diff --git "a/2879.jsonl" "b/2879.jsonl" new file mode 100644--- /dev/null +++ "b/2879.jsonl" @@ -0,0 +1,634 @@ +{"seq_id":"193857782","text":"#!/usr/bin/env python3\n\"\"\"\nConnects to LND and checks channels for issues\nWIP\n\"\"\"\n\nfrom nodeinterface import NodeInterface\n\n\ncentralitycheckcount = 23\n\nmynode = NodeInterface.fromconfig()\n\nprint('Chanid kCap RBal Alias Flags')\nmyneighbours = {}\nfor chan in mynode.ListChannels().channels:\n\n flags = []\n chanid = chan.chan_id\n alias = mynode.getAlias(chan.remote_pubkey)\n kcap = chan.capacity/1000\n remote_ratio = chan.remote_balance / chan.capacity\n\n if chan.private:\n flags.append('private')\n\n if not chan.active:\n flags.append('inactive')\n\n totalsatsmoved = chan.total_satoshis_sent + chan.total_satoshis_received\n if totalsatsmoved == 0:\n flags.append('unused')\n elif chan.total_satoshis_sent == 0:\n flags.append('never_sent')\n elif chan.total_satoshis_received == 0:\n flags.append('never_rcvd')\n\n if chan.initiator:\n if remote_ratio > 0.95: flags.append('depleted')\n else:\n if remote_ratio < 0.05: flags.append('depleted')\n\n print(chanid, f'{kcap:5.0f}{remote_ratio:6.1%} {alias[:20]:20}', *flags)\n myneighbours[chan.remote_pubkey] = {'flags':flags, 'usage':totalsatsmoved}\n\nexit()\nprint('\\nFetching graph for further analysis')\ngraph = mynode.DescribeGraph()\nprint(len(graph.nodes), len(graph.edges))\n\n\nprint(f'Checking up to {centralitycheckcount} least used public channels for removal centrality impact')\n\n\n\n\n","sub_path":"checkchannels.py","file_name":"checkchannels.py","file_ext":"py","file_size_in_byte":1440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"571439837","text":"#!/usr/bin/env python2\n\nimport web\n\nurls = (\n\t'/', 'Editor'\n)\n\napp = web.application(urls, globals())\ntemplates = web.template.render('templates')\n\nclass Editor:\n\tdef GET(self):\n\t\treturn templates.editor()\n\nif __name__ == \"__main__\":\n\tapp.run()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"640442982","text":"import tdl\n\nfrom components.fighter import Fighter\nfrom death_functions import kill_monster, kill_player\nfrom entity import Entity, get_blocking_entities_at_location\nfrom input_handlers import handle_keys\nfrom render_functions import clear_all, render_all, RenderOrder\nfrom map_utils import GameMap, make_map\nfrom game_messages import Message, MessageLog\nfrom game_states import GameStates\nfrom components.inventory import Inventory\n\n# written by Rob\n# from http://rogueliketutorials.com/tdl/1\n\n\ndef main():\n screen_width = 80\n screen_height = 50\n\n bar_width = 20\n panel_height = 7\n panel_y = screen_height - panel_height\n\n message_x = bar_width + 2\n message_width = screen_width - bar_width - 2\n message_height = panel_height - 1\n\n map_width = 80\n map_height = 43\n\n room_max_size = 10\n room_min_size = 6\n max_rooms = 30\n\n fov_algorithm = 'BASIC'\n fov_light_walls = True\n fov_radius = 10\n\n max_monsters_per_room = 3\n max_items_per_room = 2\n\n colors = {\n 'dark_wall': (32, 32, 32),\n 'dark_ground': (51, 0, 0),\n 'light_wall': (52, 52, 52),\n 'light_ground': (101, 50, 50),\n 'desaturated_green': (63, 127, 63),\n 'darker_green': (0, 127, 0),\n 'dark_red': (191, 0, 0),\n 'white': (255, 255, 255),\n 'black': (0, 0, 0),\n 'red': (255, 0, 0),\n 'orange': (255, 127, 0),\n 'light_red': (255, 114, 114),\n 'darker_red': (127, 0, 0),\n 'violet': (127, 0, 255),\n 'yellow': (255, 255, 0),\n 'blue': (0, 0, 255),\n 'green': (0, 255, 0)\n }\n\n fighter_component = Fighter(hp=30, defense=2, power=5)\n inventory_component = Inventory(26)\n player = Entity(0, 0, '@', (255, 255, 255), 'Player', blocks=True, render_order=RenderOrder.ACTOR,\n fighter=fighter_component, inventory=inventory_component)\n entities = [player]\n\n tdl.set_font('arial10x10.png', greyscale=True, altLayout=True)\n\n root_console = tdl.init(screen_width, screen_height, title='The Sorcerer of Doom')\n con = tdl.Console(screen_width, screen_height)\n panel = tdl.Console(screen_width, panel_height) # holds hp bar and message log\n\n game_map = GameMap(map_width, map_height)\n make_map(game_map, max_rooms, room_min_size, room_max_size, map_width, map_height, player, entities,\n max_monsters_per_room, max_items_per_room, colors)\n\n fov_recompute = True\n\n message_log = MessageLog(message_x, message_width, message_height)\n\n mouse_coordinates = (0, 0)\n\n game_state = GameStates.PLAYERS_TURN\n previous_game_state = game_state\n\n while not tdl.event.is_window_closed():\n if fov_recompute:\n game_map.compute_fov(player.x, player.y, fov=fov_algorithm, radius=fov_radius, light_walls=fov_light_walls)\n\n render_all(con, panel, entities, player, game_map, fov_recompute, root_console, message_log, screen_width,\n screen_height, bar_width, panel_height, panel_y, mouse_coordinates, colors, game_state)\n tdl.flush()\n\n clear_all(con, entities)\n\n fov_recompute = False\n\n for event in tdl.event.get():\n if event.type == 'KEYDOWN':\n user_input = event\n break\n elif event.type == 'MOUSEMOTION':\n mouse_coordinates = event.cell\n else:\n user_input = None\n\n if not user_input:\n continue\n\n action = handle_keys(user_input, game_state)\n\n move = action.get('move')\n pickup = action.get('pickup')\n show_inventory = action.get('show_inventory')\n drop_inventory = action.get('drop_inventory')\n inventory_index = action.get('inventory_index')\n exit = action.get('exit')\n fullscreen = action.get('fullscreen')\n\n player_turn_results = []\n\n if move and game_state == GameStates.PLAYERS_TURN:\n dx, dy = move\n destination_x = player.x + dx\n destination_y = player.y + dy\n\n if game_map.walkable[destination_x, destination_y]:\n target = get_blocking_entities_at_location(entities, destination_x, destination_y)\n\n if target:\n attack_results = player.fighter.attack(target)\n player_turn_results.extend(attack_results)\n else:\n player.move(dx, dy)\n\n fov_recompute = True # only recalc FOV when player moves\n\n game_state = GameStates.ENEMY_TURN\n\n elif pickup and game_state == GameStates.PLAYERS_TURN:\n for entity in entities:\n if entity.item and entity.x == player.x and entity.y == player.y:\n pickup_results = player.inventory.add_item(entity, colors)\n player_turn_results.extend(pickup_results)\n\n break\n else:\n message_log.add_message(Message('There is nothing here to pick up.', colors.get('yellow')))\n\n if show_inventory:\n previous_game_state = game_state\n game_state = GameStates.SHOW_INVENTORY\n\n if drop_inventory:\n previous_game_state = game_state\n game_state = GameStates.DROP_INVENTORY\n\n if inventory_index is not None and previous_game_state != GameStates.PLAYER_DEAD and inventory_index < len(\n player.inventory.items):\n item = player.inventory.items[inventory_index]\n\n if game_state == GameStates.SHOW_INVENTORY:\n player_turn_results.extend(player.inventory.use(item, colors))\n elif game_state == GameStates.DROP_INVENTORY:\n player_turn_results.extend(player.inventory.drop_item(item, colors))\n\n if exit:\n if game_state in (GameStates.SHOW_INVENTORY, GameStates.DROP_INVENTORY):\n game_state = previous_game_state\n else:\n return True\n\n if fullscreen:\n tdl.set_fullscreen(not tdl.get_fullscreen())\n\n for player_turn_result in player_turn_results:\n message = player_turn_result.get('message')\n dead_entity = player_turn_result.get('dead')\n item_added = player_turn_result.get('item_added')\n item_consumed = player_turn_result.get('consumed')\n item_dropped = player_turn_result.get('item_dropped')\n\n if message:\n message_log.add_message(message)\n\n if dead_entity:\n if dead_entity == player:\n message, game_state = kill_player(dead_entity, colors)\n else:\n message = kill_monster(dead_entity, colors)\n\n message_log.add_message(message)\n\n if item_added:\n entities.remove(item_added)\n\n game_state = GameStates.ENEMY_TURN\n\n if item_consumed:\n game_state = GameStates.ENEMY_TURN\n\n if item_dropped:\n entities.append(item_dropped)\n\n game_state = GameStates.ENEMY_TURN\n\n if game_state == GameStates.ENEMY_TURN:\n for entity in entities:\n if entity.ai:\n enemy_turn_results = entity.ai.take_turn(player, game_map, entities)\n\n for enemy_turn_result in enemy_turn_results:\n message = enemy_turn_result.get('message')\n dead_entity = enemy_turn_result.get('dead')\n\n if message:\n message_log.add_message(message)\n\n if dead_entity:\n if dead_entity == player:\n message, game_state = kill_player(dead_entity, colors)\n else:\n message = kill_monster(dead_entity, colors)\n\n message_log.add_message(message)\n\n if game_state == GameStates.PLAYER_DEAD:\n break\n\n if game_state == GameStates.PLAYER_DEAD:\n break\n else:\n game_state = GameStates.PLAYERS_TURN\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":8247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"507104412","text":"#Function to reverse arr[] from index start to end\ndef reverseArray(arr, start, end):\n while(start < end):\n temp = arr[start]\n arr[start] = arr[end]\n arr[end] = temp\n start += 1\n end = end-1\n\n#Function to left rotate arr[] of size of n by d\ndef leftRotate(arr, d):\n n = len(arr)\n reverseArray(arr, 0, d-1)\n reverseArray(arr, d, n-1)\n reverseArray(arr, 0, n-1)\n\n#Function to print an array\ndef printArray(arr):\n for i in range(0, len(arr)):\n print(arr[i])\n\narr = [ 1, 2, 3, 4, 5, 6, 7]\nleftRotate(arr, 2)\nprintArray(arr)\n","sub_path":"reverseArrayRotate.py","file_name":"reverseArrayRotate.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"129353525","text":"def restore(arr):\n print(\"D1| D2| D3| D4| D5\")\n for e1 in arr:\n count = 0\n for e2 in e1:\n if(e2[0] == '1'):\n count += 1\n if(count % 2 == 0):\n e1[e1.index('*')] = '0'\n else:\n e1[e1.index('*')] = '1'\n for e2 in e1:\n print(e2,end=\" \")\n print()\nn = int(input(\"Enter # of data:\"))\narr = []\nprint(\"D1| D2| D3| D4| D5\")\nfor _ in range(n):\n arr.append([e for e in input().split()])\nprint()\nrestore(arr)\n","sub_path":"December-25/d25.py","file_name":"d25.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"310072740","text":"import os\r\nimport random\r\nimport game_framework\r\nimport title_state\r\nimport Gameover\r\nfrom Player import*\r\nfrom enemy import*\r\nfrom background import Background\r\nfrom Resource import*\r\n\r\nfrom pico2d import*\r\n\r\n#os.chdir('C:\\\\Temp\\\\lab01')\r\n\r\nrunning = True;\r\nname = \"MainState\"\r\nScore = None\r\n\r\ncurrent_time = get_time()\r\n\r\nplayer = None\r\nbackground = None\r\nbullets = None\r\nmoons = None\r\narrows = None\r\nspecial_attack = None\r\nspecial_attack_count = None\r\nbullet_effects = None\r\nenemies1 = None\r\nenemies2 = None\r\nenemies3 = None\r\nenemies4 = None\r\ndead_enemy1 = None\r\ndead_enemy2 = None\r\ndead_enemy3 = None\r\ndead_enemy4 = None\r\nenemy_missile = None\r\nui = None\r\n\r\nenemy1_time = 0\r\nenemy2_time = 0\r\nenemy3_time = 0\r\nenemy4_time = 0\r\nenemy1_missile_time = 0\r\nenemy2_missile_time = 0\r\nenemy3_missile_time = 0\r\nenemy4_missile_time = 0\r\n\r\nclass UI():\r\n def __init__(self):\r\n self.font = load_font('ENCR10B.TTF', 30)\r\n self.special_item = load_image('special_item.png')\r\n self.life = load_image('Ayinlife.png')\r\n\r\n def draw(self):\r\n self.font.draw(620, 540, 'Score:%d'% Score, (0, 0, 0))\r\n for i in range(player.special_attack_count):\r\n self.special_item.draw(i*30 + 30, 570)\r\n for i in range(player.life):\r\n self.life.draw(i*30 + 30, 540)\r\n\r\n\r\n\r\n# 충돌 체크 함수\r\ndef collision_check(a, b):\r\n left_a, bottom_a, right_a, top_a = a.get_bb()\r\n left_b, bottom_b, right_b, top_b = b.get_bb()\r\n\r\n if left_a > right_b:\r\n return False\r\n if right_a < left_b:\r\n return False\r\n if top_a < bottom_b:\r\n return False\r\n if bottom_a > top_b:\r\n return False\r\n\r\n return True\r\n\r\ndef collision_attack_enemy():\r\n global enemies1, enemies2, enemies3, enemies4, bullets, basic_attack_effects, special_attack_effects, \\\r\n dead_enemy1, dead_enemy2, dead_enemy3, dead_enemy4, moons, arrows, special_attack\r\n All_enemies = enemies1 + enemies2 + enemies3 + enemies4\r\n\r\n for new_attack in arrows:\r\n for enemy in All_enemies:\r\n if collision_check(new_attack, enemy):\r\n new_attack_effect = Bullet_effect()\r\n new_attack_effect.x, new_attack_effect.y = new_attack.x, new_attack.y\r\n bullet_effects.append(new_attack_effect)\r\n if new_attack in arrows:\r\n arrows.remove(new_attack)\r\n enemy.hp -= 5\r\n\r\n for new_attack in moons:\r\n for enemy in All_enemies:\r\n if collision_check(new_attack, enemy):\r\n new_attack_effect = Bullet_effect()\r\n new_attack_effect.x, new_attack_effect.y = new_attack.x, new_attack.y\r\n bullet_effects.append(new_attack_effect)\r\n if new_attack in moons:\r\n moons.remove(new_attack)\r\n enemy.hp -= 10\r\n\r\ndef handle_events(frame_time):\r\n events = get_events()\r\n\r\n for event in events:\r\n if (event.type, event.key) == (SDL_KEYDOWN, SDLK_ESCAPE):\r\n game_framework.quit()\r\n else:\r\n player.handle_event(event)\r\n\r\ndef update(frame_time):\r\n global current_time, Score\r\n\r\n background.update(frame_time)\r\n collision_attack_enemy()\r\n player.update(frame_time)\r\n update_all_attack(frame_time)\r\n make_enemy(frame_time)\r\n update_all_enemy(frame_time)\r\n dead_effect_update(frame_time)\r\n collision_attack_player(frame_time)\r\n current_time += frame_time\r\n Score += frame_time\r\n if player.life < 0:\r\n game_framework.change_state(Gameover)\r\n\r\ndef draw_all():\r\n global player, dead_enemy1, dead_enemy2, dead_enemy3, dead_enemy4, enemies1, enemies2, enemies3, enemies4\\\r\n , bullet_effects, moons, arrows, ui, special_attack, enemy_missile\r\n\r\n all_attack = moons + arrows + bullet_effects + special_attack + enemy_missile\r\n all_dead_enemy = dead_enemy1 + dead_enemy2 + dead_enemy3 + dead_enemy4\r\n all_enemy = enemies1 + enemies2 + enemies3 + enemies4\r\n\r\n player.draw()\r\n for attack in all_attack:\r\n attack.draw()\r\n for dead_enemy in all_dead_enemy:\r\n dead_enemy.draw()\r\n for enemy in all_enemy:\r\n enemy.draw()\r\n ui.draw()\r\n\r\n\r\ndef draw(frame_time):\r\n global background\r\n clear_canvas()\r\n background.draw()\r\n draw_all()\r\n update_canvas()\r\n\r\ndef update_all_attack(frame_time):\r\n global arrows, moons , bullet_effects, special_attack, enemy_missile\r\n for new_attack in arrows:\r\n new_attack.update(frame_time)\r\n if new_attack.x > 800 or new_attack.x < 0:\r\n del (new_attack)\r\n\r\n for new_attack in moons:\r\n new_attack.update(frame_time)\r\n if new_attack.x > 800 or new_attack.x < 0:\r\n del (new_attack)\r\n\r\n for attack_effect in bullet_effects:\r\n attack_effect.update(frame_time)\r\n if attack_effect.frame == 5:\r\n bullet_effects.remove(attack_effect)\r\n\r\n for new_special_attack in special_attack:\r\n new_special_attack.update(frame_time)\r\n if new_special_attack.x > 3000:\r\n del(new_special_attack)\r\n\r\n for enemy_attack in enemy_missile:\r\n enemy_attack.update(frame_time)\r\n if enemy_attack.x < -10:\r\n del(enemy_attack)\r\n\r\n\r\ndef make_enemy(frame_time):\r\n global enemies1, enemies2, enemies3, enemies4, enemy1_time, enemy2_time, enemy3_time, enemy4_time, \\\r\n enemy_missile, enemy1_missile_time, enemy2_missile_time, enemy3_missile_time, enemy4_missile_time\r\n enemy1_time += frame_time\r\n enemy2_time += frame_time\r\n enemy3_time += frame_time\r\n enemy4_time += frame_time\r\n enemy1_missile_time += frame_time\r\n enemy2_missile_time += frame_time\r\n enemy3_missile_time += frame_time\r\n enemy4_missile_time += frame_time\r\n\r\n\r\n if enemy1_time > 5:\r\n if current_time > 30:\r\n new_enemy1 = Enemy1()\r\n enemies1.append(new_enemy1)\r\n new_enemy_missile = Enemy_Missile()\r\n enemy_missile.append(new_enemy_missile)\r\n new_enemy1 = Enemy1()\r\n enemies1.append(new_enemy1)\r\n enemy1_time = 0\r\n enemy1_missile_time = 0\r\n\r\n if enemy2_time > 5:\r\n if current_time > 30:\r\n new_enemy2 = Enemy2()\r\n enemies2.append(new_enemy2)\r\n new_enemy_missile = Enemy_Missile()\r\n enemy_missile.append(new_enemy_missile)\r\n new_enemy2 = Enemy2()\r\n enemies2.append(new_enemy2)\r\n enemy2_time =0\r\n enemy1_missile_time = 0\r\n\r\n if enemy3_time > 20:\r\n if current_time > 30:\r\n for i in range (3):\r\n new_enemy3 = Enemy3()\r\n enemies3.append(new_enemy3)\r\n new_enemy_missile = Enemy_Missile()\r\n enemy_missile.append(new_enemy_missile)\r\n new_enemy3 = Enemy3()\r\n enemies3.append(new_enemy3)\r\n enemy3_time =0\r\n enemy1_missile_time = 0\r\n\r\n if enemy4_time > 3:\r\n if current_time > 20:\r\n new_enemy4 = Enemy4()\r\n enemies4.append(new_enemy4)\r\n new_enemy_missile = Enemy_Missile()\r\n enemy_missile.append(new_enemy_missile)\r\n new_enemy4 = Enemy4()\r\n enemies4.append(new_enemy4)\r\n enemy4_time =0\r\n enemy1_missile_time = 0\r\n\r\n# 적 update, 적이 죽으면 이펙트 좌표에 추가\r\ndef update_all_enemy(frame_time):\r\n global enemies1, enemies2, enemies3, enemies4, dead_enemy1, dead_enemy2, dead_enemy3, dead_enemy4, Score\r\n\r\n for new_enemy1 in enemies1:\r\n new_enemy1.update(frame_time)\r\n if new_enemy1.hp <= 0:\r\n new_dead_enemy = Dead_Enemy1()\r\n new_dead_enemy.x, new_dead_enemy.y = new_enemy1.x, new_enemy1.y\r\n dead_enemy1.append(new_dead_enemy)\r\n enemies1.remove(new_enemy1)\r\n Score += 5\r\n\r\n for new_enemy2 in enemies2:\r\n new_enemy2.update(frame_time)\r\n if new_enemy2.hp <= 0:\r\n new_dead_enemy = Dead_Enemy2()\r\n new_dead_enemy.x, new_dead_enemy.y = new_enemy2.x, new_enemy2.y\r\n dead_enemy2.append(new_dead_enemy)\r\n enemies2.remove(new_enemy2)\r\n Score += 10\r\n\r\n for new_enemy3 in enemies3:\r\n new_enemy3.update(frame_time)\r\n if new_enemy3.hp <= 0:\r\n new_dead_enemy = Dead_Enemy3()\r\n new_dead_enemy.x, new_dead_enemy.y = new_enemy3.x, new_enemy3.y\r\n dead_enemy3.append(new_dead_enemy)\r\n enemies3.remove(new_enemy3)\r\n Score += 8\r\n\r\n for new_enemy4 in enemies4:\r\n new_enemy4.update(frame_time)\r\n if new_enemy4.hp <= 0:\r\n new_dead_enemy = Dead_Enemy4()\r\n new_dead_enemy.x, new_dead_enemy.y = new_enemy4.x, new_enemy4.y\r\n dead_enemy4.append(new_dead_enemy)\r\n enemies4.remove(new_enemy4)\r\n Score += 6\r\n\r\n\r\ndef dead_effect_update(frame_time):\r\n global dead_enemy1, dead_enemy2, dead_enemy3, dead_enemy4\r\n\r\n for dead_em1 in dead_enemy1:\r\n dead_em1.update(frame_time)\r\n if dead_em1.frame >= 0.01:\r\n dead_enemy1.remove(dead_em1)\r\n\r\n for dead_em2 in dead_enemy2:\r\n dead_em2.update(frame_time)\r\n if dead_em2.frame >= 0.01:\r\n dead_enemy2.remove(dead_em2)\r\n\r\n for dead_em3 in dead_enemy3:\r\n dead_em3.update(frame_time)\r\n if dead_em3.frame >= 0.01:\r\n dead_enemy3.remove(dead_em3)\r\n\r\n for dead_em4 in dead_enemy4:\r\n dead_em4.update(frame_time)\r\n if dead_em4.frame >= 0.01:\r\n dead_enemy4.remove(dead_em4)\r\n\r\ndef collision_attack_player(frame_time):\r\n global player, enemy_missile\r\n\r\n for new_attack in enemy_missile:\r\n if collision_check(new_attack, player):\r\n player.life -= 1\r\n new_attack.remove(new_attack)\r\n\r\ndef destroy_world():\r\n global player, enemies1, enemies2, enemies3, enemies4, background, player_bullet, Score, moons, arrows,\\\r\n ui, special_attack, enemy_missile\r\n all_enemies = enemies1 + enemies2 + enemies3 + enemies4\r\n del(player)\r\n del ui\r\n del(all_enemies)\r\n del(background)\r\n del(Score)\r\n del moons\r\n del arrows\r\n del dead_enemy1\r\n del dead_enemy2\r\n del dead_enemy3\r\n del dead_enemy4\r\n del enemy_missile\r\n del enemies1\r\n del enemies2\r\n del enemies3\r\n del enemies4\r\n del bullets\r\n del bullet_effects\r\n del special_attack\r\n\r\n\r\ndef enter():\r\n global background, player, background, player_bullet, enemies1, enemies2, enemies3, enemies4, \\\r\n dead_enemy1, dead_enemy2, dead_enemy3, dead_enemy4, bullet_effects, Score, arrows, moons, ui, \\\r\n special_attack, enemy_missile\r\n\r\n background = Background(800, 600)\r\n player = Player()\r\n ui = UI()\r\n enemies1 = []\r\n enemies2 = []\r\n enemies3 = []\r\n enemies4 = []\r\n arrows = []\r\n moons = []\r\n enemy_missile = []\r\n special_attack = []\r\n bullet_effects = []\r\n dead_enemy1 = []\r\n dead_enemy2 = []\r\n dead_enemy3 = []\r\n dead_enemy4 = []\r\n Score = 0\r\n\r\ndef exit():\r\n destroy_world()\r\n\r\ndef pause():\r\n pass\r\n\r\ndef resume():\r\n pass\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Game_Pro/Game_Pro/main_state.py","file_name":"main_state.py","file_ext":"py","file_size_in_byte":11096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"529698536","text":"# Files schrijven\n# Ook te vinden op: github.com/Denbergvanthijs/PROG/tree/master/Deel%207\nimport datetime as tijd\n\nwhile True:\n bestand = open(\"hardlopers.txt\", \"a+\")\n finisher = input(\"Wie is er binnengekomen? \")\n vandaag = tijd.datetime.today()\n s = vandaag.strftime(\"%a %d %b %Y om %H:%M:%S \")\n bestand.write(str(s) + str(finisher) + \"\\n\")\n","sub_path":"Deel 7/pe7_4.py","file_name":"pe7_4.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"399407815","text":"#!/usr/bin/env python\n\"\"\"\nAuthor : Iroiso . I (iroiso@live.com)\nProject: Home SDK\nLicense: Apache License 2.0\nCopyright 2011, Atlas.\n\nDescription:\nUnittests for home.core.types\n\"\"\"\nfrom home.core.types import phone, blob\nfrom home.core.commons import String\nfrom home.core.types import TypedList, TypedMap, TypedSet, CollectionException\nfrom unittest import TestCase,expectedFailure,skip\n\nclass TestPhone(TestCase):\n '''Unittests for the phone type'''\n \n def testSanity(self):\n '''Makes sure that basic usage is sane'''\n with self.assertRaises(ValueError):\n mobile = phone(\"(0248) 123-7654\")\n mobile = phone(\"+2348094486101\")\n \n def testRepr(self):\n '''Makes sure that phones get a valid python repr'''\n mobile = phone(\"+2348094486101\")\n self.assertEquals(eval(repr(mobile)), mobile)\n \n def testStr(self):\n '''Makes sure that phones are properly stringified'''\n mobile = phone(\"+2342481237654\")\n self.assertEquals(\"+2342481237654\", str(mobile))\n \nclass TestBlob(TestCase):\n '''Unittests for the blob type'''\n \n def testSanity(self):\n '''Makes sure that basic usage is sane'''\n image = blob(content=\"Some rubbish text from a file\" * 1024, mimetype=\"image/jpeg\", gzipped=True)\n self.assertTrue(image.checksum != None)\n self.assertTrue(\"gzipped\" in image.metadata)\n self.assertTrue(repr(image)) \n new = eval(repr(image))\n self.assertTrue(isinstance(new, blob))\n self.assertTrue(new == image)\n\nclass TestCollectionDifferLogic(TestCase):\n \n def testListDiffer(self):\n '''Checks that lists can detect changes correctly'''\n l = TypedList(String)\n for i in list(\"Hello World\"):\n l.append(i)\n self.assertTrue(l.rewrite())\n l.commit()\n for i in list(\"Hello Again\"):\n l.append(i)\n self.assertFalse(l.rewrite())\n mods = l.modifications()\n pre, app = mods[\"prepend\"], mods[\"append\"]\n self.assertTrue(len(pre) == 0)\n self.assertTrue(len(app) == 11)\n l.commit()\n l.pop(0); l.pop(0); l.pop(len(l)-1)\n self.assertTrue(l.rewrite())\n \n def testSetDiffer(self):\n '''Checks that sets can detect changes appropriately'''\n s = TypedSet(String)\n for i in list(\"Hello World\"):\n s.add(i)\n for i in list(\"Hello World\"):\n self.assertTrue(i in s.added())\n self.assertTrue(len(s.deleted()) == 0)\n print(\"Testing modification in sets\")\n s.commit()\n s.pop(); s.pop(); s.pop()\n self.assertTrue(len(s.added()) == 0)\n self.assertTrue(list(s.deleted()))\n s.commit()\n self.assertTrue(len(s.added()) == 0)\n self.assertTrue(len(s.deleted()) == 0)\n \n def testMapDiffer(self):\n '''Checks that maps can detect changes'''\n m = TypedMap(String, String)\n for i, v in enumerate(\"Hello World Here\"):\n m[i] = v\n self.assertTrue(list(m.added()))\n self.assertFalse(list(m.deleted()))\n self.assertFalse(list(m.modified()))\n m.commit()\n del m[0]; del m[1]; del m[2];\n self.assertTrue(list(m.deleted()))\n m[3] = \"Me\"; m[5] = \"Are\"; m[7] = \"Us\";\n self.assertTrue(list(m.modified()))\n \nclass TestCollectionLimits(TestCase):\n key = \"Key: Something really really long...\" * 65535\n value = \"Value: Something really really long...\" * 65535\n limit = 65535 + 5\n \n def testMapSanity(self):\n '''Show that Map respects Cassandra limits'''\n m = TypedMap(String, String)\n with self.assertRaises(CollectionException): m[\"hello\"] = self.value\n with self.assertRaises(CollectionException): m[self.key] = \"value\"\n with self.assertRaises(CollectionException): m[self.key] = self.value\n with self.assertRaises(CollectionException):\n for i in range(self.limit):\n m[i] = i\n \n def testListSanity(self):\n '''Show that List respects Cassandra limits'''\n l = TypedList(String)\n with self.assertRaises(CollectionException): l.append(self.value)\n with self.assertRaises(CollectionException):\n for i in range(self.limit):\n l.append(i)\n \n def testSetSanity(self):\n '''Show that Set respects Cassandra limits'''\n l = TypedSet(String)\n with self.assertRaises(CollectionException): l.add(self.value)\n with self.assertRaises(CollectionException):\n for i in range(self.limit):\n l.add(i)\n \n \n \n","sub_path":"src/tests/core/testtypes.py","file_name":"testtypes.py","file_ext":"py","file_size_in_byte":4658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"313923812","text":"# -*- coding: utf-8 -*-\nimport readline\nimport deepox as dx\nfrom modules import textparser,text2word,word2py,py2TCinput,synthTC,play\n\nprint('Welcome to deepox TC, Enjoy here!')\nprint('Type \\'quit\\' or \\'exit\\' to EXIT')\n\neng = dx.Deepox()\neng.addmod(textparser.TextParser('textparser'))\neng.addmod(text2word.Text2word('txt2word'))\neng.addmod(word2py.Word2py('word2py'))\neng.addmod(py2TCinput.Py2TCinput('py2TCinput'))\neng.addmod(synthTC.SynthTC('synthTC'))\neng.addmod(play.Play('play'))\n\nout = {}\nwhile 1:\n text = input('##')\n if text.startswith('S'):\n out['_skip_mods'] = ['textparser','txt2word','word2py','py2TCinput']\n text = text.replace('\\'','')\n text = text.replace(' ','')\n text = text.replace(' ','')\n out['_base'] = text.split(',')\n else:\n out['_skip_mods'] = []\n out['_base'] = [text]\n if len(text) == 0:\n text = 'play'\n if text.startswith('quit') or text.startswith('exit'):\n print('bye!')\n break\n if text.startswith('play'):\n if '_wav_fn' in out:\n eng.getmod('play').proc(out)\n continue\n out = eng.proc(out)\n #print out\n pass\n\n","sub_path":"T/deepox/ver1.0/cmdlineTC.py","file_name":"cmdlineTC.py","file_ext":"py","file_size_in_byte":1162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"131309103","text":"import pygame\nimport time\n\npygame.init()\n\nsound = pygame.mixer.Sound(\"Audios/Match/match_floating_cities.ogg\")\nchannel1 = sound.play()\n\n\nchannel1.set_volume(0.0, 1.0) # Now plays at 60% (previous value replaced).\n # Sound plays at full volume by default\ntime.sleep(5)\n\nchannel1.set_volume(1.0, 0.0)\ntime.sleep(5)\n\n\n","sub_path":"Code/channels.py","file_name":"channels.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"282846397","text":"import numpy as np\nimport os\nimport random\nimport _option as OPTION\n\n\nclass DataSet(object):\n def __init__(self, sequences_list, labels_list, shuffled=True,\n one_hot=True, label_used=True):\n \"\"\" Construct a DataSet.\n sequences_list: [str1,str2,...], 1D list of string\n labels_list: 1D list of int\n\n \"\"\"\n data = np.zeros([len(sequences_list), OPTION.SEQUENCE_LEN], dtype=int)\n for index, item in enumerate(sequences_list):\n offset = 0\n for token_id in item:\n if offset >= OPTION.SEQUENCE_LEN:\n break\n data[index][offset] = token_id\n offset += 1\n\n if one_hot:\n labels = np.zeros([len(labels_list), OPTION.NUM_CLASSES], dtype=int)\n for index, item in enumerate(labels_list):\n labels[index][item] = 1\n else:\n labels = np.array(labels_list, dtype=int)\n\n self._shuffled = shuffled\n self._one_hot = one_hot\n self._label_used = label_used\n self._epochs_completed = 0\n self._index_in_epoch = 0\n self._num_examples = len(sequences_list)\n\n # Shuffle the data\n if shuffled:\n perm = np.arange(self._num_examples)\n random.shuffle(perm)\n self._data = data[perm]\n if label_used:\n self._labels = labels[perm]\n else:\n self._data = data\n if label_used:\n self._labels = labels\n\n def get_dataset_size(self):\n return self._num_examples\n\n def next_batch(self, batch_size, keep_strict_batching=False):\n \"\"\" Return the next `batch_size` examples from this data set.\"\"\"\n if keep_strict_batching:\n assert batch_size <= self._num_examples\n\n if self._index_in_epoch >= self._num_examples:\n # Finished epoch\n self._epochs_completed += 1\n # Shuffle the data\n if self._shuffled:\n perm = np.arange(self._num_examples)\n np.random.shuffle(perm)\n self._data = self._data[perm]\n if self._label_used:\n self._labels = self._labels[perm]\n # Start next epoch\n self._index_in_epoch = 0\n\n start = self._index_in_epoch\n self._index_in_epoch += batch_size\n if self._index_in_epoch > self._num_examples:\n if keep_strict_batching:\n # Finished epoch\n self._epochs_completed += 1\n # Shuffle the data\n if self._shuffled:\n perm = np.arange(self._num_examples)\n np.random.shuffle(perm)\n self._data = self._data[perm]\n if self._label_used:\n self._labels = self._labels[perm]\n # Start next epoch\n start = 0\n self._index_in_epoch = batch_size\n else:\n self._index_in_epoch = self._num_examples\n end = self._index_in_epoch\n\n batch_data = self._data[start:end]\n if self._label_used:\n batch_labels = self._labels[start:end]\n\n if self._label_used:\n return batch_data, batch_labels\n else:\n return batch_data\n\n\ndef generate_data_set(time_list, data_dir=OPTION.DATA_PATH, data_name=OPTION.TRAIN_DATA_NAME, shuffled=True,\n one_hot=True, label_used=True):\n \"\"\" get train data\n\n \"\"\"\n sequences_list = []\n labels_list = []\n for t in time_list:\n lineslist = open(os.path.join(data_dir, data_name + '_%d' % t), 'r').readlines()\n count = 0\n for index, item in enumerate(lineslist):\n if index % 2 == 0:\n count = count + 1\n sequences = []\n for sens in item.strip().split('\\t'):\n if len(sequences) > OPTION.SEQUENCE_LEN:\n break\n sequences.extend(sens.strip().split(' '))\n sequences_list.append([int(v) for v in sequences])\n else:\n labels_list.append(int(item.strip()))\n\n print('time %d: %d' % (t, count))\n\n print('generating train data...')