo_name":"1234jienf/TIL","sub_path":"0830/problem/BAEKJOON_11000_강의실배정.py","file_name":"BAEKJOON_11000_강의실배정.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20027690860","text":"#!/usr/bin/env python3\n# -*- encoding: utf-8\n\nimport boto3\nimport argparse\nimport maya\nfrom datetime import datetime, timedelta\n\n\ndef get_log_events(log_group, start_time=None, end_time=None):\n \"\"\"Generate all the log events from a CloudWatch group.\n\n :param log_group: Name of the CloudWatch log group.\n :param start_time: Only fetch events with a timestamp after this time.\n Expressed as the number of milliseconds after midnight Jan 1 1970.\n :param end_time: Only fetch events with a timestamp before this time.\n Expressed as the number of milliseconds after midnight Jan 1 1970.\n\n \"\"\"\n client = boto3.client(\"logs\")\n kwargs = {\n \"logGroupName\": log_group,\n \"limit\": 10000,\n }\n\n if start_time is not None:\n kwargs[\"startTime\"] = start_time\n if end_time is not None:\n kwargs[\"endTime\"] = end_time\n\n while True:\n resp = client.filter_log_events(**kwargs)\n yield from resp[\"events\"]\n try:\n kwargs[\"nextToken\"] = resp[\"nextToken\"]\n except KeyError:\n break\n\n\ndef milliseconds_since_epoch(time_string):\n dt = maya.when(time_string)\n seconds = dt.epoch\n return seconds * 1000\n\n\ndef today_start_end():\n now = datetime.now()\n start = now.replace(hour=0, minute=0, second=0, microsecond=0)\n end = start + timedelta(days=1, microseconds=-1)\n return (start.isoformat(), end.isoformat())\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(\n description=\"Print log event messages from a CloudWatch log group.\"\n )\n parser.add_argument(\"log_group\", type=str, help=\"Name of the CloudWatch log group.\")\n parser.add_argument(\n \"--start\",\n type=str,\n default=None,\n help=\"Only print events with a timestamp after this time. (defaults to the start of today)\",\n )\n parser.add_argument(\n \"--end\",\n type=str,\n default=None,\n help=\"Only print events with a timestamp before this time. (defaults to the end of today)\",\n )\n\n args = parser.parse_args()\n\n log_group = args.log_group\n\n if args.start:\n try:\n start_time = milliseconds_since_epoch(args.start)\n except ValueError:\n exit(f\"Invalid datetime input as --start: {args.start}\")\n else:\n start_time, _ = today_start_end()\n start_time = milliseconds_since_epoch(start_time)\n\n if args.end:\n try:\n end_time = milliseconds_since_epoch(args.end)\n except ValueError:\n exit(f\"Invalid datetime input as --end: {args.end}\")\n else:\n _, end_time = today_start_end()\n end_time = milliseconds_since_epoch(end_time)\n\n with open(\"cloudwatch.log\", \"w\") as f:\n f.write(f\"start_time: {start_time}\\\\n\")\n f.write(f\"end_time: {end_time}\\\\n\")\n\n print(f\"start_time: {start_time}\")\n print(f\"end_time: {start_time}\")\n\n logs = get_log_events(\n log_group=log_group, start_time=start_time, end_time=end_time\n )\n\n for event in logs:\n message = event[\"message\"].rstrip()\n print(event[\"message\"].rstrip())\n f.write(message + \"\\n\")\n","repo_name":"agentgill/cloudwatch-utils","sub_path":"get-logs.py","file_name":"get-logs.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70491196243","text":"from urllib.parse import quote\nfrom selectolax.parser import HTMLParser\nimport aiohttp\n\nfrom utils import user_agent\n\nbase_query = 'https://google.com/search?q='\n\n\nasync def google_it(query: str, how_many: int = 1) -> str:\n async with aiohttp.request('GET', base_query + quote(query), headers={'User-Agent': user_agent}) as resp:\n text = await resp.text()\n i = 0\n\n results = []\n search_result_node = HTMLParser(text).css_first('div[eid]')\n if search_result_node is None:\n return 'Ничего не нашел'\n\n nodes = search_result_node.css_first('div > div.srg').css_first('div.srg').css('div.g')\n for node in nodes:\n node = node.css_first('div[data-ved]').css_first('div.rc')\n header_node = node.css_first('div.r').css_first('a')\n\n url = header_node.attributes['href']\n\n header_node = node.css_first('h3').css_first('div')\n\n title = header_node.text().strip()\n\n print(f'{i}: {title} {url}')\n results.append(f'Описание: {title}\\nСсылка: {url}\\n')\n i += 1\n\n if len(results) > 0:\n return '\\n'.join(results[:how_many])\n else:\n return 'Ничего не нашел'\n","repo_name":"AntonyMoes/cyberhelper","sub_path":"bot/google.py","file_name":"google.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"16322439227","text":"# License: GNU Affero General Public License v3 or later\n# A copy of GNU AGPL v3 should have been included in this software package in LICENSE.txt.\n\n\"\"\" Manages HTML construction for the Lanthipeptide module\n\"\"\"\n\nfrom jinja2 import FileSystemLoader, Environment, StrictUndefined\n\nfrom antismash.common import path\nfrom antismash.common.layers import ClusterLayer\nfrom antismash.common.secmet import Prepeptide\n\n\ndef will_handle(products) -> bool:\n \"\"\" Returns True if the products provided are relevant to the module \"\"\"\n return 'lanthipeptide' in products\n\n\nclass LanthipeptideLayer(ClusterLayer):\n \"\"\" An extended ClusterLayer for holding a list of LanthipeptideMotifs \"\"\"\n def __init__(self, record, cluster_feature):\n ClusterLayer.__init__(self, record, cluster_feature)\n self.motifs = []\n for motif in self.record.seq_record.get_cds_motifs():\n if not isinstance(motif, Prepeptide):\n continue\n if not motif.is_contained_by(self.cluster_rec):\n continue\n if motif.peptide_class == \"lanthipeptide\":\n self.motifs.append(motif)\n\n\ndef generate_details_div(cluster_layer, results, record_layer, options_layer) -> str:\n \"\"\" Generates a HTML div for the main page of results \"\"\"\n lanthi_layer = LanthipeptideLayer(record_layer, cluster_layer.cluster_rec)\n if not results:\n return \"\"\n env = Environment(loader=FileSystemLoader(path.get_full_path(__file__, \"templates\")),\n autoescape=True, undefined=StrictUndefined)\n template = env.get_template('details.html')\n motifs_in_cluster = {}\n for locus in results.clusters.get(cluster_layer.get_cluster_number(), []):\n motifs_in_cluster[locus] = results.motifs_by_locus[locus]\n details_div = template.render(record=record_layer,\n cluster=lanthi_layer,\n options=options_layer,\n results=motifs_in_cluster)\n return details_div\n\n\ndef generate_sidepanel(cluster_layer, results, record_layer, options_layer) -> str:\n \"\"\" Generates a div for the sidepanel results \"\"\"\n env = Environment(\n loader=FileSystemLoader(path.get_full_path(__file__, \"templates\")),\n autoescape=True, undefined=StrictUndefined)\n template = env.get_template('sidepanel.html')\n cluster = LanthipeptideLayer(record_layer, cluster_layer.cluster_rec)\n if not results:\n return \"\"\n record = record_layer\n motifs_in_cluster = {}\n for locus in results.clusters.get(cluster_layer.get_cluster_number(), []):\n motifs_in_cluster[locus] = results.motifs_by_locus[locus]\n sidepanel = template.render(record=record,\n cluster=cluster,\n options=options_layer,\n results=motifs_in_cluster)\n return sidepanel\n","repo_name":"agolfinos/antismash","sub_path":"antismash/modules/lanthipeptides/html_output.py","file_name":"html_output.py","file_ext":"py","file_size_in_byte":2915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"13856241758","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport random\n\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nimport numpy as np\n\nN = 20\ncenter = np.array([0.5, 0.5])\n\n\nfig = plt.figure() \n\n\np = np.random.rand(N, 2)\n#p = np.array([[5,5],[70,100],[160,180]])\n\nax = fig.add_subplot(111)\nax.grid(True, linestyle = '-', color = '0.75')\nax.set_xlim([0, 1])\nax.set_ylim([0, 1])\n\ncolors = np.random.rand(N)\nscat = plt.scatter(p[:, 0], p[:, 1], s = 100, c=colors)\nscat.set_alpha(0.5)\n\n\ndef update_plot(i,fig, scat):\n global p\n for i in range(0, N):\n cmp = p[i] != center\n if cmp[0] or cmp[1]:\n ran = random.random()\n p[i] += ran*(center-p[i])\n\n scat.set_offsets(p)\n #print('Frames: %d' %i)\n print(p)\n return scat,\n\nanim = animation.FuncAnimation(fig, update_plot, fargs = (fig, scat),frames = 100, interval = 500)\n \nplt.show()","repo_name":"youzipi/Language-Implementation-Partterns-note","sub_path":"complier/num/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29670759827","text":"\"\"\"\n * ***** BEGIN GPL LICENSE BLOCK *****\n *\n * This program is free software; you can redistribute it and/or\n * modify it under the terms of the GNU General Public License\n * as published by the Free Software Foundation; either version 2\n * of the License, or (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 Foundation,\n * Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n *\n * Contributor(s): Julien Duroure.\n *\n * ***** END GPL LICENSE BLOCK *****\n * This development is done in strong collaboration with Airbus Defence & Space\n \"\"\"\n\nfrom .primitive import *\nfrom ..rig import *\n\nclass Mesh():\n def __init__(self, index, json, gltf):\n self.index = index\n self.json = json # Mesh json\n self.gltf = gltf # Reference to global glTF instance\n self.primitives = []\n self.target_weights = []\n self.name = None\n self.skin = None\n\n\n def read(self):\n if 'name' in self.json.keys():\n self.name = self.json['name']\n self.gltf.log.debug(\"Mesh \" + self.json['name'])\n else:\n self.gltf.log.debug(\"Mesh index \" + str(self.index))\n\n cpt_idx_prim = 0\n for primitive_it in self.json['primitives']:\n primitive = Primitive(cpt_idx_prim, primitive_it, self.gltf)\n primitive.read()\n self.primitives.append(primitive)\n primitive.debug_missing()\n cpt_idx_prim += 1\n\n # reading default targets weights if any\n if 'weights' in self.json.keys():\n for weight in self.json['weights']:\n self.target_weights.append(weight)\n\n def rig(self, skin_id, mesh_id):\n if skin_id not in self.gltf.skins.keys():\n self.skin = Skin(skin_id, self.gltf.json['skins'][skin_id], self.gltf)\n self.skin.mesh_ids.append(mesh_id)\n self.gltf.skins[skin_id] = self.skin\n self.skin.read()\n self.skin.debug_missing()\n else:\n self.skin = self.gltf.skins[skin_id]\n self.skin.mesh_ids.append(mesh_id)\n\n def debug_missing(self):\n keys = [\n 'name',\n 'primitives',\n 'weights'\n ]\n\n for key in self.json.keys():\n if key not in keys:\n self.gltf.log.debug(\"MESH MISSING \" + key)\n","repo_name":"sketchfab/gltf2-blender-importer","sub_path":"gltf/mesh/mesh.py","file_name":"mesh.py","file_ext":"py","file_size_in_byte":2719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"29062386737","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom future import standard_library\n\nstandard_library.install_aliases()\n\nimport pytest\nimport mock\nimport http.client as http\nfrom decimal import Decimal as D\nfrom hamcrest import assert_that, equal_to\n\nfrom balance import constants as cst\n\nfrom brest.core.tests import security\nfrom yb_snout_api.utils import context_managers as ctx_util\nfrom yb_snout_api.tests_unit.base import TestCaseApiAppBase\nfrom yb_snout_api.tests_unit.fixtures.resource import mock_client_resource\nfrom yb_snout_api.tests_unit.fixtures.client import create_role_client, create_client\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.permissions import create_admin_role, create_role\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.invoice import create_invoice, create_custom_invoice\n\n\n@pytest.fixture(name='close_inv_role')\ndef create_close_inv_role():\n return create_role(\n (\n cst.PermissionCode.CLOSE_INVOICE,\n {cst.ConstraintTypes.firm_id: None, cst.ConstraintTypes.client_batch_id: None},\n ),\n )\n\n\n@pytest.mark.smoke\nclass TestCaseInvoiceClose(TestCaseApiAppBase):\n BASE_API = '/v1/invoice/close'\n\n @pytest.mark.parametrize(\n 'func',\n [\n pytest.param(lambda inv: setattr(inv, 'total_act_sum', inv.total_act_sum + inv.total_sum),\n id='total_act_sum < total_sum'),\n pytest.param(lambda inv: setattr(inv, 'receipt_sum', D('0')),\n id='receipt_sum == 0'),\n pytest.param(lambda inv: setattr(inv.invoice_orders[0].order, 'service_id', cst.ServiceId.DIRECT),\n id='service_id != ONE_TIME_SALE'),\n ],\n )\n @mock.patch('yb_snout_api.utils.context_managers._new_transactional_session', ctx_util.new_rollback_session)\n def test_cannot_close_invoice(self, admin_role, close_inv_role, func):\n security.set_roles([admin_role, close_inv_role])\n\n invoice = create_custom_invoice(service_id=cst.ServiceId.ONE_TIME_SALE)\n invoice.manual_turn_on(D('1000'))\n func(invoice) # нарушаем одно из условий закрытия счёта\n\n response = self.test_client.secure_post(\n self.BASE_API,\n data={'invoice_id': invoice.id, 'close_dt': self.test_session.now().strftime('%Y%m%d%H%M%S')},\n )\n assert_that(\n response.status_code,\n equal_to(http.INTERNAL_SERVER_ERROR),\n 'response code must be INTERNAL_SERVER_ERROR',\n )\n assert_that(response.get_json()['error'], 'CANNOT_CLOSE_INVOICE')\n\n def test_invalid_month(self, invoice):\n session = self.test_session\n invoice.turn_on_rows()\n\n with mock.patch('balance.mncloselib.get_task_last_status', return_value='resolved'):\n # Нельзя формировать акты, если закрыта вторая стадия\n response = self.test_client.secure_post(\n self.BASE_API,\n data={'invoice_id': invoice.id, 'close_dt': session.now().strftime('%Y%m%d%H%M%S')},\n )\n\n assert_that(\n response.status_code,\n equal_to(http.INTERNAL_SERVER_ERROR),\n 'response code must be INTERNAL_SERVER_ERROR',\n )\n assert_that(response.get_json()['error'], equal_to('CLOSED_MONTH'))\n\n\n@pytest.mark.permissions\n@pytest.mark.smoke\nclass TestCaseInvoiceClosePermissions(TestCaseApiAppBase):\n BASE_API = '/v1/invoice/close'\n\n @mock_client_resource('yb_snout_api.resources.v1.invoice.routes.close.InvoiceClose')\n @mock.patch('yb_snout_api.utils.context_managers._new_transactional_session', ctx_util.new_rollback_session)\n def test_close_by_user(self, client):\n security.set_passport_client(client)\n security.set_roles([])\n invoice = create_invoice(client=client)\n response = self.test_client.secure_post(\n self.BASE_API,\n data={\n 'invoice_id': invoice.id,\n 'close_dt': self.test_session.now().strftime('%Y%m%d%H%M%S'),\n },\n is_admin=False,\n )\n assert_that(response.status_code, equal_to(http.FORBIDDEN))\n\n @pytest.mark.parametrize(\n 'role_firm_id, res',\n [\n pytest.param(cst.SENTINEL, http.FORBIDDEN,\n id='wo role'),\n pytest.param(None, http.OK,\n id='role wo constraints'),\n pytest.param(cst.FirmId.YANDEX_OOO, http.OK,\n id='role constraints matches firm_id'),\n pytest.param(cst.FirmId.CLOUD, http.FORBIDDEN,\n id='role constraints don\\'t matches firm_id'),\n ],\n )\n @mock.patch('yb_snout_api.utils.context_managers._new_transactional_session', ctx_util.new_rollback_session)\n def test_firm_id(self, admin_role, close_inv_role, role_firm_id, res):\n roles = [admin_role]\n if role_firm_id is not cst.SENTINEL:\n roles.append((close_inv_role, {cst.ConstraintTypes.firm_id: role_firm_id}))\n security.set_roles(roles)\n\n invoice = create_custom_invoice(firm_id=cst.FirmId.YANDEX_OOO, service_id=cst.ServiceId.ONE_TIME_SALE)\n invoice.manual_turn_on(D('1000'))\n\n response = self.test_client.secure_post(\n self.BASE_API,\n data={\n 'invoice_id': invoice.id,\n 'close_dt': self.test_session.now().strftime('%Y%m%d%H%M%S'),\n },\n )\n assert_that(response.status_code, equal_to(res))\n\n @pytest.mark.parametrize(\n 'is_allowed',\n [True, False],\n )\n @mock.patch('yb_snout_api.utils.context_managers._new_transactional_session', ctx_util.new_rollback_session)\n def test_client_id(self, client, admin_role, close_inv_role, is_allowed):\n firm_id = cst.FirmId.DRIVE\n\n client_batch_id = create_role_client(client if is_allowed else None).client_batch_id\n roles = [\n admin_role,\n (\n close_inv_role,\n {cst.ConstraintTypes.firm_id: firm_id, cst.ConstraintTypes.client_batch_id: client_batch_id},\n ),\n ]\n security.set_roles(roles)\n\n invoice = create_custom_invoice(service_id=cst.ServiceId.ONE_TIME_SALE, client=client, firm_id=firm_id)\n invoice.manual_turn_on(D('1000'))\n\n response = self.test_client.secure_post(\n self.BASE_API,\n data={\n 'invoice_id': invoice.id,\n 'close_dt': self.test_session.now().strftime('%Y%m%d%H%M%S'),\n },\n )\n assert_that(response.status_code, equal_to(http.OK if is_allowed else http.FORBIDDEN))\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests_unit/resources/v1/invoice/test_invoice_close.py","file_name":"test_invoice_close.py","file_ext":"py","file_size_in_byte":6875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2379028572","text":"from __future__ import print_function\n\nimport os\nimport argparse\nimport json\nimport time\nimport collections\n\nfrom copy import deepcopy\n\nimport numpy as np\n\nfrom allennlp.data import Vocabulary, DataIterator, DatasetReader, Tokenizer, TokenIndexer\nfrom allennlp.common import Params\nfrom allennlp.data.dataset import Batch\nfrom allennlp.models import Model, archive_model, archival\nfrom allennlp.training import Trainer\n\nfrom cmv.preprocessing.cmvReader import CMVReader\nfrom cmv.rnn.cmvPredictor import CMVPredictor\nfrom cmv.rnn.cmvMultiChannelPredictor import CMVMultiChannelPredictor\n#from cmv.rnn.cmvExtractor import CMVExtractor\n\nimport argparse\nimport logging\nimport sys\nimport json\nimport os\n\nlogger = logging.getLogger(__name__) \n\ndef train_model(data_path, params, serialization_dir, cuda_device=-1, use_validation_data=True):\n\n os.makedirs(serialization_dir, exist_ok=True)\n\n handler = logging.FileHandler(os.path.join(serialization_dir, \"python_logging.log\"))\n handler.setLevel(logging.INFO)\n handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(name)s - %(message)s'))\n logging.getLogger().addHandler(handler)\n \n serialization_params = deepcopy(params).as_dict(quiet=True)\n\n with open(os.path.join(serialization_dir, \"model_params.json\"), \"w\") as param_file:\n json.dump(serialization_params, param_file, indent=4)\n \n ds_params = params.pop('dataset_reader', {})\n data_params = ds_params.pop('data', {})\n dataset_reader = CMVReader.from_params(ds_params)\n '''\n dataset_reader = CMVReader(data_path,\n tokenizer=Tokenizer.from_params(ds_params.pop('tokenizer', {})),\n token_indexers=TokenIndexer.from_params(ds_params.pop('token_indexers', {})))\n '''\n\n logger.info('Reading training data...')\n \n train_data = dataset_reader.read('train',\n **data_params)\n\n #train_data_response_only_for_vocab = dataset_reader.read('train', response_only=True)\n #train_data_op_only_for_vocab = dataset_reader.read('train', op_only=True)\n #all_datasets = [train_data_response_only_for_vocab, train_data_op_only_for_vocab]\n all_datasets = [train_data]\n datasets_in_vocab = ['train'] #_response_only_for_vocab']\n\n if use_validation_data:\n logger.info('Reading validation data...')\n validation_data = dataset_reader.read('val',\n **data_params)\n all_datasets.append(validation_data)\n datasets_in_vocab.append('val')\n else:\n validation_data = None\n\n logger.info('Creating a vocabulary using %s data.', ', '.join(datasets_in_vocab))\n vocab_params = params.pop('vocabulary', {})\n dataset = None\n if 'directory_path' not in vocab_params:\n dataset = Batch([instance for dataset in all_datasets\n for instance in dataset.instances])\n\n vocab = Vocabulary.from_params(vocab_params,\n dataset)\n vocab.save_to_files(os.path.join(serialization_dir, 'vocabulary'))\n \n model = Model.from_params(params=params.pop('model'), vocab=vocab)\n iterator = DataIterator.from_params(params.pop('iterator'))\n\n train_data.index_instances(vocab)\n if validation_data:\n validation_data.index_instances(vocab)\n\n trainer_params = params.pop(\"trainer\")\n if cuda_device is not None:\n trainer_params[\"cuda_device\"] = cuda_device\n trainer = Trainer.from_params(model,\n serialization_dir,\n iterator,\n train_data,\n validation_data,\n trainer_params)\n\n trainer.train()\n\n # Now tar up results\n archive_model(serialization_dir)\n\n return model\n \n \nparser = argparse.ArgumentParser()\n\nparser.add_argument('data_path')\nparser.add_argument('param_path',\n type=str,\n help='path to parameter file describing the model to be trained')\n \nparser.add_argument(\"logdir\",type=str)\n\nparser.add_argument(\"--cuda-device\", type=int, default=None, help='id of GPU to use (if any)')\n\nargs = parser.parse_args()\n\nparams = Params.from_file(args.param_path)\n\ntrain_model(args.data_path, params, args.logdir, args.cuda_device)\n \n","repo_name":"chridey/cmv","sub_path":"cmv/bin/train_cmv_torch.py","file_name":"train_cmv_torch.py","file_ext":"py","file_size_in_byte":4439,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"36412047773","text":"from contextlib import contextmanager\nimport os\nimport time\nimport yaml\n\nimport pytest\n\nimport libnmstate\nfrom libnmstate.schema import Interface\nfrom libnmstate.schema import LLDP\n\nfrom .testlib import assertlib\nfrom .testlib import cmdlib\nfrom .testlib import ifacelib\nfrom .testlib import statelib\nfrom .testlib.veth import create_veth_pair\nfrom .testlib.veth import remove_veth_pair\n\n\nLLDPTEST = \"lldptest\"\nLLDPTEST_PEER = \"lldptest.peer\"\n\nLLDP_TEST_NS = \"nmstate_lldp_test\"\n\nLLDP_SYSTEM_DESC = (\n \"Summit300-48 - Version 7.4e.1 (Build 5) by Release_Master \"\n \"05/27/05 04:53:11\"\n)\n\nEXPECTED_LLDP_NEIGHBOR = \"\"\"\n- system-name: Summit300-48\n type: 5\n- system-description: Summit300-48 - Version 7.4e.1 (Build 5) by Release_Master\n 05/27/05 04:53:11\n type: 6\n- system-capabilities:\n - MAC Bridge component\n - Router\n type: 7\n- _description: MAC address\n chassis-id: 00:01:30:F9:AD:A0\n chassis-id-type: 4\n type: 1\n- _description: Interface name\n port-id: 1/1\n port-id-type: 5\n type: 2\n- ieee-802-1-vlans:\n - name: v2-0488-03-0505\n vid: 488\n oui: 00:80:c2\n subtype: 3\n type: 127\n- ieee-802-3-mac-phy-conf:\n autoneg: true\n operational-mau-type: 16\n pmd-autoneg-cap: 27648\n oui: 00:12:0f\n subtype: 1\n type: 127\n- ieee-802-1-ppvids:\n - 0\n oui: 00:80:c2\n subtype: 2\n type: 127\n- management-addresses:\n - address: 00:01:30:F9:AD:A0\n address-subtype: MAC\n interface-number: 1001\n interface-number-subtype: 2\n type: 8\n- ieee-802-3-max-frame-size: 1522\n oui: 00:12:0f\n subtype: 4\n type: 127\n\"\"\"\n\nLLDP_CAPS = [\"MAC Bridge component\", \"Router\"]\n\nCHASSIS_ID = \"chassis-id\"\nCHASSIS_ID_TYPE = \"chassis-id-type\"\nMANAGEMENT_ADDRESSES = \"management-addresses\"\nADDRESS = \"address\"\nADDRESS_SUBTYPE = \"address-subtype\"\nINTERFACE_NUMBER = \"interface-number\"\nINTERFACE_NUMBER_SUBTYPE = \"interface-number-subtype\"\nAUTONEG = \"autoneg\"\nOPERATIONAL_MAU_TYPE = \"operational-mau-type\"\nPMD_AUTONEG_CAP = \"pmd-autoneg-cap\"\nPPVIDS_SUBTREE = \"ieee-802-1-ppvids\"\nPPVID = \"ppvid\"\nVLANS_SUBTREE = \"ieee-802-1-vlans\"\nNAME = \"name\"\nPORT_ID = \"port-id\"\nPORT_ID_TYPE = \"port-id-type\"\nSYSTEM_NAME = \"system-name\"\nSYSTEM_DESCRIPTION = \"system-description\"\nSYSTEM_CAPABILITIES = \"system-capabilities\"\nMFS_KEY = \"ieee-802-3-max-frame-size\"\nMAC_PHY_SUBTREE = \"ieee-802-3-mac-phy-conf\"\n\nLLDP_TEST_SYSTEM_NAME = \"Summit300-48\"\n\n\n@pytest.fixture(scope=\"module\")\ndef lldpiface_env():\n try:\n create_veth_pair(LLDPTEST, LLDPTEST_PEER, LLDP_TEST_NS)\n yield\n finally:\n remove_veth_pair(LLDPTEST, LLDP_TEST_NS)\n\n\n@pytest.fixture\ndef lldptest_up(lldpiface_env):\n with ifacelib.iface_up(LLDPTEST) as ifstate:\n yield ifstate\n\n\ndef test_enable_lldp(lldptest_up):\n with lldp_enabled(lldptest_up) as dstate:\n assertlib.assert_state_match(dstate)\n\n\ndef test_lldp_yaml(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n assert test_neighbor == yaml.safe_load(EXPECTED_LLDP_NEIGHBOR)\n\n\ndef test_lldp_system(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n seen = set()\n for tlv in test_neighbor:\n seen.add(tlv[LLDP.Neighbors.TLV_TYPE])\n if tlv[LLDP.Neighbors.TLV_TYPE] == 5:\n assert tlv[SYSTEM_NAME] == \"Summit300-48\"\n elif tlv[LLDP.Neighbors.TLV_TYPE] == 6:\n assert tlv[SYSTEM_DESCRIPTION] == LLDP_SYSTEM_DESC\n elif tlv[LLDP.Neighbors.TLV_TYPE] == 7:\n assert tlv[SYSTEM_CAPABILITIES] == LLDP_CAPS\n assert set([5, 6, 7]).issubset(seen)\n\n\ndef test_lldp_chassis(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 1, test_neighbor\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][CHASSIS_ID] == \"00:01:30:F9:AD:A0\"\n assert tlvs[0][CHASSIS_ID_TYPE] == 4\n\n\ndef test_lldp_management_addresses(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 8, test_neighbor\n )\n )\n assert len(tlvs) == 1\n test_mngt = tlvs[0][MANAGEMENT_ADDRESSES][0]\n assert test_mngt[ADDRESS] == \"00:01:30:F9:AD:A0\"\n assert test_mngt[ADDRESS_SUBTYPE] == \"MAC\"\n assert test_mngt[INTERFACE_NUMBER] == 1001\n assert test_mngt[INTERFACE_NUMBER_SUBTYPE] == 2\n\n\ndef test_lldp_macphy(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 127\n and tlv[LLDP.Neighbors.ORGANIZATION_CODE] == \"00:12:0f\"\n and tlv[LLDP.Neighbors.TLV_SUBTYPE] == 1,\n test_neighbor,\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][MAC_PHY_SUBTREE][AUTONEG] is True\n assert tlvs[0][MAC_PHY_SUBTREE][OPERATIONAL_MAU_TYPE] == 16\n assert tlvs[0][MAC_PHY_SUBTREE][PMD_AUTONEG_CAP] == 27648\n\n\ndef test_lldp_port(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 2, test_neighbor\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][PORT_ID] == \"1/1\"\n assert tlvs[0][PORT_ID_TYPE] == 5\n assert tlvs[0][LLDP.Neighbors.DESCRIPTION] == \"Interface name\"\n\n\ndef test_lldp_port_vlan(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 127\n and tlv[LLDP.Neighbors.ORGANIZATION_CODE] == \"00:80:c2\"\n and tlv[LLDP.Neighbors.TLV_SUBTYPE] == 2,\n test_neighbor,\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][PPVIDS_SUBTREE][0] == 0\n\n\ndef test_lldp_vlan(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 127\n and tlv[LLDP.Neighbors.ORGANIZATION_CODE] == \"00:80:c2\"\n and tlv[LLDP.Neighbors.TLV_SUBTYPE] == 3,\n test_neighbor,\n )\n )\n assert len(tlvs) == 1\n test_vlan = tlvs[0][VLANS_SUBTREE][0]\n assert test_vlan[NAME] == \"v2-0488-03-0505\"\n\n\ndef test_lldp_mfs(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n test_neighbor = lldp_config[LLDP.NEIGHBORS_SUBTREE][0]\n\n tlvs = list(\n filter(\n lambda tlv: tlv[LLDP.Neighbors.TLV_TYPE] == 127\n and tlv[LLDP.Neighbors.ORGANIZATION_CODE] == \"00:12:0f\"\n and tlv[LLDP.Neighbors.TLV_SUBTYPE] == 4,\n test_neighbor,\n )\n )\n assert len(tlvs) == 1\n assert tlvs[0][MFS_KEY] == 1522\n\n\ndef test_lldp_empty_neighbors(lldptest_up):\n with lldp_enabled(lldptest_up):\n dstate = statelib.show_only((LLDPTEST,))\n lldp_state = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert not lldp_state.get(LLDP.NEIGHBORS_SUBTREE, [])\n\n\ndef test_show_running_config_has_no_lldp_neighbor(lldptest_up):\n with lldp_enabled(lldptest_up):\n _send_lldp_packet()\n dstate = statelib.show_only((LLDPTEST,))\n lldp_config = dstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n assert len(lldp_config[LLDP.NEIGHBORS_SUBTREE]) == 1\n running_config = libnmstate.show_running_config()\n for iface_config in running_config[Interface.KEY]:\n if iface_config[Interface.NAME] == LLDPTEST:\n lldp_iface_config = iface_config\n break\n assert lldp_iface_config[LLDP.CONFIG_SUBTREE][LLDP.ENABLED]\n assert (\n LLDP.NEIGHBORS_SUBTREE\n not in lldp_iface_config[LLDP.CONFIG_SUBTREE]\n )\n\n\n@contextmanager\ndef lldp_enabled(ifstate):\n lldp_config = ifstate[Interface.KEY][0][LLDP.CONFIG_SUBTREE]\n lldp_config[LLDP.ENABLED] = True\n libnmstate.apply(ifstate)\n try:\n yield ifstate\n finally:\n lldp_config[LLDP.ENABLED] = False\n libnmstate.apply(ifstate)\n\n\ndef _send_lldp_packet():\n test_dir = os.path.dirname(os.path.realpath(__file__))\n cmdlib.exec_cmd(\n f\"ip netns exec {LLDP_TEST_NS} \"\n f\"tcpreplay --intf1={LLDPTEST_PEER} \"\n f\"{test_dir}/test_captures/lldp.pcap\".split(),\n check=True,\n )\n time.sleep(1)\n","repo_name":"nmstate/nmstate","sub_path":"tests/integration/lldp_test.py","file_name":"lldp_test.py","file_ext":"py","file_size_in_byte":10737,"program_lang":"python","lang":"en","doc_type":"code","stars":225,"dataset":"github-code","pt":"3"}
+{"seq_id":"73968011280","text":"## Feito por Léo\n## 20/08/22 Mod 29/08/22\n## Lib feita para Calculo Numérico\n\ndef GaussElimination(A):\n import numpy as np\n A = np.array(A)\n\n nl = len(A) \n\n nc = len(A[0])\n\n if nl>nc:\n exit(\"Nao posso resolver isso....\")\n\n for i in range(nl):\n if A[i][i] !=0:\n pivot = A[i][i]\n else:\n for j in range(i+1,nl):\n if A[j][i]:\n ans = list(A[i])\n A[i] = -A[j]\n A[j] = np.array(ans)\n pivot = A[i][i]\n break\n\n for j in range(i+1,nl):\n if not A[j][i]:\n continue\n else:\n A[j] = A[j]*pivot - A[j][i]*A[i]\n \n if A[nl-1][nl-1] == 0:\n if A[nl-1][nc-1] == 0:\n exit('Esse sistema possui raizes indeterminadas!!')\n\n else:\n exit('Esse sistema não possui raizes!!')\n \n #implementação da parte que pegar as raizes.\n\n nr = list()\n \n for i in range(nl):\n sm = 0\n rt = 0\n for j in range(i):\n sm += nr[j]*A[nl-1-i][nc-2-j]\n\n if A[nl-1-i][nl-1-i]:\n rt = (A[nl-1-i][nc-1] - sm)/A[nl-1-i][nl-1-i]\n\n nr.append(rt)\n\n nr.reverse()\n\n return nr\n\ndef LUdecomposition(A):\n import numpy as np\n \n A = np.array(A) \n nl = np.shape(A)[0]\n L = np.eye(nl)\n \n for k in np.arange(nl):\n L[k+1:nl,k] = A[k+1:nl,k]/A[k,k] \n for l in np.arange(k+1,nl):\n A[l,:] = A[l,:] - np.dot((L[l,k]),A[k,:])\n \n U = A\n \n return [L, U]\n\n\ndef gaussjacobi(A,b,x0,kmax):\n import numpy as np\n for i,line in enumerate(A):\n b[i] /= line[i]\n line /= line[i]\n M = A - np.eye(len(A))\n print(M)\n print(b)\n for i in range(kmax):\n x0 = b - M@x0\n if np.linalg.norm(x0) < 1e-3:\n break\n return [x0,kmax]\n\n\n\n","repo_name":"engleovictor/calpy","sub_path":"linsys.py","file_name":"linsys.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17112965576","text":"from collections import deque\nimport bisect\n\nn,k = map(int, input().split())\ncards = []\nfor _ in range(n):\n s = input()\n card = \"\"\n for i in range(k):\n if s[i] == \"E\":\n card += \"1\"\n elif s[i] == \"S\":\n card += \"2\"\n else:\n card += \"3\"\n cards.append(int(card))\n\ncards.sort()\n\nans = 0\n\ndq = deque([])\n\nfor i in range(n-2):\n dq.append(str(cards[i]))\n j = i+1\n while j < n-1:\n dq.append(str(cards[j]))\n tmps = \"\"\n for kk in range(k):\n if dq[0][kk] == dq[1][kk]:\n tmps += dq[0][kk]\n else:\n if dq[0][kk] != \"1\" and dq[1][kk] != \"1\":\n tmps += \"1\"\n elif dq[0][kk] != \"2\" and dq[1][kk] != \"2\":\n tmps += \"2\"\n else:\n tmps += \"3\"\n ind = bisect.bisect_left(cards, int(tmps))\n if ind < n and 0 <= ind:\n if cards[ind] == int(tmps):\n ans += 1\n d = dq.pop()\n j += 1\n d = dq.pop()\n\nprint(ans)","repo_name":"outk/competitive-programming","sub_path":"codeforces/Python/612div2b.py","file_name":"612div2b.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72342923921","text":"# use activation function\n\n# https://blog.csdn.net/u011630575/article/details/78063641\n\n# 可视化 tensorboard --logdir='logs/'\n\nimport tensorflow as tf\nimport numpy as np\n\n\ndef add_layer(intputs, in_size, out_size, n_layer, activation_function=None): # add new laryer\n layer_name = \"layer%s\" % n_layer\n with tf.name_scope(layer_name):\n\n with tf.name_scope(\"Weight\"):\n Weight = tf.Variable(tf.random_normal([in_size, out_size]), name=\"W\")\n tf.summary.histogram(layer_name+'/Weights', Weight)\n\n with tf.name_scope(\"biases\"):\n biases = tf.Variable(tf.zeros([1, out_size]), name=\"b\") + 0.1 # 推荐大于0\n tf.summary.histogram(layer_name + '/biases', biases)\n\n with tf.name_scope(\"Wx_plus_b\"):\n Wx_plus_b = tf.matmul(intputs, Weight) + biases\n\n if activation_function is None:\n outputs = Wx_plus_b\n else:\n outputs = activation_function(Wx_plus_b)\n tf.summary.histogram(layer_name + '/outputs', outputs)\n\n return outputs\n\n\nx_data = np.linspace(-1, 1, 300)[:, np.newaxis] # 创建等差数列\nnoise = np.random.normal(0, 0.05, x_data.shape) # 模拟噪点\ny_data = np.square(x_data) - 0.5 + noise # 创建结果数据集\n\nwith tf.name_scope(\"inputs\"):\n xs = tf.placeholder(tf.float32, [None, 1], name=\"input_x\") # 创建x_data占位符\n ys = tf.placeholder(tf.float32, [None, 1], name=\"input_y\") # 创建y_data占位符\n\nlayer1 = add_layer(xs, 1, 10, 1, activation_function=tf.nn.relu) # 创建输入层\nprediction = add_layer(layer1, 10, 1, 2, activation_function=None) # 创建输出层\n\nwith tf.name_scope(\"loss\"):\n loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1])) # 计算损失\n tf.summary.scalar('loss', loss)\n\nwith tf.name_scope(\"train\"):\n train_setps = tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 最小化loss\n\nwith tf.Session() as sess:\n merged = tf.summary.merge_all()\n writer = tf.summary.FileWriter(\"logs/\", sess.graph) # 将整个可视化文件写入本地\n\n sess.run(tf.global_variables_initializer())\n\n for step in range(1000):\n sess.run(train_setps, feed_dict={xs: x_data, ys: y_data})\n if step % 50 == 0:\n result = sess.run(merged, feed_dict={xs: x_data, ys: y_data})\n writer.add_summary(result, step)\n print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))\n","repo_name":"AndyandViky/ML-study","sub_path":"tensorflow/5-activation-function.py","file_name":"5-activation-function.py","file_ext":"py","file_size_in_byte":2446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28195157633","text":"def load_input(file):\n with open(file, 'r') as lines:\n return [str(line).strip() for line in lines]\n\ndef get_image(input):\n image = []\n for line in input[2:]:\n image.append([0 if x==\".\" else 1 for x in line])\n return image\n\ndef add_frame(image, background):\n framed = []\n width = len(image)+4\n framed.extend([[background]*width for i in range(2)])\n for line in image:\n temp = [background]*2\n temp.extend(line)\n temp.extend([background]*2)\n framed.append(temp)\n framed.extend([[background]*width for i in range(2)])\n return framed\n\ndef enhance(enhancement, image, background):\n filtered = [[0]*(len(image[0])-2) for i in range(len(image)-2)]\n for y in range(1, len(image)-1):\n for x in range(1, len(image[0])-1):\n square = []\n for a in range(-1,2):\n for b in range(-1,2):\n square.append(image[y+a][x+b])\n i = int(\"\".join(str(a) for a in square), 2)\n filtered[y-1][x-1] = 0 if enhancement[i] == \".\" else 1\n\n i = int(\"\".join(str(background)*9), 2)\n background = 0 if enhancement[i]==\".\" else 1\n return filtered, background\n\ndef main():\n #load file\n input = load_input(\"input.txt\")\n enhancement = input[0]\n image = get_image(input)\n background = 0\n\n #part 1\n filtered = image\n for _ in range(2):\n filtered = add_frame(filtered, background)\n filtered, background = enhance(enhancement, filtered, background)\n total = 0\n for y in range(len(filtered)):\n for x in range(len(filtered[0])):\n if filtered[y][x]==1:\n total+=1\n print(\"Part 1: \" + str(total))\n\n #part 2\n filtered = image\n for _ in range(50):\n filtered = add_frame(filtered, background)\n filtered, background = enhance(enhancement, filtered, background)\n total = 0\n for y in range(len(filtered)):\n for x in range(len(filtered[0])):\n if filtered[y][x]==1:\n total+=1\n print(\"Part 2: \" + str(total))\n \n\nif __name__ == '__main__':\n main()","repo_name":"MartynaBorak/AOC_2021","sub_path":"Day 20/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12079569383","text":"from time import time\nimport os\nimport torch\nimport torch.backends.cudnn as cudnn\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\n\nfrom loss.Dice_loss import DiceLoss\nfrom net.YNet_kernel3_dropout import net\nfrom dataset.stage1_dataset import train_fix_ds\n\nif not os.path.exists(r'/openbayes/home/Module/'):\n os.makedirs(r'/openbayes/home/Module/')\n\ncudnn.benchmark = True\nEpoch = 1000\nleaing_rate_base = 1e-5\n\nbatch_size = 1\nnum_workers = 2\npin_memory = True\n\nnet = torch.nn.DataParallel(net).cuda()\n\n# 定义数据加载\ntrain_dl = DataLoader(train_fix_ds, batch_size, True, num_workers=num_workers, pin_memory=pin_memory)\n\n# 定义损失函数\nloss_func = DiceLoss()\n\n# 定义优化器\nopt = torch.optim.Adam(net.parameters(), lr=leaing_rate_base)\n\n# 学习率衰减\nlr_decay = torch.optim.lr_scheduler.MultiStepLR(opt, [400])\n\n# 训练网络\nstart = time()\n\nfor epoch in range(Epoch):\n\n lr_decay.step()\n\n loss_step = []\n\n for step, (ct, seg, pet) in enumerate(train_dl):\n\n ct = ct.cuda()\n seg = seg.cuda()\n pet = pet.cuda()\n\n # 如果一个正样本都没有就直接结束\n if torch.numel(seg[seg > 0]) == 0:\n continue\n\n ct = Variable(ct)\n seg = Variable(seg)\n pet = Variable(pet)\n\n outputs, outputs_temp = net(ct, pet)\n loss = loss_func(outputs, outputs_temp, seg)\n\n opt.zero_grad()\n loss.backward()\n opt.step()\n\n # xzl\n loss_step.append(loss.item())\n\n loss_mean = (sum(loss_step)/len(loss_step))\n\n print(epoch,loss_mean)\n\n # if step % 20 is 0:\n # print('epoch:{}, step:{}, loss:{:.3f}, time:{:.3f} min'\n # .format(epoch, step, loss.item(), (time() - start) / 60))\n\n # 每十个个epoch保存一次模型参数\n if epoch >= 150:\n torch.save(net.state_dict(), '/openbayes/home/Module/net{}.pth'.format(epoch))\n","repo_name":"ETVP/Multi-Modal-Co-learning","sub_path":"ours/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1925,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"}
+{"seq_id":"22753459721","text":"import typing\n\nimport pytest\nfrom ellar.socket_io import TestGateway\nfrom ellar.socket_io.testing.module import RunWithServerContext\n\nfrom .sample import EventGateway, GatewayOthers, GatewayWithGuards\n\n# @pytest.fixture()\n# from asgi_lifespan import LifespanManager\n# from httpx import AsyncClient\n# async def client() -> AsyncIterator[AsyncClient]:\n# async with LifespanManager(app):\n# async with AsyncClient(app=app, base_url=\"http://localhost\") as client:\n# yield client\n\n\n@pytest.mark.asyncio\nclass TestEventGateway:\n test_client = TestGateway.create_test_module(controllers=[EventGateway])\n\n async def test_socket_connection_work(self):\n my_response_message = []\n connected_called = False\n disconnected_called = False\n\n async with self.test_client.run_with_server() as ctx:\n\n @ctx.sio.event\n async def my_response(message):\n my_response_message.append(message)\n\n @ctx.sio.event\n async def disconnect():\n nonlocal disconnected_called\n disconnected_called = True\n\n @ctx.sio.event\n async def connect(*args):\n nonlocal connected_called\n await ctx.sio.emit(\"my_event\", {\"data\": \"I'm connected!\"})\n connected_called = True\n\n await ctx.connect(socketio_path=\"/ws/\")\n await ctx.wait()\n\n assert len(my_response_message) == 2\n assert my_response_message == [\n {\"data\": \"Connected\", \"count\": 0},\n {\"data\": \"I'm connected!\"},\n ]\n assert disconnected_called and connected_called\n\n async def test_broadcast_work(self):\n sio_1_response_message = []\n sio_2_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n ctx_2 = ctx.new_socket_client_context()\n\n @ctx.sio.on(\"my_response\")\n async def my_response_case_1(message):\n sio_1_response_message.append(message)\n\n @ctx_2.sio.on(\"my_response\")\n async def my_response_case_2(message):\n sio_2_response_message.append(message)\n\n await ctx.connect(socketio_path=\"/ws/\")\n await ctx_2.connect(socketio_path=\"/ws/\")\n\n await ctx.sio.emit(\n \"my_broadcast_event\", {\"data\": \"Testing Broadcast\"}\n ) # both sio_1 and sio_2 would receive this message\n\n await ctx.wait()\n await ctx_2.wait()\n\n assert len(sio_1_response_message) == 2\n assert sio_1_response_message == [\n {\"data\": \"Connected\", \"count\": 0},\n {\"data\": \"Testing Broadcast\"},\n ]\n\n assert len(sio_2_response_message) == 2\n assert sio_2_response_message == [\n {\"data\": \"Connected\", \"count\": 0},\n {\"data\": \"Testing Broadcast\"},\n ]\n\n\n@pytest.mark.asyncio\nclass TestGatewayWithGuards:\n test_client = TestGateway.create_test_module(controllers=[GatewayWithGuards])\n\n async def test_socket_connection_work(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n\n @ctx.sio.event\n async def my_response(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecret\"}\n )\n await ctx.sio.emit(\"my_event\", {\"data\": \"Testing Broadcast\"})\n await ctx.wait()\n\n assert my_response_message == [\n {\"auth-key\": \"supersecret\", \"data\": \"Testing Broadcast\"}\n ]\n\n async def test_event_with_header_work(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n\n @ctx.sio.event\n async def my_response(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecret\"}\n )\n await ctx.sio.emit(\"my_event_header\", {\"data\": \"Testing Broadcast\"})\n await ctx.wait()\n\n assert my_response_message == [\n {\"data\": \"Testing Broadcast\", \"x_auth_key\": \"supersecret\"}\n ]\n\n async def test_event_with_plain_response(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n\n @ctx.sio.on(\"my_plain_response\")\n async def message_receive(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecret\"}\n )\n await ctx.sio.emit(\"my_plain_response\", {\"data\": \"Testing Broadcast\"})\n await ctx.wait()\n\n assert my_response_message == [\n {\"data\": \"Testing Broadcast\", \"x_auth_key\": \"supersecret\"}\n ]\n\n async def test_failed_to_connect(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n ctx = typing.cast(RunWithServerContext, ctx)\n\n @ctx.sio.on(\"error\")\n async def error(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecre\"}\n )\n await ctx.sio.emit(\"my_event_header\", {\"data\": \"Testing Broadcast\"})\n await ctx.wait()\n\n assert my_response_message == [{\"code\": 1011, \"reason\": \"Authorization Failed\"}]\n\n async def test_failed_process_message_sent(self):\n my_response_message = []\n\n async with self.test_client.run_with_server() as ctx:\n ctx = typing.cast(RunWithServerContext, ctx)\n\n @ctx.sio.on(\"error\")\n async def error(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-guard/\", headers={\"x-auth-key\": \"supersecret\"}\n )\n await ctx.sio.emit(\n \"my_event_header\", {\"doesnt_exist_key\": \"Testing Broadcast\"}\n )\n await ctx.wait()\n\n assert my_response_message == [\n {\n \"code\": 1007,\n \"reason\": [\n {\n \"loc\": [\"body\", \"data\"],\n \"msg\": \"none is not an allowed value\",\n \"type\": \"type_error.none.not_allowed\",\n }\n ],\n }\n ]\n\n\n@pytest.mark.asyncio\nclass TestGatewayExceptions:\n @pytest.mark.parametrize(\n \"debug, result\",\n [\n (\n True,\n [\n {\"code\": 1011, \"reason\": \"I dont have anything to run.\"},\n {\"code\": 1009, \"reason\": \"Message is too big\"},\n ],\n ),\n (\n False,\n [\n {\"code\": 1011, \"reason\": \"Something went wrong\"},\n {\"code\": 1009, \"reason\": \"Message is too big\"},\n ],\n ),\n ],\n )\n async def test_exception_handling_works_debug_true_or_false(self, debug, result):\n test_client = TestGateway.create_test_module(\n controllers=[GatewayOthers], config_module={\"DEBUG\": debug}\n )\n my_response_message = []\n\n async with test_client.run_with_server() as ctx:\n ctx = typing.cast(RunWithServerContext, ctx)\n ctx2 = ctx.new_socket_client_context()\n\n @ctx.sio.on(\"error\")\n async def error(message):\n my_response_message.append(message)\n\n @ctx2.sio.on(\"error\")\n async def error_2(message):\n my_response_message.append(message)\n\n await ctx.connect(socketio_path=\"/ws-others/\")\n await ctx2.connect(socketio_path=\"/ws-others/\")\n\n await ctx.sio.emit(\"my_event\", {})\n await ctx.wait()\n\n await ctx2.sio.emit(\"my_event_raise\", {})\n await ctx2.wait()\n\n assert my_response_message == result\n\n async def test_message_with_extra_args(self):\n test_client = TestGateway.create_test_module(controllers=[GatewayOthers])\n my_response_message = []\n\n async with test_client.run_with_server() as ctx:\n ctx = typing.cast(RunWithServerContext, ctx)\n\n @ctx.sio.on(\"error\")\n async def error(message):\n my_response_message.append(message)\n\n await ctx.connect(\n socketio_path=\"/ws-others/?query1=some-query1&query2=some-query2&?\"\n )\n\n await ctx.sio.emit(\"extra_args\", {})\n await ctx.wait()\n\n assert my_response_message == [\n {\"code\": 1009, \"reason\": {\"query1\": \"some-query1\", \"query2\": \"some-query2\"}}\n ]\n","repo_name":"eadwinCode/ellar","sub_path":"tests/test_socket_io/test_operation.py","file_name":"test_operation.py","file_ext":"py","file_size_in_byte":8925,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"3"}
+{"seq_id":"11558900976","text":"import asyncio\nimport contextlib\nimport functools\nimport logging\nimport os\nimport time\nimport unittest\n\n\nclass AsyncioTestCase(unittest.TestCase):\n \"\"\"\n Base class for tests that sets up an isolated event loop for each test.\n\n \"\"\"\n\n def __init_subclass__(cls, **kwargs):\n \"\"\"\n Convert test coroutines to test functions.\n\n This supports asychronous tests transparently.\n\n \"\"\"\n super().__init_subclass__(**kwargs)\n for name in unittest.defaultTestLoader.getTestCaseNames(cls):\n test = getattr(cls, name)\n if asyncio.iscoroutinefunction(test):\n setattr(cls, name, cls.convert_async_to_sync(test))\n\n @staticmethod\n def convert_async_to_sync(test):\n \"\"\"\n Convert a test coroutine to a test function.\n\n \"\"\"\n\n @functools.wraps(test)\n def test_func(self, *args, **kwargs):\n return self.loop.run_until_complete(test(self, *args, **kwargs))\n\n return test_func\n\n def setUp(self):\n super().setUp()\n self.loop = asyncio.new_event_loop()\n asyncio.set_event_loop(self.loop)\n\n def tearDown(self):\n self.loop.close()\n super().tearDown()\n\n def run_loop_once(self):\n # Process callbacks scheduled with call_soon by appending a callback\n # to stop the event loop then running it until it hits that callback.\n self.loop.call_soon(self.loop.stop)\n self.loop.run_forever()\n\n @contextlib.contextmanager\n def assertNoLogs(self, logger=\"websockets\", level=logging.ERROR):\n \"\"\"\n No message is logged on the given logger with at least the given level.\n\n \"\"\"\n with self.assertLogs(logger, level) as logs:\n # We want to test that no log message is emitted\n # but assertLogs expects at least one log message.\n logging.getLogger(logger).log(level, \"dummy\")\n yield\n\n level_name = logging.getLevelName(level)\n self.assertEqual(logs.output, [f\"{level_name}:{logger}:dummy\"])\n\n def assertDeprecationWarnings(self, recorded_warnings, expected_warnings):\n \"\"\"\n Check recorded deprecation warnings match a list of expected messages.\n\n \"\"\"\n self.assertEqual(len(recorded_warnings), len(expected_warnings))\n for recorded, expected in zip(recorded_warnings, expected_warnings):\n actual = recorded.message\n self.assertEqual(str(actual), expected)\n self.assertEqual(type(actual), DeprecationWarning)\n\n\n# Unit for timeouts. May be increased on slow machines by setting the\n# WEBSOCKETS_TESTS_TIMEOUT_FACTOR environment variable.\nMS = 0.001 * int(os.environ.get(\"WEBSOCKETS_TESTS_TIMEOUT_FACTOR\", 1))\n\n# asyncio's debug mode has a 10x performance penalty for this test suite.\nif os.environ.get(\"PYTHONASYNCIODEBUG\"): # pragma: no cover\n MS *= 10\n\n# Ensure that timeouts are larger than the clock's resolution (for Windows).\nMS = max(MS, 2.5 * time.get_clock_info(\"monotonic\").resolution)\n","repo_name":"WebKit/WebKit","sub_path":"LayoutTests/imported/w3c/web-platform-tests/tools/third_party/websockets/tests/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3031,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"30282379670","text":"from typing import Optional\n\nfrom django_tgbot.state_manager import message_types\nfrom . import BasicType\n\n\nclass Message(BasicType):\n \"\"\"\n Represents Telegram Message type:\n https://core.telegram.org/bots/api#message\n \"\"\"\n fields = {\n 'message_id': str,\n 'date': str,\n 'forward_from_message_id': str,\n 'forward_signature': str,\n 'forward_sender_name': str,\n 'forward_date': str,\n 'edit_date': str,\n 'media_group_id': str,\n 'author_signature': str,\n 'text': str,\n 'caption': str,\n 'new_chat_title': str,\n 'delete_chat_photo': BasicType.bool_interpreter,\n 'group_chat_created': BasicType.bool_interpreter,\n 'supergroup_chat_created': BasicType.bool_interpreter,\n 'channel_chat_created': BasicType.bool_interpreter,\n 'migrate_to_chat_id': str,\n 'migrate_from_chat_id': str,\n 'connected_website': str,\n }\n\n def __init__(self, obj=None):\n super(Message, self).__init__(obj)\n\n def type(self):\n field_checks = {\n 'text': message_types.Text,\n 'audio': message_types.Audio,\n 'document': message_types.Document,\n 'animation': message_types.Animation,\n 'game': message_types.Game,\n 'photo': message_types.Photo,\n 'sticker': message_types.Sticker,\n 'video': message_types.Video,\n 'voice': message_types.Voice,\n 'video_note': message_types.VideoNote,\n 'contact': message_types.Contact,\n 'dice': message_types.Dice,\n 'location': message_types.Location,\n 'venue': message_types.Venue,\n 'poll': message_types.Poll,\n 'new_chat_members': message_types.NewChatMembers,\n 'left_chat_member': message_types.LeftChatMember,\n 'new_chat_title': message_types.NewChatTitle,\n 'new_chat_photo': message_types.NewChatPhoto,\n 'delete_chat_photo': message_types.DeleteChatPhoto,\n 'group_chat_created': message_types.GroupChatCreated,\n 'supergroup_chat_created': message_types.SupergroupChatCreated,\n 'channel_chat_created': message_types.ChannelChatCreated,\n 'migrate_to_chat_id': message_types.MigrateToChatId,\n 'migrate_from_chat_id': message_types.MigrateFromChatId,\n 'pinned_message': message_types.PinnedMessage,\n 'invoice': message_types.Invoice,\n 'successful_payment': message_types.SuccessfulPayment,\n 'passport_data': message_types.PassportData\n }\n\n for key in field_checks.keys():\n if getattr(self, key, None) is not None:\n return field_checks[key]\n return None\n\n def get_message_id(self) -> str:\n return getattr(self, 'message_id', None)\n\n def get_chat(self):\n return getattr(self, 'chat', None)\n\n def get_user(self):\n return getattr(self, 'from', None)\n\n def get_from(self):\n return self.get_user()\n\n def get_reply_to_message(self):\n return getattr(self, 'reply_to_message', None)\n\n def get_text(self) -> Optional[str]:\n return getattr(self, 'text', None)\n\n def get_caption(self) -> Optional[str]:\n return getattr(self, 'caption', None)\n\n def get_reply_markup(self):\n return getattr(self, 'reply_markup', None)\n \n\n def get_photo(self):\n return getattr(self, 'photo', None)\n\n\n# Placed here to avoid import cycles\nfrom . import user, chat, messageentity, audio, document, animation, game, photosize, \\\n inlinekeyboardmarkup, passportdata, successfulpayment, invoice, poll, \\\n venue, location, contact, videonote, voice, video, sticker, dice\n\n\nMessage.fields.update({\n 'reply_to_message': Message,\n 'via_bot': user.User,\n 'pinned_message': Message,\n 'from': user.User,\n 'chat': chat.Chat,\n 'forward_from': user.User,\n 'forward_from_chat': chat.Chat,\n 'entities': {\n 'class': messageentity.MessageEntity,\n 'array': True\n },\n 'caption_entities': {\n 'class': messageentity.MessageEntity,\n 'array': True\n },\n 'audio': audio.Audio,\n 'document': document.Document,\n 'animation': animation.Animation,\n 'game': game.Game,\n 'photo': {\n 'class': photosize.PhotoSize,\n 'array': True\n },\n 'sticker': sticker.Sticker,\n 'video': video.Video,\n 'voice': voice.Voice,\n 'video_note': videonote.VideoNote,\n 'contact': contact.Contact,\n 'dice': dice.Dice,\n 'location': location.Location,\n 'venue': venue.Venue,\n 'poll': poll.Poll,\n 'new_chat_members': {\n 'class': user.User,\n 'array': True\n },\n 'left_chat_member': user.User,\n 'new_chat_photo': {\n 'class': photosize.PhotoSize,\n 'array': True\n },\n 'invoice': invoice.Invoice,\n 'successful_payment': successfulpayment.SuccessfulPayment,\n 'passport_data': passportdata.PassportData,\n 'reply_markup': inlinekeyboardmarkup.InlineKeyboardMarkup\n})\n","repo_name":"Ali-Toosi/django-tgbot","sub_path":"django_tgbot/types/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":5067,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"3"}