\n\n return DataSet(sequences_list, labels_list, shuffled=shuffled,\n one_hot=one_hot, label_used=label_used)\n","sub_path":"TextCNN-NYT/TextCNNps/TextCNNps_input.py","file_name":"TextCNNps_input.py","file_ext":"py","file_size_in_byte":4408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"580730089","text":"from keras.models import Sequential\nfrom keras.layers import Dense,BatchNormalization,Flatten,Dropout,LSTM,TimeDistributed,ConvLSTM2D,Input,Activation\nfrom keras.layers.convolutional import Conv1D\nfrom keras.layers.convolutional import MaxPooling1D\nfrom keras import regularizers\n\nfrom keras import callbacks\nimport tensorflow as tf\n\ndef get_callbacks_list(model_name):\n \"\"\"Get callbacks for a model\"\"\"\n return [\n callbacks.EarlyStopping(monitor='val_acc',patience=100,mode='max',verbose=1),\n callbacks.ModelCheckpoint(filepath='MODELS/'+model_name+'.h5',monitor='val_acc',save_best_only=True),\n callbacks.ReduceLROnPlateau(monitor='val_loss',factor=0.3,patience=5,min_lr=0.0000001,verbose=1),]\n\ndef lstm_model(trainX, trainy):\n n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]\n model = Sequential()\n model.add(LSTM(100, input_shape=(n_timesteps,n_features)))\n model.add(Dropout(0.5))\n model.add(Dense(100, activation='relu'))\n model.add(Dense(n_outputs, activation='softmax'))\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n # fit network\n return model\n\n\ndef Cnn_lstm_model(trainX, trainy,n_steps, n_length):\n # define model\n n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]\n \n # define model\n model = Sequential()\n model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu'), input_shape=(None,n_length,n_features)))\n model.add(BatchNormalization())\n model.add(TimeDistributed(Dropout(0.2)))\n \n model.add(TimeDistributed(Conv1D(filters=64, kernel_size=3, activation='relu')))\n model.add(TimeDistributed(Dropout(0.3)))\n model.add(BatchNormalization())\n \n model.add(TimeDistributed(MaxPooling1D(pool_size=2)))\n model.add(TimeDistributed(Flatten()))\n model.add(LSTM(100))\n model.add(Dropout(0.5))\n \n model.add(Dense(100, activation='relu'))\n model.add(Dense(n_outputs, activation='softmax'))\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n # fit network\n return model\n\n\ndef ConvLstm(trainX, trainy, n_steps, n_length):\n # define model\n n_timesteps, n_features, n_outputs = trainX.shape[1], trainX.shape[2], trainy.shape[1]\n # define model\n model = Sequential()\n model.add(ConvLSTM2D(filters=64, kernel_size=(1,3), activation='relu', input_shape=(n_steps, 1, n_length, n_features)))\n model.add(Dropout(0.5))\n model.add(Flatten())\n model.add(Dense(100, activation='relu'))\n model.add(Dense(n_outputs, activation='softmax'))\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n \n return model\n\ndef Mlp(trainX, trainy):\n n_input,n_output=trainX.shape[1]*trainX.shape[2],trainy.shape[1]\n model = Sequential()\n model.add(Dense(256, input_dim=n_input, kernel_initializer='uniform'))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n model.add(Dropout(0.3))\n\n model.add(Dense(32,kernel_regularizer=regularizers.l2(0.01)))\n model.add(BatchNormalization())\n model.add(Activation('relu'))\n model.add(Dropout(0.5))\n\n model.add(Dense(n_output, kernel_initializer='uniform'))\n model.add(BatchNormalization())\n model.add(Activation('softmax'))\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n return model\n\n\ndef freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):\n \"\"\"\n Freezes the state of a session into a pruned computation graph.\n\n Creates a new computation graph where variable nodes are replaced by\n constants taking their current value in the session. The new graph will be\n pruned so subgraphs that are not necessary to compute the requested\n outputs are removed.\n @param session The TensorFlow session to be frozen.\n @param keep_var_names A list of variable names that should not be frozen,\n or None to freeze all the variables in the graph.\n @param output_names Names of the relevant graph outputs.\n @param clear_devices Remove the device directives from the graph for better portability.\n @return The frozen graph definition.\n \"\"\"\n graph = session.graph\n with graph.as_default():\n freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))\n output_names = output_names or []\n output_names += [v.op.name for v in tf.global_variables()]\n input_graph_def = graph.as_graph_def()\n if clear_devices:\n for node in input_graph_def.node:\n node.device = ''\n frozen_graph = tf.graph_util.convert_variables_to_constants(\n session, input_graph_def, output_names, freeze_var_names)\n return frozen_graph\n","sub_path":"active_utils/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"145723503","text":"\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n### Load all files\nprint(\"Loading total_acc files...\")\n\n\nfolder = \"../UCI HAR Dataset/\"\n\nfile_activity_labels = folder + \"activity_labels.txt\"\nfile_features = folder + \"features.txt\"\nfile_train_x = folder + \"train/Inertial Signals/total_acc_x_train.txt\"\nfile_train_y = folder + \"train/Inertial Signals/total_acc_y_train.txt\"\nfile_train_z = folder + \"train/Inertial Signals/total_acc_z_train.txt\"\nfile_test_x = folder + \"test/Inertial Signals/total_acc_x_test.txt\" \nfile_test_y = folder + \"test/Inertial Signals/total_acc_y_test.txt\"\nfile_test_z = folder + \"test/Inertial Signals/total_acc_z_test.txt\"\n\nactivity_labels = pd.read_csv(file_activity_labels, delimiter=\" \", header=None, names=['id', 'activity'])\nfeatures = pd.read_csv(file_features, delimiter=\" \", header=None, names=['id', 'feature'])\ntrain_x = pd.read_csv(file_train_x, delimiter=\" \", header=None, skipinitialspace=True)\ntrain_y = pd.read_csv(file_train_y, delimiter=\" \", header=None, skipinitialspace=True)\ntrain_z = pd.read_csv(file_train_z, delimiter=\" \", header=None, skipinitialspace=True)\ntest_x = pd.read_csv(file_test_x, delimiter=\" \", header=None, skipinitialspace=True)\ntest_y = pd.read_csv(file_test_y, delimiter=\" \", header=None, skipinitialspace=True)\ntest_z = pd.read_csv(file_test_z, delimiter=\" \", header=None, skipinitialspace=True)\n\n### Concatenate train-, and test-sets\nprint(\"Concatenating...\")\nx = pd.concat([train_x, test_x])\ny = pd.concat([train_y, test_y])\nz = pd.concat([train_z, test_z])\n\n### For each axis, calculate the variance for each row and sum the variances\nprint(\"Calculating variances...\")\nx_var = x.apply(lambda row : row.var(), axis='columns').sum()\ny_var = y.apply(lambda row : row.var(), axis='columns').sum()\nz_var = z.apply(lambda row : row.var(), axis='columns').sum()\nvariances = [x_var, y_var, z_var]\n\nprint(\" Variance x : %0.2f\" % x_var)\nprint(\" Variance y : %0.2f\" % y_var)\nprint(\" Variance z : %0.2f\" % z_var)\nprint(\"Greatest variance : %s\" % ['x', 'y', 'z'][np.argmax(variances)])\n\n\n\n### Load body_acc files for axis with greatest variance\nprint()\nprint(\"Loading body_acc files...\")\nfile_data = ['x', 'y', 'z'][np.argmax(variances)]\nfile_data_train = folder + \"train/Inertial Signals/body_acc_\" + file_data + \"_train.txt\"\nfile_data_test = folder + \"test/Inertial Signals/body_acc_\" + file_data + \"_test.txt\"\nfile_labels_train = folder + \"train/y_train.txt\"\nfile_labels_test = folder + \"test/y_test.txt\"\n\ntraindata = pd.read_csv(file_data_train, delimiter=\" \", header=None, skipinitialspace=True)\ntestdata = pd.read_csv(file_data_test, delimiter=\" \", header=None, skipinitialspace=True)\ntrainlabels = pd.read_csv(file_labels_train, delimiter=\" \", header=None, skipinitialspace=True)\ntestlabels = pd.read_csv(file_labels_test, delimiter=\" \", header=None, skipinitialspace=True)\n\ntrainlabels['label'] = trainlabels[0].transform(lambda c : activity_labels['activity'][c-1])\ntestlabels['label'] = testlabels[0].transform(lambda c : activity_labels['activity'][c-1])\n\n### Concatenate train-, and test-set\ndataset = pd.concat([traindata, testdata], ignore_index=True)\nlabelset = pd.concat([trainlabels, testlabels], ignore_index=True)\n\n### Drop the last half of the columns to solve the overlap problem, allowing retrieval of the original signal\ndataset = dataset.loc[:, :63]\n### Convert the dataframe to a numpy array\nraw_signal = dataset.values\n### Flatten the 2D array to a 1D array by concatenating all the rows, effectively retrieving the original signal\nraw_signal = raw_signal.flatten()\n\nprint()\nprint(\"Datapoints expected : 64 * %d = %d\" % (len(dataset.index), 64 * len(dataset.index)))\nprint(\"Datapoints in raw signal = %d\" % raw_signal.size)\n\n# ### Add the labels to the dataset\n# dataset['label'] = labelset['label']\n\n# print(dataset[:10])\n","sub_path":"TS/4_4.py","file_name":"4_4.py","file_ext":"py","file_size_in_byte":3882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"237446182","text":"import pandas as pd\nimport numpy as np\nfrom datetime import *\n\n\ndf0 = pd.DataFrame([['Iphone','DHDEM26','30-10-2020 12:14','25-11-2020 12:24','400'],['Iphone','CGHTZ09','11-09-2020 12:14','22-11-2020 12:24','400'],\n['dell','LXRGN32','11-09-2020 12:14','19-11-2020 12:24','300'],['dell','LXRGN31', '11-09-2020 12:13','11-20-2020 12:24','300'],\n['Samsung ','SGSJP04', '11-09-2020 12:12','11-20-2020 12:24','250'],['Samsung ','CXMHK36', '11-09-2020 12:11','11-20-2020 12:24','250'],\n['Samsung ','CGFTK29', '11-09-2020 12:10','11-20-2020 12:24','250'],['dell','CGKTLB6','11-09-2020 12:10','11-20-2020 12:24','300'],\n['dell','null', '11-09-2020 12:10','11-20-2020 12:24','300']],columns=('PRODUCT','ZIPCODE','SHIPMENT','DELIVERY','PRICE'))\n\ndef aplist(L1,L2):\n ls = []\n if isinstance(L1, pd.core.series.Series) and isinstance(L2, pd.core.series.Series):\n ls1 = L1.to_list()\n ls2 = L2.to_list()\n ls = [i + j for i, j in zip(ls1, ls2)]\n elif isinstance(L1, list) and isinstance(L2, list):\n ls = [i + j for i, j in zip(L1, L2)]\n elif isinstance(L1, pd.core.series.Series) and isinstance(L2, str):\n ls1 = L1.to_list()\n for i in range(len(ls1)):\n ni = str(ls1[i]) + L2\n ls.append(ni)\n elif isinstance(L1, list) and isinstance(L2, str):\n for i in range(len(ls1)):\n ni = str(ls1[i]) + L2\n ls.append(ni)\n else:\n print('arg1 can be list or pd.core.series.Series and arg2 can be string')\n return ls\n\ndef sumifs(df,numeric_col,list_of_cols_as_ref):\n if len(list_of_cols_as_ref) > 1:\n st = \"\"\n for i in range(len(list_of_cols_as_ref)):\n if st == '':\n st = list_of_cols_as_ref[i]\n else:\n st = st + '-' + list_of_cols_as_ref[i]\n df[st] = df[list_of_cols_as_ref].apply(lambda x: ''.join(map(str,x)),axis=1)\n df1 = df.groupby(st)[numeric_col].sum().to_frame(name = newcol).reset_index()\n df2 = df.merge(df1, on=st)\n df2.drop(st, axis='columns', inplace=True)\n return df2\n else:\n col = list_of_cols_as_ref[0]\n df1 = df.groupby(col)[numeric_col].sum().to_frame(name = newcol).reset_index()\n df2 = df.merge(df1, on=col)\n return df2\n\n\ndef cntff(df, numeric_col, *argv):\n rngmod = len(argv) % 2\n if len(argv) > 0 and rngmod == 0:\n n = 0\n lscnt = 0\n stcnt = 0\n lsTmp = []\n ls = []\n st = \"\"\n while n < len(argv):\n if isinstance(argv[n], pd.core.series.Series):\n if len(ls) < 1:\n ls = argv[n]\n else:\n lsTmp = aplist(ls,argv[n])\n ls = lsTmp\n lscnt = lscnt + 1\n else:\n stcnt = stcnt + 1\n st = st + str(argv[n])\n n = n + 1\n df['NC1'] = pd.Series(ls)\n if stcnt == lscnt:\n df1 = df.groupby(st)[numeric_col].sum().to_frame(name = \"X\").reset_index()\n elif stcnt == 0:\n df1 = df.groupby([ls])[numeric_col].sum().to_frame(name = \"X\").reset_index()\n print(df1)\n","sub_path":"Z_ALL_FILE/Py/fnx.py","file_name":"fnx.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"559644629","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Romain Silvestri, Romain Gallay\n\n\"\"\" Manually encrypt a wep message given the WEP key\"\"\"\n\nfrom scapy.all import *\nimport binascii\nimport rc4\nimport sys\n\n#Cle wep AA:AA:AA:AA:AA\nkey='\\xaa\\xaa\\xaa\\xaa\\xaa'\n\n#lecture de message chiffré - rdpcap retourne toujours un array, même si la capture contient un seul paquet\narp = rdpcap('arp.cap')[0]\n\n# rc4 seed est composé de IV+clé\nseed = arp.iv+key\n\n# max size of data in 1 fragment\nMAX_SIZE = 36\n\ncounter = 0\nfilename = \"arp3.cap\"\n\n# message to send\nbase_msg = \"Having tried in vain at every expence considerable trouble and some danger to unite the Suliotes for the good of Greece and their own I have come to the following resolution\"\n\n# delete the file if exists\ntry:\n os.remove(filename)\nexcept OSError:\n pass\n\n\nwhile (len(base_msg) > 0):\n\n\t# take the MAX_SIZE first characters of our message\n\tmsg = base_msg[:MAX_SIZE]\n\n\t# pad the message WITH '0' if needed\n\tif (len(msg) < MAX_SIZE):\n\t\tmsg += '0'*(MAX_SIZE - len(msg))\n\n\t# copy the arp request we are using\n\tfragment = arp\n\n\t# set the MF bit to 1 if needed\n\tif len(base_msg) <= MAX_SIZE:\n\t\tfragment.FCfield &= 0xfffb\n\telse:\n\t\tfragment.FCfield |= 0x004\n\n\t# increment the counter\n\tfragment.SC = counter\n\n\t# compute the icv\n\ticv_enclair = crc32(msg)\n\n\t# encrypt the cleartext with rc4\n\tcleartext = msg + struct.pack(' 0:\n result.append(dict(\n name=genre.name,\n books=genre_books\n ))\n return result\n\n\ndef is_in_library(book):\n if is_authenticated(request):\n current_user = get_current_user_from_request(request)\n inLibrary = BookInLibrary.query.filter_by(book_id=book.id, user_id=current_user.id).first()\n if inLibrary:\n return True\n else:\n return False\n else:\n return False","sub_path":"out_date/apps/services/books_service.py","file_name":"books_service.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"602542585","text":"##################################################################################################\n#### HackerEarth program to be executed like below:-\n#### python Magical_word.py\n#### First Input denotes the number of testcases.\n#### Second Input denotes the maximum no of letters in the word.\n#### Output desired:-\n#### Final Output: String closest to magical word.\n####################################################################\n#### PROBLEM STATEMENT #############################################\n#### Dhananjay has recently learned about ASCII values.He is very fond of experimenting. With his knowledge of ASCII values and character he has developed a special word and named it Dhananjay's Magical word.\n####\n#### A word which consist of alphabets whose ASCII values is a prime number is an Dhananjay's Magical word. An alphabet is Dhananjay's Magical alphabet if its ASCII value is prime.\n#### \n#### Dhananjay's nature is to boast about the things he know or have learnt about. So just to defame his friends he gives few string to his friends and ask them to convert it to Dhananjay's Magical word. None of his friends would like to get insulted. Help them to convert the given strings to Dhananjay's Magical Word.\n#### \n#### Rules for converting:\n#### \n#### 1.Each character should be replaced by the nearest Dhananjay's Magical alphabet.\n#### \n#### 2.If the character is equidistant with 2 Magical alphabets. The one with lower ASCII value will be considered as its replacement.\n#### \n#### Input format:\n#### \n#### First line of input contains an integer T number of test cases. Each test case contains an integer N (denoting the length of the string) and a string S.\n#### \n#### Output Format:\n#### \n#### For each test case, print Dhananjay's Magical Word in a new line.\n##################################################################################################\nimport sys\nfrom bisect import bisect\n\nno_of_tcs = input()\nlines = []\nfinalarr = []\n\ndef getPrimes(limit):\n MAX=1001\n p=2\n prime=[True]*(MAX+1)\n prime[0]=prime[1]=False\n primelist=[]\n while p*p<=MAX:\n if prime[p]:\n for i in range(p*p,MAX+1,p):\n prime[i]=False\n p=p+1\n for i in range(2,MAX+1):\n if prime[i]:\n primelist.append(i)\n\n # Return the list of prime numbers\n return primelist\n\nfor tc in range(int(no_of_tcs)):\n string_length = input()\n strg = input()\n if(len(strg) == int(string_length)):\n lines.append(strg)\n else:\n sys.exit()\n\nfor i in range(0, len(lines)):\n word = lines[i]\n for j in range(0, len(word)):\n findAsciiVal = ord(word[j])\n findprimeVal = getPrimes(findAsciiVal)\n ind=bisect(findprimeVal, int(findAsciiVal))\n a=findAsciiVal - findprimeVal[ind - 1]\n b=findprimeVal[ind] - findAsciiVal\n if a<=b:\n #print('Nearest prime no. for letter ', word[j], 'aaaa', findprimeVal[ind-1])\n x = chr(findprimeVal[ind-1])\n finalarr.append(x)\n else:\n #print('Nearest prime no. for letter ', word[j],'bbbb', findprimeVal[ind])\n y = chr(findprimeVal[ind])\n finalarr.append(y)\n\nfinalStr = \"\"\nfinalStr = ''.join(finalarr)\n\nprint(finalStr)","sub_path":"Magical_word.py","file_name":"Magical_word.py","file_ext":"py","file_size_in_byte":3250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"307818992","text":"import json\n\n\nclass Utils:\n def __init__(self, bot,):\n self.bot = bot\n self.settings_file = 'data/settings.json'\n\n self.settings = self.load_json(self.settings_file)\n\n def save_json(self, filename, data):\n with open(filename, encoding='utf-8', mode=\"w\") as f:\n json.dump(data, f, indent=4, sort_keys=True, separators=(',', ' : '))\n return data\n\n def load_json(self, filename):\n with open(filename, encoding='utf-8', mode=\"r\") as f:\n data = json.load(f)\n return data\n\n def save_settings(self):\n self.save_json(self.settings_file, self.settings)\n\n async def send_cmd_help(self, context):\n if context.invoked_subcommand:\n pages = await self.bot.formatter.format_help_for(context, context.invoked_subcommand)\n for page in pages:\n await context.send(page)\n else:\n pages = await self.bot.formatter.format_help_for(context, context.command)\n for page in pages:\n await context.send(page)\n","sub_path":"utils/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"367740365","text":"from flask import Flask, jsonify, abort, json, render_template, request, Response\nimport os\nimport glob\nfrom jsonschema import validate\n\n\napp = Flask(__name__)\n\n\n@app.route('/', methods=['GET'])\ndef get_maneuver():\n return render_template('index.html')\n\n\n@app.route('/page1.html', methods=['GET'])\ndef get_page1():\n return render_template('page1.html')\n\n\n@app.route('/page2.html', methods=['GET'])\ndef get_page2():\n return render_template('page2.html')\n\n\n@app.route('/alertWindow.html', methods=['GET'])\ndef get_alert():\n return render_template('alertWindow.html')\n\n\n@app.route('/keywordsPage.html', methods=['GET'])\ndef get_keywords():\n return render_template('keywordsPage.html')\n\n\n@app.route('/UI/API/0.1//', methods=['GET'])\ndef get_app(file_name):\n jfile=open(str(file_name)+'.json','r')\n json_file=json.load(jfile)\n response = jsonify(json_file)\n response.status_code = 200\n return response\n\n\n@app.route('/UI/API/0.1/', methods=['POST'])\ndef post_app():\n maxim=0\n for filename in glob.glob('*.json'):\n filenr=filename.split('.')[0]\n if is_number(filenr) and int(filenr)>maxim:\n maxim=int(filenr)\n\n data=request.get_json()\n\n with open('JSON schema.json','r') as schema:\n schemajson = json.load(schema)\n validate(data,schemajson)\n\n with open(str(maxim+1) + '.json', 'w') as outfile:\n json.dump(data, outfile,indent=4, separators=(',', ': '))\n\n response=Response(\"\")\n response.headers['Location']='/UI/API/0.1/'+str(maxim+1)\n response.status_code=201\n return response\n\n\ndef is_number(s):\n try:\n int(s)\n return True\n except ValueError:\n return False\n\n\n@app.route('/UI/API/0.1//', methods=['DELETE'])\ndef delete_app(file_name):\n try:\n os.remove(str(file_name) + '.json')\n except OSError:\n abort(404)\n return 'No content', 204\n\n\n@app.route('/UI/API/0.1//applicationName', methods=['GET'])\ndef get_app_name(file_name):\n jfile = open(str(file_name) + '.json', 'r')\n json_file = json.load(jfile)\n response = jsonify({\"applicationName\": json_file[\"application\"]})\n response.status_code = 200\n return response\n\n\n@app.route('/UI/API/0.1//components', methods=['GET'])\ndef get_descriptor_components(file_name):\n jfile = open(str(file_name) + '.json', 'r')\n json_file = json.load(jfile)\n response = jsonify({\"components\": json_file[\"components\"]})\n response.status_code = 200\n return response\n\n\n@app.route('/UI/API/0.1//components/', methods=['GET'])\ndef get_component_id(component_id,file_name):\n jfile = open(str(file_name) + '.json', 'r')\n json_file = json.load(jfile)\n component = [component for component in json_file[\"components\"] if component['id'] == component_id]\n if len(component) == 0:\n abort(404)\n return jsonify({'component': component[0]})\n\n\n@app.route('/UI/API/0.1//components/', methods=['GET'])\ndef get_component_name(name,file_name):\n jfile = open(str(file_name) + '.json', 'r')\n json_file = json.load(jfile)\n component = [component for component in json_file[\"components\"] if component['name'] == name]\n if len(component) == 0:\n abort(404)\n return jsonify({'component': component[0]})\n\n\n@app.route('/UI/API/0.1//IP', methods=['GET'])\ndef get_app_IP(file_name):\n jfile = open(str(file_name) + '.json', 'r')\n json_file = json.load(jfile)\n response = jsonify({\"IP\": json_file[\"IP\"]})\n response.status_code = 200\n return response\n\n\n@app.route('/UI/API/0.1//budget', methods=['GET'])\ndef get_app_budget(file_name):\n jfile = open(str(file_name) + '.json', 'r')\n json_file = json.load(jfile)\n response = jsonify({\"budget\": json_file[\"budget\"]})\n response.status_code = 200\n return response\n\n\n@app.route('/UI/API/0.1//restrictions', methods=['GET'])\ndef get_app_restrictions(file_name):\n jfile = open(str(file_name) + '.json', 'r')\n json_file = json.load(jfile)\n response = jsonify({\"restrictions\": json_file[\"restrictions\"]})\n response.status_code = 200\n return response\n\n\nif __name__ == '__main__':\n app.run(debug=True, port=8080)\n","sub_path":"restapi.py","file_name":"restapi.py","file_ext":"py","file_size_in_byte":4274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"95218237","text":"#encoding:utf-8\n#!/usr/bin/env python\nfrom werkzeug.utils import secure_filename\nfrom flask import Flask, render_template, jsonify, request, make_response, send_from_directory, abort\nimport time\nimport os\nimport base64\nimport datetime\nimport random\nimport cv2\nimport numpy as np\nimport json\nimport requests\nfrom os import environ\n\napp = Flask(__name__)\nUPLOAD_FOLDER = 'upload'\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\nbasedir = os.path.abspath(os.path.dirname(__file__))\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'JPG', 'PNG', 'gif', 'GIF'])\n\n@app.route('/')\ndef home():\n\trt = { \n\t\t\"path 0\":\"/distance\",\n\t\t\"path 1\":\"/detect\"\n\t}\n\treturn jsonify(rt)\n\n@app.route('/distance')\ndef upload_test():\n\treturn render_template('distance.html')\n\n@app.route('/detect')\ndef detect():\n\treturn render_template('detect.html')\n\n@app.route('/landmarks68p')\ndef landmarks68p():\n\treturn render_template('landmarks68p.html')\n\n@app.route('/post2landmarks68p', methods=['POST'], strict_slashes=False)\ndef post_landmarks68p():\n\tf = request.files['photo1']\n\tf.save(\"4.jpg\")\n\twith open(\"4.jpg\", 'rb') as f7:\n\t\t_byte1 = f7.read()\n\timage_str1 = base64.b64encode(_byte1)\n\t\n\turl = 'http://localhost:65530/api/face/landmarks68p/'\n\tbody = {\"image_base64\": image_str1} \n\trt = requests.post(url, data=body)\n\t\n\tjsonData = json.loads(rt.text)\n\tif jsonData.get(\"ok\")==False:\n\t\treturn rt.text\n\timage = cv2.imread(\"4.jpg\", cv2.IMREAD_COLOR)\n\tface_num = 0\n\trects = jsonData.get(\"face\")\n\tfor (i, rect) in enumerate(rects):\n\t\tface_num = face_num + 1\n\n\t\tx=rect['x']\n\t\ty=rect['y']\n\t\tw=rect['width']\n\t\th=rect['height']\n\n\t\tcv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n\n\t\tcv2.putText(image, \"Face #{}\".format(i + 1), (x - 10, y - 10),\n\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n\n\t\tshape = rect[\"face_shape_list\"]\n\t\tfor point in shape:\n\t\t\tcv2.circle(image, (point[\"x\"], point[\"y\"]), 2, (0, 0, 255), -1)\n\n\tcv2.imwrite(\"static\\\\4.jpg\", image)\n\twith open(\"static\\\\4.jpg\", 'rb') as f7:\n\t\t_byte1 = f7.read()\n\n\timgb64 = str(base64.b64encode(_byte1))\n\timgb64 = imgb64[2:len(imgb64) - 1]\n\tnewHTML = ''.format(imgb64)\n\tf7 = open(\"templates\\\\a.html\", \"w\")\n\tf7.write(newHTML)\n\tf7.close()\n\treturn render_template('a.html')\n\n\n@app.route('/post2detect', methods=['POST'], strict_slashes=False)\ndef post_detect():\n\tf = request.files['photo1']\n\tf.save(\"3.jpg\")\n\twith open(\"3.jpg\", 'rb') as f7:\n\t\t_byte1 = f7.read()\n\timage_str1 = base64.b64encode(_byte1)\n\t\n\turl = 'http://localhost:65530/api/face/detect/'\n\tbody = {\"image_base64\": image_str1} \n\trt = requests.post(url, data=body)\n\t\n\t# return (rt.text)\n\n\tprint(rt.text)\n\tjsonData = json.loads(rt.text)\n\tif jsonData.get(\"ok\")==False:\n\t\treturn rt.text\n\timage = cv2.imread(\"3.jpg\", cv2.IMREAD_COLOR)\n\tface_num = 0\n\trects = jsonData.get(\"face\")\n\tfor (i, rect) in enumerate(rects):\n\t\tface_num = face_num + 1\n\n\t\tx=rect['x']\n\t\ty=rect['y']\n\t\tw=rect['width']\n\t\th=rect['height']\n\n\t\tcv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n\n\t\tcv2.putText(image, \"Face #{}\".format(i + 1), (x - 10, y - 10),\n\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n\tcv2.imwrite(\"static\\\\3.jpg\", image)\n\twith open(\"static\\\\3.jpg\", 'rb') as f7:\n\t\t_byte1 = f7.read()\n\n\timgb64 = str(base64.b64encode(_byte1))\n\timgb64 = imgb64[2:len(imgb64) - 1]\n\tnewHTML = ''.format(imgb64)\n\tf7 = open(\"templates\\\\a.html\", \"w\")\n\tf7.write(newHTML)\n\tf7.close()\n\treturn render_template('a.html')\n\n\n@app.route('/post2distance', methods=['POST'], strict_slashes=False)\ndef api_upload():\n\tf = request.files['photo1']\n\tf.save(\"1.jpg\")\n\twith open(\"1.jpg\", 'rb') as f7:\n\t\t_byte1 = f7.read()\n\timage_str1 = base64.b64encode(_byte1)\n\n\tf = request.files['photo2']\n\tf.save(\"2.jpg\")\n\twith open(\"2.jpg\", 'rb') as f7:\n\t\t_byte2 = f7.read()\n\timage_str2 = base64.b64encode(_byte2)\n\n\turl = 'http://localhost:65530/api/face/distance/'\n\tbody = {\"image1_base64\": image_str1,\"image2_base64\":image_str2} \n\trt = requests.post(url, data=body)\n\treturn rt.text\n \nif __name__ == '__main__':\n\tHOST = environ.get('SERVER_HOST', 'localhost')\n\ttry:\n\t\tPORT = int(environ.get('SERVER_PORT', '5559'))\n\texcept ValueError:\n\t\tPORT = 65530\n\n\t#多线程模式\n\t#app.run(HOST, PORT, threaded=True)\n\n\t#多进程模式\n\t#app.run(HOST, PORT, processes=3)\n\n\tapp.run(HOST, PORT, debug=True)","sub_path":"webFaceDUI/flaskup.py","file_name":"flaskup.py","file_ext":"py","file_size_in_byte":4288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"579052746","text":"\"\"\"\nAuthor(s): Tushar Sharma \n\ncreate_app function will initialize the gms_services along with\nthe modules required using init_app\n\nCalled in Flask script\n\"\"\"\n\nimport os\nimport logging\n\nfrom flask import Flask, current_app, request\nfrom gms_services.utils.flask_logs import LogSetup\nfrom flask_bcrypt import Bcrypt\nfrom pymysql.cursors import DictCursor\nfrom flaskext.mysql import MySQL\nfrom datetime import datetime as dt\n\nfrom .config import config_by_name\n\nflask_bcrypt = Bcrypt()\nmysql = MySQL(cursorclass=DictCursor)\nlogs = LogSetup()\n\ndef create_app(config_name):\n \"\"\" Method for creating a flask app\n\n after_request is used for logging\n \n :param config_name: Environment name\n :return: instance of Flask app\n :rtype: object\n \"\"\"\n gms_services = Flask(__name__)\n gms_services.config.from_object(config_by_name[config_name])\n flask_bcrypt.init_app(gms_services)\n mysql.init_app(gms_services)\n logs.init_app(gms_services)\n\n @gms_services.after_request\n def after_request(response):\n \"\"\" Logging after every request. \"\"\"\n logger = logging.getLogger(\"gms_services.access\")\n logger.info(\n \"%s [%s] %s %s %s %s %s %s %s\",\n request.remote_addr,\n dt.utcnow().strftime(\"%d/%b/%Y:%H:%M:%S.%f\")[:-3],\n request.method,\n request.path,\n request.scheme,\n response.status,\n response.content_length,\n request.referrer,\n request.user_agent\n )\n return response\n\n return gms_services\n","sub_path":"gms_services/main/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"382167789","text":"\"\"\"\n997. Find the Town Judge (Easy)\n\nIn a town, there are N people labelled from 1 to N. There is a rumor that one of these people is secretly the town judge.\n\nIf the town judge exists, then:\n\nThe town judge trusts nobody.\nEverybody (except for the town judge) trusts the town judge.\nThere is exactly one person that satisfies properties 1 and 2.\nYou are given trust, an array of pairs trust[i] = [a, b] representing that the person labelled a trusts the person labelled b.\n\nIf the town judge exists and can be identified, return the label of the town judge. Otherwise, return -1.\n\n \n\nExample 1:\n\nInput: N = 2, trust = [[1,2]]\nOutput: 2\nExample 2:\n\nInput: N = 3, trust = [[1,3],[2,3]]\nOutput: 3\nExample 3:\n\nInput: N = 3, trust = [[1,3],[2,3],[3,1]]\nOutput: -1\nExample 4:\n\nInput: N = 3, trust = [[1,2],[2,3]]\nOutput: -1\nExample 5:\n\nInput: N = 4, trust = [[1,3],[1,4],[2,3],[2,4],[4,3]]\nOutput: 3\n \n\nNote:\n\n1 <= N <= 1000\ntrust.length <= 10000\ntrust[i] are all different\ntrust[i][0] != trust[i][1]\n1 <= trust[i][0], trust[i][1] <= N\n\"\"\"\n\nclass Solution(object):\n def findJudge(self, N, trust):\n \"\"\"\n :type N: int\n :type trust: List[List[int]]\n :rtype: int\n \"\"\"\n memo = {}\n for i in range(1, N+1):\n memo[i] = 0\n for item in trust:\n a, b = item\n if a in memo:\n del memo[a]\n if b in memo:\n memo[b] += 1\n if len(memo) == 1:\n k = list(memo.keys())[0]\n if memo[k] == N-1:\n return k\n return -1\n\n\nif __name__ == \"__main__\":\n a = Solution()\n print(a.findJudge(2, [[1,2]]))\n print(a.findJudge(3, [[1,3],[2,3]]))\n print(a.findJudge(3, [[1,3],[2,3],[3,1]]))\n print(a.findJudge(3, [[1,2],[2,3]]))","sub_path":"python/leetcode/graph/997_find_town_judge.py","file_name":"997_find_town_judge.py","file_ext":"py","file_size_in_byte":1782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"260283031","text":"import gym\nimport matplotlib.pyplot as plt\nfrom garnet import *\nfrom utils import *\nfrom optimizer.tdvanilla import TD\nfrom optimizer.tdc import TDC\nfrom optimizer.vrgreedygq import VRTDC\nfrom optimizer.vrtd import VRTD\nimport time\n\n\ndef easy_simulation(env, alpha, beta, trajectory_length=50000, num_simulation=100, gamma=0.95,\n target=None):\n ini_start = time.time()\n\n print(\"Initialization...\")\n A, b, C = evaluate_AbC(env, gamma=gamma, target_policy=target)\n theta_ast = -np.matmul(np.linalg.inv(A), b)\n\n ini_theta = theta_ast + 0.8 * np.random.normal(scale=1.0, size=theta_ast.shape)\n print(\"Optimal Theta:\", theta_ast)\n print(\"Initial Theta:\", ini_theta)\n print(\"Initialization Completed. Time Spent:\", time.time() - ini_start)\n\n all_vrtdc1_hist = []\n all_vrtd1_hist = []\n all_vrtdc1000_hist = []\n all_vrtd1000_hist = []\n all_vrtdc2000_hist = []\n all_vrtd2000_hist = []\n all_vrtdc3000_hist = []\n all_vrtd3000_hist = []\n all_vrtdc4000_hist = []\n all_vrtd4000_hist = []\n all_vrtdc5000_hist = []\n all_vrtd5000_hist = []\n for _ in range(num_simulation):\n env.reset()\n current_state = env.current_state\n\n vrtdc1 = TDC(env, alpha=alpha, beta=beta, target_policy=target, gamma=gamma)\n vrtdc1.set_theta(ini_theta)\n vrtd1 = TD(env, alpha=alpha, target_policy=target, gamma=gamma)\n vrtd1.set_theta(ini_theta)\n\n vrtdc1000 = VRTDC(env, batch_size=1000, alpha=alpha, beta=beta, target_policy=target, gamma=gamma)\n vrtdc1000.set_theta(ini_theta)\n vrtd1000 = VRTD(env, batch_size=1000, alpha=alpha, target_policy=target, gamma=gamma)\n vrtd1000.set_theta(ini_theta)\n\n vrtdc2000 = VRTDC(env, batch_size=2000, alpha=alpha, beta=beta, target_policy=target, gamma=gamma)\n vrtdc2000.set_theta(ini_theta)\n vrtd2000 = VRTD(env, batch_size=2000, alpha=alpha, target_policy=target, gamma=gamma)\n vrtd2000.set_theta(ini_theta)\n\n vrtdc3000 = VRTDC(env, batch_size=3000, alpha=alpha, beta=beta, target_policy=target, gamma=gamma)\n vrtdc3000.set_theta(ini_theta)\n vrtd3000 = VRTD(env, batch_size=3000, alpha=alpha, target_policy=target, gamma=gamma)\n vrtd3000.set_theta(ini_theta)\n\n vrtdc4000 = VRTDC(env, batch_size=4000, alpha=alpha, beta=beta, target_policy=target, gamma=gamma)\n vrtdc4000.set_theta(ini_theta)\n vrtd4000 = VRTD(env, batch_size=4000, alpha=alpha, target_policy=target, gamma=gamma)\n vrtd4000.set_theta(ini_theta)\n\n vrtdc5000 = VRTDC(env, batch_size=5000, alpha=alpha, beta=beta, target_policy=target, gamma=gamma)\n vrtdc5000.set_theta(ini_theta)\n vrtd5000 = VRTD(env, batch_size=5000, alpha=alpha, target_policy=target, gamma=gamma)\n vrtd5000.set_theta(ini_theta)\n\n print(\"Start Training. Simulation:\", _ + 1)\n train_start = time.time()\n\n vrtdc1_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtdc1000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtdc2000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtdc3000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtdc4000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtdc5000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n\n vrtd1_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtd1000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtd2000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtd3000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtd4000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n vrtd5000_hist = [np.sum((vrtdc1.theta - theta_ast) ** 2)]\n count = 1\n for i in range(trajectory_length):\n next_state, reward, action = env.step()\n # action = None\n\n vrtdc1.update(current_state, reward, next_state, action)\n vrtdc1_hist.append(np.sum((vrtdc1.theta - theta_ast) ** 2))\n vrtd1.update(current_state, reward, next_state, action)\n vrtd1_hist.append(np.sum((vrtd1.theta - theta_ast) ** 2))\n\n vrtdc1000.update(current_state, reward, next_state, action)\n vrtdc1000_hist.append(np.sum((vrtdc1000.theta - theta_ast) ** 2))\n vrtd1000.update(current_state, reward, next_state, action)\n vrtd1000_hist.append(np.sum((vrtd1000.theta - theta_ast) ** 2))\n\n vrtdc2000.update(current_state, reward, next_state, action)\n vrtdc2000_hist.append(np.sum((vrtdc2000.theta - theta_ast) ** 2))\n vrtd2000.update(current_state, reward, next_state, action)\n vrtd2000_hist.append(np.sum((vrtd2000.theta - theta_ast) ** 2))\n\n vrtdc3000.update(current_state, reward, next_state, action)\n vrtdc3000_hist.append(np.sum((vrtdc3000.theta - theta_ast) ** 2))\n vrtd3000.update(current_state, reward, next_state, action)\n vrtd3000_hist.append(np.sum((vrtd3000.theta - theta_ast) ** 2))\n\n vrtdc4000.update(current_state, reward, next_state, action)\n vrtdc4000_hist.append(np.sum((vrtdc4000.theta - theta_ast) ** 2))\n vrtd4000.update(current_state, reward, next_state, action)\n vrtd4000_hist.append(np.sum((vrtd4000.theta - theta_ast) ** 2))\n\n vrtdc5000.update(current_state, reward, next_state, action)\n vrtdc5000_hist.append(np.sum((vrtdc5000.theta - theta_ast) ** 2))\n vrtd5000.update(current_state, reward, next_state, action)\n vrtd5000_hist.append(np.sum((vrtd5000.theta - theta_ast) ** 2))\n\n current_state = np.copy(next_state)\n if (i + 1) % 10000 == 0:\n print(\"Current iteration:\", i + 1, \". Time Spent:\", time.time() - train_start)\n train_start = time.time()\n count += 1\n all_vrtdc1_hist.append(vrtdc1_hist)\n all_vrtdc1000_hist.append(vrtdc1000_hist)\n all_vrtdc2000_hist.append(vrtdc2000_hist)\n all_vrtdc3000_hist.append(vrtdc3000_hist)\n all_vrtdc4000_hist.append(vrtdc4000_hist)\n all_vrtdc5000_hist.append(vrtdc5000_hist)\n all_vrtd1_hist.append(vrtd1_hist)\n all_vrtd1000_hist.append(vrtd1000_hist)\n all_vrtd2000_hist.append(vrtd2000_hist)\n all_vrtd3000_hist.append(vrtd3000_hist)\n all_vrtd4000_hist.append(vrtd4000_hist)\n all_vrtd5000_hist.append(vrtd5000_hist)\n return all_vrtdc1_hist, all_vrtdc1000_hist, all_vrtdc2000_hist, all_vrtdc3000_hist, all_vrtdc4000_hist, all_vrtdc5000_hist, \\\n all_vrtd1_hist, all_vrtd1000_hist, all_vrtd2000_hist, all_vrtd3000_hist, all_vrtd4000_hist, all_vrtd5000_hist\n\n\nnp.random.seed(91)\n\n# Compare Different Batch Sizes\nnum_states = 500\nnum_actions = 20\nbranching_factor = 50\nnum_features = 15\n\nprint(\"Set Up the Simulation Environment...\")\nenv = Garnet(num_states, num_actions, branching_factor, num_features)\nprint(\"Done.\")\n\ngamma = 0.95\nmax_num_iteration = 100000\nalpha = 0.1\nbeta = 0.02\ntarget = get_random_policy(num_states, num_actions)\nnum_simulation = 250\n\nh1, h2, h3, h4, h5, h6, tdh1, tdh2, tdh3, tdh4, tdh5, tdh6 = easy_simulation(env, alpha, beta,\n trajectory_length=max_num_iteration,\n num_simulation=num_simulation, gamma=gamma,\n target=target)\n\nfig, ax = plt.subplots()\nvrtdc_h = [h1, h2, h3, h4, h5, h6]\nvrtd_h = [tdh1, tdh2, tdh3, tdh4, tdh5, tdh6]\n\n\nnum_obs = 10000\nerrors_vrtdc = [np.mean(np.array(h)[:, -num_obs:], axis=1) for h in vrtdc_h]\nerrors_vrtd = [np.mean(np.array(h)[:, -num_obs:], axis=1) for h in vrtd_h]\n\nimport pandas as pd\n\nbatch_size_list = ['1', \"1000\", \"2000\", \"3000\", \"4000\", \"5000\"]\nDF = pd.DataFrame(columns=[\"Errors\", \"Batch Size\", \"Algorithm\"])\ncount = 0\nfor i in range(len(batch_size_list)):\n for j in range(num_simulation):\n DF.loc[count] = [errors_vrtdc[i][j], batch_size_list[i], \"VRTDC\"]\n count += 1\n DF.loc[count] = [errors_vrtd[i][j], batch_size_list[i], \"VRTD\"]\n count += 1\n\nimport seaborn as sns\n\nsns.set(style=\"ticks\", palette=\"pastel\")\nbp = sns.boxplot(x=\"Batch Size\", y=\"Errors\", data=DF, hue=\"Algorithm\", palette=[\"red\", \"blue\"])\nfig = bp.get_figure()\nfig.savefig(\"fig2-corrected.png\", dpi=300)\n\nDF.to_csv(\"out-corrected.csv\")","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":8463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"348026738","text":"import os\nimport sys, shutil\nimport custom_build_tools.build as build_tools\nfrom setuptools import setup\n\nREQUIRED_PACKAGES = [\n 'setuptools >= 39.2.0',\n 'cmake >= 3.11.0',\n 'cffi >= 1.11.5',\n 'mkl >= 2018.0.0',\n 'pyyaml >= 3.12',\n 'numpy >= 1.14.0',\n]\n\nsetup(\n name='pytorch-gpu-macosx',\n version='0.5.0',\n description='Unoffcial NVIDIA CUDA GPU support version of PyTorch for MAC OSX 10.13',\n author='Carl Cheung',\n author_email='zylo117@hotmail.com',\n url='https://github.com/zylo117/pytorch',\n install_requires=REQUIRED_PACKAGES,\n keywords='gpu cuda torch tensor machine learning', )\n\n\npytorch_src_path = '../'\n\n# modify CMakeLists\nif not os.path.exists(pytorch_src_path + 'CMakeLists_bakcup.txt'):\n shutil.copy(pytorch_src_path + 'CMakeLists.txt', pytorch_src_path + 'CMakeLists_bakcup.txt')\n cmakelists = open(pytorch_src_path + 'CMakeLists.txt', 'r')\n cml_data = cmakelists.readlines()\n for i, l in enumerate(cml_data):\n if 'CMAKE_RUNTIME_OUTPUT_DIRECTORY' in l:\n cml_data[i] = cml_data[\n i] + '\\n' + 'if(APPLE)\\n\\tset(CMAKE_FIND_FRAMEWORK LAST)\\n\\tset(CMAKE_FIND_APPBUNDLE LAST)\\nendif()\\n'\n break\n cmakelists = open('../CMakeLists.txt', 'w')\n cmakelists.writelines(cml_data)\n cmakelists.close()\n\npwd = sys.path[0]\n\n# build PT\npython_exec = build_tools.get_python_path()\npython_bin = '/'.join(build_tools.get_python_path().split('/')[:-1])\npython_lib_path = build_tools.get_python_package()\n\nbuild_tools.git_clone()\n\nprint('[INFO] Building PyTorch...')\nprint(\"[INFO] It's going to last for about 20 minutes on Intel i7-6700\")\nprint('[INFO] Build Complete')\n","sub_path":"pip/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"98288216","text":"import os, re, shutil\nimport zc.buildout\n\nstart_section = re.compile('(^|\\n)[ \\t]*<[^>]+>[ \\t]*\\n').search\nend_section = re.compile('(^|\\n)[ \\t]*]+>[ \\t]*\\n').search\n\nclass Recipe:\n # Need to think about the inheritence interface\n # it *is* reasonable to think about instances as an\n # extension of the basic egg/script-generation model.\n\n def __init__(self, buildout, name, options):\n self.options, self.name = options, name\n\n options['zeo'] = options.get('zeo', 'zeo')\n self._getdbconfig(buildout, options)\n python = buildout['buildout']['python']\n options['zeo-directory'] = buildout[options['zeo']]['location']\n options['location'] = os.path.join(\n buildout['buildout']['parts-directory'],\n self.name,\n )\n python = buildout['buildout']['python']\n options['executable'] = buildout[python]['executable']\n options['port'] = options.get('port', '8100')\n options['zconfig'] = zeoclient_tempalte % options['port']\n\n def _getdbconfig(self, buildout, options):\n dbconfig = buildout[options['database']]['zconfig']\n #import pdb; pdb.set_trace()\n dbconfig = dbconfig[start_section(dbconfig).end(0):]\n dbconfig = dbconfig[start_section(dbconfig).start(0):]\n dbconfig = dbconfig[:end_section(dbconfig).end(0):]\n dbconfig = dbconfig.replace('>', ' 1>', 1)\n options['database-config'] = dbconfig\n\n def install(self):\n \n options = self.options\n location = options['location']\n\n if os.path.exists(location):\n return location\n \n # What follows is a bit of a hack because the instance-setup mechanism\n # is a bit monolithic. We'll run mkzeoinstabce and then we'll\n # patch the result. A better approach might be to provide independent\n # instance-creation logic, but this raises lots of issues that\n # need to be stored out first.\n mkzeoinstance = os.path.join(options['zeo-directory'],\n 'bin', 'mkzeoinstance')\n\n assert os.spawnl(\n os.P_WAIT, options['executable'], options['executable'],\n mkzeoinstance, location,\n ) == 0\n\n try:\n # Now, patch the zodb option in zeo.conf\n zeo_conf_path = os.path.join(location, 'etc', 'zeo.conf')\n zeo_conf = open(zeo_conf_path).read()\n zeo_conf = zeo_conf.replace('address 8100',\n 'address %s' % options['port'],\n 1)\n zeo_conf = (\n zeo_conf[:zeo_conf.find('')]\n +\n options['database-config']\n +\n zeo_conf[zeo_conf.find('')+15:]\n )\n open(zeo_conf_path, 'w').write(zeo_conf)\n\n \n except:\n # clean up\n shutil.rmtree(location)\n raise\n \n return location\n\n def update(self):\n pass\n\nzeoclient_tempalte = \"\"\"\\\n\n \n server localhost:%s\n \n\n\"\"\"\n","sub_path":"zc.recipe.zeoinstance/trunk/src/zc/recipe/zeoinstance/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"373630639","text":"\"\"\" Comprises 20K training examples and 20K test examples\n of the CMS electromagnetic calorimeter formatted\n as 28x28 pixel monochromatic images.\n\n Label convention: Electron --> 0\n Photon --> 1\n Pion --> 2\n\"\"\"\nfrom collections import namedtuple\n\nimport numpy as np\nimport os\n\nnp.random.seed(42)\n\nDATADIR = '/home/jose/work/ml-physics/data'\n\nELEC = 'eminus_Ele-Eta0-PhiPiOver2-Energy50.npy'\nPHOT = 'gamma-Photon-Eta0-PhiPiOver2-Energy50.npy'\nPION = 'piminus_Pion-Eta0-PhiPiOver2-Energy50.npy'\n\n\ndef read_data(threshold):\n \"\"\" Read numpy arrays.\n Select images with energy above the threshold.\n Assign labels and concatenate arrays.\n Finally, shuffles and returns.\n \"\"\"\n elec = np.load(os.path.join(DATADIR, ELEC))\n phot = np.load(os.path.join(DATADIR, PHOT))\n pion = np.load(os.path.join(DATADIR, PION))\n\n elec = np.array([i for i in elec if np.sum(i) > threshold])\n phot = np.array([i for i in phot if np.sum(i) > threshold])\n pion = np.array([i for i in pion if np.sum(i) > threshold])\n\n zeros = np.zeros(elec.shape[0], np.int32)\n ones = np.ones(phot.shape[0], np.int32)\n twos = 1 + np.ones(pion.shape[0], np.int32)\n\n y = np.concatenate((zeros, ones, twos))\n X = np.concatenate((elec, phot, pion))\n p = np.random.permutation(len(y))\n return X[p], y[p]\n\n\ndef load_dataset(threshold):\n \"\"\" Split into training and validation sets.\n Return namedtuple.\n \"\"\"\n X, y = read_data(threshold)\n X = np.reshape(X, [-1, 28, 28, 1])\n m = min(len(y), 40000)\n\n X_train, X_val = X[:m//2], X[m//2:m]\n y_train, y_val = y[:m//2], y[m//2:m]\n\n Samples = namedtuple('Samples', 'images labels')\n Dataset = namedtuple('Dataset', 'train validation')\n return Dataset(Samples(X_train, y_train), Samples(X_val, y_val))\n\n\nif __name__ == '__main__':\n dataset = load_dataset(threshold=10.)\n train_images = dataset.train.images\n print(train_images.shape)\n print(train_images.max())\n","sub_path":"python/custom_dataset.py","file_name":"custom_dataset.py","file_ext":"py","file_size_in_byte":2023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"484143241","text":"import os\nimport subprocess\nimport csv\nimport codecs\nfrom pathlib import Path\n\napplicationFolderPath = str(Path.home())+\"\\\\SellerSpot\"\n\n\ndef get_installed_mongodb_version(): # used to get the version of mongodb installed\n\n # getting all the apps installed in the local system into a csv file\n os.system(\"wmic product get name,version /format:csv > installedapps.csv\")\n # opening app to read using codec pkg to handle conversion to utf-16\n spamreader = csv.reader(codecs.open(\n 'installedapps.csv', 'rU', 'utf-16'), delimiter=',', quotechar='|')\n # finding the right version of mongodb installed\n for row in spamreader:\n if len(row) > 0 and \"MongoDB\" in row[1]:\n # updating global instances\n mongoDBVersion = str('.'.join(row[2].split('.')[0:2]))\n return mongoDBVersion\n\n\ndef delete_created_csv_file(): # used to delete the app listing csv files\n subprocess.Popen(\n \"del installedapps.csv\", shell=True,\n stdout=subprocess.PIPE, creationflags=subprocess.CREATE_NO_WINDOW)\n\n\nif __name__ == \"__main__\":\n\n # navigating to the server folder\n os.chdir(applicationFolderPath+\"\\\\server\")\n\n # running server\n mongoCheck = subprocess.Popen(\n \"LocalConnectServer.exe\", shell=True, stdout=subprocess.PIPE, creationflags=subprocess.CREATE_NO_WINDOW)\n\n # getting installed mongodb version\n installedMongoDBVersion = get_installed_mongodb_version()\n\n # deleting the created csv file\n delete_created_csv_file()\n\n os.chdir(\"C:\\\\Program Files\\\\MongoDB\\\\Server\\\\\" +\n installedMongoDBVersion+\"\\\\bin\")\n\n # initializing database\n mongoCheck = subprocess.Popen(\n \"mongod.exe --config \"+applicationFolderPath+\"\\\\mongoconfig.cgf\", shell=True, stdout=subprocess.PIPE, creationflags=subprocess.CREATE_NO_WINDOW)\n","sub_path":"sellerspotServerInvoke.py","file_name":"sellerspotServerInvoke.py","file_ext":"py","file_size_in_byte":1827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"572793143","text":"# -*- coding: utf-8 -*-\n\"\"\"\nbaseline NER identifier system for project 3\n\"\"\"\n\n\ndirectory = \"C:/Users/Jingchen/OneDrive/Documents/Cornell/CS 4740_NLP/Project_3\"\ntrain_data = \"train.txt\"\ntest_data = \"test.txt\"\n\nimport random\nrandom.seed(2015)\nimport csv\n\n# define a class to store the training examples\nclass Sent_instance():\n def __init__(self):\n self.sent = \"\" # the sentence in this instance\n self.word = [] # words in sentence in form of list of strings\n self.pos = [] # the pos tags for each word in the sentence\n self.ner = [] # the ner tags for each word in the sentence\n self.net_test = [] # just to perserve a slot for test data ner tages \n self.map = {} # a mapping between words and ner tags, just for\n # the sake of baseline system\n\n\n# function to read through the train.txt to generate sentence \n# returns a list of Sent_instance objecs if reads training data, \n# returns a single Sent_instance object if reads test data by specify type = \"test\"\ndef read_data(file_location, type = \"train\"):\n with open(file_location, \"rb\") as myfile:\n data = myfile.readlines()\n # a specifial version of read just for test data set\n if type == \"test\":\n sent = \"\"\n for i in range(0, len(data), 3):\n sent += data[i] + \"\\t\"\n pos = \"\"\n for i in range(1, len(data), 3):\n pos += data[i] + \"\\t\"\n ner = \"\"\n for i in range(2, len(data), 3):\n ner += data[i] + \" \"\n \n # get rid of \"\\t\\n\" in all elements \n sent = sent.translate(None, \"\\r\\n\")\n pos = pos.translate(None, \"\\r\\n\")\n ner = ner.translate(None, \"\\r\\n\")\n \n \n test_instance = Sent_instance()\n test_instance.sent = sent[:-1]\n test_instance.word = sent.split(\"\\t\")[:-1]\n test_instance.pos = pos.split(\"\\t\")[:-1]\n test_instance.ner = ner.split(\" \")[:-1]\n return test_instance\n \n # create a list to store the final output: bunch Sent_instance objects\n list_sent_instance = [] \n \n # iterating every object in the loaded data\n # for every 3 new lines, construct a new Sent_instance object and append it\n # to the list_sent_instance\n max_idx_line = len(data)\n idx_line_loaded = 0\n while (idx_line_loaded + 3 <= max_idx_line):\n # create a sent_instance object \n new_instance = Sent_instance()\n # break strings and \n new_instance.sent = data[idx_line_loaded][:-2] # -2 to get rid of \"\\r\\n\"\n new_instance.word = data[idx_line_loaded][:-2].split(\"\\t\")\n new_instance.pos = data[idx_line_loaded+1][:-2].split(\"\\t\")\n new_instance.ner = data[idx_line_loaded+2][:-2].split(\"\\t\")\n \n list_sent_instance.append(new_instance)\n idx_line_loaded += 3\n return list_sent_instance\n\n# read training and test data\ndata = read_data(directory+\"/\"+train_data)\ntest = read_data(directory+\"/\"+test_data, type = \"test\")\n\n# a function that tranform a list of Sent_instance to a dictionary mapping each word\n# to its ner tags. One word can have several ner tags\ndef build_map(list_instance):\n map_dic = {} \n for instance in list_instance: \n for i, word in enumerate(instance.word):\n if word not in map_dic.keys(): \n map_dic[word] = []\n map_dic[word].append(instance.ner[i])\n else:\n map_dic[word].append(instance.ner[i])\n return map_dic\n\nbase_map = build_map(data) # surprisingly takes more 90 seconds\n\n# funtion to predict based on baseline predictor\n# it only works with Sent_instance and dictionary\n# it returns a dictionary whose keys are index (int), values are tuples of \n# the word to be predicted and its ner tag\ndef base_predict(test_instance, dic_map):\n prediction = {}\n for i in range(len(test_instance.ner)):\n word_to_pred = test_instance.word[i]\n if word_to_pred in dic_map.keys():\n ner_predict = random.choice(dic_map[word_to_pred])\n else:\n ner_predict = 'UNK' # meaning it's not in the training data\n prediction[i] = (word_to_pred, ner_predict) \n return prediction\n\nbase_pred = base_predict(test, base_map) # takes about 1 min\n\n# a function to print the predictions to the format required\ndef submission_transform(predictions):\n raw_print = {\"ORG\": [], \"MISC\": [], \"PER\": [], \"LOC\": []} \n for k, v in predictions.items():\n for ner_tag in raw_print.keys(): \n if ner_tag in v[1]:\n raw_print[ner_tag].append(k)\n \n # now the values in raw_print should be a list like [1,4,8...]\n final_print = {} \n for key in raw_print.keys():\n l = raw_print[key]\n ranges = sum((list(t) for t in zip(l, l[1:]) if t[0]+1 != t[1]), [])\n iranges = iter(l[0:1] + ranges + l[-1:])\n final_print[key] = [str(n) + '-' + str(next(iranges)) for n in iranges]\n return raw_print, final_print\n\n\nraw_print, final_print = submission_transform(base_pred)\n\n# Output results\ndef submission_print(final_print, file_name):\n baseline_submission = [['Type', 'Prediction']]\n for k, v in final_print.items():\n baseline_submission.append([k, \" \".join(v)])\n with open(file_name + '.csv', 'wb') as file:\n csv_writer = csv.writer(file, delimiter=',')\n for row in baseline_submission:\n csv_writer.writerow(row)\n\nsubmission_print(final_print, \"baseline_submission\")\n\n\n","sub_path":"CS 4740_NLP/Named Entity Recognition_HMM/nlp_p3_baseline.py","file_name":"nlp_p3_baseline.py","file_ext":"py","file_size_in_byte":5500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"300510270","text":"from crypto.constants import TRANSACTION_MULTI_SIGNATURE_REGISTRATION\nfrom crypto.transactions.builder.multi_signature_registration import MultiSignatureRegistration\n\n\ndef test_multi_signature_registration_transaction():\n \"\"\"Test if a second signature registration transaction gets built\n \"\"\"\n keysgroup = [\n '03a02b9d5fdd1307c2ee4652ba54d492d1fd11a7d1bb3f3a44c4a05e79f19de933',\n '13a02b9d5fdd1307c2ee4652ba54d492d1fd11a7d1bb3f3a44c4a05e79f19de933',\n '23a02b9d5fdd1307c2ee4652ba54d492d1fd11a7d1bb3f3a44c4a05e79f19de933',\n ]\n transaction = MultiSignatureRegistration(2, 255, keysgroup)\n transaction.sign('secret')\n transaction.second_sign('second secret')\n transaction_dict = transaction.to_dict()\n assert transaction_dict['signature']\n assert transaction_dict['type'] is TRANSACTION_MULTI_SIGNATURE_REGISTRATION\n transaction.verify() # if no exception is raised, it means the transaction is valid\n","sub_path":"tests/transactions/builder/test_multi_signature_registration.py","file_name":"test_multi_signature_registration.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"651634401","text":"# -*- coding: utf-8 -*-\n# ------------------------------------------------------------------------------\n#\n# Copyright 2018-2019 Fetch.AI Limited\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# ------------------------------------------------------------------------------\n\"\"\"This module contains generic tools for AEA end-to-end testing.\"\"\"\n\nfrom pathlib import Path\nfrom typing import Any, Dict, List\n\nimport yaml\n\nfrom aea.cli.utils.config import handle_dotted_path\nfrom aea.configurations.base import PublicId\nfrom aea.connections.stub.connection import write_envelope\nfrom aea.mail.base import Envelope\n\n\ndef write_envelope_to_file(envelope: Envelope, file_path: str) -> None:\n \"\"\"\n Write an envelope to a file.\n\n :param envelope: Envelope.\n :param file_path: the file path\n\n :return: None\n \"\"\"\n with open(Path(file_path), \"ab+\") as f:\n write_envelope(envelope, f)\n\n\ndef read_envelope_from_file(file_path: str):\n \"\"\"\n Read an envelope from a file.\n\n :param file_path the file path.\n\n :return: envelope\n \"\"\"\n lines = []\n with open(Path(file_path), \"rb+\") as f:\n lines.extend(f.readlines())\n\n assert len(lines) == 2, \"Did not find two lines.\"\n line = lines[0] + lines[1]\n to_b, sender_b, protocol_id_b, message, end = line.strip().split(b\",\", maxsplit=4)\n to = to_b.decode(\"utf-8\")\n sender = sender_b.decode(\"utf-8\")\n protocol_id = PublicId.from_str(protocol_id_b.decode(\"utf-8\"))\n assert end in [b\"\", b\"\\n\"]\n\n return Envelope(to=to, sender=sender, protocol_id=protocol_id, message=message,)\n\n\ndef _nested_set(dic: Dict, keys: List, value: Any) -> None:\n \"\"\"\n Nested set a value to a dict.\n\n :param dic: target dict\n :param keys: list of keys.\n :param value: a value to set.\n\n :return: None.\n \"\"\"\n for key in keys[:-1]:\n dic = dic.setdefault(key, {})\n dic[keys[-1]] = value\n\n\ndef force_set_config(dotted_path: str, value: Any) -> None:\n \"\"\"\n Set an AEA config without validation.\n\n Run from agent's directory.\n\n Allowed dotted_path:\n 'agent.an_attribute_name'\n 'protocols.my_protocol.an_attribute_name'\n 'connections.my_connection.an_attribute_name'\n 'contracts.my_contract.an_attribute_name'\n 'skills.my_skill.an_attribute_name'\n 'vendor.author.[protocols|connections|skills].package_name.attribute_name\n\n :param dotted_path: dotted path to a setting.\n :param value: a value to assign. Must be of yaml serializable type.\n\n :return: None.\n \"\"\"\n settings_keys, file_path, _ = handle_dotted_path(dotted_path)\n\n settings = {}\n with open(file_path, \"r\") as f:\n settings = yaml.safe_load(f)\n\n _nested_set(settings, settings_keys, value)\n\n with open(file_path, \"w\") as f:\n yaml.dump(settings, f, default_flow_style=False)\n","sub_path":"aea/test_tools/generic.py","file_name":"generic.py","file_ext":"py","file_size_in_byte":3350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"382607448","text":"import random\n\nfrom deap import tools\nfrom operator import eq\nfrom copy import deepcopy\n\n\nclass myParetoFront():\n\n def __init__(self, max_len, similar=eq):\n self.keys = list()\n self.items = list()\n self.max_len = max_len\n self.similar = similar\n\n def insert(self, item):\n item = deepcopy(item)\n\n self.items.append(item)\n self.keys.append(item.fitness)\n\n def remove(self, index):\n\n del self.keys[index]\n del self.items[index]\n\n def replace(self, index, item):\n item = deepcopy(item)\n\n self.keys[index] = item\n self.items[index] = item.fitness\n\n def update(self, population):\n\n removed = 0\n\n for ind in population:\n is_dominated = False\n dominates_one = False\n has_twin = False\n to_remove = []\n for i, hofer in enumerate(self): # hofer = hall of famer\n if not dominates_one and hofer.fitness.dominates(ind.fitness):\n is_dominated = True\n break\n elif ind.fitness.dominates(hofer.fitness):\n dominates_one = True\n to_remove.append(i)\n elif ind.fitness == hofer.fitness and self.similar(ind, hofer):\n has_twin = True\n break\n\n for i in reversed(to_remove): # Remove the dominated hofer\n self.remove(i)\n removed += 1\n if not is_dominated and not has_twin:\n if(len(self) < self.max_len):\n self.insert(ind)\n\n return removed\n\n def __len__(self):\n return len(self.items)\n\n def __getitem__(self, i):\n return self.items[i]\n\n def __iter__(self):\n return iter(self.items)\n\n def __reversed__(self):\n return reversed(self.items)\n\n def __str__(self):\n return str(self.items)\n\n\ndef nsga2Algorithm(population, toolbox, cxpb, mutpb, ngen, stats=None, halloffame=None, verbose=__debug__):\n\n logbook = tools.Logbook()\n logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])\n\n # Evaluate the individuals with an invalid fitness\n invalid_ind = [ind for ind in population if not ind.fitness.valid]\n fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n for ind, fit in zip(invalid_ind, fitnesses):\n ind.fitness.values = fit\n\n if halloffame is not None:\n halloffame.update(population)\n\n numOfIndividuals = len(population)\n\n # This is just to assign the crowding distance to the individuals\n # no actual selection is done\n population = toolbox.select(population, len(population))\n\n record = stats.compile(population) if stats else {}\n logbook.record(gen=0, evals=len(invalid_ind), **record)\n\n if verbose:\n print(logbook.stream)\n # Begin the generational process\n\n removed = 0\n for gen in range(1, ngen + 1):\n # Vary the population\n\n # Dodawanie osobników aż populacja będzie podzielna przez 4\n while len(population) % 4 != 0:\n population.append(\n population[random.randint(0, len(population) - 1)])\n\n offspring = tools.selTournamentDCD(population, len(population))\n\n offspring = [toolbox.clone(ind) for ind in offspring]\n\n for ind1, ind2 in zip(offspring[::2], offspring[1::2]):\n if random.random() <= cxpb:\n ind1, ind2 = toolbox.mate(ind1, ind2)\n\n if random.random() <= mutpb:\n toolbox.mutate(ind1)\n if random.random() <= mutpb:\n toolbox.mutate(ind2)\n del ind1.fitness.values, ind2.fitness.values\n\n # Evaluate the individuals with an invalid fitness\n invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n for ind, fit in zip(invalid_ind, fitnesses):\n ind.fitness.values = fit\n\n if halloffame is not None:\n removed += halloffame.update(offspring)\n\n # Select the next generation population\n population = toolbox.select(population + offspring, numOfIndividuals)\n record = stats.compile(population) if stats else {}\n logbook.record(gen=gen, evals=len(invalid_ind), **record)\n if verbose:\n print(logbook.stream)\n return population, logbook, removed\n","sub_path":"wielokryterialne/nsga2_alg.py","file_name":"nsga2_alg.py","file_ext":"py","file_size_in_byte":4418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"434196417","text":"import zipfile\nimport os\n\n#unzips file and removes older files in directory\ndef unzip_file(file, destination, deleteZip = True):\n if os.path.exists(destination):\n filelist = [f for f in os.listdir(destination)]\n for f in filelist:\n os.remove(os.path.join(destination, f))\n\n with zipfile.ZipFile(file, 'r') as zip_ref:\n zip_ref.extractall(destination)\n\n if deleteZip:\n os.remove(file)","sub_path":"patcher/file_utils.py","file_name":"file_utils.