+{"seq_id":"16602936191","text":"def solution(citations):\n\n # citations 내림차순으로 정렬\n citations.sort(reverse = True)\n \n for i in range(len(citations)):\n if citations[i] < i + 1: # 논문의 인용수가 논문수보다 작아질 때\n return i\n elif citations[i] == i + 1: # 논문의 인용수와 논문수가 같을 때\n return i + 1\n \n return len(citations) # 논문의 인용수와 논문수가 같거나 작아지지 않을 때\n","repo_name":"eeeeeddy/Coding_Test_Practice","sub_path":"프로그래머스/lv2/42747. H－Index/H－Index.py","file_name":"H－Index.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8026201987","text":"import cv2\nimport math\nimport numpy as np\nimport pywt\nimport bandelette_1\nimport bandelette_inverse\nimport huff\nimport algo_RLE\nimport quantif_scalaire\nimport matplotlib.pyplot as plt\n\ndef afficher(img) :\n i = 0\n shape = img.shape\n while i < shape[0] :\n j = 0\n while j < shape[1] :\n print(\"img[\",i,\"]\",\"[\",j,\"]\",img[i][j])\n if img[i][j] != 0 :\n return False\n j += 1\n i += 1\n return True\ndef calcule_PSNR(max,mse) :\n pnsr = 10 * math.log(math.pow(max,2)/mse,10)\n return pnsr\ndef s(i1,i2) :\n i=0\n val = 0\n shape = i1.shape\n while i < abs(shape[0]) :\n j = 0\n while j < abs(shape[1]) :\n val += pow(i1[i][j]-i2[i][j],2)\n j += 1\n i += 1\n return val\ndef calcule_mse(img1,img2,n):\n mse = s(img1,img2)/n\n return mse\ndef calcule_taille_rle(list):\n taille= len(list) * 8\n return taille\n\ndef array_list(mat,l,c):\n i=0\n list=[]\n while i bool:\n try:\n self._sm_client.describe_endpoint(EndpointName=endpoint_name)\n except ClientError:\n return False\n return True\n\n def connect_to_endpoint(self, endpoint_name: str) -> None:\n if not self._does_endpoint_exist(endpoint_name):\n raise Exception(f\"Endpoint {endpoint_name} does not exist.\")\n self._endpoint_name = endpoint_name\n self._variant_name = \"AllTraffic\"\n self._resource_id = \"endpoint/{}/variant/{}\".format(\n self._endpoint_name, self._variant_name\n )\n\n def create_endpoint(\n self,\n arn: str,\n endpoint_name: str,\n instance_type: str,\n n_instances: int = 1,\n recreate: bool = False,\n role: Optional[str] = None,\n ) -> None:\n import sagemaker\n\n if role is None:\n role = get_role()\n\n if self._does_endpoint_exist(endpoint_name):\n if recreate:\n self.connect_to_endpoint(endpoint_name)\n self.delete_endpoint()\n else:\n raise Exception(\n f\"Endpoint {endpoint_name} already exists and recreate={recreate}.\"\n )\n\n model = sagemaker.ModelPackage(\n role=role,\n model_data=None,\n sagemaker_session=self._sm_session, # makes sure the right region is used\n model_package_arn=arn,\n )\n\n validation_params = dict(\n model_data_download_timeout=2400,\n container_startup_health_check_timeout=2400,\n )\n\n try:\n model.deploy(\n n_instances,\n instance_type,\n endpoint_name=endpoint_name,\n **validation_params,\n )\n except ParamValidationError:\n model.deploy(n_instances, instance_type, endpoint_name=endpoint_name)\n\n self.connect_to_endpoint(endpoint_name)\n\n def register_scalable_target(self, max_capacity, min_capacity=1):\n return self._aas_client.register_scalable_target(\n ServiceNamespace=\"sagemaker\",\n ResourceId=self._resource_id,\n ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n MinCapacity=min_capacity,\n MaxCapacity=max_capacity,\n )\n\n def set_step_autoscaling(self, policy_name, policy_configuration: Dict):\n return self._aas_client.put_scaling_policy(\n PolicyName=policy_name,\n ServiceNamespace=\"sagemaker\",\n ResourceId=self._resource_id,\n ScalableDimension=\"sagemaker:variant:DesiredInstanceCount\",\n PolicyType=\"StepScaling\",\n StepScalingPolicyConfiguration=policy_configuration,\n )\n\n def set_metric_alarm(self, policy_arn, **kwargs):\n kwargs[\"Dimensions\"] = [\n {\"Name\": \"EndpointName\", \"Value\": self._endpoint_name},\n {\"Name\": \"VariantName\", \"Value\": self._variant_name},\n ]\n kwargs[\"AlarmActions\"] = [policy_arn]\n\n return self._cw_client.put_metric_alarm(**kwargs)\n\n def create_transform_job(\n self,\n arn: str,\n n_instances: int,\n instance_type: str,\n input_path: str,\n output_path: str,\n role: Optional[str] = None,\n wait: bool = True,\n logs: bool = True,\n ):\n import sagemaker\n\n if role is None:\n role = get_role()\n\n model = sagemaker.ModelPackage(\n name=arn.split(\"/\")[-1],\n role=role,\n model_data=None,\n sagemaker_session=self._sm_session, # makes sure the right region is used\n model_package_arn=arn,\n )\n\n uid = uuid.uuid4().hex\n # if input path is a local path, upload to default s3 bucket\n if not input_path.startswith(\"s3://\") and os.path.exists(input_path):\n csv_path_with_ids = prefix_csv_with_ids(input_path=input_path)\n s3_input_path = self._sm_session.upload_data(\n path=csv_path_with_ids, key_prefix=f\"input/{uid}\"\n )\n print(f\"Input file uploaded to {s3_input_path}.\")\n\n download_output_path = None\n # if output path is a local path, change to default s3 bucket,\n # add job name and random uuid\n if not output_path.startswith(\"s3://\"):\n download_output_path = output_path\n output_path = os.path.join(\n \"s3://\",\n self._sm_session.default_bucket(),\n \"output\",\n model.name,\n uid,\n )\n\n transformer = model.transformer(\n instance_count=n_instances,\n instance_type=instance_type,\n output_path=output_path,\n strategy=\"MultiRecord\",\n )\n\n transformer.transform(\n data=s3_input_path,\n content_type=\"text/csv\",\n split_type=\"Line\",\n wait=wait,\n logs=logs,\n )\n\n if download_output_path is not None:\n download_s3_folder(\n path=output_path,\n local_dir=download_output_path,\n )\n print(f\"Output downloaded to {download_output_path}.\")\n\n def _invoke_endpoint(self, texts: Union[str, List[str]]):\n if self._endpoint_name is None:\n raise Exception(\n \"No endpoint connected. \" \"Run connect_to_endpoint() first.\"\n )\n\n if isinstance(texts, str):\n data = json.dumps({\"data\": {\"text\": texts}})\n else:\n data = json.dumps({\"data\": [{\"text\": text} for text in texts]})\n\n return self._sm_runtime_client.invoke_endpoint(\n EndpointName=self._endpoint_name,\n ContentType=\"application/json\",\n Body=data,\n )\n\n def embed(self, texts: Union[str, List[str]]):\n response = self._invoke_endpoint(texts)\n resp = json.loads(response[\"Body\"].read().decode())\n return resp[\"data\"]\n\n def delete_endpoint(self) -> None:\n if self._endpoint_name is None:\n raise Exception(\"No endpoint connected.\")\n try:\n self._sm_client.delete_endpoint(EndpointName=self._endpoint_name)\n except ClientError:\n print(\"Endpoint not found, skipping deletion.\")\n\n try:\n self._sm_client.delete_endpoint_config(\n EndpointConfigName=self._endpoint_name\n )\n except ClientError:\n print(\"Endpoint config not found, skipping deletion.\")\n\n def close(self) -> None:\n try:\n self._sm_runtime_client.close()\n self._sm_client.close()\n except AttributeError:\n print(\n \"SageMaker client could not be closed. \"\n \"This might be because you are using an old version of SageMaker.\"\n )\n raise\n","repo_name":"jina-ai/jina-sagemaker","sub_path":"jina_sagemaker/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":7989,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71405486483","text":"import numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom matplotlib.colors import hsv_to_rgb\nfrom celluloid import Camera\nfrom ast import literal_eval\nfrom matplotlib.ticker import (MultipleLocator, FormatStrFormatter, AutoMinorLocator)\nimport matplotlib.ticker as ticker\nimport nibabel as nib\nimport matplotlib as mpl\n\nimport argparse\n\nif __name__ == '__main__':\n\tparser = argparse. ArgumentParser ()\n\trequiredNamed = parser.add_argument_group('Required arguments')\n\trequiredNamed. add_argument ('--input_dir','-in', help = \"path of input directory\")\n\targs = parser.parse_args()\n\n\t# READ PREDICTIONS\n\tdf = pd. read_csv (\"%s/Outputs/predictions.csv\"%args.input_dir, sep = ';', header = 0, index_col = 0)\n\tregions = list (df. columns)\n\n\tindex = df. index\n\ttitle = 'Brain activity predictions'\n\n\tROIS_desc = pd. read_csv (\"brain_areas.tsv\", sep = '\\t', header = 0)\n\n\n\tcolors_of_rois = []\n\n# 1000 distinct colors:\n\tcolors = [hsv_to_rgb([(i * 0.618033988749895) % 1.0, 1, 1]) for i in range(1000)]\n\n\tfor k in range (1, len (regions) + 1):\n\t\tcolors_of_rois. append (colors[k])\n\n\t# SAVE LEGENDS SEPARATELY\n\tfig = plt.figure()\n\tfig_legend = plt.figure(figsize=(6, 1))\n\tax = fig.add_subplot(111)\n\tbars = ax.bar(range(len (regions)), range(len (regions)), color=colors_of_rois, label=regions)\n\tfig_legend.legend(bars.get_children(), regions, loc='center', frameon=False, ncol = 3)\n\tfig_legend. savefig (\"%s/Outputs/legend.png\"%args.input_dir)\n\tplt.clf ()\n\tplt. cla ()\n\tplt. close ()\n\t#exit (1)\n\n\tmpl.style.use('seaborn')\n\t# SAVE PREDICTIONS AS A VIDEO\n\tfig, ax = plt.subplots (nrows = len (regions), ncols = 1, figsize=(10,5.6), sharex=True)\n\t#fig.text(0.5, 0.04, 'Time (s)', ha='center')\n\tfig.text(0.5, 0.04, 'Time (s)', ha='center')\n\tfig.subplots_adjust(\n\t top=0.981,\n\t bottom=0.09,\n\t left=0.03,\n\t right=0.8,\n\t hspace=0.2,\n\t wspace=0.2\n\t)\n\t#plt.tight_layout(pad=0.05)\n\t#fig.text(0.04, 0.5, title, va='center', rotation='vertical')\n\n\tcamera = Camera(fig)\n\tlegend_image = plt. figure (figsize = (3,5))\n\n\n\tfor j in range (len (regions)):\n\t\tax [j]. set_xlim (np.min (index), np. max (index) + 1)\n\t\tax [j]. xaxis.set_minor_locator(MultipleLocator(5))\n\t\tax [j]. set_ylim (0, 1.1)\n\t\tax [j]. yaxis. set_major_locator (ticker. MultipleLocator (1))\n\t\tax [j]. set_yticklabels([])\n\n\tfor i in range (1,len (index)):\n\t\tfor j in range (len (regions)):\n\t\t\tax[j]. plot (index [:i], df. iloc [:i, j], linewidth = 2, color = colors_of_rois [j], alpha = 1, label = regions[j])\n\t\t\tax[j]. legend(['%s'%regions[j]], bbox_to_anchor=(1, 1), loc=2, borderaxespad=0.05, markerscale = 0.5, handlelength=0.5, fontsize = 10)\n\t\tcamera. snap()\n\n\n\tanim = camera.animate (repeat = False, interval = 1205)\n\n\tanim.save(\"%s/Outputs/predictions_video.mp4\"%args.input_dir, extra_args=['-vcodec', 'h264', '-pix_fmt', 'yuv420p'])\n\tfig. savefig (\"%s/Outputs/predictions.png\"%args.input_dir)\n","repo_name":"Hmamouche/NeuroTSConvers","sub_path":"demo/animation.py","file_name":"animation.py","file_ext":"py","file_size_in_byte":2886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34906992866","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys\nfrom base64 import b64encode\nimport requests\nimport upyun\n\ntry:\n import urllib.parse\n import queue\nexcept Exception as e:\n import Queue as queue\n import urllib\n\n# ----------待拉取的服务名操作员信息-------------\norigin_bucket = '' # (必填) 待拉取的服务名\norigin_username = '' # (必填) 待拉取的服务名下授权的操作员名\norigin_password = '' # (必填) 待拉取服务名下授权操作员的密码\nhost = '' # (必填) 待拉取的服务名的访问域名, 请使用 http// 或者 https:// 开头, 比如 'http://techs.upyun.com'\norigin_path = '' # (必填) 待拉取的资源路径 \n# --------------------------------------------\n\n# ----------目标迁移服务名, 操作员信息-------------\ntarget_bucket = '' # (必填) 文件迁移的目标服务名\ntarget_username = '' # (必填) 文件迁移的目标服务名的授权操作员名\ntarget_password = '' # (必填) 文件迁移的目���服务名的授权操作员的密码\nsave_as_prefix = '' # (必填) 目标服务名的保存路径的前置路径\n# --------------------------------------------\n\nnotify_url = '' # 将回调地址改成自己的服务器地址, 用来接收又拍云 POST 过来的异步拉取结果\n\n# --------------------------------------------\n\n\nqueue = queue.LifoQueue()\n\n\ndef push_tasks(url, up):\n fetch_data = [\n {\n 'url': host + url, # 需要拉取文件的 URL\n 'random': False, # 是否追加随机数, 默认 false\n 'overwrite': True, # 是否覆盖，默认 True\n 'save_as': save_as_prefix + url\n }\n ]\n if not notify_url:\n print('第 19 行 notify_url 参数不能为空')\n sys.exit(0)\n result = up.put_tasks(fetch_data, notify_url, 'spiderman')\n return result\n\n\ndef do_http_request(method, key, upyun_iter):\n uri = '/' + origin_bucket + (lambda x: x[0] == '/' and x or '/' + x)(key)\n try:\n uri = urllib.parse.quote(uri)\n except Exception as e:\n if isinstance(uri, unicode):\n uri = uri.encode('utf-8')\n uri = urllib.quote(uri)\n headers = {\n 'Authorization': 'Basic ' + b64encode(\n (origin_username + ':' + origin_password).encode()).decode(),\n 'User-Agent': 'up-python-script',\n 'X-List-Limit': '300'\n }\n if upyun_iter is not None or upyun_iter is not 'g2gCZAAEbmV4dGQAA2VvZg':\n headers['x-list-iter'] = upyun_iter\n\n url = \"http://v0.api.upyun.com\" + uri\n requests.adapters.DEFAULT_RETRIES = 5\n session = requests.session()\n try:\n response = session.request(method, url, headers=headers, timeout=30)\n status = response.status_code\n if status == 200:\n try:\n content = response.content.decode()\n except Exception as e:\n content = response.content\n try:\n iter_header = response.headers['x-upyun-list-iter']\n except Exception as e:\n iter_header = 'g2gCZAAEbmV4dGQAA2VvZg'\n data = {\n 'content': content,\n 'iter_header': iter_header\n }\n return data\n else:\n return None\n except Exception as e:\n return None\n\n\ndef sort_data(key, upyun_iter):\n result = do_http_request('GET', key, upyun_iter)\n if not result:\n return None\n content = result['content']\n items = content.split('\\n')\n content = [dict(zip(['name', 'type', 'size', 'time'],\n x.split('\\t'))) for x in items] + result['iter_header'].split()\n return content\n\n\ndef get_list(path):\n upyun_iter = None\n up = upyun.UpYun(target_bucket, target_username, target_password)\n while True:\n while upyun_iter != 'g2gCZAAEbmV4dGQAA2VvZg':\n res = sort_data(path, upyun_iter)\n if res:\n upyun_iter = res[-1]\n for i in res[:-1]:\n try:\n if not i['name']:\n continue\n new_path = '/' + i['name'] if path == '/' else path + '/' + i['name']\n if i['type'] == 'F':\n queue.put(new_path)\n elif i['type'] == 'N':\n print(new_path)\n push_tasks(new_path, up)\n else:\n sys.exit(0)\n except Exception as e:\n print(e)\n else:\n if not queue.empty():\n path = queue.get()\n upyun_iter = None\n queue.task_done()\n else:\n if not queue.empty():\n path = queue.get()\n upyun_iter = None\n queue.task_done()\n else:\n break\n\n\nif __name__ == '__main__':\n get_list(origin_path)\n print(\"Done!\")","repo_name":"upyun/upyun-uss-script","sub_path":"migrate_file_between_buckets/transfer_file.py","file_name":"transfer_file.py","file_ext":"py","file_size_in_byte":5001,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"16182354690","text":"# This is a code for WebApp for predicting the sales.\n\nimport pickle\nimport streamlit as st\nwith open('sales_predictor.pickle', 'rb') as file:\n model = pickle.load(file)\n\nprint(model)\n\nst.title('Sales Predictor')\n\nTV = st.number_input(label ='Enter how many ads were broadcasted through TV: ')\nradio = st.number_input(label = 'Enter how many ads were broadcasted through radio: ')\nnewspaper = st.number_input(label = 'Enter how many ads were posted in newspaper: ')\n\n\nif st.button('Submit'):\n \n y_pred = model.predict([[TV, radio, newspaper]])\n st.write('Sales: ',y_pred[0])","repo_name":"Kushal-Shr/Sales-Predictor-WebApp","sub_path":"predict_sales.py","file_name":"predict_sales.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36716273320","text":"li# -*- coding: utf-8 -*-\n\"\"\"\n@author: 东风\n@file: fec.py\n@time: 2019/11/29 17:40\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame, Series\nfrom pprint import pprint\n\npath = 'D:/data/pydata_book2/datasets/fec/P00000001-ALL.csv'\nfec = pd.read_csv(path, low_memory=False)\n\n# 信息加载\npprint(fec.info())\n\n# 打印一条样本记录\n# print(fec.iloc[123456])\n\n# 使用unique获得所有不同的政治候选人名单\n# unique_cands = fec.cand_nm.unique()\n# pprint(unique_cands)\n# pprint(unique_cands[2])\n\nparties = {\n 'Bachmann, Michelle': 'Republican',\n 'Cain, Herman': 'Republican',\n 'Gingrich, Newt': 'Republican',\n 'Huntsman, Jon': 'Republican',\n 'Johnson, Gary Earl': 'Republican',\n 'McCotter, Thaddeus G': 'Republican',\n 'Obama, Barack': 'Democrat',\n 'Paul, Ron': 'Republican',\n 'Pawlenty, Timothy': 'Republican',\n 'Perry, Rick': 'Republican',\n \"Roemer, Charles E. 'Buddy' III\": 'Republican',\n 'Romney, Mitt': 'Republican',\n 'Santorum, Rick': 'Republican'\n}\n\n# print(fec.cand_nm[123456: 123461])\n# print(fec.cand_nm[123456: 123461].map(parties))\n","repo_name":"cuibinghua84/pydata","sub_path":"python_data_analysis/14-数据分析案例/fec.py","file_name":"fec.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17089105277","text":"# Is the actual A* algorithm\n# Code adapted from https://www.youtube.com/watch?v=ob4faIum4kQ&t=562s\n# 4/4/22\n# author = Charlotte Miller\n\nimport pygame\nimport random\nimport queue\nfrom MazeGeneration import *\n\nclass State(object):\n def __init__(self, value, parent, start = 0, goal = 0):\n self.children = []\n self.parent = parent\n self.value = value\n self.dist = 0\n if parent:\n self.path = parent.path[:]\n self.path.append(value)\n self.start = parent.start\n self.goal = parent.goal\n else:\n self.path = [value]\n self.start = start\n self.goal = goal\n\nclass StateString(State):\n def __init__(self, value, parent, start = 0, goal = 0):\n super(StateString, self).__init__(value, parent, start, goal)\n self.dist = self.GetDist()\n\n def GetDist(self):\n if self.value == self.goal:\n return 0\n return self.goal[0] - self.value[0], self.goal[1] - self.value[1]\n\n def CreateChildren(self, maze):\n parentcell = maze.cell_at(self.value[0], self.value[1])\n parentx = self.value[0]\n parenty = self.value[1]\n if not self.children:\n if not parentcell.walls['N']:\n self.children.append(maze.cell_at(parentx, parenty + 1))\n if not parentcell.walls['S']:\n self.children.append(maze.cell_at(parentx, parenty - 1))\n if not parentcell.walls['E']:\n self.children.append(maze.cell_at(parentx + 1, parenty))\n if not parentcell.walls['W']:\n self.children.append(maze.cell_at(parentx - 1, parenty))\n print(\"parentcell:\", parentcell)\n\nclass AStarSolver:\n def __init__(self, start, goal, nx, ny):\n self.path = []\n self.visitedQueue = []\n self.priorityQueue = queue.PriorityQueue()\n self.start = start\n self.goal = goal\n self.maze = maze\n self.maze.gen_maze()\n print(self.maze)\n self.maze.draw_background()\n\n def Solve(self):\n startState = StateString(self.start, 0, self.start, self.goal)\n # number of child nodes that have been visited\n count = 0\n self.priorityQueue.put((0, count, startState))\n while(not self.path and self.priorityQueue.qsize()):\n closestChild = self.priorityQueue.get()[2]\n closestChild.CreateChildren(self.maze)\n self.visitedQueue.append(closestChild.value)\n for child in closestChild.children:\n dist = goal[0] - child.x, goal[1] - child.y\n print(dist)\n if (child.x, child.y) not in self.visitedQueue:\n count += 1\n if not dist:\n self.path = child.path\n break\n self.priorityQueue.put((dist, count, child))\n if not self.path:\n print(\"Goal is impossible to reach\")\n return self.path\n\nif __name__ == \"__main__\":\n start = 0, 0\n goal = 14, 14\n a = AStarSolver(start, goal, 15, 15)\n a.Solve()","repo_name":"RavenclawLunatic/MazeGenerating","sub_path":"A*Pathfinder.py","file_name":"A*Pathfinder.py","file_ext":"py","file_size_in_byte":3117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31365927810","text":"import datetime\nimport random\nimport sys\nimport Agencia as Ag\nimport Correntista as Co\nimport Devedor\nimport Disponivel\n\n\nclass ContaBancaria:\n def __init__(self, nome_do_correntista: str, cpf: str,) -> None:\n\n self.__estado = Disponivel.Disponivel()\n self.agencia = Ag.Agencia(f\"Agência P{random.randint(100, 999)}\", random.randint(100, 999))\n self.correntista = Co.Correntista(nome_do_correntista, cpf)\n self.numero_da_conta = random.randint(10000, 99999)\n self.saldo = 0\n self.limite = 50\n self.data_abertura = datetime.datetime.now()\n self.data_encerramento = None\n self.dinheiro_disponivel = self.saldo + self.limite\n\n def realizar_saque(self, quantidade) -> None:\n quantidade_sacada = self.__estado.realizar_saque(self.dinheiro_disponivel, quantidade)\n self.saldo -= quantidade_sacada\n self.__verificar_estado()\n\n def realizar_deposito(self, quantidade) -> None:\n quantidade_depositada = self.__estado.realizar_deposito(quantidade)\n self.saldo += quantidade_depositada\n self.__verificar_estado()\n\n def encerrar_conta(self) -> None:\n self.data_encerramento = self.__estado.encerrar_conta()\n if self.data_encerramento is not None:\n sys.exit()\n\n def __verificar_estado(self) -> None:\n if self.limite + self.saldo < 0:\n self.__estado = Devedor.Devedor()\n elif self.saldo + self.limite >= 0:\n self.__estado = Disponivel.Disponivel\n\n self.dinheiro_disponivel = self.saldo + self.limite\n","repo_name":"SFaguiar/analise_e_projeto_de_software_2av","sub_path":"2q/ContaBancaria.py","file_name":"ContaBancaria.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17592245487","text":"#WAP: addition of two nos with command more precise \r\n#with filtering\r\n\r\nimport sys\r\ndef Addition(no1,no2):\r\n return no1 + no2\r\n \r\ndef main():\r\n if (len(sys.argv) < 3) or (len(sys.argv) > 3):\r\n print(\"Insufficient aruments\")\r\n return\r\n \r\n ret = Addition(int(sys.argv[1]),int(sys.argv[2]))\r\n print(\"Addition of {} and {} is {} :\".format(sys.argv[1],sys.argv[2],ret))\r\n \r\nif __name__ == \"__main__\":\r\n main()\r\n ","repo_name":"PrathameshDhumal/Python-programming","sub_path":"commands/command3.py","file_name":"command3.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6378875580","text":"import threading\nimport datetime\nimport pytz\nimport time\nimport copy\nimport re\nfrom ibapi.wrapper import EWrapper\nfrom ibapi.client import EClient\nfrom ibapi import contract\nfrom StockValues_Google import StockValues_Google\n\n'''\nCreated on 11th November 2017\n\n@author: rob dobson\n'''\n\nclass MarketDataWrapper(EWrapper):\n \"\"\"\n The wrapper deals with the action coming back from the IB gateway or TWS instance\n We override methods in EWrapper that will get called when this action happens\n \"\"\"\n\n def __init__(self):\n pass\n\nclass MarketDataClient(EClient):\n \"\"\"\n The client method\n We don't override native methods, but instead call them from our own wrappers\n \"\"\"\n def __init__(self, wrapper):\n ## Set up with a wrapper inside\n EClient.__init__(self, wrapper)\n\nclass PriceGetter(MarketDataWrapper, MarketDataClient):\n _REQ_ID_PRICE_BASE = 10000000\n _REQ_ID_DETAILS_BASE = 200000\n _nextReqId = 1\n _mapPriceReqIdToStockInfo = {}\n _mapSymbolToPriceReqId = {}\n _mapDetailsReqIdToPriceReqId = {}\n _IB_SymbolMappings = {\n \"INDEXSP:.INX\": { \"symbol\": \"SPX\", \"exchange\": \"CBOE\", \"currency\":\"USD\", \"secType\":\"IND\" },\n \"INDEXDJX:.DJI\": {\"symbol\": \"INDU\", \"exchange\": \"NYSE\", \"currency\": \"USD\", \"secType\": \"IND\"},\n # \"INDEXFTSE:UKX\": {\"symbol\": \"TICK-LSE\", \"exchange\": \"LSE\", \"currency\": \"GBP\", \"secType\": \"IND\"},\n \"AV-B.L\": {\"symbol\": \"AV.B\", \"exchange\": \"LSE\", \"currency\": \"GBP\", \"secType\": \"STK\"},\n }\n\n def __init__(self, ipaddress, portid, clientid, symbolChangedCallback):\n self._symbolChangedCallback = symbolChangedCallback\n # Initialise both base classes\n MarketDataWrapper.__init__(self)\n MarketDataClient.__init__(self, wrapper=self)\n # Connect using the passed params\n self.connect(ipaddress, portid, clientid)\n # Start a thread which runs the run() function inside EClient in the IB API\n self.getterThread = threading.Thread(target = self.run)\n self.getterThread.start()\n setattr(self, \"_thread\", self.getterThread)\n # A thread for adding the market requests\n self.requestAddStockList = []\n self.requestRemoveStockList = []\n self.requestStockListChanged = False\n self.requestListLock = threading.Lock()\n self.requestThreadRunning = True\n self.requestThread = threading.Thread(target = self.requestMarketData)\n self.requestThread.start()\n # A lock for the dictionary used to access symbol values\n self.mapsLock = threading.Lock()\n # Start the google getter too\n self._stockValuesFromGoogle = StockValues_Google(self.symbolChangedCallback)\n\n def error(self, reqId:int, errorCode:int, errorString:str):\n if errorCode == 200 or errorCode == 504: # security not found OR not connected\n with self.mapsLock:\n if reqId in self._mapPriceReqIdToStockInfo:\n sym = self._mapPriceReqIdToStockInfo[reqId][\"ySymbol\"]\n print(\"StockValues: Symbol not matched\", sym, \"errorMsg\", errorString)\n self._mapPriceReqIdToStockInfo.pop(reqId)\n if sym in self._mapSymbolToPriceReqId:\n self._mapSymbolToPriceReqId.pop(sym)\n # Add to list of symbols to get from google\n self._stockValuesFromGoogle.addStock(sym)\n else:\n print(\"StockValues: Unknown symbol not found\")\n else:\n if reqId == -1:\n print(\"StockValues: IB INFO msgCode\", errorCode, \"msgStr\", errorString)\n else:\n print(\"StockValues: IB INFO reqId\", reqId, \"msgCode\", errorCode, \"msgStr\", errorString)\n\n def stop(self):\n # Setting this flag avoids calling disconnect() twice (which throws an error)\n self._stockValuesFromGoogle.stop()\n self.done = True\n self.requestThreadRunning = False\n\n def setStocks(self, stockList):\n # Send full list to google getter (for slow rate updates - e.g. when market closed)\n self._stockValuesFromGoogle.setAllStocks(stockList)\n # Record list to be got\n with self.requestListLock:\n self.requestAddStockList = []\n with self.mapsLock:\n for sym in stockList:\n if sym not in self._mapSymbolToPriceReqId:\n self.requestAddStockList.append(sym)\n for sym, reqId in self._mapSymbolToPriceReqId.items():\n if sym not in stockList:\n self.requestRemoveStockList.append(sym)\n if len(self.requestAddStockList) > 0 or len(self.requestRemoveStockList) > 0:\n self.requestStockListChanged = True\n\n def clear(self):\n # Stop getting each symbol from the market data feed\n for reqId, stockItem in self._mapPriceReqIdToStockInfo.items():\n self.cancelMktData(reqId)\n # Lock here as we're changing the mappings\n self.mapsLock.acquire()\n # Clear maps\n self._mapSymbolToPriceReqId.clear()\n self._mapPriceReqIdToStockInfo.clear()\n self._mapDetailsReqIdToPriceReqId.clear()\n # Release lock\n self.mapsLock.release()\n # Set reqId back\n self._nextReqId = 1\n\n def addYSymbol(self, ySymbol):\n # Check if this stock is already being acquired\n if ySymbol in self._mapSymbolToPriceReqId:\n return\n # Get the contract (stockInfo) in the form needed for interactiveBrokers API\n stockInfo = self.makeStockInfo(ySymbol)\n # Ticker Id is used to communicate with the API - called reqId in the API\n curPriceReqId = self._REQ_ID_PRICE_BASE + self._nextReqId\n stockInfo[\"priceReqId\"] = curPriceReqId\n # Info used by the IB API\n contractInfo = self.getContractInfo(stockInfo)\n stockInfo[\"contractInfo\"] = contractInfo\n # Add to the lookups for the symbol and tickId\n self.mapsLock.acquire()\n self._mapSymbolToPriceReqId[ySymbol] = curPriceReqId\n self._mapPriceReqIdToStockInfo[curPriceReqId] = stockInfo\n self.mapsLock.release()\n # Start the API getting this symbol\n self.reqMktData(curPriceReqId, contractInfo, \"\", False, False, [])\n # No reuse of tickIds currently\n self._nextReqId += 1\n\n def removeSymbol(self, ySymbol):\n # Check we have the symbol\n if ySymbol in self._mapSymbolToPriceReqId:\n # Remove from maps\n tickId = self._mapSymbolToPriceReqId[ySymbol]\n self.mapsLock.acquire()\n self._mapSymbolToPriceReqId.pop(ySymbol, None)\n self._mapPriceReqIdToStockInfo.pop(tickId, None)\n self.mapsLock.release()\n # Stop the API getting this symbol\n self.cancelMktData(tickId)\n\n def makeStockInfo(self, ySymbol):\n nowInUk = datetime.datetime.now(pytz.timezone('GB'))\n # Base record\n stkInfo = {\"ySymbol\": ySymbol, \"symbol\": ySymbol, \"name\": \"\", \"exchange\": \"SMART\",\n \"currency\": \"USD\", \"secType\": \"STK\", \"includeExpired\": False,\n \"failCount\": 0, \"time\": nowInUk}\n if ySymbol in self._IB_SymbolMappings:\n for key, val in self._IB_SymbolMappings[ySymbol].items():\n stkInfo[key] = val\n else:\n # Split YSymbol apart to find underlying exchange and symbol\n # Attempt to handle Yahoo style ticker symbols\n dotPos = ySymbol.rfind(\".\")\n if dotPos > 0:\n if ySymbol[dotPos+1:] == \"L\":\n stkInfo[\"exchange\"] = \"LSE\"\n stkInfo[\"currency\"] = \"GBP\"\n symb = ySymbol[:dotPos]\n if len(symb) == 2:\n symb += \".\"\n stkInfo[\"symbol\"] = symb\n else:\n atPos = ySymbol.rfind(\"@\")\n if atPos > 0:\n stkInfo[\"primaryExchange\"] = ySymbol[atPos+1:]\n symb = ySymbol[:atPos]\n if len(symb) == 2:\n symb += \".\"\n stkInfo[\"symbol\"] = symb\n return stkInfo\n\n def getContractInfo(self, stockInfo):\n c = contract.Contract()\n c.conId = 0\n c.symbol = stockInfo[\"symbol\"]\n c.exchange = stockInfo[\"exchange\"]\n c.currency = stockInfo[\"currency\"]\n c.secType = stockInfo[\"secType\"]\n if \"primaryExchange\" in stockInfo:\n c.primaryExchange = stockInfo[\"primaryExchange\"]\n c.includeExpired = stockInfo[\"includeExpired\"]\n return c\n\n def requestMarketData(self):\n while self.requestThreadRunning:\n # Check if there is new data\n if self.requestStockListChanged:\n self.requestStockListChanged = False\n # Add new stocks\n stockList = []\n with self.requestListLock:\n for stk in self.requestAddStockList:\n stockList.append(stk)\n # Go through adding stocks\n for stk in stockList:\n self.addYSymbol(stk)\n # Remove unwanted stocks\n stockList = []\n with self.requestListLock:\n for stk in self.requestRemoveStockList:\n stockList.append(stk)\n # Go through adding stocks\n for stk in stockList:\n self.removeSymbol(stk)\n time.sleep(1)\n\n def marketDataCallback(self, reqId, tickType, price, attrib):\n pass\n\n def setValInStockDict(self, sym, elemName, val, stockDict):\n if elemName not in stockDict or stockDict[elemName] is None or stockDict[elemName] != val:\n stockDict[elemName] = val\n return sym\n return None\n\n def setChangeInStockDict(self, sym, stockDict):\n if \"close\" in stockDict and stockDict[\"close\"] is not None and \"price\" in stockDict and stockDict[\"price\"] is not None:\n last = stockDict[\"price\"]\n stockDict[\"change\"] = priceChange = last - stockDict[\"close\"]\n if last != 0:\n stockDict[\"chg_percent\"] = 100 * (priceChange) / last\n\n def tickPrice(self, reqId, tickType:int, price:float, attrib):\n # Acquire lock on maps\n symbolDataChanged = None\n with self.mapsLock:\n if reqId in self._mapPriceReqIdToStockInfo:\n stockInfo = self._mapPriceReqIdToStockInfo[reqId]\n sym = stockInfo[\"ySymbol\"]\n if tickType == 1: # bid_price\n # Not this does not set symbolDataChanged\n self.setValInStockDict(sym, \"bid_price\", price, stockInfo)\n elif tickType == 2: # ask_price\n # Not this does not set symbolDataChanged\n self.setValInStockDict(sym, \"ask_price\", price, stockInfo)\n elif tickType == 4: # last\n symbolDataChanged = self.setValInStockDict(sym, \"price\", price, stockInfo)\n if symbolDataChanged:\n self.setChangeInStockDict(sym, stockInfo)\n elif tickType == 6: # high\n symbolDataChanged = self.setValInStockDict(sym, \"high\", price, stockInfo)\n elif tickType == 7: # low\n symbolDataChanged = self.setValInStockDict(sym, \"low\", price, stockInfo)\n elif tickType == 9: # close\n symbolDataChanged = self.setValInStockDict(sym, \"close\", price, stockInfo)\n if \"price\" not in stockInfo or stockInfo[\"price\"] is None:\n self.setValInStockDict(sym, \"price\", price, stockInfo)\n if symbolDataChanged:\n self.setChangeInStockDict(sym, stockInfo)\n elif tickType == 14: # open\n symbolDataChanged = self.setValInStockDict(sym, \"open\", price, stockInfo)\n if \"price\" not in stockInfo or stockInfo[\"price\"] is None:\n self.setValInStockDict(sym, \"price\", price, stockInfo)\n else:\n print(\"StockValues: Unhandled tickPrice\", tickType, \"price\", price)\n if symbolDataChanged:\n nowInUk = datetime.datetime.now(pytz.timezone('GB'))\n stockInfo[\"time\"] = nowInUk\n # Check if detailed info (long name of stock etc) already requested\n if \"detailsReqId\" not in stockInfo:\n # Request contract details for this symbol\n detailsReqId = self._REQ_ID_DETAILS_BASE + self._nextReqId\n self.reqContractDetails(detailsReqId, stockInfo[\"contractInfo\"])\n self._mapDetailsReqIdToPriceReqId[detailsReqId] = stockInfo[\"priceReqId\"]\n stockInfo[\"detailsReqId\"] = detailsReqId\n self._nextReqId += 1\n # Callback if data has changed\n if symbolDataChanged is not None:\n self._symbolChangedCallback(symbolDataChanged)\n\n def tickSize(self, reqId:int, tickType:int, size:int):\n # Acquire lock on maps\n symbolDataChanged = None\n with self.mapsLock:\n if reqId in self._mapPriceReqIdToStockInfo:\n stockInfo = self._mapPriceReqIdToStockInfo[reqId]\n sym = stockInfo[\"ySymbol\"]\n if tickType == 0: # bid_size\n self.setValInStockDict(sym, \"bid_size\", size, stockInfo)\n elif tickType == 3: # ask_size\n self.setValInStockDict(sym, \"ask_size\", size, stockInfo)\n elif tickType == 5: # last_size\n self.setValInStockDict(sym, \"last_size\", size, stockInfo)\n elif tickType == 8: # volume\n symbolDataChanged = self.setValInStockDict(sym, \"volume\", size, stockInfo)\n else:\n print(\"StockValues: Unhandled tickSize\", tickType, \"size\", size)\n if symbolDataChanged:\n nowInUk = datetime.datetime.now(pytz.timezone('GB'))\n stockInfo[\"time\"] = nowInUk\n # Callback if data has changed\n if symbolDataChanged is not None:\n self._symbolChangedCallback(symbolDataChanged)\n\n def tickString(self, reqId:int, tickType:int, value:str):\n if tickType == 32: # ask_exch\n pass\n elif tickType == 33: # bid_exch\n pass\n elif tickType == 45: # last_timestamp\n pass\n elif tickType == 84: # last_exch\n pass\n else:\n print(\"StockValues: Unhandled tickString\", tickType, \"str\", value)\n\n def tickGeneric(self, reqId:int, tickType:int, value:float):\n print(\"StockValues: Unhandled tickGeneric\", tickType, \"float\", value)\n\n def tickEFP(self, reqId:int, tickType:int, basisPoints:float,\n formattedBasisPoints:str, totalDividends:float,\n holdDays:int, futureLastTradeDate:str, dividendImpact:float,\n dividendsToLastTradeDate:float):\n print(\"StockValues: Unhandled tickEFP\", tickType, \"basisPoints\", basisPoints,\n \"formattedBasisPoints\", formattedBasisPoints, \"totalDividends\", totalDividends,\n \"holdDays\", holdDays, \"futureLastTradeDate\", futureLastTradeDate,\n \"dividendImpact\", dividendImpact, \"dividendsToLastTradeDate\", dividendsToLastTradeDate)\n\n def tickSnapshotEnd(self, reqId:int):\n print(\"StockValues: Unhandled tickSnapshotEnd\", reqId)\n\n def contractDetails(self, reqId:int, contractDetails):\n self.mapsLock.acquire()\n if reqId in self._mapDetailsReqIdToPriceReqId:\n priceReqId = self._mapDetailsReqIdToPriceReqId[reqId]\n if priceReqId in self._mapPriceReqIdToStockInfo:\n stockInfo = self._mapPriceReqIdToStockInfo[priceReqId]\n stockInfo[\"name\"] = contractDetails.longName\n self._symbolChangedCallback(stockInfo[\"ySymbol\"])\n self.mapsLock.release()\n\n def contractDetailsEnd(self, reqId:int):\n pass\n\n def getStockInfoData(self, ySymbol):\n if ySymbol not in self._mapSymbolToPriceReqId:\n return self._stockValuesFromGoogle.getStockInfoData(ySymbol)\n stockInfo = self._stockValuesFromGoogle.getStockInfoData(ySymbol)\n if stockInfo is None:\n stockInfo = {}\n reqId = self._mapSymbolToPriceReqId[ySymbol]\n with self.mapsLock:\n for k,v in self._mapPriceReqIdToStockInfo[reqId].items():\n if k != \"price\" or v != 0:\n stockInfo[k] = v\n return stockInfo\n\n def symbolChangedCallback(self, symbol):\n self._symbolChangedCallback(symbol)\n\nclass StockValues_InteractiveBrokers:\n bOnlyUpdateWhileMarketOpen = False\n \n def __init__(self):\n self.openhour = 8\n self.openmin = 0\n self.closehour = 16\n self.closemin = 30\n self.tradingdow = 0, 1, 2, 3, 4\n self._priceGetter = PriceGetter(\"127.0.0.1\", 4001, 10, self.symbolDataChanged)\n self._dictOfStocksChangedSinceUIUpdate = {}\n self._lockOnStockChangeList = threading.Lock()\n \n def setStocks(self, stockList):\n # Set list of symbols got by the API\n self._priceGetter.setStocks(stockList)\n\n def symbolDataChanged(self, symbol):\n with self._lockOnStockChangeList:\n self._dictOfStocksChangedSinceUIUpdate[symbol] = True\n # print(\"UpdateTo\", symbol, \"len\", len(self._dictOfStocksChangedSinceUIUpdate))\n\n def getMapOfStocksChangedSinceUIUpdated(self):\n changedStockDict = {}\n with self._lockOnStockChangeList:\n changedStockDict = copy.copy(self._dictOfStocksChangedSinceUIUpdate)\n self._dictOfStocksChangedSinceUIUpdate = {}\n return changedStockDict\n\n def getStockData(self, sym):\n return self._priceGetter.getStockInfoData(sym)\n\n def setOnlyUpdateWhenMarketOpen(self, onlyWhenOpen):\n self.bOnlyUpdateWhileMarketOpen = onlyWhenOpen\n \n def run(self):\n pass\n\n def stop(self):\n self._priceGetter.stop()\n\n def getMarketOpenStatus(self):\n nowInUk = datetime.datetime.now(pytz.timezone('GB'))\n open_time = nowInUk.replace( hour=self.openhour, minute=self.openmin, second=0 )\n close_time = nowInUk.replace( hour=self.closehour, minute=self.closemin, second=0 )\n open_day = False\n for day in self.tradingdow:\n if ( day == nowInUk.weekday() ):\n open_day = True\n marketOpen = (nowInUk > open_time) and (nowInUk < close_time) and open_day\n return \"Market Open\" if marketOpen else \"Market Closed\"\n\n","repo_name":"robdobsn/QtStockTicker","sub_path":"StockValues_InteractiveBrokers.py","file_name":"StockValues_InteractiveBrokers.py","file_ext":"py","file_size_in_byte":18904,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"40259223904","text":"import tensorflow as tf\r\nfrom tensorflow.keras import backend as K\r\n\r\n#---------------------------------------------------#\r\n# ボックスを調整し、実際の画像に合わせる\r\n#---------------------------------------------------#\r\ndef yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image):\r\n #-----------------------------------------------------------------#\r\n # y軸を前に置くのは、予測ボックスと画像の幅・高さを簡単にかけるためです\r\n #-----------------------------------------------------------------#\r\n box_yx = box_xy[..., ::-1]\r\n box_hw = box_wh[..., ::-1]\r\n input_shape = K.cast(input_shape, K.dtype(box_yx))\r\n image_shape = K.cast(image_shape, K.dtype(box_yx))\r\n\r\n if letterbox_image:\r\n #-----------------------------------------------------------------#\r\n # ここで求められるoffsetは、画像の有効領域が画像の左上角からのオフセットです\r\n # new_shapeは幅と高さの縮小を示します\r\n #-----------------------------------------------------------------#\r\n new_shape = K.round(image_shape * K.min(input_shape/image_shape))\r\n offset = (input_shape - new_shape)/2./input_shape\r\n scale = input_shape/new_shape\r\n\r\n box_yx = (box_yx - offset) * scale\r\n box_hw *= scale\r\n\r\n box_mins = box_yx - (box_hw / 2.)\r\n box_maxes = box_yx + (box_hw / 2.)\r\n boxes = K.concatenate([box_mins[..., 0:1], box_mins[..., 1:2], box_maxes[..., 0:1], box_maxes[..., 1:2]])\r\n boxes *= K.concatenate([image_shape, image_shape])\r\n return boxes\r\n\r\n#---------------------------------------------------#\r\n# 各特徴レイヤーの予測値を実数に調整する\r\n#---------------------------------------------------#\r\ndef get_anchors_and_decode(feats, anchors, num_classes, input_shape, calc_loss=False):\r\n num_anchors = len(anchors)\r\n #------------------------------------------#\r\n # grid_shapeは特徴レイヤーの高さと幅を示します\r\n #------------------------------------------#\r\n grid_shape = K.shape(feats)[1:3]\r\n #--------------------------------------------------------------------#\r\n # 各特徴点の座標情報を生成する。生成されるshapeは (13, 13, num_anchors, 2)\r\n #--------------------------------------------------------------------#\r\n grid_x = K.tile(K.reshape(K.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, num_anchors, 1])\r\n grid_y = K.tile(K.reshape(K.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], num_anchors, 1])\r\n grid = K.cast(K.concatenate([grid_x, grid_y]), K.dtype(feats))\r\n #---------------------------------------------------------------#\r\n # 各特徴点の座標情報を生成する。生成されるshapeは (13, 13, num_anchors, 2)\r\n #---------------------------------------------------------------#\r\n anchors_tensor = K.reshape(K.constant(anchors), [1, 1, num_anchors, 2])\r\n anchors_tensor = K.tile(anchors_tensor, [grid_shape[0], grid_shape[1], 1, 1])\r\n\r\n #---------------------------------------------------#\r\n # 予測結果を(batch_size,13,13,3,85)に調整する\r\n # 85は、4 + 1 + 80に分解できます\r\n # 4は中心の幅と高さの調整パラメータを示します\r\n # 1はボックスの信頼度を示します\r\n # 80はカテゴリの信頼度を示します\r\n #---------------------------------------------------#\r\n feats = K.reshape(feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])\r\n #------------------------------------------#\r\n #事前に設定されたボックスをデコードし、正規化する\r\n #------------------------------------------#\r\n box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[::-1], K.dtype(feats))\r\n box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats))\r\n #------------------------------------------#\r\n #予測ボックスの信頼度を取得\r\n #------------------------------------------#\r\n box_confidence = K.sigmoid(feats[..., 4:5])\r\n box_class_probs = K.sigmoid(feats[..., 5:])\r\n\r\n #---------------------------------------------------------------------#\r\n # 損失計算時にはgrid, feats, box_xy, box_whを返します\r\n # 予測時にはbox_xy, box_wh, box_confidence, box_class_probsを返します\r\n #---------------------------------------------------------------------#\r\n if calc_loss == True:\r\n return grid, feats, box_xy, box_wh\r\n return box_xy, box_wh, box_confidence, box_class_probs\r\n\r\n#---------------------------------------------------#\r\n# 画像の予測\r\n#---------------------------------------------------#\r\ndef DecodeBox(outputs,\r\n anchors,\r\n num_classes,\r\n input_shape,\r\n #-----------------------------------------------------------#\r\n # 13x13の特徴レイヤーに対応するアンカーは[116,90],[156,198],[373,326]\r\n # 26x26の特徴レイヤーに対応するアンカーは[30,61],[62,45],[59,119]\r\n # 52x52の特徴レイヤーに対応するアンカーは[10,13],[16,30],[33,23]\r\n #-----------------------------------------------------------#\r\n anchor_mask = [[6, 7, 8], [3, 4, 5], [0, 1, 2]],\r\n max_boxes = 100,\r\n confidence = 0.5,\r\n nms_iou = 0.3,\r\n letterbox_image = True):\r\n\r\n image_shape = K.reshape(outputs[-1],[-1])\r\n\r\n box_xy = []\r\n box_wh = []\r\n box_confidence = []\r\n box_class_probs = []\r\n for i in range(len(anchor_mask)):\r\n sub_box_xy, sub_box_wh, sub_box_confidence, sub_box_class_probs = \\\r\n get_anchors_and_decode(outputs[i], anchors[anchor_mask[i]], num_classes, input_shape)\r\n box_xy.append(K.reshape(sub_box_xy, [-1, 2]))\r\n box_wh.append(K.reshape(sub_box_wh, [-1, 2]))\r\n box_confidence.append(K.reshape(sub_box_confidence, [-1, 1]))\r\n box_class_probs.append(K.reshape(sub_box_class_probs, [-1, num_classes]))\r\n box_xy = K.concatenate(box_xy, axis = 0)\r\n box_wh = K.concatenate(box_wh, axis = 0)\r\n box_confidence = K.concatenate(box_confidence, axis = 0)\r\n box_class_probs = K.concatenate(box_class_probs, axis = 0)\r\n\r\n #------------------------------------------------------------------------------------------------------------#\r\n # 画像がネットワークに入力される前に、letterbox_imageを使用して画像周りにグレーのバーが追加されます。\r\n # したがって、生成されたbox_xy、box_whはグレーのバーがある画像に対応しています。\r\n # これらを修正し、グレーのバー部分を取り除く必要があります。box_xy、およびbox_whをy_min, y_max, xmin, xmaxに調整します。\r\n # letterbox_imageを使用していない場合でも、正規化されたbox_xy、box_whを元の画像サイズに対して調整する必要があります。\r\n #------------------------------------------------------------------------------------------------------------#\r\n boxes = yolo_correct_boxes(box_xy, box_wh, input_shape, image_shape, letterbox_image)\r\n\r\n box_scores = box_confidence * box_class_probs\r\n\r\n #-----------------------------------------------------------#\r\n # スコアがscore_thresholdよりも大きいかどどうかを判断する必要があります\r\n #-----------------------------------------------------------#\r\n mask = box_scores >= confidence\r\n max_boxes_tensor = K.constant(max_boxes, dtype='int32')\r\n boxes_out = []\r\n scores_out = []\r\n classes_out = []\r\n for c in range(num_classes):\r\n #-----------------------------------------------------------#\r\n # すべてのbox_scores >= score_thresholdのボックスとスコアを取り出します。\r\n #-----------------------------------------------------------#\r\n class_boxes = tf.boolean_mask(boxes, mask[:, c])\r\n class_box_scores = tf.boolean_mask(box_scores[:, c], mask[:, c])\r\n\r\n #-----------------------------------------------------------#\r\n # 非極大抑制：\r\n # ある範囲内でスコアが最大のボックスを保持します。\r\n #-----------------------------------------------------------#\r\n nms_index = tf.image.non_max_suppression(class_boxes, class_box_scores, max_boxes_tensor, iou_threshold=nms_iou)\r\n\r\n #-----------------------------------------------------------#\r\n # 非極大抑制後の結果を取得します。\r\n # 以下の3つは、それぞれボックスの位置、スコア、カテゴリを示しています。\r\n #-----------------------------------------------------------#\r\n class_boxes = K.gather(class_boxes, nms_index)\r\n class_box_scores = K.gather(class_box_scores, nms_index)\r\n classes = K.ones_like(class_box_scores, 'int32') * c\r\n\r\n boxes_out.append(class_boxes)\r\n scores_out.append(class_box_scores)\r\n classes_out.append(classes)\r\n boxes_out = K.concatenate(boxes_out, axis=0)\r\n scores_out = K.concatenate(scores_out, axis=0)\r\n classes_out = K.concatenate(classes_out, axis=0)\r\n\r\n return boxes_out, scores_out, classes_out\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import matplotlib.pyplot as plt\r\n import numpy as np\r\n\r\n def sigmoid(x):\r\n s = 1 / (1 + np.exp(-x))\r\n return s\r\n #---------------------------------------------------#\r\n # 各特徴層の予測値を実際の値に調整します。\r\n #---------------------------------------------------#\r\n def get_anchors_and_decode(feats, anchors, num_classes):\r\n # feats [batch_size, 13, 13, 3 * (5 + num_classes)]\r\n # anchors [3, 2]\r\n # num_classes\r\n # 3\r\n num_anchors = len(anchors)\r\n #------------------------------------------#\r\n # grid_shapeは特徴層の高さと幅を示しています\r\n # grid_shape [13, 13]\r\n #------------------------------------------#\r\n grid_shape = np.shape(feats)[1:3]\r\n #--------------------------------------------------------------------#\r\n # 各特徴点の座標情報を取得します。生成されるshapeは（13, 13, num_anchors, 2）\r\n #--------------------------------------------------------------------#\r\n grid_x = np.tile(np.reshape(np.arange(0, stop=grid_shape[1]), [1, -1, 1, 1]), [grid_shape[0], 1, num_anchors, 1])\r\n grid_y = np.tile(np.reshape(np.arange(0, stop=grid_shape[0]), [-1, 1, 1, 1]), [1, grid_shape[1], num_anchors, 1])\r\n grid = np.concatenate([grid_x, grid_y], -1)\r\n #---------------------------------------------------------------#\r\n # 各特徴点の座標情報を生成する。生成されるshapeは (13, 13, num_anchors, 2)\r\n # [1, 1, 3, 2]\r\n # [13, 13, 3, 2]\r\n #---------------------------------------------------------------#\r\n anchors_tensor = np.reshape(anchors, [1, 1, num_anchors, 2])\r\n anchors_tensor = np.tile(anchors_tensor, [grid_shape[0], grid_shape[1], 1, 1])\r\n\r\n #---------------------------------------------------#\r\n # 予測結果を（batch_size, 13, 13, 3, 85）に調整します。\r\n # 85は、4 + 1 + 80に分割できます。\r\n # 4は中心の幅と高さの調整パラメータを表します。\r\n # 1はボックスの信頼度を表します。\r\n # 80はクラスの信頼度を表します。\r\n # [batch_size, 13, 13, 3 * (5 + num_classes)]\r\n # [batch_size, 13, 13, 3, 5 + num_classes]\r\n #---------------------------------------------------#\r\n feats = np.reshape(feats, [-1, grid_shape[0], grid_shape[1], num_anchors, num_classes + 5])\r\n #------------------------------------------#\r\n # 先行バウンディングボックスをデコードし、正規化します。\r\n #------------------------------------------#\r\n box_xy = sigmoid(feats[..., :2]) + grid\r\n box_wh = np.exp(feats[..., 2:4]) * anchors_tensor\r\n #------------------------------------------#\r\n # 予測ボックスの信頼度を取得します。\r\n #------------------------------------------#\r\n box_confidence = sigmoid(feats[..., 4:5])\r\n box_class_probs = sigmoid(feats[..., 5:])\r\n\r\n box_wh = box_wh / 32\r\n anchors_tensor = anchors_tensor / 32\r\n fig = plt.figure()\r\n ax = fig.add_subplot(121)\r\n plt.ylim(-2,15)\r\n plt.xlim(-2,15)\r\n plt.scatter(grid_x,grid_y)\r\n plt.scatter(5,5,c='black')\r\n plt.gca().invert_yaxis()\r\n\r\n anchor_left = grid_x - anchors_tensor/2\r\n anchor_top = grid_y - anchors_tensor/2\r\n print(np.shape(anchors_tensor))\r\n print(np.shape(box_xy))\r\n rect1 = plt.Rectangle([anchor_left[5,5,0,0],anchor_top[5,5,0,1]],anchors_tensor[0,0,0,0],anchors_tensor[0,0,0,1],color=\"r\",fill=False)\r\n rect2 = plt.Rectangle([anchor_left[5,5,1,0],anchor_top[5,5,1,1]],anchors_tensor[0,0,1,0],anchors_tensor[0,0,1,1],color=\"r\",fill=False)\r\n rect3 = plt.Rectangle([anchor_left[5,5,2,0],anchor_top[5,5,2,1]],anchors_tensor[0,0,2,0],anchors_tensor[0,0,2,1],color=\"r\",fill=False)\r\n\r\n ax.add_patch(rect1)\r\n ax.add_patch(rect2)\r\n ax.add_patch(rect3)\r\n\r\n ax = fig.add_subplot(122)\r\n plt.ylim(-2,15)\r\n plt.xlim(-2,15)\r\n plt.scatter(grid_x,grid_y)\r\n plt.scatter(5,5,c='black')\r\n plt.scatter(box_xy[0,5,5,:,0],box_xy[0,5,5,:,1],c='r')\r\n plt.gca().invert_yaxis()\r\n\r\n pre_left = box_xy[...,0] - box_wh[...,0]/2\r\n pre_top = box_xy[...,1] - box_wh[...,1]/2\r\n\r\n rect1 = plt.Rectangle([pre_left[0,5,5,0],pre_top[0,5,5,0]],box_wh[0,5,5,0,0],box_wh[0,5,5,0,1],color=\"r\",fill=False)\r\n rect2 = plt.Rectangle([pre_left[0,5,5,1],pre_top[0,5,5,1]],box_wh[0,5,5,1,0],box_wh[0,5,5,1,1],color=\"r\",fill=False)\r\n rect3 = plt.Rectangle([pre_left[0,5,5,2],pre_top[0,5,5,2]],box_wh[0,5,5,2,0],box_wh[0,5,5,2,1],color=\"r\",fill=False)\r\n\r\n ax.add_patch(rect1)\r\n ax.add_patch(rect2)\r\n ax.add_patch(rect3)\r\n\r\n plt.show()\r\n\r\n feat = np.random.normal(0,0.5,[4,13,13,75])\r\n anchors = [[142, 110],[192, 243],[459, 401]]\r\n get_anchors_and_decode(feat,anchors,20)\r\n\r\n\r\n'''\r\nYOLOによるオブジェクト検出のためのバウンディングボックスのデコーディングと調整を行う関数を定義します。\r\nyolo_correct_boxes: 予測されたバウンディングボックスの座標を実際の画像サイズに合わせて調整します。\r\nget_anchors_and_decode: 各特徴レイヤーの予測値を実際のバウンディングボックスの座標に変換します。\r\nDecodeBox: YOLOの出力から実際のバウンディングボックス、スコア、およびクラスを取得します。\r\nこれには、信頼度しきい値と非極大抑制を使用して、重要なバウンディングボックスのみを選択します。\r\nこれらの関数は、YOLOの出力を解釈し、検出されたオブジェクトの位置とクラスを正確に特定するために使用されます。\r\n'''\r\n","repo_name":"ultra-supara/automatic_train","sub_path":"utils/utils_bbox.py","file_name":"utils_bbox.py","file_ext":"py","file_size_in_byte":15568,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23320358550","text":"from flask import render_template, redirect, request\n\nfrom flask_app import app\n\nfrom flask_app.models.cookie import Cookie_order\n\n@app.route('/')\ndef index():\n return redirect('/cookies')\n\n@app.route('/cookies')\ndef cookie_summary():\n return render_template('cookies.html', cookies=Cookie_order.get_all())\n\n@app.route('/cookies/new')\ndef new():\n return render_template('new_cookie.html')\n\n@app.route('/cookies/create', methods = ['POST'])\ndef create_order():\n if Cookie_order.validate_order(request.form) == True:\n print(request.form)\n Cookie_order.save(request.form)\n return redirect('/cookies')\n else:\n print('not valid')\n return redirect('/cookies/new')\n\n@app.route('/cookies/edit/')\ndef edit_order(id):\n data = {\n \"id\":id\n }\n return render_template('edit_order.html', cookie=Cookie_order.get_one(data))\n\n@app.route('/cookies/update', methods = ['POST'])\ndef update():\n if Cookie_order.validate_order(request.form) == True:\n Cookie_order.update(request.form)\n return redirect('/')\n else:\n return redirect(f'/cookies/edit/{request.form[\"id\"]}')","repo_name":"tiffanyliang333/Coding-Dojo-Python-Full-Stack","sub_path":"flask_mysql/validation/cookie_orders_validation/flask_app/controllers/cookies.py","file_name":"cookies.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7055077091","text":"# http://adventofcode.com/2015/day/14\nfrom __future__ import print_function\nfrom collections import defaultdict\n\n\ndef calc_distance(reindeer, in_time):\n name, speed, speed_time, rest_time = reindeer\n cycles, rem = divmod(in_time, speed_time + rest_time)\n return (cycles * speed_time + min(speed_time, rem)) * speed, name\n\n\ndef calc_race(data, in_time):\n return [calc_distance(reindeer, in_time) for reindeer in data]\n\n\ndef award_points(data, in_time, tally):\n race = calc_race(data, in_time)\n winner_distance = max(race)[0]\n # check for multiple winners\n winners = filter(lambda r: r[0] == winner_distance, race)\n for _, winner in winners:\n tally[winner] += 1\n\n\ndef process(data):\n # part 1\n race = calc_race(data, 2503)\n winning = max(race)\n print(\"Part 1:\", winning[0])\n # part 2\n tally = defaultdict(int)\n for tick in range(1, 2503 + 1):\n award_points(data, tick, tally)\n winner = max(tally, key = lambda key: tally[key])\n print(\"Part 2:\", tally[winner])\n\n\ndef parse(line):\n line = line.strip().split()\n name, speed, speed_time, rest_time = line[0], int(line[3]), int(line[6]), int(line[13])\n return name, speed, speed_time, rest_time\n\n\ndef load_data(fileobj):\n data = [parse(line) for line in fileobj]\n return data\n\n\ndef main(file):\n print(file)\n with open(file) as f:\n process(load_data(f))\n\n\nif __name__ == \"__main__\":\n # main(\"test.txt\")\n main(\"input.txt\")\n","repo_name":"PetrPrazak/AdventOfCode","sub_path":"2015/14/aoc2015_14.py","file_name":"aoc2015_14.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39982484695","text":"import json\nfrom django.http import JsonResponse\nimport os\n# Create your views here.\nfrom django.shortcuts import render\nfrom django.http import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\ncurrent_app_dir = os.path.dirname(os.path.abspath(__file__ ))\ndbg=open('symptoms.txt','a')\napi_url=[]\n@csrf_exempt\ndef chatbot_view(request):\n if request.method == 'POST':\n user_message = request.POST.get('message')\n bot_response = process_user_message(user_message)\n return JsonResponse({'message': bot_response})\n return render(request, 'chatbot_app/chatbot.html')\n\ndef process_user_message(user_message):\n with open(os.path.join(current_app_dir, 'medicalterms.json'), 'r') as json_file:\n data = json.load(json_file)\n search_query = user_message\n for item in data:\n if search_query.lower() in item['name'].lower() or search_query.lower() in item['text'].lower():\n return (item['text'])\n return \"I don't know\"\n","repo_name":"Saurabhpal052/medinavi","sub_path":"MediNavi/chatbot/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5671360013","text":"# -*- coding: utf-8 -*-\n\nfrom os import path\nfrom scipy.misc import imread\nfrom wordcloud import WordCloud, ImageColorGenerator\n\nd = path.dirname(__file__)\n\nstopwordsHR = ['a', 'ako', 'ali', 'bi', 'bih', 'bila', 'bili', 'bilo', 'bio',\n'bismo', 'biste', 'biti', 'bumo', 'da', 'do', 'duž', 'ga', 'hoće', 'hoćemo',\n'hoćete', 'hoćeš', 'hoću', 'i', 'iako', 'ih', 'ili', 'iz', 'ja', 'je',\n'jedna', 'jedne', 'jedno', 'jer', 'jesam', 'jesi', 'jesmo', 'jest', 'jeste',\n'jesu', 'jim', 'joj', 'još', 'ju', 'kada', 'kako', 'kao', 'koja', 'koje',\n'koji', 'kojima', 'koju', 'kroz','li', 'me', 'mene', 'meni', 'mi', 'mimo',\n'moj', 'moja', 'moje', 'mu', 'na', 'nad', 'nakon', 'nam', 'nama', 'nas',\n'naš', 'naša', 'naše', 'našeg', 'ne', 'nego', 'neka', 'neki', 'nekog', 'neku',\n'nema', 'netko', 'neće', 'nećemo', 'nećete', 'nećeš', 'neću', 'nešto', 'ni',\n'nije', 'nikoga', 'nikoje', 'nikoju', 'nisam', 'nisi', 'nismo', 'niste',\n'nisu', 'njega', 'njegov', 'njegova', 'njegovo', 'njemu', 'njezin', 'njezina',\n'njezino', 'njih', 'njihov', 'njihova', 'njihovo', 'njim', 'njima', 'njoj',\n'nju', 'no', 'o', 'od', 'odmah', 'on', 'ona', 'oni', 'ono', 'ova', 'pa',\n'pak', 'po', 'pod', 'pored', 'prije', 's', 'sa', 'sam', 'samo', 'se', 'sebe',\n'sebi', 'si', 'smo', 'ste', 'su', 'sve', 'svi', 'svog', 'svoj', 'svoja',\n'svoje', 'svom', 'ta', 'tada', 'taj', 'tako', 'te', 'tebe', 'tebi', 'ti',\n'to', 'toj', 'tome', 'tu', 'tvoj', 'tvoja', 'tvoje', 'u', 'uz', 'vam', 'vama',\n'vas', 'vaš', 'vaša', 'vaše', 'već', 'vi', 'vrlo', 'za', 'zar', 'će', 'ćemo',\n'ćete', 'ćeš', 'ću', 'što', 'e', 'ima', 'ce', 'kad', 'sad', 'sto', 'ovo',\n'onda', 'ma', 'zato', 'ko', 'jo', 'tko', 'im', 'gdje', 'sta', 'šta', 'bez',\n'jos', 'kod', 'ovaj', 'treba', 'nek', 'vise', 'zasto', 'al', 'ono', 'dok', 'mogu',\n'ove', 'tamo', 'neko', 'jako', 'jel', 'zbog', 'nebi', 'http', 'ovi', 'ove', 'one',\n'on', 'https', 'kaj', 'svaka', 'niti', 'u', 'kakva', 'oko', 'ovu', 'niko', 'di',\n'onaj', 'nesto', 'com', 'neke', 'imam', 'ide', 'eto', 'imamo', 'isto', 'ide', 'tak',\n'preko', 'bit', 'baš', 'tom', 'tek', 'ipak', 'bar', 'www', 'netko', 'cu', ]\n\n# Sources - contains the names of sources to process\n# To be able to process a source we need two files:\n# [SOURCE_NAME]_comments.txt - the file used as a source for the wordcloud\n# [SOURCE_NAME].png - Image that the wordcloud will be shaped/colored into\nsourceNames = []\n\nfor sourceName in sourceNames:\n\t# Read the whole text.