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"172353338","text":"import requests\nimport csv\nfrom bs4 import BeautifulSoup\n\n\ndef makesoup(url):\n page = requests.get(url)\n #page.encoding = 'utf-8'\n soupdata = BeautifulSoup(page.text.encode('utf-8'), 'html.parser')\n return soupdata\n\n\nsoup = makesoup('http://www.the-numbers.com/movie/records/All-Time-International-Box-Office')\n\nplayee = []\nplayer = []\n#playlist = []\n#realplayer = []\n\nfor record in soup.findAll('tr'):\n # print(record.text)\n for players in record.findAll('td'):\n player.append(players.text)\n playlist = [player[i:i + 6] for i in list(range(0, len(player), 6))]\nprint(*playlist,sep='\\n')\n","sub_path":"parsing/parse-2.py","file_name":"parse-2.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"421414709","text":"from django.shortcuts import render, redirect\nfrom .models import Book, Author\ndef index(request):\n \n context = {\n \"all_books\": Book.objects.all(),\n \"all_authors\": Author.objects.all()\n }\n return render(request, \"index.html\", context)\n\ndef index2(request):\n context = {\n \"all_books\": Book.objects.all(),\n \"all_authors\": Author.objects.all()\n }\n return render(request, \"addauthors.html\", context)\n\ndef processbook(request):\n booktitle = request.POST['booktitle']\n bookdesc = request.POST['bookdesc']\n # bookId = Book.objects.get(id=request.POST) ????\n newBook = Book.objects.create(title = booktitle, desc = bookdesc)\n return redirect(\"/\")\n\ndef processauthor(request):\n authorFname = request.POST['authorFname']\n authorLname = request.POST['authorLname']\n notes = request.POST['notes']\n \n newAuthor = Author.objects.create(first_name = authorFname, last_name = authorLname, notes = notes)\n return redirect(\"/addauthors\")\n\ndef viewBook(request, bookId):\n context = {\n \"book\": Book.objects.get(id = bookId),\n \"all_authors\": Author.objects.all()\n }\n return render(request, \"viewbook.html\", context)\n\ndef addbook(request, authorId):\n authorObject = Author.objects.get(id = authorId)\n \n authorObject.books.add(Book.objects.get(id = request.POST['bookId']))\n \n return redirect(f\"/viewauthor/{authorId}\")\n\ndef addauthor(request, bookId):\n bookObject = Book.objects.get(id = bookId)\n bookObject.authors.add(Author.objects.get(id = request.POST['authorId']))\n \n return redirect(f\"/viewbook/{bookId}\")\n\ndef viewAuthor(request, authorId):\n context = {\n \"author\" : Author.objects.get(id = authorId),\n \"all_books\": Book.objects.all()\n }\n return render(request, \"viewauthor.html\", context)\n","sub_path":"books_authors_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"527620901","text":"import pyautogui\r\nimport time\r\n\r\nsize = pyautogui.size()\r\n\r\nisready = False\r\ningame = False\r\n\r\nsize_multiplier = size[0] / 2560, size[1] / 1440\r\n\r\nprint(f\"{size[0]}, {size[1]} px multiplier={size_multiplier[0]}, {size_multiplier[1]}\")\r\n\r\n\r\ndef multi(x, y):\r\n global size_multiplier\r\n xy = x * size_multiplier[0], y * size_multiplier[1]\r\n xy = round(xy[0]), round(xy[1])\r\n return xy\r\n\r\n\r\ndef line_angle():\r\n print(\"\")\r\n\r\n\r\nrdy_btn_pos = multi(2400, 1080)\r\nrdy_btn_col = 248, 255, 34\r\n\r\nrdy_btn_die_pos = multi(2300, 900)\r\nrdy_btn_die_col = 0, 119, 255\r\n\r\nbus_icn_pos = multi(2185, 450)\r\nbus_icn_col = 112, 167, 66\r\n\r\ntime.sleep(2)\r\n\r\nwhile True: \r\n if not isready:\r\n if pyautogui.pixelMatchesColor(rdy_btn_pos[0], rdy_btn_pos[1], rdy_btn_col, tolerance=32):\r\n pyautogui.click(rdy_btn_pos[0], rdy_btn_pos[1], interval=0.2)\r\n print(\"ready'ed up\")\r\n isready = True\r\n ingame = False\r\n elif pyautogui.pixelMatchesColor(rdy_btn_die_pos[0], rdy_btn_die_pos[1], rdy_btn_die_col, tolerance=32):\r\n pyautogui.click(rdy_btn_die_pos[0], rdy_btn_die_pos[1], interval=0.2)\r\n print(\"ready'ed up\")\r\n isready = True\r\n ingame = False\r\n else:\r\n print(\"waiting for ready button...\")\r\n time.sleep(1)\r\n\r\n if isready:\r\n if pyautogui.pixelMatchesColor(bus_icn_pos[0], bus_icn_pos[1], bus_icn_col, tolerance=32):\r\n print(\"on bus waiting 15 seconds...\")\r\n time.sleep(15)\r\n pyautogui.press('space')\r\n time.sleep(3)\r\n pyautogui.press('space', interval=0.2)\r\n ingame = True\r\n isready = False\r\n else:\r\n print(\"waiting for bus...\")\r\n time.sleep(1)\r\n\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"339662844","text":"import sys\nfrom PyQt5.QtWidgets import QApplication, QWidget, QDialog, QPushButton\nfrom PyQt5.QtCore import Qt\n\n\nclass DialogDemo(QWidget):\n\n def __init__(self, parent=None):\n super(DialogDemo, self).__init__(parent)\n\n self.setWindowTitle('Dialog')\n self.resize(350, 300)\n\n self.btn = QPushButton(self)\n self.btn.setText('show dialog')\n self.btn.move(50, 50)\n self.btn.clicked.connect(self.show_dialog)\n\n @staticmethod\n def show_dialog():\n dialog = QDialog()\n btn = QPushButton('ok', dialog)\n btn.move(50, 50)\n dialog.setWindowTitle('Dialog')\n dialog.setWindowModality(Qt.ApplicationModal)\n dialog.exec_()\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n win = DialogDemo()\n win.show()\n sys.exit(app.exec_())","sub_path":"ch01_basic_widgets/qt20_Dialog.py","file_name":"qt20_Dialog.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"387861059","text":"\nimport random\nfrom UnoException import *\n\nColors = ['R', 'G', 'B', 'Y', 'N']\nWords = ['D', 'S', 'U', 'W', 'F']\nMAX_CARD = 108\nMAX_DEAL = 7\n\nclass Card:\n def __init__(self, color, value):\n\n if (not color in Colors) and (not value in ['W', 'F']):\n raise InvalidCardColor(color)\n self.color = color\n\n if type(value) == int:\n self.value = str(value)\n self.is_normal = True\n elif value in Words:\n self.value = value\n self.is_normal = False\n else:\n raise InvalidCardValue(value)\n\n self.status = 0\n self.playerId = 0\n\n def __repr__(self):\n if type(self.value) == int:\n return \"%s%d\" % (self.color, self.value)\n else:\n return \"%s%s\" % (self.color, self.value)\n def toString(self):\n return self.__repr__()\n\nclass Deck:\n def __init__(self):\n cards = []\n\n # start adding cards\n for color in Colors:\n if color != 'N':\n cards.append(Card(color, 0))\n for i in xrange(1, 10):\n cards.append(Card(color, i)) \n cards.append(Card(color, i)) \n\n for w in Words:\n if w in ['D', 'U', 'S']:\n cards.append(Card(color, w))\n cards.append(Card(color, w))\n else:\n cards.append(Card('N', w))\n\n self.cards = cards\n\n def lookup(self, card_str):\n card = [c for c in self.cards if\\\n c.value == card_str[1] and c.color == card_str[0] and c.status < 2]\n if len(card) > 0:\n return card\n else:\n raise InvalidCard(card_str) \n\n def deal(self, n):\n random.shuffle(self.cards)\n count = 0\n for i in xrange(0, MAX_DEAL):\n for j in xrange(1, n + 1):\n self.cards[count].status = 1\n self.cards[count].playerId = j\n count += 1\n\n def reshuffle(self):\n for c in self.cards:\n if c.status == 2:\n c.status = 0\n if c.playerId != 0:\n c.playerId = 0\n\n def next(self):\n pool = [c for c in self.cards if c.status == 0]\n\n if len(pool) == 0:\n raise DeckFull('Deck is full, maybe reshuffle')\n\n card = None\n while not card or card.status != 0:\n card = random.choice(self.cards)\n return card\n \n def toString(self, f = None):\n cards = self.cards\n if not f is None:\n cards = f(cards)\n r = \"\"\n for c in cards:\n r = r + c.__repr__()\n\n return r\n\n def dispose(self):\n for c in self.cards:\n c.status = 2\n\n self.cards = []\n\n","sub_path":"server/Deck.py","file_name":"Deck.py","file_ext":"py","file_size_in_byte":2810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"621414762","text":"import time\nimport socket\n\n\nclass Load:\n\n def __init__(self, load5, load10, load15):\n self.load5 = load5\n self.load10 = load10\n self.load15 = load15\n\n\ndef read_load_info():\n FILE = \"/proc/loadavg\"\n with open(FILE, \"r\") as f:\n data = f.read()\n return tuple(data.split()[:3])\n\n\ndef send_to_server(load):\n HOST = ('localhost', 7777)\n s = socket.socket()\n s.connect(HOST)\n s.send(str(load.__dict__))\n s.close()\n\n\ndef main():\n interval = 5\n\n while True:\n l5, l10, l15 = read_load_info()\n l = Load(l5, l10, l15)\n send_to_server(l)\n time.sleep(interval)\n\n\nif __name__ == '__main__':\n exit(main())\n","sub_path":"python/moniload/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"78266487","text":"# urllib3/filepost.py\n# Copyright 2008-2013 Andrey Petrov and contributors (see CONTRIBUTORS.txt)\n#\n# This module is part of urllib3 and is released under\n# the MIT License: http://www.opensource.org/licenses/mit-license.php\n\nimport codecs\nimport mimetypes\n\nfrom uuid import uuid4\nfrom io import BytesIO\n\nfrom .packages import six\nfrom .packages.six import b\nfrom .fields import RequestField\n\nwriter = codecs.lookup('utf-8')[3]\n\n\ndef choose_boundary():\n \"\"\"\n Our embarassingly-simple replacement for mimetools.choose_boundary.\n \"\"\"\n return uuid4().hex\n\n\ndef iter_field_objects(fields):\n \"\"\"\n Iterate over fields.\n\n Supports list of (k, v) tuples and dicts, and lists of\n :class:`~urllib3.fields.RequestField`.\n\n \"\"\"\n if isinstance(fields, dict):\n i = six.iteritems(fields)\n else:\n i = iter(fields)\n\n for field in i:\n if isinstance(field, RequestField):\n yield field\n else:\n yield RequestField.from_tuples(*field)\n\n\ndef iter_fields(fields):\n \"\"\"\n Iterate over fields.\n\n .. deprecated ::\n\n The addition of `~urllib3.fields.RequestField` makes this function\n obsolete. Instead, use :func:`iter_field_objects`, which returns\n `~urllib3.fields.RequestField` objects, instead.\n\n Supports list of (k, v) tuples and dicts.\n\n \"\"\"\n if isinstance(fields, dict):\n return ((k, v) for k, v in six.iteritems(fields))\n\n return ((k, v) for k, v in fields)\n\n\ndef encode_multipart_formdata(fields, boundary=None, form_data_encoding=None,\n field_encoding_style=None):\n \"\"\"\n Encode a dictionary of ``fields`` using the multipart/form-data MIME format.\n\n :param fields:\n Dictionary of fields or list of (key, :class:`~urllib3.fields.RequestField`).\n\n :param boundary:\n If not specified, then a random boundary will be generated using\n :func:`mimetools.choose_boundary`.\n\n :param form_data_encoding:\n Encoding used to format the request body, i.e. the content of\n text fields for which unicode strings have been provided,\n and the content of header fields for file names and field names.\n The correct choice might depend on some HTML form for which the\n current request is an answer.\n\n :param field_encoding_style: Method of header field generation.\n Possible values are ``HTML5`` and ``RFC2231``, the former being\n the default. Both standards give conflicting instructions on\n how to encode non-ASCII file names. Depending on the server\n implementation, one choice might work while the other might not.\n \"\"\"\n body = BytesIO()\n if boundary is None:\n boundary = choose_boundary()\n\n if form_data_encoding is None:\n form_data_encoding = 'utf-8'\n factory = codecs.lookup(form_data_encoding)[3]\n # HTML 5 draft requires use of xmlcharrefreplace:\n # http://www.w3.org/TR/html51/forms.html#multipart-form-data\n writer = factory(body, errors='xmlcharrefreplace')\n\n for field in iter_field_objects(fields):\n body.write(b('--%s\\r\\n' % (boundary)))\n\n writer.write(field.render_headers(\n field_encoding_style=field_encoding_style))\n writer.reset() # flush\n data = field.data\n\n if isinstance(data, int):\n data = str(data) # Backwards compatibility\n\n if isinstance(data, six.text_type):\n writer.write(data)\n writer.reset() # flush\n else:\n body.write(data)\n\n body.write(b'\\r\\n')\n\n body.write(b('--%s--\\r\\n' % (boundary)))\n\n content_type = str('multipart/form-data; boundary=%s' % boundary)\n\n return body.getvalue(), content_type\n","sub_path":"urllib3/filepost.py","file_name":"filepost.py","file_ext":"py","file_size_in_byte":3701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"490472095","text":"import time\nimport os\nimport urllib.request\nfrom urllib.request import urlretrieve\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\n\n \n\nbrowser = webdriver.Chrome()\ndriver = webdriver.Chrome(\n executable_path='/Users/voidmemories/Documents/code/py/chromedriver')\nbrowser.get('http://kletecheresults.contineo.in/')\n\n\ncap = input()\nf=open(\"final.txt\",\"a\")\n\n\nfor i in range(10, 99):\n f1=f2=f3=0\n u = browser.find_element_by_id(\"usn\")\n c = browser.find_element_by_id(\"osolCatchaTxt0\")\n # new=\"01fe17bcs\"+i\n u.send_keys(f\"01fe17bcs0{i}\")\n c.send_keys(cap, Keys.TAB, Keys.TAB, Keys.ENTER)\n\n iter = browser.find_elements_by_class_name(\"headingdateWhite\")\n\n for j in iter:\n xxx=j.get_attribute(\"textContent\")\n print(xxx)\n f.write(xxx)\n f.write('\\n')\n\n iter2=browser.find_elements_by_class_name(\"box\")\n\n for j in iter2:\n xxx=j.get_attribute(\"textContent\")\n print(xxx)\n f.write(xxx)\n f.write('\\n')\n\n \n\n images = browser.find_elements_by_tag_name(\"img\")\n for image in images:\n src = image.get_attribute(\"src\")\n urllib.request.urlretrieve(src, f\"{image}.jpg\")\n# browser.find_element_by_link_text('Print Provisional Grade Card').click()\n f.write('\\n')\n f.write('\\n')\n f.write('\\n')\n f.write('\\n')\n browser.back()\nf.close()\n","sub_path":"scrap.py","file_name":"scrap.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"388622833","text":"from math import sqrt\n\nn = 900 # The total number of items to be shuffled\nr = 3 # The number of rounds\n\n# The set of batch lengths that are going to be analised\nl = [5, 10, 15, (int(sqrt(n))), 50, 90, 100, 150, 300, 600, 900]\n\n# Get the probability of item\ndef get_prob(l, i):\n # If the batch size is less than the number of batches\n if l < n / l:\n return ((1 / l) * ((1 / l) ** (i + 1)))\n\n # If the batch size is equal or greater than the number of batches\n else:\n return ((1 / l) * ((1 / (n / l)) ** (i + 1)))\n\n\nif __name__ == '__main__':\n\n# Iterate the over the number of rounds\n for i in range(r):\n print(f'round{i+1}')\n\n # analyse the choosen batches sizes by finding the attacker winning the game\n for j in range(len(l)):\n print('{:<4}: {}'.format(l[j], get_prob(l[j], i)))\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"277142382","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 19 08:29:03 2018\n\n@author: s-long.bao\n\"\"\"\n\nimport pickle\nfrom torch import nn\nfrom torch import optim\nimport torch\nimport pyodbc\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os.path\nimport csv\n\n\n#import seaborn as sns\n#import torch.utils.data\n#import torch.nn.functional as F\n#\n#from sklearn.utils import shuffle\n#from keras.models import Model\n#from keras.layers import Input, Embedding, Dot, Add, Flatten\n#from keras.regularizers import l2\n#from keras.optimizers import SGD, Adam\n\n\nconn = pyodbc.connect(\"Driver={SQL Server Native Client 11.0};\"\n \"Server=RSSVMKDB-CLUS;\"\n \"uid=readonly;pwd=readonly\")\ncursor = conn.cursor()\n\ndata_folder = os.path.join(\"X:\\\\\",\"07マーケティング推進データ分析\", \"20181219_セカンドデビュー分析\")\nfile_to_open = os.path.join(data_folder, \"SQLQuery1.sql\")\nos.path.exists(file_to_open)\n\nwith open(file_to_open) as f:\n sql = f.read()\n f.close()\n sqlcomm = sql.split(';')\n for command in sqlcomm:\n cursor.execute(command)\n try:\n result = cursor.fetchall()\n except Exception as exception:\n continue \n with open('sql_results.csv', 'w') as fp:\n file = csv.writer(fp)\n file.writerows(result)\n fp.close()\n \ncursor.close()\nconn.close() \n\n# read files\ndf = pd.read_csv('sql_results.csv',encoding='cp932' , header=None)\ndf.columns = ['OFFICE_CD','CLIENT_CD','DEBUT_DATE','KBN','SHOT_SD_FLG','ADD_SD_FLG']\n\n","sub_path":"DataScienceProject/0.Test_Script/0.workplace_script/experiment/dbconnection.py","file_name":"dbconnection.py","file_ext":"py","file_size_in_byte":1562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"20026934","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.utils.model_zoo as model_zoo\nfrom functools import reduce\nfrom torch.autograd import Variable\nfrom torch.nn.parameter import Parameter\nfrom torchsummary import summary\nimport os\n# from .sync_batchnorm.batchnorm import SynchronizedBatchNorm2d\n# from .Convolution import SeparableConv2d\nimport math\n\nmodel_urls = {\n 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\n 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\n 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',\n}\n\ndef show(keys):\n s = ''\n for i,k in enumerate(keys):\n s += k + ' '\n if i != 0 and i % 5 == 0:\n print(s)\n s = ''\n print(len(keys))\nclass SKtune_unit(nn.Module):\n def __init__(self, channels, M=2, G=32, r=16, stride=1 ,L=32):\n \"\"\" Constructor\n Args:\n in_channels: input channel dimensionality.\n out_channels: output channel dimensionality.\n M: the number of branchs.\n G: num of convolution groups.\n r: the ratio for compute d, the length of z.\n mid_features: the channle dim of the middle conv with stride not 1, default out_features/2.\n stride: stride.\n L: the minimum dim of the vector z in paper.\n \"\"\"\n super(SKtune_unit, self).__init__()\n d = max(int(channels//r), L)\n self.channels = channels\n self.gap1 = nn.AdaptiveAvgPool2d((1,1))\n # self.gap2 = nn.AdaptiveAvgPool2d((1,1))\n self.fc1 = nn.Sequential(nn.Conv2d(channels, d, kernel_size=1, stride=1, bias=False),\n nn.BatchNorm2d(d),\n nn.ReLU(inplace=False))\n self.fc2 = nn.Conv2d(d,channels*M,1,1,bias=False)\n self.softmax = nn.Softmax(dim=1)\n \n def forward(self,x,pre_x):\n batch_size = x.size(0)\n\n output = [x,pre_x]\n U = reduce(lambda x,y:x+y,output)\n s = self.gap1(U)\n z = self.fc1(s)\n a_b = self.fc2(z)\n a_b = a_b.view(batch_size,self.M,self.channels,-1)\n a_b = self.softmax(a_b)\n a_b = list(a_b.chunk(self.M,dim = 1))\n a_b = list(map(lambda x:x.view(batch_size,self.channels,1,1),a_b))\n V = list(map(lambda x,y:x*y, output,a_b))\n V = reduce(lambda x,y:x+y,V)\n\n return V\nclass BasicBlock(nn.Module):\n expansion = 1\n\n def __init__(self, inplanes, planes, stride=1, dilation=1, bn_momentum=0.1,sparable = False):\n super(BasicBlock, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, bias=False,padding=1)\n self.bn1 = nn.BatchNorm2d(planes)\n self.relu = nn.ReLU(inplace=True)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n padding=dilation, dilation=dilation, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n self.stride = stride\n\n def forward(self, x):\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n\n out = self.conv2(out)\n out = self.bn2(out)\n\n return out\n\n\nclass Bottleneck(nn.Module):\n expansion = 4\n\n def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, bn_momentum=0.1,sparable = False):\n super(Bottleneck, self).__init__()\n \n if sparable:\n pass\n # self.conv1 = SeparableConv2d(inplanes, planes, kernel_size=1, bias=False)\n # self.conv2 = SeparableConv2d(planes, planes, kernel_size=3, stride=stride,\n # dilation=dilation, bias=False)\n # self.conv3 = SeparableConv2d(planes, planes * self.expansion, kernel_size=1, bias=False)\n else:\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n padding=dilation, dilation=dilation, bias=False)\n self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes, momentum=bn_momentum)\n \n \n self.bn2 = nn.BatchNorm2d(planes, momentum=bn_momentum)\n \n \n self.bn3 = nn.BatchNorm2d(planes * self.expansion, momentum=bn_momentum)\n self.relu = nn.ReLU(inplace=True)\n self.downsample = downsample\n self.stride = stride\n\n def forward(self, x):\n residual = x\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n\n out = self.conv2(out)\n out = self.bn2(out)\n out = self.relu(out)\n\n out = self.conv3(out)\n out = self.bn3(out)\n\n if self.downsample is not None:\n residual = self.downsample(x)\n\n out += residual\n out = self.relu(out)\n\n return out\n\n\n\nclass SKtune_block(nn.Module):\n def __init__(self,block,\\\n inplanes, planes, stride=1, dilation=1, downsample=None, bn_momentum=0.1,sparable = False,M=2):\n super(SKtune_block, self).__init__()\n self.block = block(inplanes, planes,\\\n stride=stride, dilation=dilation, bn_momentum=bn_momentum,sparable = sparable)\n self.parallel_block = block(inplanes, planes, \\\n stride=stride, dilation=dilation, bn_momentum=bn_momentum,sparable = sparable)\n self.sk_layer = SKtune_unit(planes,M=M)\n self.relu = nn.ReLU(inplace=True)\n self.downsample = downsample\n \n def forward(self,x):\n residual = x\n out = self.block(x)\n parallel_out = self.parallel_block(x)\n out = self.sk_layer(out,parallel_out)\n if self.downsample is not None:\n residual = self.downsample(x)\n out += residual\n out = self.relu(out)\n return out\n\n\nclass ResNet(nn.Module):\n\n def __init__(self, block, layers, bn_momentum=0.1, pretrained=False, output_stride=16,mode ='resnet50',sparable = False):\n self.mode = mode\n if output_stride == 16:\n dilations = [1, 1, 1, 2]\n strides = [1, 2, 2, 1]\n elif output_stride == 8:\n dilations = [1, 1, 2, 4]\n strides = [1, 2, 1, 1]\n elif output_stride == 32:\n dilations = [1, 1, 1, 1]\n strides = [1, 2, 2, 2]\n else:\n raise Warning(\"output_stride must be 8 or 16!\")\n self.inplanes = 64\n super(ResNet, self).__init__()\n self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,\n bias=False)\n self.bn1 = nn.BatchNorm2d(64, momentum=bn_momentum)\n self.relu = nn.ReLU(inplace=True)\n self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0],\n bn_momentum=bn_momentum,sparable = sparable)\n self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1],\n bn_momentum=bn_momentum,sparable = sparable)\n self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2],\n bn_momentum=bn_momentum,sparable = sparable)\n self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3],\n bn_momentum=bn_momentum,sparable = sparable)\n\n self._init_weight()\n\n if pretrained:\n self._load_pretrained_model()\n\n def _make_layer(self, block, planes, blocks, sparable,stride=1, dilation=1, bn_momentum=0.1):\n downsample = None\n if stride != 1 or self.inplanes != planes * block.expansion:\n downsample = nn.Sequential(\n nn.Conv2d(self.inplanes, planes * block.expansion,\n kernel_size=1, stride=stride, bias=False),\n nn.BatchNorm2d(planes * block.expansion, momentum=bn_momentum),\n )\n\n layers = []\n layers.append(SKtune_block(block,self.inplanes, planes, stride, dilation, downsample, bn_momentum=bn_momentum,sparable=sparable))\n self.inplanes = planes * block.expansion\n for i in range(1, blocks):\n layers.append(SKtune_block(block,self.inplanes, planes, dilation=dilation, bn_momentum=bn_momentum))\n\n return nn.Sequential(*layers)\n\n def _load_pretrained_model(self):\n pretrain_dict = model_zoo.load_url(model_urls[self.mode],model_dir=os.path.join(os.getcwd(),\"models/\"))\n model_dict = {}\n state_dict = self.state_dict()\n for k, v in pretrain_dict.items():\n if k in state_dict:\n # print(\"ok\",k)\n model_dict[k] = v\n else:\n # print(k)\n tk = k.split('.',2)\n if len(tk) < 3:\n continue\n k = tk[0]+'.'+tk[1]+'.block.'+tk[2]\n # print(k)\n if k in state_dict:\n # print('ok',k)\n model_dict[k] = v\n \n state_dict.update(model_dict)\n self.load_state_dict(state_dict)\n # print(prekey)\n for name, m in self.named_modules():\n # print(name,m)\n # if name in prekey:\n # print('ok')\n # else:\n # print('No')\n if isinstance(m,nn.Conv2d) and \"parallel\" not in name:\n # print(name)\n m.weight.requires_grad = False\n # show(model_dict.keys())\n # show(pretrain_dict.keys())\n print(\"Having loaded imagenet-pretrained successfully!\")\n def forward(self, x):\n # output = {}\n x = self.conv1(x)\n x = self.bn1(x)\n x = self.relu(x)\n x = self.maxpool(x)\n x = self.layer1(x)\n # output['x1'] = x\n low_level_feat = x\n x = self.layer2(x)\n # output[\"x2\"] = x\n x = self.layer3(x)\n # output[\"x3\"] = x\n x = self.layer4(x)\n # output[\"x4\"] = x\n # x = self.avgpool(x)\n # x = x.view(x.size(0), -1)\n # x = self.fc(x)\n\n return x, low_level_feat\n # return output\n\n def _init_weight(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n # m.weight.data.normal_(0, math.sqrt(2. / n))\n torch.nn.init.kaiming_normal_(m.weight)\n elif isinstance(m, nn.BatchNorm2d):\n m.weight.data.fill_(1)\n m.bias.data.zero_()\n\n\ndef sk_resnet50(bn_momentum=0.1, pretrained=False, output_stride=16,sparable = False):\n \"\"\"Constructs a ResNet-50 model.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = ResNet(Bottleneck, [3, 4, 6, 3], bn_momentum, pretrained, output_stride,mode ='resnet50',sparable = sparable)\n return model\n\n\ndef mean_image_subtraction(images, means=[123.68, 116.78, 103.94]):\n '''\n image normalization\n :param images: bs * w * h * channel \n :param means:\n :return:\n '''\n num_channels = images.data.shape[1]\n if len(means) != num_channels:\n raise ValueError('len(means) must match the number of channels')\n for i in range(num_channels):\n images.data[:,i,:,:] -= means[i]\n\n return images\n\n\ndef sk_resnet101(bn_momentum=0.1, pretrained=False, output_stride=16,sparable = False):\n \"\"\"Constructs a ResNet-101 model.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = ResNet(Bottleneck, [3, 4, 23, 3], bn_momentum, pretrained, output_stride,mode ='resnet101',sparable = sparable)\n return model\n\ndef sk_resnet18(bn_momentum=0.1, pretrained=False, output_stride=16,sparable = False):\n \"\"\"Constructs a ResNet-18 model.\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n \"\"\"\n model = ResNet(BasicBlock, [2, 2, 2, 2], bn_momentum, pretrained, output_stride,mode ='resnet18',sparable = sparable)\n return model\n\nif __name__ == \"__main__\":\n # with torch.no_grad():\n model = resnet18(pretrained=True)\n k = []\n # for item in model.state_dict().keys():\n # if \"tracked\" in item or 'parallel' in item or 'sk' in item:\n # continue\n # k.append(item)\n # show(k)\n i = 0\n for name, m in model.named_modules():\n if isinstance(m,nn.Conv2d):\n print(name, m.weight.requires_grad)","sub_path":"Model/sk_tune.py","file_name":"sk_tune.py","file_ext":"py","file_size_in_byte":12829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"322945272","text":"# ---------------------------------------------------------------- \n# Independent Study 496, Student Stree Prediction\n#\n# file_name: clustering_students.py\n# Functionality: clustering students, \n# return dict: map: group_ids(str) -> list_of_student_id(list(str))\n# Author: Yunfei Luo\n# Start date: EST Feb.22th.2020\n# Last update: EST Feb.27th.2020\n# ----------------------------------------------------------------\n\nimport os\nimport sys\nfrom src.utils.read_utils import read_pickle\n\nif __name__ == '__main__':\n method = None\n try:\n method = sys.argv[1]\n except:\n method = 'one_for_each'\n #student_groups = clustering(student_list, data['data'], method)\n\n groups_file_path = 'Data/student_groups/' + method + '.pkl'\n print('get group file from: ' + groups_file_path)\n student_groups = read_pickle(groups_file_path) \n \n # check how students are distributed\n print(\"student distribution: \")\n rev_groups = dict()\n for student in student_groups:\n if rev_groups.get(student_groups[student]) != None:\n rev_groups[student_groups[student]].append(student)\n else:\n rev_groups[student_groups[student]] = [student]\n for group in rev_groups:\n print(group + ': ' + str(rev_groups[group]))\n ","sub_path":"src/experiments/clustering/check_groups.py","file_name":"check_groups.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"269724192","text":"#!/usr/bin/python\n\n# Copyright 2014 Cloudbase Solutions Srl\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport base64\nimport functools\n\nimport six\nfrom winrm import protocol\n\nfrom argus import exceptions\n\n__all__ = (\n 'WinRemoteClient',\n)\n\n\ndef _base64_read_file(filepath, size=8192):\n with open(filepath, 'rb') as stream:\n reader = functools.partial(stream.read, size)\n for data in iter(reader, b''):\n encoded = base64.b64encode(data)\n if six.PY3:\n # Get a string instead.\n encoded = encoded.decode()\n yield encoded\n\n\nclass WinRemoteClient(object):\n \"\"\"Get a remote client to a Windows instance.\n\n :param hostname: The ip where the client should be connected.\n :param username: The username of the client.\n :param password: The password of the remote client.\n :param transport_protocol:\n The transport for the WinRM protocol. Only http and https makes\n sense.\n \"\"\"\n def __init__(self, hostname, username, password,\n transport_protocol='http'):\n self.hostname = \"{protocol}://{hostname}:{port}/wsman\".format(\n protocol=transport_protocol,\n hostname=hostname,\n port=5985 if transport_protocol == 'http' else 5986)\n self.username = username\n self.password = password\n\n @staticmethod\n def _run_command(protocol_client, shell_id, command):\n try:\n command_id = protocol_client.run_command(shell_id, command)\n stdout, stderr, exit_code = protocol_client.get_command_output(\n shell_id, command_id)\n if exit_code:\n raise exceptions.ArgusError(\n \"Executing command {command!r} failed with \"\n \"exit code {exit_code!r} and output {output!r}\"\n .format(command=command,\n exit_code=exit_code,\n output=stdout))\n\n return stdout, stderr, exit_code\n finally:\n protocol_client.cleanup_command(shell_id, command_id)\n\n def _run_commands(self, commands):\n protocol_client = self.get_protocol()\n shell_id = protocol_client.open_shell()\n try:\n results = [self._run_command(protocol_client, shell_id, command)\n for command in commands]\n finally:\n protocol_client.close_shell(shell_id)\n return results\n\n def get_protocol(self):\n protocol.Protocol.DEFAULT_TIMEOUT = \"PT3600S\"\n return protocol.Protocol(endpoint=self.hostname,\n transport='plaintext',\n username=self.username,\n password=self.password)\n\n def run_remote_cmd(self, cmd):\n \"\"\"Run the given remote command.\n\n The command will be executed on the remote underlying server.\n It will return a tuple of three elements, stdout, stderr\n and the return code of the command.\n \"\"\"\n return self._run_commands([cmd])[0]\n\n def copy_file(self, filepath, remote_destination):\n \"\"\"Copy the given filepath in the remote destination.\n\n The remote destination is the file name where the content\n of filepath will be written.