\n\ttext = open(path.join(d, sourceName + '_comments.txt')).read()\n\tcoloring = imread(path.join(d, sourceName + \".png\"))\n\n\t# Generate a word cloud image \n\twordcloud = WordCloud(\n\t\tmask=coloring,\n\t\tfont_path='/Users/igorrinkovec/Library/Fonts/LobsterTwo-Regular.otf',\n\t\trelative_scaling=.5,\n\t\tbackground_color=\"white\",\n\t\twidth=3000,\n\t\theight=3000,\n\t\tstopwords=stopwordsHR).generate(text)\n\timage_colors = ImageColorGenerator(coloring)\n\n\t# Display the generated image:\n\t# the matplotlib way:\n\timport matplotlib.pyplot as plt\n\tplt.imshow(wordcloud.recolor(color_func=image_colors))\n\tplt.axis(\"off\")\n\tfig = plt.gcf();\n\tfig.set_size_inches(30, 30)\n\tfig.savefig(sourceName + '_cloud.png', dpi=100)\n","repo_name":"TheWildHorse/FacebookCommentsWordcloud","sub_path":"wordcloud_generator/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"sh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29795758740","text":"## Imports\nimport random\n\nfrom Animal import animal\nfrom world.Water import water\nfrom world.Food import food\n\nclass empty_space:\n type = \"empty\"\n finished = False\n\n##########################\n## Sim helper functions ##\n##########################\n\ndef create_animals(number_of_animals, world_size):\n animals = []\n for i in range(number_of_animals):\n # Generate traits of animal\n endurance = random.randint(1, 10)\n strength = random.randint(1, 10)\n speed = random.randint(1, 10)\n reproductive_score = random.randint(1, 100)\n position = random.randint(1, world_size-1)\n\n # Create animal object and add to list of animals\n new_animal = animal(endurance, strength, speed, reproductive_score, position)\n animals.append(new_animal)\n\n return animals\n\ndef create_world(world_size):\n world = [None, ] * world_size\n amount_water_remaining = world_size // 10\n amount_food_remaining = world_size // 10\n\n while amount_water_remaining:\n sickness_chance = random.randint(1, 100)\n amount = random.randint(1, 3)\n new_water = water(sickness_chance, amount)\n while True:\n location = random.randint(0, world_size-1)\n if world[location] is None:\n world[location] = new_water\n break\n amount_water_remaining -= 1\n\n while amount_food_remaining:\n nutrition = random.randint(1, 5)\n amount = random.randint(1, 3)\n sickness_chance = random.randint(1, 100)\n\n while True:\n location = random.randint(0, world_size-1)\n if world[location] is None:\n new_food = food(nutrition, amount, sickness_chance)\n world[location] = new_food\n break\n amount_food_remaining -= 1\n\n for n, space in enumerate(world):\n if space is None:\n world[n] = empty_space()\n\n return world\n\ndef create_food(world, food_missing):\n world_size = len(world)\n while food_missing:\n nutrition = random.randint(1, 5)\n amount = random.randint(1, 3)\n sickness_chance = random.randint(1, 100)\n\n new_food = food(nutrition, amount, sickness_chance)\n while True:\n location = random.randint(0, world_size-1)\n if world[location].type == \"empty\":\n world[location] = new_food\n break\n food_missing -= 1\n return world\n\ndef create_water(world, water_missing):\n world_size = len(world)\n while water_missing:\n sickness_chance = random.randint(1, 100)\n amount = random.randint(1, 3)\n new_water = water(sickness_chance, amount)\n while True:\n location = random.randint(0, world_size-1)\n # print (f\"L: {location}\")\n if world[location].type == \"empty\":\n world[location] = new_water\n break\n water_missing -= 1\n return world\n\n## Sim object\nclass sim:\n def __init__(self):\n self.world_size = 100\n self.animals = create_animals(1, self.world_size) # List of animal objects\n self.world = create_world(self.world_size)\n self.food_missing = 0\n self.water_missing = 0\n\n ## Sim main loop function\n def run(self):\n cycles = 0\n animals_to_be_added = []\n while len(self.animals) > 0: # and cycles < 100\n print (f\"Cycle: {cycles}\")\n print (f\"📘📘📘Population: {len(self.animals)}\")\n #print ([animal.total_health for animal in self.animals])\n\n for animal_index, animal in enumerate(self.animals):\n animal.total_health = animal.hunger * 0.33 + animal.thirst * 0.33 + animal.fatigue * 0.33\n if animal.sickness:\n animal.total_health -= 5\n if animal.total_health <= 0:\n #print (f\"Animal died, Hunger: {animal.hunger} Thirst: {animal.thirst} Fatigue: {animal.fatigue} number_reproduced: {animal.number_reproduced}\")\n self.animals.pop(animal_index)\n continue\n\n reached_goal = animal.move(self.world)\n if reached_goal:\n if animal.goal_type == \"sleep\":\n animal.rest()\n elif animal.goal_type == \"food\":\n self.world = animal.eat(self.world)\n elif animal.goal_type == \"water\":\n self.world = animal.drink(self.world)\n #print (\"drink\")\n\n # Check for possible_mates\n mates_nearby = [[mate, index] for index, mate in enumerate(self.animals) if abs(mate.position - mate.position) < 2]\n possible_mates = [[mate, index] for mate, index in mates_nearby if mate.total_health + animal.reproductive_score > 150 and animal.total_health + mate.reproductive_score > 150]\n if possible_mates:\n nearest_mate = possible_mates.pop()\n while possible_mates:\n next_mate = possible_mates.pop()\n if abs(animal.position - next_mate[0].position) < abs(animal.position - nearest_mate[0].position):\n nearest_mate = next_mate\n\n mate, mate_index = nearest_mate\n new_animal = animal.reproduce(mate)\n\n mate.fatigue -= 10\n animal.fatigue -= 10\n self.animals[mate_index] = mate\n animals_to_be_added.append(new_animal)\n\n # Update list to reflect new animal state\n self.animals[animal_index] = animal\n\n\n # Update world to reflect resources that are gone\n for n, space in enumerate(self.world):\n if space.finished:\n self.world[n] = empty_space()\n if space.type == \"food\":\n self.food_missing += 1\n elif space.type == \"water\":\n self.water_missing += 1\n\n if self.food_missing:\n self.world = create_food(self.world, self.food_missing)\n\n if self.water_missing:\n self.world = create_water(self.world, self.water_missing)\n\n self.food_missing, self.water_missing = 0, 0\n\n self.animals.extend(animals_to_be_added)\n animals_to_be_added = []\n cycles += 1\n\n\n## Run the sim fucntion\nif __name__ == '__main__':\n s = sim()\n s.run()\n","repo_name":"NoahBlack012/ecosystem_v2","sub_path":"src/simulation/Sim.py","file_name":"Sim.py","file_ext":"py","file_size_in_byte":6611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16312241274","text":"import speech_recognition as sr \nfrom gtts import gTTS \nimport time\nimport playsound\nimport os\nimport pyttsx3 \nimport webbrowser\nimport requests\nfrom bs4 import BeautifulSoup\nimport time\nfrom googlesearch import search\nimport subprocess\nimport datetime\nimport random\nfrom pynput.keyboard import Key, Controller\nimport psutil\nimport wikipedia\nfrom deep_translator import GoogleTranslator\nimport threading\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\n\n\n##DİL\ndef speak_t(text):\n tts = gTTS(text=text, lang=\"tr\")\n filename = \"voice.mp3\"\n tts.save(filename)\n playsound.playsound(filename)\n\n\ndef speak(text):\n engine = pyttsx3.init('sapi5')\n engine.setProperty('rate', 160)\n engine.setProperty('voice', engine.getProperty('voices')[0].id)\n engine.say(text)\n engine.runAndWait()\n\n\ndef get_audio():\n r = sr.Recognizer()\n \n with sr.Microphone() as source:\n r.adjust_for_ambient_noise(source)\n audio = r.listen(source)\n said = \"\"\n\n try:\n said = r.recognize_google(audio,language=\"tr-TR\")\n print(said)\n except Exception as e:\n print(\"Exception: \" + str(e))\n return said.lower()\n\n\ndef get_audio_eng():\n r = sr.Recognizer()\n with sr.Microphone() as source:\n audio = r.listen(source)\n said = \"\"\n\n try:\n said = r.recognize_google(audio, language=\"en-US\")\n print(said)\n except Exception as e:\n print(\"Exception: \" + str(e))\n return said.lower()\n\n\ndef get_audio_withoutwriting():\n r = sr.Recognizer()\n with sr.Microphone() as source:\n audio = r.listen(source)\n said = \"\"\n try:\n said = r.recognize_google(audio,language=\"tr-TR\")\n except Exception as e:\n print(\"\")\n return said.lower()\n\n\ndef get_audio_withoutwriting_eng():\n r = sr.Recognizer()\n with sr.Microphone() as source:\n audio = r.listen(source)\n said = \"\"\n try:\n said = r.recognize_google(audio, language='en-US')\n except Exception as e:\n print(\"\")\n return said.lower()\n\n\ndef countdown(t):\n from Func import countdown\n countdown(t)\n\n\ndef alarm_tanıma(t):\n from Func import alarm_tanıma\n alarm_tanıma(t)\n\n\ndef film_bul(irating):\n from Func import film_bul\n film_bul(irating)\n\n\ndef film_seç():\n from Func import film_seç\n film_seç()\n\n\ndef bekleme(answer):\n from Func import bekleme\n bekleme(answer)\n\n\ndef googlesearch(answer):\n from Func import googlesearch\n googlesearch(answer)\n\n\ndef not_alma(text):\n from Func import not_alma\n not_alma(text)\n\n\ndef notları_oku():\n from Func import notları_oku\n\n\ndef notları_göster():\n from Func import notları_göster\n\n\ndef notları_yazdır():\n from Func import notları_yazdır\n\n\ndef notları_temizle():\n from Func import notları_temizle\n\n\ndef liste_aç():\n from Func import liste_aç\n\n\ndef listeye_yaz():\n from Func import listeye_yaz\n\n\ndef ses_kıs(ses):\n from Func import ses_kıs\n ses_kıs(ses)\n\n\ndef ses_aç(ses):\n from Func import ses_aç\n ses_aç(ses)\n\n\ndef hava_durumu():\n from Func import hava_durumu\n hava_durumu()\n\n\ndef translate():\n from Func import translate\n\nWAKE = [\"uyan\", \"kalk\", \"sana da\", \"alita\", \"harita\", \"alita kalk\", \"alita uyan\", \"burada mısın\", \"günaydın\",\n \"iyi günler\", \"iyi akşamlar\"]\nSHUT = [\"kapat kendini\", \"Kapat kendini\", \"mute\", \"mut\", \"kapan\", \"kendini kapat\", \"kapan\", \"uyu alita\",\n \"işim bitti alita\", \"bu kadardı alita\", \"bu kadar\"]\nWHATSAPP = [\"whatsapp\"]\nSEARCH_STR = [\"google'da arat\", \"search up google\", \"search at google\"]\nFILM_SEARCH_STR = [\"film bul\", \"bize film bul\", \"bize bir film bul\", \"film bulabilir musun\"]\nTAKE_NOTE = [\"not alır mısın\", \"şunu not alır mısın\", \"not al\", \"notlara şunu ekle\", \"notlara şunu ekler misin\",\"şunu yaz\", \"şunu notlara yaz\", \"notlarıma şunu ekler misin\"]\nNOT_YAZDIR_STR = [\"notlarımı ekrana yazdır\", \"notları yazdır\", \"notlarımı ekranda göster\"]\nDATE_STR = [\"bugünün tarihi ne\", \"tarihi söyle\", \"bugünün tarihini söyle\", \"tarih ne\"]\nGREETINGS_STR = [\"merhaba\", \"Merhaba\", \"hey alita\", \"Hey alita\"]\nCONDITION = [\"nasılsın\", \"nasılsınız alita\", \"Nasılsın alita\", \"nasılsın alita\"]\nNAME_STR = [\"adın ne\", \"Adın ne\", \"Adını söyle\", \"İsmin ne\", \"ismini söyle\", \"ismin ne\", \"İsmin ne senin\",\n \"Adın ne senin\", \"Adın ne senin\", \"ismini söyle bakalım\"]\nDCMUTE_STR = [\"discord'da modemi\", \"mutemi\", \"mutemi kapat\", \"Mute mi\", \"sustur\", \"mute mi\", \"mutele\"]\nSS_STR = [\"seal\", \"SS al\", \"eses al\", \"Eses al\"]\nALTIN_STR = [\"altın kaç tl\", \"altın ne kadar\", \"altın kaç olmuş\", \"altın kaç oldu\"]\nDOLAR_STR = [\"dolar kaç tl\", \"dolar ne kadar\", \"dolar kaç olmuş\", \"dolar kaç oldu\"]\nEURO_STR = [\"euro kaç oldu\", \"euro kaç tl\", \"euro ne kadar\", \"Euro ne kadar\", \"euro kaç olmuş\"]\nSLEEP_STR = [\"bilgisayarı uyut\", \"uyku moduna geç\"]\nCHESS_STR = [\"satranç oynayacağım\", \"satranç oynayalım\", \"satranç sitesini\", \"satranç\"]\nINTEGRALSITE_STR = [\"integral hesaplayıcı\", \"integral sitesini\", \"integral hesaplayıcı sını\",\n \"hesaplanacak integrallerin var\"]\nARASTIRMA_STR = [\"bunu araştır\", \"buna bak\", \"bu ne araştır\", \"araştırma yap\", \"araştır\"]\n\nCHATBOT =[\"konuşma botu\",\"konuşma botunu aç\",\"chatbot\",\"cat bot aç\",\"cat bot\",\"chat botunu aç\",\"chat botun aç\"]\nCHATBOT_KAPATMA=[\"konuşma botunu kapat\",\"kapat kendini\",\"botu durdur\",\"kendini durdur\",\"yeterli\",\"sus artık\",\"tamam sus\"]\n\ndef nn_chatbot(training=1):\n speak(\"Chatbot is loading...\")\n tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-large\")\n model = AutoModelForCausalLM.from_pretrained(\"microsoft/DialoGPT-large\").to(\"cuda\")\n \n step=0\n speak(\"All ready\")\n while True:\n text=get_audio()\n for words in CHATBOT_KAPATMA:\n if text==words:\n break\n if text!=\"\":\n text=GoogleTranslator(source=\"auto\", target=\"en\").translate(text)\n print(text)\n new_user_input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors='pt').to(\"cuda\")\n bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n\n chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).to(\"cuda\")\n\n speak(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))\n step=training\n else:\n print(\"\")\n\n\n\n\nkeyboard = Controller()\n\ncondition2 = 1 #İngilizce olması\ncondition3 = 0 #yazıp yazmaması\n\n# MAIN LOOP\n\n\nAWAKE = True\nspeak(\"Device connected...\")\nif 4 <= int(datetime.datetime.now().strftime(\"%H\")) <= 11:\n speak(\"Good Morning, sir\")\nelif 11 < int(datetime.datetime.now().strftime(\"%H\")) < 18:\n speak(\"Good Afternoon, sir\")\nelif int(datetime.datetime.now().strftime(\"%H\")) >= 18 or int(datetime.datetime.now().strftime(\"%H\")) < 4:\n speak(\"Good evening, sir\")\n\nwhile True:\n if not AWAKE:\n text = get_audio_withoutwriting()\n for words in WAKE:\n if text.count(words) > 0:\n AWAKE = True\n speak(\"On your command\")\n break\n\n while AWAKE:\n if condition3 == 1 and condition2 == 1:\n text = get_audio_eng()\n elif condition3 == 1 and condition2 == 0:\n text = get_audio_withoutwriting_eng()\n elif condition2 == 0 and condition3 == 0:\n text = get_audio_withoutwriting()\n elif condition2 == 1 and condition3 == 0:\n text = get_audio()\n\n if text.count(\"dili değiştir\") > 0 or text.count(\"change the language\") > 0:\n if condition3 == 0:\n condition3 = 1\n speak(\"It's English\")\n elif condition3 == 1:\n condition3 = 0\n speak(\"It's Turkish\")\n # -------------------------------------------------------------------\n if text.count(\"yazılı kalsın\") > 0 or text.count(\"yazılı olsun\") > 0 or text.count(\"write what i say\") > 0:\n speak(\"Are you sure?\")\n answer1 = get_audio()\n if answer1.count(\"evet\") > 0 or answer1.count(\"yes\") > 0 or answer1.count(\"he\") > 0:\n speak(\"Alright...\")\n condition2 = 1\n if text.count(\"dediklerimi yazma\") > 0 or text.count(\"dediklerim yazılı olmasın\") > 0 or text.count(\n \"don't write what i say\") > 0:\n speak(\"Are you sure?\")\n answer2 = get_audio()\n if answer1.count(\"evet\") > 0 or answer1.count(\"yes\") > 0 or answer1.count(\"he\") > 0:\n speak(\"Okey...\")\n condition2 = 0\n for words in CHATBOT:\n if text==words:\n nn_chatbot()\n if \"söylemek istediğin bir şey var mı\" in text:\n speak(\"Yes. Fuck you beykan...\")\n # --------------------------------------------------------------------\n for words1 in SHUT:\n if text.count(words1) > 0:\n speak(\"As you wish sir\")\n AWAKE = False\n if text.count(\"burada mısın\") > 0 or text.count(\"açık mısın\") > 0:\n speak(\"Yes,I am\")\n for i in GREETINGS_STR:\n if text.count(i) > 0:\n speak(\"Hi, how can I help you?\")\n for i in CONDITION:\n if text.count(i) > 0:\n liste = [\"Not bad\", \"Doing fine\", \"Cool\", \"So so\", \"Fine\"]\n condition = random.randint(0, 4)\n speak(liste[condition])\n speak(\"How can I help you?\")\n for i in NAME_STR:\n if text.count(i) > 0:\n liste1 = [\"My name is Alita\", \"Alita\", \"They call me Alita\", \"Master named me Alita\"]\n condition1 = random.randint(0, 3)\n speak(liste1[condition1])\n # \"\"\"***************ARAŞTIRMA************************\"\"\"\n if text.count(\"araştır\") > 0 or text.count(\"internetten bak\") > 0 or text.count(\"search for\"):\n if text.count(\"araştır\") > 0:\n text = text.replace(\"araştır \", \"\")\n print(text)\n if text.count(\"search\") > 0:\n text = text.replace(\"search for \", \"\")\n print(text)\n try:\n print(wikipedia.summary(text, sentences=2))\n speak(\"Do you want me to read it?\")\n read = get_audio()\n if read.count(\"evet\") > 0:\n speak(wikipedia.summary(text, sentences=1))\n except:\n try:\n wikipedia.set_lang('tr')\n print(wikipedia.summary(text, sentences=2))\n speak(\"I found it on Turkish resources.Do you want me to read it?\")\n read = get_audio()\n if read.count(\"evet\") > 0:\n speak_t(wikipedia.summary(text, sentences=1))\n os.remove(\"voice.mp3\")\n except:\n speak(\"I could not find any information...\")\n for words in SEARCH_STR:\n if text.count(words) > 0:\n if text.count(\"search at google\") > 0:\n text = text.replace(\"search at google \", \"\")\n if text.count(\"search up google\") > 0:\n text = text.replace(\"search up google \", \"\")\n if text.count(\"google'da arat\") > 0:\n text = text.replace(\"google'da arat \", \"\")\n googlesearch(text)\n for i in FILM_SEARCH_STR:\n if text.count(i) > 0:\n speak(\"What rating do you want?\")\n try:\n while True:\n a = get_audio()\n if a == \"vazgeçtim\":\n speak(\"I am not searching for a film\")\n break\n elif float(a) >= 10:\n speak(\"That is not a valid number for imdb\")\n elif float(a) < 8:\n speak(\"Really!\")\n elif 8 <= float(a) < 10:\n film_bul(float(a))\n speak(\"Here you are\")\n while True:\n speak(\"Did you make your choice?\")\n answer = get_audio()\n bekleme(answer)\n break\n film_seç()\n except:\n speak(\"There is a mistake\")\n # if text.count(\"haritada şunu arar mısın\")>0 or text.count(\"haritada arama yap\")>0 or text.count(\"haritada ara\")>0:\n # answer3=get_audio()\n # answer3.replace(\" \",\"+\")\n # webbrowser.open(f\"https://www.google.com/maps/{answer3}\")\n # \"\"\"*********************************************************************\"\"\"\n # \"\"\"****************************NOTLAR***********************************\"\"\"\n for i in TAKE_NOTE:\n if text.count(i) > 0:\n speak(\"What would you like me to write down?\")\n\n note = get_audio()\n not_alma(note)\n if text.count(\"notlarımı oku\") > 0:\n speak(\"Yes sir\")\n notları_oku()\n if text.count(\"notlarımı göster\") > 0:\n speak(\"Yes sir\")\n notları_göster()\n for i in NOT_YAZDIR_STR:\n if text.count(i) > 0:\n notları_yazdır()\n if text.count(\"notları temizle\") > 0:\n notları_temizle()\n speak(\"Sector clear\")\n # \"\"\"********************************************************************\"\"\"\n # \"\"\"***************************DATE İŞLERİ******************************\"\"\"\n if text.count(\"Bugün günlerden ne\") > 0 or text.count(\"günlerden ne\") > 0:\n day_talk = \"Today is \" + datetime.datetime.now().strftime(\"%A\")\n speak(day_talk)\n for i in DATE_STR:\n if text.count(i) > 0:\n date_talk = \"We are in \" + datetime.datetime.now().strftime(\"%x\")\n speak(date_talk)\n if text.count(\"Hangi aydayız\") or text.count(\"hangi aydayız\") > 0:\n month_talk = \"We are in \" + datetime.datetime.now().strftime(\"%B\")\n speak(month_talk)\n if text.count(\"hangi yıldayız\") > 0:\n year_talk = \"We are in \" + datetime.datetime.now().strftime(\"%Y\")\n speak(year_talk)\n if text.count(\"saat kaç\") > 0 or text.count(\"Saati söyle\") > 0:\n hour_talk = \"It is \" + datetime.datetime.now().strftime(\"%X\")\n speak(hour_talk)\n # \"\"\"**************************************************************************\"\"\"\n if text.count(\"sağırlaştır\") > 0 or text.count(\"sağırlaştır beni\") > 0:\n keyboard.press(Key.f10)\n keyboard.release(Key.f10)\n for i in SS_STR:\n if text.count(i) > 0:\n speak(\"Ss taken\")\n keyboard.press(Key.ctrl_l)\n keyboard.press(Key.f8)\n keyboard.release(Key.ctrl_l)\n keyboard.release(Key.f8)\n for i in DOLAR_STR:\n if text.count(i) > 0:\n url = \"https://kur.doviz.com/serbest-piyasa/amerikan-dolari\"\n response = requests.get(url)\n içerik = response.content\n soup = BeautifulSoup(içerik, \"html.parser\")\n piyasa = soup.find_all(\"span\", {\"class\": \"value\"})\n dolar = piyasa[1].text\n speak(\"Dolar is \" + dolar)\n print(dolar)\n for i in ALTIN_STR:\n if text.count(i) > 0:\n url = \"https://kur.doviz.com/serbest-piyasa/amerikan-dolari\"\n response = requests.get(url)\n içerik = response.content\n soup = BeautifulSoup(içerik, \"html.parser\")\n piyasa = soup.find_all(\"span\", {\"class\": \"value\"})\n altın = piyasa[0].text\n speak(\"Gold is \" + altın)\n print(altın)\n for i in EURO_STR:\n if text.count(i) > 0:\n url = \"https://kur.doviz.com/serbest-piyasa/amerikan-dolari\"\n response = requests.get(url)\n içerik = response.content\n soup = BeautifulSoup(içerik, \"html.parser\")\n piyasa = soup.find_all(\"span\", {\"class\": \"value\"})\n euro = piyasa[2].text\n speak(\"Euro is \" + euro)\n print(euro)\n for i in SLEEP_STR:\n if text.count(i) > 0:\n speak(\"Sleep mode activated\")\n os.system(\"rundll32.exe powrprof.dll,SetSuspendState Sleep \")\n if text.count(\"liste yarat\") > 0:\n liste_aç()\n if text.count(\"listeye yaz\") > 0:\n listeye_yaz()\n # **************************AÇMA**********************************************\n if text.count(\"aç\") > 0 and text.count(\"sesi\") == 0 and text.count(\"kaç\") == 0:\n speak(\"Yes sir\")\n for i in WHATSAPP:\n if text.count(i) > 0:\n webbrowser.open(\"https://web.whatsapp.com/\")\n if text.count(\"youtube'u\") > 0 or text.count(\"Youtube'u\") > 0:\n webbrowser.open(\"https://youtube.com/\")\n if text.count(\"netflix'i\") > 0 or text.count(\"netflix'i\") > 0:\n webbrowser.open(\"https://www.netflix.com/browse\")\n if text.count(\"classroom u\") > 0 or text.count(\"classroom\") > 0:\n webbrowser.open(\"https://classroom.google.com/h\")\n if text.count(\"ders sistemini\") > 0:\n webbrowser.open(\"https://lms.gazi.edu.tr/\")\n if text.count(\"sınav sistemini\") > 0:\n webbrowser.open(\"https://gazi-sinav-lms.almscloud.net/Home/Login\")\n if text.count(\"spotify\") > 0 or text.count(\"spotify'ı\") > 0:\n spotifyPath = \"C:\\\\Users\\\\Murat\\AppData\\\\Local\\\\Microsoft\\\\WindowsApps\\\\Spotify.exe\"\n os.startfile(spotifyPath)\n if text.count(\"valorant\") > 0 or text.count(\"valorant'ı \") > 0:\n valorantPath = \"C:\\\\Users\\Murat\\\\Desktop\\OYUN\\\\VALORANT\"\n os.startfile(valorantPath)\n if text.count(\"cs yi\") > 0 or text.count(\"counter aç\") > 0:\n csPath = \"C:\\\\Users\\\\Murat\\\\Desktop\\\\OYUN\\\\Counter-Strike Global Offensive\"\n os.startfile(csPath)\n if text.count(\"discord\") > 0 or text.count(\"discord'u\") > 0:\n dcPath = \"C:\\\\Users\\\\Murat\\\\Desktop\\\\PROGRAM\\\\Discord\"\n os.startfile(dcPath)\n if text.count(\"olasılık hocasının notlarını\") > 0 or text.count(\"olasılık notlarını\"):\n webbrowser.open(\"https://drive.google.com/drive/folders/1RvmriC5va3kZEhskdoAEhE7xdLT0xeqY\")\n if text.count(\"email\") > 0 or text.count(\"gmail'i\") > 0 or text.count(\"gmail\") > 0:\n webbrowser.open(\"https://mail.google.com/mail/u/0/#inbox\")\n if text.count(\"udemy\") > 0:\n webbrowser.open(\"https://www.udemy.com/\")\n if text.count(\"google'ı\") > 0 or text.count(\"google\") > 0:\n webbrowser.open(\"https://www.google.com/\")\n if text.count(\"çalışma alanımı\") > 0 or text.count(\"günlüğü\") > 0 or text.count(\"çalışma alanını\") > 0:\n webbrowser.open(\"https://app.clickup.com/4600131/v/b/li/42560592\")\n for phrases in CHESS_STR:\n if text.count(phrases) > 0:\n speak(\"Which site do you want?\")\n answer4 = get_audio()\n if answer4.count(\"farketmez\") > 0 or answer4.count(\"chess\") > 0:\n webbrowser.open(\"https://www.chess.com/home\")\n if answer4.count(\"lichess\") > 0:\n webbrowser.open(\"https://lichess.org/\")\n for i in INTEGRALSITE_STR:\n if text.count(i) > 0:\n webbrowser.open(\"https://www.symbolab.com/solver/integral-calculator\")\n for i in DCMUTE_STR:\n if text.count(i) > 0:\n keyboard.press(Key.f9)\n keyboard.release(Key.f9)\n # \"\"\"*********************************************************************************\"\"\"\n if text.count(\"yüzdelerini\") > 0 or text.count(\"yüzdeler\") > 0:\n speak(f\"memory %{psutil.virtual_memory()[2]} used\")\n print(f\"Memory %{psutil.virtual_memory()[2]} used...\")\n speak(f\"cpu %{psutil.cpu_percent(interval=1)} used\")\n print(f\"cpu %{psutil.cpu_percent(interval=1)} used...\")\n speak(\"disk usage is {}\".format(psutil.disk_usage(\"/\")[3]))\n print(\"Disk usage is {}...\".format(psutil.disk_usage(\"/\")[3]))\n if text.count(\"bunu bana hatırlat\") > 0 or text.count(\"bunu unutma\") > 0:\n with open(\"memory.txt\", \"r+\", encoding=\"utf-8\") as file:\n speak(\"What you want to remember?\")\n memory = get_audio()\n for i in memory:\n file.write(i)\n file.write(\"\\n\")\n if text.count(\"hatırlamam gereken\") > 0 or text.count(\"unutmamam gereken\") > 0:\n with open(\"Memory/memory.txt\", \"r\", encoding=\"utf-8\") as file:\n speak(file.read())\n if text.count(\"ses ayarları\") > 0 or text.count(\"sesi\") > 0 or text.count(\"müziği\") > 0:\n if text.count(\"kıs\") > 0 or text.count(\"azalt\") > 0:\n print(\"kıs\")\n ses_kıs(25)\n if text.count(\"arttır\") > 0 or text.count(\"çoğalt\") > 0:\n print(\"aç\")\n ses_aç(25)\n if text.count(\"kapat\") > 0:\n print(\"kapat\")\n ses_kıs(50)\n if text.count(\"fulle\") > 0 or text.count(\"aç\"):\n print(\"full\")\n ses_aç(50)\n if text.count(\"ekranı değiştir\") > 0 or text.count(\"ekran değiş\") > 0 or text.count(\"ekranı değiş\") > 0:\n keyboard.press(Key.alt)\n keyboard.press(Key.tab)\n keyboard.release(Key.alt)\n keyboard.release(Key.tab)\n if text.count(\"hava nasıl\") > 0 or text.count(\"hava durumu\") > 0:\n hava_durumu()\n if text.count(\"çeviri\") > 0:\n translate()\n if text.count(\"alarm\") > 0 and text.count(\"kur\") > 0:\n speak(\"Alarm set\")\n p1=threading.Thread(target=alarm_tanıma,args=[text])\n p1.start()\n\n\n if text.count(\"uyandır\")>0 and text.count(\"beni\"):\n speak(\"Alarm set\")\n p1 = threading.Thread(target=alarm_tanıma, args=[text])\n p1.start()\n p1.join()\n\n while True:\n text=get_audio_withoutwriting()\n speak(\"Time is up sir, wake up\")\n if text.count(\"tamam\")>0 or text.count(\"kapat\")>0 or text.count(\"sus\")>0:\n break\n\n\n if text.count(\"antrenman moduna geç\") > 0:\n go = \"\"\n while go.count(\"bitti\") == 0:\n speak(\"What do you want to do?\")\n go = get_audio_withoutwriting()\n if go.count(\"kardiyo\") > 0:\n speak(\"when you are ready\")\n while True:\n go = get_audio_withoutwriting()\n if go.count(\"başlat\") > 0 or go.count(\"go\") > 0:\n speak(\"Burpees for 1 minute\")\n speak(\"1, 2, 3, go\")\n countdown(60) # 60\n speak(\"move on to planck for 30 second\")\n time.sleep(2)\n countdown(30) # 30\n speak(\"move on to high knees for 30 second\")\n countdown(30) # 30\n speak(\"move on to planck for 30 second\")\n countdown(30) # 30\n speak(\"leg raise, 20 times\")\n go = get_audio_withoutwriting()\n if go.count(\"bitti\") > 0:\n speak(\"You have been amazing so far, sir\")\n break\n if text.count(\"sınav moduna geç\") > 0:\n speak(\"War mode Activated\")\n while True:\n yardım = get_audio_withoutwriting()\n if yardım.count(\"sınav bitti\") > 0:\n speak(\"I hope you have done well, sir\")\n break\n if yardım.count(\"olasılık hocasının notlarını\") > 0 or yardım.count(\"olasılık notlarını\"):\n webbrowser.open(\"https://drive.google.com/drive/folders/1RvmriC5va3kZEhskdoAEhE7xdLT0xeqY\")\n if yardım.count(\"çeviri\") > 0:\n translate()\n for i in INTEGRALSITE_STR:\n if text.count(i) > 0:\n webbrowser.open(\"https://www.symbolab.com/solver/integral-calculator\")\n for i in DCMUTE_STR:\n if text.count(i) > 0:\n keyboard.press(Key.f9)\n keyboard.release(Key.f9)\n if text.count(\"classroom u\") > 0 or text.count(\"classroom\") > 0:\n webbrowser.open(\"https://classroom.google.com/h\")\n if text.count(\"ders sistemini\") > 0:\n webbrowser.open(\"https://lms.gazi.edu.tr/\")\n if text.count(\"sınav sistemini\") > 0:\n webbrowser.open(\"https://gazi-sinav-lms.almscloud.net/Home/Login\")\n if text.count(\"spotify\") > 0 or text.count(\"spotify'ı\") > 0:\n spotifyPath = \"C:\\\\Users\\\\Murat\\AppData\\\\Local\\\\Microsoft\\\\WindowsApps\\\\Spotify.exe\"\n os.startfile(spotifyPath)\n for i in WHATSAPP:\n if text.count(i) > 0:\n webbrowser.open(\"https://web.whatsapp.com/\")\n if text.count(\"youtube'u\") > 0 or text.count(\"Youtube'u\") > 0:\n webbrowser.open(\"https://youtube.com/\")\n if text.count(\"saat kaç\") > 0 or text.count(\"Saati söyle\") > 0:\n hour_talk = \"It is \" + datetime.datetime.now().strftime(\"%X\")\n speak(hour_talk)\n if text.count(\"araştır\") > 0 or text.count(\"internetten bak\") > 0 or text.count(\"search for\"):\n if text.count(\"araştır\") > 0:\n text = text.replace(\"araştır \", \"\")\n print(text)\n if text.count(\"search\") > 0:\n text = text.replace(\"search for \", \"\")\n print(text)\n try:\n print(wikipedia.summary(text, sentences=2))\n speak(\"Do you want me to read it?\")\n read = get_audio()\n if read.count(\"evet\") > 0:\n speak(wikipedia.summary(text, sentences=1))\n except:\n try:\n wikipedia.set_lang('tr')\n print(wikipedia.summary(text, sentences=2))\n speak(\"I found it on Turkish resources.Do you want me to read it?\")\n read = get_audio()\n if read.count(\"evet\") > 0:\n speak_t(wikipedia.summary(text, sentences=1))\n os.remove(\"voice.mp3\")\n except:\n speak(\"I could not find any information...\")\n for words in SEARCH_STR:\n if text.count(words) > 0:\n if text.count(\"search at google\") > 0:\n text = text.replace(\"search at google \", \"\")\n if text.count(\"search up google\") > 0:\n text = text.replace(\"search up google \", \"\")\n if text.count(\"google'da arat\") > 0:\n text = text.replace(\"google'da arat \", \"\")\n googlesearch(text)\n \n \n","repo_name":"uraninjo/Alita-Asisstant-","sub_path":"ALITA.py","file_name":"ALITA.py","file_ext":"py","file_size_in_byte":28543,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"482823364","text":"import tensorflow as tf\nimport tensorflow_datasets as tfds\nfrom tensorflow import keras\nfrom tensorflow.keras.layers import Dense, Embedding, LSTM, Bidirectional\nfrom tensorflow.keras.preprocessing.text import Tokenizer\n\n# set GPU as device\ntf.config.list_physical_devices('GPU')\n\n# Prepare the data\nchatbot_data = tfds.builder(\"imdb_reviews\").as_dataset(split=\"train[:10%]\", as_supervised=True)\ntokenizer = Tokenizer()\ntokenizer.fit_on_texts(chatbot_data)\ndata = tokenizer.texts_to_sequences(chatbot_data)\n\n# Build the model\nmodel = keras.Sequential()\nmodel.add(Embedding(len(tokenizer.word_index)+1, 128))\nmodel.add(Bidirectional(LSTM(64)))\nmodel.add(Dense(64, activation='relu'))\nmodel.add(Dense(len(tokenizer.word_index)+1, activation='softmax'))\n\n# Compile and train the model\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(data, labels, epochs=10)\n","repo_name":"Werby213/pythonl","sub_path":"CV2PPG/CRR/my_projects/chatGPT/Neural_network/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70890356883","text":"from res import id as id, values as values, constants as constants\nfrom util import utils as utils\nimport handler.database as db\nimport pandas as pd\nimport numpy as np\nimport talib as ta\n\n\ndef check_double_top(price_action, indices_maxima, obv):\n\n isDoubleTop = False\n\n for i in range(0, len(indices_maxima) - 1):\n highVal = price_action[id.high].iloc[indices_maxima[i]]\n closeVal = price_action[id.close].iloc[indices_maxima[i]]\n nextHighVal = price_action[id.high].iloc[indices_maxima[i + 1]]\n minVal = price_action[id.low].iloc[indices_maxima[i]:indices_maxima[i + 1]].min()\n\n if nextHighVal >= closeVal and nextHighVal <= highVal and obv[indices_maxima[i]] >= obv[indices_maxima[ i +1]]:\n for j in range(indices_maxima[ i +1] + 1, len(price_action ) -1):\n if price_action[id.close].iloc[j] < minVal:\n isDoubleTop = True\n break\n return isDoubleTop\n\ndef check_double_bottom(price_action, indices_minima, obv):\n\n isDoubleBottom = False\n\n for i in range(0, len(indices_minima) - 1):\n lowVal = price_action[id.low].iloc[indices_minima[i]]\n closeVal = price_action[id.close].iloc[indices_minima[i]]\n nextLowVal = price_action[id.low].iloc[indices_minima[i + 1]]\n maxVal = price_action[id.high].iloc[indices_minima[i]:indices_minima[i + 1]].min()\n\n if nextLowVal <= closeVal and nextLowVal >= lowVal and obv[indices_minima[i]] >= obv[indices_minima[i + 1]]:\n for j in range(indices_minima[i + 1] + 1, len(price_action) - 1):\n if price_action[id.close].iloc[j] > maxVal:\n isDoubleBottom = True\n break\n return isDoubleBottom\n\ndef double_top_double_bottom(key, dataList, time_frame):\n\n price_action = pd.DataFrame(dataList, columns=[id.time, id.open, id.close, id.high, id.low, id.volume])\n if price_action.empty:\n return\n\n window_size = constants.strategy_params[id.window_size]\n # To determine up/down trend and the strength\n upTrend = utils.__uptrend(price_action.iloc[- 2 *window_size - 3:-3][id.close].values, window_size)\n downTrend = utils.__downtrend(price_action.iloc[- 2 *window_size - 3:-3][id.close].values, window_size)\n\n # ADX to determine the strength of the trend and OBV to get volume of trades placed\n adx = ta.ADX(price_action[id.high], price_action[id.low], price_action[id.close], window_size)\n obv = ta.OBV(price_action[id.close], price_action[id.volume])\n\n # Calculate the local maxima and minima in the window frame\n local_minima, local_maxima, indices_minima, indices_maxima = utils.__local_min_max(np.array(price_action[id.high]))\n\n notifier = {\n values.double_top: False,\n values.double_bottom: False\n }\n\n if upTrend and adx[len(adx ) -1] >= constants.strategy_params[id.trend_strength]:\n notifier[values.double_top] = check_double_top(price_action, indices_maxima, obv)\n if downTrend and adx[len(adx ) -1] >= constants.strategy_params[id.trend_strength]:\n notifier[values.double_bottom] = check_double_bottom(price_action, indices_minima, obv)\n\n if notifier[values.double_top]:\n try:\n #db.insert_strategy(key, time_frame, values.double_top, price_action.iloc[-1][id.time])\n return {\n id.name: id.double_top,\n id.key: key,\n id.price_action: dataList\n }\n except:\n print('Unable to add to database')\n return {}\n\n if notifier[values.double_bottom]:\n try:\n #db.insert_strategy(key, time_frame, values.double_bottom, price_action.iloc[-1][id.time])\n return {\n id.name: id.double_bottom,\n id.key: key,\n id.price_action: dataList\n }\n except:\n print('Unable to add to database')\n return {}","repo_name":"CryptoBullMarket/popo-socket","sub_path":"strategy/doubleTopDoubleBottom.py","file_name":"doubleTopDoubleBottom.py","file_ext":"py","file_size_in_byte":3932,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"45383571608","text":"from machine import ADC\nimport time\n\nclass POS:\n def __init__(self,baselines) -> None:\n self.baselines = baselines\n self.minsSet = False\n self.minV1 = 0\n self.minV2 = 0\n self.maxV1 = 0\n self.maxV2 = 0\n self.orbitCompleteTime = 7680000;\n\n def get(self):\n \n # updateVREF();\n vrefs = []\n vrefs.append(ADC(28).read_u16()) # ADC_2\n vrefs.append(ADC(27).read_u16()) # ADC_1\n vrefs.append(ADC(26).read_u16()) # ADC_0\n\n current1 = (self.baselines[0] - vrefs[0]) / 0.1;\n current2 = (self.baselines[1] - vrefs[1]) / 0.1;\n\n volt1 = current1 * vrefs[2];\n volt2 = current2 * vrefs[2];\n # using these two voltage measurements we will calculate the angle to the sun\n\n # posPercent is the percent between 180 and 0\n # volt1 in the front pannel volt2 is the back\n if self.minsSet:\n if (volt1 > volt2):\n posPercent = (volt1 - self.minV1) / (self.maxV1 - self.minV1);\n pos = 180 * posPercent;\n else:\n posPercent = (volt2 - self.minV2) / (self.maxV2 - self.minV2);\n pos = (180 * posPercent) * -1;\n\n # Send pos immediately\n # Estimated degrees to the sun\n\n # Add 180 to bring pos to a 360 then get percent\n posPercent = (pos + 180) / 360;\n\n # percent of orbit complete in time from peak light\n timePercent = (time.ticks_ms() / self.orbitCompleteTime) * 100;\n\n # Get the average of the estimated percents\n avgPercent = (posPercent + timePercent) / 2;\n\n avgDeg = 360 * avgPercent;\n # POSUPDATE+degs\n return avgDeg\n else:\n if (time.ticks_ms() <= self.orbitCompleteTime):\n testV1 = ADC(26).read_u16();\n testV2 = ADC(27).read_u16();\n\n # set maxV* to the max voltage we see\n if (testV1 > self.maxV1):\n self.maxV1 = testV1\n if (testV2 > self.maxV2):\n self.maxV2 = testV2\n # set maxV* to the min voltage we see\n if (testV1 < self.minV1):\n self.minV1 = testV1\n if (testV2 < self.minV2):\n self.minV2 = testV2\n else:\n self.minsSet = True\n return False\n","repo_name":"lakefox/CARD_SAT","sub_path":"code/lib/getpos.py","file_name":"getpos.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5723871342","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 3 11:26:10 2020\n\n@author: Zhaoliang\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random as rnd\nfrom scipy import linalg\nimport binary_HT_1d.Gaussian_dist as Gd\n\nclass Compare_pln2statis_hist(object):\n \n def __init__(self, mean_pln = None, Sigma_pln = None,name = 'plan'):\n if(mean_pln is None or Sigma_pln is None):\n raise ValueError(\"Set proper statistics.\")\n \n self.mean_Pln = mean_pln\n self.Sigma_Pln = Sigma_pln\n self.name = name\n \n def calculate_stat(self,data):\n mean_stat = np.mean(data)\n var_stat = np.var(data)\n return mean_stat,var_stat\n \n def numer_compare(self,data):\n mean_stat,var_stat = self.calculate_stat(data)\n mean_error = np.abs(mean_stat- self.mean_Pln)\n var_error = np.abs(var_stat - self.Sigma_Pln)\n return mean_error,var_error\n \n def visualization_compare(self,data):\n mean_stat,var_stat = self.calculate_stat(data)\n dim = len(data)\n bins = dim/10\n x_stat = np.linspace(np.min(data)-1,np.max(data)+1,bins)\n x_pln = np.linspace(np.floor(np.min(data)),np.ceil(np.max(data)),bins)\n \n plt.figure()\n plt.title(\"The error histogram\")\n plt.hist(data, bins=int(bins), normed=True, alpha=1, histtype='stepfilled',\n color='steelblue', edgecolor='none')\n plt.ylabel(\"Frequency\")\n plt.xlabel(\"Error in bins\")\n Gd.plotGaussian(x_stat,mean_stat,var_stat,'v-g','mean=$\\mu_{statis}$,var=$Sigma_{statis}$')\n Gd.plotGaussian(x_pln,self.mean_Pln,self.Sigma_Pln,'.-r','mean=$\\mu_{%s}$, var=$Sigma_{%s}$'%(self.name,self.name))\n plt.legend()\n plt.title(\"Statistical histogram vs. planning mean and variance\")\n\n \n#if __name__ == '__main__':","repo_name":"CREATE-knowledge-planning/Sensing_planning_framework","sub_path":"utility_functions/CompP2SHist.py","file_name":"CompP2SHist.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"20842314321","text":"from PyQt5 import Qt\n\n\nfrom .ui import UILoadable\n\n\nclass Options(object):\n def __init__(self):\n # variables and default values\n self.optlist = [\n \"LOCAL_maxUnbound\",\n \"SA_minaccratio\",\n \"SA_tol\",\n \"SA_stopT\",\n \"SA_maxiter\",\n \"SA_meltratio\",\n \"SA_direct\",\n \"BI_stab\",\n \"BI_length\",\n \"BI_report\",\n \"BI_savehist\",\n \"BI_histFile\",\n \"workDirectory\",\n \"manualFile\",\n \"seed\",\n \"warning_chi2_low\",\n \"warning_chi2_high\",\n \"autoWizardOnLoad\",\n ]\n self.dfltlist = [\n 0,\n 0.0,\n 0.0,\n 0.1,\n 0,\n 0.97,\n True,\n 5e3,\n 5e4,\n 5e2,\n False,\n \"\",\n str(Qt.QDir.currentPath()),\n str(Qt.QDir.currentPath()) + \"/html/User Manual.html\",\n 12345,\n 0.6,\n 1.4,\n True,\n ]\n self.reset()\n\n def reset(self):\n for opt, dflt in zip(self.optlist, self.dfltlist):\n setattr(self, opt, dflt)\n\n def _pprint(self):\n print(str(self))\n for opt, dflt in zip(self.optlist, self.dfltlist):\n v = getattr(self, opt)\n print(\"\\t%s, %s %r (default=%s %r)\" % (opt, type(v), v, type(dflt), dflt))\n\n\n@UILoadable\nclass OptionsDlg(Qt.QDialog):\n \"\"\"Dialog containing options for PAScual. The options are members of this object.\"\"\"\n\n def __init__(self, parent=None):\n super(OptionsDlg, self).__init__(parent)\n self.loadUi()\n # set validators\n self.BI_lengthLE.setValidator(Qt.QDoubleValidator(self))\n self.BI_stabLE.setValidator(Qt.QDoubleValidator(self))\n self.BI_reportLE.setValidator(Qt.QDoubleValidator(self))\n self.seedLE.setValidator(Qt.QIntValidator(self))\n # connections\n self.buttonBox.clicked.connect(self.onclicked)\n self.manualFilePB.clicked.connect(self.onChangeManualFile)\n self.workDirectoryPB.clicked.connect(self.onChangeWorkDirectory)\n self.BI_savehistPB.clicked.connect(self.onChangeHistoryFile)\n\n # set options\n self.reset()\n\n def onChangeWorkDirectory(self):\n filename = Qt.QFileDialog.getExistingDirectory(\n self,\n \"Select work directory (it must be user-writable)\",\n self.workDirectoryLE.text(),\n )\n if filename:\n self.workDirectoryLE.setText(filename)\n\n def onChangeManualFile(self):\n filename, _ = Qt.QFileDialog.getOpenFileName(\n self, \"Select User Manual File\", self.manualFileLE.text(), \"(*.html *.htm)\"\n )\n if filename:\n self.manualFileLE.setText(filename)\n\n def onChangeHistoryFile(self):\n filename, _ = Qt.QFileDialog.getSaveFileName(\n self,\n \"BI history File Selection\",\n self.BI_savehistLE.text(),\n \"ASCII (*.txt)\\nAll (*)\",\n Qt.QFileDialog.DontConfirmOverwrite | Qt.QFileDialog.DontUseNativeDialog,\n )\n if filename:\n self.BI_savehistLE.setText(filename)\n\n def onclicked(self, button):\n if self.buttonBox.buttonRole(button) == Qt.QDialogButtonBox.ResetRole:\n self.reset()\n\n def reset(self):\n self.setOptions(Options())\n\n def getOptions(self):\n \"\"\"returns an options object filled with values from the dialog\"\"\"\n options = Options()\n # LOCAL\n # Max number of unbound LMA runs between L-BFGS-B optimisations\n # (this only takes place if first LMA is outside bounds)\n options.LOCAL_maxUnbound = int(self.LOCAL_maxUnboundSB.value())\n # SA\n # If the acceptance ratio drps below this value, the SA stops.\n options.SA_minaccratio = float(self.SA_minaccratioSB.value())\n # Tolerance for stopping the SA\n options.SA_tol = float(self.SA_tolSB.value())\n # Stop temperature for SA (put to 0 to disable). (SA_stopT>1 is not recommended)\n options.SA_stopT = float(self.SA_stopTSB.value())\n # Max number of iterations in the SimAnn fit\n options.SA_maxiter = int(self.SA_maxiterSB.value())\n # The \"melting\" phase of the SA will stop when this acceptance ratio is reached\n options.SA_meltratio = float(self.SA_meltratioSB.value())\n # Whether to use the direct mode in NNRLA for SA (asymetrical transit prob)\n options.SA_direct = bool(self.SA_directCB.isChecked())\n # BI\n # This many steps (multiplied by the order of the searching space!) of BI\n # will be done and not considered for statistical purposes.\n # Put this to 0 to skip stabilisation.\n options.BI_stab = float(self.BI_stabLE.text())\n # This many steps (multiplied by the order of the searching space)\n # will be calculated by BI\n options.BI_length = float(self.BI_lengthLE.text())\n # A report will be shown every this steps during BI (put to -1 for no reports).\n # Be Careful: too much reports may slow down the calc.\n options.BI_report = float(self.BI_reportLE.text())\n # This controls wheter the fitpar history should be saved (=FileName)\n # or not (=False).\n options.BI_savehist = bool(self.BI_savehistCB.isChecked())\n # Caution!: this will increase the RAM requeriments approximately by\n # 11Bytes*BI_length*(3+2*NC)^2 , where NC is the number of components!\n options.BI_histFile = str(self.BI_savehistLE.text())\n # PATHS\n options.workDirectory = str(self.workDirectoryLE.text())\n options.manualFile = str(self.manualFileLE.text())\n # MISC\n options.seed = int(self.seedLE.text()) # Seed for pseudorandom generator\n options.warning_chi2_low = float(self.warning_chi2_lowSB.value())\n options.warning_chi2_high = float(self.warning_chi2_highSB.value())\n options.autoWizardOnLoad = bool(self.autoWizardOnLoadCB.isChecked())\n return options\n\n def setOptions(self, options):\n \"\"\"set dialog values from the passed options object\"\"\"\n # LOCAL\n self.LOCAL_maxUnboundSB.setValue(int(options.LOCAL_maxUnbound))\n # SA\n self.SA_minaccratioSB.setValue(float(options.SA_minaccratio))\n self.SA_tolSB.setValue(float(options.SA_tol))\n self.SA_stopTSB.setValue(float(options.SA_stopT))\n self.SA_maxiterSB.setValue(int(options.SA_maxiter))\n self.SA_meltratioSB.setValue(float(options.SA_meltratio))\n self.SA_directCB.setChecked(bool(options.SA_direct))\n # BI\n self.BI_stabLE.setText(\"%g\" % float(options.BI_stab))\n self.BI_lengthLE.setText(\"%g\" % float(options.BI_length))\n self.BI_reportLE.setText(\"%g\" % float(options.BI_report))\n self.BI_savehistCB.setChecked(bool(options.BI_savehist))\n self.BI_savehistLE.setText(str(options.BI_histFile))\n # PATHS\n self.workDirectoryLE.setText(str(options.workDirectory))\n self.manualFileLE.setText(str(options.manualFile))\n # MISC\n self.seedLE.setText(str(options.seed))\n self.warning_chi2_lowSB.setValue(float(options.warning_chi2_low))\n self.warning_chi2_highSB.setValue(float(options.warning_chi2_high))\n self.autoWizardOnLoadCB.setChecked(bool(options.autoWizardOnLoad))\n\n\nif __name__ == \"__main__\":\n import sys\n\n app = Qt.QApplication(sys.argv)\n form = OptionsDlg(None)\n form.show()\n sys.exit(app.exec())\n","repo_name":"cpascual/PAScual","sub_path":"PAScual/PASoptions.py","file_name":"PASoptions.py","file_ext":"py","file_size_in_byte":7597,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"9933974199","text":"import os\r\nimport sys\r\nimport io\r\nimport json\r\nfrom google.cloud import vision\r\nfrom PIL import Image as PIL_Image\r\nfrom PIL import ImageOps\r\nfrom PIL.ExifTags import TAGS\r\nfrom exif import Image as exif_Image\r\n\r\n# connect Google environment variable\r\n# USER will need to update this `credential_path with (.json) file with Google creds\r\ncredential_path = \"\"\r\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credential_path\r\n\r\nADDITION = \"./\"\r\n\r\n\r\ndef image_process_only(raw_url):\r\n processed_label_findings_dict = get_image_processing_dict(raw_url)\r\n\r\n results_JSON = json.dumps(processed_label_findings_dict)\r\n\r\n print(\"Begin printing processing results\\n\" + results_JSON + \"\\nFinished printing processing results\")\r\n\r\n return results_JSON\r\n\r\n\r\ndef image_process_with_save(raw_url):\r\n processed_label_findings_dict = get_image_processing_dict(raw_url)\r\n\r\n results_JSON = json.dumps(processed_label_findings_dict)\r\n\r\n image_add_METADATA(processed_label_findings_dict, raw_url)\r\n\r\n print(\"\\nNewly processed Metadata labels have been saved within the image.\")\r\n\r\n return results_JSON\r\n\r\n\r\ndef image_del_METADATA(raw_url):\r\n print(\"\\nBegin deleting Metadata.\")\r\n\r\n # load image, wrangle URL if needed\r\n del_METADATA_url = url_wrangler(raw_url, 1)\r\n original = PIL_Image.open(del_METADATA_url)\r\n save_image_path = del_METADATA_url\r\n\r\n # rotate image to correct orientation before removing EXIF data\r\n original = ImageOps.exif_transpose(original)\r\n\r\n # save image without tags (metadata is cleared)\r\n stripped = PIL_Image.new(original.mode, original.size)\r\n stripped.putdata(list(original.getdata()))\r\n original.close()\r\n stripped.save(save_image_path)\r\n stripped.close()\r\n\r\n print(\"\\nMetadata has been deleted from image.\")\r\n\r\n return None\r\n\r\n\r\ndef url_wrangler(raw_url, needed_url_type):\r\n \"\"\"Update and decide the proper url name convention to be used,\r\n basically do we/do not want './' in url string\r\n default :: 1 is assumed to be 'current_url_type'\r\n --> needed_url_type[arg] = 1 --- 'photos/nyc.JPG'\r\n --> needed_url_type[arg] = 2 --- './photos/nyc.JPG'\r\n \"\"\"\r\n checked_url, current_url_type = raw_url, 1\r\n\r\n # figure out the current labeling within the given raw_url\r\n if checked_url[0:2] == ADDITION:\r\n current_url_type = 2\r\n\r\n # decide if the raw_url is fit for the url_type_needed\r\n if current_url_type == needed_url_type:\r\n pass\r\n # update the raw_url (if needed) to the url_type_needed\r\n elif current_url_type < needed_url_type:\r\n temp = ADDITION + checked_url\r\n checked_url = temp\r\n else:\r\n # need to rid the first 2 bytes of the string\r\n temp = checked_url\r\n checked_url = temp[2:]\r\n\r\n return checked_url\r\n\r\n\r\ndef image_add_METADATA(label_dict, raw_url):\r\n print(\"\\nBegin adding and saving image Metadata.\")\r\n\r\n add_METADATA_url = url_wrangler(raw_url, 2)\r\n with open(f\"{add_METADATA_url}\", \"r+b\") as image_file:\r\n image_file = exif_Image(image_file)\r\n\r\n # update the labels within the metadata tags\r\n results_JSON_labels = json.dumps(label_dict[\"labels\"])\r\n image_file.image_description = results_JSON_labels\r\n\r\n # save image file with metadata updates\r\n with open(f\"{add_METADATA_url}\", \"wb\") as image_file_updated:\r\n image_file_updated.write(image_file.get_file())\r\n\r\n image_file_updated.close()\r\n\r\n print(\"\\nFinished adding and saving image Metadata.\")\r\n\r\n return None\r\n\r\n\r\ndef image_check_METADATA(raw_url):\r\n \"\"\"\r\n Mostly this function is implemented to be able to check/test/debug\r\n the metadata of a image after del or add\r\n \"\"\"\r\n print(\"\\nBegin checking current image metadata.\")\r\n\r\n check_METADATA_url = url_wrangler(raw_url, 1)\r\n file_name = os.path.abspath(check_METADATA_url)\r\n\r\n # for current/saved metadata extraction, using PIL's Image, not of Google API Vision\r\n check_image = PIL_Image.open(file_name)\r\n exifdata = check_image.getexif()\r\n\r\n exifdata_dict = {}\r\n updated_exifdata_dict = exifdata_extractor(exifdata, exifdata_dict)\r\n\r\n results_JSON = json.dumps(updated_exifdata_dict)\r\n\r\n print(\"\\nCurrent image metadata: \\n\" + results_JSON + \"\\nChecking current image metadata is complete.\\n\")\r\n\r\n check_image.close()\r\n\r\n return results_JSON\r\n\r\n\r\ndef get_image_processing_dict(raw_url):\r\n load_image_url = url_wrangler(raw_url, 1)\r\n file_name = os.path.abspath(load_image_url)\r\n try:\r\n with io.open(file_name, 'rb') as image_file:\r\n content = image_file.read()\r\n image = vision.Image(content=content)\r\n print(\"\\nBegin processing image:\\n\" + file_name)\r\n except:\r\n print('Error with loading photo')\r\n exit(1)\r\n\r\n processed_label_findings = {\"labels\": []}\r\n\r\n # create a Google API vision classed object\r\n client = vision.ImageAnnotatorClient()\r\n\r\n processed_label_findings = processing_for_labels(processed_label_findings, client, image)\r\n processed_label_findings = processing_for_logos(processed_label_findings, client, image)\r\n processed_label_findings = processing_for_landmark(processed_label_findings, client, image)\r\n processed_label_findings = processing_for_text(processed_label_findings, client, image)\r\n\r\n image_file.close()\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef processing_for_labels(processed_label_findings, client, image):\r\n label_response = client.label_detection(image=image)\r\n\r\n for label in label_response.label_annotations:\r\n processed_label_findings[\"labels\"].append(label.description)\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef processing_for_logos(processed_label_findings, client, image):\r\n logo_response = client.logo_detection(image=image)\r\n\r\n for logo in logo_response.logo_annotations:\r\n processed_label_findings[\"labels\"].append(logo.description)\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef processing_for_landmark(processed_label_findings, client, image):\r\n landmark_response = client.landmark_detection(image=image)\r\n\r\n for landmark in landmark_response.landmark_annotations:\r\n processed_label_findings[\"labels\"].append(landmark.description)\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef processing_for_text(processed_label_findings, client, image):\r\n text_response = client.text_detection(image=image)\r\n\r\n # counter needed as this would give a string with \\n listing all\r\n counter_to_skip_first = 0\r\n for text in text_response.text_annotations:\r\n if counter_to_skip_first != 0:\r\n processed_label_findings[\"labels\"].append(text.description)\r\n else:\r\n counter_to_skip_first += 1\r\n\r\n return processed_label_findings\r\n\r\n\r\ndef exifdata_extractor(exifdata, exifdata_dict):\r\n for tag_id in exifdata:\r\n # get the tag name, instead of human unreadable tag id\r\n tag, data = TAGS.get(tag_id, tag_id), exifdata.get(tag_id)\r\n\r\n # ignore all byte-saved information, it is probably TIFF-type associated\r\n if isinstance(data, bytes):\r\n pass\r\n else:\r\n exifdata_dict[tag] = data\r\n return exifdata_dict\r\n\r\n\r\nif __name__ == '__main__':\r\n raw_url = sys.argv[1]\r\n","repo_name":"potatoe88888/CS361--microservice","sub_path":"imageMETADATA.py","file_name":"imageMETADATA.py","file_ext":"py","file_size_in_byte":7230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19978278254","text":"\"\"\"Logic for Celebrations, i.e. Feasts and commemorations of Saints.