\n \"\"\"\n\n # TODO(cpopa): This powershell dance is a little complicated,\n # find a simpler way to send a file over a remote server,\n # without relying on OpenStack infra.\n get_string_cmd = (\"[System.Text.Encoding]::UTF8.GetString(\"\n \"[System.Convert]::FromBase64String('{}'))\")\n commands = []\n for command in _base64_read_file(filepath):\n remote_command = (\n \"powershell \\\"{content}\\\" >> {remote_destination}\"\n .format(content=get_string_cmd.format(command),\n remote_destination=remote_destination))\n\n commands.append(remote_command)\n self._run_commands(commands)\n","sub_path":"argus/remote_client.py","file_name":"remote_client.py","file_ext":"py","file_size_in_byte":4577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"546948491","text":"#!/usr/bin/env python3\n# -*- Coding:utf-8 -*-\n\"\"\"\n################################################################################\nImplementation of View,Write,Reply,Forward windows:one class per kind.\nCode is factored here for reuse :a Write window is a customized View window,\nand Reply and Forward are custom Write windows.\nWindows defined in this file are created by the list windows,\nin response to user actions.\n################################################################################\n\"\"\"\nfrom SharedNames import * #program-wide global objects\n\n###############################################################################\n# message view window - also a superclass of write,reply,forward\n###############################################################################\n\nfrom minghu6.internet import email as mailtools\n\nclass ViewWindow(windows.PopupWindow,mailtools.MailParser):\n \"\"\"\n a Toplevel,with extra protocol and embedded TextEditor;\n inherits saveParts,partList from mailtools.MailParser;\n mixed in custom subclass logic by direct inheritance here;\n \"\"\"\n\n modelabel='View' #used in window titles\n\n\n okayToOpenParts=getattr(mailconfig, 'okayToOpenParts', True)\n verifyPartOpens=getattr(mailconfig, 'verifyPartOpens', False)\n maxPartButtons=getattr(mailconfig, 'maxPartButtons', 8)\n skipTextOnHtmlPart=getattr(mailconfig, 'skipTextOnHtmlPart', False)\n\n tempPartDir='TempParts'\n\n #all view windows use same dialog:remembers last dir\n partsDialog=Directory(title=appname+': Select parts save directory')\n\n def __init__(self,headermap,showtext,origmessage=None):\n '''\n header map is origmessage,or custom hdr dict for writing;\n showtext is main part of the message:parsed or custom;\n origmessage is parsed email.message.Message for view mail windows\n :param headermap:\n :param showtext:\n :param origmessage:\n :return:\n '''\n windows.PopupWindow.__init__(self,appname,self.modelabel)\n self.origMessage=origmessage\n self.makeWidgets(headermap,showtext)\n\n def makeWidgets(self,headermap,showtext):\n '''\n add headers,actions ,attachments,text editor\n :param headermap:\n :param showtext:\n :return:\n '''\n actionsframe=self.makeHeaders(headermap)\n if self.origMessage and self.okayToOpenParts:\n self.makePartButtons()\n\n self.editor=textEditor.TextEditorComponentMinimal(self)\n myactions=self.actionButtons()\n for(label,callback) in myactions:\n b=Button(actionsframe,text=label,command=callback)\n b.config(bg='beige',relief=RIDGE,bd=2)\n b.pack(side=TOP,expand=YES,fill=BOTH)\n\n self.editor.pack(side=BOTTOM)\n self.update()\n self.editor.setAllText(showtext)\n lines=len(showtext.splitlines())\n lines=min(lines+3,getattr(mailconfig, 'viewheight', 20))\n self.editor.setHeight(lines)\n self.editor.setWidth(80)\n if getattr(mailconfig, 'viewbg', None):\n self.editor.setBg(mailconfig.viewbg)\n if getattr(mailconfig, 'viewfg', None):\n self.editor.setFg(mailconfig.viewfg)\n if getattr(mailconfig, 'viewfont', None):\n self.editor.setFont(mailconfig.viewfont)\n\n def makeHeaders(self,headermap):\n '''\n add header entry fields,return action buttons frames;\n :param headermap:\n :return:\n '''\n top=Frame(self)\n top.pack(side=TOP,fill=X)\n\n left=Frame(top)\n left.pack(side=LEFT,expand=NO,fill=BOTH)\n\n middle=Frame(top)\n middle.pack(side=LEFT,expand=YES,fill=X)\n\n #headers set may be extended in mailconfig (Bcc,other?)\n self.userHdrs=()\n showhdrs=('From','To','Cc','Subject')\n if getattr(mailconfig, 'viewheaders', None):\n self.userHdrs=mailconfig.viewheaders\n showhdrs+=self.userHdrs\n\n addhdrs=('From','To','Cc','Bcc')\n\n self.hdrFields=[]\n for(i,header) in enumerate(showhdrs):\n lab =Label(middle,text=header+':',justify=LEFT)\n ent=Entry(middle)\n lab.grid(row=i,column=0,sticky=EW)\n ent.grid(row=i,column=1,sticky=EW)\n middle.rowconfigure(i,weight=1)\n hdrvalue=headermap.get(header,'?')\n\n if header not in addhdrs:\n hdrvalue=self.decodeHeader(hdrvalue)\n else:\n hdrvalue=self.decodeAddrHeader(hdrvalue)\n\n try:\n ent.insert('0',hdrvalue)\n except:\n import traceback\n traceback.print_exc()\n print('En..., May be a bug of tcl/tk')\n\n self.hdrFields.append(ent)\n middle.columnconfigure(1,weight=1)\n return left\n\n def actionButtons(self):\n return [('Cancel',self.destroy),\n ('Parts',self.onParts),\n ('Split',self.onSplit)]\n\n def makePartButtons(self):\n\n def makeButton(parent,text,callback):\n link=Button(parent,text=text,command=callback,relief=SUNKEN)\n if getattr(mailconfig, 'partfg', None):\n link.config(fg=mailconfig.partfg)\n\n if getattr(mailconfig, 'partbg', None):\n link.config(bg=mailconfig.partbg)\n\n link.pack(side=LEFT,fill=X,expand=YES)\n\n parts=Frame(self)\n parts.pack(side=TOP,expand=NO,fill=X)\n for (count,partname) in enumerate(self.partsList(self.origMessage)):\n if count==self.maxPartButtons:\n makeButton(parts,'...',self.onSplit)\n break\n openpart=(lambda partname=partname:self.onOnePart(partname))\n makeButton(parts,partname,openpart)\n\n def onOnePart(self,partname):\n '''\n locate selected part for button and save and open;\n okay if multiple mails open:resaves each time selected;\n we could probably just use web browser directly here;\n\n Caveat:tempPartDir is relative to cwd - poss anywhere;\n Caveat:tempOartDir is never cleaned up:meight be large,\n could use tempfile module (just like the HTML main text\n part display code in onView of the list window client)\n :param partname:\n :return:\n '''\n try:\n savedir=self.tempPartDir\n message=self.origMessage\n (contype,savepath)=self.saveOnePart(savedir,partname,message)\n\n except:\n showerror(appname, 'Error while writing part file')\n printStack(sys.exc_info())\n else:\n self.openParts([(contype,os.path.abspath(savepath))])\n\n\n def onParts(self):\n '''\n show message part/attachment in pop-up window;\n uses same file naming scheme as save on Split;\n if non-multipart ,string part=full body text\n :return:\n '''\n\n partnames=self.partsList(self.origMessage)\n msg='\\n'.join(['Message parts:\\n']+partnames)\n showinfo(appname,msg)\n\n def onSplit(self):\n '''\n pop up save dir dialog and save all parts/attachments there;\n if desired,pop up HTML and multimedia parts in web browser,\n text in TextEditor,and well-known doctypes on windows;\n could show parts in View windows where embedded etxt editor\n would provide a save button ,but most are not readable text\n :return:\n '''\n savedir=self.partsDialog.show()\n if savedir:\n try:\n partfiles=self.saveParts(savedir,self.origMessage)\n except:\n showerror(appname, 'Error while writing part files')\n printStack(sys.exc_info())\n else:\n if self.okayToOpenParts:\n self.openParts(partfiles)\n\n def askOpen(self,appname,prompt):\n if not self.verifyPartOpens:\n return True\n else:\n return askyesno(appname,prompt)\n\n def openParts(self,partfiles):\n '''\n auto-open well known and safe file types, but only if verified\n by the user in a pop up; other types must be opened manually\n from save dir; at this point, the named parts have been already\n MIME-decoded and saved as raw bytes in binary-mode files, but text\n parts may be in any Unicode encoding; PyEdit needs to know the\n encoding to decode, webbrowsers may have to guess or be told;\n\n caveat: punts for type application/octet-stream even if it has\n safe filename extension such as .html; caveat: image/audio/video\n could be opened with the book's playfile.py; could also do that\n if text viewer fails: would start notepad on Windows via startfile;\n webbrowser may handle most cases here too, but specific is better;\n :param partfiles:\n :return:\n '''\n\n def textPartEncoding(fullfilename):\n\n partname=os.path.basename(fullfilename)\n for(filename,contype,part) in self.walkNamedParts(self.origMessage):\n if filename==partname:\n return part.get_content_charset()\n\n assert False,'Text part not found'\n\n for (contype,fullfilename) in partfiles:\n maintype=contype.split('/')[0]\n extension=os.path.splitext(fullfilename)[1]\n basename=os.path.basename(fullfilename)\n\n #HTML and XML text,web pages,some media\n if contype in ['text/html','text/xml']:\n browserOpened=False\n if self.askOpen(appname,'Open \"%s\" in browser?'%basename):\n try:\n webbrowser.open_new('file://'+fullfilename)\n browserOpened=True\n except:\n showerror(appname,'Browser failed:trying editor')\n\n if not browserOpened or not self.skipTextOnHtmlPart:\n try:\n encoding=textPartEncoding(fullfilename)\n textEditor.TextEditorMainPopup(parent=self,\n winTitle=' - %s email part'\n %(encoding or '?'),\n localFirst=fullfilename,\n loadEncode=encoding)\n\n except:\n showerror(appname, 'Error opening text viewer')\n\n\n #text/plain text/x-python,etc.;4E:encoding,may fail\n elif maintype=='text':\n if self.askOpen(appname,'Open text part \"%s\"'%basename):\n try:\n encoding=textPartEncoding(fullfilename)\n textEditor.TextEditorMainPopup(parent=self,\n winTitle=' - %s email part'%\n (encoding or '?'),\n loadFirst=fullfilename,\n loadEncode=encoding)\n except:\n showerror(appname, 'Error opening text viewer')\n\n #multimedia types:Windows opens mediaplayer,imageviewer,etc.\n elif maintype in ['image','audio','video']:\n if self.askOpen(appname,'Open media part \"%s\"?'%basename):\n try:\n webbrowser.open_new('file://' + fullfilename)\n except:\n showerror(appname, 'Error opening browser')\n\n #common Windows doccuments:Word,Excel,Adobe,archives,etc.\n elif (sys.platform[:3]=='win' and\n maintype=='application'and\n extension in ['.doc','.docx','.xls','.xlsx',\n '.pdf','.zip','.tar','.wmv']):\n\n if self.askOpen(appname,'Open part \"%s\"?'%basename):\n os.startfile(fullfilename)\n\n else:\n msg='Cannot open part: \"%s\"\\n Open manually in:\"%s\"'\n msg=msg%(basename,os.path.dirname(fullfilename))\n showinfo(appname,msg)\n\n\n\n###############################################################################\n# message edit windows - write, reply, forward\n###############################################################################\n\n\nclass WriteWindow(ViewWindow, mailtools.MailSenderAuth):\n \"\"\"\n customized view display for composed new mail\n inherits sendMessage from mailtools.MailSender\n \"\"\"\n modelabel = 'Write'\n\n def __init__(self,headermap,starttext,\n smtpserver=None, smtpuser=None, smtpPassword=None):\n\n ViewWindow.__init__(self,headermap,starttext)\n mailtools.MailSenderAuth.__init__(self,\n smtpserver=smtpserver,\n smtpuser=smtpuser,\n smtpPassword=smtpPassword)\n\n self.smtpPassword=smtpPassword\n\n self.attaches=[]\n self.openDialog=None\n\n def actionButtons(self):\n return [('Cancel',self.quit),\n ('Parts',self.onParts),\n ('Attach',self.onAttach),\n ('Send',self.onSend)]\n\n def onParts(self):\n # caveat: deletes not currently supported\n if not self.attaches:\n showinfo(appname, 'Nothing attached')\n else:\n msg = '\\n'.join(['Already attached:\\n'] + self.attaches)\n showinfo(appname, msg)\n\n def onAttach(self):\n '''\n attach a flle to the mail:\n name added here will be added as part on Send\n :return:\n '''\n if not self.openDialog:\n self.openDialog=Open(title=appname+': Select Attachment File')\n filename=self.openDialog.show()\n if filename:\n self.attaches.append(filename)\n\n def resolveUnicodeEncodings(self):\n\n def isTextKind(filename):\n contype,encoding=mimetypes.guess_type(filename)\n if contype is None or encoding is not None:\n return False\n\n maintype,subtype=contype.split('/',1)\n return maintype=='text'\n\n bodytextEncoding=getattr(mailconfig, 'mainTextEncoding', 'utf-8')\n if bodytextEncoding == None:\n asknow=askstring('PyMailGUI','Enter main text Unicode encoding name')\n bodytextEncoding=asknow or 'latin-1'\n\n if bodytextEncoding!='utf-8':\n try:\n bodytext=self.editor.getAllText()\n bodytext.encode(bodytextEncoding)\n except (UnicodeError,LookupError): #lookup:bad encoding name\n bodytextEncoding='utf-8'\n\n #resolve any text part attachment encodings\n attachesEncodings=[]\n config=getattr(mailconfig, 'attachmentTextEncoding', 'utf-8')\n for filename in self.attaches:\n if not isTextKind(filename):\n attachesEncodings.append(None)\n elif config!=None:\n attachesEncodings.append(config)\n else:\n prompt='Enter Unicode encoding name for %'%filename\n asknow=askstring('PyMailGUI',prompt)\n attachesEncodings.append(asknow or 'latin-1')\n\n #last chance:use utf-8\n choice=attachesEncodings[-1]\n if choice !=None and choice !='utf-8':\n try:\n attachbytes=open(filename,'rb').read()\n attachbytes.decode(choice)\n except (UnicodeError,LookupError,IOError):\n attachesEncodings[-1]='utf-8'\n\n return bodytextEncoding,attachesEncodings\n\n def onSend(self):\n\n #resolve Unicode encoding for text parts;\n bodytextEncoding,attachesEncodings=self.resolveUnicodeEncodings()\n\n #get components from GUI ;\n fieldvalues=[entry.get() for entry in self.hdrFields]\n From,To,Cc,Subj=fieldvalues[:4]\n extraHdrs=[('Cc',Cc),('X-Mailer',appname+' (Python)')]\n extraHdrs+=list(zip(self.userHdrs,fieldvalues[4:]))\n bodytext=self.editor.getAllText()\n\n Tos=self.splitAddresses(To)\n for (ix,(name,value)) in enumerate(extraHdrs):\n if value:\n if value=='?':\n extraHdrs[ix]=(name,'')\n elif name.lower() in ['cc','bcc']:\n extraHdrs[ix]=(name,self.splitAddresses(value))\n\n #withdraw to disallow send during send\n # caveat: might not be foolproof - user may deiconify if icon visible\n self.withdraw()\n self.getPassword()\n popup=popuputil.BusyBoxNowait(appname,'Sending message')\n sendingBusy.inc()\n threadtools.startThread(\n action=self.sendMessage,\n args=(From,Tos,Subj,extraHdrs,bodytext,self.attaches,\n saveMailSeparator,bodytextEncoding,attachesEncodings),\n context=(popup,),\n onExit=self.onSendExit,\n onFail=self.onSendFail\n )\n\n def onSendExit(self,popup):\n \"\"\"\n erase wait window, erase view window, decr send count;\n sendMessage call auto saves sent message in local file;\n can't use window.addSavedMails: mail text unavailable;\n \"\"\"\n\n popup.quit()\n self.destroy()\n sendingBusy.dec()\n\n sentname=os.path.abspath(getattr(mailconfig, 'sentmailfile', ''))\n if sentname in openSaveFiles.keys():\n window=openSaveFiles[sentname]\n window.loadMailFileThread()\n\n def onSendFail(self,exc_info,popup):\n popup.quit()\n self.deiconify()\n self.lift()\n showerror(appname,'Send failed: \\n%s\\n%s'%exc_info[:2])\n printStack(exc_info)\n mailtools.MailSenderAuth.smtpPassword=None\n sendingBusy.dec()\n\n def askSmtpPassword(self,password=None):\n '''\n get password if needed from GUI here, in main thread;\n\n Caveat: may try this again in thread if no input first\n time, so goes into a loop until input is provided;\n :return:\n '''\n\n while not password:\n prompt=('Password for %s on %s?'%\n (self.smtpUser,self.smtpServerName))\n password=popuputil.askPasswordWindow(appname,prompt,\n default=self.smtpPassword)\n\n return password\n\nclass ReplyWindow(WriteWindow):\n \"\"\"\n customized write display for replaying\n text and headers set up by list window\n \"\"\"\n modelabel = 'Reply'\n\nclass ForwardWindow(WriteWindow):\n \"\"\"\n customized reply display for forwarding\n text and headers set up by list window\n \"\"\"\n modelabel = 'Forward'\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"ViewWindows.py","file_name":"ViewWindows.py","file_ext":"py","file_size_in_byte":19075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"92961707","text":"import sys\nimport os.path\nimport datetime\nimport inspect\n\n\nlevels=['error','warning','info','debug']\n\nclass Level():\n def __init__(self):\n self.ERROR=0\n self.WARNING=1\n self.INFO=2\n self.DEBUG=3\n \nlevel=Level()\n\nclass Log():\n def __init__(self,\n filename=None,\n stderr=True,\n syslog=False,\n stdout=False):\n self.stderr=stderr\n self.syslog=syslog\n self.stdout=stdout\n self.filename=filename\r\n self.loglevel=3\r\n if self.filename:\r\n self.__logfile=open(self.filename,'a')\n self.date_format='%c'\n self.__stderr=sys.stderr\n self.__stdout=sys.stdout\n def open(self, filename):\n self.filename=filename\n self.__logfile=open(self.filename,'a')\n def close(self):\n self.__logfile.close()\n self.filename=None\n def caller(self):\n filename=os.path.basename(inspect.stack()[2][1])\n linenum=inspect.stack()[2][2]\n method=inspect.stack()[2][3]\n return \"%s: %s (%s)\" % (filename, linenum, method)\n def write(self,msg,level=3,caller=None):\n datestamp=datetime.datetime.today().strftime(self.date_format)\n if not caller:\n caller=self.caller()\n #caller='fake caller'\n msg='[%s][%s] %s: %s\\n' % (datestamp,levels[level],caller,msg)\r\n if level <= self.loglevel:\n if self.stderr:\n self.__stderr.write(msg)\n if self.stdout:\n self.__stderr.write(msg)\r\n if self.filename:\r\n self.__logfile.write(msg)\n def flush(self):\n if self.stderr:\n self.__stderr.flush()\n if self.stdout:\n self.__stdout.flush()\n if self.filename:\n self.__logfile.flush()\n\nlogfile=Log(stderr=True)\n \n \n \n","sub_path":"PuttyKnife/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":1882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"315197709","text":"import logging\nimport random\nlogger = logging.getLogger('server-msgs')\n\n\ndef read_line(line):\n return parse_numeric(line['command'], line)\n\n\ndef parse_numeric(numeric, line):\n \"\"\"Manipulates the bot accordingly to the numerical code\n received from the server.\n\n Keyword arguments:\n numeric -- numeric code received from the server\n line -- the full line received from the server\n\n \"\"\"\n response = []\n welcome = '001'\n channel_failed = {'403': 'no such channel',\n '405': 'too many channels',\n '471': 'channel limit',\n '473': 'channel is invite only',\n '474': 'banned from the channel'}\n nickname_failed = ['432', '433']\n unknown_command = '421'\n if numeric == welcome:\n # registered\n logger.info('Registered successfully!')\n response.append(('set_registered', True))\n\n elif numeric in channel_failed.keys():\n # channel join failed\n logger.warn('Joining the channel failed: ' + channel_failed[numeric])\n response.append(None)\n\n elif numeric in nickname_failed:\n # nickname register failed\n logger.warn('Setting nickname failed')\n failed_nick = line['trailing'].split()[0]\n response.append(('set_nick', failed_nick + str(random.randint(0, 100))))\n\n elif numeric == unknown_command:\n # something weird happened\n logger.warn('Tried sending an unknown command')\n response.append(None)\n\n else:\n response.append(None)\n\n return response\n","sub_path":"irc_exceptions.py","file_name":"irc_exceptions.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"324621409","text":"import pymongo\nimport requests\nfrom pyquery import PyQuery as pq\nfrom urllib.parse import urljoin\n\npage = 1\n############### mongo 部分 ###############\nDATABASE_IP = '127.0.0.1'\nDATABASE_PORT = 27017\nDATABASE_NAME = 'sun'\nclient = pymongo.MongoClient(DATABASE_IP, DATABASE_PORT)\ndb = client.sun\ndb.authenticate('dba', 'dba')\ncollection = db.imooc\n############# mongo 部分 end #############\n\ndef main(page):\n print(f'正在爬取{page}页数据')\n try:\n with requests.Session() as s:\n res = s.get('https://www.imooc.com/course/list?page={}'.format(page))\n d = pq(res.text)\n get_content(d)\n except Exception as e:\n print(e)\n finally:\n page += 1\n main(page)\n\ndef get_content(d):\n courses = d.items('.course-card-container')\n for course in courses:\n title = course.find('.course-card-name').text()\n des = course.find('.course-card-desc').text()\n level = course.find('.course-card-info>span:eq(0)').text()\n users = course.find('.course-card-info>span:eq(1)').text()\n labels = course.find('.course-label').text().split(' ')\n url = urljoin('https://www.imooc.com/learn/', course.find('a').attr('href'))\n img_url = urljoin('https://img3.mukewang.com/', course.find('img').attr('src'))\n dict = {\n 'title': title,\n 'des': des,\n 'level': level,\n 'users': users,\n 'labels': labels,\n 'url': url,\n 'img_url': img_url\n }\n collection.insert_one(dict)\n\nif __name__ == '__main__':\n main(page)","sub_path":"imooc/imooc.py","file_name":"imooc.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"118903865","text":"\"\"\"\nType annotations for mediaconvert service client.\n\n[Open documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html)\n\nUsage::\n\n ```python\n import boto3\n from mypy_boto3_mediaconvert import MediaConvertClient\n\n client: MediaConvertClient = boto3.client(\"mediaconvert\")\n ```\n\"\"\"\nimport sys\nfrom typing import Any, Dict, List, Type, overload\n\nfrom botocore.client import BaseClient, ClientMeta\n\nfrom .literals import (\n BillingTagsSourceType,\n DescribeEndpointsModeType,\n JobStatusType,\n JobTemplateListByType,\n OrderType,\n PresetListByType,\n PricingPlanType,\n QueueListByType,\n QueueStatusType,\n SimulateReservedQueueType,\n StatusUpdateIntervalType,\n)\nfrom .paginator import (\n DescribeEndpointsPaginator,\n ListJobsPaginator,\n ListJobTemplatesPaginator,\n ListPresetsPaginator,\n ListQueuesPaginator,\n)\nfrom .type_defs import (\n AccelerationSettingsTypeDef,\n CreateJobResponseTypeDef,\n CreateJobTemplateResponseTypeDef,\n CreatePresetResponseTypeDef,\n CreateQueueResponseTypeDef,\n DescribeEndpointsResponseTypeDef,\n GetJobResponseTypeDef,\n GetJobTemplateResponseTypeDef,\n GetPolicyResponseTypeDef,\n GetPresetResponseTypeDef,\n GetQueueResponseTypeDef,\n HopDestinationTypeDef,\n JobSettingsTypeDef,\n JobTemplateSettingsTypeDef,\n ListJobsResponseTypeDef,\n ListJobTemplatesResponseTypeDef,\n ListPresetsResponseTypeDef,\n ListQueuesResponseTypeDef,\n ListTagsForResourceResponseTypeDef,\n PolicyTypeDef,\n PresetSettingsTypeDef,\n PutPolicyResponseTypeDef,\n ReservationPlanSettingsTypeDef,\n UpdateJobTemplateResponseTypeDef,\n UpdatePresetResponseTypeDef,\n UpdateQueueResponseTypeDef,\n)\n\nif sys.version_info >= (3, 8):\n from typing import Literal\nelse:\n from typing_extensions import Literal\n\n__all__ = (\"MediaConvertClient\",)\n\nclass BotocoreClientError(BaseException):\n MSG_TEMPLATE: str\n\n def __init__(self, error_response: Dict[str, Any], operation_name: str) -> None:\n self.response: Dict[str, Any]\n self.operation_name: str\n\nclass Exceptions:\n BadRequestException: Type[BotocoreClientError]\n ClientError: Type[BotocoreClientError]\n ConflictException: Type[BotocoreClientError]\n ForbiddenException: Type[BotocoreClientError]\n InternalServerErrorException: Type[BotocoreClientError]\n NotFoundException: Type[BotocoreClientError]\n TooManyRequestsException: Type[BotocoreClientError]\n\nclass MediaConvertClient(BaseClient):\n \"\"\"\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html)\n \"\"\"\n\n meta: ClientMeta\n\n @property\n def exceptions(self) -> Exceptions:\n \"\"\"\n MediaConvertClient exceptions.\n \"\"\"\n def associate_certificate(self, *, Arn: str) -> Dict[str, Any]:\n \"\"\"\n Associates an AWS Certificate Manager (ACM) Amazon Resource Name (ARN) with AWS\n Elemental MediaConvert.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.associate_certificate)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#associate_certificate)\n \"\"\"\n def can_paginate(self, operation_name: str) -> bool:\n \"\"\"\n Check if an operation can be paginated.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.can_paginate)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#can_paginate)\n \"\"\"\n def cancel_job(self, *, Id: str) -> Dict[str, Any]:\n \"\"\"\n Permanently cancel a job.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.cancel_job)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#cancel_job)\n \"\"\"\n def close(self) -> None:\n \"\"\"\n Closes underlying endpoint connections.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.close)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#close)\n \"\"\"\n def create_job(\n self,\n *,\n Role: str,\n Settings: \"JobSettingsTypeDef\",\n AccelerationSettings: \"AccelerationSettingsTypeDef\" = None,\n BillingTagsSource: BillingTagsSourceType = None,\n ClientRequestToken: str = None,\n HopDestinations: List[\"HopDestinationTypeDef\"] = None,\n JobTemplate: str = None,\n Priority: int = None,\n Queue: str = None,\n SimulateReservedQueue: SimulateReservedQueueType = None,\n StatusUpdateInterval: StatusUpdateIntervalType = None,\n Tags: Dict[str, str] = None,\n UserMetadata: Dict[str, str] = None\n ) -> CreateJobResponseTypeDef:\n \"\"\"\n Create a new transcoding job.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.create_job)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#create_job)\n \"\"\"\n def create_job_template(\n self,\n *,\n Name: str,\n Settings: \"JobTemplateSettingsTypeDef\",\n AccelerationSettings: \"AccelerationSettingsTypeDef\" = None,\n Category: str = None,\n Description: str = None,\n HopDestinations: List[\"HopDestinationTypeDef\"] = None,\n Priority: int = None,\n Queue: str = None,\n StatusUpdateInterval: StatusUpdateIntervalType = None,\n Tags: Dict[str, str] = None\n ) -> CreateJobTemplateResponseTypeDef:\n \"\"\"\n Create a new job template.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.create_job_template)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#create_job_template)\n \"\"\"\n def create_preset(\n self,\n *,\n Name: str,\n Settings: \"PresetSettingsTypeDef\",\n Category: str = None,\n Description: str = None,\n Tags: Dict[str, str] = None\n ) -> CreatePresetResponseTypeDef:\n \"\"\"\n Create a new preset.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.create_preset)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#create_preset)\n \"\"\"\n def create_queue(\n self,\n *,\n Name: str,\n Description: str = None,\n PricingPlan: PricingPlanType = None,\n ReservationPlanSettings: \"ReservationPlanSettingsTypeDef\" = None,\n Status: QueueStatusType = None,\n Tags: Dict[str, str] = None\n ) -> CreateQueueResponseTypeDef:\n \"\"\"\n Create a new transcoding queue.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.create_queue)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#create_queue)\n \"\"\"\n def delete_job_template(self, *, Name: str) -> Dict[str, Any]:\n \"\"\"\n Permanently delete a job template you have created.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.delete_job_template)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#delete_job_template)\n \"\"\"\n def delete_policy(self) -> Dict[str, Any]:\n \"\"\"\n Permanently delete a policy that you created.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.delete_policy)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#delete_policy)\n \"\"\"\n def delete_preset(self, *, Name: str) -> Dict[str, Any]:\n \"\"\"\n Permanently delete a preset you have created.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.delete_preset)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#delete_preset)\n \"\"\"\n def delete_queue(self, *, Name: str) -> Dict[str, Any]:\n \"\"\"\n Permanently delete a queue you have created.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.delete_queue)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#delete_queue)\n \"\"\"\n def describe_endpoints(\n self,\n *,\n MaxResults: int = None,\n Mode: DescribeEndpointsModeType = None,\n NextToken: str = None\n ) -> DescribeEndpointsResponseTypeDef:\n \"\"\"\n Send an request with an empty body to the regional API endpoint to get your\n account API endpoint.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.describe_endpoints)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#describe_endpoints)\n \"\"\"\n def disassociate_certificate(self, *, Arn: str) -> Dict[str, Any]:\n \"\"\"\n Removes an association between the Amazon Resource Name (ARN) of an AWS\n Certificate Manager (ACM) certificate and an AWS Elemental MediaConvert\n resource.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.disassociate_certificate)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#disassociate_certificate)\n \"\"\"\n def generate_presigned_url(\n self,\n ClientMethod: str,\n Params: Dict[str, Any] = None,\n ExpiresIn: int = 3600,\n HttpMethod: str = None,\n ) -> str:\n \"\"\"\n Generate a presigned url given a client, its method, and arguments.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.generate_presigned_url)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#generate_presigned_url)\n \"\"\"\n def get_job(self, *, Id: str) -> GetJobResponseTypeDef:\n \"\"\"\n Retrieve the JSON for a specific completed transcoding job.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.get_job)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#get_job)\n \"\"\"\n def get_job_template(self, *, Name: str) -> GetJobTemplateResponseTypeDef:\n \"\"\"\n Retrieve the JSON for a specific job template.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.get_job_template)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#get_job_template)\n \"\"\"\n def get_policy(self) -> GetPolicyResponseTypeDef:\n \"\"\"\n Retrieve the JSON for your policy.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.get_policy)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#get_policy)\n \"\"\"\n def get_preset(self, *, Name: str) -> GetPresetResponseTypeDef:\n \"\"\"\n Retrieve the JSON for a specific preset.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.get_preset)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#get_preset)\n \"\"\"\n def get_queue(self, *, Name: str) -> GetQueueResponseTypeDef:\n \"\"\"\n Retrieve the JSON for a specific queue.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.get_queue)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#get_queue)\n \"\"\"\n def list_job_templates(\n self,\n *,\n Category: str = None,\n ListBy: JobTemplateListByType = None,\n MaxResults: int = None,\n NextToken: str = None,\n Order: OrderType = None\n ) -> ListJobTemplatesResponseTypeDef:\n \"\"\"\n Retrieve a JSON array of up to twenty of your job templates.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.list_job_templates)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#list_job_templates)\n \"\"\"\n def list_jobs(\n self,\n *,\n MaxResults: int = None,\n NextToken: str = None,\n Order: OrderType = None,\n Queue: str = None,\n Status: JobStatusType = None\n ) -> ListJobsResponseTypeDef:\n \"\"\"\n Retrieve a JSON array of up to twenty of your most recently created jobs.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.list_jobs)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#list_jobs)\n \"\"\"\n def list_presets(\n self,\n *,\n Category: str = None,\n ListBy: PresetListByType = None,\n MaxResults: int = None,\n NextToken: str = None,\n Order: OrderType = None\n ) -> ListPresetsResponseTypeDef:\n \"\"\"\n Retrieve a JSON array of up to twenty of your presets.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.list_presets)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#list_presets)\n \"\"\"\n def list_queues(\n self,\n *,\n ListBy: QueueListByType = None,\n MaxResults: int = None,\n NextToken: str = None,\n Order: OrderType = None\n ) -> ListQueuesResponseTypeDef:\n \"\"\"\n Retrieve a JSON array of up to twenty of your queues.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.list_queues)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#list_queues)\n \"\"\"\n def list_tags_for_resource(self, *, Arn: str) -> ListTagsForResourceResponseTypeDef:\n \"\"\"\n Retrieve the tags for a MediaConvert resource.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.list_tags_for_resource)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#list_tags_for_resource)\n \"\"\"\n def put_policy(self, *, Policy: \"PolicyTypeDef\") -> PutPolicyResponseTypeDef:\n \"\"\"\n Create or change your policy.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.put_policy)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#put_policy)\n \"\"\"\n def tag_resource(self, *, Arn: str, Tags: Dict[str, str]) -> Dict[str, Any]:\n \"\"\"\n Add tags to a MediaConvert queue, preset, or job template.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.tag_resource)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#tag_resource)\n \"\"\"\n def untag_resource(self, *, Arn: str, TagKeys: List[str] = None) -> Dict[str, Any]:\n \"\"\"\n Remove tags from a MediaConvert queue, preset, or job template.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.untag_resource)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#untag_resource)\n \"\"\"\n def update_job_template(\n self,\n *,\n Name: str,\n AccelerationSettings: \"AccelerationSettingsTypeDef\" = None,\n Category: str = None,\n Description: str = None,\n HopDestinations: List[\"HopDestinationTypeDef\"] = None,\n Priority: int = None,\n Queue: str = None,\n Settings: \"JobTemplateSettingsTypeDef\" = None,\n StatusUpdateInterval: StatusUpdateIntervalType = None\n ) -> UpdateJobTemplateResponseTypeDef:\n \"\"\"\n Modify one of your existing job templates.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.update_job_template)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#update_job_template)\n \"\"\"\n def update_preset(\n self,\n *,\n Name: str,\n Category: str = None,\n Description: str = None,\n Settings: \"PresetSettingsTypeDef\" = None\n ) -> UpdatePresetResponseTypeDef:\n \"\"\"\n Modify one of your existing presets.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.update_preset)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#update_preset)\n \"\"\"\n def update_queue(\n self,\n *,\n Name: str,\n Description: str = None,\n ReservationPlanSettings: \"ReservationPlanSettingsTypeDef\" = None,\n Status: QueueStatusType = None\n ) -> UpdateQueueResponseTypeDef:\n \"\"\"\n Modify one of your existing queues.\n\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Client.update_queue)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/client.html#update_queue)\n \"\"\"\n @overload\n def get_paginator(\n self, operation_name: Literal[\"describe_endpoints\"]\n ) -> DescribeEndpointsPaginator:\n \"\"\"\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Paginator.DescribeEndpoints)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/paginators.html#describeendpointspaginator)\n \"\"\"\n @overload\n def get_paginator(\n self, operation_name: Literal[\"list_job_templates\"]\n ) -> ListJobTemplatesPaginator:\n \"\"\"\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Paginator.ListJobTemplates)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/paginators.html#listjobtemplatespaginator)\n \"\"\"\n @overload\n def get_paginator(self, operation_name: Literal[\"list_jobs\"]) -> ListJobsPaginator:\n \"\"\"\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Paginator.ListJobs)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/paginators.html#listjobspaginator)\n \"\"\"\n @overload\n def get_paginator(self, operation_name: Literal[\"list_presets\"]) -> ListPresetsPaginator:\n \"\"\"\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Paginator.ListPresets)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/paginators.html#listpresetspaginator)\n \"\"\"\n @overload\n def get_paginator(self, operation_name: Literal[\"list_queues\"]) -> ListQueuesPaginator:\n \"\"\"\n [Show boto3 documentation](https://boto3.amazonaws.com/v1/documentation/api/1.26.45/reference/services/mediaconvert.html#MediaConvert.Paginator.ListQueues)\n [Show boto3-stubs documentation](https://vemel.github.io/boto3_stubs_docs/mypy_boto3_mediaconvert/paginators.html#listqueuespaginator)\n \"\"\"\n","sub_path":"typings/mypy_boto3/mediaconvert/client.pyi","file_name":"client.pyi","file_ext":"pyi","file_size_in_byte":22390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"421255790","text":"#!/usr/bin python\r\nfrom importlib import import_module\r\nimport os\r\nfrom flask import (\r\n Blueprint, render_template, Response\r\n)\r\n\r\nvideo_streaming = Blueprint('video_streaming_app', __name__)\r\n\r\n# import camera driver\r\nif os.environ.get('CAMERA'):\r\n Camera = import_module('src.video.camera.camera_' + os.environ['CAMERA']).Camera\r\nelse:\r\n from src.video.camera.camera import Camera\r\n\r\n\r\n@video_streaming.route('/')\r\ndef index():\r\n \"\"\"Video streaming home page.\"\"\"\r\n return render_template('main.html')\r\n\r\n\r\ndef gen(camera):\r\n \"\"\"Video streaming generator function.\"\"\"\r\n while True:\r\n frame = camera.get_frame()\r\n yield (b'--frame\\r\\n'\r\n b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n')\r\n\r\n\r\n@video_streaming.route('/video_feed')\r\ndef video_feed():\r\n \"\"\"Video streaming route. Put this in the src attribute of an img tag.\"\"\"\r\n return Response(gen(Camera()),\r\n mimetype='multipart/x-mixed-replace; boundary=frame')","sub_path":"autonomous_car/drive_native/src_can_remove/video/streaming.py","file_name":"streaming.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"37027072","text":"t = 0\n\ndef setup():\n size(600, 600)\n rectMode(CENTER)\n \ndef draw():\n global t\n background(255)\n translate(width / 2, height / 2)\n \n rotated_clockwise(t)\n rotated_counterclockwise(t)\n t += 1\n \ndef rotated_square(p, s):\n pushMatrix()\n translate(p, 0)\n rotate(radians(3 * t))\n rect(0, 0, s, s)\n popMatrix()\n \ndef rotated_clockwise(t):\n rotate(radians(t))\n for i in range(12):\n rotated_square(50, 10)\n rotated_square(200, 30)\n rotate(radians(360 / 12))\n \ndef rotated_counterclockwise(t):\n rotate(radians(-2 * t))\n for i in range(12):\n rotated_square(100, 20)\n rotated_square(300, 40)\n rotate(radians(360 / 12))","sub_path":"processing/rotated_squares.py","file_name":"rotated_squares.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"535971809","text":"name = 'Tommy'\nnumbers = [1,2,2,4,5,6,'Hello','Tommy',3.4,3.5]\nx = 1\nwhile x != 10000:\n\tprint(f'searching for number, round number {x}')\n\tx = x + 1\n\tif x == 5:\n\t\tprint('=======================')\n\t\tprint('The End')\n\t\tbreak\n\nprint('=======================')\nr = 0\nfor y in numbers:\n\tprint(f'loop type:: {type(y)} :: value is :: {y}')\n","sub_path":"while_loop.py","file_name":"while_loop.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"351495547","text":"# -*- coding: utf-8 -*-\n# Copyright (c) 2015 The Pycroft Authors. See the AUTHORS file.\n# This file is part of the Pycroft project and licensed under the terms of\n# the Apache License, Version 2.0. See the LICENSE file for details.\n\nfrom sqlalchemy import Column, ForeignKey, CheckConstraint, \\\n PrimaryKeyConstraint, func, or_, and_, true, literal_column, \\\n select, cast, TEXT\nfrom sqlalchemy.dialects import postgresql\nfrom sqlalchemy.orm import relationship, backref, Query\nfrom sqlalchemy.types import BigInteger, Enum, Integer\n\nfrom pycroft.model.base import ModelBase\nfrom pycroft.model.ddl import DDLManager, Function, Trigger, View\nfrom pycroft.model.types import DateTimeTz\nfrom pycroft.model.user import User\nfrom pycroft.model.host import IP, Host, Interface\n\nddl = DDLManager()\n\n\nclass TrafficEvent(object):\n timestamp = Column(DateTimeTz, server_default=func.current_timestamp(), nullable=False)\n amount = Column(BigInteger, CheckConstraint('amount >= 0'),\n nullable=False)\n\n\nclass TrafficVolume(TrafficEvent, ModelBase):\n __table_args__ = (\n PrimaryKeyConstraint('ip_id', 'type', 'timestamp'),\n )\n type = Column(Enum(\"Ingress\", \"Egress\", name=\"traffic_direction\"),\n nullable=False)\n ip_id = Column(Integer, ForeignKey(IP.id, ondelete=\"CASCADE\"),\n nullable=False, index=True)\n ip = relationship(IP, backref=backref(\"traffic_volumes\",\n cascade=\"all, delete-orphan\"))\n user_id = Column(Integer, ForeignKey(User.id, ondelete='CASCADE'),\n nullable=True, index=True)\n user = relationship(User,\n backref=backref(\"traffic_volumes\",\n cascade=\"all, delete-orphan\"),\n uselist=False)\n packets = Column(Integer, CheckConstraint('packets >= 0'),\n nullable=False)\n\n\nTrafficVolume.__table__.add_is_dependent_on(IP.__table__)\n\n\npmacct_traffic_egress = View(\n name='pmacct_traffic_egress',\n query=(\n Query([])\n .add_columns(TrafficVolume.packets.label('packets'),\n TrafficVolume.amount.label('bytes'),\n TrafficVolume.timestamp.label('stamp_inserted'),\n TrafficVolume.timestamp.label('stamp_updated'),\n IP.address.label('ip_src'))\n .select_from(TrafficVolume)\n .filter_by(type='Egress')\n .join(IP)\n .statement # turns our `Selectable` into something compilable\n ),\n)\nddl.add_view(TrafficVolume.__table__, pmacct_traffic_egress)\n\npmacct_expression_replacements = dict(\n tv_tname=TrafficVolume.__tablename__,\n tv_type=TrafficVolume.type.key,\n tv_ip_id=TrafficVolume.ip_id.key,\n tv_timestamp=TrafficVolume.timestamp.key,\n tv_amount=TrafficVolume.amount.key,\n tv_packets=TrafficVolume.packets.key,\n tv_user_id=TrafficVolume.user_id.key,\n ip_tname=IP.__tablename__,\n ip_id=str(IP.id.expression),\n ip_interface_id=str(IP.interface_id.expression),\n ip_address=str(IP.address.expression),\n host_tname=Host.__tablename__,\n host_id=str(Host.id.expression),\n host_owner_id=str(Host.owner_id.expression),\n interface_tname=Interface.__tablename__,\n interface_id=str(Interface.id.expression),\n interface_host_id=str(Interface.host_id.expression),\n)\npmacct_egress_upsert = Function(\n name=\"pmacct_traffic_egress_insert\", arguments=[], language=\"plpgsql\", rtype=\"trigger\",\n definition=\"\"\"BEGIN\n INSERT INTO traffic_volume ({tv_type}, {tv_ip_id}, \"{tv_timestamp}\", {tv_amount}, {tv_packets}, {tv_user_id})\n SELECT\n 'Egress',\n {ip_id},\n date_trunc('day', NEW.stamp_inserted),\n NEW.bytes,\n NEW.packets,\n {host_owner_id}\n FROM {ip_tname}\n JOIN {interface_tname} ON {ip_interface_id} = {interface_id}\n JOIN {host_tname} ON {interface_host_id} = {host_id}\n WHERE NEW.ip_src = {ip_address}\n ON CONFLICT ({tv_ip_id}, {tv_type}, \"{tv_timestamp}\")\n DO UPDATE SET ({tv_amount}, {tv_packets}) = ({tv_tname}.{tv_amount} + NEW.bytes,\n {tv_tname}.{tv_packets} + NEW.packets);\n RETURN NULL;\n END;\"\"\".format(**pmacct_expression_replacements),\n)\npmacct_egress_upsert_trigger = Trigger(\n name='pmacct_traffic_egress_insert_trigger', table=pmacct_traffic_egress.table,\n events=[\"INSERT\"], function_call=\"pmacct_traffic_egress_insert()\", when=\"INSTEAD OF\"\n)\n\nddl.add_function(TrafficVolume.__table__, pmacct_egress_upsert)\nddl.add_trigger(TrafficVolume.__table__, pmacct_egress_upsert_trigger)\n\n\npmacct_traffic_ingress = View(\n name='pmacct_traffic_ingress',\n query=(\n Query([])\n .add_columns(TrafficVolume.packets.label('packets'),\n TrafficVolume.amount.label('bytes'),\n TrafficVolume.timestamp.label('stamp_inserted'),\n TrafficVolume.timestamp.label('stamp_updated'),\n IP.address.label('ip_dst'))\n .select_from(TrafficVolume)\n .filter_by(type='Ingress')\n .join(IP)\n .statement # turns our `Selectable` into something compilable\n ),\n)\nddl.add_view(TrafficVolume.__table__, pmacct_traffic_ingress)\n\n\npmacct_ingress_upsert = Function(\n name=\"pmacct_traffic_ingress_insert\", arguments=[], language=\"plpgsql\", rtype=\"trigger\",\n definition=\"\"\"BEGIN\n INSERT INTO traffic_volume ({tv_type}, {tv_ip_id}, \"{tv_timestamp}\", {tv_amount}, {tv_packets}, {tv_user_id})\n SELECT\n 'Ingress',\n {ip_id},\n date_trunc('day', NEW.stamp_inserted),\n NEW.bytes,\n NEW.packets,\n {host_owner_id}\n FROM {ip_tname}\n JOIN {interface_tname} ON {ip_interface_id} = {interface_id}\n JOIN {host_tname} ON {interface_host_id} = {host_id}\n WHERE NEW.ip_dst = {ip_address}\n ON CONFLICT ({tv_ip_id}, {tv_type}, \"{tv_timestamp}\")\n DO UPDATE SET ({tv_amount}, {tv_packets}) = ({tv_tname}.{tv_amount} + NEW.bytes,\n {tv_tname}.{tv_packets} + NEW.packets);\n RETURN NULL;\n END;\"\"\".format(**pmacct_expression_replacements),\n)\npmacct_ingress_upsert_trigger = Trigger(\n name='pmacct_traffic_ingress_insert_trigger', table=pmacct_traffic_ingress.table,\n events=[\"INSERT\"], function_call=\"pmacct_traffic_ingress_insert()\", when=\"INSTEAD OF\"\n)\n\nddl.add_function(TrafficVolume.__table__, pmacct_ingress_upsert)\nddl.add_trigger(TrafficVolume.__table__, pmacct_ingress_upsert_trigger)\n\n\ndef traffic_history_query():\n events = (select([func.sum(TrafficVolume.amount).label('amount'),\n literal_column('day'),\n cast(TrafficVolume.type, TEXT).label('type')]\n )\n .select_from(\n func.generate_series(\n func.date_trunc('day', literal_column('arg_start')),\n func.date_trunc('day', literal_column('arg_end')),\n '1 day'\n ).alias('day')\n .outerjoin(TrafficVolume.__table__, and_(\n func.date_trunc('day', TrafficVolume.timestamp) == literal_column('day'),\n TrafficVolume.user_id == literal_column('arg_user_id'))\n )\n )\n .group_by(literal_column('day'), literal_column('type'))\n ).cte()\n\n events_ingress = select([events]).where(or_(events.c.type == 'Ingress', events.c.type == None)).cte()\n events_egress = select([events]).where(or_(events.c.type == 'Egress', events.c.type == None)).cte()\n\n hist = (select([func.coalesce(events_ingress.c.day, events_egress.c.day).label('timestamp'),\n events_ingress.c.amount.label('ingress'),\n events_egress.c.amount.label('egress')])\n .select_from(events_ingress.join(events_egress,\n events_ingress.c.day == events_egress.c.day,\n full=true))\n .order_by(literal_column('timestamp'))\n )\n\n return hist\n\n\ntraffic_history_function = Function(\n 'traffic_history', ['arg_user_id int', 'arg_start timestamptz', 'arg_end timestamptz'],\n 'TABLE (\"timestamp\" timestamptz, ingress numeric, egress numeric)',\n str(\n traffic_history_query().compile(\n dialect=postgresql.dialect(),\n compile_kwargs={'literal_binds': True}\n )\n ),\n volatility='stable',\n)\n\nddl.add_function(\n TrafficVolume.__table__,\n traffic_history_function\n)\n\n\nclass TrafficHistoryEntry:\n def __init__(self, timestamp, ingress, egress):\n self.timestamp = timestamp\n self.ingress = ingress\n self.egress = egress\n\n def __repr__(self):\n return str(self.__dict__)\n\n\nddl.register()\n","sub_path":"pycroft/model/traffic.py","file_name":"traffic.py","file_ext":"py","file_size_in_byte":9063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"15890491","text":"from typing import List\n\n\nclass Solution:\n #https://www.bilibili.com/video/BV1pB4y1A747?from=search&seid=8098118433140481209\n # n = len(nums)\n # m ——> times\n # n * x = sum + m(n-1)\n # x = min + m\n # n *(min + m) = sum + m(n-1)\n # n * min = sum -m\n # m = sum - n * min\n def minMoves(self, nums: List[int]) -> int:\n n = len(nums)\n return sum(nums) - n * min(nums)\n def minMoves(self, nums: List[int]) -> int:\n nums.sort()\n steps = 0\n n = len(nums)\n maxinum = -float('inf')\n while True:\n same_count = 0\n for i in range(n):\n if i < n - 1 and nums[i] == nums[i + 1]:\n same_count += 1\n if same_count == n - 1 and i == n - 1:\n return steps\n for i in range(n):\n if nums[i] > maxinum:\n m = i\n maxinum = nums[i]\n nums[i] += 1\n nums[m] -= 1\n steps += 1\n\nsolution = Solution()\nprint(solution.minMoves([1, 2, 3]))\nprint(solution.minMoves([1, 1, 1]))","sub_path":"Math/453-MinimumMovestoEqualArrayElements.py","file_name":"453-MinimumMovestoEqualArrayElements.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"73423593","text":"#!/usr/bin/python\n#\n# Copyright 2018-2022 Polyaxon, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport os\nimport pytest\nimport tempfile\nimport uuid\n\nfrom mock import patch\n\nfrom polyaxon.client.impersonate import create_context_auth, impersonate\nfrom polyaxon.schemas.api.authentication import AccessTokenConfig\nfrom polyaxon.utils.test_utils import BaseTestCase\n\n\n@pytest.mark.client_mark\nclass TestImpersonate(BaseTestCase):\n def test_create_context_auth(self):\n token = uuid.uuid4().hex\n context_mount = tempfile.mkdtemp()\n context_mount_auth = \"{}/.auth\".format(context_mount)\n\n # Login without updating the token and without persistence\n if os.path.exists(context_mount_auth):\n os.remove(context_mount_auth)\n\n assert os.path.exists(context_mount_auth) is False\n create_context_auth(AccessTokenConfig(token=token), context_mount_auth)\n assert os.path.exists(context_mount_auth) is True\n\n @patch(\"polyaxon_sdk.RunsV1Api.impersonate_token\")\n @patch(\"polyaxon_sdk.UsersV1Api.get_user\")\n @patch(\"polyaxon.client.impersonate.create_context_auth\")\n def test_login_impersonate(self, create_context, get_user, impersonate_token):\n\n impersonate(owner=\"owner\", project=\"project\", run_uuid=uuid.uuid4().hex)\n assert impersonate_token.call_count == 1\n assert get_user.call_count == 1\n assert create_context.call_count == 1\n","sub_path":"core/tests/test_client/test_impersonate.py","file_name":"test_impersonate.py","file_ext":"py","file_size_in_byte":1934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"170131355","text":"__author__ = 'wolfram'\n\nimport random\nimport sys\n\na = sys.stdin.readline().split()\na = [int(i) for i in a]\ndef sort(list):\n if len(list) <= 1:\n return list\n pivot = random.choice(list)\n less = [x for x in list if x < pivot]\n equal = [x for x in list if x == pivot]\n greater = [x for x in list if x > pivot]\n\n return sort(less) + equal + sort(greater)\n\na = sort(a)\na = [str(i) for i in a]\nsys.stdout.write(' '.join(a))","sub_path":"lab5/Fomin/quick_sort.py","file_name":"quick_sort.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"340024866","text":"import pytest\nimport search_journalctl\n\n\ndef canned_search_journalctl(get_log_output=None):\n \"\"\"Create a search_journalctl object with canned get_log_output method\"\"\"\n module = search_journalctl\n if get_log_output:\n module.get_log_output = get_log_output\n return module\n\n\nDEFAULT_TIMESTAMP = 1496341364\n\n\ndef get_timestamp(modifier=0):\n return DEFAULT_TIMESTAMP + modifier\n\n\ndef get_timestamp_microseconds(modifier=0):\n return get_timestamp(modifier) * 1000000\n\n\ndef create_test_log_object(stamp, msg):\n return '{{\"__REALTIME_TIMESTAMP\": \"{}\", \"MESSAGE\": \"{}\"}}'.format(stamp, msg)\n\n\n@pytest.mark.parametrize('name,matchers,log_input,expected_matches,expected_errors', [\n (\n 'test with valid params',\n [\n {\n \"start_regexp\": r\"Sample Logs Beginning\",\n \"regexp\": r\"test log message\",\n \"unit\": \"test\",\n },\n ],\n [\n create_test_log_object(get_timestamp_microseconds(), \"test log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"Sample Logs Beginning\"),\n ],\n [\"test log message\"],\n [],\n ),\n (\n 'test with invalid json in log input',\n [\n {\n \"start_regexp\": r\"Sample Logs Beginning\",\n \"regexp\": r\"test log message\",\n \"unit\": \"test-unit\",\n },\n ],\n [\n '{__REALTIME_TIMESTAMP: ' + str(get_timestamp_microseconds()) + ', \"MESSAGE\": \"test log message\"}',\n ],\n [],\n [\n [\"invalid json\", \"test-unit\", \"test log message\"],\n ],\n ),\n (\n 'test with invalid regexp',\n [\n {\n \"start_regexp\": r\"Sample Logs Beginning\",\n \"regexp\": r\"test [ log message\",\n \"unit\": \"test\",\n },\n ],\n [\n create_test_log_object(get_timestamp_microseconds(), \"test log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"sample log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"fake log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"dummy log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"Sample Logs Beginning\"),\n ],\n [],\n [\n [\"invalid regular expression\"],\n ],\n ),\n], ids=lambda argval: argval[0])\ndef test_get_log_matches(name, matchers, log_input, expected_matches, expected_errors):\n def get_log_output(matcher):\n return log_input\n\n module = canned_search_journalctl(get_log_output)\n matched_regexp, errors = module.get_log_matches(matchers, 500, 60 * 60)\n\n assert set(matched_regexp) == set(expected_matches)\n assert len(expected_errors) == len(errors)\n\n for idx, partial_err_set in enumerate(expected_errors):\n for partial_err_msg in partial_err_set:\n assert partial_err_msg in errors[idx]\n\n\n@pytest.mark.parametrize('name,matcher,log_count_lim,stamp_lim_seconds,log_input,expected_match', [\n (\n 'test with matching log message, but out of bounds of log_count_lim',\n {\n \"start_regexp\": r\"Sample Logs Beginning\",\n \"regexp\": r\"dummy log message\",\n \"unit\": \"test\",\n },\n 3,\n get_timestamp(-100 * 60 * 60),\n [\n create_test_log_object(get_timestamp_microseconds(), \"test log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"sample log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"fake log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"dummy log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"Sample Logs Beginning\"),\n ],\n None,\n ),\n (\n 'test with matching log message, but with timestamp too old',\n {\n \"start_regexp\": r\"Sample Logs Beginning\",\n \"regexp\": r\"dummy log message\",\n \"unit\": \"test\",\n },\n 100,\n get_timestamp(-10),\n [\n create_test_log_object(get_timestamp_microseconds(), \"test log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"sample log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"fake log message\"),\n create_test_log_object(get_timestamp_microseconds(-1000), \"dummy log message\"),\n create_test_log_object(get_timestamp_microseconds(-1000), \"Sample Logs Beginning\"),\n ],\n None,\n ),\n (\n 'test with matching log message, and timestamp within time limit',\n {\n \"start_regexp\": r\"Sample Logs Beginning\",\n \"regexp\": r\"dummy log message\",\n \"unit\": \"test\",\n },\n 100,\n get_timestamp(-1010),\n [\n create_test_log_object(get_timestamp_microseconds(), \"test log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"sample log message\"),\n create_test_log_object(get_timestamp_microseconds(), \"fake log message\"),\n create_test_log_object(get_timestamp_microseconds(-1000), \"dummy log message\"),\n create_test_log_object(get_timestamp_microseconds(-1000), \"Sample Logs Beginning\"),\n ],\n create_test_log_object(get_timestamp_microseconds(-1000), \"dummy log message\"),\n ),\n], ids=lambda argval: argval[0])\ndef test_find_matches_skips_logs(name, matcher, log_count_lim, stamp_lim_seconds, log_input, expected_match):\n match = search_journalctl.find_matches(log_input, matcher, log_count_lim, stamp_lim_seconds)\n assert match == expected_match\n","sub_path":"openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/openshift_health_checker/test/search_journalctl_test.py","file_name":"search_journalctl_test.py","file_ext":"py","file_size_in_byte":5728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"534260946","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.neighbors import NearestNeighbors\n\nfrom property_anomaly_detector.features.feature_engineer import normalize_features\nfrom property_anomaly_detector.features.read_df import read_df\nfrom property_anomaly_detector.database import database\n\n\ndef detect(df) -> pd.DataFrame:\n \"\"\"\n\n Calculate the outlier score for each property in the given dataset. The higher\n the score the higher is the probability of being a scam. Scams are usually\n advertisements of properties with a relatively small price when comparing to\n similar ones.\n\n The most important attributes to calculate the score are : The property_type,\n shared_occupancy, latitude, longitude, num_bedrooms, num_recepts.\n\n Since properties with different types and shared_occupancy have very distinct distributions\n it was decided to perform a group by in the dataset using them. Using a distance based approach\n to calculate the outlier scores with the property type variable is not a good idea. First, to do\n it is necessary to use the OneHotEncoder and this will produce a dataset with a lot of variables.\n Consequently increasing the processing time. If a given property type does not have a lot of properties\n the algorithm will use properties of other types changing the monthly rental price dramatically when\n calculating it.\n\n The algorithm uses a group by operation on the property_type and shared_occupancy and then\n applies the KNearestNeighbors using the latitude, longitude, num_bedrooms and num_recepts. This will\n get the near property neighbors with a similar amount of bedrooms and receipts. Finally it calculates\n the median monthly_rental_price of the N neighbors and subtract with the property monthly_rental_price.\n\n\n :param df: A pandas dataframe with the properties\n :return: The same given dataframe but with the follow additional columns :\n\n - Neighbors_median\n The monthly_rental_price median of the N property neighbors\n - Outlier_score\n The difference between the property monthly_rental_price and\n the its neighbors monthly_rental_price median multiplied by\n -1.\n\n \"\"\"\n groups = []\n # The config problem of grouping by districts is that a few districts\n # do not have enough amount of properties.\n for name, group in df.groupby(['property_type', 'shared_occupancy']):\n features = ['latitude', 'longitude', 'num_bedrooms', 'num_recepts']\n df_ml = normalize_features(group[features].values)\n\n # If the dataset is smaller than the number of neighbors is necessary to reduce\n # the n_neighbors value, otherwise an exception will be raised\n n_neighbors = min([20, len(df_ml)])\n\n nbrs = NearestNeighbors(n_neighbors=n_neighbors, algorithm='ball_tree').fit(df_ml)\n distances, indices = nbrs.kneighbors(df_ml)\n\n neighbors_median = np.median(\n group['monthly_rental_price'].iloc[indices.reshape(1, -1)[0]].values.reshape(len(df_ml), n_neighbors),\n axis=1\n ).reshape(-1, 1)\n\n group['neighbors_median'] = neighbors_median\n\n # Calculate the deviation\n group['outlier_score'] = (group['monthly_rental_price'].values.reshape(-1, 1) - neighbors_median)\n # Since we care more about the lowest values it's necessary to invert the signals\n # this way makes more intuitive for stakeholders\n group['outlier_score'] *= -1\n\n groups.append(group)\n\n return pd.concat(groups)\n\n\ndef detect_db(property: dict = None, save_results=False):\n \"\"\"\n\n Read the properties stored in the database and apply calculate the outlier scores\n of them.\n\n :param property: A dictionary with a property data for the case of which you want to\n calculate the score of an individual property.\n :param save_results: A boolean, if true then it saves the result dataframe in a Mongo\n collection called anomalies\n\n :return: A pandas dataframe with the database properties and the follow columns :\n\n - Neighbors_median\n The monthly_rental_price median of the N property neighbors\n - Outlier_score\n The difference between the property monthly_rental_price and\n the its neighbors monthly_rental_price median multiplied by\n -1.\n \"\"\"\n\n df = read_df(\n {},\n projection={\n 'latitude': True,\n 'longitude': True,\n 'outcode': True,\n 'furnished_state': True,\n 'rental_prices.shared_occupancy': True,\n 'details_url': True,\n 'property_type': True,\n 'status': True,\n 'num_bedrooms': True,\n 'num_bathrooms': True,\n 'num_floors': True,\n 'num_recepts': True,\n 'rental_prices.per_month': True\n },\n flatten_attributes=True,\n drop_outliers=True\n )\n\n # Insert the given property as the first value of the dataframe\n # since this is not sorted is possible to retrieve later\n # using pandas iloc function\n if property is not None:\n property['_id'] = 'property'\n property['shared_occupancy'] = 0 if property['shared_occupancy'] == 'N' else 1\n df = pd.concat([\n pd.DataFrame([property]),\n df\n ])\n\n df = detect(\n df=df\n )\n\n if save_results:\n database.save_top_outliers(df.sort_values(by=\"outlier_score\", ascending=False)[:100])\n\n return df\n\n\ndef classify_property(property: dict):\n \"\"\"\n Calculate the score of the given property data\n :param property: A dictionary with the property data\n :return: A tuple with the given property anomaly score and the\n highest outlier found when applying the calculation for comparison.