\"\"\"\n\nfrom datetime import date\n\n_PASCHA_LOOKUP_TABLE = {\n \"2015\": date(2015, 4, 12),\n \"2016\": date(2016, 5, 1),\n \"2017\": date(2017, 4, 16),\n \"2018\": date(2018, 4, 8),\n \"2019\": date(2019, 4, 28),\n \"2020\": date(2020, 4, 19),\n \"2021\": date(2021, 5, 2),\n \"2022\": date(2022, 4, 24),\n \"2023\": date(2023, 4, 16),\n \"2024\": date(2024, 5, 5),\n \"2025\": date(2025, 4, 20),\n}\n\n\nclass DateNotFound(Exception):\n \"\"\"Represents a date lookup that failed.\"\"\"\n\n\ndef get_date_of_pascha(datelike):\n \"\"\"Return the date of Pascha given a datetime.date-like object.\n\n Raises a DateNotFound exception if it cannot find the date.\n \"\"\"\n year_string = datelike.strftime(\"%Y\")\n try:\n return _PASCHA_LOOKUP_TABLE[year_string]\n except KeyError as key_error:\n msg = f\"Could not find the date of Pascha for {year_string}.\"\n raise DateNotFound(msg) from key_error\n","repo_name":"nicholaskachur/doxacal","sub_path":"doxacal/celebrations.py","file_name":"celebrations.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"29499342267","text":"from ASG import *\r\nfrom ATOM3Type import *\r\nfrom ATOM3List import *\r\nfrom ATOM3Constraint import *\r\nfrom ATOM3Attribute import *\r\nfrom copy import *\r\n\r\nfrom graph_ASG_ERmetaMetaModel import *\r\n\r\nclass ASG_ERmetaMetaModel(ASG, ATOM3Type):\r\n\r\n def __init__(self, parent=None, ASGroot = None):\r\n ASG.__init__(self, 'ERmetaMetaModel', ASGroot, [\"ERentity\", \"ERrelationship\", \"ASG_ERmetaMetaModel\"] )\r\n ATOM3Type.__init__(self)\r\n self.parent = parent\r\n self.name = ATOM3String('ERModel')\r\n self.attributes = ATOM3List([1,1,1, None], ATOM3Attribute, parent.types)\r\n self.constraints = ATOM3List([1,1,1, None], ATOM3Constraint)\r\n self.containerFrame = None\r\n\r\n self.graphClass_ = graph_ASG_ERmetaMetaModel\r\n\r\n self.keyword_ = self.name\r\n\r\n self.generatedAttributes = { 'name' : ('ATOM3String', ),\r\n 'attributes' : ('ATOM3List', 'ATOM3Attribute'),\r\n 'constraints': ('ATOM3List', 'ATOM3Constraint')}\r\n\r\n def show(self, parent, parentWindow):\r\n self.containerFrame = Frame(parent)\r\n Label(self.containerFrame, text=\"name\").grid(row=0, column=0, sticky=W)\r\n self.name.show (self.containerFrame, parentWindow).grid(row=0, column=1, sticky=W) # pack the widget to edit the name\r\n\r\n Label(self.containerFrame, text=\"atributes\").grid(row=1, column=0, sticky=W)\r\n self.attributes.show(self.containerFrame, parentWindow).grid(row=1, column=1, sticky=W) # attributes to generate\r\n\r\n Label(self.containerFrame, text=\"constraints\").grid(row=2, column=0, sticky=W)\r\n self.constraints.show(self.containerFrame, parentWindow).grid(row=2, column=1, sticky=W) # constraints to generate\r\n return self.containerFrame\r\n\r\n def open(self, parent, parentWindow):\r\n \"Method to open an ATOM3 instance to edit the entities (to be overwritten)\"\r\n from ATOM3\t import ATOM3\r\n a = ATOM3 (parentWindow , \"EntityRelationship\", 0, 1, self)\r\n return a\r\n\r\n def toString(self):\r\n return (self.name.toString(), self.attributes.toString(), self.constraints.toString())\r\n\r\n def clone(self):\r\n \"creates an exact copy of this object\"\r\n #cloneObject = ASG_UMLmetaMetaModel(self.parent)\r\n #cloneObject.listNodes = copy.copy(self.listNodes)\r\n #cloneObject.name = self.name.clone()\r\n #cloneObject.attributes = self.attributes.clone()\r\n #cloneObject.constraints = self.constraints.clone()\r\n #cloneObject.containerFrame= self.containerFrame\r\n #cloneObject.keyword_ = cloneObject.keyword_\r\n #return cloneObject\r\n return self\r\n\r\n def copy(self, other):\r\n \"copies each field of the other object into its own state\"\r\n ATOM3Type.copy(self, other)\t\t\t# call the ancestor (copies the parent field)\r\n self.name = other.name\r\n self.constraints = other.constraints\r\n self.attributes = other.attributes\r\n self.keyword_ = self.name\r\n self.containerFrame= other.containerFrame\r\n\r\n def destroy(self):\r\n ASGNode.GGcheckSetNone(self)\r\n self.destroyNodes() \r\n self.name.destroy()\r\n self.attributes.destroy()\r\n self.constraints.destroy()\r\n self.containerFrame = None\r\n\r\n def allClassesDistinct(self):\r\n for node1 in self.listNodes[\"ERentity\"]:\r\n for node2 in self.listNodes[\"ERentity\"]:\r\n if (node1 != node2) and (node1.keyword_.toString() == node2.keyword_.toString()):\r\n return (\"allClassesDistinct: classes have the same keyword: \", node1.keyword_.toString())\r\n return None\r\n\r\n def allRelationshipsDistinct(self):\r\n for node1 in self.listNodes[\"ERrelationship\"]:\r\n for node2 in self.listNodes[\"ERrelationship\"]:\r\n if (node1 != node2) and (node1.keyword_.toString() == node2.keyword_.toString()):\r\n return (\"allRelationshipsDistinct: Relationships have the same keyword: \", node1.keyword_.toString())\r\n return None\r\n\r\n def preCondition(self, actionID, * params):\r\n if actionID==ASG.SAVE:\r\n res = self.allClassesDistinct()\r\n if res: return res\r\n res = self.allRelationshipsDistinct()\r\n if res: return res\r\n return None\r\n\r\n def invalid(self):\r\n \"Decides if the attribute is valid, that is, if the initial value (if any) is valid\"\r\n import string\r\n vname = self.name.getValue()\r\n # check if we have a name\r\n if (not vname) or (vname == \"\"): # the name is mandatory\r\n return \"Entity name must be specified\"\r\n # now check that the name is valid (a variable name)\r\n if string.count(vname, \" \") > 0:\r\n return \"Invalid entity name, no white spaces allowed\"\r\n # check first character\r\n if (vname[0] >= '0') and (vname[0] <= '9'): # a number\r\n return \"Invalid variable name, first character must be a letter or '_'\"\r\n if vname[0] != '_' and (vname[0]<'A' or vname[0]>'z'):\r\n return \"Invalid entity name, first character must be a letter or '_'\"\r\n # now check for the rest of not allowed characters...\r\n for c in range(len(vname)-1):\r\n if vname[c+1] < 'A' or vname[c+1] > 'z': # not a letter\r\n if vname[c+1] < '0' or vname[c+1] > '9': # not a number\r\n if vname[c+1] != '_': # not underscore\r\n return \"Invalid model name, character '\"+vname[c+1]+\"' is not allowed\"\r\n return ASG.invalid(self)\r\n\r\n \r\n","repo_name":"AILab-FOI/LSMASOMM","sub_path":"atom3/Kernel/ERModels/ASG_ERmetaMetaModel.py","file_name":"ASG_ERmetaMetaModel.py","file_ext":"py","file_size_in_byte":5634,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"6004903782","text":"from tkinter import *\nfrom tkinter.messagebox import showinfo\n\n# criando crasse frame / creating a frame crass\nclass myGui(Frame):\n def __init__(self, parent=None): \n Frame.__init__(self, parent)\n botao = Button(self, text='aperte\\npress', command= self.reply)\n botao.pack()\n\n def reply(self):\n showinfo(title='popup', message='botao apertado\\nbutton pressed')\n\nif __name__ == '__main__':\n window = myGui()\n window.pack()\n window.mainloop()\n\n ","repo_name":"jonasht/programmingPython-book","sub_path":"1-chapter-aSneakPreview/5-step5-addingA-GUI/2-using-OOP-ForGUI/tkinter002.py","file_name":"tkinter002.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"1577908837","text":"import json\nfrom datetime import datetime, timedelta\n\nfrom experts_dw import db, pure_json\nfrom experts_etl import loggers\nfrom pureapi import client\n\n# defaults:\n\ntransaction_record_limit = 100\n# Named for the Pure API endpoint:\npure_api_record_type = 'changes'\n\n# functions:\n\ndef ensure_valid_startdate(cursor, api_version, startdate=None):\n if startdate is None:\n change_history_date = pure_json.max_change_history_inserted_date(cursor, api_version=api_version)\n change_date = pure_json.max_change_inserted_date(cursor, api_version=api_version)\n if change_history_date is None and change_date is not None:\n startdate = change_date\n if change_date is None and change_history_date is not None:\n startdate = change_history_date\n if change_date is not None and change_history_date is not None:\n startdate = max(change_date, change_history_date)\n if startdate is None:\n # Default to 1 day ago (yesterday):\n startdate = datetime.now() - timedelta(days=1)\n return startdate\n\ndef required_fields_exist(api_document):\n for field in ['uuid','familySystemName','version','changeType']:\n if field not in api_document:\n return False\n return True\n\n# entry point/public api:\n\ndef run(\n startdate=None,\n startdate_str=None, # yyyy-MM-dd or yyyy-MM-dd_HH-mm-ss format\n transaction_record_limit=transaction_record_limit,\n experts_etl_logger=None,\n api_version=None\n):\n if experts_etl_logger is None:\n experts_etl_logger = loggers.experts_etl_logger()\n experts_etl_logger.info('starting: extracting/loading raw json', extra={'pure_api_record_type': pure_api_record_type})\n\n client_config = client.Config() if api_version is None else client.Config(version=api_version)\n api_version = client_config.version\n\n with db.cx_oracle_connection() as connection:\n try:\n cursor = connection.cursor()\n family_system_names = pure_json.collection_family_system_names_for_api_version(\n cursor,\n api_version=api_version\n )\n\n if startdate_str is None:\n startdate_str = ensure_valid_startdate(cursor, api_version, startdate).isoformat()\n\n documents_to_insert = {}\n for api_document in client.get_all_changes_transformed(startdate_str, config=client_config):\n if not required_fields_exist(api_document):\n continue\n if api_document.familySystemName not in family_system_names:\n continue\n\n # We make this a dict with unique keys to avoid attempts to insert duplicates:\n documents_to_insert[f'{api_document.uuid}:{api_document.version}'] = {\n 'uuid': api_document.uuid,\n 'pure_version': api_document.version,\n 'change_type': api_document.changeType,\n 'family_system_name': api_document.familySystemName,\n 'inserted': datetime.now(),\n 'json_document': json.dumps(api_document),\n }\n\n if len(documents_to_insert) % transaction_record_limit == 0:\n pure_json.insert_change_documents(\n cursor,\n documents=list(documents_to_insert.values()),\n api_version=api_version\n )\n documents_to_insert = {}\n connection.commit()\n\n if documents_to_insert:\n pure_json.insert_change_documents(\n cursor,\n documents=list(documents_to_insert.values()),\n api_version=api_version\n )\n connection.commit()\n\n except Exception as e:\n connection.rollback()\n formatted_exception = loggers.format_exception(e)\n experts_etl_logger.error(\n f'exception encountered during extractin/loading raw json: {formatted_exception}',\n extra={'pure_api_record_type': pure_api_record_type}\n )\n\n experts_etl_logger.info('ending: extracting/loading raw json', extra={'pure_api_record_type': pure_api_record_type})\n","repo_name":"UMNLibraries/experts_etl","sub_path":"experts_etl/pure_to_edw/changes.py","file_name":"changes.py","file_ext":"py","file_size_in_byte":4276,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"43098387094","text":"import okhound\nimport pyaudio\n\n\nCHUNKSIZE = 1024\n\naudio = pyaudio.PyAudio()\nstream = audio.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True, frames_per_buffer=CHUNKSIZE)\n\nokhound.setThreshold(0.4)\n\nwhile True:\n\ttry:\n\t\taudio = stream.read(CHUNKSIZE)\n\texcept IOError:\n\t\tprint(\"Skipped frame\")\n\t\tcontinue\n\n\tphraseSpotted = okhound.processSamples(audio)\n\tif phraseSpotted: break\n\n\nprint(\"'Ok Hound' spotted! Sensitivity: {0}\".format(okhound.getThreshold()))\n","repo_name":"alexdong/okhound","sub_path":"example_pyaudio.py","file_name":"example_pyaudio.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15445388842","text":"import time\nfrom pathlib import Path\nimport dataGenerator as dg\nfrom regexChecker import RegexChecker as rc\nfrom toolBox import *\n\nif __name__==\"__main__\":\n data_folder = Path(\"data/\")\n file = data_folder / \"email_test.csv\"\n\n test_emails = dg.create_email()\n modified_emails = dg.add_text(test_emails)\n dg.write_to_csv_for_test(modified_emails, file)\n\n expected_emails = dg.email_quant\n print(\"There are \" + str(expected_emails) + \" email entities\")\n\n time.sleep(3)\n\n nlp = spacy.load('ja_ginza_nopn', disable=[\"tagger\", \"parser\", \"ner\", \"textcat\"])\n stop_file = \"C:\\\\Users\\\\Ko.In\\\\Desktop\\\\PiiExtractionData\\\\StopKey_pii.csv\"\n add_stop_words(read_to_list(stop_file, find_encoding(stop_file)), nlp)\n df = read_to_df(file, find_encoding(file))\n\n raw_msgs = list(df[0])\n\n new_msgs = pre_process(raw_msgs)\n\n email_regex = rc.email_regex()\n phone_regex = rc.phone_regex()\n\n result = []\n count = 0\n\n docs = nlp.pipe(new_msgs)\n\n for msg in docs:\n match = False\n row = {}\n\n email = re.findall(email_regex, str(msg))\n if len(email) > 0:\n print(\"Found email at \" + str(count) + \": \" + \",\".join([str(n) for n in email]))\n row[\"Email\"] = \"\".join([str(n) for n in email])\n match = True\n\n if match == True:\n result.append(row)\n count += 1\n\n output_df = pd.DataFrame(result)\n print(output_df)\n\n actual_emails = len(output_df.index)\n print(\"The actual detected emails entities are: \" + str(actual_emails))\n print(\"Accuracy of email extraction is: \" + str(actual_emails/expected_emails))\n","repo_name":"koasurion/PiiChecker3","sub_path":"emailAccuracy.py","file_name":"emailAccuracy.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74369892242","text":"import numpy as np\nimport tensorflow as tf\nimport math\nimport pickle\nimport operator\nimport gensim\nfrom gensim.models import word2vec\nimport logging\n\ndef readDataBible():\n '''\n Detects sentences based on the occurance of the full_stop_char_sans variable\n Inserts that between every 2 sentences as well\n As of now, removes occurances of ''\n '''\n filename = 'Bible/bible_translation.pickle'\n list_of_words = []\n words = {}\n word_count = 0\n full_stop_char_sans = 2404\n\n with open(filename, 'rb') as f:\n d = pickle.load(f)\n for spair in d:\n sans = spair[0]\n sans_words = []\n # sans.replace(ord(full_stop_char_sans),'')\n\n for w in sans.split(chr(full_stop_char_sans)):\n w1 = w.split(' ')\n sans_words.extend(w1)\n if len(w1) != 0 and w1[0] != '':\n sans_words.append(chr(full_stop_char_sans))\n\n for word in sans_words:\n '''\n Some preprocessing can be done here\n It will definitely help\n '''\n if word is '':\n continue\n if word not in words:\n words[word] = 1\n word_count += 1\n else:\n words[word] += 1\n list_of_words.append(word)\n\n return list_of_words\n\ndef main():\n '''\n list_of_words, words, word_count=readDataBible()\n\n list_of_words contains words in the order in which they appear. As it is\n word_count is the number of unique words in the vocabulary\n words is a dictionary. Key is word and value is the number of occurances of that word\n '''\n\n embedding_size = 64\n batch_size = 64\n window_size = 5\n\n print(readDataBible())\n input()\n logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)\n model = word2vec.Word2Vec(readDataBible(),sg=1,size=embedding_size,window=window_size,workers=4)\n\n print(model.wv.index2word)\n \n print(model.wv.similarity('मम','तत्'))\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"Genius1237/NeuralMT","sub_path":"Code/word2vec_gensim.py","file_name":"word2vec_gensim.py","file_ext":"py","file_size_in_byte":2095,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"4368170680","text":"from typing import List, Dict, Optional\nfrom uuid import uuid4\n\n\nclass Annotation:\n def __init__(self, cls, selected_text, notes=None, raw=None):\n self.cls = cls\n self.selected_text = selected_text\n self.notes = notes\n self.raw = raw\n\n @classmethod\n def create_from_labeler(cls, project, obj):\n annotation_class = 'unknown'\n for fc in project.fragment_classes:\n if fc['id'] == obj['categoryId']:\n annotation_class = fc['name']\n\n return cls(annotation_class, obj['selectedText'], raw=obj)\n\n @classmethod\n def deserialize(cls, obj):\n return cls(obj['annotationCategory'], obj['selectedText'])\n\n def serialize(self, format='df'):\n if format == 'df':\n return {\n 'class': self.cls,\n 'selected_text': self.selected_text,\n 'notes': self.notes\n }\n elif format == 'api':\n return self.raw\n else:\n raise Exception('Unsupported serialization format.')\n\n\n\nclass Label:\n @classmethod\n def deserialize_from_task(cls, task, response):\n annotations = [Annotation.deserialize(a) for a in response['contents']['annotations']]\n return cls(\n task.document.id,\n task.document.external_id,\n response['contents'].get('categoryName'),\n response['contents'].get('categoryId'),\n annotations,\n id=response['id']\n )\n\n @classmethod\n def deserialize(cls, label):\n if label['annotations']:\n annotations = [Annotation.deserialize(a) for a in label['annotations']]\n else:\n annotations = None\n\n document_class = label.get('documentCategory')\n\n return cls(\n label['documentId'], # TODO-1: replace with id\n label['documentId'], # TODO-2: this is external id\n document_class,\n None, # document_class_id\n annotations=annotations\n # TODO-2: we need to pull id so that it isn't auto-generated\n )\n\n @classmethod\n def create_from_labeler(cls, project, document, document_class, annotations):\n class_id = document_class.get('id')\n class_name = document_class.get('name')\n\n if not annotations:\n annotations = []\n annotations = [Annotation.create_from_labeler(project, a) for a in annotations]\n\n return cls(document['id'], document['external_id'], class_name, class_id, annotations)\n\n def __init__(self, document_id, document_external_id, class_name, class_id, annotations, id=uuid4()):\n self.id: str = id\n self.document_id: str = document_id\n self.document_external_id: str = document_external_id\n self.cls: str = class_name\n self.cls_id: str = class_id\n\n if not annotations:\n annotations = []\n self.annotations: List[Annotation] = annotations # TODO-2: convert away from dict to Annotation class\n # TODO-3: creator information and other metadata, coming from API\n\n def serialize(self, project=None, format='api'):\n if len(self.annotations) > 0:\n serialized = {\n 'annotations': [a.serialize(format=format) for a in self.annotations]\n }\n else:\n serialized = { 'annotations': None }\n\n if format == 'api':\n serialized['document_id'] = self.document_id\n serialized['class'] = self.cls\n serialized['class_id'] = self.cls_id\n elif format == 'df':\n serialized['document_id'] = self.document_external_id\n serialized['label'] = self.cls\n else:\n raise Exception('Unsupported serialization format.')\n\n return serialized\n","repo_name":"Witch27/Python-tiptaptoe","sub_path":"venv/lib/python3.9/site-packages/markers/label.py","file_name":"label.py","file_ext":"py","file_size_in_byte":3777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11601007715","text":"# All Imports\n#-------------------------------------------------------------------------------\nimport pygame\n# from movement import *\nfrom pygame.locals import *\nimport sys\nfrom config import *\nfrom blocks import blocks\nfrom player import player as p\nfrom movement import collision_test, move\nfrom sprites import *\nfrom load import *\n\n\n#Clock Configuration\n#-------------------------------------------------------------------------------\nclock = pygame.time.Clock()\n\n#Pygame Initialization\n#-------------------------------------------------------------------------------\npygame.init()\npygame.display.set_caption(\"CloackMan\")\n\n#Window Configuration\n#-------------------------------------------------------------------------------\nWINDOW_SIZE = (WIN_WIDTH, WIN_HEIGHT)\n\n\n#Block/Background Configuration\n#-------------------------------------------------------------------------------\ngrass=blocks.transformsprites(\"blocks/tile001.png\")\ndirt=blocks.transformsprites(\"blocks/tile013.png\")\nscene = pygame.image.load(\"spritesheets/background/background.png\")\nscreen = pygame.display.set_mode(WINDOW_SIZE,0,32)\ndisplay = pygame.Surface((320, 240))\ntrue_display=pygame.transform.scale(display, WINDOW_SIZE)\nscreen.blit(true_display, (0,0))\n# **scaling the display to window size\ntrue_scene=pygame.transform.scale(scene, BACKGROUND)\ntrue_scroll = [0,0]\n\ntilemap = map.load_map('tilemap')\n\nrect = pygame.Rect(100,100,32,32)\np1 = p(rect)\n\nanimation_database = {}\n\nanimation_database['run'] = sprites.load_animation('player_animations/run',[7,7,7,7,7,7])\nanimation_database['idle'] = sprites.load_animation('player_animations/idle',[7,7,7,7])\nanimation_database['jump'] = sprites.load_animation('player_animations/jump',[4,4,4])\nanimation_database['jumpdown'] = sprites.load_animation('player_animations/jumpdown',[4,4,4])\n\n#Game Loop\nwhile True:\n screen.blit(true_scene, (0,-480))\n tile_rects = []\n\n# Below is the scrolling feaature within the game that allows the screen to essentially follow the p1.player.\n true_scroll[0] += (p1.player_rect.x-true_scroll[0]-222)/20\n true_scroll[1] += (p1.player_rect.y-true_scroll[1]-212)/20\n scroll = true_scroll.copy()\n scroll[0] = int(scroll[0])\n scroll[1] = int(scroll[1])\n y = 0\n# Populates the tiles with the correct images on to the screen in occordance with the tilemap.\n for row in tilemap:\n x = 0\n for tile in row:\n if tile == '2':\n screen.blit(grass, (x * TILE_SIZE - true_scroll[0], y * TILE_SIZE - true_scroll[1]))\n if tile == '1':\n screen.blit(dirt, (x * TILE_SIZE - true_scroll[0], y * TILE_SIZE - true_scroll[1]))\n if tile != '0':\n tile_rects.append(pygame.Rect(x * TILE_SIZE, y * TILE_SIZE, TILE_SIZE, TILE_SIZE))\n x += 1\n y += 1\n# This is the coordinate/movement detection for the player [0,1]=[x,y].\n p1.player_movement = [0,0]\n if p1.moving_right == True:\n p1.player_movement[0] += 4\n if p1.moving_left == True:\n p1.player_movement[0] -= 4\n p1.player_movement[1] += p1.vertical_momentum\n p1.vertical_momentum += 0.2\n if p1.vertical_momentum > 4:\n p1.vertical_momentum = 4\n\n# This is the key & direction detection for the sprite's frame.\n if p1.player_movement[0] == 0:\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'idle')\n if p1.player_movement[0] > 0 and collisions['bottom']==True:\n p1.player_flip = False\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'run')\n if p1.player_movement[0] < 0 and collisions['bottom']==True:\n p1.player_flip = True\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'run')\n if p1.player_movement[0] > 0:\n if p1.vertical_momentum < 0 :\n p1.player_flip = False\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'jump')\n if p1.player_movement[0] < 0:\n if p1.vertical_momentum < 0 :\n p1.player_flip = True\n p1.player_action,p1.player_frame = sprites.change_action(p1.player_action,p1.player_frame,'jump')\n p1.player_rect,collisions = move(p1.player_rect, p1.player_movement, tile_rects)\n\n if collisions['bottom']:\n p1.air_timer = 0\n p1.vertical_momentum = 0\n p1.jumping = False\n else:\n p1.air_timer += 1\n \n p1.player_frame += 1\n if p1.player_frame >= len(animation_database[p1.player_action]):\n p1.player_frame = 0\n p1.player_img_id = animation_database[p1.player_action][p1.player_frame]\n p1.player = animation_frames[p1.player_img_id]\n screen.blit(pygame.transform.flip(p1.player,p1.player_flip,False),(p1.player_rect.x-scroll[0],p1.player_rect.y-scroll[1]))\n\n for event in pygame.event.get():\n\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n\n if event.type == KEYDOWN:\n if event.key == K_RIGHT:\n p1.moving_right = True\n if event.key == K_LEFT:\n p1.moving_left = True\n if event.key == K_UP:\n if (p1.air_timer < 6):\n p1.jumping=True\n p1.falling=False\n p1.vertical_momentum = -6\n p1.player_movement[1] = -5.2\n if event.type == KEYUP:\n if event.key == K_RIGHT:\n p1.moving_right = False\n if event.key == K_LEFT:\n p1.moving_left = False\n if event.key == K_UP:\n p1.jumping = False\n\n\n pygame.display.update()\n clock.tick(60)\n","repo_name":"ojimba01/CloakmanV3","sub_path":"cloakmanv3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71285793361","text":"import sys\n#sys.path.append(\"../\")\nfrom PyQt5.QtWidgets import *\nfrom japrp.audio_backends.audio_backend_vlc import VlcBackend\nfrom japrp.audio_backends.audio_backend_pyqt5 import QtMediaPlayerWrapper\n\n_BACKEND = \"vlc\"\n\nclass Player(QWidget):\n\n def __init__(self, value_dict=None, parent=None):\n super().__init__(parent=parent)\n self.layout = QGridLayout()\n if value_dict is None:\n value_dict = {}\n self._station_name = value_dict.get(\"name\", \"NO NamE\")\n self._icon = value_dict.get(\"icon\", \"NO ICON\")\n self.url = value_dict.get(\"url\")\n self.station_name = QLabel(self._station_name)\n self.icon = QLabel(self._icon)\n self.playBtn = QPushButton(\"Play\")\n self.stopBtn = QPushButton(\"Stop\")\n self.playBtn.clicked.connect(self.start_playing)\n self.stopBtn.clicked.connect(self.stop_playing)\n if _BACKEND == \"vlc\":\n self.player = VlcBackend()\n else:\n self.player = QtMediaPlayerWrapper()\n\n self.layout.addWidget(self.station_name, 0, 0, 1, 3)\n self.layout.addWidget(self.icon, 1, 0, 1, 3)\n self.layout.addWidget(self.playBtn, 2, 0, 1, 1)\n self.layout.addWidget(self.stopBtn, 2, 1, 1, 1)\n self.setLayout(self.layout)\n\n def start_playing(self):\n #self.stream = subprocess.Popen([\"python\", \"-m\", \"streamer_script.py\"], stdout=sys.stdout)\n if self.url == \"\" or self.url is None:\n pass\n else:\n self.player.set_media(self.url, media_type='infer')\n if _BACKEND == \"vlc\":\n if self.player.media_player.is_playing():\n self.player.pause()\n else:\n self.player.play()\n else:\n self.player.play()\n\n def stop_playing(self):\n if self.player is not None:\n self.player.stop()\n\n\n def set_url(self, url):\n #URL = \"http://bob.hoerradar.de/radiobob-hartesaite-mp3-hq?sABC=6020sp0s%230%23r731s0685son37qn82q119rrn30n0ss5%23zrqvncynlre&=&amsparams=playerid:mediaplayer;skey:1612774415\" # BBC\n #URL = \"http://mp3-live.swr3.de/swr3_m.m3u\"\n self.url = url\n\n\n\n\n\n def closeEvent(self, event):\n #quit_msg = \"Are you sure you want to exit the program?\"\n if self.player is not None:\n self.player.stop()\n\n\n\nif __name__ == \"__main__\":\n URL = \"http://bob.hoerradar.de/radiobob-hartesaite-mp3-hq?sABC=6020sp0s%230%23r731s0685son37qn82q119rrn30n0ss5%23zrqvncynlre&=&amsparams=playerid:mediaplayer;skey:1612774415\" # BBC\n app = QApplication(sys.argv)\n player = Player()\n player.set_url(URL)\n player.show()\n sys.exit(app.exec())","repo_name":"Banjio/japrp","sub_path":"japrp/app_parts/player_qt.py","file_name":"player_qt.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70589737363","text":"from django.contrib.auth.models import User\nfrom django.test import TestCase\nfrom django.urls import reverse\n\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase, APIClient\n\nfrom professors.models import Professor\n\ndef get_or_none(Model, **conditions):\n try:\n model = Model.objects.get(**conditions)\n except Model.DoesNotExist:\n model = None\n\n return model\n\nADMIN_USER_DATA = {\n 'username': 'fake-admin-user',\n 'password': 'fake-admin-user'\n}\n\nUSER_DATA = {\n 'username': 'fake-user',\n 'password': 'fake-user'\n}\n\nPROFESSOR_1_DATA = {\n 'first_name': 'Gary',\n 'last_name': 'Gillespie',\n 'biography': 'Professor for CSE 12, CSE 15L, and CSE 110.'\n}\n\nPROFESSOR_2_DATA = {\n 'first_name': 'Rick',\n 'last_name': 'Ord',\n 'biography': 'Professor for CSE 11 and CSE 30.'\n}\n\nclass ProfessorAPITestCase(APITestCase):\n\n @classmethod\n def setUpTestData(cls):\n\n cls.client = APIClient()\n\n cls.admin_user = User(**ADMIN_USER_DATA)\n cls.admin_user.is_staff = True\n cls.admin_user.save()\n \n cls.user = User(**USER_DATA)\n cls.user.save()\n\n cls.professor_1 = Professor(**PROFESSOR_1_DATA)\n cls.professor_1.save()\n\n def test_post_professor_list_admin(self):\n\n self.client.force_authenticate(self.admin_user)\n\n response = self.client.post(reverse('professor_list'), PROFESSOR_2_DATA)\n professor = get_or_none(Professor, **PROFESSOR_2_DATA)\n\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n self.assertNotEqual(professor, None)\n self.client.logout()\n\n def test_post_professor_list_no_admin(self):\n\n self.client.force_authenticate(self.user)\n\n response = self.client.post(reverse('professor_list'), PROFESSOR_2_DATA)\n professor = get_or_none(Professor, **PROFESSOR_2_DATA)\n \n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n self.assertEqual(professor, None)\n self.client.logout()\n\n def test_get_professor_list_admin(self):\n\n self.client.force_authenticate(self.admin_user)\n\n response = self.client.get(reverse('professor_list'))\n\n self.assertEqual(len(response.data['results']), 1)\n\n def test_get_professore_list_no_admin(self):\n\n self.client.force_authenticate(self.user)\n\n response = self.client.get(reverse('professor_list'))\n \n self.assertEqual(len(response.data['results']), 1)","repo_name":"chtka/GradeTracker","sub_path":"professors/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1155492920","text":"\nprint (\"Aşağıdaki işlemlerden birini seçinizi.\\n1- Toplama \\n2- Çıkarma\")\n\nToplama = 1\nÇıkarma = 2\n\noutput = \"İlk sayı: {}, ikinci sayı: {}\"\nİşlem = int (input(\"Lütfen işlem seçiniz.\"))\n\nif İşlem == 1 :\n birinci_input = int(input(\"Lütfen ilk sayıyı giriniz.\"))\n ikinci_input = int(input(\"Lütfen ikinci sayıyı giriniz.\"))\n print(output.format(birinci_input, ikinci_input), \"için toplama işleminin sonucu\", (birinci_input + ikinci_input))\nelif İşlem == 2 :\n birinci_input = int(input(\"Lütfen ilk sayıyı giriniz.\"))\n ikinci_input = int(input(\"Lütfen ikinci sayıyı giriniz.\"))\n\n print(output.format(birinci_input, ikinci_input), \"için çıkarma işleminin sonucu\", (birinci_input - ikinci_input))\nelse:\n print (\"Yanlış seçim yaptınız.\")\n\n\n","repo_name":"busraylmz00/Homework1","sub_path":"Homework_1.py","file_name":"Homework_1.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35658229161","text":"import json\nimport asyncio\nimport pyppeteer\nimport pydantic\nimport re\nfrom typing import List, Optional, Union\nimport datetime\nimport yt_dlp\nfrom moviepy.editor import *\nimport uuid\nfrom enum import Enum\n\ndef milliseconds_to_time(milliseconds: int) -> datetime.time:\n \"\"\"Convert milliseconds to datetime.time\"\"\"\n\n return datetime.time(\n int(milliseconds / 1000) // 3600, \n (int(milliseconds / 1000) % 3600) // 60, \n int(milliseconds / 1000) % 60\n )\n\n\ndef seconds_to_time(seconds: Union[int, float]) -> datetime.time:\n \"\"\"Convert seconds to datetime.time\"\"\"\n\n if isinstance(seconds, float):\n seconds = int(round(seconds))\n\n return datetime.time(seconds // 3600, (seconds % 3600) // 60, seconds % 60)\n\n\ndef time_to_seconds(time: datetime.time) -> int:\n \"\"\"Convert datetime.time to milliseconds\"\"\"\n\n return (time.hour * 60 + time.minute) * 60 + time.second\n\n\ndef valid_filename(filename: str):\n \"\"\"Convert a string into a valid filename\"\"\"\n\n return re.sub(\n r'(?u)[^-\\w.]', '', \n str(filename).strip().replace(' ', '_')\n )\n\n\nclass Thumbnail(pydantic.BaseModel):\n \"\"\"Data model for Thumbnail data\"\"\"\n\n url: str\n width: int\n height: int\n\n\nclass Chapter(pydantic.BaseModel):\n \"\"\"Data model for Chapter data\"\"\"\n\n title: str\n start_time: datetime.time\n end_time: datetime.time\n thumbnails: List[Thumbnail]\n\n\nclass Heatmap(pydantic.BaseModel):\n \"\"\"Data model for Heatmap data\"\"\"\n\n chapter: Optional[Chapter]\n start_time: Optional[datetime.time]\n end_time: Optional[datetime.time]\n intensity_score_normalised: Optional[float]\n\n\nclass VideoHeatmap(pydantic.BaseModel):\n \"\"\"Data model for YouTube video Heatmap data\"\"\"\n\n url: str\n heatmaps: List[Heatmap]\n \n @property\n def chapters(self) -> List[Chapter]:\n \"\"\"Get video chapters from heatmaps\"\"\"\n\n return [heatmap.chapter for heatmap in self.heatmaps if heatmap.chapter]\n\n def most_intense_heatmaps(self, merge: bool=True, count: int=3) -> List[Heatmap]:\n \"\"\"Return the most intense heatmaps, optionally joining by chapter title\"\"\"\n\n sorted_heatmaps = list(sorted(self.heatmaps, key=lambda x: x.intensity_score_normalised, reverse=True))\n\n if not merge or sum(1 for x in sorted_heatmaps if x.chapter) < count:\n return sorted_heatmaps[:count]\n\n merged_heatmaps = {}\n for heatmap in sorted_heatmaps:\n\n # Skip if heatmap has no chapter\n if not heatmap.chapter:\n continue\n\n # Add heatmap if not already exist and so far less than count added\n if not merged_heatmaps.get(heatmap.chapter.title) and len(merged_heatmaps) <= count:\n merged_heatmaps[heatmap.chapter.title] = heatmap\n\n else:\n merged_heatmap = merged_heatmaps.get(heatmap.chapter.title)\n\n if not merged_heatmap:\n continue\n\n # Update heatmap times if different to existing\n if heatmap.start_time <= merged_heatmap.start_time:\n merged_heatmap.start_time = heatmap.start_time\n \n if merged_heatmap.end_time <= heatmap.end_time:\n merged_heatmap.end_time = heatmap.end_time\n\n return list(merged_heatmaps.values())\n\n\n def most_intense_chapters(self, merge: bool=True, count: int=3) -> List[Chapter]:\n \"\"\"Return the most intense chapters\"\"\"\n\n return [heatmap.chapter for heatmap in self.most_intense_heatmaps(merge, count) if heatmap.chapter]\n\n\nclass ClipAlign(str, Enum):\n \"\"\"Enumeration for clip allignment\"\"\"\n\n left = \"left\"\n center = \"center\"\n right = \"right\"\n\n\nclass Video:\n \"\"\"Class to manage, download, and clip video\"\"\"\n\n url: str\n ydl_info: dict\n\n def __init__(self, url: str) -> None:\n self.url = url\n self.ydl_info = {}\n\n\n def download(self):\n \"\"\"Download YouTube video using YT-Dlp\"\"\"\n\n ydl_opts = {\n \"outtmpl\": \"/tmp/videos/%(id)s.%(ext)s\",\n }\n\n with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n self.ydl_info = ydl.extract_info(self.url, download=True)\n\n\n def generate_clip(self, file_name: str, clip_file_name: str, start_time: datetime.time, end_time: datetime.time) -> None:\n \"\"\"Generate clip using start time and end time, returning file name\"\"\"\n \n clip = VideoFileClip(f\"tmp/videos/{file_name}\").subclip(\n time_to_seconds(start_time) if time_to_seconds(start_time) > 0 else 0, \n time_to_seconds(end_time) if time_to_seconds(end_time) < self.ydl_info[\"duration\"] else None\n )\n \n \"\"\"\n -vf: \n This option is used to apply video filtergraph to the input video.\n In this case, the filtergraph is a series of filter commands that will be applied to the input video.\n The filtergraph is split into 3 parts:\n 1. split [original][copy] : This filter command creates a copy of the input video and assigns it to the label \"original\" and \"copy\"\n 2. [copy] crop=ih*9/16:ih:iw/2-ow/2:0, scale=1080:1920, gblur=sigma=50[blurred] : This filter command applies crop, scale and gblur to the video labeled as \"copy\"\n 3. [original] scale=-1:1080, crop=1080:1080:iw/2-ow/2:ih/2-oh/2 [scaled]: This filter command applies scale and crop to the video labeled as \"original\"\n 4. [blurred][scaled] overlay=(main_w-overlay_w)/2:(main_h-overlay_h)/2 : This filter command overlays the blurred video on top of the scaled video\n\n -auto-alt-ref:\n This option is used to enable use of alternate reference frames in VP9. It is used to improve the efficiency of the video compression.\n 0 means it is disabled.\n\n -c:a:\n This option is used to set the codec for the audio stream.\n In this case, the audio stream is copied from the input file to the output file, without any changes.\n\n -c:v:\n This option is used to set the codec for the video stream.\n In this case, the codec for the video stream is set to h264.\n \"\"\"\n ffmpeg_params = [\n \"-vf\", \n \"split [original][copy]; [copy] crop=ih*9/16:ih:iw/2-ow/2:0, scale=1080:1920, gblur=sigma=50[blurred]; [original] scale=-1:1080, crop=1080:1080:iw/2-ow/2:ih/2-oh/2 [scaled]; [blurred][scaled] overlay=(main_w-overlay_w)/2:(main_h-overlay_h)/2\", \n \"-auto-alt-ref\", \"0\", \n \"-c:a\", \"copy\",\n \"-c:v\", \"h264\", \n ]\n\n try:\n clip.write_videofile(\n f\"tmp/clips/{clip_file_name}\",\n preset=\"ultrafast\",\n ffmpeg_params=ffmpeg_params,\n )\n except Exception as e:\n print(f\"Error: {str(e)}\")\n\n clip.close()\n\n\n def generate_chapter_clips(self, chapters: List[Chapter], align: Optional[ClipAlign]=ClipAlign.left, length: Optional[int]=None):\n \"\"\"Generate clips from chapters\"\"\"\n\n for chapter in chapters:\n\n start_time = None\n end_time = None\n\n if length:\n\n if align == ClipAlign.left:\n start_time = chapter.start_time\n end_time = seconds_to_time(time_to_seconds(chapter.start_time) + length)\n\n elif align == ClipAlign.right:\n start_time = seconds_to_time(time_to_seconds(chapter.start_time) - length)\n end_time = chapter.end_time\n\n else:\n middle = (time_to_seconds(chapter.start_time) + time_to_seconds(chapter.end_time)) // 2\n start_time = seconds_to_time(middle - (length/2))\n end_time = seconds_to_time(middle + (length/2))\n\n self.generate_clip(\n f\"{self.ydl_info['id']}.webm\", \n f\"{valid_filename(chapter.title)}.mp4\",\n start_time or chapter.start_time,\n end_time or chapter.end_time,\n )\n\n\n def generate_heatmap_clips(self, heatmaps: List[Heatmap], align: Optional[ClipAlign]=ClipAlign.left, length: Optional[int]=None):\n \"\"\"Generate clips from heatmaps\"\"\"\n\n for heatmap in heatmaps:\n\n start_time = None\n end_time = None\n\n if length:\n\n if align == ClipAlign.left:\n start_time = heatmap.start_time\n end_time = seconds_to_time(time_to_seconds(heatmap.start_time) + length)\n\n elif align == ClipAlign.right:\n start_time = seconds_to_time(time_to_seconds(heatmap.start_time) - length)\n end_time = heatmap.end_time\n\n else:\n middle = (time_to_seconds(heatmap.start_time) + time_to_seconds(heatmap.end_time)) // 2\n start_time = seconds_to_time(middle - (length/2))\n end_time = seconds_to_time(middle + (length/2))\n\n self.generate_clip(\n f\"{self.ydl_info['id']}.webm\", \n f\"{heatmap.chapter.title if heatmap.chapter else uuid.uuid4()}.mp4\",\n start_time or heatmap.start_time,\n end_time or heatmap.end_time,\n )\n\n\nasync def get_page_content(url: str) -> str:\n \"\"\"Get page HTML content from URL\"\"\"\n\n # Create browser\n browser = await pyppeteer.launch()\n page = await browser.newPage()\n\n # Open video page and get HTML content\n await page.goto(url)\n html_content = await page.content()\n\n # Close page and browser\n await browser.close()\n\n return html_content\n\n\ndef get_initial_data(html_content: str) -> dict:\n \"\"\"Get YouTube initial data from video URL\"\"\"\n\n # Get intitial video data\n yt_initial_data = re.search(\n r\"var ytInitialData = (.*?);<\",\n html_content,\n re.DOTALL,\n ).group(1)\n\n # Return YouTube intitial JSON data as dict\n return json.loads(yt_initial_data)\n\n\ndef get_marker_data(yt_initial_data: dict) -> dict:\n \"\"\"Get YouTube marker data from intitial data\"\"\"\n\n return yt_initial_data[\"playerOverlays\"][\"playerOverlayRenderer\"][\"decoratedPlayerBarRenderer\"][\"decoratedPlayerBarRenderer\"][\"playerBar\"][\"multiMarkersPlayerBarRenderer\"][\"markersMap\"]\n\n\ndef get_chapters(marker_data: dict, video: Video) -> List[Chapter]:\n \"\"\"Get YouTube chapters from initial data\"\"\"\n\n chapter_data = []\n\n # Check if chapter data exists in marker data\n for data in marker_data:\n if data[\"key\"] == \"DESCRIPTION_CHAPTERS\":\n chapter_data = [chapter[\"chapterRenderer\"] for chapter in data[\"value\"][\"chapters\"]]\n break\n\n chapters = []\n\n # Iterate over each chapter\n for i in range(0, len(chapter_data)):\n\n chapter = chapter_data[i]\n\n end_time = None\n if i+1 < len(chapter_data):\n end_time = milliseconds_to_time(chapter_data[i+1][\"timeRangeStartMillis\"])\n else:\n end_time = seconds_to_time(video.ydl_info[\"duration\"])\n\n chapters.append(\n Chapter(\n title=chapter[\"title\"][\"simpleText\"],\n start_time=milliseconds_to_time(chapter[\"timeRangeStartMillis\"]),\n end_time=end_time,\n thumbnails=[Thumbnail(**thumbnail) for thumbnail in chapter[\"thumbnail\"][\"thumbnails\"]],\n )\n )\n\n return chapters\n\n\ndef get_heatmaps(marker_data: dict, chapters: Optional[List[Chapter]]=[]) -> List[Heatmap]:\n \"\"\"Get YouTube heatmap data from marker data\"\"\"\n\n heatmap_data = []\n\n # Check if chapter data exists in intital data\n for data in marker_data:\n if data[\"key\"] == \"HEATSEEKER\":\n heatmap_data = [heatmap[\"heatMarkerRenderer\"] for heatmap in data[\"value\"][\"heatmap\"][\"heatmapRenderer\"][\"heatMarkers\"]]\n break\n\n heatmaps = []\n\n # Iterate over each heatmap\n for heatmap in heatmap_data:\n\n matching_chapter = None\n\n # Find a matching chapter timestamp if exists\n for chapter in chapters:\n\n if milliseconds_to_time(heatmap[\"timeRangeStartMillis\"]) >= chapter.start_time:\n matching_chapter = chapter\n \n heatmaps.append(\n Heatmap(\n chapter=matching_chapter,\n start_time=milliseconds_to_time(heatmap[\"timeRangeStartMillis\"]),\n end_time=milliseconds_to_time(heatmap[\"timeRangeStartMillis\"] + heatmap[\"markerDurationMillis\"]),\n intensity_score_normalised=heatmap[\"heatMarkerIntensityScoreNormalized\"]\n )\n )\n\n return heatmaps\n\n\ndef get_video_heatmap(video: Video) -> VideoHeatmap:\n \"\"\"Get YouTube video heatmap from URL\"\"\"\n\n event_loop = asyncio.get_event_loop()\n html_content = event_loop.run_until_complete(asyncio.gather(get_page_content(video.url)))[0]\n event_loop.close()\n\n initial_data = get_initial_data(html_content)\n marker_data = get_marker_data(initial_data)\n chapters = get_chapters(marker_data, video)\n heatmaps = get_heatmaps(marker_data, chapters)\n\n return VideoHeatmap(\n url=video.url,\n heatmaps=heatmaps,\n )\n","repo_name":"BadgerHobbs/Heatmap-Clips","sub_path":"heatmap_clips.py","file_name":"heatmap_clips.py","file_ext":"py","file_size_in_byte":13164,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"9483732839","text":"currentIndex = 0\r\nmiddleIndex = 12\r\n\r\nfor i in range(5):\r\n\tdata = input().split()\r\n\tfound = False\r\n\r\n\tfor index, value in enumerate(data):\r\n\t\tif value == '1':\r\n\t\t\tcurrentIndex += index\r\n\t\t\tfound = True\r\n\r\n\tif found:\r\n\t\tbreak\r\n\r\n\tcurrentIndex += 5\r\n\r\ndifferent = abs(currentIndex - middleIndex)\r\n\r\nrowMove = different // 5\r\ncolumnMove = different % 5\r\n\r\nif columnMove == 4:\r\n\tcolumnMove -= 2\r\n\r\nprint(rowMove + columnMove)\r\n","repo_name":"hhanhvu99/Personal-CS114.L21.KHCL","sub_path":"Practice/Matrix.py","file_name":"Matrix.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1514400706","text":"import interfaceTools as it\nimport deconvolution as dv\n\nentryIterations,entryWeight,dropdownImg, dropdownPSF = (None,None,None,None)\n\ndef deconvolutionEvent():\n\tglobal entryIterations, entryWeight, dropdownImg, dropdownPSF\n\topcDeconv = it.NewWindow('Deconvolution Options','300x180') #Objeto de la clase NewWindow\n\topcDeconv.createLabel('Image: ',20,20)\n\topcDeconv.createLabel('PSF: ',20,50)\n\topcDeconv.createLabel('Iterations: ',20,80)\n\topcDeconv.createLabel('Weight: ',20,110)\n\tentryIterations = opcDeconv.createEntry('50',110,80)\n\tentryWeight = opcDeconv.createEntry('20',110,110)\n\tdropdownImg = opcDeconv.createCombobox(110,20)\n\tdropdownPSF = opcDeconv.createCombobox(110,50)\n\topcDeconv.createButton('OK', deconvolutionKernel, 'bottom')\n\ndef deconvolutionKernel():\n\tglobal entryIterations, entryWeight, dropdownImg, dropdownPSF\n\tdv.deconvolutionMain(it.filesPath[dropdownImg.current()],it.filesPath[dropdownPSF.current()],entryIterations.get(),entryWeight.get())\n\n#Se crea la ventana principal del programa\nit.createWindowMain()\n#Se crea menu desplegable\nmenu = it.createMenu()\n#Se añaden las opciones del menu\nopc1 = it.createOption(menu)\nit.createCommand(opc1, \"Abrir\", it.openFile)\nit.createCommand(opc1, \"Guardar\", it.saveFile)\nit.createCommand(opc1, \"Salir\", it.mainWindow.quit)\nit.createCascade(menu, 'Archivo', opc1)\n\nopc2 = it.createOption(menu)\nit.createCommand(opc2, \"Deconvolution\", deconvolutionEvent)\n#it.createCommand(opc2, \"Guardar\", it.saveFile)\n#it.createCommand(opc2, \"Salir\", mainWindow.quit)\nit.createCascade(menu, 'Image', opc2)\n\nit.createStatusBar()\n#statusBar = it.createStatusBar()\n#statusBar['text']=dv.message\n\nit.mainWindow.mainloop()","repo_name":"CharlieBrianML/IFCSoftDeconv","sub_path":"IFCSoftDeconv.py","file_name":"IFCSoftDeconv.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8331780688","text":"from tqdm import trange\nimport numpy as np\nimport pandas as pd\n\nimport cvxpy as cp\n\nfrom tqdm import trange\n\n\ndef feature_engineer(return_predictions):\n \"\"\"\n param return_predictions: pandas DataFrame of return predictions\n\n Splits each row into positive and negative parts\n \"\"\"\n return_predictions_pos = return_predictions.copy()\n return_predictions_neg = return_predictions.copy()\n return_predictions_pos[return_predictions_pos < 0] = 0\n return_predictions_neg[return_predictions_neg > 0] = 0\n return pd.concat([return_predictions_pos, return_predictions_neg], axis=1)\n\n\ndef tune_alpha(returns, return_predictors, alphas, memory=1000):\n correlations = []\n rmses = []\n all_weights_list = []\n for alpha in alphas:\n combinator = ReturnCombinator()\n # combinator.build(250, 7, alpha)\n # returns_hats, all_weights = combinator.fit(returns.iloc[:,0], returns_predictions)\n\n returns_hats, all_weights = combinator.fit(\n pd.DataFrame(returns), return_predictors, memory, alpha\n )\n\n correlations.append(\n np.corrcoef(\n returns_hats.values.flatten(),\n returns.loc[returns_hats.index].values.flatten(),\n )[0, 1]\n )\n rmses.append(\n np.sqrt(\n np.mean(\n (\n returns_hats.values.flatten()\n - returns.loc[returns_hats.index].values.flatten()\n )\n ** 2\n )\n )\n )\n all_weights_list.append(all_weights)\n\n return alphas, correlations, rmses, all_weights_list\n\n\n# def get_corr(returns, alpha):\n# combinator = ReturnCombinator()\n# combinator.build(250, 7, alpha)\n\n# returns_predictions = ewma_predictors(returns, [1, 5, 10, 21, 63, 125, 250])\n\n\n# returns_hats, all_weights = combinator.fit(returns, returns_predictions)\n# return np.corrcoef(returns_hats.values.flatten(), returns.loc[returns_hats.index].values.flatten())[0,1]\n\n\ndef _combinator_problem(memory, K, alpha):\n \"\"\"\n param memory: number of days to look back\n param K: number of predictors\n param alpha: regularization parameter in combination problem\n \"\"\"\n\n weights = cp.Variable(K)\n return_param = cp.Parameter(memory)\n return_prediction_param = cp.Parameter((memory, K))\n\n # halflife = 250\n # beta = np.exp(np.log(0.5) / halflife)\n # decay_weights = np.array([beta ** i for i in range(memory)])\n # decay_weights = decay_weights / np.sum(decay_weights)\n # decay_weights = decay_weights[::-1]\n # decay_weights = np.ones_like(decay_weights)\n\n objective = cp.Minimize(\n cp.sum_squares(return_param - return_prediction_param @ weights)\n + alpha * cp.norm1(weights)\n )\n # constraints = [cp.sum(weights)==1]\n # constraints = [weights<=1, weights>=-1]\n\n # problem = cp.Problem(objective, constraints)\n\n problem = cp.Problem(objective)\n\n return problem, weights, return_param, return_prediction_param\n\n\n# def _combinator_problem2(memory, n, K, alpha):\n# \"\"\"\n# param memory: number of days to look back\n# param K: number of predictors\n# param alpha: regularization parameter in combination problem\n# \"\"\"\n\n# weights = cp.Variable((K,n))\n# returns_param = cp.Parameter((memory, n))\n# returns_predictions_param = [cp.Parameter((memory, K)) for _ in range(n)]\n\n# # halflife = 250\n# # beta = np.exp(np.log(0.5) / halflife)\n# # decay_weights = np.array([beta ** i for i in range(memory)])\n# # decay_weights = decay_weights / np.sum(decay_weights)\n# # decay_weights = decay_weights[::-1]\n# # decay_weights = np.ones_like(decay_weights)\n\n# objective = 0\n# for i in range(n):\n# objective += cp.sum_squares(\n# returns_param[:,i] - returns_predictions_param[i] @ weights[:,i]\n# )\n# objective += alpha * cp.norm1(weights)\n\n# problem = cp.Problem(cp.Minimize(objective))\n\n# return problem, weights, returns_param, returns_predictions_param\n\n\nclass ReturnCombinator:\n def __init__(self) -> None:\n pass\n\n def _get_weights(self, returns, returns_predictions, scale, alpha, weighted_hf=500):\n \"\"\"\n param returns: pandas DataFrame of observed returns\n param returns_predictions: pandas DataFrame of various return predictors\n param scale: pandas series of scales for weighted least squares\n param alpha: regularization parameter in combination problem\n param weighted_hf: halflife for weighted least squares\n\n return: pandas series of weights\n \"\"\"\n\n beta = np.exp(np.log(0.5) / weighted_hf)\n decay_weights = np.array([beta**i for i in range(len(returns_predictions))])\n decay_weights = decay_weights / np.sum(decay_weights) * len(returns_predictions)\n decay_weights = decay_weights[::-1]\n b = decay_weights.reshape(-1, 1)\n # b = np.ones_like(b)\n\n D = np.diag(scale.values)\n\n weights = np.linalg.solve(\n (returns_predictions.values.T * b.T) @ returns_predictions.values\n + alpha * D,\n (returns_predictions.values.T * b.T) @ returns.values,\n )\n\n return pd.Series(weights, index=returns_predictions.columns)\n\n # return np.linalg.inv((returns_predictions.T * b.T) @ returns_predictions + alpha * D) @ (returns_predictions.T * b.T) @ returns\n\n def _get_scales(self, returns_predictions, halflife=250):\n \"\"\"\n param returns_predictions: pandas DataFrame of various return predictors\n\n return: pandas series of scales\n \"\"\"\n squared_returns_predictions = [\n prediction**2 for prediction in returns_predictions\n ]\n\n variances = [\n squared_prediction.ewm(halflife).mean().shift(1)\n for squared_prediction in squared_returns_predictions\n ]\n\n # scales = [1 / variance for variance in variances]\n scales = [variance for variance in variances]\n\n return scales\n\n def fit(self, returns, returns_predictions, memory, alpha):\n \"\"\"\n param returns: pandas DataFrame of observed returns\n param return_predictions: list of pandas DataFrames of various\n return predictors\n \"\"\"\n\n n = len(returns_predictions)\n scales = self._get_scales(returns_predictions)\n\n times = returns_predictions[0].index\n for i in range(n):\n assert (times == returns_predictions[i].index).all()\n \"predictors must have same indices\"\n\n assert set(times).issubset(returns.index)\n \"returns must have all predictor indices\"\n\n # Create empty pandas dataframe to store weights\n all_weights = [\n np.nan * returns_predictions[i].iloc[memory:].copy() for i in range(n)\n ]\n\n # for t in trange(memory, len(times)):\n # for t in range(2, len(times)):\n for t in trange(memory, len(times)): # Note: scales are None for day one\n t_start = times[max(0, t - memory)]\n t_end = times[t - 1]\n\n returns_temp = returns.loc[t_start:t_end]\n\n t_next = times[t]\n for i, asset in enumerate(returns.columns):\n scale = scales[i].loc[t_next]\n all_weights[i].loc[t_next] = self._get_weights(\n returns_temp[asset],\n returns_predictions[i].loc[t_start:t_end],\n scale,\n alpha,\n )\n\n returns_hats = {}\n for i in range(n):\n returns_hat = returns_predictions[i].iloc[memory:] * all_weights[i]\n returns_hats[i] = returns_hat.sum(axis=1)\n\n returns_hats = pd.DataFrame(returns_hats)\n returns_hats.columns = returns.columns\n\n return returns_hats, all_weights\n\n\n# def get_r_hat(returns):\n# return_predictions = ewma_predictors(returns, [1, 5, 10, 21, 63, 125, 250])\n# returns_hat, all_weights = ewma_return_combination(returns.iloc[1:], return_predictions, alpha=0.0001, memory=250)\n\n# return returns_hat, all_weights\n\n\nclass ReturnCombinatorOld:\n def __init__(self) -> None:\n pass\n\n def build(self, memory, K, alpha):\n \"\"\"\n param memory: number of days to look back\n param n: number of assets\n param K: number of predictors\n param alpha: regularization parameter in combination problem\n \"\"\"\n self.K = K\n self.memory = memory\n\n (\n self.problem,\n self.weights,\n self.returns_param,\n self.returns_predictions_param,\n ) = _combinator_problem(memory, K, alpha)\n\n def fit(self, returns, returns_predictions):\n \"\"\"\n param returns: pandas DataFrame of observed returns\n param return_predictions: list of pandas DataFrames of various\n return predictors\n \"\"\"\n assert self.K is not None\n \"build the model first\"\n\n assert returns_predictions.shape[1] == self.K\n \"number of predictors must match built model\"\n\n times = returns_predictions.index\n\n assert set(times).issubset(returns.index)\n \"returns must have all predictor indices\"\n\n # Create empty pandas dataframe to store weights\n all_weights = np.nan * returns_predictions.iloc[self.memory :].copy()\n\n for t in trange(self.memory, len(times)):\n t_start = times[t - self.memory]\n t_end = times[t - 1]\n self.returns_param.value = returns.loc[t_start:t_end].values\n self.returns_predictions_param.value = returns_predictions.loc[\n t_start:t_end\n ].values\n self.problem.solve(ignore_dpp=True)\n\n t_next = times[t]\n all_weights.loc[t_next] = self.weights.value\n\n returns_hat = (returns_predictions.iloc[self.memory :] * all_weights).sum(\n axis=1\n )\n\n return returns_hat, all_weights\n\n # def fit(self, returns, returns_predictions):\n # \"\"\"\n # param returns: pandas DataFrame of observed returns\n # param return_predictions: list of pandas DataFrames of various\n # return predictors\n # \"\"\"\n # assert self.K is not None; \"build the model first\"\n\n # for i in range(self.n):\n # assert returns_predictions[i].shape[1] == self.K; \"number of predictors must match built model\"\n # assert returns.shape[1] == self.n; \"number of assets must match built model\"\n\n # times = returns_predictions[0].index\n # for i in range(self.n):\n # assert returns_predictions[i].index.equals(times); \"predictor indices must match\"\n\n # assert set(times).issubset(returns.index); \"returns must have all predictor indices\"\n\n # # Create empty pandas dataframe to store weights\n # all_weights = [np.nan * predictor.iloc[self.memory:].copy() for predictor in returns_predictions]\n\n # for t in trange(self.memory, len(times)):\n # t_start = times[t-self.memory]\n # t_end = times[t-1]\n # self.returns_param.value = returns.loc[t_start:t_end].values\n # for i in range(self.n):\n # self.returns_predictions_param[i].value = returns_predictions[i].loc[t_start:t_end].values\n # self.problem.solve(ignore_dpp=True)\n\n # t_next = times[t]\n # for i in range(self.n):\n # all_weights[i].loc[t_next] = self.weights.value[:,i]\n\n # returns_hats = {}\n # for i in range(self.n):\n # returns_hat = returns_predictions[i].iloc[self.memory:] * all_weights[i]\n # returns_hats[i] = returns_hat.sum(axis=1)\n\n # returns_hats = pd.DataFrame(returns_hats)\n\n # return returns_hats, all_weights\n\n\ndef ewma_return_combination(returns, return_predictions, alpha, memory):\n \"\"\"\n param returns: pandas Series of observed returns\n param return_predictions: pandas DataFrame of various return predictions\n param alpha: regularization parameter in combination problem\n param memory: number of days to look back in combination problem\n \"\"\"\n times = returns.index\n\n problem, weights, return_param, return_prediction_param = _combinator_problem(\n memory, return_predictions.shape[1], alpha\n )\n\n # Create empty pandas datafrema to store weights of shape (T, K)\n all_weights = np.nan * return_predictions.iloc[memory:].copy()\n\n for t in trange(memory, returns.shape[0]):\n t_start = times[t - memory]\n t_end = times[t - 1]\n return_param.value = returns.loc[t_start:t_end].values\n return_prediction_param.value = return_predictions.loc[t_start:t_end].values\n problem.solve(ignore_dpp=True)\n\n t_next = times[t]\n all_weights.loc[t_next] = weights.value.T\n\n returns_hat = ((all_weights * return_predictions).dropna()).sum(axis=1)\n\n return returns_hat, all_weights\n\n\ndef daily_predictors(returns, lags):\n \"\"\"\n param returns: pandas Series of observed returns\n param lags: list of lags to use in daily predictors\n \"\"\"\n\n return_predictions = {}\n\n for lag in lags:\n return_predictions[lag] = returns.shift(lag).dropna()\n return_predictions = pd.DataFrame(return_predictions, index=returns.index)\n\n return return_predictions\n\n\ndef ewma_predictors(returns, halflives):\n \"\"\"\n param returns: pandas Series of observed returns\n param halflives: list of halflives to use in EWMA\n \"\"\"\n\n return_predictions = {}\n\n for halflife in halflives:\n return_predictions[halflife] = (\n returns.ewm(halflife=halflife).mean().shift(1).dropna()\n )\n return_predictions = pd.DataFrame(return_predictions, index=returns.index[1:])\n\n return return_predictions\n\n\n# def combinator_problem(returns, K, alpha):\n# T, n = returns.shape\n\n# weights = cp.Variable(K)\n# return_prediction_params = {i: cp.Parameter((T, n)) for i in range(K)}\n\n# print(returns.shape)\n# print(cp.sum([weights[i] * return_prediction_params[i] for i in range(K)]).shape)\n\n# print(returns.shape)\n# print(return_prediction_params[0].shape)\n# print((returns - return_prediction_params[0]).shape)\n\n\n# objective = cp.Minimize(\n# cp.sum_squares(returns - cp.sum([weights[i] * return_prediction_params[i] for i in range(K)])) + alpha * cp.norm1(weights)\n# )\n# problem = cp.Problem(objective)\n\n# return problem, weights, return_prediction_params\n\n\n# def return_combination(returns, return_predictions, alpha):\n# \"\"\"\n# param returns: pandas DataFrame of observed returns\n# param return_predictions: dictionary of various return predictions\n# each prediction is a pandas DataFrame\n# param alpha: regularization parameter in combination problem\n\n# return: weight to put on each predictior\n# \"\"\"\n# K = len(return_predictions)\n\n# problem, weights, return_prediction_params = combinator_problem(returns, K, alpha)\n","repo_name":"cvxgrp/cov_pred_finance","sub_path":"experiments/utils/return_combination.py","file_name":"return_combination.py","file_ext":"py","file_size_in_byte":15070,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"}
+{"seq_id":"30118246707","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING\nfrom dm.skills.Hero._hero import HeroSkill\nfrom utilities import CooldownType\n\nif TYPE_CHECKING:\n from dm.core.contexts import AttackContext\n from dm.core.game.game import DMGame\n from dm.core.objects.unit import DMUnit\n################################################################################\n\n__all__ = (\"Venom\",)\n\n################################################################################\nclass Venom(HeroSkill):\n\n def __init__(self, state: DMGame, parent: DMUnit = None):\n\n super().__init__(\n state, parent,\n _id=\"SKL-362\",\n name=\"Venom\",\n description=(\n \"When attacking enemy, apply Poison as much as 8 % of target's \"\n \"current LIFE.\"\n ),\n rank=5,\n cooldown=CooldownType.Passive\n )\n\n################################################################################\n def on_attack(self, ctx: AttackContext) -> None:\n\n # If we're attacking\n if self.owner == ctx.source:\n # Apply Poison\n ctx.target.add_status(\"Poison\", 0.08 * ctx.target.life, self)\n\n################################################################################\n","repo_name":"AllegroVivo/DungeonDefense","sub_path":"dm/skills/Hero/Grand/Venom.py","file_name":"Venom.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3790840026","text":"MAX_CYCLE = 2 ** 17 - 1\n\nclass LFSR:\n def __init__(self, initial_state, feedback_states):\n self.initial_state = initial_state\n self.feedback_states = feedback_states\n self.state = None\n self.reset()\n\n def next(self):\n r = self.state[0]\n feedback_bit = sum(self.state[x] for x in self.feedback_states) % 2\n for i in range(len(self.state) - 1):\n self.state[i] = self.state[i + 1]\n self.state[len(self.state) - 1] = feedback_bit\n return r\n\n def take(self, n):\n return (self.next() for _ in range(n))\n\n def reset(self):\n self.state = self.initial_state[:]\n\n\ndef calc_cycle(lfsr):\n lfsr.reset()\n lfsr.next()\n c = 1\n while lfsr.state != lfsr.initial_state:\n lfsr.next()\n c += 1\n return c\n\n\ndef test(lfsr):\n print('first 30 bits: ', list(lfsr.take(30)))\n c = calc_cycle(lfsr)\n print('cycle length = %d' % c)\n print('precentage of max cycle: %d%%' % (float(c) / MAX_CYCLE * 100))\n\n\ndef main():\n print('max cycle length: %d' % MAX_CYCLE)\n initial_state = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n key1 = [0, 2, 3, 5]\n print('Test a')\n test(LFSR(initial_state, key1))\n key2 = [0, 8]\n print('Test b')\n test(LFSR(initial_state, key2))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"randomUserNameThatIsRandom/Crypto","sub_path":"hw2/ex1_q7.py","file_name":"ex1_q7.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11741116198","text":"from django.shortcuts import render, redirect\nfrom video.models import Video1\nfrom video.forms import VideoForm, AddForm\nfrom rest_framework import routers\n# from video.serializers import APItestSerializer\nfrom rest_framework import viewsets\nfrom django.contrib.auth.decorators import login_required\n\n# Create your views here.\n\n@login_required(login_url='login')\ndef index(request):\n if request.method == 'POST':\n video = VideoForm(request.POST)\n if video.is_valid():\n ag = video.cleaned_data['agegroup']\n sb = video.cleaned_data['subject']\n to = video.cleaned_data['topic']\n vi = video.cleaned_data['video']\n reg = video(agegroup = ag, subject = sb)\n reg.save()\n else:\n video = VideoForm()\n video=Video1.objects.all()\n return render (request, 'index.html', {\"video\":video})\n\n\n\ndef addvideoview(request):\n if request.method == 'POST':\n addd = AddForm(request.POST)\n if addd.is_valid():\n addd.save()\n return redirect('/home/')\n else:\n \n \n addd = AddForm() \n return render(request, 'addvideo.html', {'form':addd})\n \ndef deleteview(request):\n if request.method == \"POST\":\n data = request.POST \n id = data.get('id')\n \n \n \n\n\n\n# class APItestViewSet(viewsets.ModelViewSet):\n# serializer_class = APItestSerializer\n# queryset = APItest.objects.all()","repo_name":"ashish1700/API_Registration","sub_path":"video/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73997376721","text":"# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, val=0, next=None):\n# self.val = val\n# self.next = next\nclass Solution:\n def mergeInBetween(self, list1: ListNode, a: int, b: int, list2: ListNode) -> ListNode:\n \n head = list1\n \n # Find (a-1)th node\n for _ in range(a-1):\n list1 = list1.next\n \n a_minus_1_node = list1\n \n # Now points to a\n list1 = list1.next\n \n # Attach list2 start\n a_minus_1_node.next = list2\n \n # Get to bth node\n for _ in range(b - a + 1):\n list1 = list1.next\n \n # Get end of list2\n while list2.next:\n list2 = list2.next\n \n # Attach end of list2 to list1\n list2.next = list1\n \n return head\n \n","repo_name":"KajalGada/leetcode-python","sub_path":"leetcode 1669 Merge In Between Linked Lists/sol1.py","file_name":"sol1.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1980300721","text":"# %%\nfrom pathlib import Path\nimport pandas as pd\nimport os\nos.getcwd()\n\ndata_path = Path(\"data\")\ninput_path = data_path / \"split\"\n\ndata_path = Path(\"data\")\n\npd.options.display.max_rows = 63\n\n# %%\ndf = pd.read_csv(data_path / \"labels_racism.csv\", sep=\"|\")\n\nwith (data_path /\"racist_data/racist_words_lemma.txt\").open(\"r\") as f:\n racist_words_lemma = f.read().splitlines()\n\n\ndff = pd.DataFrame([])\n\nfor i in racist_words_lemma:\n df_counts = pd.DataFrame({\"word\":[i],\n \"count\":sum(df['message'].apply(lambda x: x.count(i)))})\n df_counts.set_index('word')\n dff = dff.append(df_counts)\n\n\n\ndff.set_index('word')\n","repo_name":"david26694/datathon-racism","sub_path":"eda/count_words.py","file_name":"count_words.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41142280886","text":"import requests\nimport frappe\nfrom frappe import _\nimport json\nfrom frappe.utils.password import get_decrypted_password\nfrom frappe.utils import today\nfrom json.decoder import JSONDecodeError\nfrom frappe.utils.data import format_date\ndef execute(filters=None):\n\tcolumns = get_columns()\n\tdata=get_data(filters)\n\treturn columns, data\n\n\ndef get_columns():\n columns = [\n {\n \"label\": (\"Description\"),\n \"fieldname\": \"heading\",\n \"fieldtype\": \"Data\",\n \"width\": 250\n\t\t},\n {\n \"label\": (\"Value\"),\n \"fieldname\": \"amount\",\n \"fieldtype\": \"Data\",\n \"width\": 250\n\t\t},\n ]\n return columns\n\n\n\ndef get_data(filters={}):\n\t\tdata = []\n\t\tcondition = \"\"\n\t\tif filters.get(\"company\") != \"TEAM CYCLE WORLD PVT. LTD.\":\n\t\t\tcondition=f\"\"\" and branch = '{filters.get(\"branch\")}'\"\"\"\n\t\tno_of_amt = frappe.db.sql(f\"\"\"\n\t\tSELECT SUM(grand_total) as amt_si , COUNT(name) as no_of_si\n\t\tFROM `tabSales Invoice`\n\t\tWHERE docstatus != 2\n\t\t{condition}\n\t\tAND company = \"{filters.get(\"company\")}\"\n\t\tAND sales_type_link != \"Order\"\n\t\tAND DATE(posting_date) = DATE(\"{filters.get('posting_date')}\");\n\t\"\"\",as_dict = 1)\n\t\tif len(no_of_amt):\n\t\t\tdata.append({'heading':'Total Sales Amount','amount':no_of_amt[0]['amt_si'] or 0})\n\t\t\tdata.append({'heading':'Total No of Invoice','amount':int(no_of_amt[0]['no_of_si'] or 0)})\n\t\tbicycle = []\n\t\tparent_grps = ['BICYCLES']\n\t\tfor i in parent_grps:\n\t\t\tbicycle.extend(frappe.get_all('Item Group', filters={'parent_item_group': ['in', i]}, pluck='name'))\n\t\t\tparent_grps.extend(frappe.get_all('Item Group', filters={'parent_item_group': ['in', i], 'is_group': 1}, pluck='name'))\n\t\tbicycle += parent_grps\n\t\tif filters.get(\"company\") != \"TEAM CYCLE WORLD PVT. LTD.\":\n\t\t\tcondition=f\"\"\" and si.branch = '{filters.get(\"branch\")}'\"\"\"\n\t\tsi_cycle = frappe.db.sql(f\"\"\"\n\t\t\tSELECT SUM(sii.qty) as no_of_bi\n\t\t\tFROM `tabSales Invoice` si\n\t\t\tLEFT JOIN `tabSales Invoice Item` sii ON sii.parent = si.name\n\t\t\tWHERE si.docstatus != 2 \n\t\t\tAND DATE(si.posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\t\tAND si.company = \"{filters.get('company')}\"\n\t\t\t{condition}\n\t\t\tAND sii.item_group IN %(bicycle)s\n\t\t\"\"\", {'bicycle': bicycle}, as_dict=1)\n\t\tif len(si_cycle):\n\t\t\tdata.append({'heading':'Total No of Bicycle Sold','amount':int(si_cycle[0]['no_of_bi'] or 0) })\n\t\taccess = []\n\t\tparent_grps = ['ACCESSORIES']\n\t\tfor i in parent_grps:\n\t\t\taccess.extend(frappe.get_all('Item Group', filters={'parent_item_group':['in', i]}, pluck='name'))\n\t\t\t\n\t\t\tparent_grps.extend(frappe.get_all('Item Group', filters={'parent_item_group':['in', i], 'is_group':1}, pluck='name'))\n\t\taccess+=parent_grps\n\t\tsi_access = frappe.db.sql(f\"\"\"SELECT SUM(sii.qty) as no_of_ac\n\t\tFROM `tabSales Invoice` si\n\t\tLEFT JOIN `tabSales Invoice Item` sii ON sii.parent = si.name\n\t\tWHERE si.docstatus != 2 \n\t\t\t\tAND si.posting_date = DATE(\"{filters.get('posting_date')}\")\n\t\tAND si.company = \"{filters.get(\"company\")}\"\n\t\t{condition} AND\n\t\tsii.item_group IN %(access)s\n\t\"\"\",{\"access\": tuple(access)}, as_dict = 1)\n\t\tif len(si_access):\n\t\t\tdata.append({'heading':'Total No of Accessories Sold','amount':int(si_access[0]['no_of_ac'] or 0) })\n\t\tsum = 0.0\n\t\texchange_count = 0\n\t\tmop = frappe.db.sql(f\"\"\"\n\t\tSELECT sip.mode_of_payment, SUM(sip.amount) as mop\n\t\tFROM `tabSales Invoice` si\n\t\tLEFT JOIN `tabSales Invoice Payment` sip ON si.name = sip.parent\n\t\tWHERE \n\t\t si.docstatus != 2 {condition}\n\t\tAND si.company = \"{filters.get(\"company\")}\"\n\t\tAND DATE(si.posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\tGROUP BY sip.mode_of_payment\n\t\"\"\", as_dict = True)\n\t\tif(filters.get(\"company\") == \"TEAM CYCLE WORLD PVT. LTD.\"):\n\t\t\tpe = frappe.db.sql(f\"\"\"\n\t\t\tSELECT mode_of_payment, SUM(paid_amount) as mop\n\t\t\tFROM `tabPayment Entry`\n\t\t\tWHERE \n\t\t\tdocstatus != 2 AND payment_type = \"Receive\"\n\t\t\tAND company = \"{filters.get(\"company\")}\"\n\t\t\tAND DATE(posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\t\tGROUP BY mode_of_payment\n\t\t\t\"\"\", as_dict = True)\n\t\t\tmop+=pe\n\t\tfor i in mop:\n\t\t\tif i['mop']:\n\t\t\t\tdata.append({'heading': i['mode_of_payment'], 'amount': i['mop'] or 0})\n\t\t\t\tsum+=i['mop']\n\t\tif(sum):\n\t\t\tdata.append({'heading':'Total MOP Amount','amount':sum or 0})\n\n\t\tcash = frappe.get_all(\"Account\",filters={'company':filters.get(\"company\"),'account_type':'Cash'},pluck='name')\n\t\tif filters.get(\"company\") != \"TEAM CYCLE WORLD PVT. LTD.\":\n\t\t\tcondition=f\"\"\" and branch = '{filters.get(\"branch\")}'\"\"\"\n\t\tno_of_pi_amt = frappe.db.sql(f\"\"\"\n\t\tSELECT SUM(credit) as outward , SUM(debit) as inward\n\t\tFROM `tabGL Entry`\n\t\tWHERE is_cancelled = 0\n\t\t{condition}\n\t\tAND company = \"{filters.get(\"company\")}\"\n\t\tAND DATE(posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\tAND account IN {tuple(cash)}\n\t\"\"\", as_dict = 1)\n\t\tif len(no_of_pi_amt):\n\t\t\tdata.append({'heading':'Cash Outward','amount':no_of_pi_amt[0]['outward'] or 0})\n\t\t\tdata.append({'heading':'Cash Inward','amount':no_of_pi_amt[0]['inward'] or 0})\n\t\tadvance = frappe.db.sql(f\"\"\"\n\t\tSELECT SUM(credit) as adv\n\t\tFROM `tabGL Entry`\n\t\tWHERE is_cancelled = 0\n\t\t{condition}\n\t\tAND company = \"{filters.get(\"company\")}\"\n\t\tAND DATE(posting_date) = DATE(\"{filters.get('posting_date')}\")\n\t\tAND against IN %(cash)s AND\n\t\tagainst_voucher_type = \"Sales Order\"\n\t\"\"\", {\"cash\": cash}, as_dict = 1)\n\t\tif len(advance):\n\t\t\tdata.append({'heading':'Cash in Advance','amount':advance[0]['adv'] or 0})\n\t\tif filters.get(\"company\") != \"TEAM CYCLE WORLD PVT. LTD.\":\n\t\t\tcondition=f\"\"\" and si.branch = '{filters.get(\"branch\")}'\"\"\"\n\t\tmop_exe = frappe.db.sql(f\"\"\"\n\t\tSELECT COUNT(sip.name) as mop_ex\n\t\tFROM `tabSales Invoice` si\n\t\tLEFT JOIN `tabSales Invoice Payment` sip ON si.name = sip.parent\n\t\tWHERE \n\t\t si.docstatus != 2 {condition}\n\t\tAND si.company = \"{filters.get(\"company\")}\"\n\t\tAND DATE(si.posting_date) = DATE(\"{filters.get('posting_date')}\") AND\n\t\tsip.amount > 0 AND\n\t\tsip.mode_of_payment = \"Exchange\"\n\t\"\"\", as_dict = True)\n\t\tdata.append({'heading': 'Exchange cycle received today', 'amount':int(mop_exe[0]['mop_ex'] or 0) })\n\t\texe_sold = frappe.db.sql(f\"\"\"\n\t\tSELECT SUM(sii.qty) as no_of_si_exe\n\t\tFROM `tabSales Invoice` si\n\t\tLEFT JOIN `tabSales Invoice Item` sii ON sii.parent = si.name\n\t\tWHERE si.docstatus != 2 \n\t\tAND si.posting_date = DATE(\"{filters.get('posting_date')}\")\n\t\tAND si.company = \"{filters.get(\"company\")}\" \n\t\t{condition} AND\n\t\tsii.item_group = \"Exchange\"\n\t\"\"\", as_dict = 1)\n\t\tdata.append({'heading': 'Exchange cycle Sold today', 'amount':int(exe_sold[0]['no_of_si_exe'] or 0 ) })\n\t\tget_branch = frappe.get_value(\"Branch\",filters.get(\"branch\"),'sales_warehouse')\n\t\t\n\t\tbin = frappe.db.sql(f\"\"\"\n\t\tSELECT actual_qty as act_qty\n\t\tFROM `tabBin`\n\t\tWHERE \n\t\titem_code in (Select name from `tabItem` where item_group = \"Exchange\")\n\t\tAND warehouse = %(get_branch)s\n\t\"\"\", {\"get_branch\": get_branch}, as_dict = 1)\n\t\tif len(bin):\n\t\t\tdata.append({'heading':'Total Exchange Cycle Available','amount':int(bin[0]['act_qty'] or 0 ) })\n\t\t\n\n\t\treturn data \n\n\nimport frappe\nimport json\nfrom frappe.utils.password import get_decrypted_password\n\n\n@frappe.whitelist()\ndef message_loop():\n\tif not frappe.db.get_value(\"Company API Credentials\", {\"enable\":1, \"use_this_to_send_daily_summary\":1}, \"name\"):\n\t\treturn\n\tcompanies = frappe.db.get_all(\"Company\", fields=[\"name\"])\n\tfor company in companies:\n\t\tbranches = frappe.get_all(\"Branch\", filters={\"company\": company.name}, pluck=\"name\")\n\t\tif(len(branches) == 0):\n\t\t\tbranches = [None]\n\t\tfor branch in branches:\n\t\t\tmessage_txt = get_report_content({'company': company.name, 'branch': branch, 'posting_date': today()})\n\t\t\tif(branch):\n\t\t\t\tuser = frappe.get_all(\"User\",filters={\"company\":company.name,\"role_profile_name\":\"Branch Admin\"},pluck=\"email\")\n\t\t\t\tuser_permission = frappe.get_all(\"User Permission\",filters={\"user\":[\"in\",user],\"allow\":\"Branch\",\"for_value\":branch},pluck=\"user\")\n\t\t\t\tfor i in user_permission:\n\t\t\t\t\tmobile_no = frappe.get_value(\"User\",{\"company\":company.name,\"email\":i, \"enabled\":1},\"mobile_no\")\n\t\t\t\t\tif not mobile_no:\n\t\t\t\t\t\twhatsapp = frappe.new_doc(\"Whatsapp Log\")\n\t\t\t\t\t\twhatsapp.update({\n\t\t\t\t\t\t\t\"response\":f\"Mobile Number Not found for user {i}, Company:{company.name}, Branch:{branch}\", \n\t\t\t\t\t\t\t'status':'Not Sent',\n\t\t\t\t\t\t\t'branch':branch,\n\t\t\t\t\t\t\t'message':message_txt,\n\t\t\t\t\t\t\t'user':i,\n\t\t\t\t\t\t\t'company':company.name\n\t\t\t\t\t\t\t})\n\t\t\t\t\t\twhatsapp.save(ignore_permissions = True)\n\t\t\t\t\t\tfrappe.db.commit()\n\t\t\t\t\telse:\n\t\t\t\t\t\tmobile_no = \"\".join(mobile_no.split(\" \"))\n\t\t\t\t\t\tsend_message(company.name,i,message_txt,f\"91{mobile_no}\",branch)\n\t\t\telse:\n\t\t\t\tuser = frappe.get_all(\"User\",filters={\"company\":company.name,\"role_profile_name\":\"HO Admin Super Admin\", \"enabled\":1},pluck=\"email\")\n\t\t\t\tfor i in user:\n\t\t\t\t\tmobile_no = frappe.get_value(\"User\",{\"company\":company.name,\"email\":i},\"mobile_no\")\n\t\t\t\t\tif not mobile_no:\n\t\t\t\t\t\twhatsapp = frappe.new_doc(\"Whatsapp Log\")\n\t\t\t\t\t\twhatsapp.update({\n\t\t\t\t\t\t\t\"response\":f\"Mobile Number Not found for user {i}, Company:{company.name}, Branch:{branch}\", \n\t\t\t\t\t\t\t'status':'Not Sent',\n\t\t\t\t\t\t\t\"message\":message_txt,\n\t\t\t\t\t\t\t'user':i,\n\t\t\t\t\t\t\t'company':company.name\n\t\t\t\t\t\t\t})\n\t\t\t\t\t\twhatsapp.save(ignore_permissions = True)\n\t\t\t\t\t\tfrappe.db.commit()\n\t\t\t\t\telse:\n\t\t\t\t\t\tmobile_no = \"\".join(mobile_no.split(\" \"))\n\t\t\t\t\t\tsend_message(company.name,i,message_txt,f\"91{mobile_no}\")\n\ndef send_message(company,user,message_txt,mobile_no,branch=None):\n\tresponse=\"\"\n\turl_message = \"\"\n\ttry: \n\t\tnumber\t= mobile_no\n\t\tchild_name = frappe.db.get_value(\"Company API Credentials\", {\"enable\":1, \"use_this_to_send_daily_summary\":1}, \"name\")\n\t\tif not child_name:\n\t\t\treturn\n\t\tinstance_id = frappe.db.get_value(\"Company API Credentials\", child_name, \"instance_id\")\n\t\t\n\t\taccess = get_decrypted_password(\"Company API Credentials\",child_name, \"access_token\")\n\t\turl=frappe.db.get_value(\"Company API Credentials\", child_name, \"api_url\")\n\t\tenabled= frappe.db.get_single_value(\"API Credentials\", \"enable\") and frappe.db.get_value(\"Company API Credentials\", child_name, \"enable\")\n\t\tif enabled == 1:\n\t\t\turl_message = f\"{url}?number={number}&type=text&message={message_txt}&instance_id={instance_id}&access_token={access}\"\n\t\t\tpayload={}\n\t\t\theaders = {}\n\t\t\tresponse = requests.request(\"GET\", url_message, headers=headers, data=payload)\n\t\t\twhatsapp = frappe.new_doc(\"Whatsapp Log\")\n\t\t\ttry:\n\t\t\t\tresponse = response.json()\n\t\t\t\tif(response.get('status') == 'error'):\n\t\t\t\t\twhatsapp.update({'status':'Failed', \"response\":json.dumps(response, indent=4),})\n\t\t\t\telse:\n\t\t\t\t\twhatsapp.update({'status':'Success', \"response\":json.dumps(response, indent=4),})\n\t\t\texcept JSONDecodeError:\n\t\t\t\tfrappe.log_error(response.text)\n\t\t\t\twhatsapp.update({'status':'Failed', \"success_html\":response.text})\n\t\t\texcept Exception as e:\n\t\t\t\tfrappe.log_error(title=\"Whatsapp Error\", message=f\"\"\"Error: {company}\\n{user}\\n{mobile_no}\\n{branch}\\n{response.text}\\n{url_message}\\n{e}\"\"\")\n\t\t\t\tfrappe.db.commit()\n\t\t\t\n\t\t\twhatsapp.update({\"request\":url_message,\"message\":message_txt,\"number\":number, \"branch\":branch,\"company\":company,\"user\":user})\n\t\t\twhatsapp.save(ignore_permissions = True)\n\t\t\tfrappe.db.commit()\n\texcept Exception as e:\n\t\tfrappe.log_error(title=\"Whatsapp Error\", message=f\"\"\" {company}\\n{user}\\n{mobile_no}\\n{branch}\\n{response}\\n{url_message}\\n{e}\"\"\")\n\t\tfrappe.db.commit()\n\n\ndef get_report_content(filters):\n\treport = frappe.get_doc(\"Report\", \"Daily Summary\")\n\n\tfilters = frappe.parse_json(filters) if filters else {}\n\n\tcolumns, data = report.get_data(\n\t\tfilters=filters,\n\t)\n\treport_data = \"\"\n\tfor i in data:\n\t\treport_data += f\"%0A{i['heading']}%09-%09{i['amount']}\"\n\tbranch_txt = \"\"\n\tif filters.get(\"branch\"):\n\t\tbranch_txt = f\"*BRANCH* : {filters.get('branch')} %0A\"\n\ttext_msg = f\"\"\"\n*Daily Summary*%09%09%09%0A\n*COMPANY* : {filters.get(\"company\")}%0A\n{branch_txt}\n*DATE* : {format_date(filters.get(\"posting_date\"), \"dd-mm-yyyy\")}\n{report_data}\n\"\"\"\n\treturn text_msg","repo_name":"thirvusoft/Cycle-World-Franchise","sub_path":"cycle_world/cycle_world/report/daily_summary/daily_summary.py","file_name":"daily_summary.py","file_ext":"py","file_size_in_byte":11747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72437329360","text":"import sqlite3\r\n\r\nsquare = lambda n: n * n # creamos la funcion que enviaremos a la base de datos\r\n\r\n\r\nconn = sqlite3.connect(\"Northwind.db\") # instanciamos la base de datos\r\nconn.create_function(\r\n \"square\", 1, square\r\n) # creamos la funcion dentro de la base de datos(\"nombre de la funcion a crear\",cuantos parametros al usuario,la funcion)\r\n\r\ncursor = conn.cursor()\r\ncursor.execute(\r\n \"\"\"\r\n SELECT * FROM Products\r\n GROUP BY ProductID\r\n ORDER BY ProductID\r\n \"\"\"\r\n)\r\n\r\nresults = cursor.fetchall()\r\nprint(results)\r\n","repo_name":"zgamerHT/miexperiencia","sub_path":"UDF.py","file_name":"UDF.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24069329705","text":"import numpy as np\n\n\nclass Vehicle:\n def __init__(self, name, board, fuel=100):\n self.name = name\n self.position = self.get_pos(board)\n self.orientation = self.get_orientation()\n self.fuel = fuel\n\n def get_pos(self, board):\n position = []\n for i in range(6):\n for j in range(6):\n if board.grid[i][j] == self.name:\n position.append([i, j])\n return position\n\n def get_orientation(self):\n # check same row\n if self.position[0][0] == self.position[1][0]:\n return 'X'\n else:\n return 'Y'\n\n # need to check head from top of the car, leftmost element of the pos list\n # check fuel, orientation, if going out of board, and if next pos is free\n # remove 1 from each ROW\n def can_move_up(self, current_board):\n return self.fuel > 0 \\\n and self.orientation == 'Y' \\\n and self.position[0][0] - 1 >= 0 \\\n and current_board.grid[self.position[0][0] - 1][self.position[0][1]] == '.'\n\n # check the last element of the pos list\n def can_move_down(self, current_board):\n return self.fuel > 0 \\\n and self.orientation == 'Y' \\\n and self.position[-1][0] + 1 < 6 \\\n and current_board.grid[self.position[-1][0] + 1][self.position[0][1]] == '.'\n\n # check first element of the pos list (left most pos of the car)\n def can_move_left(self, current_board):\n return self.fuel > 0 \\\n and self.orientation == 'X' \\\n and self.position[0][1] - 1 >= 0 \\\n and current_board.grid[self.position[0][0]][self.position[0][1] - 1] == '.'\n\n # check LAST element of the pos list (right most pos of the car)\n def can_move_right(self, current_board):\n # need check col of last pos if its less than 6\n return self.fuel > 0 \\\n and self.orientation == 'X' \\\n and self.position[-1][1] + 1 < 6 \\\n and current_board.grid[self.position[0][0]][self.position[-1][1] + 1] == '.'\n\n def move(self, current_board, direction):\n # remove from grid\n for pos in self.position:\n current_board.grid[pos[0]][pos[1]] = '.'\n\n # new position\n if direction == 'right':\n for pos in self.position:\n pos[1] = pos[1] + 1\n elif direction == 'left':\n for pos in self.position:\n pos[1] = pos[1] - 1\n elif direction == 'up':\n for pos in self.position:\n pos[0] = pos[0] - 1\n elif direction == 'down':\n for pos in self.position:\n pos[0] = pos[0] + 1\n\n # fuel and grid update\n self.fuel -= 1\n current_board.change_fuel()\n\n def print_vehicle(self):\n print(self.name, end=' ')\n print(self.position, end=' ')\n print(self.fuel)\n","repo_name":"stevenvuong00/comp472Project2","sub_path":"src/vehicle.py","file_name":"vehicle.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43360317862","text":"import logging\n\nimport aiohttp\n\nLOG = logging.getLogger(\"obu\")\n\nclass teltonikaRUT9x:\n async def __aenter__(self):\n self.session = aiohttp.ClientSession()\n await self._get_session_id()\n return self\n\n async def __aexit__(self,exc_type,exc,tb):\n await self.session.close()\n\n def __init__(self,url,username=\"root\",password=\"\"):\n \"\"\"Makes an teltonikaRUT9x object to read JSON_RPC from the RUT9x mobile router.\n \n Parameters\n ----------\n url : string\n URL to router\n username : string\n username to log into the router, default: root\n password : string\n Password for username, default: \"\"\n \"\"\"\n\n self._url = url\n payload = { \n \"jsonrpc\": \"2.0\", \n \"id\": 1, \n \"method\": \"call\", \n \"params\": [ \n \"00000000000000000000000000000000\", \n \"session\", \n \"login\",\n {\"username\": \"\", \"password\": \"\", \"timeout\": 0 } \n ] \n }\n payload[\"params\"][3][\"username\"] = username\n payload[\"params\"][3][\"password\"] = password\n \n self.payload = payload\n \n async def _get_session_id(self):\n async with self.session.post(self._url,json=self.payload) as resp:\n LOG.info(\"Request JSON-RPC session ID from router\")\n resp = await resp.json()\n self._sessionId = resp[\"result\"][1][\"ubus_rpc_session\"]\n LOG.info(\"Session id retrieved\")\n \n async def readSignalStrength(self):\n \"\"\"\n Reads the RSRP, SINR and RSRQ from the RUT9x \n\n Returns\n -------\n dict: with RSRP, SINR and RSPQ.\n \"\"\"\n\n payload = {\n \"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"call\", \"params\":\n [ \n \"\",\n \"file\",\n \"exec\",\n {\n \"command\":\"gsmctl\",\n \"params\":[\"-WZM\" ]\n }\n ]\n }\n payload[\"params\"][0] = self._sessionId\n \n async with self.session.post(self._url,json=payload) as r:\n r = await r.json()\n \n stdout = r[\"result\"][1][\"stdout\"].split(\"\\n\")\n \n result = {\n \"RSRP\" : \"\",\n \"SINR\" : \"\",\n \"RSRQ\" : \"\"\n }\n i = 0\n for x in result:\n result[x] = stdout[i]\n i += 1\n #print(result)\n LOG.info(result)\n return result\n\n async def readGPS(self):\n \"\"\" Reads the latitude and longitude\n\n Returns\n -------\n dict:\n a dict with the latitude and longitude \n \"\"\"\n\n payload = {\n \"jsonrpc\": \"2.0\", \"id\": 1, \"method\": \"call\", \"params\":\n [ \n \"\",\n \"file\",\n \"exec\",\n {\n \"command\":\"gpsctl\",\n \"params\":[\"-ix\" ]\n }\n ]\n }\n payload[\"params\"][0] = self._sessionId\n #payload = json.dumps(payload)\n #print(payload)\n async with self.session.post(self._url,json=payload) as r:\n r = await r.json()\n #print(r.text)\n stdout = r[\"result\"][1][\"stdout\"].split(\"\\n\")\n #print(stdout)\n result = {\n \"Lat\" : \"\",\n \"Lon\" : \"\"\n }\n i = 0\n for x in result:\n result[x] = stdout[i]\n i += 1\n #print(result)\n LOG.info(result)\n return result\n ","repo_name":"svinz/msc_project_onboardunit","sub_path":"src/ReadJSONRPC.py","file_name":"ReadJSONRPC.py","file_ext":"py","file_size_in_byte":3641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34949562114","text":"import warnings\nfrom copy import deepcopy\n\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport umap\nfrom hdbscan import HDBSCAN\nfrom h5.constants import BARCODE, ID, SAMPLE\nfrom h5.data.assay import Assay as H5_Assay\nfrom plotly.subplots import make_subplots\nfrom scipy import stats\nfrom scipy.cluster import hierarchy\nfrom scipy.spatial.distance import pdist\nfrom scipy.stats import gaussian_kde\nfrom sklearn.cluster import DBSCAN, KMeans\nfrom sklearn.decomposition import PCA\nfrom sklearn.neighbors import NearestNeighbors\n\nfrom mosaic.constants import (\n COLORS,\n LABEL,\n ORG_BARCODE,\n PALETTE,\n PCA_LABEL,\n READS,\n SCALED_LABEL,\n UMAP_LABEL,\n)\nfrom mosaic.plotting import plt, require_seaborn, sns\nfrom mosaic.utils import clipped_values, get_indexes\n\n\nclass _Assay(H5_Assay):\n \"\"\"\n Abstract class for all assays\n\n Each of :class:`mosaic.dna.Dna`, :class:`mosaic.cnv.Cnv`,\n and :class:`mosaic.protein.Protein` are inherited from this base class,\n hence all of them have the following functionality:\n\n `_Assay` objects can be filtered using Python's slice notation. It requires two\n keys (barcodes, ids). Both could be a list of positions, a Boolean list,\n or a list of the respective values.\n\n Load the sample.\n\n >>> import mosaic.io as mio\n >>> sample = mio.load('/path/to/h5')\n\n Select the first 250 cells (these aren't necessarily the cells\n with the highest reads, they're arbitrary cells) and the dna\n variants obtained from filtering.\n\n >>> select_bars = sample.dna.barcodes()[: 250]\n >>> select_vars = sample.dna.filter_variants()\n\n Slice the dna assay for the selected cells and variants.\n\n >>> filtered_dna = sample.dna[select_bars, select_vars]\n\n Note: This example is for DNA, but the same can be done for\n any `_Assay` object.\n\n .. rubric:: Modifying data\n .. autosummary::\n\n add_layer\n add_row_attr\n add_col_attr\n add_metadata\n del_layer\n del_row_attr\n del_col_attr\n del_metadata\n\n .. rubric:: Selecting data\n .. autosummary::\n\n barcodes\n clustered_barcodes\n ids\n clustered_ids\n drop\n get_palette\n set_palette\n get_labels\n set_labels\n set_selected_labels\n rename_labels\n get_attribute\n\n .. rubric:: Tertiary analysis methods\n .. autosummary::\n\n scale_data\n run_pca\n run_umap\n run_lda\n cluster\n group_clusters\n\n .. rubric:: Visualizations\n .. autosummary::\n\n scatterplot\n heatmap\n violinplot\n ridgeplot\n stripplot\n read_distribution\n feature_scatter\n fishplot\n barplot\n\n .. rubric:: Statistics\n .. autosummary::\n\n feature_signature\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n \"\"\"\n Calls h5.data.assay.__init__.\n \"\"\"\n super().__init__(*args, **kwargs)\n self.selected_bars = {}\n self.selected_ids = np.array([])\n self.title = \", \".join(self.metadata[SAMPLE].flatten())\n\n self._rename_barcodes()\n\n pal = None\n if PALETTE in self.metadata:\n pal = self.get_palette()\n\n labs = self.get_labels()\n self.set_labels(labs)\n\n if pal is not None and set(pal.keys()) == set(labs):\n self.set_palette(pal)\n\n # --------- Interactivity\n\n def __getitem__(self, key):\n \"\"\"\n Implemented for convenience when analyzing\n multiple samples in a Jupyter Notebook.\n\n Parameters\n ----------\n key : 2-tuple\n Both elements of the tuple must be an\n array of values, indices or bools. The first\n element is used to filter barcodes and the\n second element is used to filter ids.\n\n Returns\n -------\n :class:`mosaic.assay._Assay`\n A copy of itself with the relevant\n barcodes and ids selected.\n\n Raises\n ------\n KeyError\n When the tuple provided does not match the\n expected format.\n \"\"\"\n def _isstrlike(val):\n if (isinstance(val, list) and isinstance(val[0], str)) or (isinstance(val, np.ndarray) and isinstance(val[0], str)):\n return True\n return False\n\n if isinstance(key, tuple):\n assay = deepcopy(self)\n key_row, key_col = key\n\n if _isstrlike(key_row):\n key_row = get_indexes(assay.barcodes(), key_row)\n\n if _isstrlike(key_col):\n key_col = get_indexes(assay.ids(), key_col)\n\n if key_row is not None:\n assay.select_rows(key_row)\n\n if key_col is not None:\n assay.select_columns(key_col)\n else:\n raise KeyError(f\"Expected tuple, got {type(key)}\")\n\n return assay\n\n def _rename_barcodes(self):\n \"\"\"\n Appends the name of the sample to the barcode\n in case of multi sample files. This is required to\n avoid duplicate barcodes from colliding.\n\n In case two different samples have duplicate\n barcodes, then there are high chances of\n misidentification of the cell signature. Moreover,\n many plots and analyses assume that barcodes\n are unique. They fail in case of duplicate\n barcodes. This is the easiset workaround to tackle\n both the situations.\n\n The original barcodes are stored in the org_barcode\n row attribute.\n \"\"\"\n\n if self.metadata[SAMPLE].shape[0] > 1:\n msg = 'Multiple samples detected. Appending the sample name to the barcodes to avoid duplicate barcodes.'\n msg += f' The original barcodes are stored in the {ORG_BARCODE} row attribute'\n\n warnings.warn(msg)\n\n self.add_row_attr(ORG_BARCODE, self.barcodes())\n self.add_row_attr(BARCODE, self.barcodes() + '-' + self.row_attrs[SAMPLE])\n\n def drop(self, values, value_type=None):\n \"\"\"\n Drops the given values from the assay.\n\n Parameters\n ----------\n values : list-like\n The values that are to be dropped,\n which could be barcodes or ids.\n value_type : str\n Accepted values are `id`, `barcode`, and\n `None`. When `None`, the type is guessed\n based on the values.\n\n Returns\n -------\n :class:`mosaic.assay._Assay`\n A copy of itself with the relevant\n ids removed.\n\n Raises\n ------\n ValueError\n Raise when the type of the value (barcode or id)\n is not determinable, and value_type is expected.\n \"\"\"\n\n remaining_bars = None\n remaining_ids = None\n\n def check_complete(array):\n nonlocal values\n if not np.isin(values, array).all():\n raise ValueError('Not all the given values are present in the assay.')\n\n if value_type == 'id':\n check_complete(self.ids())\n remaining_ids = ~np.isin(self.ids(), values)\n elif value_type == 'barcode':\n check_complete(self.barcodes())\n remaining_bars = ~np.isin(self.barcodes(), values)\n elif value_type is None:\n if np.isin(values, self.ids()).any():\n check_complete(self.ids())\n remaining_ids = ~np.isin(self.ids(), values)\n elif np.isin(values, self.barcodes()).any():\n check_complete(self.barcodes())\n remaining_bars = ~np.isin(self.barcodes(), values)\n else:\n raise ValueError('Not all the given values are present in the assay.')\n else:\n raise ValueError('value_type must be `id`, `barcode`, or `None`.')\n\n return self[remaining_bars, remaining_ids]\n\n def del_layer(self, key):\n \"\"\"\n Delete a layer from the assay layers.\n\n Parameters\n ----------\n key : str\n The name of the layer.\n \"\"\"\n if key in self.layers:\n del self.layers[key]\n\n def del_row_attr(self, key):\n \"\"\"\n Delete an attribute from the row_attrs.\n\n Parameters\n ----------\n key : str\n The name of the row attribute.\n \"\"\"\n if key in self.row_attrs:\n del self.row_attrs[key]\n\n def del_col_attr(self, key):\n \"\"\"\n Delete an attribute from the col_attrs.\n\n Parameters\n ----------\n key : str\n The name of the column attribute.\n \"\"\"\n if key in self.col_attrs:\n del self.col_attrs[key]\n\n def del_metadata(self, key):\n \"\"\"\n Delete an attribute from the metadata.\n\n Parameters\n ----------\n key : str\n The name of the metadata.\n \"\"\"\n if key in self.metadata:\n del self.metadata[key]\n\n def get_attribute(self, attribute, features=None, constraint=None):\n \"\"\"\n Retrieve any attribute in the assay\n\n Returns an np.array which could either be\n a row attribute, column attribute, layer\n or the passed attribute itself.\n\n Parameters\n ----------\n attribute : str / np.ndarray\n The attribute to be searched for in the assay.\n\n features : str\n In case the attribute is a layer or col, then\n the subset of features to select\n\n constraint : str\n One of the following is accepted.\n 1. None\n No contraint.\n 2. 'row'\n The first dimension must be equal\n to the number of cells.\n 3. 'col'\n The second dimension must be equal\n to the number of ids or the number of\n features given in the input.\n 4. 'row+col'\n The dimension must be exactly\n (number of cells, number of ids).\n The layers have this shape.\n\n Returns\n -------\n pd.DataFrame\n The array of the attribute with the given name\n found in the assay layer, row attributes,\n or col attributes in that order. If a constraint\n is provided, the array is reshaped appropriately\n if possible. In case `constraint` is provided then\n the columns and index are named based on the\n barcodes, ids, or a range depending on where\n the given attribute is found.\n\n Raises\n ------\n ValueError\n When the attribute is not found or\n when the constraint is not satisfied.\n TypeError\n When the attribute is neither a str\n not an np.ndarray\n\n Notes\n -----\n In case the constraint can reshape the array\n into the expected shape then no error will be raised.\n Eg. An assay with 100 barcodes and 10 ids has a shape\n (100, 10). When the attribute 'barcode' is fetched\n constrained by 'col' it will not raise an error,\n but rather return a 10x10 dataframe of barcodes.\n\n >>> assay.shape\n (100, 10)\n >>> attr = assay.get_attribute('barcode', constrain='col')\n >>> attr.shape\n (10, 10)\n\n Possible expected behavior\n >>> assay.shape\n (100, 11)\n >>> attr = assay.get_attribute('barcode', constrain='col')\n ValueError - 'The given attribute does not have the expected\n shape nor could be reshaped appropriately.'\n \"\"\"\n\n # Find where the attribute is\n if isinstance(attribute, str):\n title = attribute\n\n if attribute in self.row_attrs and attribute in self.layers:\n warnings.warn(f'{attribute} found in the row attributes and the layers both.'\n f' Defaulting to the {attribute} in the layers.')\n\n if attribute in self.col_attrs and attribute in self.layers:\n warnings.warn(f'{attribute} found in the col attributes and the layers both.'\n f' Defaulting to the {attribute} in the layers.')\n\n if attribute in self.col_attrs and attribute in self.row_attrs:\n warnings.warn(f'{attribute} found in the col attributes and the row attributes both.'\n f' Defaulting to the {attribute} in the row attributes.')\n\n if attribute in self.layers:\n attribute = self.layers[attribute]\n if features is None:\n features = self.ids()\n elif np.isin(features, self.ids()).all():\n inds = [np.where(self.ids() == f)[0][0] for f in features]\n attribute = attribute[:, inds]\n else:\n raise ValueError('Not all features are found in the arrays ids')\n elif attribute in self.row_attrs:\n attribute = self.row_attrs[attribute]\n elif attribute in self.col_attrs:\n attribute = self.col_attrs[attribute]\n if features is None:\n features = self.ids()\n elif np.isin(features, self.ids()).all():\n inds = [np.where(self.ids() == f)[0][0] for f in features]\n attribute = attribute[inds, :]\n else:\n raise ValueError('Not all features are found in the arrays ids')\n else:\n raise ValueError(f'{attribute} not found in either the row attributes, col attributes or the layers')\n elif isinstance(attribute, np.ndarray):\n title = '-'\n else:\n raise TypeError(\"'attribute' must be of type str or np.ndarray\")\n\n # Constraint the attribute\n attribute = attribute.copy()\n err_msg = 'The given attribute does not have the expected shape nor could be reshaped appropriately.'\n if constraint == 'row':\n try:\n attribute = attribute.reshape((self.shape[0], -1))\n except ValueError:\n raise ValueError(f'{err_msg} Given {attribute.shape}, expected ({self.shape[0]}, *).')\n elif constraint == 'col':\n try:\n if features is None:\n attribute = attribute.reshape((-1, self.shape[1]))\n else:\n attribute = attribute.reshape((-1, len(features)))\n except ValueError:\n raise ValueError(f'{err_msg} Given {attribute.shape}, expected (*, {self.shape[1]}).')\n elif constraint == 'row+col':\n try:\n if features is None:\n attribute = attribute.reshape(self.shape)\n else:\n attribute = attribute.reshape((self.shape[0], len(features)))\n except ValueError:\n raise ValueError(f'{err_msg} Given {attribute.shape}, expected {self.shape}.')\n elif constraint is not None:\n raise TypeError(f'Constraint must be one of None, \"row\", \"col\", \"row+col\". Given {constraint}')\n\n # Mark the indices and columns appropriately\n attribute = pd.DataFrame(attribute)\n if constraint is not None:\n if features is None:\n attribute.columns = (np.arange(attribute.shape[1]) + 1).astype(str)\n elif len(features) == attribute.shape[1]:\n attribute.columns = features\n else:\n raise ValueError(f\"Expected {attribute.shape[1]} features, given {len(features)}\")\n\n if 'row' in constraint:\n attribute.index = self.barcodes()\n\n attribute.index.name = title\n\n return attribute\n\n def barcodes(self, label=None):\n \"\"\"\n Get the list of barcodes.\n\n Parameters\n ----------\n label : list\n The list of labels whose barcodes are to be retrieved.\n If `None`, then all barcodes are returned.\n\n Returns\n -------\n numpy.ndarray\n An array of barcode ids.\n \"\"\"\n\n bars = np.array(self.row_attrs[BARCODE])\n\n if isinstance(label, str):\n label = np.array([label])\n\n if label is None:\n return bars\n else:\n return bars[get_indexes(self.get_labels(), label)]\n\n def clustered_barcodes(self, orderby=None, splitby=LABEL, override=False):\n \"\"\"\n Hierarchical clustering of barcodes.\n\n If the layer is not provided, the barcodes are\n returned according to the labels.\n\n If a layer is provided, then further hierarchical\n clustering is performed based on the values\n in this layer for each label.\n\n Parameters\n ----------\n orderby : str / np.ndarray\n An attribute with at least one dimension equal\n to the number of barcodes. Uses :meth:`_Assay.get_attribute`\n to retrieve the values constrained by `row`.\n splitby : bool\n Whether to order the barcodes based on the given\n labels or not. Only applicable when labels `orderby`\n is `None`. Uses :meth:`_Assay.get_attribute` to\n retrieve the values constrained by `row`. The shape\n must be equal to `(#cells)`\n override : bool\n Continue clustering even if the number of `id`\n is greater than 1,000.\n\n Returns\n -------\n numpy.ndarray\n Ordered barcodes\n\n Raises\n ------\n ValueError\n When the shape of orderby is not appropriate\n\n Exception\n When the number of `id` in the assay is greater\n than 1,000. This can take a long time\n to process, hence denied.\n\n Warning\n If the number of `id` is greater than 1,000 and\n override is passed.\n \"\"\"\n\n if splitby is None:\n splitby = np.array(['-'] * self.shape[0])\n else:\n splitby = self.get_attribute(splitby, constraint='row').values.flatten()\n\n if len(splitby) != self.shape[0]:\n raise ValueError('Only 1-D values for `splitby` are permitted.')\n\n if orderby is None:\n return self.barcodes()[splitby.argsort()]\n else:\n df = self.get_attribute(orderby, constraint='row')\n\n if df.shape[1] > 10 ** 3:\n message = f\"Clustering using {df.shape[1]} features. This might take a long time.\"\n if override:\n warnings.warn(message)\n else:\n raise Exception(f\"{message} Pass 'override' to continue clustering.\")\n\n df.loc[:, LABEL] = splitby\n\n # Cluster at the level of labels\n if len(set(splitby)) > 1:\n df_avg = df.groupby(LABEL).mean()\n\n Z = hierarchy.linkage(df_avg, metric='cityblock', method='average')\n order = hierarchy.leaves_list(Z)[::-1]\n label_order = df_avg.index[order]\n\n df.loc[:, LABEL] = pd.Categorical(splitby, label_order)\n\n df = df.sort_values(by=LABEL)\n\n # Cluster barcodes within each label\n leaf_order = []\n cells_done = 0\n for lab, df_lab in df.groupby(LABEL):\n df_lab = df_lab.drop(LABEL, axis=1).copy()\n if (df_lab.shape[0] == 1):\n order = np.array([0])\n else:\n Z = hierarchy.linkage(df_lab, metric='cityblock', method='average')\n order = hierarchy.leaves_list(Z)[::-1]\n leaf_order.extend(order + cells_done)\n cells_done += len(order)\n\n return df.index[leaf_order].values\n\n def ids(self):\n \"\"\"\n Get the list of ids.\n\n Returns\n -------\n np.ndarray\n The list of ids in the column attribute 'id'.\n \"\"\"\n return self.col_attrs[ID]\n\n def clustered_ids(self, orderby, features=None, override=False):\n \"\"\"\n Hierarchical clustering of ids.\n\n Parameters\n ----------\n orderby : str / np.ndarray\n An attribute with at least one dimension equal\n to the number of ids in the assay. Uses\n :meth:`_Assay.get_attribute` to retrieve the value\n constrained by `col`\n features : list-like\n The subset of ids to use and return, in case orderby\n is found in the layers, otherwise it is the name\n of the ids to be given to the orderby attribute.\n override : bool\n Continue clustering even if the number of `id`\n is greater than 1,000.\n\n Returns\n -------\n numpy.ndarray\n ordered ids\n\n Raises\n ------\n ValueError\n When the labels are not set.\n\n Exception\n When number of `id` in the assay is greater\n than 1,000. This can take a long time\n to process, hence denied.\n\n Warning\n If the number of `id` is greater than 1,000 and\n override is passed.\n \"\"\"\n\n orderby = self.get_attribute(orderby, constraint='col', features=features)\n features = orderby.columns\n orderby = orderby.values\n\n if orderby.shape[1] > 10 ** 3:\n message = f\"Clustering using {orderby.shape[1]} ids. This might take a long time.\"\n if override:\n warnings.warn(message)\n else:\n raise Exception(f\"{message} Pass 'override' to continue clustering.\")\n\n df = pd.DataFrame(orderby, columns=features)\n\n # No need to reorder if fewer than 2 features\n if df.T.shape[0] < 2:\n return df.columns\n\n Z = hierarchy.linkage(df.T, metric='cityblock', method='average')\n order = hierarchy.leaves_list(Z)[::-1]\n\n return df.columns[order]\n\n def get_palette(self):\n \"\"\"\n Get the label color palette.\n\n Returns\n -------\n dictionary\n The keys are the names of the labels\n and the values are the hex color codes.\n \"\"\"\n if PALETTE not in self.metadata:\n self.set_palette()\n\n return {v[0]: v[1] for v in self.metadata[PALETTE]}\n\n def set_palette(self, palette=None):\n \"\"\"\n Set the label color palette.\n\n Modifies the `palette` metadata of the assay.\n\n Parameters\n ----------\n palette : dictionary\n The color values for each label.\n A subset of labels is not accepted.\n\n Raises\n ------\n TypeError\n When the provided palette\n is not a dictionary.\n ValueError\n When one of the labels is not\n present in the given palette.\n \"\"\"\n\n unique_labels = np.unique(self.get_labels())\n pal = []\n\n if palette is None:\n for i in range(len(unique_labels)):\n pal.append([unique_labels[i], COLORS[i % len(COLORS)]])\n else:\n if not isinstance(palette, dict):\n raise TypeError('The palette should be of type dictionary.')\n\n for lab in unique_labels:\n if lab not in palette:\n raise ValueError(f'The color value {lab} is missing from the given palette - {palette}.')\n pal.append([lab, palette[lab]])\n\n self.add_metadata(PALETTE, np.array(pal))\n\n def get_labels(self, barcodes=None):\n \"\"\"\n Get the labels corresponding to the barcodes.\n\n Parameters\n ----------\n barcodes : list-like / None\n The set of barcodes for which the\n labels are to be returned. The order\n of the barcodes is maintained. The\n LABEL row attribute is returned when\n `barcodes` is `None`\n\n Returns\n -------\n np.array\n The list of labels.\n \"\"\"\n if LABEL not in self.row_attrs:\n return np.array(['-'] * self.shape[0])\n\n all_labs = self.row_attrs[LABEL].copy()\n if barcodes is None:\n return all_labs\n\n all_bars = self.barcodes()\n labels = []\n for bar in barcodes:\n labels.append(all_labs[np.where(all_bars == bar)[0][0]])\n\n labels = np.array(labels)\n\n return labels\n\n def set_labels(self, clusters, others_label='others'):\n \"\"\"\n Set the labels for the barcodes.\n\n Modifies the `id` row attribute of the assay.\n\n Parameters\n ----------\n clusters : list or dictionary\n If a list, then the order of the barcodes\n should be considered because the labels\n are set in the same order as the\n order of barcodes.\n others_label : str\n In case not all barcodes are labeled,\n then this is the label of the remaining\n barcodes.\n \"\"\"\n\n if isinstance(clusters, np.ndarray) or isinstance(clusters, list):\n labels = np.array(clusters)\n else:\n barcodes = self.barcodes()\n labels = np.array([others_label] * len(barcodes), dtype=object)\n for cluster_name in clusters:\n bars = clusters[cluster_name]\n if not np.isin(bars, self.barcodes()).all():\n raise ValueError(f'One of the barcodes in {cluster_name} was not found in the assay.')\n indexes = get_indexes(barcodes, bars)\n labels[indexes] = cluster_name\n\n self.add_row_attr(LABEL, labels.astype(str))\n self.set_palette()\n\n def set_selected_labels(self):\n \"\"\"\n Custom clustering from scatter plots.\n\n Setting the labels of the barcodes based on\n the clusters selected on the scatter plot.\n\n Modifies the `id` row attribute of the assay.\n \"\"\"\n\n i = 1\n labs = {}\n pal = {}\n for k in self.selected_bars:\n if self.selected_bars[k].size > 0:\n labs[str(i)] = self.selected_bars[k]\n pal[str(i)] = k\n i += 1\n\n self.set_labels(labs)\n self.set_palette(pal)\n\n def rename_labels(self, label_map):\n \"\"\"\n Change the name of the labels.\n\n Parameters\n ----------\n label_map : dictionary\n The key is the existing name of the label, and\n the value is the new name of the label.\n \"\"\"\n\n palette = self.get_palette()\n labels = self.get_labels().copy()\n\n for k in label_map:\n labels = np.where(labels == k, label_map[k], labels)\n palette[label_map[k]] = palette[k]\n del palette[k]\n\n self.add_row_attr(LABEL, labels)\n self.set_palette(palette)\n\n # --------- Tertiary analysis methods\n\n def cluster(self, attribute, method='dbscan', **kwargs):\n \"\"\"\n Identify clusters of cells.\n\n Modifies the `label` row attribute of the assay.\n\n Parameters\n ----------\n attribute : str / np.array\n The attribute to use as coordinates for clustering.\n Uses :meth:`_Assay.get_attribute` to retrieve the values\n constrained by `row`.