\n \"\"\"\n df = detect_db(property=property)\n property_anomaly_score = df.query(\"_id == 'property'\").iloc[0]['outlier_score']\n highest_anomaly_score = df.sort_values(by=\"outlier_score\", ascending=False).iloc[0]['outlier_score']\n return property_anomaly_score, highest_anomaly_score\n\n\nif __name__ == \"__main__\":\n print(detect_db(save_results=True))\n","sub_path":"packages/property_anomaly_detector/build/lib/property_anomaly_detector/anomaly/detect_anomalies.py","file_name":"detect_anomalies.py","file_ext":"py","file_size_in_byte":6186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"607113827","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\nimport pydle\nfrom requests import get\nfrom multiprocessing import Process\nimport socket\nimport random\n# import deps.pypsrp\n# import deps.sys\n# def check_valid_key():\nimport subprocess\nimport os, sys, time\nimport requests\n\n# subprocess.call('C:\\Windows\\System32\\powershell.exe Get-Process', shell=True)\ndef tor_identity():\n from torrequest import TorRequest\n global ip\n print(\"Loading new Tor identity ...\")\n tr=TorRequest()\n response= requests.get('http://ipecho.net/plain')\n print (\"My Original IP Address:\", response.text)\n\n tr.reset_identity() #Reset Tor identity\n response= tr.get('http://ipecho.net/plain')\n ip = response.text\n print (\"New Ip Address\",response.text)\n\n\nclass MyOwnBot(pydle.Client):\n async def on_connect(self):\n await self.join('#net')\n\n async def alert_msg(message):\n await self.message(target, message)\n\n async def on_message(self, target, source, message):\n # don't respond to our own messages, as this leads to a positive feedback loop\n if message.startswith(\"!target \"):\n message = message.replace(\"!target \",\"\")\n if message.startswith(ip):\n command = message.replace(ip, \"\")\n await self.message(target, \"order received !\")\n p = subprocess.Popen([\"powershell.exe\",\n command], \n stdout=sys.stdout)\n p.communicate()\n pass\n\n if message == \"!ip\":\n await self.message(target, format(ip))\n\n if message == \"!os\":\n await self.message(target, getos())\n\n if message == \"!spawn_shell\":\n port = random.randint(1025,3000)\n message = \"shell available on \"+ip+\":\"+ str(port)\n await self.message(target, message)\n p = Process(target=shell(port))\n p.start()\n\n if message == \"!stop_shell\":\n p.terminate()\n p.join()\n message = \"Shell stopped\"\n await self.message(target, message)\n\n if message.startswith(\"!ps invoke\"):\n command = message.replace(\"!ps invoke\", \"\")\n PowershellInvoke.invoke(command)\n # await self.message(target, message)\n\nclass PowershellInvoke():\n def __init__(self, command):\n self.command = command\n\n def invoke(command):\n # os.system(command)\n # s = subprocess.check_call([\"/bin/bash\", command] , shell = False) \n # s = subprocess.call(\"/bin/bash\", command)\n # print(\", return code\", s)\n\n p = subprocess.Popen([\"powershell.exe\",\n command], \n stdout=sys.stdout)\n p.communicate()\n print(p.communicate())\n\n # process=subprocess.Popen([\"powershell\",\"Get-Childitem C:\\\\Windows\\\\*.log\"],stdout=subprocess.PIPE)\n # result=process.communicate()[0]\n # print(result)\n\ndef shell(port):\n try:\n server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n hote = ''\n print(hote)\n server.bind((hote,port))\n server.listen(5)\n server.accept()\n while (True):\n message = server.recv(1024).decode(\"utf8\")\n if message == \"!stop\":\n server.send(\"Server stopping...\".encode(\"utf8\"))\n server.close()\n break\n if message == \"!getlog\":\n process=subprocess.Popen([\"powershell\",\"Get-Childitem C:\\\\Windows\\\\*.log\"], stdout=subprocess.PIPE)\n result=process.communicate()[0]\n print(result)\n if message == None:\n pass\n\n except OSError:\n server.close() \n finally:\n server.close()\n\ndef getos():\n import platform\n return platform.system() \n \ndef main():\n tor_identity()\n client = MyOwnBot(\"BoBiBot\", realname=\"bot\")\n while True:\n client.run('localhost', tls=False, tls_verify=False)\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"C2/Agent.py","file_name":"Agent.py","file_ext":"py","file_size_in_byte":4017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"423599546","text":"from django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom .models import dish,comments,cart_item,cart,profile,order_history,order_item\nfrom django.contrib import messages\nfrom django.contrib.auth import get_user_model\nfrom django.http import JsonResponse\nimport re\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.template.loader import render_to_string\nfrom django.utils.html import strip_tags\n# Create your views here.\ndef home(request):\n Dish=dish.objects.all()\n cdishes=dish.objects.all().order_by(\"-price\")[:5]\n print(cdishes,Dish)\n return render(request,\"home.html\",{\"Dish\":Dish,\"cdishes\":cdishes})\n\n@login_required\ndef dish_add(request):\n if request.user.is_superuser:\n\n if request.method==\"POST\":\n name=request.POST.get(\"name\",\"none\")\n price=request.POST.get(\"price\",0)\n desc=request.POST.get(\"desc\",\"none\")\n img=request.FILES[\"img\"]\n print(name,price,desc,img)\n d=dish(name=name,price=price,desc=desc,img=img,user_id=request.user.id)\n d.save()\n return redirect(\"/\")\n else:\n return render(request,\"dishform.html\")\n else:\n messages.info(request,\"YOU are not authorized to enter dishes\")\n return render(request,\"dishform.html\")\ndef add(request,id):\n if request.method==\"POST\":\n d=dish.objects.get(pk=id)\n c,created=cart.objects.get_or_create(user=request.user)\n c1,created1=cart_item.objects.get_or_create(cart=c,name=d.name,price=d.price)\n c1.quantity+=1\n c1.total=c1.quantity*c1.price\n c.save()\n c1.save()\n k=c.cart_item_set.all()\n print(c.user,c1.name,c1.cart,c1.quantity,k)\n return redirect(\"/dish/\"+id)\n else:\n if request.is_ajax:\n d=dish.objects.get(pk=id)\n c,created=cart.objects.get_or_create(user=request.user)\n c1,created1=cart_item.objects.get_or_create(cart=c,name=d.name,price=d.price)\n c1.quantity+=1\n c1.total=c1.quantity*c1.price\n c.save()\n c1.save()\n return HttpResponse(c1.quantity)\ndef dish_add2(request,id):\n if request.is_ajax:\n pt=cart.objects.get(user=request.user)\n pt1=pt.cart_item_set.all()\n t=0\n for i in pt1:\n t+=i.total\n\n c1=cart_item.objects.get(pk=id)\n c1.quantity+=1\n c1.total=c1.quantity*c1.price\n t+=c1.price\n c1.save()\n return JsonResponse({\"quantity\":c1.quantity,\"total\":c1.total,\"t\":t})\n c1=cart_item.objects.get(pk=id)\n c1.quantity+=1\n c1.total=c1.quantity*c1.price\n c1.save()\n \n return redirect(\"/cart/\"+str(request.user.id))\ndef final(request,id):\n u1=get_user_model().objects.get(pk=id)\n c1=cart.objects.get(user=u1)\n ci1=c1.cart_item_set.all()\n p1=profile.objects.get(user=u1)\n print(p1.address)\n total=0\n for i in ci1:\n total+=i.total\n f=total+20\n return render(request,\"final.html\",{\"total\":total,\"profile\":p1,\"f\":f,\"cart\":c1})\ndef delete(request,id):\n if request.method==\"POST\":\n c1=cart_item.objects.get(pk=id)\n d=dish.objects.get(name=c1.name)\n u=request.META.get(\"HTTP_REFERER\")\n u=u.split(\"/\")[-2]\n \n if c1.quantity>1:\n c1.quantity-=1\n c1.total=c1.quantity*c1.price\n c1.save()\n else:\n c1.delete()\n if u==\"dish\":\n return redirect(\"/dish/\"+str(d.id))\n return redirect(\"/cart/\"+str(request.user.id))\n else:\n if request.is_ajax:\n c2=cart.objects.get(user=request.user)\n d=dish.objects.get(pk=id)\n try:\n c1=cart_item.objects.get(name=d.name,cart=c2)\n except:\n return HttpResponse(0)\n if c1.quantity>1:\n c1.quantity-=1\n c1.total=c1.quantity*c1.price\n c1.save()\n else:\n c1.delete()\n if c1 is not None:\n return HttpResponse(c1.quantity)\n return HttpResponse(0)\ndef delete2(request,id):\n c1=cart_item.objects.get(pk=id)\n d=dish.objects.get(name=c1.name)\n u=request.META.get(\"HTTP_REFERER\")\n u=u.split(\"/\")[-2]\n pt=cart.objects.get(user=request.user)\n pt1=pt.cart_item_set.all()\n t=0\n for i in pt1:\n t+=i.total\n \n if c1.quantity>1:\n c1.quantity-=1\n c1.total=c1.quantity*c1.price\n c1.save()\n t-=c1.price\n return JsonResponse({\"quantity\":c1.quantity,\"total\":c1.total,\"t\":t})\n else:\n c1.delete()\n return HttpResponse(0)\n\ndef checkout(request,id):\n if request.method==\"POST\":\n c=cart.objects.get(pk=id)\n c.payment=request.POST[\"payment\"]\n c.save()\n return redirect(\"/\")\n else:\n c=cart.objects.get(pk=id)\n return render(request,\"checkout.html\",{\"cart\":c})\ndef order_home(request,id):\n c1=cart.objects.get(pk=id)\n ci1=c1.cart_item_set.all()\n user=get_user_model().objects.get(pk=request.user.id)\n p=profile.objects.get(user=user)\n Dish=dish.objects.all()\n cdishes=dish.objects.all().order_by(\"-price\")[:5]\n \n o=order_history(user=request.user)\n o.save()\n for k in ci1:\n b=order_item(cart=o,name=k.name,price=k.price)\n b.quantity=k.quantity\n b.total=k.total\n b.save()\n d=dish.objects.get(name=k.name)\n d.sold+=k.quantity\n d.save()\n \n subject,from_email,to='Order Confirmation Mesage','saad.shaikh0@gmail.com',[request.user.email]\n html_content=render_to_string(\"email.html\",{\"dishes\":ci1,\"cart\":c1,\"profile\":p,\"user\":user})\n text_content=strip_tags(html_content)\n msg=EmailMultiAlternatives(subject,text_content,from_email,to)\n msg.attach_alternative(html_content,\"text/html\")\n msg.send()\n # zerosms.sms(phno=9766999626,passwd=\"iamagoodboy\",message=\"Your order is confirmed\",receivernum=9766999626)\n \n phonenum=\"9766999626\"\n password=\"iamagoodboy\"\n receivernum1=\"9766999626\"\n zerosms.sms(phno=phonenum,passwd=password,message=\"hello\",receivernum=receivernum1)\n c1.delete()\n\n return redirect(\"/\")\n #return render(request,\"home.html\",{\"Dish\":Dish,\"cdishes\":cdishes})\ndef cart_show(request,id):\n \n c,created=cart.objects.get_or_create(user=request.user)\n c1=c.cart_item_set.all().order_by(\"name\")\n total=0\n if len(c1)==0:\n messages.info(request,\"Your shopping cart is empty\")\n else:\n for i in c1:\n total+=i.total\n print(i.name,i.total)\n \n\n return render(request,\"cart.html\",{\"cart\":c,\"items\":c1,\"total\":total})\n \n\ndef comment_add(request,id):\n d=dish.objects.get(pk=id)\n c=request.POST[\"comment\"]\n c1=comments(text=c,name=request.user.first_name,dish=d)\n c1.save()\n return redirect(\"/dish/\"+id)\n@login_required\ndef dishes(request,id):\n d=dish.objects.get(pk=id)\n c=d.comments_set.all()\n od=list(dish.objects.exclude(name=d.name))\n try:\n c1=cart.objects.get(user=request.user)\n except cart.DoesNotExist:\n c1=None\n item=None\n if c1 is not None:\n try:\n item=c1.cart_item_set.get(name=d.name)\n except cart_item.DoesNotExist:\n item=None\n \n t=[]\n for j in range(0,len(od),3):\n k=[]\n if j 0:\n\t\t# While candidates have less then 2 nodes not in common crossover would\n\t\t# not produce any new candidates, so mutation is forced.\n\t\t# E.g., any mutation between (1,2,3,4) and (2,3,4,5) will not produce\n\t\t# any new candidate.\n\t\tif len(candidate1) - len(common) == 1:\n\t\t\t# If the two candidates differ by 1 element, perform a random mutation\n\t\t\t# once.\n\t\t\tif args[\"mutation_operator\"] == ea_global_random_mutation:\n\t\t\t\tcandidate1 = ea_global_random_mutation(prng, [candidate1], args)[0]\n\t\t\t\tcandidate2 = ea_global_random_mutation(prng, [candidate2], args)[0]\n\t\t\telse:\n\t\t\t\tcandidate1 = ea_global_random_mutation(prng, candidate1, args)\n\t\t\t\tcandidate2 = ea_global_random_mutation(prng, candidate2, args)\n\t\telif len(candidate1) == len(common):\n\t\t\t# If the two candidates are identical, perform a random mutation twice.\n\t\t\tfor _ in range(2):\n\t\t\t\tif args[\"mutation_operator\"] == ea_global_random_mutation:\n\t\t\t\t\tcandidate1 = ea_global_random_mutation(prng, [candidate1], args)[0]\n\t\t\t\t\tcandidate2 = ea_global_random_mutation(prng, [candidate2], args)[0]\n\t\t\t\telse:\n\t\t\t\t\tcandidate1 = ea_global_random_mutation(prng, candidate1, args)\n\t\t\t\t\tcandidate2 = ea_global_random_mutation(prng, candidate2, args)\n\n\t\tmax_trials -= 1\n\t\tcommon = list(set(candidate1).intersection(set(candidate2)))\n\n\tif max_trials==0:\n\t\treturn [candidate2, candidate1]\n\n\tcandidate1_to_swap = candidate1.copy()\n\tcandidate2_to_swap = candidate2.copy()\n\tc1_common = {}\n\tc2_common = {}\n\n\t# get the nodes of each candidate that can be swapped\n\tfor c in common:\n\t\tcandidate1_to_swap.pop(candidate1_to_swap.index(c))\n\t\tcandidate2_to_swap.pop(candidate2_to_swap.index(c))\n\t\tidx1 = candidate1.index(c)\n\t\tidx2 = candidate2.index(c)\n\t\tc1_common[idx1] = c\n\t\tc2_common[idx2] = c\n\n\t# choose swap position\n\n\tswap_idx = prng.randint(1, len(candidate1_to_swap) - 1)\n\tswap = candidate1_to_swap[swap_idx:]\n\tcandidate1_to_swap[swap_idx:] = candidate2_to_swap[swap_idx:]\n\tcandidate2_to_swap[swap_idx:] = swap\n\n\tfor (idx, c) in c1_common.items():\n\t\tcandidate1_to_swap.insert(idx, c)\n\tfor (idx, c) in c2_common.items():\n\t\tcandidate2_to_swap.insert(idx, c)\n\n\t# if set(candidate1_to_swap) == set(candidate2) or set(candidate1_to_swap)==set(candidate1):\n\t# \tprint(candidate1_to_swap, candidate2_to_swap)\n\t# \texit(0)\n\n\treturn [candidate1_to_swap, candidate2_to_swap]\n","sub_path":"code/src/ea/crossovers.py","file_name":"crossovers.py","file_ext":"py","file_size_in_byte":2774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"189301205","text":"import re\r\nimport logging\r\nfrom mechanize import Browser\r\nfrom faker import Faker\r\nfrom random import randint\r\n\r\n\r\nscholen=['Avans', 'HAS', 'Haagse hogeschool', 'Avans Den Bosch'];\r\nstudies=['Informatica', 'Bedrijfsinformatica', 'Economie', 'Zorg', 'Communicatie media design'];\r\n\r\nbr = Browser();\r\nfake = Faker(\"nl_NL\");\r\n# Ignore robots.txt\r\nbr.set_handle_robots(False)\r\n# Google demands a user-agent that isn't a robot\r\nbr.addheaders = [('User-agent', 'Firefox')]\r\n\r\nfor x in range(100000):\r\n\tresponse = br.open(\"http://compensatiewijzer.nl/bereken-nu/\");\r\n\tbr.select_form(nr=1);\r\n\tbr.form.find_control(\"your-name\").value = fake.name();\r\n\tbr.form.find_control(\"your-email\").value = fake.email();\r\n\tbr.form.find_control(\"hogeschool\").value = scholen[randint(0, len(scholen)-1)];\r\n\tbr.form.find_control(\"huidigestudie\").value = studies[randint(0, len(studies)-1)];\r\n\tbr.form.find_control(\"instroom\").value = str(randint(1, 4));\r\n\tbr.submit();\r\n","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"108649188","text":"\"\"\"This file is Avature (Variant 2) spider created on top of the ATSSpiderTerms\nscrapy crawl avature_spx -a mining_job_id=9999 -a iteration=1 -a url=\"http://spx.avature.net\" -a extract=1\n\nsample url:\n http://spx.avature.net\n\"\"\"\nfrom urlparse import urljoin, urlparse\n\nfrom scrapy.http import Request\nfrom scrapy.selector import Selector\n\nfrom brightcorp.spiders.avature import Avature\nfrom brightcorp.items import BrightcorpItemLoader\n\n\nclass Avature_Spx(Avature):\n\n \"\"\" Avature sub crawler. \"\"\"\n\n name = 'avature_spx' # unique identifier for this spider\n\n def parse(self, response):\n \"\"\"Recurse pages\n \"\"\"\n self.set_meta_language(response)\n\n sel = Selector(response)\n\n jobs = sel.xpath(\"//h2[@class='jobTitle']\")\n for job in jobs:\n location = job.xpath(\"./following-sibling::p[@class='jobLocation']/text()\")\n\n job_url = job.xpath(\"a/@href\").extract()\n if job_url:\n job_url = job_url[0].strip()\n if job_url:\n url = urljoin(response.url, job_url)\n request = Request(url, callback=self.parse_job_callback())\n request.meta['location'] = ''.join(location.extract())\n\n yield request\n\n next_page = sel.xpath('//a[contains(@class, \"pagination\") and .=\"Next >>\"]/@href').extract()\n if next_page:\n next_page_url = next_page[0]\n yield Request(next_page_url, callback=self.parse)\n\n def parse_job(self, response):\n \"\"\"Extract job data\n \"\"\"\n sel = Selector(response)\n loader = BrightcorpItemLoader(selector=sel)\n\n referencenumber = self.get_referencenumber(response)\n\n loader.add_xpath(\"title\", \"//h3[@class='jobInfoTitle']/text()\")\n loader.add_xpath(\"description\", \"//div[@class='jobDescription']\")\n loader.add_value(\"location\", response.meta['location'])\n loader.add_value(\"referencenumber\", referencenumber)\n loader.add_value(\"company\", \"SPX Corporation\")\n loader.add_value(\"url\", response.url)\n\n yield loader.load_item()\n\n def set_custom_item(self, response):\n \"\"\"Pass reference number and job data not found in job description page\n \"\"\"\n referencenumber = self.get_referencenumber(response)\n\n self.loader.add_value(\"location\", response.meta['location'])\n self.loader.add_value(\"referencenumber\", referencenumber)\n self.loader.add_value(\"company\", \"SPX Corporation\")\n\n @staticmethod\n def get_referencenumber(response):\n \"\"\"Extract referencenumber\n \"\"\"\n sel = Selector(response)\n\n subdomain = urlparse(response.url).netloc.split('.')[0]\n\n referencenumber = response.url.split('/')[-1]\n referencenumber = \"%s-%s\" % (subdomain, referencenumber)\n\n return referencenumber\n","sub_path":"brightcorp/brightcorp/spiders/avature_spx.py","file_name":"avature_spx.py","file_ext":"py","file_size_in_byte":2877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"55696619","text":"\na = 3\nprint(repr(a))#3\n\nb = 'machine leanring'\nprint(repr(b)) #'machine leanring'\n\nclass Developer:\n def __init__(self,name):\n self.name = name\n \n def __repr__(self):\n return f\"my name is {self.name} and a developer\"\n\nmike = Developer(\"mike\")\nprint(repr(mike)) #my name is mike and a developer\n","sub_path":"operator_and_function_overloading_in_custom_python_class/demo_05.py","file_name":"demo_05.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"95829013","text":"#!/usr/bin/python2.7\n\n#### Christian Orellana\n#### June 2020\n#### Speed Test Hourly Checks\n\n\n'''\n /$$$$$$ /$$ /$$ \n /$$__ $$| $$ |__/ \n| $$ \\__/| $$$$$$$ /$$$$$$ /$$ /$$$$$$$\n| $$ | $$__ $$ /$$__ $$| $$ /$$_____/\n| $$ | $$ \\ $$| $$ \\__/| $$| $$$$$$ \n| $$ $$| $$ | $$| $$ | $$ \\____ $$\n| $$$$$$/| $$ | $$| $$ | $$ /$$$$$$$/\n \\______/ |__/ |__/|__/ |__/|_______/ \n \n \n\nThis script is designed to run hourly and append Speedtest cli results to a local csv file\nand then post onto Twitter a gif based on the speed results.\n\nThis is using Twurl to post to Twitter and runs on a cron job to run hourly.\n'''\n\n\n### IMPORT \nimport tweepy \nimport random \nimport pandas as pd\nfrom datetime import datetime, time\nfrom subprocess import check_output\nimport os\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt \n\n\n### GLOBAL VARIABLES\nnow = datetime.now()\nhour = now.strftime('%H:%M')\ndate = now.strftime('%A_%B_%d_%Y')\ndateNoBars = now.strftime('%A %B %d %Y')\ncsvLocation = '/home/flexorflux/Desktop/spectrumChecker/data/{}.csv'.format(date)\ndownloadSpeed = 0\n\n### FUNCTIONS\ndef createCSV():\n \n \"Create a csv file if it doesn't exist\"\n if os.path.exists(csvLocation):\n pass\n else:\n data = {}\n columns = [\"time\", \"server name\", \"server id\", \"latency\", \"jitter\", \"packet loss\", \"download\", \"upload\", \"download bytes\", \"upload bytes\", \"share url\"]\n df = pd.DataFrame(data, columns=columns)\n df.to_csv(csvLocation, index=False)\n \ndef speedList():\n \n \"Run the Speedtest with the CLI\"\n bashOutput = check_output(['/usr/bin/speedtest', '-s', '16888', '-f', 'csv'])\n bashOuttoList = list(bashOutput.split(\",\"))\n bashOuttoList = [i.replace('\"', '') for i in bashOuttoList]\n bashOuttoList.insert(0, hour)\n bashOuttoList.remove(' NY')\n return bashOuttoList\n \ndef appendSpeedList():\n \n \"Take the speedtest results and add it to the csv file\"\n df = pd.read_csv(csvLocation)\n df.loc[len(df)] = speedList()\n df['download'] = df['download'].astype(int)\n df.to_csv(csvLocation, index=False)\n \ndef evaluateAndTweet():\n \n \"Bash or Compliment Spectrum based on the results\"\n consumerKey = ''\n consumerSecret = '' \n accessToken = '' \n accessTokenSecret = '' \n \n auth = tweepy.OAuthHandler(consumerKey, consumerSecret) \n auth.set_access_token(accessToken, accessTokenSecret) \n api = tweepy.API(auth) \n \n df = pd.read_csv(csvLocation)\n downloadSpeed = df['download'].iloc[-1]\n downloadSpeed = (downloadSpeed / 1048576).round(decimals=0)\n \n fileNumber = random.randint(1,10)\n\n if downloadSpeed >= 40:\n tweet = \"Nice job #Spectrum! My internet speed is at {}MBps, you guys are awesome!\".format(downloadSpeed)\n media = api.media_upload(\"/home/flexorflux/Desktop/spectrumChecker/images/hellyea/hellyea{}.gif\".format(fileNumber))\n api.update_status(status=tweet, media_ids=[media.media_id])\n elif downloadSpeed in range(20, 40):\n tweet = \"I guess it's decent #Spectrum, my internet could be a tad better though since it's at {}MBps\".format(downloadSpeed)\n media = api.media_upload(\"/home/flexorflux/Desktop/spectrumChecker/images/couldBeBetter/better{}.gif\".format(fileNumber))\n api.update_status(status=tweet, media_ids=[media.media_id])\n else:\n tweet = \"#Spectrum? wtf is going on with my internet? it's at {}MBps...\".format(downloadSpeed)\n media = api.media_upload(\"/home/flexorflux/Desktop/spectrumChecker/images/wtf/wtf{}.gif\".format(fileNumber))\n api.update_status(status=tweet, media_ids=[media.media_id])\n\ndef convertToMB(x):\n \n return x / 1048576\n \n print(\"converted\")\n\ndef createPlot():\n \n \"Check if its the end of the day and create a plot with data of the data\"\n df = pd.read_csv(csvLocation)\n lastHour = df['time'].iloc[-1]\n lastHour = datetime.strptime(lastHour, \"%H:%M\")\n df['download'] = df['download'].apply(convertToMB)\n averageInternet = df['download'].mean()\n averageInternet = averageInternet.round(decimals=0)\n \n if lastHour >= datetime.strptime(\"23:00\",\"%H:%M\"):\n plt.plot(df['time'], df['download'])\n plt.xlabel(\"Time of Day\")\n plt.ylabel(\"Internet Speed (MBps)\")\n plt.title(\"My Home's Internet Speed for {}\".format(dateNoBars))\n plt.xticks(rotation=30, fontsize=5)\n plt.savefig(\"/home/flexorflux/Desktop/spectrumChecker/plots/{}.png\".format(date))\n \n consumerKey = ''\n consumerSecret = '' \n accessToken = '' \n accessTokenSecret = '' \n \n auth = tweepy.OAuthHandler(consumerKey, consumerSecret) \n auth.set_access_token(accessToken, accessTokenSecret) \n api = tweepy.API(auth)\n \n tweet = \"Hey #Spectrum, just FYI, here are my speed results for today, {}, with an average of {} MBps.\".format(dateNoBars, averageInternet)\n media = api.media_upload(\"/home/flexorflux/Desktop/spectrumChecker/plots/{}.png\".format(date))\n api.update_status(status=tweet, media_ids=[media.media_id])\n \n\n### EXECUTE\n\ncreateCSV()\nappendSpeedList()\nevaluateAndTweet()\ncreatePlot()\n\n","sub_path":"spectrumChecker.py","file_name":"spectrumChecker.py","file_ext":"py","file_size_in_byte":5095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"383398799","text":"# This files contains your custom actions which can be used to run\n# custom Python code.\n#\n# See this guide on how to implement these action:\n# https://rasa.com/docs/rasa/custom-actions\n\n\n# This is a simple example for a custom action which utters \"Hello World!\"\n\nfrom typing import Any, Text, Dict, List\nfrom rasa_sdk import Action, Tracker, FormValidationAction\nfrom rasa_sdk.executor import CollectingDispatcher\nfrom rasa_sdk.events import SlotSet, AllSlotsReset\nfrom rasa_sdk.types import DomainDict\n\nimport json\nimport requests\nimport datetime\n\nurl =\"http://localhost:1337/\"\nrutepost = \"json\"\nruteget = \"appointments\"\nrutegetuser = \"usuarios\"\nclass ActionHelloWorld(Action):\n\n def name(self) -> Text:\n return \"action_hello_world\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n dispatcher.utter_message(text=\"Hello World!\")\n\n return []\n\n\nclass Actionrecievedni(Action):\n def name(self) -> Text:\n return \"action_ask_dniuser\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n \n dispatcher.utter_message(text=\"Cual es tu numero de identificación\")\n \n return []\n\n\n\nclass ActionReceiveDay(Action):\n def name(self) -> Text:\n return \"action_ask_day\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n \n dispatcher.utter_message(text=\"Que día quisiera la cita\")\n \n return []\n\n\nclass ActionReceiveHour(Action):\n def name(self) -> Text:\n return \"action_ask_hour\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n \n\n dispatcher.utter_message(text=\"Que hora quisiera su cita\")\n \n return []\n\n\n\nclass ActionReceiveMonth(Action):\n def name(self) -> Text:\n return \"action_ask_month\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n \n \n \n dispatcher.utter_message(text=\"Que mes quisiera la cita\")\n\n\n return []\n\n\n\n\nclass ActionInformForm(Action):\n\n def name(self) -> Text:\n return \"action_inform_form\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n\n\n \n slot = tracker.get_slot(\"month\")\n monthe = slot[0].lower()\n\n \n\n slot2 = tracker.get_slot(\"day\")\n\n day = slot2[0]\n\n slot3 = tracker.get_slot(\"hour\")\n \n month = None\n\n if isinstance(slot3, str):\n hour = slot3.replace(' ','').split(\":\")\n else:\n hour = slot3[0].replace(' ','')\n\n \n\n\n slot4 = tracker.get_slot(\"dni_user\")\n\n dni = slot4[0]\n\n year = \"2021\"\n\n entities = tracker.latest_message['entities']\n\n for e in entities:\n if e['entity'] == 'timer':\n timer = e['value']\n\n if isinstance(slot, str):\n monthe = slot\n \n else:\n monthe = slot[0].lower()\n \n \n\n print(monthe)\n if monthe == \"enero\":\n month = \"01\"\n elif monthe == \"febrero\":\n month = \"02\"\n elif monthe == \"marzo\":\n month = \"03\" \n elif monthe == \"abril\":\n month = \"04\"\n elif monthe == \"mayo\":\n month = \"05\"\n elif monthe == \"junio\":\n month = \"06\"\n elif monthe == \"julio\":\n month = \"07\" \n elif monthe == \"agosto\":\n month = \"08\" \n elif monthe == \"septiembre\":\n month = \"09\" \n elif monthe == \"octubre\":\n month = \"10\" \n elif monthe == \"noviembre\":\n month = \"11\" \n elif monthe == \"diciembre\":\n month = \"12\" \n \n if timer == \"tarde\":\n print(\"Hora que creo\",hour) \n\n\n\n year1 = int(year)\n month1 = int(month)\n day1 = int(day)\n\n date = datetime.datetime(year1, month1, day1 , 8).isoformat()\n\n\n if isinstance(hour, str):\n hour1 = int(hour)\n time = datetime.time(hour1)\n time1 = str(time)\n else:\n time = datetime.time(int(hour[0]), int(hour[1]))\n time1 = str(time)\n \n \n\n\n r = requests.get('https://appointments-carvajal.herokuapp.com/users')\n\n data = r.json()\n username = \"\"\n\n\n if isinstance(slot4, str):\n dni = slot_value.replace(' ','')\n else:\n dni = slot4[0].replace(' ','')\n \n\n for i in range(len(data)):\n x = data[i]\n if dni == x[\"dni\"]:\n username = x[\"name\"]\n\n \n data = {\n \"appointment_type\": \"Cita Medica\",\n \"appoinment_with\": \"Carlos\",\n \"datetime\": date,\n \"hours\": [{\"client_name\": username,\"available\":False, \"time\":time1} ]\n}\n\n send = json.dumps(data)\n\n \n headers = {'content-type' : 'application/json'}\n try:\n r = requests.post(\"https://appointments-carvajal.herokuapp.com/appointments\",headers=headers ,data = send)\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n raise SystemExit(err)\n\n dispatcher.utter_message(text=\"Cita creada con exito\")\n\n return [AllSlotsReset()]\n\n\n \n\n\nclass ActionRequestCitaCercana(Action):\n\n def name(self) -> Text:\n return \"action_citacercana\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n \n horarios = [\"6:00:00.000\", \"7:00:00.000\", \"8:00:00.000\", \"9:00:00.000\", \"10:00:00.000\", \"11:00:00.000\", \"12:00:00.000\", \"13:00:00.000\",\n \"14:00:00.000\", \"15:00:00.000\", \"16:00:00.000\", \"17:00:00.000\", \"18:00:00.000\",\"6:30:00.000\", \"7:30:00.000\",\"8:30:00.000\",\"9:30:00.000\",\n \"10:30:00.000\",\"11:30:00.000\",\"12:30:00.000\",\"13:30:00.000\",\"14:30:00.000\",\"15:30:00.000\",\"16:30:00.000\",\"17:30:00.000\", \"18:30:00.000\"]\n try:\n r = requests.get('https://appointments-carvajal.herokuapp.com/appointments')\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n raise SystemExit(err)\n \n\n data = r.json()\n date = data[0]\n l = date[\"datetime\"].split(\"-\")\n m = l[2]\n t = (m.split(\"T\"))[0]\n \n\n for i in range(len(data)):\n x = data[i]\n y = x[\"hours\"]\n z = y[0]\n\n time = z['time']\n p = len(horarios)\n\n\n\n for j in range(p):\n if time == horarios[j]:\n horarios.pop(j)\n break\n \n\n\n # print(response.text)\n\n dispatcher.utter_message(text=\"Es estas\")\n return []\n\n\n\n\nclass ActionRequestCitaEspecifica(Action):\n\n def name(self) -> Text:\n return \"action_cita_especifica\"\n\n def run(self, dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any]) -> List[Dict[Text, Any]]:\n\n\n\n\n\n # day = tracker.get_slot(\"day_request\")\n # month = tracker.get_slot(\"month_request\")\n\n\n entities = tracker.latest_message['entities']\n monthe= \"\"\n for e in entities:\n if e['entity'] == 'day_request':\n day = e['value']\n \n\n if e['entity'] == 'month_request':\n monthe = e['value'] \n \n\n month = \"\"\n if monthe == \"enero\":\n month = \"01\"\n elif monthe == \"febrero\":\n month = \"02\"\n elif monthe == \"marzo\":\n month = \"03\" \n elif monthe == \"abril\":\n month = \"04\"\n elif monthe == \"mayo\":\n month = \"05\"\n elif monthe == \"junio\":\n month = \"06\"\n elif monthe == \"julio\":\n month = \"07\" \n elif monthe == \"agosto\":\n month = \"08\" \n elif monthe == \"septiembre\":\n month = \"09\" \n elif monthe == \"octubre\":\n month = \"10\" \n elif monthe == \"noviembre\":\n month = \"11\" \n elif monthe == \"diciembre\":\n month = \"12\" \n\n\n horarios = [\"6:00:00.000\",\"6:30:00.000\", \"7:00:00.000\", \"7:30:00.000\", \"8:00:00.000\", \"8:30:00.000\",\"9:00:00.000\",\"9:30:00.000\",\n \"10:00:00.000\",\"10:30:00.000\", \"11:00:00.000\", \"11:30:00.000\",\"12:00:00.000\",\"12:30:00.000\", \"13:00:00.000\",\"13:30:00.000\",\n \"14:00:00.000\", \"14:30:00.000\",\"15:00:00.000\", \"15:30:00.000\", \"16:00:00.000\", \"16:30:00.000\", \"17:00:00.000\",\"17:30:00.000\", \"18:00:00.000\",\n \"18:30:00.000\"]\n\n\n try:\n r = requests.get('https://appointments-carvajal.herokuapp.com/appointments')\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n raise SystemExit(err)\n \n\n data = r.json()\n # date = data[0]\n # l = date[\"datetime\"].split(\"-\")\n # m = l[2]\n # t = (m.split(\"T\"))[0]\n # k = l[1]\n\n\n\n\n \n \n \n \n for i in range(len(data)):\n x = data[i]\n l = x[\"datetime\"].split(\"-\")\n\n\n m = l[2]\n\n\n # Day\n t = (m.split(\"T\"))[0]\n\n \n # Month\n k = l[1]\n\n\n\n y = x[\"hours\"]\n z = y[0]\n\n time = z['time']\n p = len(horarios)\n\n if k == month and t == day:\n \n for j in range(p):\n\n if time == horarios[j]:\n horarios.pop(j)\n break\n \n textr= \"\"\n for b in range(len(horarios)):\n if horarios[b] == \"6:00:00.000\":\n textr+= \"6 de la mañana, \"\n if horarios[b] == \"6:30:00.000\":\n textr+= \"6 y 30 de la mañana, \"\n if horarios[b] == \"7:00:00.000\":\n textr+= \"7 de la mañana, \"\n if horarios[b] == \"7:30:00.000\":\n textr+= \"7 y 30 de la mañana, \" \n if horarios[b] == \"8:00:00.000\":\n textr+= \"8 de la mañana, \"\n if horarios[b] == \"8:30:00.000\":\n textr+= \"8 y 30 de la mañana, \"\n if horarios[b] == \"9:00:00.000\":\n textr+= \"9 de la mañana, \"\n if horarios[b] == \"9:30:00.000\":\n textr+= \"9 y 30 de la mañana, \"\n if horarios[b] == \"10:00:00.000\":\n textr+= \"10 de la mañana, \"\n if horarios[b] == \"10:30:00.000\":\n textr+= \"10 y 30 de la mañana, \"\n if horarios[b] == \"11:00:00.000\":\n textr+= \"11 de la mañana, \"\n if horarios[b] == \"11:30:00.000\":\n textr+= \"11 y 30 de la mañana, \"\n if horarios[b] == \"12:00:00.000\":\n textr+= \"12 de la tarde, \"\n if horarios[b] == \"12:30:00.000\":\n textr+= \"12 y 30 de la tarde, \"\n if horarios[b] == \"13:00:00.000\":\n textr+= \"13 de la tarde, \"\n if horarios[b] == \"13:30:00.000\":\n textr+= \"13 y 30 de la tarde, \"\n if horarios[b] == \"14:00:00.000\":\n textr+= \"14 de la tarde, \"\n if horarios[b] == \"14:30:00.000\":\n textr+= \"14 y 30 de la tarde, \"\n if horarios[b] == \"15:00:00.000\":\n textr+= \"15 de la tarde, \"\n if horarios[b] == \"15:30:00.000\":\n textr+= \"15 y 30 de la tarde, \"\n\n\n if horarios[b] == \"16:00:00.000\":\n textr+= \"16 de la tarde, \"\n if horarios[b] == \"16:30:00.000\":\n textr+= \"16 y 30 de la tarde, \"\n\n if horarios[b] == \"17:00:00.000\":\n textr+= \"17 de la tarde, \"\n if horarios[b] == \"17:30:00.000\":\n textr+= \"17 y 30 de la tarde, \" \n\n if horarios[b] == \"18:00:00.000\":\n textr+= \"18 de la tarde, \"\n if horarios[b] == \"18:30:00.000\":\n textr+= \"18 y 30 de la tarde, \" \n\n \n\n \n dispatcher.utter_message(text=textr)\n return []\n\n\n\n\ndef defaultconverter(o):\n if isinstance(o, datetime.datetime):\n return o.__str__()\n\n\nclass ValidateCitaForm(FormValidationAction):\n def name(self) -> Text:\n return \"validate_cita_form\"\n\n \n\n def validate_dni_user(\n self,\n slot_value: Any,\n dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: DomainDict,\n ) -> Dict[Text, Any]:\n \"\"\"Validate `first_name` value.\"\"\"\n \n r = requests.get('https://appointments-carvajal.herokuapp.com/users')\n\n data = r.json()\n check = False\n dni = \"\"\n if isinstance(slot_value, str):\n dni = slot_value.replace(' ','')\n \n else:\n dni = slot_value[0].replace(' ','')\n \n \n # print(dni)\n for i in range(len(data)):\n x = data[i]\n if dni == x[\"dni\"]:\n \n check = True \n\n if check == False:\n\n dispatcher.utter_message(text=f\"Esa identificacion no esta registrada en el sistema, porfavor ingrese una nueva\")\n return {\"dni_user\": None}\n else:\n return {\"dni_user\": slot_value}","sub_path":"actions/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":13927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"261663515","text":"#!/usr/bin/python\nfrom stanfordcorenlp import StanfordCoreNLP as snlp\nfrom nltk.tree import *\nimport numpy as np\n\nclass Constree:\n\tdef __init__(self):\n\t\tself.nlp = snlp(r'./resources/stanford-corenlp-full-2018-01-31')\n\n\tdef parseSentence(self,sentence):\n\t\tparsing = self.nlp.parse(sentence)\n\t\tself.ptree = ParentedTree.fromstring(parsing)\n\n\tdef get_lca_length(self,location1, location2):\n\t\ti = 0\n\t\twhile i < len(location1) and i < len(location2) and location1[i] == location2[i]:\n\t\t\ti+=1\n\t\treturn i\n\n\tdef get_labels_from_lca(self, lca_len, location):\n\t\tlabels = []\n\t\tfor i in range(lca_len, len(location)):\n\t\t\tlabels.append(self.ptree[location[:i]].label())\n\t\treturn labels\n\n\tdef findPathLen(self, index1, index2):\n\t\t'''Returns the pathlen of between two words given their positions in the sentence '''\n\t\tleaf_index1 = index1\n\t\tleaf_index2 = index2\n\n\t\tlocation1 = self.ptree.leaf_treeposition(leaf_index1)\n\t\tlocation2 = self.ptree.leaf_treeposition(leaf_index2)\n\n\t\t#find length of least common ancestor (lca)\n\t\tlca_len = self.get_lca_length(location1, location2)\n\n\t\t#find path from the node1 to lca\n\t\tlabels1 = self.get_labels_from_lca(lca_len, location1)\n\t\t#ignore the first element, because it will be counted in the second part of the path\n\t\tresult = labels1[1:]\n\t\t#inverse, because we want to go from the node to least common ancestor\n\t\tresult = result[::-1]\n\n\t\t#add path from lca to node2\n\t\tresult = result + self.get_labels_from_lca(lca_len, location2)\n\t\tif len(result)==0:\n\t\t\treturn 0\n\t\treturn len(result) + 1\n\n\tdef adjMatrix(self,sentence,l=0.1):\n\t\t'''Returns the adjacency matrix using path len as the distance between two nodes'''\n\t\tself.parseSentence(sentence)\n\t\tleaf_values = self.ptree.leaves()\n\t\tn = len(leaf_values)\n\t\tadj = np.zeros((n,n))\n\t\tfor i in range(n):\n\t\t\tfor j in range(n):\n\t\t\t\tadj[i][j] = self.findPathLen(i,j)\n\t\t\t\tadj[j][i] = adj[i][j]\n\t\tdiam = np.max(adj)\n\t\tadj = adj/diam\n\t\tadj = -adj*adj/(2*l*l)\n\t\tadj = np.exp(adj)\n\t\tself.adj = adj\n\t\treturn adj\n\n\tdef degMatrix(self, sentence=None, l=0.1, adj = None):\n\t\t'''Returns the degree matrix'''\n\t\tif adj is None:\n\t\t\tadj = self.adjMatrix(sentence,l)\n\t\tleaf_values = self.ptree.leaves()\n\t\tn = len(leaf_values)\n\t\tsum_arr = np.apply_along_axis(np.sum,1,adj)\n\t\tdegMat = np.zeros((n,n),dtype=np.float64)\n\t\tfor i in range(sum_arr.shape[0]):\n\t\t\tdegMat[i][i] = sum_arr[i]\n\t\treturn degMat\n\n\tdef getReuiredParameters(self, sentence, aspect_words_indexes, l=0.1):\n\t\t'''Returns DeltaInverse_mm and Wights_ma'''\n\t\tW = self.adjMatrix(sentence, l)\n\t\t\n\t\tdeg = self.degMatrix(adj=W)\n\t\tD = deg - W\n\t\tDI = np.linalg.inv(D)\n\t\tDI_mm = np.delete(np.delete(DI,aspect_words_indexes,0), aspect_words_indexes, 1)\n\t\t\n\t\tW_am = np.delete(W[aspect_words_indexes], aspect_words_indexes, 1)\n\t\tW_m1 = np.transpose([np.mean(W_am,0)])\n\t\tW_mm = np.delete(np.delete(W,aspect_words_indexes,0), aspect_words_indexes, 1)\n\t\treturn W_mm, W_m1\n\ndef main():\n\tc = Constree()\n\tsentence = 'i saw a dog today.'\n\tprint(c.adjMatrix(sentence))\n\nif __name__ == '__main__':\n\tmain()","sub_path":"constree.py","file_name":"constree.py","file_ext":"py","file_size_in_byte":2998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"453413458","text":"# Based on pdb_query\n\n# Import all of the files\n# Get mean chain length\n# Get max chain length\n# Remove outliers if needed\n# Observe the dictionary\n# Choose 40 families and num select points.\n# Choose num_select points\n\n\n# Imports\nfrom Bio import PDB\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nfrom matplotlib import pyplot\nfrom mpl_toolkits.mplot3d import Axes3D\nimport random\n\n\n\n# Gets list with [x,y,z] and num select amount of points.\n# If amount of points is smaller then the list size -> randomly choose num select points from the list\n# If amount of points is larger than the list size -> Take all of the points and 0 pad the difference.\n# points array is list\n# num_select is integer\n\n\ndef scatter_plot(x_vals,y_vals,z_vals):\n\n fig = pyplot.figure()\n ax = Axes3D(fig)\n ax.scatter(x_vals,y_vals,z_vals)\n pyplot.show()\n\n return\n\n\ndef randomly_choose_points(points_array , num_select):\n pdb_points = np.zeros((num_select,3))\n len_points = len(points_array)\n np_points_array = np.array(points_array)\n index = np.random.choice(range(len_points), np.min([len_points,num_select]), replace=False)\n np_points_chosen = np_points_array[index]\n pdb_points[0:len(np_points_chosen)] = np_points_chosen\n return np.expand_dims(pdb_points,axis=0)\n\n\ntrain_example_num = 400\nfamily_num = 40\n\n# Need to do something for all of the families together\n# Create a dict where's every key in the dict is PDB file and the values are x,y,z coordinates\n\n# Families folder located in tryout\nfamilies_folder = 'chosen families'\n# Get all dirs in the folder\nentries = os.listdir(families_folder)\n#Initiate dict\ndict_families = {}\n# For all of the families - files in the folder\nfor family_name in entries:\n print(family_name)\n dict_pdbs = {}\n # Get all PDBS from the folder\n pdbs = os.listdir(families_folder +'/' + family_name)\n pdb_index = 0\n pdbs_available = len(pdbs)\n indices = np.random.choice(range(pdbs_available), train_example_num, replace=False)\n# Run over all of the PDBS\n for ind in indices:\n pdb = pdbs[ind]\n# print(pdb)\n # Get x,y,z coordinate of the atom\n parser = PDB.PDBParser()\n io = PDB.PDBIO()\n struct = parser.get_structure(pdb,families_folder +'/' + family_name + '/' + pdb)\n dict_pdbs[pdb] = []\n for model in struct:\n for chain in model:\n for residue in chain:\n for atom in residue:\n x,y,z = atom.get_coord()\n dict_pdbs[pdb].append([x,y,z])\n # print(x,y,z)\n dict_families[family_name] = dict_pdbs\n\n\n# Having the dict, select amount of points and get coordinates\nnum_select = 512\nX_train = np.zeros([train_example_num*family_num,num_select,3])\nX_labels = np.zeros(train_example_num*family_num)\n\n\nnone_zero_dict = {}\n\n# Set label initial value\nlabel = 0\nfor family_key in dict_families.keys():\n non_zero_count = 0\n non_zero_mean = 0 \n pdb_xyz_concat = np.zeros([1,num_select,3])\n family_dict = dict_families[family_key]\n for key in dict_families[family_key].keys():\n len_pdb = len(family_dict[key])\n if len_pdb < num_select:\n non_zero_count += 1\n non_zero_mean += len_pdb\n# print(key , len_pdb)\n pdb_xyz = randomly_choose_points(family_dict[key], num_select)\n pdb_xyz_concat = np.concatenate((pdb_xyz_concat, pdb_xyz), axis=0)\n # scatter_plot(pdb_xyz[0][:,0],pdb_xyz[0][:,1],pdb_xyz[0][:,2])\n family_data = pdb_xyz_concat[1:,:,:]\n family_label = np.ones([family_data.shape[0]]) * label\n print(family_key)\n print(['None zero count',non_zero_count])\n if non_zero_count == 0:\n Mean_length = 512\n if non_zero_count > 0:\n Mean_length = (non_zero_mean*non_zero_count/train_example_num) + 512*(train_example_num-non_zero_count)/train_example_num\n \n print(['Mean length',Mean_length ])\n print()\n# print(families_folder +'/' + family_key + '/' + family_key + '.npy')\n\n X_train[label*train_example_num:(label+1)*train_example_num,:,:] = family_data\n X_labels[label*train_example_num:(label+1)*train_example_num] = family_label\n label = label + 1\n none_zero_dict[family_key] = [non_zero_count,Mean_length]\n\n\n\nnp.save('X_train' + '.npy', X_train) # save\nnp.save('X_label' + '.npy', X_labels) # save\n\nf = open(\"none_zero_dict.txt\",\"w\")\nf.write( str(none_zero_dict) )\nf.close()","sub_path":"oren_proj/pdb_to_np_array.py","file_name":"pdb_to_np_array.py","file_ext":"py","file_size_in_byte":4473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"245000063","text":"import pandas as pd\nimport pickle\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom xgboost import XGBClassifier\nimport matplotlib.pyplot as plt\nimport sys\nimport prepare_data\nfrom impyute.imputation.cs import mice\n\nsys.setrecursionlimit(100000) # Increase the recursion limit of the OS\n\ntraining_dataset_output_path = \"data_files/training_data/\"\n\n\ndef plot_dataframe(plot_df):\n df = plot_df.copy()\n df = df.reset_index()\n print(df)\n df.plot(x='index', y='sleep_or_wake', kind='line')\n plt.show()\n\n\ndef save_model(clf):\n Pkl_Filename = \"Pickle_\" + get_classifier_name(clf) + \".pkl\"\n\n with open(Pkl_Filename, 'wb') as file:\n pickle.dump(clf, file)\n\n\ndef train_data(clf, X, y):\n print(\"Splitting into train and test data\")\n train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2)\n\n print(\"Fitting \" + get_classifier_name(clf) + \"...\")\n clf.fit(train_X, train_y)\n\n print(\"Generating prediction...\")\n clfPred = clf.predict(test_X)\n\n acc = accuracy_score(test_y, clfPred)\n f1 = f1_score(test_y, clfPred)\n print('\\nAccuracy:', accuracy_score(test_y, clfPred))\n print('F1 score:', f1_score(test_y, clfPred))\n print('Recall:', recall_score(test_y, clfPred))\n print('Precision:', precision_score(test_y, clfPred))\n print('\\n clasification report:\\n', classification_report(test_y, clfPred))\n print('\\n confussion matrix:\\n', confusion_matrix(test_y, clfPred))\n\n # save_model(clf)\n\n\ndef load_model(clf, X, y):\n print(\"Loading saved \" + get_classifier_name(clf) + \" model...\")\n Pkl_Filename = \"Pickle_\" + get_classifier_name(clf) + \".pkl\"\n # Load the Model back from file\n with open(Pkl_Filename, 'rb') as file:\n Pickled_Ada_Model = pickle.load(file)\n\n score = Pickled_Ada_Model.score(X, y)\n # Print the Score\n print(\"Test score: {0:.2f} %\".format(100 * score))\n\n # Predict the Labels using the reloaded Model\n Ypredict = Pickled_Ada_Model.predict(X)\n\n\ndef generate_training_data():\n print(\"Retrieving dataset...\")\n dataset = prepare_data.get_sleep_model_training_data()\n\n boolean_columns = [\"discrete:app_state:is_active\", \"discrete:app_state:is_inactive\",\n \"discrete:app_state:is_background\",\n \"discrete:app_state:missing\", \"discrete:battery_plugged:is_ac\",\n \"discrete:battery_plugged:is_usb\",\n \"discrete:battery_plugged:is_wireless\", \"discrete:battery_plugged:missing\",\n \"discrete:battery_state:is_unknown\", \"discrete:battery_state:is_unplugged\",\n \"discrete:battery_state:is_not_charging\", \"discrete:battery_state:is_discharging\",\n \"discrete:battery_state:is_charging\", \"discrete:battery_state:is_full\",\n \"discrete:battery_state:missing\", \"discrete:on_the_phone:is_False\",\n \"discrete:on_the_phone:is_True\",\n \"discrete:on_the_phone:missing\", \"discrete:ringer_mode:is_normal\",\n \"discrete:ringer_mode:is_silent_no_vibrate\", \"discrete:ringer_mode:is_silent_with_vibrate\",\n \"discrete:ringer_mode:missing\", \"discrete:wifi_status:is_not_reachable\",\n \"discrete:wifi_status:is_reachable_via_wifi\", \"discrete:wifi_status:is_reachable_via_wwan\",\n \"discrete:wifi_status:missing\"]\n\n print(\"Cleaning and preparing dataframe\")\n\n dataset['sleep_or_wake'] = (dataset['sleep_or_wake'] == 'S').astype(int)\n\n # handle missing values\n # from sklearn.impute import SimpleImputer\n # imp_mean = SimpleImputer( strategy='mean') #for median imputation replace 'mean' with 'median'\n # imp_mean.fit(dataset)\n # dataset[dataset.columns] = imp_mean.transform(dataset)\n\n dataset[dataset.columns] = mice(dataset.values)\n print(\"impute finished\")\n\n # dataset = dataset.fillna(0)\n\n # dataset.to_csv(training_dataset_output_path + 'sleep_model_training_data.csv', index=False)\n dataset = dataset.drop(['timestamp'], axis=1)\n\n X = dataset.drop(['sleep_or_wake'], axis=1)\n y = dataset['sleep_or_wake']\n\n # # normalizing\n scaler = MinMaxScaler(feature_range=(0, 1))\n X[X.columns] = scaler.fit_transform(X[X.columns])\n\n return X, y\n\n\ndef get_classifier_name(clf):\n return str(clf.__class__.__name__)\n\n\ndef calculate_feature_importance(X, y):\n model = XGBClassifier()\n\n print(\"Fitting \" + get_classifier_name(model) + \"...\")\n\n model.fit(X, y)\n\n print(\"Top 10 most important features:\")\n df_feature_imp = pd.DataFrame(model.feature_importances_, index=X.columns,\n columns=['feature importance']).sort_values(\n 'feature importance', ascending=False)\n print(df_feature_imp.iloc[:10, ])\n\n\nif __name__ == '__main__':\n X, y = generate_training_data()\n clf = AdaBoostClassifier(n_estimators=100, random_state=0)\n # calculate_feature_importance()\n train_data(clf, X, y)\n","sub_path":"train_sleep_model.py","file_name":"train_sleep_model.py","file_ext":"py","file_size_in_byte":5346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"401098634","text":"# coding=utf-8\n# ycat\t\t\t2018-8-2\t create\n# seerAgv的API封装 \nimport sys, os\nimport threading\nimport time\nimport socket\nimport setup \n\nif __name__ == '__main__':\n\tsetup.setCurPath(__file__)\nimport json\nimport buffer\nimport log\nimport local\nimport tcpSocketLong\nimport lock as lockImp\nimport driver.seerAgv.agvServer as agvServer\n\ng_cmd = {\n\t\"robot_status_info_req\": 1000,\n\t\"robot_status_run_req\": 1002,\n\t\"robot_status_mode_req\": 1003,\n\t\"robot_status_loc_req\": 1004,\n\t\"robot_status_speed_req\": 1005,\n\t\"robot_status_area_req\": 1011,\n\t\"robot_status__emergency_req\": 1012,\n\t\"robot_status_io_req\": 1013,\n\t\"robot_status_task_req\": 1020,\n\t\"robot_status_all1_req\": 1100,\n\t\"robot_status_alarm_res\": 1050,\n\t\"robot_status_station_res\": 1301,\n\t\"robot_status_init_req\": 1111,\n\t\"robot_status_map_req\": 1300,\n\t\"robot_status_pgv_req\": 1017,\n\n\t\"robot_control_reloc_req\": 2002,\n\t\"robot_control_comfirmloc_req\": 2003,\n\t\"robot_control_motion_req\": 2010,\n\t\"robot_control_loadmap_req\": 2022,\n\n\t\"robot_task_cancel_req\": 3003,\n\t\"robot_task_gopoint_req\": 3050,\n\t\"robot_task_gotarget_req\": 3051,\n\t\"robot_task_translate_req\": 3055,\n\t\"robot_task_turn_req\": 3056,\n\t\"robot_task_spin_req\":3057,\n\t\"robot_task_gointo_shelf_req\": 3063,\n\t\"robot_task_uwb_follow_req\": 3071,\n\n\t\"robot_status_fork_req\": 1028,\n\n\n\t\"robot_other_setdo_reqs\": 6002,\n\t\"robot_other_speaker_req\": 6000,\n\t\"robot_other_pause_audio_req\": 6010,\n\t\"robot_other_resume_audio_req\": 6011,\n\t\"robot_other_stop_audio_req\": 6012,\n\t\"robot_other_uploadaudio_req\": 6030,\n\t\"robot_other_downloadaudio_req\": 6031,\n\t\"robot_other_audio_list_req\": 6033,\n\t\"robot_other_set_fork_height_req\": 6040, #货叉的上升下降\n\n\t\"robot_other_wait_req\": 6600,\n\t\"robot_other_robot01\": 6601,\n\t\"robot_other_robot02\": 6602,\n\t\"robot_other_robot03\": 6603,\n\t\"robot_other_robot04\": 6604,\n\n\t\"robot_other_jack_load_req\": 6070,\n\t\"robot_other_jack_unload_req\": 6071,\n\t\"robot_other_jack_stop_req\": 6072,\n\n\t\"robot_other_robot07\": 6080,\t#顶升\n\t\"robot_other_robot08\": 6081,\t#下降\n\t\"robot_other_robot09\": 6082,\t#停止\n\t\"robot_other_robot10\": 6060,\t# 上到位状态查询\n\t\"robot_other_robot11\": 6061,\t# 下到位状态查询\n\n\t\"robot_other_robot12\": 6062,\t# 上限位状态查询\n\t\"robot_other_robot13\": 6063,\t# 下限位状态查询\n\t\"robot_other_robot14\": 6064,\t# 顶升距离查询\n\n\t\"robot_other_robot16\": 6066,\t# 控制左旋转命令\n\t\"robot_other_robot17\": 6067,\t# 控制右旋转命令\n\t\"robot_other_robot18\": 6068,\t# 旋转运动停止命令\n\t\"robot_other_robot19\": 6069,\t# 旋转运动距离查询\n\t\"robot_other_robot27\": 6065,\t# 旋转归中命令\n\t\"robot_other_robot28\": 6075,\t# 旋转归中状态查询\n\t\"robot_other_robot32\": 6090,\t# 旋转清0\n\t\"robot_other_robot33\": 6091,\t# 旋转清0查询\n\n\t\"robot_other_robot20\": 6083,\t# 机械臂上料\n\t\"robot_other_robot21\": 6084,\t# 机械臂下料\n\t\"robot_other_robot22\": 6085,\t# 机械臂上料状态查询\n\t\"robot_other_robot23\": 6086,\t# 机械臂下料状态查询\n\n\t\"robot_other_robot24\": 6072, # 机械臂上料状态查询\n\t\"robot_other_robot25\": 6073, # 机械臂下料状态查询\n\t\"robot_other_robot26\": 6074, # 机械臂下料状态查询\n\n\t\"robot_config_downloadmap_req\": 4011,\n\t\"robot_config_uploadmap_req\": 4010,\n\t\"robot_config_removemap_req\": 4012,\n\t\"robot_config_require_req\": 4001,\n\t\"robot_config_release_req\": 4002,\n\n\n\t\"taskchain_wait_countdown\": 10010,\n\t\"taskchain_wait_other1\": 10011,\n\t\"taskchain_wait_other2\": 10012,\n\n\n\t\"robot_other_roller_front_roll_req\": 6151, # 预留6，\n\t\"robot_other_roller_back_roll_req\": 6152, # 预留6，\n\t\"robot_other_roller_left_roll_req\": 6153, # 预留6，\n\t\"robot_other_roller_right_roll_req\": 6154, # 预留6，\n\t\"robot_other_roller_front_load_req\": 6155, # 辊筒(皮带)前上料，\n\t\"robot_other_roller_front_unload_req\": 6156, # 辊筒(皮带)前下料，\n\t\"robot_other_roller_front_pre_load_req\": 6157, # 预留6，\n\t\"robot_other_roller_back_load_req\": 6158, # 预留6，\n\t\"robot_other_roller_back_unload_req\": 6159, # 预留6，\n\t\"robot_other_roller_back_pre_load_req\": 6160, # 预留6，\n\t\"robot_other_roller_left_load_req\": 6161, # 预留6，\n\t\"robot_other_roller_left_unload_req\": 6162, # 预留6，\n\t\"robot_other_roller_right_load_req\": 6163, # 预留6，\n\t\"robot_other_roller_right_unload_req\": 6164, # 预留6，\n\t\"robot_other_roller_left_pre_load_req\": 6165, # 预留6，\n\t\"robot_other_roller_right_pre_load_req\": 6166, # 预留6，\n\t\"robot_other_roller_stop_req\": 6167, # 停止，\n\t\"robot_other_roller_reset_req\": 6178, # 停止，\n\t\"robot_other_roller_left_right_inverse_req\": 6168, # 辊筒(皮带)左右反向，\n\t\"robot_other_roller_front_back_inverse_req\": 6169, # 辊筒(皮带)前后反向，\n\n\t\"robot_other_robot170\": 6170, # 层数设定\n\t\"robot_other_robot175\": 6175, # 辊筒(皮带)前上料查询\n\t\"robot_other_robot176\": 6176, # 辊筒(皮带)前下料查询\n\t\"robot_other_robot177\": 6177, # 指定的层数是否有料查询\n\t\"robot_other_robot178\": 6178, # 辊筒(皮带)信息\n\n\t\"robot_other_robot179\": 6179, # 开关夹信息\n\t\"robot_other_robot180\": 6180, # 开关夹打开\n\t\"robot_other_robot181\": 6181, # 开关夹关闭\n\t\"robot_other_robot182\": 6182, # 上下料完成按钮状态查询\n\t\n\t\"robot_config_clear_goodsshape_req\": 4356, # 清除货架描述文件\n\t\"robot_config_set_shelfshape_req\":4357, # 设置货架描述文件\n\t\"robot_config_DI_req\": 4140, # 配置 DI\n\t\"robot_other_getdi_req\": 6021, # 获取虚拟 DI 状态\n\t\"robot_other_setdi_req\": 6020, # 设置虚拟 DI\n\t\"robot_other_setdo_req\": 6001, # 设置 DO\n\n\t\"robot_status_jack_req\": 1027, # 查询顶升机构状态\n}\n\nAPI_PORT_STATE = 19204\nAPI_PORT_CTRL = 19205\nAPI_PORT_TASK = 19206\nAPI_PORT_CONFIG = 19207\nAPI_PORT_KERNEL = 19208\nAPI_PORT_OTHER = 19210\nAPI_PORT_PERPHERIAL = 19214\n\ng_lock_type = {\n\tAPI_PORT_STATE:\t\"lock_status\",\n\tAPI_PORT_CTRL:\t\"lock_ctrl\",\n\tAPI_PORT_TASK:\t\"lock_task\",\n\tAPI_PORT_CONFIG:\"lock_conf\",\n\tAPI_PORT_KERNEL:\"lock_core\",\n\tAPI_PORT_OTHER:\t\"lock_other\",\n\tAPI_PORT_PERPHERIAL:\"lock_perpherial\"\n}\n\n#主动检测每个端口\ndef checkLink(agvId):\n\tip = g_agvList[agvId][\"ip\"]\n\tports = [API_PORT_STATE, API_PORT_CTRL, API_PORT_TASK, API_PORT_CONFIG,API_PORT_KERNEL, API_PORT_OTHER]\n\tfor p in ports:\n\t\tlock = _getLock(agvId,g_lock_type[p])\n\t\tlockImp.acquire(lock)\n\t\ttry:\n\t\t\tif _getClient(agvId,ip,p) is None:\n\t\t\t\tlog.info(agvId,\"checkLink failed\",ip,p)\n\t\t\t\treturn False\n\t\t\tif API_PORT_STATE == p:\n\t\t\t\t_getClient(agvId,ip,p).clear()\n\t\tfinally:\n\t\t\tlockImp.release(lock)\n\treturn True\n\n\n# 机器人状态 API\ndef ctrl(agvId, type, data):\n\treturn _handle(agvId, API_PORT_CTRL, type, data)\n\n\n# 机器人控制 API\ndef status(agvId, type, data):\n\treturn _handle(agvId, API_PORT_STATE, type, data)\n\n\n# 机器人任务 API\ndef task(agvId, type, data):\n\tlog.info('-------------------------来到driver中的agvApi下的task函数中------------------（12）--------------------')\n\tfor i in range(5):\n\t\ttry:\n\t\t\tret = _handle(agvId, API_PORT_TASK, type, data)\n\t\t\treturn ret\n\t\texcept Exception as e:\n\t\t\tlog.exception(\"agvApi send task, agvId=\"+agvId,e)\n\t\t\tif i == 4:\n\t\t\t\traise\n\t\ttime.sleep(1)\n\t\t\n \n# 机器人配置 API\ndef conf(agvId, type, data):\n\treturn _handle(agvId, API_PORT_CONFIG, type, data)\n\n\n# 机器人核心 API\ndef core(agvId, type, data):\n\treturn _handle(agvId, API_PORT_KERNEL, type, data)\n\n\n# 其他API\ndef other(agvId, type, data):\n\tlog.info('------------------------other函数-----------------------------------------')\n\treturn _handle(agvId, API_PORT_OTHER, type, data)\n\n#自研API\ndef perpherial(agvId, type, data):\n\tfor i in range(5):\n\t\ttry:\n\t\t\tret = _handle(agvId, API_PORT_PERPHERIAL, type, data)\n\t\t\treturn ret\n\t\texcept Exception as e:\n\t\t\tlog.exception(\"agvApi send custom, agvId=\"+agvId,e)\n\t\t\tif i == 4:\n\t\t\t\traise \n\t\ttime.sleep(1)\n\t\ndef getAgvInfo(agvId):\n\tglobal g_agvList\n\treturn g_agvList[agvId]\n\n\ndef _handle(agvId, port, type, data):\n\tglobal g_cmd\n\tlockKey = g_lock_type[port]\n\ttypeId = type\n\tif isinstance(type, str):\n\t\ttypeId = g_cmd[type]\n\tlock = _getLock(agvId, lockKey)\n\ttry:\n\t\tlockImp.acquire(lock)\n\t\tresult = _request(agvId, port, typeId, data)\n\t\treturn result\n\tfinally:\n\t\tlockImp.release(lock)\n\n\ndef _loadAgvList():\n\tproName = local.get(\"project\",\"name\") \n\tfile = \"/../../agvCtrl/projects/\"+proName+\"/\" + local.get(\"project\",\"agvcfg\") #TODO\n\t#file = \"../../agvCtrl/projects/\"+proName+\"/\" + local.get(\"project\",\"agvcfg\") \n\twith open(os.path.abspath(os.path.dirname(__file__) + file), 'r') as f:\t\t\n\t\tagvList = {}\n\t\taa = json.load(f)\n\t\tfor a in aa:\n\t\t\tif aa[a][\"enable\"].lower() == \"false\":\n\t\t\t\tcontinue\n\t\t\tagvList[a] = aa[a]\n\t\treturn agvList\n\n\ng_agvList = _loadAgvList()\ng_sn = -1\n\ndef _getLock(agvId, lockKey):\n\tglobal g_agvList\n\tif lockKey not in g_agvList[agvId]:\n\t\tg_agvList[agvId][lockKey] = lockImp.create(\"agv.\"+agvId+\".\"+lockKey)\n\treturn g_agvList[agvId][lockKey]\n\n\ndef _request(agvId, port, type, data):\n\tif port == API_PORT_PERPHERIAL and \"perpherialIP\" in g_agvList[agvId]:\n\t\tip = g_agvList[agvId][\"perpherialIP\"]\n\telse:\n\t\tip = g_agvList[agvId][\"ip\"]\n\t# log.debug(\"send type \" + str(type) + \" \" + agvId)\n\tbuf = encodeData(agvId, data, type)\n\t# 长连接\n\tresult = sendCmd(agvId, buf, ip, port, type)\n\t# log.debug(\"read type \" + str(type) + \" \" + agvId)\n\treturn result\n \ndef sendCmd(agvId, buf, ip, port, type):\n\tif not isClient(agvId):\n\t\treturn agvServer.sendCmd(agvId, buf, ip, port, type)\n\t\t\n\t_send(agvId,ip, port, buf.buf)\n\twhile True:\n\t\twhile True:\n\t\t\theadData = _recv(agvId,ip, port, 1)\n\t\t\tif headData[0] != 0x5A:\n\t\t\t\tcontinue\n\t\t\theadData = _recv(agvId,ip, port, 1)\n\t\t\tif headData[0] == 0x01:\n\t\t\t\tbreak\n\t\t\t\t\n\t\theadData = _recv(agvId,ip, port, 14) \n\t\tbodylen,retType = getDataLen(agvId, headData, ip ,port, buf.buf) \n\t\tif bodylen == 0:\n\t\t\treturn {}\n\t\trecData = _recv(agvId,ip, port, bodylen) \n\t\tinBuf = buffer.inBuffer(recData)\n\t\trecJsonData = inBuf.getStr(inBuf.remainLen)#.replace(\"'\", \"\\\"\")\n\t\tobj = json.loads(recJsonData)\n\t\tresult= _checkError(agvId, type, obj) \n\t\treturn result \n\t\t\n\ndef encodeData(agvId, data, type):\n\tdef _getSn():\n\t\tglobal g_sn\n\t\tg_sn += 1\n\t\tif g_sn >= 65535:\n\t\t\tg_sn = 0\n\t\treturn g_sn\n\n\tjsonData = \"\"\n\tif data:\n\t\tjsonData = json.dumps(data, separators=(',', ':'))\n\tbuf = buffer.outBuffer()\n\tbuf.setBytes(b\"\\x5A\\x01\")\n\tsn = _getSn()\n\tbuf.setUInt16(sn)\n\tbuf.setUInt32(len(jsonData))\n\tbuf.setUInt16(type)\n\tbuf.setBytes([0x0] * 6)\n\tbuf.setStr(jsonData)\n\t# if type != 1100:\n\t# \tprint(\"_request type \" + str(type) + \" \" + agvId + \" \" + jsonData)\n\treturn buf\n\n\ndef getDataLen(agvId, recData, ip ,port, raw):\n\tif recData is None:\n\t\tlog.error(agvId, \"agv head is none\")\n\t\traise IOError(\"AGV协议头为空\")\n\tinBuf = buffer.inBuffer(recData) \n\tsn= inBuf.getUInt16()\n\tlen= inBuf.getUInt32() \n\tretType = inBuf.getUInt16()\n\tif retType >= 60000:\n\t\tlog.error(agvId, \"decode error:\", retType)\n\t\traise IOError(\"AGV协议返回错误码\" + str(retType)) \n\treturn len,retType\n\n\ndef _checkError(agvId, type, data):\n\tif \"ret_code\" not in data:\n\t\treturn data\n\tcode = data[\"ret_code\"]\n\tif code == 0:\n\t\treturn data\n\tmsg = \"\"\n\tif \"err_msg\" in data:\n\t\tmsg = data[\"err_msg\"]\n\traise IOError(agvId + \"执行\" + str(type) + \"错误\" + \",code=\" + str(code) + \",msg=\" + msg)\n\ndef isClient(agvId):\n\tglobal g_agvList\n\tif agvId in g_agvList:\n\t\tif \"tcpType\" in g_agvList[agvId]:\n\t\t\treturn g_agvList[agvId][\"tcpType\"] == \"client\"\n\treturn True\n\t\ndef _getClient(agvId,ip,port):\n\tif isClient(agvId):\n\t\treturn tcpSocketLong._getClient(agvId,ip,port)\n\treturn agvServer.getClient(agvId,ip, port)\n\t\ndef _send(agvId,ip, port, buf):\n\treturn tcpSocketLong.send(agvId,ip, port, buf)\n\ndef _recv(agvId,ip, port, len):\n\treturn tcpSocketLong.recv(agvId,ip, port, len)\n\nfor a in g_agvList:\n\tif not isClient(a):\n\t\tagvServer.start()\n\t\tbreak\n\t\t\n################## unit test ##################\ndef test_request():\n\timport utility\n\timport socket\n\timport threading, time\n\tglobal g_sn\n\tg_sn = 0\n\t# 这例子的type收发不一样的\n\td22 = b\"\\x5A\\x01\\x00\\x01\\x00\\x00\\x00\\x3C\\x2A\\xFC\\x00\\x00\\x00\\x00\\x00\\x00\\x7B\\x22\\x72\\x65\\x74\\x5F\\x63\\x6F\\x64\\x65\\x22\\x3A\\x30\\x2C\\x22\\x78\\x22\\x3A\\x36\\x2E\\x30\\x2C\\x22\\x79\\x22\\x3A\\x32\\x2E\\x30\\x2C\\x22\\x61\\x6E\\x67\\x6C\\x65\\x22\\x3A\\x31\\x2E\\x35\\x37\\x2C\\x22\\x63\\x6F\\x6E\\x66\\x69\\x64\\x65\\x6E\\x63\\x65\\x22\\x3A\\x30\\x2E\\x39\\x7D\"\n\n\tdef runFunc(server):\n\t\tclient, address = server.accept()\n\t\tdata = client.recv(4096)\n\t\td = [0x5A, 0x01, 0x00, 0x01, 0x00, 0x00, 0x00, 0x1C, 0x07, 0xD2, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7B, 0x22,\n\t\t\t 0x78, 0x22, 0x3A, 0x31, 0x30, 0x2E, 0x30, 0x2C, 0x22, 0x79, 0x22, 0x3A, 0x33, 0x2E, 0x30, 0x2C, 0x22, 0x61,\n\t\t\t 0x6E, 0x67, 0x6C, 0x65, 0x22, 0x3A, 0x30, 0x7D]\n\t\tobj = json.loads(\n\t\t\tb\"\\x7B\\x22\\x78\\x22\\x3A\\x31\\x30\\x2E\\x30\\x2C\\x22\\x61\\x6E\\x67\\x6C\\x65\\x22\\x3A\\x30\\x2C\\x22\\x79\\x22\\x3A\\x33\\x2E\\x30\\x7D\".decode(\n\t\t\t\t\"utf-8\"))\n\t\ttry:\n\t\t\tassert len(data) == len(d)\n\t\t\tassert obj == {\"x\": 10.0, \"y\": 3.0, \"angle\": 0}\n\t\t\tclient.send(d22)\n\t\texcept Exception as e:\n\t\t\tprint(\"agvApi\",e)\n\t\t\traise e\n\t\ttime.sleep(0.1)\n\n\tserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\tserver.bind(('192.168.31.111', 19205))\n\tserver.listen(19205)\n\tt = threading.Thread(target=runFunc, args=(server,))\n\tt.start()\n\ttime.sleep(2)\n\td = {\"x\": 10.0, \"y\": 3.0, \"angle\": 0}\n\tglobal g_agvList\n\tg_agvList[\"AGVTEST\"] = {\"ip\": \"192.168.31.196\"}\n\td2 = _request(\"AGVTEST\", 19205, 2002, d)\n\tobj = {\"ret_code\": 0,\n\t\t \"x\": 6.0,\n\