\n method : str\n dbscan, hdbscan, kmeans, or graph-community (Louvain\n on a shared nearest neighbor graph).\n kwargs : dictionary\n Passed to the appropriate clustering method.\n\n Available parameters for the methods are:\n 1. dbscan : eps (float)\n A measure of how close the points are.\n 2. hdbscan : min_cluster_size (int)\n The number of cells in a cluster.\n 3. kmeans : n_clusters (int)\n The number of clusters to be generated.\n 4. graph-community : k (int)\n The number of nearest neighbors to consider\n in the shared nearest neighbor graph generation.\n\n Raises\n ------\n ValueError\n When the row attribute is not available in the assay.\n Exception\n When an unsupported clustering method is provided.\n \"\"\"\n\n supported_methods = {\n 'dbscan': DBSCAN,\n 'hdbscan': HDBSCAN,\n 'kmeans': KMeans,\n 'graph-community': self._community_clustering\n }\n\n if method in supported_methods:\n data = self.get_attribute(attribute, constraint='row').values\n if method == 'graph-community':\n self._community_clustering(data, **kwargs)\n else:\n clusterer = supported_methods[method](**kwargs).fit\n output = clusterer(data)\n labels, idx, cnt = np.unique(output.labels_, return_inverse=True, return_counts=True)\n\n # Order by counts and maintain -1 for outliers\n order = cnt.argsort()[::-1]\n clones = labels[order] != -1\n labels[order[clones]] = np.arange(clones.sum()) + 1\n labels[order[~clones]] = -1\n labels = labels[idx]\n\n self.set_labels(labels)\n else:\n raise Exception(f'{method} not supported. Try {supported_methods.keys()}.')\n\n def _community_clustering(self, data, k=20, show_plot=False, **kwargs):\n \"\"\"\n Louvain clustering\n\n Modifies the `id` row attribute of the assay.\n\n This is a graph-based clustering approach where\n the graph structure uses the Jaccard similarity coefficient as\n the edge weights, which is followed by a community\n cluster detection method (Louvain).\n\n Currently, the highest k value is used to set labels.\n The user expected is expected to try various k values.\n\n The plot for k vs number of clusters is provided to\n the user to interpret the accuracy of the k selected.\n\n Parameters\n ----------\n data : numpy.ndarray\n The array to use to create an SNN graph.\n k : int\n The number of nearest neighbors to consider\n when creating the SNN graph.\n show_plot : bool\n If true, then a plot for various k-values is\n shown to ascertain whether the provided k-value\n is sufficient.\n kwargs : dict\n Passed to the NearestNeighbors algorithm.\n\n Raises\n ------\n ValueError\n When the igraph package is not installed\n \"\"\"\n\n try:\n import igraph\n except ImportError as err:\n raise ValueError(\"The igraph library is required for community clustering.\") from err\n\n # Find the nearest neighbors\n print(\"Creating the Shared Nearest Neighbors graph.\")\n knn = NearestNeighbors(n_neighbors=k, algorithm='ball_tree', **kwargs).fit(data)\n dist, nbrs = knn.kneighbors(data)\n\n # Set the edges\n v1 = 0\n edges = []\n for v1_nbrs in nbrs:\n for v2 in v1_nbrs:\n edges.append((v1, v2))\n v1 += 1\n\n edges = np.array(edges)\n\n # Set the weight using Jaccard similarity\n # Optimize this step further, if possible\n print('-' * 50)\n i = 0\n weights = []\n similarities = {}\n nbrs = [set(t) for t in nbrs]\n for e in edges:\n if (i % (edges.shape[0] // 50) == 0):\n print('#', flush=True, end='')\n\n i += 1\n if (e[0], e[1]) not in similarities:\n intersection = len(nbrs[e[0]].intersection(nbrs[e[1]]))\n common = 2 * k - intersection\n similarity = intersection / common\n similarities[(e[1], e[0])] = similarity\n else:\n similarity = similarities[(e[0], e[1])]\n\n weights.append(similarity)\n\n # Construct the graphs and identify clusters for various values of k less than the given value\n print(\"\\nIdentifying clusters using Louvain community detection.\")\n edges = np.array(edges).reshape(len(nbrs), k, 2)\n weights = np.array(weights).reshape(len(nbrs), k, 1)\n sizes = np.linspace(10, k, 5) if show_plot else [k]\n clusters_found = []\n for size in sizes:\n size = int(size)\n sub_edges = np.array(edges)[:, :size, :]\n sub_edges = sub_edges.reshape(\n sub_edges.shape[0] * sub_edges.shape[1], sub_edges.shape[2])\n sub_weights = np.array(weights)[:, :size]\n sub_weights = sub_weights.reshape(\n sub_weights.shape[0] * sub_weights.shape[1])\n\n g = igraph.Graph()\n g.add_vertices(len(self.barcodes()))\n g.add_edges(sub_edges)\n g.es['weight'] = sub_weights\n\n vc = g.community_multilevel(weights='weight')\n vc_labels = np.array(vc.as_cover().membership).flatten()\n\n clusters_found.append((size, len(set(vc_labels))))\n\n print(f'\\nNumber of clusters found: {clusters_found[-1][1]}.')\n print(f'Modularity: {vc.modularity:.3f}')\n\n if show_plot:\n x = np.array(clusters_found)[:, 0]\n y = np.array(clusters_found)[:, 1]\n plt.figure(figsize=(10, 10))\n plt.scatter(x, y)\n plt.ylabel('Number of clusters found')\n plt.xlabel('Number of nearest neighbors used')\n\n labels, idx, cnt = np.unique(vc_labels, return_inverse=True, return_counts=True)\n labels[cnt.argsort()[::-1]] = np.arange(len(labels)) + 1\n labels = labels[idx]\n self.set_labels(labels)\n\n def group_clusters(self, attribute):\n \"\"\"\n Relabel clusters based on expression.\n\n Takes an assay object and reorders the\n cluster labels so that the clusters are\n grouped hierarchically. Ordering uses\n the median cluster value in the layer\n denoted by the input parameter \"layer.\"\n\n For example, after implementation, all protein\n clusters with high CD3 might be numbered next\n to each other.\n\n Parameters\n ----------\n layer : str / np.array\n The name of the attribute from the assay object used\n to hierarchically group the clusters. Uses\n :meth:`_Assay.get_attribute` to retrieve the values\n constrained by `row`\n\n \"\"\"\n\n labels = self.get_labels()\n\n u, lbl = np.unique(labels, return_inverse=True)\n\n nc = len(np.unique(lbl))\n\n if nc == 1:\n return\n\n data = self.get_attribute(attribute, constraint='row').values\n z = np.zeros((nc, len(self.ids())))\n for jj in range(nc):\n z[jj, :] = np.median(data[lbl == jj, :], axis=0)\n\n D = pdist(z, 'cosine')\n tree = hierarchy.linkage(D)\n s = hierarchy.leaves_list(hierarchy.optimal_leaf_ordering(tree, D))\n\n s = np.argsort(s) + 1\n\n self.set_labels(s[lbl])\n\n def scale_data(self, layer, output_label=SCALED_LABEL):\n \"\"\"\n Z-score normalization\n\n Ensures that all `id` have a mean\n of 0 and a standard deviation of 1.\n\n Adds `scaled_counts` to the layers.\n\n Parameters\n ----------\n layer : str / np.array\n The data to scale. Uses :meth:`_Assay.get_attribute`\n to retrieve the value constrained by `row+col`.\n output_label : str, default SCALED_LABEL\n The name of the output layer.\n \"\"\"\n\n data = self.get_attribute(layer, constraint='row+col').values\n data = data - data.mean(axis=0)\n data = data / data.std(axis=0)\n self.add_layer(output_label, data)\n\n def run_pca(self, attribute, components, output_label=PCA_LABEL, show_plot=False, **kwargs):\n \"\"\"\n Principal component analysis\n\n Adds `output_label` to the row attributes.\n\n Parameters\n ----------\n attribute : str\n The attribute to be used for PCA. Uses\n :meth:`_Assay.get_attribute` to retrieve\n the values constrained by `row`.\n components : int\n The number of PCA components to reduce to.\n output_label : str, default PCA_LABEL\n The name of the row attribute to store\n the data in.\n show_plot : bool\n Show the plot for the explained variance\n by the PCAs.\n kwargs : dict\n Passed to the PCA.\n\n Raises\n ------\n ValueError\n When both layer and attribute are provided.\n Only one is permitted at a time.\n \"\"\"\n\n data = self.get_attribute(attribute, constraint='row').values\n components = np.minimum(components, data.shape[1])\n\n pca = PCA(n_components=components, **kwargs)\n pca.fit(data.T)\n pca_data = pca.components_.T\n self.add_row_attr(output_label, pca_data)\n\n if show_plot:\n plt.figure(figsize=(10, 10))\n plt.scatter(np.arange(components), pca.explained_variance_ratio_)\n plt.xlabel('Principal component')\n plt.ylabel('Explained variance ratio')\n plt.title(self.title)\n\n def run_umap(self, attribute, output_label=UMAP_LABEL, **kwargs):\n \"\"\"\n Perform UMAP on the given data.\n\n Adds `output_label` to the row attributes.\n\n Parameters\n ----------\n attribute : str\n The attribute to be used for UMAP. Uses\n :met:`_Assay.get_attribute` to retrieve the values\n constrained by `row`.\n output_label : str, default UMAP_LABEL\n The name of the row attribute where the UMAP output is.\n kwargs : dictionary\n Passed to UMAP.\n\n Raises\n ------\n ValueError\n When both layer and attribute are provided.\n Only one is permitted at a time.\n \"\"\"\n\n data = self.get_attribute(attribute, constraint='row').values\n\n fit = umap.UMAP(**kwargs)\n u = fit.fit_transform(data)\n\n # Sets UMAP x/y range ~[-10,10]\n u = stats.zscore(u) * 5\n\n self.add_row_attr(output_label, u)\n\n def run_lda(self, layer, attribute, output_label, cycles=1):\n \"\"\"\n Perform LDA on a row attribute using a layer to type clusters.\n\n The row attribute is dimensionally reduced using UMAP.\n\n Adds `output_label` to the row attributes.\n\n Parameters\n ----------\n layer : str / np.array\n The layer used for cluster typing. Uses\n :meth:`_Assay.get_attribute` to retrieve\n the value constrained by `row+col`.\n attribute : str / np.array\n The row attribute to be used for UMAP. Uses\n :meth:`_Assay.get_attribute` to retrieve\n the value constrained by `row`.\n output_label : str\n The name of the row attribute where the UMAP output is.\n cycles : int\n The number of rounds of LDA to perform.\n kwargs : dictionary\n Passed to UMAP.\n \"\"\"\n\n cnt = 0\n\n data = self.get_attribute(layer, constraint='row+col').values\n attribute = self.get_attribute(layer, constraint='row').values\n self.add_row_attr(output_label, attribute)\n\n for jj in range(cycles):\n\n u = self.row_attrs[output_label]\n nc = np.minimum(30, int(data.shape[0] / 20))\n\n kmeans = KMeans(n_clusters=nc, random_state=1).fit(u)\n lbl = kmeans.labels_\n\n clust_profile = np.zeros((nc, data.shape[1], 2))\n\n for ii in range(nc):\n clust_profile[ii, :, 0] = np.median(data[lbl == ii, :], axis=0)\n clust_profile[ii, :, 1] = np.std(data[lbl == ii, :], axis=0)\n\n clust_project = np.zeros((data.shape[0], nc))\n for ii in range(nc):\n coord = np.absolute(\n (data - clust_profile[ii, :, 0][None, :]) / (clust_profile[ii, :, 1][None, :] + 0.0001))\n clust_project[:, ii] = 1 / (np.sum(np.minimum(coord, 10), axis=1) + .1)\n\n X = clust_project\n\n self.add_row_attr(output_label, X)\n\n self.run_umap(attribute=output_label,\n output_label=output_label,\n n_neighbors=50,\n metric='cosine',\n min_dist=0,\n spread=1,\n repulsion_strength=3,\n negative_sample_rate=10,\n random_state=42)\n\n cnt = cnt + 1\n print(f'Round {cnt} complete.')\n\n # --------- Plotting\n\n def scatterplot(self, attribute, colorby=None, features=None, title=''):\n \"\"\"\n Scatter plot for all barcodes.\n\n Requires a 2-D row attribute.\n\n If `colorby` is None, then this plot can be used\n to create custom clusters using the plotly lasso tool.\n\n Not selecting any cell deselects all clusters, and double\n clicking refocuses all points.\n\n Parameters\n ----------\n attribute : str / np.array\n The row attribute to use as coordinates,\n This row attribute must be 2D. Uses :meth:`_Assay.get_attribute`\n to retrieve the values constrained by `row`.\n The shape must be `(#cells, 2)`.\n colorby : str / np.array\n The values used to color the scatterplot.\n Uses :meth:`_Assay.get_attribute` to retrieve\n the values constrained by `row`.\n The shape must be `(#cells, *)` if 2D, otherwise\n the shape must be `(#cells)`.\n 1. None\n The scatterplot is not colored and\n highlighting using the lasso tool\n can be used to select subpopulations.\n 2. Any 1D attribute\n In case 'label' is provided then the\n stored paltte is used. If the values\n are strings, then a discrete color map\n is assumed. For numerical values a\n continuous color scale is used.\n 3. Any 2D attribute\n One dimension must match the number of\n cells in the assay.\n 4. 'density'\n Colors the plot based on the density of\n the nearby points.\n features : list-like\n In the case that `colorby` is a layer then\n features sets the subset of features to\n color the plot by.\n\n In the case that `colorby` is a 2D array\n features sets the title of each scatterplot.\n In this case, the length of `features`\n must match the number of columns in the\n `colorby` 2D array.\n title : str\n The title given to the plot after\n the name of the assay.\n\n Returns\n -------\n fig : plotly.graph_objs.FigureWidget\n\n Raises\n ------\n ValueError\n Raised when the attribute is not present in the assay\n or when length of `features` does not match the second\n dimension of `colorby`.\n \"\"\"\n\n if features is not None and colorby is None:\n raise ValueError(\"'colorby' must be provided when features are given.\")\n\n # Get data\n if isinstance(attribute, str):\n xlabel, ylabel = f'{attribute}-1', f'{attribute}-2'\n else:\n xlabel, ylabel = 'x', 'y'\n\n data = self.get_attribute(attribute, constraint='row+col', features=[xlabel, ylabel])\n data.loc[:, 'hovertext'] = 'label: ' + self.get_labels().astype(object) + '
' + self.barcodes().astype(object)\n\n # Determine source of colorby\n colorby_type = colorby if isinstance(colorby, str) else '----'\n if colorby_type == 'density':\n x = data.iloc[:, 0]\n y = data.iloc[:, 1]\n xy = np.vstack([x, y])\n colorby = gaussian_kde(xy)(xy)\n idx = colorby.argsort()\n data, colorby = data.iloc[idx, :], colorby[idx][:, None]\n elif colorby is not None:\n feature_values = self.get_attribute(colorby, constraint='row', features=features)\n colorby = feature_values.values\n features = feature_values.columns.values\n\n # Plot\n layout = dict(xaxis=dict(zeroline=False, title_text=xlabel),\n yaxis=dict(zeroline=False, title_text=ylabel),\n title=f'{self.title}{title}',\n legend_tracegroupgap=0,\n dragmode='pan',\n hovermode='closest',\n template='simple_white',\n coloraxis_colorbar_title=colorby_type)\n\n if colorby is None:\n p = px.scatter(data, x=xlabel, y=ylabel, hover_data=['hovertext'])\n p.data[0].marker.color = np.array([COLORS[0]] * self.shape[0])\n fw = go.FigureWidget(data=p.data, layout=layout)\n fw.update_layout(dragmode='lasso', width=600, height=550)\n fw.update_traces(marker_size=4, hovertemplate='%{customdata}
')[-1] for i in d.customdata.flatten()])\n d.marker.color = np.array([d.marker.color] * len(d.x))\n else:\n fw.update_layout(dragmode='zoom', width=600, height=550)\n\n else:\n val_1, val_99 = clipped_values(colorby.flatten())\n\n datas = pd.DataFrame()\n for feature in features:\n d = data.copy()\n d.loc[:, 'value'] = feature_values.loc[:, feature].values\n d.loc[:, 'name'] = feature\n datas = pd.concat([datas, d])\n\n nplots = len(features)\n nrows = round(nplots ** 0.5)\n ncols = nplots // nrows + min(1, nplots % nrows)\n\n p = px.scatter(datas, x=xlabel, y=ylabel, color='value', hover_data=['hovertext'],\n facet_row_spacing=max(0.01, 0.2 / nrows), facet_col_spacing=max(0.01, 0.2 / ncols),\n facet_col='name', facet_col_wrap=ncols, color_discrete_sequence=COLORS,\n width=max(600, 350 * ncols), height=max(350 * nrows, 550 / ncols))\n p.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n\n fw = go.Figure(p)\n\n if not isinstance(val_1, str):\n layout.update(coloraxis=dict(cauto=False,\n cmax=val_99,\n cmin=val_1,\n colorbar=dict(thickness=25,\n len=1 / nrows,\n yanchor=\"top\",\n y=1.035,\n x=1.05,\n ticks=\"outside\")))\n fw.update_layout(layout)\n fw.update_traces(marker_size=4, hovertemplate='%{customdata}
%{x}%{customdata} ',\n showlegend=False,\n showscale=False)\n fig.add_trace(vals, row=1, col=2)\n\n # Add dividers\n if ordered:\n for i in range(len(cnt_ordered_labs) - 1):\n start = cnt_ordered_labs[:i + 1].sum()\n fig.add_hline(start - 0.5, line_color='rgba(255,255,255,1)')\n else:\n tickvals = []\n ticktexts = []\n\n # Update layout\n val_1, val_99 = clipped_values(data.values.flatten())\n fig.update_layout(coloraxis_colorscale='magma',\n legend_tracegroupgap=0,\n width=800,\n height=800,\n title_text=f'{self.title}{title}',\n template='plotly_white',\n coloraxis=dict(cmax=val_99,\n cmin=val_1,\n cauto=False,\n colorbar=dict(thickness=15,\n len=0.3,\n yanchor=\"bottom\",\n y=0,\n ticks=\"outside\",\n title=data.index.name)),\n yaxis=dict(type='category',\n tickvals=tickvals,\n ticktext=ticktexts,\n tickformat='{^text}',\n range=[0, data.shape[0]]),\n yaxis2=dict(type='category',\n range=[0, data.shape[0]]),\n xaxis=dict(ticktext=data.columns, # Added for heatmap selection\n showticklabels=False),\n xaxis2=dict(tickangle=-90,\n tickvals=np.arange(data.shape[1]),\n ticktext=data.columns,\n tickmode='array'))\n\n ann_text = f'{data.shape[0]} cells, {data.shape[1]} features'\n if convolve != 0 and ordered:\n ann_text = ann_text + '
Values smoothed using a moving average.'\n\n ann = go.layout.Annotation(text=ann_text,\n xanchor='left',\n yanchor='bottom',\n align='left',\n showarrow=False,\n xref='x2',\n yref='y2',\n x=-0.5,\n y=data.shape[0],\n font_size=10)\n fig.add_annotation(ann)\n\n self.selected_ids = np.array([])\n self.__heatmap = go.FigureWidget(fig)\n self.__heatmap.data[1].on_click(self._heatmap_selection)\n\n return self.__heatmap\n\n def violinplot(self, attribute, splitby=None, features=None, title=''):\n \"\"\"\n Violin plot for all barcodes.\n\n Parameters\n ----------\n attribute : str / np.array\n The attribute used to plot the values on the violinplot.\n Uses :meth:`_Assay.get_attribute` to retrieve the values.\n constrained by 'row'.\n splitby : str / np.array\n The values used to split and color the violinplot.\n Uses :meth:`_Assay.get_attribute` to retrieve\n the values. The shape must be `(#cells)`.\n 1. None\n The violinplot is not colored.\n 2. Any 1D attribute\n In case 'label' is provided then the\n stored paltte is used. A discrete color map\n is used for all other cases.\n features : list-like\n In the case that `attribute` is a layer then\n features sets the subset of features to plot.\n Otherwise it sets the name of each feature shown.\n title : str\n The title given to the plot after\n the name of the assay.\n\n Returns\n -------\n fig : plotly.graph_objects.Figure\n \"\"\"\n\n data = self.get_attribute(attribute, constraint='row', features=features)\n if features is None:\n features = data.columns.values\n\n layout = dict(width=min(1200, data.shape[1] * 200),\n height=550,\n xaxis=dict(zeroline=False),\n yaxis=dict(zeroline=False),\n title=f'{self.title}{title}',\n dragmode='zoom',\n hovermode='closest',\n template='simple_white')\n\n if splitby is None:\n data = data.melt(var_name=ID)\n fig = go.Figure(layout=layout)\n\n for i, df_i in data.groupby(ID):\n fig.add_trace(go.Violin(x=df_i[ID],\n y=df_i['value'],\n scalemode='width',\n scalegroup=i,\n box_visible=True,\n box_width=0.05,\n spanmode='hard',\n points=False,\n line_color=COLORS[0],\n showlegend=False))\n else:\n splitby = self.get_attribute(splitby, constraint='row')\n if splitby.shape[1] != 1:\n raise ValueError('Only one dimensional `splitby` values are permitted.')\n\n data.loc[:, LABEL] = splitby.values\n data = data.melt(id_vars=LABEL, var_name=ID)\n\n un_labs = np.unique(splitby)\n line_colors = {}\n for i in range(len(un_labs)):\n line_colors[un_labs[i]] = self.get_palette()[un_labs[i]] if splitby.index.name == LABEL else COLORS[i]\n\n fig = go.Figure(layout=layout)\n for grp, df in data.groupby([LABEL, ID]):\n lab, i = grp\n showlegend = features[0] == str(i)\n fig.add_trace(go.Violin(x=df[ID],\n y=df['value'],\n scalemode='width',\n scalegroup=str(i) + str(lab),\n box_visible=True,\n box_width=0.02,\n meanline_visible=True,\n spanmode='hard',\n points=False,\n legendgroup=str(lab),\n name=str(lab),\n line_color=line_colors[lab],\n showlegend=showlegend))\n\n fig.update_layout(violinmode='group',\n width=min(1200, data.shape[1] * len(set(self.get_labels())) * 100))\n\n return fig\n\n def ridgeplot(self, attribute, splitby=None, features=None, title=''):\n \"\"\"\n Ridge plot for all barcodes.\n\n Parameters\n ----------\n attribute : str / np.array\n The attribute used to plot the values on the ridgeplot.\n Uses :meth:`_Assay.get_attribute` to retrieve the values.\n constrained by 'row'.\n splitby : str / np.array\n The values used to split and color the ridgeplot.\n Uses :meth:`_Assay.get_attribute` to retrieve\n the values. The shape must be `(#cells)`.\n 1. None\n The ridgeplot is not colored.\n 2. Any 1D attribute\n In case 'label' is provided then the\n stored paltte is used. A discrete color map\n is used for all other cases.\n features : list-like\n In the case that `attribute` is a layer then\n features sets the subset of features to plot.\n Otherwise it sets the name of each feature shown.\n title : str\n The title given to the plot after\n the name of the assay.\n\n Returns\n -------\n fig: plotly.graph_objects.Figure\n \"\"\"\n\n data = self.get_attribute(attribute, constraint='row', features=features)\n features = data.columns.values\n name = f'{data.index.name}'\n\n layout = dict(xaxis=dict(zeroline=False, showgrid=False),\n title=f'{self.title} {name} {title}',\n legend_tracegroupgap=0,\n dragmode='lasso',\n hovermode='closest',\n template='simple_white')\n\n if splitby is None:\n data = data.melt(var_name=ID)\n fig = go.Figure(layout=layout)\n\n for i, df_i in data.groupby(ID):\n fig.add_trace(go.Violin(y=df_i[ID],\n x=df_i['value'],\n scalemode='width',\n scalegroup=i,\n box_visible=True,\n box_width=0.05,\n spanmode='soft',\n orientation='h',\n side='positive',\n width=3,\n points=False,\n line_color=COLORS[0],\n line_width=1,\n showlegend=False))\n\n fig.update_layout(xaxis_showgrid=False,\n xaxis_zeroline=False,\n xaxis_title=name,\n yaxis_categoryarray=features,\n width=800,\n height=max(600, len(features) * 50),)\n else:\n nplots = data.shape[1]\n nrows = round(nplots ** 0.5)\n ncols = nplots // nrows + min(1, nplots % nrows)\n\n splitby = self.get_attribute(splitby, constraint='row')\n if splitby.shape[1] != 1:\n raise ValueError('Only one dimensional `splitby` values are permitted.')\n\n data.loc[:, LABEL] = splitby.values\n data = data.melt(id_vars=LABEL, var_name=ID)\n un_labs = np.unique(data[LABEL])\n\n line_colors = {}\n for i in range(len(un_labs)):\n line_colors[un_labs[i]] = self.get_palette()[un_labs[i]] if splitby.index.name == LABEL else COLORS[i]\n\n fig = make_subplots(rows=nrows,\n cols=ncols,\n subplot_titles=features,\n horizontal_spacing=0.1 / ncols,\n vertical_spacing=0.2 / nrows)\n\n for grp, df in data.groupby([LABEL, ID]):\n lab, i = grp\n showlegend = features[0] == i\n row_num = np.where(features == i)[0][0] // ncols + 1\n col_num = np.where(features == i)[0][0] % ncols + 1\n\n fig.add_trace(go.Violin(y=df[LABEL],\n x=df['value'],\n scalemode='width',\n spanmode='soft',\n legendgroup=str(lab),\n name=str(lab),\n line_color=line_colors[lab],\n orientation='h',\n side='positive',\n width=3,\n points=False,\n scalegroup=i,\n showlegend=showlegend,\n line_width=1),\n row=row_num, col=col_num)\n\n fig.update_layout(layout,\n width=max(500, 300 * ncols),\n height=max(500, max(300 * nrows, 30 * len(un_labs) * nrows)))\n fig.update_yaxes(tickvals=un_labs,\n ticktext=[''] * len(un_labs),\n showgrid=True,\n type='category')\n\n return fig\n\n def stripplot(self, attribute, colorby=None, features=None, title=''):\n \"\"\"\n Strip plot for all barcodes.\n\n Generates a strip plot for the given ids (x-axis), and\n shows the layer (y-axis) and color_layer (color) for all barcodes.\n Additional lateral jitter is added to help visualize the distribution.\n\n Parameters\n ----------\n attribute : str / np.array\n The attribute used to plot the values on the ridgeplot.\n Uses :meth:`_Assay.get_attribute` to retrieve the values.\n constrained by 'row'.\n colorby : str / np.array\n The values used to color the stripplot.\n Uses :meth:`_Assay.get_attribute` to retrieve\n the values constrained by 'row'. It must be the\n same shape as attribute.\n features : list-like\n In the case that `attribute` is a layer then\n features sets the subset of features to plot.\n Otherwise it sets the name of each feature shown.\n title : str\n The title given to the plot after\n the name of the assay.\n\n Returns\n -------\n fig: plotly.graph_objects.Figure\n \"\"\"\n\n attribute = self.get_attribute(attribute, constraint='row', features=features)\n\n if colorby is None:\n colorby = pd.DataFrame(np.full_like(attribute, COLORS[0], dtype='%{{x:.2f}}% ')[-1] for i in trace.customdata.flatten()])[inds]\n # Remove bars from the color in which they are present\n # Add bars to the new color\n for k in self.selected_bars:\n if k != self.__brush_color:\n shift = np.isin(self.selected_bars[k], bars)\n self.selected_bars[self.__brush_color] = np.append(self.selected_bars[self.__brush_color], self.selected_bars[k][shift])\n self.selected_bars[k] = self.selected_bars[k][~shift]\n\n # Change the color of the bars which are moved\n new_colors = trace.marker.color.copy()\n new_colors[inds] = self.__brush_color\n trace.marker.color = new_colors\n\n def _heatmap_selection(self, trace, points, selector):\n \"\"\"\n Heatmap id selection.\n\n The selection of the ids on the heatmap\n created using plotly.\n\n This method is passed to the on_click\n method of the FigureWidget object in\n plotly. It is not meant to be used as-is.\n \"\"\"\n\n def switch_color(text):\n style_open = \"\"\n style_close = \"\"\n if style_open in text:\n t = text.replace(style_open, '').replace(style_close, '')\n else:\n t = style_open + str(text) + style_close\n\n return t\n\n if len(points.xs) != 0:\n click_point = points.xs[0]\n click_pos = np.where(trace.x == click_point)[0][0]\n ticks = self.__heatmap.layout.xaxis.ticktext.copy()\n hovertext = trace.x.copy()\n\n # Add tick to selected ids\n old_id = ticks[click_pos]\n new_id = switch_color(old_id)\n ticks[click_pos] = new_id\n if len(self.selected_ids) > 0 and new_id in self.selected_ids:\n self.selected_ids = np.delete(self.selected_ids, np.where(self.selected_ids == new_id))\n else:\n self.selected_ids = np.append(self.selected_ids, old_id)\n\n # Update color of tick text\n if set(self.__heatmap.layout.xaxis2.ticktext) == set(self.__heatmap.layout.xaxis.ticktext):\n self.__heatmap.update_layout(xaxis2_ticktext=ticks)\n self.__heatmap.update_layout(xaxis_ticktext=ticks)\n\n # Update color of hover text\n old_hovertext = hovertext[click_pos]\n new_hovertext = switch_color(old_hovertext)\n hovertext[click_pos] = new_hovertext\n\n trace.x = hovertext\n\n # --------- Statistics\n\n def feature_signature(self, layer, exclude_missing=False):\n \"\"\"\n T-Test on given data.\n\n Generate feature signatures for each cluster/feature pair across all barcodes\n using the supplied assay and layer. For each cluster/feature, returns median feature,\n standard deviation of feature, p-value, and the t-statistic of the feature in the cluster\n compared to all other clusters.\n\n Parameters\n ----------\n layer : string\n Layer name to be evaluated.\n exclude_missing : bool\n Whether missing dna data is considered.\n\n Returns\n -------\n med : dataframe\n The median value of the layer for each cluster/feature pair.\n stdev : dataframe\n The standard deviation of the layer for each cluster/feature pair.\n pval : dataframe\n The feature p-value of for each cluster vs other clusters.\n tstat : dataframe\n The value of the t-statistic corresponding to the pval.\n \"\"\"\n\n dat = deepcopy(self.layers[layer])\n features = self.ids()\n lbl = self.get_labels()\n u, uind = np.unique(lbl, return_inverse=True)\n nc = len(u)\n nf = len(features)\n\n med = pd.DataFrame(data=np.zeros((nf, nc)), index=features, columns=u)\n stdev = pd.DataFrame(data=np.zeros((nf, nc)), index=features, columns=u)\n pval = pd.DataFrame(data=np.zeros((nf, nc)), index=features, columns=u)\n tstat = pd.DataFrame(data=np.zeros((nf, nc)), index=features, columns=u)\n\n for i in range(nc):\n for j in range(nf):\n ind = (uind == i)\n nind = np.invert(ind)\n if (exclude_missing is True) & (self.name == 'dna'):\n ind = ind & (self.layers['NGT'][:, j] < 3)\n nind = nind & (self.layers['NGT'][:, j] < 3)\n\n if np.sum(ind) == 0:\n med.iloc[j, i] = np.nan\n pval.iloc[j, i] = 1\n else:\n med.iloc[j, i] = np.median(dat[ind, j], axis=0)\n stdev.iloc[j, i] = np.std(dat[ind, j], axis=0)\n if np.sum(ind) > 1:\n tstat.iloc[j, i], pval.iloc[j, i] = stats.ttest_ind(dat[ind, j], dat[nind, j], axis=0)\n else:\n pval.iloc[j, i] = 1\n\n return med, stdev, pval, tstat\n","repo_name":"haochenz96/mosaic","sub_path":"src/mosaic/assay.py","file_name":"assay.py","file_ext":"py","file_size_in_byte":88615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"1528638798","text":"# By Neerja Thakkar for \"Balancing sensitivity and specificity in\n# distinguishing TCR groups by CDR sequence similarity\"\n# See README for license information\n\n# command line arguments:\n# for beta:\n# python2 structural_cluster_histograms.py results/stcr_results/ STCR_global_beta_SW \"datasets/stcr_data/B3-10_11_12_13-A,datasets/stcr_data/B3-10_11-A,datasets/stcr_data/B3-12_A,datasets/stcr_data/B3-12-B,datasets/stcr_data/B3-13_14-A,datasets/stcr_data/B3-14-A\"\n# for alpha:\n# python2 structural_cluster_histograms.py results/stcr_results/ STCR_global_alpha_SW \"datasets/stcr_data/A3-9-A,datasets/stcr_data/A3-10-A,datasets/stcr_data/A3-13-B,datasets/stcr_data/A3-13-A,datasets/stcr_data/A3-10_11_12-A\"\n\n\nfrom matplotlib import pyplot as plt\nimport random\n\n\nfrom sw_scoring import getDistanceSW\nfrom parse_sequences import txttoseqlist\nimport seaborn as sns\nimport sys\n\n\n# inputs (from command line): list of cluster files, directory, title\nif len(sys.argv) < 3:\n print(\"need the following arguments: output directory, file title, cluster files\")\n sys.exit()\nelse:\n output_directory = sys.argv[1]\n title = sys.argv[2]\n cluster_file_list = sys.argv[3].split(',')\n\n\n# return a list with the min scores of cdr1 compared to cdrs2, cdr_list1 = cdr_list2\ndef calculate_min_distances_within(cdr_list1, cdr_list2):\n scores = []\n # for each cdr in cdrs1, find the min score\n for cdr1 in cdr_list1:\n min = 1000000\n for cdr2 in cdr_list2:\n if cdr1 != cdr2:\n score = getDistanceSW(cdr1, cdr2, length_dep=True, gap_penalty=-10)\n if score < min:\n min = score\n scores.append(min)\n return scores\n\n# return a list with the min scores of cdr1 compared to cdrs2, cdr_list1 != cdr_list2\n# same method as above except that the cdr in cdr list 2 must not be in cdr list 1\ndef calculate_min_distances_between(cdr_list1, cdr_list2):\n scores = []\n # for each cdr in cdrs1, find the min score\n for cdr1 in cdr_list1:\n min = 100000\n for cdr2 in cdr_list2:\n if cdr1 != cdr2 and cdr2 not in cdr_list1:\n score = getDistanceSW(cdr1, cdr2, length_dep=True, gap_penalty=-10)\n if score < min:\n min = score\n scores.append(min)\n return scores\n\n\n# generate a PDF and a CDF of the data\ndef generate_plots(self_scores, non_self_scores, output_directory, title):\n sns.set(font_scale=2) # crazy big\n\n # density plot - PDF\n figure = sns.kdeplot(self_scores, shade=True)\n figure = sns.kdeplot(non_self_scores, shade=True)\n figure.set(ylim=(0, 5))\n fig = figure.get_figure()\n fig.show()\n fig.savefig(output_directory + title + \"_kde_updated.pdf\")\n\n plt.figure() # new figure\n figure = sns.kdeplot(self_scores, shade=True, cumulative = True)\n figure = sns.kdeplot(non_self_scores, shade=True, cumulative = True)\n figure.set(ylim=(0, 1))\n fig = figure.get_figure()\n fig.savefig(output_directory + title + \"_kde_cumulative_updated.pdf\")\n\nprint(\"calculating self-scores...\")\n# calculate self-scores\nall_self_scores = []\nnum_self_scores = 0\nfor file in cluster_file_list:\n cdrs = txttoseqlist(file)\n cdrs = set(cdrs)\n cdrs = list(cdrs)\n scores = calculate_min_distances_within(cdrs, cdrs)\n all_self_scores += scores\n num_self_scores += len(scores)\nprint(all_self_scores)\n\nprint(\"calculating non-self scores...\")\n# calculate non-self scores\nall_non_self_scores = []\nnum_non_self_scores = 0\nfor file1 in cluster_file_list:\n for file2 in cluster_file_list:\n if file1 != file2:\n cdrs1 = list(set(txttoseqlist(file1)))\n cdrs2 = list(set(txttoseqlist(file2)))\n scores = calculate_min_distances_between(cdrs1, cdrs2)\n all_non_self_scores += scores\n num_non_self_scores += len(scores)\n\nfor i in range(len(all_non_self_scores)):\n if all_non_self_scores[i] == 1.0:\n all_non_self_scores[i] = random.uniform(0.9, 1.0)\n\nprint(all_non_self_scores)\n\n\ngenerate_plots(all_self_scores, all_non_self_scores, output_directory, title)\n\n","repo_name":"neerjathakkar/Distinguishing-TCR-Groups","sub_path":"structural_cluster_histograms.py","file_name":"structural_cluster_histograms.py","file_ext":"py","file_size_in_byte":4106,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"70098998481","text":"from collections import defaultdict\nfrom enum import Enum\nfrom queue import Queue\nfrom market import Order\n\n\n\nclass OrderBook:\n def __init__(self) -> None:\n self.bids = defaultdict(Queue)\n self.offers = defaultdict(Queue)\n\n @property\n def highest_bid(self):\n if self.bids:\n return max(price for price in self.bids.keys() if self.bids[price].empty() is not True)\n else:\n return None\n\n @property\n def lowest_offer(self):\n if self.offers:\n return min(price for price in self.offers.keys() if self.offers[price].empty() is not True)\n else:\n return float('inf')\n\n def order(self, order:Order):\n if order.type == Type.BID:\n self.bids[order.price].put(order)\n elif order.type == Type.OFFER:\n self.offers[order.price].put(order)\n\n def match(self, order:Order):\n if order.type == Type.BID:\n if order.price >= self.lowest_offer:\n matched_bid = self.bids[order.price].get()\n matched_offer = self.offers[self.lowest_offer].get()\n print('MATCH BID {0} = OFFER {1}'.format(matched_bid, matched_offer))\n return matched_bid, matched_offer\n else:\n print(\"No Match\")\n return None, None\n if order.type == Type.OFFER:\n if not self.bids[order.price].empty():\n matched_bid = self.bids[order.price].get()\n matched_offer = self.offers[order.price].get()\n print('MATCH BID {0} = OFFER {1}'.format(matched_bid, matched_offer))\n\n else:\n print(\"No Match\")\n\n def match_all(self):\n executed_bids = []\n executed_offers = []\n bids = self.bids\n for _, orders in bids.items():\n for order in list(orders.queue):\n bids,offers = self.match(order)\n if bids != None: executed_bids.append(bids)\n if offers != None: executed_offers.append(offers)\n \n return executed_bids, executed_offers\n\n def show(self):\n print('---BID---')\n for price,orders in self.bids.items():\n for order in list(orders.queue):\n print(order)\n print('--OFFER--')\n for price,orders in self.offers.items():\n for order in list(orders.queue):\n print(order)\n\nif __name__ == \"__main__\":\n book = OrderBook()\n\n orders = [\n Order(1,5,Type.OFFER),\n Order(2,5,Type.OFFER),\n Order(3,6,Type.OFFER),\n Order(4,6,Type.OFFER),\n Order(4,7,Type.OFFER),\n Order(5,4,Type.BID),\n Order(6,4,Type.BID),\n Order(7,4,Type.BID),\n Order(8,5,Type.BID),\n Order(9,6,Type.BID),\n Order(9,7,Type.BID),\n Order(10,4,Type.OFFER),\n ]\n\n for order in orders:\n book.order(order)\n\n bids, offers = book.match_all()\n print(bids)\n print(offers)\n print('{0} : {1}'.format(book.highest_bid, book.lowest_offer))\n book.show()\n","repo_name":"jc-doyle/market-model","sub_path":"orderbook.py","file_name":"orderbook.py","file_ext":"py","file_size_in_byte":3058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"5975017884","text":"\n#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\" A photosphere class. \"\"\"\n\nfrom __future__ import division, absolute_import, print_function\n\n__author__ = \"Andy Casey
Сервис стал корневым
' in mailoutbox[0].body\n assert mailoutbox[0].to == [data.meta_other.owner.email]\n\n\n@patch('plan.services.tasks.drop_requests')\n@patch('plan.services.tasks.notify_staff')\ndef test_close_service(notify_staff, drop_requests, data):\n tasks.close_service_admin(data.service.id)\n notify_staff.apply_async.assert_called_once_with(\n args=[data.service.id],\n countdown=settings.ABC_DEFAULT_COUNTDOWN\n )\n drop_requests.apply_async.assert_called_once_with(\n args=[data.service.id],\n countdown=settings.ABC_DEFAULT_COUNTDOWN\n )\n\n\ndef test_update_idm_roles_count(patch_tvm):\n service = factories.ServiceFactory()\n mock_response = MockIdmResponse({\n 'meta': {\n 'total_count': 2,\n 'next': None\n },\n 'objects': ['some test objects'],\n })\n\n def mocked_session_send(*args, **kwargs):\n return Response(200, mock_response.response)\n\n with patch('requests.Session.send') as patched:\n patched.side_effect = mocked_session_send\n tasks.update_service_idm_roles_count(service.id)\n\n service.refresh_from_db()\n assert service.idm_roles_count == 2\n\n\ndef test_update_puncher_rules_count_from_api():\n service = factories.ServiceFactory()\n\n def mocked_session_send(*args, **kwargs):\n response = {\n 'count': 5,\n 'rules': []\n }\n\n return Response(200, json.dumps(response))\n\n with patch('requests.Session.send') as patched:\n patched.side_effect = mocked_session_send\n tasks.update_service_puncher_rules_count_from_api([service.id])\n\n service.refresh_from_db()\n assert service.puncher_rules_count == 5\n\n\ndef test_sync_owners(owner_role):\n user = factories.StaffFactory()\n another_user = factories.StaffFactory()\n\n service_with_no_owner = factories.ServiceFactory(slug='no_owner')\n factories.ServiceMemberFactory(\n service=service_with_no_owner,\n role=owner_role,\n staff=user,\n )\n service_with_no_owner.owner = None\n service_with_no_owner.save()\n\n service_with_two_heads = factories.ServiceFactory(slug='two_heads')\n to_delete = factories.ServiceMemberFactory(service=service_with_two_heads, role=owner_role, staff=user)\n factories.ServiceMemberFactory(service=service_with_two_heads, role=owner_role, staff=service_with_two_heads.owner)\n assert models.ServiceMember.objects.filter(service=service_with_two_heads, role=owner_role).count() == 2\n\n service_with_different_head_and_owner = factories.ServiceFactory(slug='different_head_and_owner')\n factories.ServiceMemberFactory(service=service_with_different_head_and_owner, role=owner_role, staff=user)\n service_with_different_head_and_owner.owner = another_user\n service_with_different_head_and_owner.save()\n\n with patch('plan.api.idm.actions.deprive_role') as deprive_role:\n tasks.sync_owners()\n\n deprive_role.assert_called_once_with(\n to_delete, comment='Неактуальный руководитель'\n )\n\n service_with_no_owner.refresh_from_db()\n service_with_no_owner.fetch_owner()\n assert service_with_no_owner.owner is not None\n\n service_with_different_head_and_owner.refresh_from_db()\n service_with_different_head_and_owner.fetch_owner()\n head_sm = models.ServiceMember.objects.get(service=service_with_different_head_and_owner, role=owner_role)\n head = head_sm.staff\n assert service_with_different_head_and_owner.owner == head\n\n\ndef test_sync_owners_two_heads(owner_role):\n \"\"\"\n Роль старого руководителя по какой-то причине создана позже, чем нового.\n Остаться должна роль, указанная в service.owner\n \"\"\"\n\n user = factories.StaffFactory()\n service_with_two_heads = factories.ServiceFactory(slug='two_heads')\n factories.ServiceMemberFactory(\n service=service_with_two_heads,\n role=owner_role,\n staff=service_with_two_heads.owner\n )\n to_delete = factories.ServiceMemberFactory(service=service_with_two_heads, role=owner_role, staff=user)\n\n assert models.ServiceMember.objects.filter(service=service_with_two_heads, role=owner_role).count() == 2\n\n with patch('plan.api.idm.actions.deprive_role') as deprive_role:\n tasks.sync_owners()\n\n deprive_role.assert_called_once_with(\n to_delete, comment='Неактуальный руководитель'\n )\n\n\ndef test_notify_services_owners(mailoutbox):\n top_owner = factories.StaffFactory()\n child_owner = factories.StaffFactory()\n top_service = factories.ServiceFactory(owner=top_owner)\n factories.ServiceFactory(parent=top_service, owner=child_owner)\n factories.ServiceFactory(owner=top_owner)\n\n tasks.notify_services_owners()\n assert len(mailoutbox) == 2\n\n\ndef test_cleanup_service_requests():\n source = factories.ServiceFactory()\n move_request_a = factories.ServiceMoveRequestFactory(\n service=source,\n updated_at=timezone.now(),\n state=models.ServiceMoveRequest.REQUESTED,\n )\n move_request_b = factories.ServiceMoveRequestFactory(\n service=source,\n updated_at=timezone.now() + timezone.timedelta(seconds=1),\n state=models.ServiceMoveRequest.REQUESTED,\n )\n other_request = factories.ServiceMoveRequestFactory(\n state=models.ServiceMoveRequest.REQUESTED,\n )\n tasks.cleanup_service_requests()\n\n for request in [move_request_a, move_request_b, other_request]:\n request.refresh_from_db()\n\n assert move_request_a.state == models.ServiceMoveRequest.REJECTED\n assert move_request_b.state == models.ServiceMoveRequest.REQUESTED\n assert other_request.state == models.ServiceMoveRequest.REQUESTED\n\n\ndef test_find_memberships_in_staff():\n factories.ServiceFactory(slug='meta_other')\n service = factories.ServiceFactory()\n member1 = factories.ServiceMemberFactory(service=service)\n member2 = factories.ServiceMemberFactory(service=service)\n member3 = factories.ServiceMemberFactory(service=service)\n\n with patch('plan.staff.tasks.MembershipStaffImporter.get_objects') as get_objects:\n get_objects.return_value = [\n {\n 'id': member1.id,\n 'person': {'login': member1.staff.login},\n 'group': {'url': f'svc_{member1.service.slug}'},\n },\n {\n 'id': member1.id,\n 'person': {'login': member1.staff.login},\n 'group': {'url': f'svc_{member1.service.slug}_{member1.role.scope.slug}'},\n },\n {\n 'id': member3.id,\n 'person': {'login': member3.staff.login},\n 'group': {'url': f'svc_{member3.service.slug}'},\n },\n {\n 'id': member3.id,\n 'person': {'login': member3.staff.login},\n 'group': {'url': f'role_svc_{member3.service.slug}_{member3.role.scope.slug}'},\n },\n ]\n tasks.find_memberships_in_staff()\n\n for member, found in [\n (member1, True),\n (member2, False),\n (member3, True),\n ]:\n member.refresh_from_db()\n assert (member.found_in_staff_at is not None) == found\n\n\n@pytest.mark.parametrize('ok', [True, False])\ndef test_find_interactive_services(robot, ok, patch_tvm, responsible_role):\n service = factories.ServiceFactory()\n moves = []\n for i in range(10):\n moves.append(factories.ServiceMoveRequestFactory(service=service, completed_at=timezone.now()))\n with patch('plan.idm.manager.Manager._run_request') as run_request:\n response = Mock()\n response.ok = ok\n response.text = 'x'\n response.json.return_value = {}\n run_request.return_value = response\n tasks.find_interactive_services()\n\n for i in range(10):\n moves[i].refresh_from_db()\n if ok:\n assert moves[i].interactive_at is not None\n else:\n assert moves[i].interactive_at is None\n\n\ndef test_finish_all_service_move_requests():\n service_a = factories.ServiceFactory(parent=None, slug='A')\n service_b = factories.ServiceFactory(parent=None, slug='B')\n service_c = factories.ServiceFactory(parent=None, slug='C')\n requests = [\n factories.ServiceMoveRequestFactory(\n service=source,\n destination=destination,\n state=models.ServiceMoveRequest.PROCESSING_ABC,\n # чтобы письмо не собиралось\n from_creation=True,\n )\n for source, destination in [(service_b, service_a), (service_c, service_b)]\n ]\n tasks.finish_all_service_move_requests()\n for request in requests:\n request.refresh_from_db()\n assert request.state == models.ServiceMoveRequest.COMPLETED\n service_b.refresh_from_db()\n assert service_b.parent == service_a\n service_c.refresh_from_db()\n assert service_c.parent == service_b\n\n\n@pytest.mark.parametrize('async_mode', [False])\ndef test_move_service_abc_side(async_mode):\n \"\"\"Меняется родитель перемещаемого сервиса и обновляются уровни всех его потомков.\"\"\"\n service_a = factories.ServiceFactory(parent=None, slug='A')\n service_b = factories.ServiceFactory(parent=None, slug='B')\n factories.ServiceFactory(parent=service_b, slug='C')\n request = factories.ServiceMoveRequestFactory(\n service=service_b,\n destination=service_a,\n state=models.ServiceMoveRequest.PROCESSING_ABC,\n # чтобы письмо не собиралось\n from_creation=True,\n )\n with override_switch('async_move_service_abc_side', active=async_mode):\n tasks.move_service_abc_side(request.id)\n request.refresh_from_db()\n assert request.state == models.ServiceMoveRequest.COMPLETED\n service_b.refresh_from_db()\n assert service_b.parent == service_a\n with patch('plan.unistat.tasks.fix_service_closuretree_levels') as fix_service_closuretree_levels_mock:\n assert service_level_broken() == 0\n fix_service_closuretree_levels_mock.apply_async.assert_not_called()\n\n\ndef test_move_service_abc_side_to_root(service):\n \"\"\"Сервис перемещается на верхний уровень, уровни всех его потомков обновляются.\"\"\"\n factories.ServiceFactory(parent=service, slug='C')\n request = factories.ServiceMoveRequestFactory(\n service=service,\n destination=None,\n state=models.ServiceMoveRequest.PROCESSING_ABC,\n # чтобы письмо не собиралось\n from_creation=True,\n )\n tasks.move_service_abc_side(request.id)\n request.refresh_from_db()\n assert request.state == models.ServiceMoveRequest.COMPLETED\n service.refresh_from_db()\n assert service.parent is None\n with patch('plan.unistat.tasks.fix_service_closuretree_levels') as fix_service_closuretree_levels_mock:\n assert service_level_broken() == 0\n fix_service_closuretree_levels_mock.apply_async.assert_not_called()\n\n\ndef test_deep_disable_membership_inheritance():\n root = factories.ServiceFactory()\n factories.ServiceFactory(parent=root, membership_inheritance=True)\n node = factories.ServiceFactory(parent=root, membership_inheritance=True)\n factories.ServiceFactory(parent=node, membership_inheritance=True)\n factories.ServiceFactory(parent=node, membership_inheritance=False)\n services = (root, *root.get_descendants())\n\n assert any(s.membership_inheritance is True for s in services)\n call_command('deep_disable_membership_inheritance', service_id=root.id)\n for s in services:\n s.refresh_from_db()\n assert all(s.membership_inheritance is False for s in services)\n\n\ndef test_sync_department_members_state():\n active_state = models.ServiceMember.states.ACTIVE\n deprved_state = models.ServiceMember.states.DEPRIVED\n service = factories.ServiceFactory()\n department_deprived = factories.ServiceMemberDepartmentFactory(\n state=deprved_state,\n service=service,\n )\n member_should_be_deprived = factories.ServiceMemberFactory(\n from_department=department_deprived,\n state=active_state,\n service=service,\n )\n member_out_of_department = factories.ServiceMemberFactory(\n state=active_state,\n service=service,\n )\n department_active = factories.ServiceMemberDepartmentFactory(\n state=active_state,\n service=service,\n )\n member_should_stay_active = factories.ServiceMemberFactory(\n from_department=department_active,\n state=active_state,\n service=service,\n )\n\n tasks.sync_department_members_state()\n\n member_out_of_department.refresh_from_db()\n member_should_stay_active.refresh_from_db()\n\n assert member_should_stay_active.state == active_state\n assert member_out_of_department.state == active_state\n\n deprived_member = models.ServiceMember.all_states.get(pk=member_should_be_deprived.pk)\n assert deprived_member.state == deprved_state\n\n\n@pytest.mark.parametrize('tag_slug', settings.REVIEW_REQUIRED_TAG_SLUGS)\n@pytest.mark.parametrize('review_required', [True, False, None])\n@pytest.mark.parametrize('service_review_required', [True, False])\ndef test_update_service_review_policy(review_required_tags, review_required, service_review_required, tag_slug):\n service = factories.ServiceFactory()\n tag = models.ServiceTag.objects.get(slug=tag_slug)\n if service_review_required:\n service.tags.add(tag)\n\n with patch('plan.idm.nodes.Node.update') as node_update_mock:\n tasks.update_service_review_policy(service_slug=service.slug, review_required=review_required)\n\n node_update_mock.assert_has_calls([\n {'review_required': review_required is None and service_review_required or review_required}\n for _ in service.role_set.all()\n ])\n\n\n@pytest.mark.parametrize('tag_slug', settings.REVIEW_REQUIRED_TAG_SLUGS)\ndef test_check_service_review_policy_enabled(review_required_tags, tag_slug):\n fake_manager = object()\n tag = models.ServiceTag.objects.get(slug=tag_slug)\n\n class FakeFetchDataMethod:\n def __init__(self, results: list = None):\n self._results = results or []\n self.call_count = 0\n\n def __call__(self, *args, **kwargs):\n result = self._results[self.call_count]\n self.call_count += 1\n return result\n\n service = factories.ServiceFactory()\n\n sox_service = factories.ServiceFactory()\n sox_service.tags.add(tag)\n custom_roles = [\n factories.RoleFactory(service=service),\n factories.RoleFactory(service=sox_service),\n ]\n default_roles_count = models.Role.objects.filter(service=None).count()\n\n fake_fetch_responses = [\n {'review_required': bool(random.randint(0, 1))}\n for _ in range(len([role for role in custom_roles if role.service.review_required]) + default_roles_count)\n ]\n\n with patch('plan.api.idm.actions.idm_manager', return_value=fake_manager), \\\n patch(\n 'plan.idm.nodes.Node.fetch_data',\n new=FakeFetchDataMethod(fake_fetch_responses),\n ) as node_fetch_data_mock, \\\n patch('plan.idm.nodes.Node.update') as update_node_mock:\n tasks.check_service_review_policy_enabled()\n\n assert node_fetch_data_mock.call_count == \\\n len(fake_fetch_responses)\n update_node_mock.assert_has_calls([\n ((), {'manager': fake_manager, 'review_required': True})\n for fake_response in fake_fetch_responses if not fake_response['review_required']\n ])\n\n\n@pytest.mark.parametrize('diff_source', ['name', 'name_en', 'use_for_hr', 'leaf_code', 'parent'])\ndef test_sync_oebs_structure(data, yql_query_oebs_mock, diff_source):\n \"\"\"\n Проверяем синхронизацию данных с oebs выгрузкой в YT\n У одного сервиса сменили родителя, другой из выгрузки пропал\n Соответственно такие же изменения должны произойти у нас\n \"\"\"\n\n oebs_type = factories.ResourceTypeFactory(code=settings.OEBS_PRODUCT_RESOURCE_TYPE_CODE)\n factories.ServiceResourceFactory(\n service=data.service,\n resource=factories.ResourceFactory(type=oebs_type),\n type_id=oebs_type.id,\n state='granted',\n attributes={\n 'parent_oebs_id': 'GROUP_CODE',\n 'leaf_oebs_id': 'FAKE_ONE' if diff_source == 'leaf_code' else 'LEAF_CODE',\n },\n )\n\n data.service.name = 'some'\n data.service.name_en = 'some_en'\n data.service.use_for_group_only = True\n data.service.use_for_hr = False\n data.service.use_for_procurement = False\n data.service.use_for_revenue = True\n data.service.oebs_parent_id = 42\n\n if diff_source == 'name_en':\n deviation_reason = OEBS_DEVIATIONS_REASONS.NAME\n data.service.name_en = 'hello'\n elif diff_source == 'name':\n deviation_reason = OEBS_DEVIATIONS_REASONS.NAME\n data.service.name = 'hello'\n elif diff_source == 'use_for_hr':\n deviation_reason = OEBS_DEVIATIONS_REASONS.FLAG\n data.service.use_for_hr = True\n elif diff_source == 'leaf_code':\n deviation_reason = OEBS_DEVIATIONS_REASONS.RESOURCE\n else:\n deviation_reason = OEBS_DEVIATIONS_REASONS.PARENT\n\n data.metaservice.oebs_parent_id = 50\n data.metaservice.oebs_data = {\n 'smth': 'else',\n 'deviation_reason': 'flag'\n }\n data.service.save()\n data.metaservice.save()\n\n tasks.sync_oebs_structure()\n yql_query_oebs_mock.assert_called_once()\n\n data.service.refresh_from_db()\n data.metaservice.refresh_from_db()\n\n assert data.service.oebs_parent_id == 30\n assert data.service.oebs_data == {\n 'leaf_oebs_id': 'LEAF_CODE', 'name': 'some',\n 'name_en': 'some_en', 'oebs_parent_id': 30,\n 'parent_oebs_id': 'GROUP_CODE',\n 'use_for_group_only': True, 'use_for_hr': False,\n 'use_for_procurement': False,\n 'use_for_revenue': True,\n 'deviation_reason': deviation_reason,\n }\n\n assert data.metaservice.oebs_parent_id is None\n assert data.metaservice.oebs_data == {}\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/unit/services/test_tasks.py","file_name":"test_tasks.py","file_ext":"py","file_size_in_byte":40232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"8390255993","text":"from flask import Flask, render_template, request\nfrom parcer import parcing_base_horoscope, parcing_personal_horoscope\nfrom scripts import random_img, zodiac_sign, name_zodiac, create_home\nfrom scripts.birthday import write_and_read_birthday\nfrom scripts.constructor import constructor_horoscope\nimport create_db\n\napp = Flask(__name__)\n\n\n@app.route('/home')\ndef home():\n zodiac_numb = 12\n images = create_home.template_home(zodiac_numb)\n\n return render_template('home.html', data=images)\n\n\n@app.route('/zodiac_sign/- ' + line\n in_list = True\n else:\n if in_list:\n in_list = False\n line += '
This method defines the handler for the POST request of the create service\n script. It returns a message wrapped into a dictionary about the process of the insertion operation.
\n\nDescription:\n \n\n
Request headers:\n
- \n
Authorization: Bearer token in the format \"Bearer XXXX\"
\n
Request payload:\n
- \n
service_name (str):
\n service_validity (str):
\n service_owner_geoville (str):
\n service_comment (str):
\n external (str):
\n
Result:\n \n\n \"\"\"\n\n try:\n req_args = api.payload\n gemslog(LogLevel.INFO, f'Request payload: {req_args}', 'API-create_service')\n\n if check_service_name_existence(req_args['service_name'], database_config_file, database_config_section_api):\n error = BadRequestError('Service name exists already', '', '')\n gemslog(LogLevel.WARNING, f\"'message': {error.to_dict()}\", 'API-create_service')\n return {'message': error.to_dict()}, 400\n\n service_comment = None if 'service_comment' not in req_args else req_args['service_comment']\n service_id = generate_service_id_hash(req_args['service_name'], req_args['service_owner_geoville'])\n\n db_query = \"\"\"INSERT INTO customer.services\n ( \n service_id, service_name, service_validity, service_owner_geoville, service_comment, external\n ) \n VALUES\n (\n %s, %s, %s, %s, %s, %s\n );\n \"\"\"\n\n execute_database(db_query, (service_id, req_args['service_name'], req_args['service_validity'],\n req_args['service_owner_geoville'], service_comment, req_args['external']),\n database_config_file, database_config_section_api, True)\n\n except KeyError as err:\n error = BadRequestError(f'Key error resulted in a BadRequest: {err}', api.payload, traceback.format_exc())\n gemslog(LogLevel.WARNING, f\"'message': {error.to_dict()}\", 'API-create_service')\n return {'message': error.to_dict()}, 400\n\n except AttributeError:\n error = ServiceUnavailableError('Could not connect to the database server', '', '')\n gemslog(LogLevel.ERROR, f\"'message': {error.to_dict()}\", 'API-create_service')\n return {'message': error.to_dict()}, 503\n\n except Exception:\n error = InternalServerErrorAPI('Unexpected error occurred', api.payload, traceback.format_exc())\n gemslog(LogLevel.ERROR, f\"'message': {error.to_dict()}\", 'API-create_service')\n return {'message': error.to_dict()}, 500\n\n else:\n gemslog(LogLevel.INFO, 'Successfully created new service', 'API-create_service')\n return {'service_id': service_id}, 201\n","repo_name":"eea/CLMS_Production_System","sub_path":"03_Service_and_Data_Dissemination_API/services/backend_api/src/resources/resources_crm/create_service/create_service.py","file_name":"create_service.py","file_ext":"py","file_size_in_byte":6419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"10665549287","text":"# -*- coding：utf-8 -*-\r\n# &Author AnFany\r\n\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndata = pd.read_csv(r'C:\\Users\\GWT9\\Desktop\\PRSA_data_2010.1.1-2014.12.31.csv')\r\n\r\n# 因为决策树回归和决策树分类程序，相似性很大，唯一的不同在于分类计算基尼系数，而回归计算MSE。\r\n# 因此回归的数据结构在此处和分类的相似\r\n\r\n# 因为原始数据中有些变量的值是离散的，但是是整数表示，因此这里需要将其变为字符串，\r\n# 这主要涉及的变量包括：year\tmonth\tday\thour\r\n\r\n'''第一部分：缺失值的处理'''\r\n# 因为Pm2.5是目标数据，对于缺失值直接删除这一条记录\r\n\r\n# 定义删除目标值为空值的行的函数, 其他列为缺失值则自动填充的函数,并将目标变量放置在数据集最后一列\r\ndef DeleteTargetNan(exdata, targetstr):\r\n # 首先判断目标字段是否有缺失值\r\n if exdata[targetstr].isnull().any():\r\n # 首先确定缺失值的行数\r\n loc = exdata[targetstr][data[targetstr].isnull().values == True].index.tolist()\r\n # 然后删除这些行\r\n exdata = exdata.drop(loc)\r\n # 凡是有缺失值的再一起利用此行的均值填充\r\n exdata = exdata.fillna(exdata.mean())\r\n # 将目标字段至放在最后的一列\r\n targetnum = exdata[targetstr].copy()\r\n del exdata[targetstr]\r\n exdata[targetstr] = targetnum\r\n return exdata\r\n\r\n# 因为此数据集中的year，month, day, hour均为整数型，再程序中会被当做连续的变量\r\n\r\n# 删除原始数据中不需要的字段名\r\ndef Shanchu(exdata, aiduan=['No']):\r\n for ai in aiduan:\r\n if ai in exdata.keys():\r\n del exdata[ai]\r\n return exdata\r\n\r\n\r\n# 因此这里将这4个变量变为字符串的形式\r\ndef TeshuHandle(exdata, ziduan=['year', 'month', 'day', 'hour'], tianjiastr=['年', '月', '日', '时']):\r\n for j, k in zip(ziduan, tianjiastr):\r\n if j in exdata.keys():\r\n exdata[j] = ['%d%s' % (j, k) for j in exdata[j]]\r\n return exdata\r\n\r\n# 数据处理后最终的数据集\r\n\r\nfirst = DeleteTargetNan(data, 'pm2.5')\r\ntwo = Shanchu(first)\r\nthird = TeshuHandle(two)\r\n\r\n# 将数据集按照7；1.5:1.5的比例分为训练，测试、预测数据集\r\n\r\ndef fenge(exdata, per=[0.15, 0.15]):\r\n # 总长度\r\n lent = len(exdata)\r\n alist = np.arange(lent)\r\n np.random.shuffle(alist)\r\n\r\n # 验证\r\n shu = int(lent * per[0])\r\n yu = int(lent * per[1])\r\n\r\n yanzheng = np.random.choice(alist, shu, replace=False)\r\n\r\n # 预测\r\n shengxai = np.array([i for i in alist if i not in yanzheng])\r\n\r\n yuce = np.random.choice(shengxai, yu, replace=False)\r\n\r\n # 训练\r\n train = np.array([j for j in alist if j not in yanzheng and j not in yuce])\r\n\r\n # 返回数据集\r\n dadata = {}\r\n dadata[0] = {}\r\n\r\n dadata[0]['train'] = exdata[train]\r\n dadata[0]['test'] = exdata[yanzheng]\r\n\r\n return dadata, exdata[yuce]\r\n\r\ndeeer = fenge(third.values)\r\n\r\n# 数据\r\ndt_data = deeer[0]\r\ntest_data = deeer[1]\r\n\r\n\r\n# 数据结构和决策树分类是相同的，\r\n","repo_name":"Anfany/Machine-Learning-for-Beginner-by-Python3","sub_path":"Decision Tree/DT_Regression/Data_DT_Regression.py","file_name":"Data_DT_Regression.py","file_ext":"py","file_size_in_byte":3105,"program_lang":"python","lang":"zh","doc_type":"code","stars":405,"dataset":"github-code","pt":"3"} +{"seq_id":"27769196130","text":"import requests\r\nimport json\r\n\r\n#To get the latest randomness from cloudflare:\r\nr = requests.get('https://drand.cloudflare.com/public/latest', headers={ 'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:31.0) Gecko/20130401 Firefox/31.0'})\r\ndata = r.json()\r\nlatest_randomness= data[\"randomness\"]\r\n\r\n#Digits is a list containing the hex's individual digits:\r\ndigits = list(latest_randomness.strip(' '))\r\n\r\nnum_length = len(digits)\r\n\r\n#Pairs is a list containing the pairs of digits that will later be converted:\r\npairs = [digits[i] + digits[i+1] for i in range(0, num_length-1, 2)]\r\n\r\nif (num_length % 2 == 1):\r\n pairs.append(digits[num_length-1])\r\n\r\n#Converts hex pairs to integers modulo 80:\r\nfor i in range(len(pairs)):\r\n pairs[i]= int(pairs[i],16) % 80\r\n \r\n#To remove any duplicate numbers:\r\npairs= list(set(pairs))\r\n\r\n#To get the last KINO winning numbers from the given link\r\nreq=requests.get(\"https://api.opap.gr/draws/v3.0/1100/last-result-and-active\")\r\ndata=req.json()\r\nkino_numbers= data[\"last\"][\"winningNumbers\"][\"list\"]\r\n\r\ncount=0 #How many of the pairs' numbers would win in the last KINO drawing\r\n\r\nfor i in range(len(pairs)):\r\n for k in range(len(kino_numbers)):\r\n if (pairs[i]==kino_numbers[k]):\r\n count+=1\r\n kino_numbers.pop(k) #If a number is found, remove it to make the search faster\r\n break\r\n continue\r\n\r\nprint(\"{} of the generated numbers would win in the last KINO drawing\".format(count))","repo_name":"MarinaTsl/Assignment_13","sub_path":"latest_randomness.py","file_name":"latest_randomness.py","file_ext":"py","file_size_in_byte":1475,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"2931046250","text":"# pylint: disable=too-many-locals,too-many-statements,too-many-nested-blocks\n\"\"\"\nGet optimal resolution method is defined here.\n\"\"\"\nfrom __future__ import annotations\n\nimport itertools as it\n\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\nfrom loguru import logger\nfrom matplotlib.axes import Axes\nfrom networkx.algorithms.community import greedy_modularity_communities\n\nfrom derevo.compatability import get_compatability_graph\n\n\ndef get_best_resolution(\n plants: pd.DataFrame,\n plants_with_limitations_resistance: pd.DataFrame,\n plants_suitable_for_light: pd.DataFrame,\n cohabitation_attributes: pd.DataFrame,\n graph_axes: Axes | None = None,\n) -> pd.DataFrame:\n \"\"\"\n Return dataframe with calculated best resolutions for current collection of species\n and limitation factors / light variants.\n\n If `graph_axes` is given, scatter plot will be drawn on it.\n \"\"\"\n plants = plants.copy()\n plants_with_lim_resist = plants_with_limitations_resistance.copy()\n plants_with_lig_resist = plants_suitable_for_light.copy()\n compatability_graph = get_compatability_graph(plants, cohabitation_attributes)\n lig_list = [1, 2, 3]\n lim_list = [1, 2, 3, 4, 5, 6]\n\n res_list = [x / 10 for x in range(0, 21, 1)]\n result: list[list] = []\n for lig in range(len(lig_list) + 1):\n for lig_subset in it.combinations(lig_list, lig):\n if len(list(lig_subset)) == 0:\n continue\n filtered_plants = plants_with_lig_resist[plants_with_lig_resist.light_type_id.isin(list(lig_subset))]\n light_comp = list(pd.unique(filtered_plants[\"name_ru\"]))\n for lim in range(len(lim_list) + 1):\n for lim_subset in it.combinations(lim_list, lim):\n filtered_plants = plants_with_lim_resist[\n plants_with_lim_resist.limitation_factor_id.isin(list(lim_subset))\n ]\n limitations_comp = filtered_plants.groupby(\"name_ru\").count().reset_index()\n limitations_comp = limitations_comp[\n limitations_comp.limitation_factor_id == limitations_comp.limitation_factor_id.max()\n ]\n limitations_comp = list(pd.unique(limitations_comp[\"name_ru\"]))\n\n # filtering species by environmental factors\n df_comp = plants[plants[\"name_ru\"].isin(light_comp)]\n if len(lim_subset) > 0:\n df_comp = df_comp[df_comp[\"name_ru\"].isin(limitations_comp)]\n\n comp_graph = compatability_graph.copy()\n comp_graph = comp_graph.subgraph(df_comp[\"name_ru\"])\n default_size_of_community = comp_graph.number_of_nodes()\n default_number_of_negative_edges = len(nx.to_pandas_edgelist(comp_graph).query(\"weight == 1\"))\n for res in res_list:\n communities_list = greedy_modularity_communities(comp_graph, weight=\"weight\", resolution=res)\n size_of_biggest_community = max(len(com) for com in communities_list)\n composition = [list(com) for com in communities_list if len(com) == size_of_biggest_community]\n if len(composition) == default_size_of_community:\n c_graph = comp_graph.subgraph(list(it.chain.from_iterable(composition)))\n number_of_remaining_nodes = c_graph.number_of_nodes()\n number_of_negative_edges = len(nx.to_pandas_edgelist(c_graph).query(\"weight == 1\"))\n tag = \"One node community\"\n result.append(\n [\n \", \".join(map(str, list(lig_subset)))\n + \"; \"\n + \", \".join(map(str, list(lim_subset))),\n list(pd.unique(df_comp[\"name_ru\"])),\n communities_list,\n number_of_remaining_nodes,\n number_of_negative_edges,\n res,\n default_size_of_community,\n default_number_of_negative_edges,\n tag,\n ]\n )\n elif len(composition) > 1:\n for comp in composition:\n c_graph = comp_graph.subgraph(comp)\n number_of_remaining_nodes = c_graph.number_of_nodes()\n number_of_negative_edges = len(nx.to_pandas_edgelist(c_graph).query(\"weight == 1\"))\n tag = \"Variable community\"\n result.append(\n [\n \", \".join(map(str, list(lig_subset)))\n + \"; \"\n + \", \".join(map(str, list(lim_subset))),\n list(pd.unique(df_comp[\"name_ru\"])),\n communities_list,\n number_of_remaining_nodes,\n number_of_negative_edges,\n res,\n default_size_of_community,\n default_number_of_negative_edges,\n tag,\n ]\n )\n else:\n c_graph = comp_graph.subgraph(list(it.chain.from_iterable(composition)))\n number_of_remaining_nodes = c_graph.number_of_nodes()\n number_of_negative_edges = len(nx.to_pandas_edgelist(c_graph).query(\"weight == 1\"))\n tag = \"Default community\"\n result.append(\n [\n \", \".join(map(str, list(lig_subset)))\n + \"; \"\n + \", \".join(map(str, list(lim_subset))),\n list(pd.unique(df_comp[\"name_ru\"])),\n communities_list,\n number_of_remaining_nodes,\n number_of_negative_edges,\n res,\n default_size_of_community,\n default_number_of_negative_edges,\n tag,\n ]\n )\n logger.debug(\"lim_subset done: {}\", list(lim_subset))\n logger.debug(\"lig_subset done: {}\", list(lig_subset))\n df_result = pd.DataFrame(\n result,\n columns=[\n \"variant_id\",\n \"precomposition\",\n \"communities_list\",\n \"size\",\n \"negative_edges\",\n \"resolution\",\n \"total_size\",\n \"total_edge_number\",\n \"tag\",\n ],\n )\n df_result[\"share_of_nodes\"] = df_result[\"size\"] / df_result[\"total_size\"]\n df_result[\"share_of_negative_edges\"] = df_result[\"negative_edges\"] / df_result[\"total_edge_number\"]\n df_result[\"composition_improvement\"] = df_result[\"share_of_nodes\"] - df_result[\"share_of_negative_edges\"]\n df_result[\"communities\"] = df_result.communities_list.map(\n lambda x: [list(com) for com in x if len(com) == max(len(com) for com in x)]\n )\n df_result[\"communities\"] = df_result.communities.map(lambda x: list(it.chain.from_iterable(x)))\n df_result[\"size\"] = df_result.communities.map(len)\n\n if graph_axes is not None:\n x_axis = list(df_result[\"resolution\"])\n y_axis = list(df_result[\"composition_improvement\"])\n\n graph_axes.scatter(x_axis, y_axis, marker=0)\n graph_axes.set_xticks(np.arange(min(x_axis), max(x_axis), 0.1), rotation=90)\n graph_axes.set_yticks(np.arange(min(y_axis), max(y_axis), 0.1))\n graph_axes.grid(True)\n graph_axes.set_xlabel(\"resolution\")\n graph_axes.set_ylabel(\"composition improvement\")\n\n return df_result\n","repo_name":"egov-itmo/derevo","sub_path":"method/derevo/optimal_resolution.py","file_name":"optimal_resolution.py","file_ext":"py","file_size_in_byte":8529,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"} +{"seq_id":"72733722320","text":"from openerp import api, fields, models, _\nfrom urllib import urlencode\nfrom urlparse import urljoin\nfrom openerp.exceptions import UserError\n\n\nclass SaleOrderExtend(models.Model):\n _inherit = 'sale.order'\n\n @api.multi\n def unlink(self):\n for order in self:\n if order.state not in ['draft','to_accept']:\n raise UserError(_('You can only delete draft quotations or Quotations Awaiting Acceptance!'))\n self.env.cr.execute(\"delete from sale_order where id = %s\" % order.id)\n\n\n @api.multi\n def copy(self, default=None):\n self.ensure_one()\n if default is None:\n default = {}\n res = super(SaleOrderExtend, self).copy(default)\n res.onchange_reach_limit()\n return res\n\n @api.multi\n def action_send_to_manager(self, msg):\n company_email = self.env.user.company_id.email.strip()\n sender_person_email = self.env.user.partner_id.email.strip()\n sender_person_name = self.env.user.name\n res = {}\n self.state = 'to_accept'\n\n if company_email and sender_person_email:\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_sale_send_to_manager')\n ctx = {}\n ctx.update({'sale_id': self.id})\n the_url = self._get_sale_order_url('sale', 'menu_sale_order', 'action_orders', ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_show_receive_send_to_manager')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {'system_email': company_email,\n 'sender_person_name': sender_person_name,\n 'sender_person_email': sender_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url': the_url,\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n\n #Simulates Notification Dialog Box\n res = {\n 'name': 'Send to Manager Notification',\n 'view_mode': 'form',\n 'res_model': 'send.manager.wizard',\n 'type': 'ir.actions.act_window',\n 'target': 'new'\n }\n\n return res\n\n\n @api.multi\n def action_disapprove(self,msg):\n reason_for_dispproval = msg\n self.disapproved_by_user_id = self.env.user\n self.state = 'no_sale'\n\n # Send Email\n company_email = self.env.user.company_id.email.strip()\n disapprove_person_email = self.disapproved_by_user_id.partner_id.email.strip()\n disapprove_person_name = self.disapproved_by_user_id.name\n\n if company_email and disapprove_person_email:\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_sale_disapproved')\n ctx = {}\n ctx.update({'sale_id': self.id})\n the_url = self._get_sale_order_url('sale', 'menu_sale_order', 'action_orders', ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_disapprove_sale_order_email')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {'system_email': company_email,\n 'disapprove_person_name': disapprove_person_name,\n 'disapprove_person_email': disapprove_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url': the_url,\n 'reason_for_dispproval': reason_for_dispproval,\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n\n if user_ids :\n self.message_subscribe_users(user_ids=user_ids)\n # For Similar Odoo Kind of Email. Works fine\n #self.message_post( _(\"Sales Order has been Disapproved with reason: \" + reason_for_dispproval + \"\") ,subject='Please See the Disapproved Sales Order', subtype='mail.mt_comment')\n\n #Just Log the Note Only\n self.message_post(_(\"Sales Order has been Disapproved with reason: \" + reason_for_dispproval + \"\"), subject='Please See the Disapproved Sales Order')\n\n @api.multi\n def action_approve(self):\n res = super(SaleOrderExtend, self).action_confirm()\n self.approved_by_user_id = self.env.user\n\n # Send Email\n company_email = self.env.user.company_id.email.strip()\n approve_person_email = self.approved_by_user_id.partner_id.email.strip()\n approve_person_name = self.approved_by_user_id.name\n\n if company_email and approve_person_email:\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_sale_approved')\n ctx = {}\n ctx.update({'sale_id': self.id})\n the_url = self._get_sale_order_url('sale', 'menu_sale_order', 'action_orders', ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_approve_sale_order_email')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {'system_email': company_email,\n 'approve_person_name': approve_person_name,\n 'approve_person_email': approve_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url': the_url\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n if user_ids :\n self.message_subscribe_users(user_ids=user_ids)\n\n return res\n\n\n @api.multi\n def action_confirm(self):\n self.state = 'so_to_approve'\n self.confirmed_by_user_id = self.env.user\n\n # Give a SO ID\n if self.so_name:\n self.name = self.so_name\n else:\n self.quote_name = self.name\n self.name = self.env['ir.sequence'].get('so_id_code')\n self.so_name = self.name\n\n #Send FYI email notification\n company_email = self.env.user.company_id.email and self.env.user.company_id.email.strip()\n confirm_person_email = self.confirmed_by_user_id.partner_id.email.strip()\n confirm_person_name = self.confirmed_by_user_id.partner_id.name\n\n if company_email and confirm_person_email :\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_quote_confirmed')\n ctx = {}\n ctx.update({'sale_id':self.id})\n the_url = self._get_sale_order_url('sale','menu_sale_order','action_orders',ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_quotation_confirmed_email')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {\n 'system_email': company_email,\n 'confirm_person_name': confirm_person_name ,\n 'confirm_person_email' :confirm_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url':the_url\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n if user_ids:\n self.message_subscribe_users(user_ids=user_ids)\n\n #Send email for approval or disapproval\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_quote_confirmed_to_approve')\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_quotation_confirmed_email_to_approve')\n for user in group_obj.users:\n if user.partner_id.email and user.partner_id.email.strip():\n user_ids.append(user.id)\n ctx = {\n 'system_email': company_email,\n 'confirm_person_name': confirm_person_name,\n 'confirm_person_email': confirm_person_email,\n 'notify_person_email': user.partner_id.email,\n 'notify_person_name': user.partner_id.name,\n 'url': the_url\n }\n mail_template.with_context(ctx).send_mail(self.id, force_send=False)\n if user_ids :\n self.message_subscribe_users(user_ids=user_ids)\n\n return\n\n # It acts as a helper for other sub classess to call the parent action_confirm() method\n @api.multi\n def action_confirm_subclass(self):\n res = super(SaleOrderExtend, self).action_confirm()\n\n @api.multi\n def action_cancel(self):\n res = super(SaleOrderExtend,self).action_cancel()\n\n #Send Email\n company_email = self.env.user.company_id.email.strip()\n sales_person_email = self.user_id.partner_id.email.strip()\n confirm_person_email = self.env.user.partner_id.email.strip()\n\n if company_email and sales_person_email and confirm_person_email and (sales_person_email != confirm_person_email ):\n # Custom Email Template\n mail_template = self.env.ref('kin_sales_double_validation.mail_templ_sale_canceled')\n ctx = {}\n ctx.update({'sale_id':self.id})\n the_url = self._get_sale_order_url('sale','menu_sale_order','action_orders',ctx)\n\n ctx = {'system_email': company_email,\n 'confirm_person_name': self.env.user.name ,\n 'confirm_person_email' :confirm_person_email,\n 'url':the_url\n }\n mail_template.with_context(ctx).send_mail(self.id,force_send=False)\n return res\n\n def onchange_reach_limit(self):\n for order in self:\n amount_total = order.amount_total\n sale_confirm_limit = self.env.user.sale_confirm_limit\n is_use_sale_confirm_limit = self.env.user.is_use_sale_confirm_limit\n if (amount_total < sale_confirm_limit) and is_use_sale_confirm_limit and order.state in ['draft','sent','to_accept']:\n self.env.cr.execute(\"update sale_order set state = 'so_to_approve' where id = %s\" % (self.id))\n elif ((amount_total >= sale_confirm_limit) and is_use_sale_confirm_limit and order.state in ['draft','sent']) :\n self.env.cr.execute(\"update sale_order set state = 'to_accept' where id = %s\" % (self.id))\n return\n\n\n\n @api.model\n def create(self, vals):\n order = super(SaleOrderExtend, self).create(vals)\n return order\n\n def _get_sale_order_url(self, module_name,menu_id,action_id, context=None):\n fragment = {}\n res = {}\n model_data = self.env['ir.model.data']\n base_url = self.env['ir.config_parameter'].get_param('web.base.url')\n fragment['menu_id'] = model_data.get_object_reference(module_name,menu_id)[1]\n fragment['model'] = 'sale.order'\n fragment['view_type'] = 'form'\n fragment['action']= model_data.get_object_reference(module_name,action_id)[1]\n query = {'db': self.env.cr.dbname}\n\n# for displaying tree view. Remove if you want to display form view\n# fragment['page'] = '0'\n# fragment['limit'] = '80'\n# res = urljoin(base_url, \"?%s#%s\" % (urlencode(query), urlencode(fragment)))\n\n\n # For displaying a single record. Remove if you want to display tree view\n\n fragment['id'] = context.get(\"sale_id\")\n res = urljoin(base_url, \"?%s#%s\" % (urlencode(query), urlencode(fragment)))\n return res\n\n @api.multi\n def action_send_for_confirm(self):\n\n for order in self:\n order.state = 'waiting'\n order.is_show_btn_send_for_confirm = False\n order.is_show_btn_approve = False\n order.is_show_btn_confirm = True\n\n is_po_check = order.env.user.company_id.is_po_check\n # is PO Check\n if is_po_check:\n if order.client_order_ref:\n client_order_ref = order.client_order_ref.strip()\n\n if len(client_order_ref) <= 0:\n raise UserError(\n 'Please Ensure that the Quote is confirmed from the customer and that the PO reference is set. e.g. you may put the po number, email, contact name, number of the customer that confirmed the Quote on the PO reference field')\n\n else:\n raise UserError(\n 'Please Ensure that the Quote is confirmed from the customer and that the PO reference is set. e.g. you may put the po number, email, contact name, number of the customer that confirmed the Quote')\n\n #Send Email\n company_email = order.env.user.company_id.email and order.env.user.company_id.email.strip()\n sales_person_email = order.user_id.partner_id.email.strip()\n\n if company_email and sales_person_email :\n # Custom Email Template\n mail_template = order.env.ref('kin_sales_double_validation.mail_templ_send_for_confirmation')\n ctx = {}\n ctx.update({'sale_id':order.id})\n the_url = order._get_sale_order_url('sale','menu_sale_order','action_orders',ctx)\n\n user_ids = []\n group_obj = self.env.ref('kin_sales_double_validation.group_receive_send_for_confirm_email')\n for user in group_obj.users :\n if user.partner_id.email and user.partner_id.email.strip() :\n user_ids.append(user.id)\n ctx = {'system_email': company_email,\n 'confirm_person_email':user.partner_id.email,\n 'confirm_person_name': user.partner_id.name ,\n 'url':the_url\n }\n mail_template.with_context(ctx).send_mail(order.id,force_send=False)\n\n if user_ids :\n order.message_subscribe_users(user_ids=user_ids)\n\n #self.message_post( _('Sales Order has been created %s.') % (self.name),subject='Please See the Created Sales Order', subtype='mail.mt_comment')\n\n partner_id = fields.Many2one(states={'draft': [('readonly', False)], 'sent': [('readonly', False)], 'to_accept': [('readonly', False)], 'waiting': [('readonly', False)], 'so_to_approve': [('readonly', False)]})\n partner_invoice_id = fields.Many2one(states={'draft': [('readonly', False)], 'sent': [('readonly', False)], 'to_accept': [('readonly', False)], 'waiting': [('readonly', False)], 'so_to_approve': [('readonly', False)]}, track_visibility='always')\n partner_shipping_id = fields.Many2one(states={'draft': [('readonly', False)], 'sent': [('readonly', False)], 'to_accept': [('readonly', False)], 'waiting': [('readonly', False)], 'so_to_approve': [('readonly', False)]}, track_visibility='always')\n pricelist_id = fields.Many2one('product.pricelist', string='Pricelist', required=True, readonly=True,\n states={'draft': [('readonly', False)],'to_accept': [('readonly', False)], 'sent': [('readonly', False)]},\n help=\"Pricelist for current sales order.\")\n #state = fields.Selection(selection_add=[('waiting', 'Waiting Approval')]) # it worked but , i could not sequence it\n state = fields.Selection([\n ('draft', 'Quotation'),\n ('sent', 'Quote Sent'),\n ('to_accept', 'Quote Awaiting Acceptance'),\n ('waiting', 'Quote Awaiting Conversion to SO'),\n ('so_to_approve', 'Sale Order To Approve'),\n ('sale', 'Sale Order Approved'),\n ('no_sale', 'Sale Order Disapproved'),\n ('credit_limit_by_pass_request', 'Credit Limit By Pass Request'),\n ('credit_limit_by_pass_confirm', 'Credit Limit By Pass Confirmed'),\n ('credit_limit_by_pass_approve', 'Credit Limit By Pass Approved'),\n ('credit_limit_by_pass_disapprove', 'Credit Limit By Pass DisApproved'),\n ('credit_limit_by_pass_cancel', 'Credit Limit By Pass Cancelled'),\n ('done', 'Done'),\n ('cancel', 'Cancelled'),\n ], string='Status', readonly=True, copy=False, index=True, track_visibility='onchange', default='draft')\n\n confirmed_by_user_id = fields.Many2one('res.users',string= 'Confirmed By')\n approved_by_user_id = fields.Many2one('res.users', string='Approved By')\n disapproved_by_user_id = fields.Many2one('res.users', string='Disapproved By')\n date_order = fields.Datetime(string='Order Date', required=True, readonly=True, index=True,\n states={'draft': [('readonly', False), ('required', False)],\n 'sent': [('readonly', False)], 'waiting': [('readonly', False)],\n 'so_to_approve': [('readonly', False)]}, copy=False)\n\n\n\n\nclass ResUsersExtend(models.Model):\n _inherit = 'res.users'\n\n sale_confirm_limit = fields.Float('Sales Confirmation Limit',help='If user exceeds this limit, expose the confirmation button for validation')\n is_use_sale_confirm_limit = fields.Boolean('Use Sales Confirm Limit')\n\n","repo_name":"kingsleyuk2003/ODEX","sub_path":"addons/kin_sales_double_validation/sale.py","file_name":"sale.py","file_ext":"py","file_size_in_byte":18270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"38397716932","text":"from io import BytesIO\n\nimport requests\nfrom starlette.responses import StreamingResponse\nfrom fastapi import FastAPI, Request, Form, File, UploadFile\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi.templating import Jinja2Templates\nfrom fastapi.logger import logger\n\napp = FastAPI()\n\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n\ntemplates = Jinja2Templates(directory=\"templates\")\n\n@app.get(\"/\")\nasync def get_root(request: Request):\n return templates.TemplateResponse(\"index.html\", {\"request\": request})\n\n\n@app.post(\"/process_text\")\nasync def process_text(text: str = Form(...)):\n req = requests.get(\"http://text2speech/text2speech\", params={\"text\": text})\n fd = BytesIO(req.content)\n\n return StreamingResponse(fd, headers=req.headers, media_type=req.headers['Content-Type'])\n\n\n@app.post(\"/process_audio\")\nasync def process_audio(audio: UploadFile = File(...)):\n\n file_data = {'file': (audio.filename, audio.file, audio.content_type)}\n req = requests.post(\"http://speech2text/\", files=file_data)\n\n response = {\"text\": req.text}\n logger.info(response)\n return response\n\n\n@app.post(\"/translate\")\nasync def translate(text: str = Form(...), source_language: str = Form(...), target_language: str = Form(...)):\n data = {\"source\": source_language, \"target\": target_language, \"q\": text}\n req = requests.get(\"http://taln.upf.edu/mmt-es_en/translate\", params=data)\n\n json_response = req.json()\n text = json_response['data']['translation']\n \n response = {\"text\": text}\n logger.info(response)\n return response\n\n\nif __name__ == \"__main__\":\n import uvicorn\n uvicorn.run(app, host=\"0.0.0.0\", port=8000)","repo_name":"rcarlini-upf/ingenious","sub_path":"integration/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"33747446750","text":"import os\r\nimport cv2\r\nfrom visander.util import ImageSampler\r\nfrom visander.core import Entity\r\nfrom visander.color import BgrIntensityComponent, GrayscaleIntensityComponent,\\\r\n get_intensity, ensure_grayscale\r\n\r\nclass DirectoryBasedImageSource(object):\r\n '''\r\n An image source which is given a directory and will walk \r\n all files in the directory and all subdirectory and read in and\r\n return those files which look like images (based on the file extension)\r\n '''\r\n \r\n def __init__(self, root_dir):\r\n self.root_dir = root_dir\r\n self.image_extensions = ['.jpg','.png','.bmp','.tif','.tiff']\r\n \r\n @property\r\n def entities(self):\r\n '''\r\n A generator function which returns all of the images in this image source.\r\n '''\r\n for root,_,filenames in os.walk(self.root_dir):\r\n for filename in filenames:\r\n _,extension = os.path.splitext(filename)\r\n if extension.lower() in self.image_extensions:\r\n image = cv2.imread(os.path.join(root,filename))\r\n if image.shape[2] == 3:\r\n yield Entity(BgrIntensityComponent(image))\r\n else:\r\n yield Entity(GrayscaleIntensityComponent(image)) \r\n","repo_name":"westonpace/visander","sub_path":"src/visander/io/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"17256756838","text":"#utility file for langevin_sim_mfpt_opt.py\n#bt TW 9th Oct 2023.\n# Path: langevin_approach/util.py\n\nimport numpy as np\nfrom scipy.linalg import eig\nfrom scipy.linalg import expm\nfrom scipy.linalg import inv\nfrom scipy.optimize import minimize\nfrom math import pi\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\nfrom tqdm import tqdm\n\nimport openmm\nimport config\n\n\"\"\"\n\ndef gaussian_2d(x, y, ax, bx, by, cx, cy): #deprecated\n return ax * np.exp(-((x-bx)**2/(2*cx**2) + (y-by)**2/(2*cy**2)))\n\"\"\"\ndef gaussian_2D(params, x, y):\n A, x0, y0, sigma_x, sigma_y = params\n return A * np.exp(-((x - x0)**2 / (2 * sigma_x**2) + (y - y0)**2 / (2 * sigma_y**2)))\n\ndef random_initial_bias_2d(initial_position, num_gaussians = 20):\n # initial position is a list e.g. [3,3]\n # note this is in \n #returns a set of random ax,ay, bx, by, cx, cy for the 2d Gaussian function\n\n #we convert the initial position from openmm quantity object to array with just the value.\n initial_position = initial_position.value_in_unit_system(openmm.unit.md_unit_system)[0] #this is in nm.\n rng = np.random.default_rng()\n a = np.ones(num_gaussians) * 0.1#* 4 #\n #ay = np.ones(num_gaussians) * 0.1 #there's only one amplitude!\n bx = rng.uniform(initial_position[0]-1, initial_position[0]+1, num_gaussians)\n by = rng.uniform(initial_position[1]-1, initial_position[1]+1, num_gaussians)\n cx = rng.uniform(1.0, 5.0, num_gaussians)\n cy = rng.uniform(1.0, 5.0, num_gaussians)\n #\"gaussian_param should be in A, x0, y0, sigma_x, sigma_y format.\"\n return np.concatenate((a, bx, by, cx, cy))\n\ndef get_total_bias_2d(x,y, gaussian_params):\n \"\"\"\n here we get the total bias at x,y.\n note: we used the transposed K matrix, we need to apply transposed total gaussian bias.\n \"\"\"\n N = x.shape[0] #N is the number of grid points.\n total_bias = np.zeros((N,N))\n num_gaussians = len(gaussian_params)//5\n a = gaussian_params[:num_gaussians]\n bx = gaussian_params[num_gaussians:2*num_gaussians]\n by = gaussian_params[2*num_gaussians:3*num_gaussians]\n cx = gaussian_params[3*num_gaussians:4*num_gaussians]\n cy = gaussian_params[4*num_gaussians:5*num_gaussians]\n for i in range(num_gaussians):\n total_bias = total_bias + gaussian_2D([a[i], bx[i], by[i], cx[i], cy[i]], x, y,)\n\n return total_bias\n\ndef compute_free_energy(K, kT):\n \"\"\"\n In 2D senario, we just need to reshape the peq and F.\n\n K is the transition matrix\n kT is the thermal energy\n peq is the stationary distribution #note this was defined as pi in Simian's code.\n F is the free energy\n eigenvectors are the eigenvectors of K\n\n first we calculate the eigenvalues and eigenvectors of K\n then we use the eigenvalues to calculate the equilibrium distribution: peq.\n then we use the equilibrium distribution to calculate the free energy: F = -kT * ln(peq)\n \"\"\"\n N = int(np.sqrt(K.shape[0]))\n evalues, evectors = eig(K)\n\n #sort the eigenvalues and eigenvectors\n index = np.argsort(evalues) #sort the eigenvalues, the largest eigenvalue is at the end of the list\n evalues_sorted = evalues[index] #sort the eigenvalues based on index\n\n #calculate the equilibrium distribution\n peq = evectors[:, index[-1]].T #normalize the eigenvector\n peq = peq / np.sum(peq)\n peq = peq.real\n #take the real part of the eigenvector i.e. the probability distribution at equilibrium.\n #calculate the free energy\n F = -kT * np.log(peq) #add a small number to avoid log(0))\n #F = F.reshape(N, N)\n return [peq, F, evectors, evalues, evalues_sorted, index]\n\ndef compute_free_energy_power_method(K, kT=0.5981):\n \"\"\"\n this use the power method to calculate the equilibrium distribution.\n num_iter is the number of iterations.\n \"\"\"\n num_iter = 1000\n N = K.shape[0]\n peq = np.ones(N) / N #initialise the peq\n for i in range(num_iter):\n peq = np.dot(peq, K)\n peq = peq / np.sum(peq)\n F = -kT * np.log(peq)\n return [peq, F]\n\ndef kemeny_constant_check(mfpt, peq):\n N2 = mfpt.shape[0]\n kemeny = np.zeros((N2, 1))\n for i in range(N2):\n for j in range(N2):\n kemeny[i] = kemeny[i] + mfpt[i, j] * peq[j]\n #print(\"Performing Kemeny constant check...\")\n #print(\"the min/max of the Kemeny constant is:\", np.min(kemeny), np.max(kemeny))\n \"\"\"\n if np.max(kemeny) - np.min(kemeny) > 1e-6:\n print(\"Kemeny constant check failed!\")\n raise ValueError(\"Kemeny constant check failed!\")\"\"\"\n return kemeny\n\ndef mfpt_calc(peq, K):\n \"\"\"\n peq is the probability distribution at equilibrium.\n K is the transition matrix.\n N is the number of states.\n \"\"\"\n N = K.shape[0] #K is a square matrix.\n onevec = np.ones((N, 1)) #, dtype=np.float64\n Qinv = np.linalg.inv(peq.T * onevec - K.T)\n\n mfpt = np.zeros((N, N)) #, dtype=np.float64\n for j in range(N):\n for i in range(N):\n #to avoid devided by zero error:\n if peq[j] == 0:\n mfpt[i, j] = 0\n else:\n mfpt[i, j] = 1 / peq[j] * (Qinv[j, j] - Qinv[i, j])\n \n #result = kemeny_constant_check(N, mfpt, peq)\n return mfpt\n\ndef Markov_mfpt_calc(peq, M):\n N = M.shape[0]\n onevec = np.ones((N, 1))\n Idn = np.diag(onevec[:, 0])\n A = (peq.reshape(-1, 1)) @ onevec.T #was peq.T @ onevec.T\n A = A.T\n Qinv = inv(Idn + A - M)\n mfpt = np.zeros((N, N))\n for j in range(N):\n for i in range(N):\n term1 = Qinv[j, j] - Qinv[i, j] + Idn[i, j]\n if peq[j] * term1 == 0:\n mfpt[i, j] = 1000000000000\n else:\n mfpt[i, j] = 1/peq[j] * term1\n #result = kemeny_constant_check(N, mfpt, peq)\n return mfpt\n\ndef try_and_optim_M(M, working_indices, N=20, num_gaussian=10, start_index=0, end_index=0, plot = False):\n \"\"\"\n here we try different gaussian params 1000 times\n and use the best one (lowest mfpt) to local optimise the gaussian_params\n \n returns the best gaussian params\n\n input:\n M: the working transition matrix, square matrix.\n working_indices: the indices of the working states.\n num_gaussian: number of gaussian functions to use.\n start_state: the starting state. note this has to be converted into the index space.\n end_state: the ending state. note this has to be converted into the index space.\n index_offset: the offset of the index space. e.g. if the truncated M (with shape [20, 20]) matrix starts from 13 to 33, then the index_offset is 13.\n \"\"\"\n #here we find the index of working_indices.\n # e.g. the starting index in the working_indices is working_indices[start_state_working_index]\n # and the end is working_indices[end_state_working_index]\n \n start_state_working_index = np.argmin(np.abs(working_indices - start_index))\n end_state_working_index = np.argmin(np.abs(working_indices - end_index))\n \n start_state_working_index_xy = np.unravel_index(working_indices[start_state_working_index], (N, N), order='C')\n end_state_working_index_xy = np.unravel_index(working_indices[end_state_working_index], (N, N), order='C')\n print(\"Try and Optim from state:\", start_state_working_index_xy, \"to state:\", end_state_working_index_xy)\n\n #now our M/working_indices could be incontinues. #N = M.shape[0]\n x,y = np.meshgrid(np.linspace(0, 2*np.pi, N), np.linspace(0, 2*np.pi, N)) #hard coded here. we need to change this.\n best_mfpt = 1e20 #initialise the best mfpt np.inf\n\n #here we find the x,y maximum and minimun in xy coordinate space, with those working index\n #we use this to generate the random gaussian params.\n working_indices_xy = np.unravel_index(working_indices, (N, N), order='C')\n\n for try_num in range(1000):\n rng = np.random.default_rng()\n a = np.ones(num_gaussian) * 2\n bx = rng.uniform(0, 2*np.pi, num_gaussian)\n by = rng.uniform(0, 2*np.pi, num_gaussian)\n cx = rng.uniform(0.3, 1.5, num_gaussian)\n cy = rng.uniform(0.3, 1.5, num_gaussian)\n gaussian_params = np.concatenate((a, bx, by, cx, cy))\n\n total_bias = get_total_bias_2d(x,y, gaussian_params)\n M_biased = np.zeros_like(M)\n\n #we truncate the total_bias to the working index.\n working_bias = total_bias[working_indices_xy] #say M is in shape[51,51], working bias will be in [51] shape.\n\n #now we have a discontinues M matrix. we need to apply the bias to the working index.\n for i in range(M.shape[0]):\n for j in range(M.shape[1]):\n u_ij = working_bias[j] - working_bias[i]\n M_biased[i,j] = M[i,j] * np.exp(-u_ij / (2*0.5981))\n M_biased[i,i] = M[i,i]\n #epsilon_offset = 1e-15\n #M_biased = M_biased / (np.sum(M_biased, axis=0)[:, None] + 1e-15)\n for i in range(M_biased.shape[0]):\n if np.sum(M_biased[i, :]) > 0:\n M_biased[i, :] = M_biased[i, :] / np.sum(M_biased[i, :])\n else:\n M_biased[i, :] = 0\n \n #note our M_biased is in working index. M.shape = (num_working_states, num_working_states)\n peq,F,_,_,_,_ = compute_free_energy(M_biased.T.astype(np.float64), kT=0.5981)\n #print(peq)\n #print(sum(peq))\n\n mfpts_biased = Markov_mfpt_calc(peq, M_biased)\n mfpt_biased = mfpts_biased[start_state_working_index, end_state_working_index]\n\n if try_num % 100 == 0:\n kemeny_constant_check(mfpts_biased, peq)\n print(\"random try:\", try_num, \"mfpt:\", mfpt_biased)\n if best_mfpt > mfpt_biased:\n best_mfpt = mfpt_biased\n best_params = gaussian_params\n\n print(\"best mfpt:\", best_mfpt)\n \n #now we use the best params to local optimise the gaussian params\n\n def mfpt_helper(gaussian_params, M, start_state_working_index = start_state_working_index, end_state_working_index = end_state_working_index, kT=0.5981, working_indices=working_indices):\n #print(\"Try and Optim from state:\", start_state_working_index_xy, \"to state:\", end_state_working_index_xy)\n total_bias = get_total_bias_2d(x,y, gaussian_params)\n M_biased = np.zeros_like(M)\n\n #we truncate the total_bias to the working index.\n working_bias = total_bias[working_indices_xy] #say M is in shape[51,51], working bias will be in [51] shape.\n\n #now we have a discontinues M matrix. we need to apply the bias to the working index.\n for i in range(M.shape[0]):\n for j in range(M.shape[1]):\n u_ij = working_bias[j] - working_bias[i]\n M_biased[i,j] = M[i,j] * np.exp(-u_ij / (2*0.5981))\n M_biased[i,i] = M[i,i]\n #epsilon_offset = 1e-15\n #M_biased = M_biased / (np.sum(M_biased, axis=0)[:, None] + 1e-15)\n for i in range(M_biased.shape[0]):\n if np.sum(M_biased[i, :]) > 0:\n M_biased[i, :] = M_biased[i, :] / np.sum(M_biased[i, :])\n else:\n M_biased[i, :] = 0\n \n #note our M_biased is in working index. M.shape = (num_working_states, num_working_states)\n peq,F,_,_,_,_ = compute_free_energy(M_biased.T.astype(np.float64), kT=0.5981)\n \n mfpts_biased = Markov_mfpt_calc(peq, M_biased)\n mfpt_biased = mfpts_biased[start_state_working_index, end_state_working_index]\n return mfpt_biased\n\n \n res = minimize(mfpt_helper, \n best_params, \n args=(M,\n start_state_working_index, \n end_state_working_index,\n working_indices), \n method='Nelder-Mead', \n bounds= [(0.1, 2)]*num_gaussian + [(0, 2*np.pi)]*num_gaussian + [(0, 2*np.pi)]*num_gaussian + [(0.3, 1.5)]*num_gaussian + [(0.3, 1.5)]*num_gaussian,\n tol=1e0)\n \n #print(\"local optimisation result:\", res.x)\n return res.x\n\ndef save_CV_total(CV_total, time_tag, prop_index):\n np.save(f\"./data/{time_tag}_{prop_index}_CV_total.npy\", CV_total[-1])\n\ndef save_gaussian_params(gaussian_params, time_tag, prop_index):\n np.save(f\"./data/{time_tag}_{prop_index}_gaussian_params.npy\", gaussian_params)\n\n\n\ndef apply_fes(system, particle_idx, gaussian_param=None, pbc = False, name = \"FES\", amp = 7, mode = \"gaussian\", plot = False, plot_path = \"./fes_visualization.png\"):\n \"\"\"\n this function apply the bias given by the gaussian_param to the system.\n \"\"\"\n pi = np.pi #we need convert this into nm.\n #at last we add huge barrier at the edge of the box. since we are not using pbc.\n #this is to prevent the particle from escaping the box.\n # if x<0, push the atom back to x=0\n\n\n k = 5 # Steepness of the sigmoid curve\n max_barrier = \"1e2\" # Scaling factor for the potential maximum\n offset = 0.5 #the offset of the boundary energy barrier.\n # Defining the potentials using a sigmoid function\n left_pot = openmm.CustomExternalForce(f\"{max_barrier} * (1 / (1 + exp({k} * x - (-{offset}))))\")\n right_pot = openmm.CustomExternalForce(f\"{max_barrier} * (1 / (1 + exp(-{k} * (x - (2 * {pi} + {offset})))))\")\n bottom_pot = openmm.CustomExternalForce(f\"{max_barrier} * (1 / (1 + exp({k} * y - (-{offset}))))\")\n top_pot = openmm.CustomExternalForce(f\"{max_barrier} * (1 / (1 + exp(-{k} * (y - (2 * {pi} + {offset})))))\")\n\n left_pot.addParticle(particle_idx)\n right_pot.addParticle(particle_idx)\n bottom_pot.addParticle(particle_idx)\n top_pot.addParticle(particle_idx)\n\n system.addForce(left_pot)\n system.addForce(right_pot)\n system.addForce(bottom_pot)\n system.addForce(top_pot)\n\n \n #unpack gaussian parameters\n if mode == \"gaussian\":\n num_gaussians = int(len(gaussian_param)/5)\n A = gaussian_param[0::5] * amp #*7\n x0 = gaussian_param[1::5]\n y0 = gaussian_param[2::5]\n sigma_x = gaussian_param[3::5]\n sigma_y = gaussian_param[4::5]\n\n #now we add the force for all gaussians.\n energy = \"0\"\n force = openmm.CustomExternalForce(energy)\n for i in range(num_gaussians):\n if pbc:\n energy = f\"A{i}*exp(-periodicdistance(x,0,0, x0{i},0,0)^2/(2*sigma_x{i}^2) - periodicdistance(0,y,0, 0,y0{i},0)^2/(2*sigma_y{i}^2))\"\n force = openmm.CustomExternalForce(energy)\n else:\n energy = f\"A{i}*exp(-(x-x0{i})^2/(2*sigma_x{i}^2) - (y-y0{i})^2/(2*sigma_y{i}^2))\"\n force = openmm.CustomExternalForce(energy)\n\n #examine the current energy term within force.\n\n print(force.getEnergyFunction())\n\n force.addGlobalParameter(f\"A{i}\", A[i])\n force.addGlobalParameter(f\"x0{i}\", x0[i])\n force.addGlobalParameter(f\"y0{i}\", y0[i])\n force.addGlobalParameter(f\"sigma_x{i}\", sigma_x[i])\n force.addGlobalParameter(f\"sigma_y{i}\", sigma_y[i])\n force.addParticle(particle_idx)\n #we append the force to the system.\n system.addForce(force)\n if plot:\n #plot the fes.\n x = np.linspace(0, 2*np.pi, 100)\n y = np.linspace(0, 2*np.pi, 100)\n X, Y = np.meshgrid(x, y)\n Z = np.zeros_like(X)\n for i in range(num_gaussians):\n Z += A[i] * np.exp(-(X-x0[i])**2/(2*sigma_x[i]**2) - (Y-y0[i])**2/(2*sigma_y[i]**2))\n plt.figure()\n plt.imshow(Z, cmap=\"coolwarm\", extent=[0, 2*np.pi,0, 2*np.pi], vmin=0, vmax=amp *12/7, origin=\"lower\")\n plt.xlabel(\"x\")\n plt.xlim([-1, 2*np.pi+1])\n plt.ylim([-1, 2*np.pi+1])\n plt.ylabel(\"y\")\n plt.title(\"FES mode = gaussian, pbc=False\")\n plt.colorbar()\n plt.savefig(plot_path)\n plt.close()\n fes = Z\n\n if mode == \"multiwell\":\n \"\"\"\n here we create a multiple well potential.\n essentially we deduct multiple gaussians from a flat surface, \n with a positive gaussian acting as an additional barrier.\n note we have to implement this into openmm CustomExternalForce.\n the x,y is [0, 2pi]\n eq:\n U(x,y) = amp * (1 #flat surface\n - A_i*exp(-(x-x0i)^2/(2*sigma_xi^2) - (y-y0i)^2/(2*sigma_yi^2))) ... #deduct gaussians\n + A_j * exp(-(x-x0j)^2/(2*sigma_xj^2) - (y-y0j)^2/(2*sigma_yj^2)) #add a sharp positive gaussian\n \"\"\"\n if pbc:\n raise NotImplementedError(\"pbc not implemented for multi-well potential.\")\n else:\n num_wells = 9\n num_barrier = 1\n\n #here's the well params\n A_i = np.array([0.9, 0.3, 0.5, 1, 0.2, 0.4, 0.9, 0.9, 0.9]) * amp #this is in kcal/mol.\n x0_i = [1.12, 1, 3, 4.15, 4, 5.27, 5.5, 6, 1] # this is in nm.\n y0_i = [1.34, 2.25, 2.31, 3.62, 5, 4.14, 4.5, 1.52, 5]\n sigma_x_i = [0.5, 0.3, 0.4, 2, 0.9, 1, 0.3, 0.5, 0.5]\n sigma_y_i = [0.5, 0.3, 1, 0.8, 0.2, 0.3, 1, 0.6, 0.7]\n\n #here's the barrier params\n # for example we define a diagonal barrier at x = pi\n A_j = np.array([0.3]) * amp\n x0_j = [np.pi]\n y0_j = [np.pi]\n sigma_x_j = [3]\n sigma_y_j = [0.3]\n\n #now we add the force for all gaussians.\n #note all energy is in Kj/mol unit.\n energy = str(amp * 4.184) #flat surface\n force = openmm.CustomExternalForce(energy)\n force.addParticle(particle_idx)\n system.addForce(force)\n for i in range(num_wells):\n energy = f\"-A{i}*exp(-(x-x0{i})^2/(2*sigma_x{i}^2) - (y-y0{i})^2/(2*sigma_y{i}^2))\"\n force = openmm.CustomExternalForce(energy)\n\n #examine the current energy term within force.\n\n print(force.getEnergyFunction())\n\n force.addGlobalParameter(f\"A{i}\", A_i[i] * 4.184) #convert kcal to kj\n force.addGlobalParameter(f\"x0{i}\", x0_i[i])\n force.addGlobalParameter(f\"y0{i}\", y0_i[i])\n force.addGlobalParameter(f\"sigma_x{i}\", sigma_x_i[i])\n force.addGlobalParameter(f\"sigma_y{i}\", sigma_y_i[i])\n force.addParticle(particle_idx)\n #we append the force to the system.\n system.addForce(force)\n \n for i in range(num_barrier):\n energy = f\"A{i+num_wells}*exp(-(x-x0{i+num_wells})^2/(2*sigma_x{i+num_wells}^2) - (y-y0{i+num_wells})^2/(2*sigma_y{i+num_wells}^2))\"\n force = openmm.CustomExternalForce(energy)\n\n #examine the current energy term within force.\n\n print(force.getEnergyFunction())\n\n force.addGlobalParameter(f\"A{i+num_wells}\", A_j[i])\n force.addGlobalParameter(f\"x0{i+num_wells}\", x0_j[i])\n force.addGlobalParameter(f\"y0{i+num_wells}\", y0_j[i])\n force.addGlobalParameter(f\"sigma_x{i+num_wells}\", sigma_x_j[i])\n force.addGlobalParameter(f\"sigma_y{i+num_wells}\", sigma_y_j[i])\n force.addParticle(particle_idx)\n #we append the force to the system.\n system.addForce(force)\n \n if plot:\n #plot the fes.\n x = np.linspace(0, 2*np.pi, 100)\n y = np.linspace(0, 2*np.pi, 100)\n X, Y = np.meshgrid(x, y)\n Z = np.zeros_like(X)\n Z += amp * 4.184 #flat surface\n for i in range(num_wells):\n Z -= A_i[i] * np.exp(-(X-x0_i[i])**2/(2*sigma_x_i[i]**2) - (Y-y0_i[i])**2/(2*sigma_y_i[i]**2))\n for i in range(num_barrier):\n Z += A_j[i] * np.exp(-(X-x0_j[i])**2/(2*sigma_x_j[i]**2) - (Y-y0_j[i])**2/(2*sigma_y_j[i]**2))\n \n #add the x,y boundary energy barrier.\n total_energy_barrier = np.zeros_like(X)\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (X - (-offset))))) #left\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (X - (2 * pi + offset))))) #right\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (Y - (-offset)))))\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (Y - (2 * pi + offset)))))\n Z += total_energy_barrier\n\n plt.figure()\n plt.imshow(Z, cmap=\"coolwarm\", extent=[0, 2*np.pi,0, 2*np.pi], vmin=0, vmax=amp* 12/7 * 4.184, origin=\"lower\")\n plt.xlabel(\"x\")\n plt.xlim([-1, 1+2*np.pi])\n plt.ylim([-1, 1+2*np.pi])\n plt.ylabel(\"y\")\n plt.title(\"FES mode = multiwell, pbc=False\")\n plt.colorbar()\n plt.savefig(plot_path)\n plt.close()\n fes = Z\n \n if mode == \"funnel\":\n \"\"\"\n this is funnel like potential.\n we start wtih a flat fes, then add/deduct sphrical gaussians\n eq:\n U = 0.7* amp * cos(2 * p * (sqrt((x-pi)^2 + (y-pi)^2))) #cos function. periodicity determines the num of waves.\n - amp exp(-((x-pi)^2+(y-pi)^2))\n + 0.4*amp*((x-pi/8)^2 + (y-pi/8)^2)\n \"\"\"\n if pbc:\n raise NotImplementedError(\"pbc not implemented for funnel potential.\")\n else:\n plot_3d = False\n periodicity = 8\n energy = f\"0.7*{amp} * cos({periodicity} * (sqrt((x-{pi})^2 + (y-{pi})^2))) - 0.6* {amp} * exp(-((x-{pi})^2+(y-{pi})^2)) + 0.4*{amp}*((x-{pi}/8)^2 + (y-{pi}/8)^2)\"\n \n force = openmm.CustomExternalForce(energy)\n force.addParticle(particle_idx)\n system.addForce(force)\n if plot:\n if plot_3d:\n import plotly.graph_objs as go\n\n # Define the x, y, and z coordinates\n x = np.linspace(0, 2*np.pi, 100)\n y = np.linspace(0, 2*np.pi, 100)\n X, Y = np.meshgrid(x, y)\n Z = np.zeros_like(X)\n Z += 0.9* amp * np.cos(periodicity * (np.sqrt((X-np.pi)**2 + (Y-np.pi)**2))) #cos function. periodicity determines the num of waves.\n Z -= 0.6* amp * np.exp(-((X-np.pi)**2/0.5+(Y-np.pi)**2)/0.5)\n Z += 0.4*amp*(((X-np.pi)/8)**2 + ((Y-np.pi)/8)**2)\n\n #add the x,y boundary energy barrier.\n total_energy_barrier = np.zeros_like(X)\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (X - 0)))) #left\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (X - 2 * pi)))) #right\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (Y - 0))))\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (Y - 2 * pi))))\n Z += total_energy_barrier\n\n # Create the 3D contour plot\n fig = go.Figure(data=[go.Surface(z=Z, x=X, y=Y, cmin = 0, cmax = amp *12/7)])\n fig.update_traces(contours_z=dict(show=True, usecolormap=True,\n highlightcolor=\"limegreen\", project_z=True))\n fig.update_layout(title='FES mode = funnel, pbc=False', autosize=True,\n width=800, height=800,\n scene = {\n \"xaxis\": {\"nticks\": 5},\n \"yaxis\": {\"nticks\": 5},\n \"zaxis\": {\"nticks\": 5},\n \"camera_eye\": {\"x\": 1, \"y\": 1, \"z\": 0.4},\n \"aspectratio\": {\"x\": 1, \"y\": 1, \"z\": 0.4}\n }\n )\n #margin=dict(l=65, r=50, b=65, t=90))\n #save fig.\n fig.write_image(plot_path)\n fes = Z\n \n else:\n #plot the fes.\n x = np.linspace(0, 2*np.pi, 100)\n y = np.linspace(0, 2*np.pi, 100)\n X, Y = np.meshgrid(x, y)\n Z = np.zeros_like(X)\n Z += 0.4* amp * np.cos(periodicity * (np.sqrt((X-np.pi)**2 + (Y-np.pi)**2))) #cos function. periodicity determines the num of waves.\n Z += 0.7* amp * np.exp(-((X-np.pi)**2/0.5+(Y-np.pi)**2/0.5))\n Z += 0.2*amp*(((X-np.pi)/8)**2 + ((Y-np.pi)/8)**2)\n\n #add the x,y boundary energy barrier.\n total_energy_barrier = np.zeros_like(X)\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (X - 0)))) #left\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (X - 2 * pi)))) #right\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(k * (Y - 0))))\n total_energy_barrier += float(max_barrier) * (1 / (1 + np.exp(-k * (Y - 2 * pi))))\n Z += total_energy_barrier\n\n plt.figure()\n plt.imshow(Z, cmap=\"coolwarm\", extent=[0, 2*np.pi,0, 2*np.pi], vmin=0, vmax=amp *12/7, origin=\"lower\")\n plt.xlabel(\"x\")\n plt.xlim([-1, 2*np.pi+1])\n plt.ylim([-1, 2*np.pi+1])\n plt.ylabel(\"y\")\n plt.title(\"FES mode = funnel, pbc=False\")\n plt.colorbar()\n plt.savefig(plot_path)\n plt.close()\n fes = Z\n\n return system, fes #return the system and the fes (2D array for plotting.)\n\ndef sum_of_gaussians(params, x, y, n_gaussians, N=100):\n total = np.zeros((N,N))\n \n A = params[0::5]\n x0 = params[1::5]\n y0 = params[2::5]\n sigma_x = params[3::5]\n sigma_y = params[4::5]\n\n for i in range(n_gaussians):\n total += gaussian_2D([A[i], x0[i], y0[i], sigma_x[i], sigma_y[i]], x, y)\n return total\n\ndef apply_bias(system, particle_idx, gaussian_param, pbc = False, name = \"BIAS\", num_gaussians = 20):\n \"\"\"\n this applies a bias using customexternal force class. similar as apply_fes.\n note this leaves a set of global parameters Ag, x0g, y0g, sigma_xg, sigma_yg.\n as these parameters can be called and updated later.\n note this is done while preparing the system before assembling the context.\n \"\"\"\n assert len(gaussian_param) == 5 * num_gaussians, \"gaussian_param should be in A, x0, y0, sigma_x, sigma_y format.\"\n\n #unpack gaussian parameters gaussian_params = np.concatenate((a, bx, by, cx, cy))\n num_gaussians = len(gaussian_param)//5\n A = gaussian_param[:num_gaussians]\n x0 = gaussian_param[num_gaussians:2*num_gaussians]\n y0 = gaussian_param[2*num_gaussians:3*num_gaussians]\n sigma_x = gaussian_param[3*num_gaussians:4*num_gaussians]\n sigma_y = gaussian_param[4*num_gaussians:5*num_gaussians]\n\n #now we add the force for all gaussians. with num_gaussians terms.\n energy = \"0\"\n force = openmm.CustomExternalForce(energy)\n for i in range(num_gaussians):\n if pbc:\n raise NotImplementedError(\"pbc not implemented for gaussian potential.\")\n energy = f\"Ag{i}*exp(-periodicdistance(x,0,0, x0g{i},0,0)^2/(2*sigma_xg{i}^2) - periodicdistance(0,y,0, 0,y0g{i},0)^2/(2*sigma_yg{i}^2))\"\n force = openmm.CustomExternalForce(energy)\n else:\n energy = f\"Ag{i}*exp(-(x-x0g{i})^2/(2*sigma_xg{i}^2) - (y-y0g{i})^2/(2*sigma_yg{i}^2))\" #in openmm unit, kj/mol, nm.\n force = openmm.CustomExternalForce(energy)\n\n #examine the current energy term within force.\n\n print(force.getEnergyFunction())\n\n force.addGlobalParameter(f\"Ag{i}\", A[i] * 4.184) #convert to kJ/mol\n force.addGlobalParameter(f\"x0g{i}\", x0[i]) #convert to nm\n force.addGlobalParameter(f\"y0g{i}\", y0[i])\n force.addGlobalParameter(f\"sigma_xg{i}\", sigma_x[i])\n force.addGlobalParameter(f\"sigma_yg{i}\", sigma_y[i])\n force.addParticle(particle_idx)\n #we append the force to the system.\n system.addForce(force)\n \n print(\"system added with bias.\")\n return system\n\ndef update_bias(simulation, gaussian_param, name = \"BIAS\", num_gaussians = 20):\n \"\"\"\n given the gaussian_param, update the bias\n note this requires the context object. or a simulation object.\n # the context object can be accessed by simulation.context.\n \"\"\"\n assert len(gaussian_param) == 5 * num_gaussians, \"gaussian_param should be in A, x0, y0, sigma_x, sigma_y format.\"\n\n #unpack gaussian parameters gaussian_params = np.concatenate((a, bx, by, cx, cy))\n num_gaussians = len(gaussian_param)//5\n A = gaussian_param[:num_gaussians]\n x0 = gaussian_param[num_gaussians:2*num_gaussians]\n y0 = gaussian_param[2*num_gaussians:3*num_gaussians]\n sigma_x = gaussian_param[3*num_gaussians:4*num_gaussians]\n sigma_y = gaussian_param[4*num_gaussians:5*num_gaussians]\n\n #now we update the GlobalParameter for all gaussians. with num_gaussians terms. and update them in the system.\n #note globalparameter does NOT need to be updated in the context.\n for i in range(num_gaussians):\n simulation.context.setParameter(f\"Ag{i}\", A[i] * 4.184) #convert to kJ/mol\n simulation.context.setParameter(f\"x0g{i}\", x0[i]) #convert to nm\n simulation.context.setParameter(f\"y0g{i}\", y0[i])\n simulation.context.setParameter(f\"sigma_xg{i}\", sigma_x[i])\n simulation.context.setParameter(f\"sigma_yg{i}\", sigma_y[i])\n \n print(\"system bias updated\")\n return simulation\n","repo_name":"DerienFe/RL_MFPT","sub_path":"2D_langevin_sim/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":30034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"41294610209","text":"import math\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# number of points\nnum_samples = 500\n\ndef painting(xc, yc, xr, yr):\n fig, ax = plt.subplots()\n\n # make a simple unit circle\n theta = np.linspace(0, 2*np.pi, num_samples)\n a, b = 1*np.cos(theta), 1*np.sin(theta)\n plt.plot(a, b, linestyle='-', linewidth=2, color='black', label='Circle')\n\n # painting the points\n plt.scatter(xr, yr, color='blue')\n plt.scatter(xc, yc, color='red')\n plt.axis([-1.5, 1.5, -1.5, 1.5])\n ax.set(xlabel='$x_1$', ylabel='$x_2$', title='Train data')\n plt.show()\n\ndef generate_points(num_samples):\n # generate the num_samples points within a rectangle [-1,5,1.5, -1.5, 1.5]\n point = -1.5+np.random.rand(num_samples, 2)*3\n px, py = list(point[:,0]), list(point[:,1])\n\n # divide points into two classes: in the circle and not.\n xc, yc = [], [] # points in the unit circle\n xr, yr = [], [] # points not in the unit circle\n for i in range(len(px)):\n if math.sqrt(px[i]**2+py[i]**2) <= 1:\n xc.append(px[i])\n yc.append(py[i])\n else:\n xr.append(px[i])\n yr.append(py[i])\n\n return xc, yc, xr, yr\n\ndef main():\n xc, yc, xr, yr = generate_points(num_samples)\n painting(xc, yc, xr, yr)\n\nif __name__ == '__main__':\n main()\n","repo_name":"htma/nn-trainer","sub_path":"plot_points.py","file_name":"plot_points.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"30755007287","text":"# all global variables\n\ndatabase_path = '' # path for database\nmat_prefs_path = '' # path for mat_prefs\nmat_prefs = '' # actual mat_prefs student/section prefs\nsec_sec_matrix_path = '' # path for section/section prefs\nmatrix_sections = '' # actual sec_sec_matrix\n\nparameters = ''\npara_path = ''\n\n# conflict and preference parameters\nsec_time_con = -10000000\nsec_sameclass_pref = 2000\nsec_sameprof_pref = 2000\n\nstu_time_con = -5000\nstu_class_con = -500\nstu_prof_con = -500\nstu_year_pref = 1000\nstu_div_pref = 50000\nstu_skill_pref = 1000\nstu_time_pref = 500\nstu_class_pref = 500\nstu_prof_pref = 500\n","repo_name":"Katiedaisey/scheduling","sub_path":"interface/globalvars.py","file_name":"globalvars.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"} +{"seq_id":"7483335856","text":"import aiohttp\nimport asyncio\nimport urllib.parse\nimport os\nimport logging\nfrom discord import File, TextChannel, User\nfrom discord.errors import NotFound\nimport config\nfrom utils.image import modify_avatar\nfrom utils import tag\n\n\nclass AvatarManager:\n def __init__(\n self,\n loop,\n db,\n http_session: aiohttp.ClientSession,\n fetch_channel\n ):\n self.loop = loop\n self.db = db\n self.http_session = http_session\n self.fetch_channel = fetch_channel\n\n\n async def fetch(self, user: User) -> str:\n res = self.db.execute(\n \"\"\"\n SELECT original_avatar_url,\n modified_avatar_url,\n modified_avatar_message_id\n FROM users WHERE id = ?\n \"\"\",\n (user.id,)\n )\n data = res.fetchone()\n\n avatar_channel = await self.fetch_channel(config.AVATAR_STORE_CHANNEL_ID)\n\n if data is not None:\n (\n original_avatar_url,\n modified_avatar_url,\n modified_avatar_message_id\n ) = data\n\n # User hasn't changed their avatar since last time they did\n # |imitate, so we can use the cached modified avatar.\n if self._avatar_url_id(user.display_avatar.url) == self._avatar_url_id(original_avatar_url):\n return modified_avatar_url\n\n # Else, user has changed their avatar.\n # Respect the user's privacy by deleting the message with their old\n # avatar.\n # Don't wait for this operation to complete before continuing.\n asyncio.create_task(\n self._delete_message(avatar_channel, modified_avatar_message_id)\n )\n\n # User has changed their avatar since last time they did |imitate or has\n # not done |imitate before, so we must create a modified version of\n # their avatar.\n # We employ the classic Discord As A CDN method to cache the modified\n # avatars by posting them to a Discord channel and storing the message\n # IDs for later.\n modified_avatar, file_format = await modify_avatar(user)\n message = await avatar_channel.send(\n file=File(modified_avatar, f\"{user.id}.{file_format}\")\n )\n\n # Update the avatar database with the new avatar URL.\n self.db.execute(\n \"\"\"\n UPDATE users\n SET original_avatar_url = ?,\n modified_avatar_url = ?,\n modified_avatar_message_id = ?\n WHERE id = ?\n \"\"\",\n (\n user.display_avatar.url,\n message.attachments[0].url,\n message.id,\n user.id\n )\n )\n return message.attachments[0].url\n\n\n async def _delete_message(\n self,\n channel: TextChannel,\n message_id: int\n ) -> None:\n try:\n message = await channel.fetch_message(message_id)\n except NotFound:\n logging.warn(\n f\"Tried to delete message {message_id} from the avatar store, \"\n \"but it doesn't exist.\"\n )\n else:\n await message.delete()\n\n\n def _avatar_url_id(self, url: str) -> str:\n path = urllib.parse.urlparse(url).path\n return os.path.splitext(path)[0]\n","repo_name":"garlic-os/parrot","sub_path":"database/avatar_manager.py","file_name":"avatar_manager.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"} +{"seq_id":"6767440779","text":"import cv2\nimport numpy as np\nimport os\nfrom matplotlib import pyplot as plot\n\n\n########################################\n# Ler imagem original\nimg_path = \"Resources/Semaforo_verm_5.jpg\"\nimg = cv2.imread(img_path)\n\nredPixels=0\nyellowPixels=0\ngreenPixels=0\nnumero_redlights=0\nnumero_greenlights=0\nnumero_yellowlights=0\n\n\n# Aplicar Blur\nimg_blur = cv2.medianBlur(img,5)\n\n# Converter para HSV\nimg_hsv = cv2.cvtColor(img_blur, cv2.COLOR_BGR2HSV)\n\n# Criar Array com o numpy para consultar dados recebidos doa HSV\nimg_HSV_values = np.array(img_hsv)\n\n# # Mostrar dados array valores HSV\n# print(img_HSV_values)\n\n\n\n\n# Mostrar imagem original\ncv2.imshow('Original', img)\n\ncv2.waitKey()\n\n\n\n########################################\n# Red Color Range de HSV\nlower_range_red = np.array([161, 155, 84])\nupper_range_red = np.array([179, 255, 255])\n\n# Criar mascara à volta da zona RED\nred_mask = cv2.inRange(img_hsv, lower_range_red, upper_range_red)\n\n# Função para apenas a zona vermelha\nred_only = cv2.bitwise_and(img_hsv, img, mask=red_mask)\n\n# Mostrar mascaras aplicadas à zona red\ncv2.imshow(\"mascara red\", red_mask)\n#cv2.imshow(\"mascara red\", red_only)\nredPixels = cv2.countNonZero(red_mask)\n\nprint(\"Pixels Vermelhos \" + str(redPixels))\ncv2.waitKey()\n\n\n\n########################################\n# Green Color Range de HSV\nlower_range_green = np.array([48, 208, 0])\nupper_range_green = np.array([83, 255, 255])\n\n# Criar máscara à volta da zona Green\ngreen_mask = cv2.inRange(img_hsv, lower_range_green, upper_range_green)\n\n# Mostrar apenas a zona Green\ngreen_only = cv2.bitwise_and(img_hsv, img, mask=green_mask)\n\n# Mostrar mascaras aplicadas à zona red\ncv2.imshow(\"mascara Green\", green_mask)\n#cv2.imshow(\"mascara Green\", green_only)\ngreenPixels = cv2.countNonZero(green_mask)\n\nprint(\"Pixels Verdes \" + str(greenPixels))\ncv2.waitKey()\n\n\n\n########################################\n# Yellow Color Range de HSV\nlower_range_yellow = np.array([12, 173, 155])\nupper_range_yellow = np.array([42, 212, 255])\n\n# Criar máscara à volta da zona yellow\nyellow_mask = cv2.inRange(img_hsv, lower_range_yellow, upper_range_yellow)\n\n# Mostrar apenas a zona Green\nyellow_only = cv2.bitwise_and(img_hsv, img, mask=yellow_mask)\n\n# Mostrar mascaras aplicadas à zona red\ncv2.imshow(\"mascara Yellow\", yellow_mask)\n#cv2.imshow(\"mascara Yellow\", yellow_only)\nyellowPixels = cv2.countNonZero(yellow_mask)\n\nprint(\"Pixels Amarelos \" + str(yellowPixels))\ncv2.waitKey()\n\n\nif redPixels > 0:\n numero_redlights = numero_redlights + 1\n print (\"Está Amarelo em \"+str(numero_redlights)+\" Imagens\")\n\nif greenPixels > 0:\n numero_redlights = numero_redlights + 1\n print (\"Está Verde em \"+str(numero_greenlights)+\" Imagens\")\n\nif yellowPixels > 0:\n print (\"Está Amarelo em \"+str(numero_yellowlights)+\" Imagens\")\n\ncv2.waitKey()\n\n\n\n","repo_name":"blamelas/python-color-detection","sub_path":"ColorDetection.py","file_name":"ColorDetection.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"} +{"seq_id":"12598112543","text":"\"\"\"\nDashboard view and supporting methods\n\"\"\"\n\n\nimport datetime\nimport logging\nfrom collections import defaultdict\n\nfrom django.conf import settings\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import redirect\nfrom django.urls import reverse\nfrom django.utils.translation import gettext as _\nfrom django.views.decorators.csrf import ensure_csrf_cookie\nfrom edx_django_utils import monitoring as monitoring_utils\nfrom edx_django_utils.plugins import get_plugins_view_context\nfrom edx_toggles.toggles import WaffleFlag\nfrom ipware.ip import get_client_ip\nfrom opaque_keys.edx.keys import CourseKey\nfrom openedx_filters.learning.filters import DashboardRenderStarted\nfrom pytz import UTC\n\nfrom lms.djangoapps.bulk_email.api import is_bulk_email_feature_enabled\nfrom lms.djangoapps.bulk_email.models import Optout\nfrom common.djangoapps.course_modes.models import CourseMode\nfrom common.djangoapps.edxmako.shortcuts import render_to_response, render_to_string\nfrom common.djangoapps.entitlements.models import CourseEntitlement\nfrom lms.djangoapps.commerce.utils import EcommerceService\nfrom lms.djangoapps.courseware.access import has_access\nfrom lms.djangoapps.learner_home.waffle import should_redirect_to_learner_home_mfe\nfrom lms.djangoapps.experiments.utils import get_dashboard_course_info, get_experiment_user_metadata_context\nfrom lms.djangoapps.verify_student.services import IDVerificationService\nfrom openedx.core.djangoapps.catalog.utils import (\n get_programs,\n get_pseudo_session_for_entitlement,\n get_visible_sessions_for_entitlement\n)\nfrom openedx.core.djangoapps.credit.email_utils import get_credit_provider_attribute_values, make_providers_strings\nfrom openedx.core.djangoapps.plugins.constants import ProjectType\nfrom openedx.core.djangoapps.programs.models import ProgramsApiConfig\nfrom openedx.core.djangoapps.programs.utils import ProgramDataExtender, ProgramProgressMeter\nfrom openedx.core.djangoapps.site_configuration import helpers as configuration_helpers\nfrom openedx.core.djangoapps.user_api.accounts.utils import is_secondary_email_feature_enabled\nfrom openedx.core.djangoapps.util.maintenance_banner import add_maintenance_banner\nfrom openedx.core.djangolib.markup import HTML, Text\nfrom openedx.features.content_type_gating.models import ContentTypeGatingConfig\nfrom openedx.features.course_duration_limits.access import get_user_course_duration, get_user_course_expiration_date\nfrom openedx.features.enterprise_support.api import (\n get_dashboard_consent_notification,\n get_enterprise_learner_portal_context,\n)\nfrom openedx.features.enterprise_support.utils import is_enterprise_learner\nfrom openedx.core.djangoapps.geoinfo.api import country_code_from_ip\nfrom common.djangoapps.student.api import COURSE_DASHBOARD_PLUGIN_VIEW_NAME\nfrom common.djangoapps.student.helpers import cert_info, check_verify_status_by_course, get_resume_urls_for_enrollments\nfrom common.djangoapps.student.models import (\n AccountRecovery,\n CourseEnrollment,\n CourseEnrollmentAttribute,\n DashboardConfiguration,\n PendingSecondaryEmailChange,\n UserProfile\n)\nfrom common.djangoapps.util.milestones_helpers import get_pre_requisite_courses_not_completed\nfrom xmodule.modulestore.django import modulestore # lint-amnesty, pylint: disable=wrong-import-order\n\nlog = logging.getLogger(\"edx.student\")\n\nEXPERIMENTS_NAMESPACE = 'student.experiments'\n\n\ndef get_org_black_and_whitelist_for_site():\n \"\"\"\n Returns the org blacklist and whitelist for the current site.\n\n Returns:\n (org_whitelist, org_blacklist): A tuple of lists of orgs that serve as\n either a blacklist or a whitelist of orgs for the current site. The\n whitelist takes precedence, and the blacklist is used if the\n whitelist is None.\n \"\"\"\n # Default blacklist is empty.\n org_blacklist = None\n # Whitelist the orgs configured for the current site. Each site outside\n # of edx.org has a list of orgs associated with its configuration.\n org_whitelist = configuration_helpers.get_current_site_orgs()\n\n if not org_whitelist:\n # If there is no whitelist, the blacklist will include all orgs that\n # have been configured for any other sites. This applies to edx.org,\n # where it is easier to blacklist all other orgs.\n org_blacklist = configuration_helpers.get_all_orgs()\n\n return org_whitelist, org_blacklist\n\n\ndef _get_recently_enrolled_courses(course_enrollments):\n \"\"\"\n Given a list of enrollments, filter out all but recent enrollments.\n\n Args:\n course_enrollments (list[CourseEnrollment]): A list of course enrollments.\n\n Returns:\n list[CourseEnrollment]: A list of recent course enrollments.\n \"\"\"\n seconds = DashboardConfiguration.current().recent_enrollment_time_delta\n time_delta = (datetime.datetime.now(UTC) - datetime.timedelta(seconds=seconds))\n return [\n enrollment for enrollment in course_enrollments\n # If the enrollment has no created date, we are explicitly excluding the course\n # from the list of recent enrollments.\n if enrollment.is_active and enrollment.created > time_delta\n ]\n\n\ndef _create_recent_enrollment_message(course_enrollments, course_modes): # lint-amnesty, pylint: disable=unused-argument\n \"\"\"\n Builds a recent course enrollment message.\n\n Constructs a new message template based on any recent course enrollments\n for the student.\n\n Args:\n course_enrollments (list[CourseEnrollment]): a list of course enrollments.\n course_modes (dict): Mapping of course ID's to course mode dictionaries.\n\n Returns:\n A string representing the HTML message output from the message template.\n None if there are no recently enrolled courses.\n\n \"\"\"\n recently_enrolled_courses = _get_recently_enrolled_courses(course_enrollments)\n\n if recently_enrolled_courses:\n enrollments_count = len(recently_enrolled_courses)\n course_name_separator = ', '\n # If length of enrolled course 2, join names with 'and'\n if enrollments_count == 2:\n course_name_separator = _(' and ')\n\n course_names = course_name_separator.join(\n [enrollment.course_overview.display_name for enrollment in recently_enrolled_courses]\n )\n\n platform_name = configuration_helpers.get_value('platform_name', settings.PLATFORM_NAME)\n\n return render_to_string(\n 'enrollment/course_enrollment_message.html',\n {\n 'course_names': course_names,\n 'enrollments_count': enrollments_count,\n 'platform_name': platform_name,\n 'course_id': recently_enrolled_courses[0].course_overview.id if enrollments_count == 1 else None\n }\n )\n\n\ndef get_course_enrollments(user, org_whitelist, org_blacklist, course_limit=None):\n \"\"\"\n Given a user, return a filtered set of their course enrollments.\n\n Arguments:\n user (User): the user in question.\n org_whitelist (list[str]): If not None, ONLY courses of these orgs will be returned.\n org_blacklist (list[str]): Courses of these orgs will be excluded.\n course_limit: Number courses to load in dashboard if set to None then all the courses would be load.\n\n Returns:\n generator[CourseEnrollment]: a sequence of enrollments to be displayed\n on the user's dashboard.\n \"\"\"\n for enrollment in CourseEnrollment.enrollments_for_user_with_overviews_preload(user, course_limit):\n\n # If the course is missing or broken, log an error and skip it.\n course_overview = enrollment.course_overview\n if not course_overview:\n log.error(\n \"User %s enrolled in broken or non-existent course %s\",\n user.username,\n enrollment.course_id\n )\n continue\n\n # Filter out anything that is not in the whitelist.\n if org_whitelist and course_overview.location.org not in org_whitelist:\n continue\n\n # Conversely, filter out any enrollments in the blacklist.\n elif org_blacklist and course_overview.location.org in org_blacklist:\n continue\n\n # Else, include the enrollment.\n else:\n yield enrollment\n\n\ndef get_filtered_course_entitlements(user, org_whitelist, org_blacklist):\n \"\"\"\n Given a user, return a filtered set of their course entitlements.\n\n Arguments:\n user (User): the user in question.\n org_whitelist (list[str]): If not None, ONLY entitlements of these orgs will be returned.\n org_blacklist (list[str]): CourseEntitlements of these orgs will be excluded.\n\n Returns:\n generator[CourseEntitlement]: a sequence of entitlements to be displayed\n on the user's dashboard.\n \"\"\"\n course_entitlement_available_sessions = {}\n unfulfilled_entitlement_pseudo_sessions = {}\n course_entitlements = list(CourseEntitlement.get_active_entitlements_for_user(user))\n filtered_entitlements = []\n pseudo_session = None\n course_run_key = None\n\n for course_entitlement in course_entitlements:\n course_entitlement.update_expired_at()\n available_runs = get_visible_sessions_for_entitlement(course_entitlement)\n\n if not course_entitlement.enrollment_course_run:\n # Unfulfilled entitlements need a mock session for metadata\n pseudo_session = get_pseudo_session_for_entitlement(course_entitlement)\n unfulfilled_entitlement_pseudo_sessions[str(course_entitlement.uuid)] = pseudo_session\n\n # Check the org of the Course and filter out entitlements that are not available.\n if course_entitlement.enrollment_course_run:\n course_run_key = course_entitlement.enrollment_course_run.course_id\n elif available_runs:\n course_run_key = CourseKey.from_string(available_runs[0]['key'])\n elif pseudo_session:\n course_run_key = CourseKey.from_string(pseudo_session['key'])\n\n if course_run_key:\n # If there is no course_run_key at this point we will be unable to determine if it should be shown.\n # Therefore it should be excluded by default.\n if org_whitelist and course_run_key.org not in org_whitelist:\n continue\n elif org_blacklist and course_run_key.org in org_blacklist:\n continue\n\n course_entitlement_available_sessions[str(course_entitlement.uuid)] = available_runs\n filtered_entitlements.append(course_entitlement)\n\n return filtered_entitlements, course_entitlement_available_sessions, unfulfilled_entitlement_pseudo_sessions\n\n\ndef complete_course_mode_info(course_id, enrollment, modes=None):\n \"\"\"\n We would like to compute some more information from the given course modes\n and the user's current enrollment\n\n Returns the given information:\n - whether to show the course upsell information\n - numbers of days until they can't upsell anymore\n \"\"\"\n if modes is None:\n modes = CourseMode.modes_for_course_dict(course_id)\n\n mode_info = {'show_upsell': False, 'days_for_upsell': None}\n # we want to know if the user is already enrolled as verified or credit and\n # if verified is an option.\n if CourseMode.VERIFIED in modes and enrollment.mode in CourseMode.UPSELL_TO_VERIFIED_MODES:\n mode_info['show_upsell'] = True\n mode_info['verified_sku'] = modes['verified'].sku\n mode_info['verified_bulk_sku'] = modes['verified'].bulk_sku\n # if there is an expiration date, find out how long from now it is\n if modes['verified'].expiration_datetime:\n today = datetime.datetime.now(UTC).date()\n mode_info['days_for_upsell'] = (modes['verified'].expiration_datetime.date() - today).days\n mode_info['expiration_datetime'] = modes['verified'].expiration_datetime.date()\n\n return mode_info\n\n\ndef get_verification_error_reasons_for_display(verification_error_codes):\n \"\"\"\n Returns the display text for the given verification error codes.\n \"\"\"\n verification_errors = []\n verification_error_map = {\n 'photos_mismatched': _('Photos are mismatched'),\n 'id_image_missing_name': _('Name missing from ID photo'),\n 'id_image_missing': _('ID photo not provided'),\n 'id_invalid': _('ID is invalid'),\n 'user_image_not_clear': _('Learner photo is blurry'),\n 'name_mismatch': _('Name on ID does not match name on account'),\n 'user_image_missing': _('Learner photo not provided'),\n 'id_image_not_clear': _('ID photo is blurry'),\n }\n\n for error in verification_error_codes:\n error_text = verification_error_map.get(error)\n if error_text:\n verification_errors.append(error_text)\n\n return verification_errors\n\n\ndef reverification_info(statuses):\n \"\"\"\n Returns reverification-related information for *all* of user's enrollments whose\n reverification status is in statuses.\n\n Args:\n statuses (list): a list of reverification statuses we want information for\n example: [\"must_reverify\", \"denied\"]\n\n Returns:\n dictionary of lists: dictionary with one key per status, e.g.\n dict[\"must_reverify\"] = []\n dict[\"must_reverify\"] = [some information]\n \"\"\"\n reverifications = defaultdict(list)\n\n # Sort the data by the reverification_end_date\n for status in statuses:\n if reverifications[status]:\n reverifications[status].sort(key=lambda x: x.date)\n return reverifications\n\n\ndef credit_statuses(user, course_enrollments):\n \"\"\"\n Retrieve the status for credit courses.\n\n A credit course is a course for which a user can purchased\n college credit. The current flow is:\n\n 1. User becomes eligible for credit (submits verifications, passes the course, etc.)\n 2. User purchases credit from a particular credit provider.\n 3. User requests credit from the provider, usually creating an account on the provider's site.\n 4. The credit provider notifies us whether the user's request for credit has been accepted or rejected.\n\n The dashboard is responsible for communicating the user's state in this flow.\n\n Arguments:\n user (User): The currently logged-in user.\n course_enrollments (list[CourseEnrollment]): List of enrollments for the\n user.\n\n Returns: dict\n\n The returned dictionary has keys that are `CourseKey`s and values that\n are dictionaries with:\n\n * eligible (bool): True if the user is eligible for credit in this course.\n * deadline (datetime): The deadline for purchasing and requesting credit for this course.\n * purchased (bool): Whether the user has purchased credit for this course.\n * provider_name (string): The display name of the credit provider.\n * provider_status_url (string): A URL the user can visit to check on their credit request status.\n * provider_id (string): A unique alphanumeric (plus hyphens) string identifying the provider.\n * request_status (string): Either \"pending\", \"approved\", or \"rejected\"\n * error (bool): If true, an unexpected error occurred when retrieving the credit status,\n so the user should contact the support team.\n\n Example:\n >>> credit_statuses(user, course_enrollments)\n {\n CourseKey.from_string(\"edX/DemoX/Demo_Course\"): {\n \"course_key\": \"edX/DemoX/Demo_Course\",\n \"eligible\": True,\n \"deadline\": 2015-11-23 00:00:00 UTC,\n \"purchased\": True,\n \"provider_name\": \"Hogwarts\",\n \"provider_status_url\": \"http://example.com/status\",\n \"provider_id\": \"HSWW\",\n \"request_status\": \"pending\",\n \"error\": False\n }\n }\n\n \"\"\"\n from openedx.core.djangoapps.credit import api as credit_api\n\n # Feature flag off\n if not settings.FEATURES.get(\"ENABLE_CREDIT_ELIGIBILITY\"):\n return {}\n\n request_status_by_course = {\n request[\"course_key\"]: request[\"status\"]\n for request in credit_api.get_credit_requests_for_user(user.username)\n }\n\n credit_enrollments = {\n enrollment.course_id: enrollment\n for enrollment in course_enrollments\n if enrollment.mode == \"credit\"\n }\n\n # When a user purchases credit in a course, the user's enrollment\n # mode is set to \"credit\" and an enrollment attribute is set\n # with the ID of the credit provider. We retrieve *all* such attributes\n # here to minimize the number of database queries.\n purchased_credit_providers = {\n attribute.enrollment.course_id: attribute.value\n for attribute in CourseEnrollmentAttribute.objects.filter(\n namespace=\"credit\",\n name=\"provider_id\",\n enrollment__in=list(credit_enrollments.values())\n ).select_related(\"enrollment\")\n }\n\n provider_info_by_id = {\n provider[\"id\"]: provider\n for provider in credit_api.get_credit_providers()\n }\n\n statuses = {}\n for eligibility in credit_api.get_eligibilities_for_user(user.username):\n course_key = CourseKey.from_string(str(eligibility[\"course_key\"]))\n providers_names = get_credit_provider_attribute_values(course_key, 'display_name')\n status = {\n \"course_key\": str(course_key),\n \"eligible\": True,\n \"deadline\": eligibility[\"deadline\"],\n \"purchased\": course_key in credit_enrollments,\n \"provider_name\": make_providers_strings(providers_names),\n \"provider_status_url\": None,\n \"provider_id\": None,\n \"request_status\": request_status_by_course.get(course_key),\n \"error\": False,\n }\n\n # If the user has purchased credit, then include information about the credit\n # provider from which the user purchased credit.\n # We retrieve the provider's ID from the an \"enrollment attribute\" set on the user's\n # enrollment when the user's order for credit is fulfilled by the E-Commerce service.\n if status[\"purchased\"]:\n provider_id = purchased_credit_providers.get(course_key)\n if provider_id is None:\n status[\"error\"] = True\n log.error(\n \"Could not find credit provider associated with credit enrollment \"\n \"for user %s in course %s. The user will not be able to see their \"\n \"credit request status on the student dashboard. This attribute should \"\n \"have been set when the user purchased credit in the course.\",\n user.id, course_key\n )\n else:\n provider_info = provider_info_by_id.get(provider_id, {})\n status[\"provider_name\"] = provider_info.get(\"display_name\")\n status[\"provider_status_url\"] = provider_info.get(\"status_url\")\n status[\"provider_id\"] = provider_id\n\n if not status[\"provider_name\"] and not status[\"provider_status_url\"]:\n status[\"error\"] = True\n log.error(\n \"Could not find credit provider info for [%s] in [%s]. The user will not \"\n \"be able to see their credit request status on the student dashboard.\",\n provider_id, provider_info_by_id\n )\n\n statuses[course_key] = status\n\n return statuses\n\n\ndef show_load_all_courses_link(user, course_limit, course_enrollments):\n \"\"\"\n By default dashboard will show limited courses based on the course limit\n set in configuration.\n\n A link would be provided provided at the bottom to load all the courses if there are any courses.\n \"\"\"\n\n if course_limit is None:\n return False\n\n total_enrollments = CourseEnrollment.enrollments_for_user(user).count()\n return len(course_enrollments) < total_enrollments\n\n\ndef get_dashboard_course_limit():\n \"\"\"\n get course limit from configuration\n \"\"\"\n course_limit = getattr(settings, 'DASHBOARD_COURSE_LIMIT', None)\n return course_limit\n\n\ndef check_for_unacknowledged_notices(context):\n \"\"\"\n Checks the notices apps plugin context to see if there are any unacknowledged notices the user needs to take action\n on. If so, build a redirect url to the first unack'd notice.\n \"\"\"\n notice_url = None\n\n notices = context.get(\"plugins\", {}).get(\"notices\", {}).get(\"unacknowledged_notices\")\n if notices:\n # We will only show one notice to the user one at a time. Build a redirect URL to the first notice in the\n # list of unacknowledged notices.\n notice_url = f\"{settings.LMS_ROOT_URL}{notices[0]}?next={settings.LMS_ROOT_URL}/dashboard/\"\n\n return notice_url\n\n\n@login_required\n@ensure_csrf_cookie\n@add_maintenance_banner\ndef student_dashboard(request): # lint-amnesty, pylint: disable=too-many-statements\n \"\"\"\n Provides the LMS dashboard view\n\n TODO: This is lms specific and does not belong in common code.\n Note:\n To load the all courses set course_limit=None as parameter in GET. If its not None then default course\n limit will be used that is set in configuration\n Arguments:\n request: The request object.\n\n Returns:\n The dashboard response.\n\n \"\"\"\n user = request.user\n if not UserProfile.objects.filter(user=user).exists():\n return redirect(reverse('account_settings'))\n\n if should_redirect_to_learner_home_mfe(user):\n return redirect(settings.LEARNER_HOME_MICROFRONTEND_URL)\n\n platform_name = configuration_helpers.get_value(\"platform_name\", settings.PLATFORM_NAME)\n\n enable_verified_certificates = configuration_helpers.get_value(\n 'ENABLE_VERIFIED_CERTIFICATES',\n settings.FEATURES.get('ENABLE_VERIFIED_CERTIFICATES')\n )\n display_course_modes_on_dashboard = configuration_helpers.get_value(\n 'DISPLAY_COURSE_MODES_ON_DASHBOARD',\n settings.FEATURES.get('DISPLAY_COURSE_MODES_ON_DASHBOARD', True)\n )\n activation_email_support_link = configuration_helpers.get_value(\n 'ACTIVATION_EMAIL_SUPPORT_LINK', settings.ACTIVATION_EMAIL_SUPPORT_LINK\n ) or settings.SUPPORT_SITE_LINK\n hide_dashboard_courses_until_activated = configuration_helpers.get_value(\n 'HIDE_DASHBOARD_COURSES_UNTIL_ACTIVATED',\n settings.FEATURES.get('HIDE_DASHBOARD_COURSES_UNTIL_ACTIVATED', False)\n )\n empty_dashboard_message = configuration_helpers.get_value(\n 'EMPTY_DASHBOARD_MESSAGE', None\n )\n disable_unenrollment = configuration_helpers.get_value(\n 'DISABLE_UNENROLLMENT',\n settings.FEATURES.get('DISABLE_UNENROLLMENT')\n )\n\n disable_course_limit = request and 'course_limit' in request.GET\n course_limit = get_dashboard_course_limit() if not disable_course_limit else None\n\n # Get the org whitelist or the org blacklist for the current site\n site_org_whitelist, site_org_blacklist = get_org_black_and_whitelist_for_site()\n course_enrollments = list(get_course_enrollments(user, site_org_whitelist, site_org_blacklist, course_limit))\n\n # Get the entitlements for the user and a mapping to all available sessions for that entitlement\n # If an entitlement has no available sessions, pass through a mock course overview object\n (course_entitlements,\n course_entitlement_available_sessions,\n unfulfilled_entitlement_pseudo_sessions) = get_filtered_course_entitlements(\n user,\n site_org_whitelist,\n site_org_blacklist\n )\n\n # Record how many courses there are so that we can get a better\n # understanding of usage patterns on prod.\n monitoring_utils.accumulate('num_courses', len(course_enrollments))\n\n # Sort the enrollment pairs by the enrollment date\n course_enrollments.sort(key=lambda x: x.created, reverse=True)\n\n # Retrieve the course modes for each course\n enrolled_course_ids = [enrollment.course_id for enrollment in course_enrollments]\n __, unexpired_course_modes = CourseMode.all_and_unexpired_modes_for_courses(enrolled_course_ids)\n course_modes_by_course = {\n course_id: {\n mode.slug: mode\n for mode in modes\n }\n for course_id, modes in unexpired_course_modes.items()\n }\n\n # Check to see if the student has recently enrolled in a course.\n # If so, display a notification message confirming the enrollment.\n enrollment_message = _create_recent_enrollment_message(\n course_enrollments, course_modes_by_course\n )\n course_optouts = Optout.objects.filter(user=user).values_list('course_id', flat=True)\n\n # Display activation message\n activate_account_message = ''\n if not user.is_active:\n activate_account_message = Text(_(\n \"Check your {email_start}{email}{email_end} inbox for an account activation link from {platform_name}. \"\n \"If you need help, contact {link_start}{platform_name} Support{link_end}.\"\n )).format(\n platform_name=platform_name,\n email_start=HTML(\"\"),\n email_end=HTML(\"\"),\n email=user.email,\n link_start=HTML(\"\").format(\n activation_email_support_link=activation_email_support_link,\n ),\n link_end=HTML(\"\"),\n )\n\n enterprise_message = get_dashboard_consent_notification(request, user, course_enrollments)\n\n recovery_email_message = recovery_email_activation_message = None\n if is_secondary_email_feature_enabled():\n try:\n pending_email = PendingSecondaryEmailChange.objects.get(user=user) # lint-amnesty, pylint: disable=unused-variable\n except PendingSecondaryEmailChange.DoesNotExist:\n try:\n account_recovery_obj = AccountRecovery.objects.get(user=user) # lint-amnesty, pylint: disable=unused-variable\n except AccountRecovery.DoesNotExist:\n recovery_email_message = Text(\n _(\n \"Add a recovery email to retain access when single-sign on is not available. \"\n \"Go to {link_start}your Account Settings{link_end}.\")\n ).format(\n link_start=HTML(\"\").format(\n account_setting_page=reverse('account_settings'),\n ),\n link_end=HTML(\"\")\n )\n else:\n recovery_email_activation_message = Text(\n _(\n \"Recovery email is not activated yet. \"\n \"Kindly visit your email and follow the instructions to activate it.\"\n )\n )\n\n # Disable lookup of Enterprise consent_required_course due to ENT-727\n # Will re-enable after fixing WL-1315\n consent_required_courses = set()\n\n # Account activation message\n account_activation_messages = [\n message for message in messages.get_messages(request) if 'account-activation' in message.tags\n ]\n\n # Global staff can see what courses encountered an error on their dashboard\n staff_access = False\n errored_courses = {}\n if has_access(user, 'staff', 'global'):\n # Show any courses that encountered an error on load\n staff_access = True\n errored_courses = modulestore().get_errored_courses()\n\n show_courseware_links_for = {\n enrollment.course_id: has_access(request.user, 'load', enrollment.course_overview)\n for enrollment in course_enrollments\n }\n\n # Find programs associated with course runs being displayed. This information\n # is passed in the template context to allow rendering of program-related\n # information on the dashboard.\n meter = ProgramProgressMeter(request.site, user, enrollments=course_enrollments)\n ecommerce_service = EcommerceService()\n inverted_programs = meter.invert_programs()\n\n urls, programs_data = {}, {}\n bundles_on_dashboard_flag = WaffleFlag(f'{EXPERIMENTS_NAMESPACE}.bundles_on_dashboard', __name__) # lint-amnesty, pylint: disable=toggle-missing-annotation\n\n # TODO: Delete this code and the relevant HTML code after testing LEARNER-3072 is complete\n if bundles_on_dashboard_flag.is_enabled() and inverted_programs and list(inverted_programs.items()):\n if len(course_enrollments) < 4:\n for program in inverted_programs.values():\n try:\n program_uuid = program[0]['uuid']\n program_data = get_programs(uuid=program_uuid)\n program_data = ProgramDataExtender(program_data, request.user).extend()\n skus = program_data.get('skus')\n checkout_page_url = ecommerce_service.get_checkout_page_url(*skus)\n program_data['completeProgramURL'] = checkout_page_url + '&bundle=' + program_data.get('uuid')\n programs_data[program_uuid] = program_data\n except: # pylint: disable=bare-except\n pass\n\n # Construct a dictionary of course mode information\n # used to render the course list. We re-use the course modes dict\n # we loaded earlier to avoid hitting the database.\n course_mode_info = {\n enrollment.course_id: complete_course_mode_info(\n enrollment.course_id, enrollment,\n modes=course_modes_by_course[enrollment.course_id]\n )\n for enrollment in course_enrollments\n }\n\n # Determine the per-course verification status\n # This is a dictionary in which the keys are course locators\n # and the values are one of:\n #\n # VERIFY_STATUS_NEED_TO_VERIFY\n # VERIFY_STATUS_SUBMITTED\n # VERIFY_STATUS_APPROVED\n # VERIFY_STATUS_MISSED_DEADLINE\n #\n # Each of which correspond to a particular message to display\n # next to the course on the dashboard.\n #\n # If a course is not included in this dictionary,\n # there is no verification messaging to display.\n verify_status_by_course = check_verify_status_by_course(user, course_enrollments)\n cert_statuses = {\n enrollment.course_id: cert_info(request.user, enrollment)\n for enrollment in course_enrollments\n }\n\n # only show email settings for Mongo course and when bulk email is turned on\n show_email_settings_for = frozenset(\n enrollment.course_id for enrollment in course_enrollments if (\n is_bulk_email_feature_enabled(enrollment.course_id)\n )\n )\n\n # Verification Attempts\n # Used to generate the \"you must reverify for course x\" banner\n verification_status = IDVerificationService.user_status(user)\n verification_errors = get_verification_error_reasons_for_display(verification_status['error'])\n\n # Gets data for midcourse reverifications, if any are necessary or have failed\n statuses = [\"approved\", \"denied\", \"pending\", \"must_reverify\"]\n reverifications = reverification_info(statuses)\n\n enrolled_courses_either_paid = frozenset(\n enrollment.course_id for enrollment in course_enrollments\n if enrollment.is_paid_course()\n )\n\n # Checks if a course enrollment redeemed using a voucher is refundable\n enrolled_courses_voucher_refundable = frozenset(\n enrollment.course_id for enrollment in course_enrollments\n if enrollment.is_order_voucher_refundable()\n )\n\n # If there are *any* denied reverifications that have not been toggled off,\n # we'll display the banner\n denied_banner = any(item.display for item in reverifications[\"denied\"])\n\n # get list of courses having pre-requisites yet to be completed\n courses_having_prerequisites = frozenset(\n enrollment.course_id for enrollment in course_enrollments\n if enrollment.course_overview.pre_requisite_courses\n )\n courses_requirements_not_met = get_pre_requisite_courses_not_completed(user, courses_having_prerequisites)\n\n if 'notlive' in request.GET:\n redirect_message = _(\"The course you are looking for does not start until {date}.\").format(\n date=request.GET['notlive']\n )\n elif 'course_closed' in request.GET:\n redirect_message = _(\"The course you are looking for is closed for enrollment as of {date}.\").format(\n date=request.GET['course_closed']\n )\n elif 'access_response_error' in request.GET:\n # This can be populated in a generalized way with fields from access response errors\n redirect_message = request.GET['access_response_error']\n else:\n redirect_message = ''\n\n # Filter out any course enrollment course cards that are associated with fulfilled entitlements\n for entitlement in [e for e in course_entitlements if e.enrollment_course_run is not None]:\n course_enrollments = [\n enr for enr in course_enrollments if entitlement.enrollment_course_run.course_id != enr.course_id\n ]\n\n show_account_activation_popup = request.COOKIES.get(settings.SHOW_ACTIVATE_CTA_POPUP_COOKIE_NAME, None)\n\n enrollments_fbe_is_on = []\n for enrollment in course_enrollments:\n course_key = CourseKey.from_string(str(enrollment.course_id))\n gated_content = ContentTypeGatingConfig.enabled_for_enrollment(\n user=user,\n course_key=course_key\n )\n duration = get_user_course_duration(user, enrollment.course)\n deadline = duration and get_user_course_expiration_date(user, enrollment.course)\n fbe_is_on = deadline and gated_content\n if fbe_is_on:\n enrollments_fbe_is_on.append(course_key)\n\n ip_address = get_client_ip(request)[0]\n country_code = country_code_from_ip(ip_address).upper()\n\n context = {\n 'urls': urls,\n 'programs_data': programs_data,\n 'enterprise_message': enterprise_message,\n 'consent_required_courses': consent_required_courses,\n 'enrollment_message': enrollment_message,\n 'redirect_message': Text(redirect_message),\n 'account_activation_messages': account_activation_messages,\n 'activate_account_message': activate_account_message,\n 'course_enrollments': course_enrollments,\n 'course_entitlements': course_entitlements,\n 'course_entitlement_available_sessions': course_entitlement_available_sessions,\n 'unfulfilled_entitlement_pseudo_sessions': unfulfilled_entitlement_pseudo_sessions,\n 'course_optouts': course_optouts,\n 'staff_access': staff_access,\n 'errored_courses': errored_courses,\n 'show_courseware_links_for': show_courseware_links_for,\n 'all_course_modes': course_mode_info,\n 'cert_statuses': cert_statuses,\n 'credit_statuses': credit_statuses(user, course_enrollments),\n 'show_email_settings_for': show_email_settings_for,\n 'reverifications': reverifications,\n 'verification_display': verification_status['should_display'],\n 'verification_status': verification_status['status'],\n 'verification_expiry': verification_status['verification_expiry'],\n 'verification_status_by_course': verify_status_by_course,\n 'verification_errors': verification_errors,\n 'denied_banner': denied_banner,\n 'billing_email': settings.PAYMENT_SUPPORT_EMAIL,\n 'show_account_activation_popup': show_account_activation_popup,\n 'user': user,\n 'logout_url': reverse('logout'),\n 'platform_name': platform_name,\n 'enrolled_courses_either_paid': enrolled_courses_either_paid,\n 'enrolled_courses_voucher_refundable': enrolled_courses_voucher_refundable,\n 'provider_states': [],\n 'courses_requirements_not_met': courses_requirements_not_met,\n 'nav_hidden': True,\n 'inverted_programs': inverted_programs,\n 'show_program_listing': ProgramsApiConfig.is_enabled(),\n 'show_dashboard_tabs': True,\n 'disable_courseware_js': True,\n 'display_course_modes_on_dashboard': enable_verified_certificates and display_course_modes_on_dashboard,\n 'display_sidebar_account_activation_message': not(user.is_active or hide_dashboard_courses_until_activated),\n 'display_dashboard_courses': (user.is_active or not hide_dashboard_courses_until_activated),\n 'empty_dashboard_message': empty_dashboard_message,\n 'enrollments_fbe_is_on': enrollments_fbe_is_on,\n 'recovery_email_message': recovery_email_message,\n 'recovery_email_activation_message': recovery_email_activation_message,\n 'show_load_all_courses_link': show_load_all_courses_link(user, course_limit, course_enrollments),\n # TODO START: clean up as part of REVEM-199 (START)\n 'course_info': get_dashboard_course_info(user, course_enrollments),\n # TODO START: clean up as part of REVEM-199 (END)\n 'disable_unenrollment': disable_unenrollment,\n 'country_code': country_code,\n # TODO: clean when experiment(Merchandise 2U LOBs - Dashboard) would be stop. [VAN-1097]\n 'is_enterprise_user': is_enterprise_learner(user),\n }\n\n # Include enterprise learner portal metadata and messaging\n enterprise_learner_portal_context = get_enterprise_learner_portal_context(request)\n context.update(enterprise_learner_portal_context)\n\n context_from_plugins = get_plugins_view_context(\n ProjectType.LMS,\n COURSE_DASHBOARD_PLUGIN_VIEW_NAME,\n context\n )\n context.update(context_from_plugins)\n\n notice_url = check_for_unacknowledged_notices(context)\n if notice_url:\n return redirect(notice_url)\n\n course = None\n context.update(\n get_experiment_user_metadata_context(\n course,\n user,\n )\n )\n if ecommerce_service.is_enabled(request.user):\n context.update({\n 'use_ecommerce_payment_flow': True,\n 'ecommerce_payment_page': ecommerce_service.payment_page_url(),\n })\n\n # Gather urls for course card resume buttons.\n resume_button_urls = ['' for entitlement in course_entitlements]\n for url in get_resume_urls_for_enrollments(user, course_enrollments).values():\n resume_button_urls.append(url)\n # There must be enough urls for dashboard.html. Template creates course\n # cards for \"enrollments + entitlements\".\n context.update({\n 'resume_button_urls': resume_button_urls\n })\n\n dashboard_template = 'dashboard.html'\n try:\n # .. filter_implemented_name: DashboardRenderStarted\n # .. filter_type: org.openedx.learning.dashboard.render.started.v1\n context, dashboard_template = DashboardRenderStarted.run_filter(\n context=context, template_name=dashboard_template,\n )\n except DashboardRenderStarted.RenderInvalidDashboard as exc:\n response = render_to_response(exc.dashboard_template, exc.template_context)\n except DashboardRenderStarted.RedirectToPage as exc:\n response = HttpResponseRedirect(exc.redirect_to or reverse('account_settings'))\n except DashboardRenderStarted.RenderCustomResponse as exc:\n response = exc.response\n else:\n response = render_to_response(dashboard_template, context)\n\n if show_account_activation_popup:\n response.delete_cookie(\n settings.SHOW_ACTIVATE_CTA_POPUP_COOKIE_NAME,\n domain=settings.SESSION_COOKIE_DOMAIN,\n path='/',\n )\n\n return response\n","repo_name":"openedx/edx-platform","sub_path":"common/djangoapps/student/views/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":39601,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"} +{"seq_id":"24045700912","text":"# Author: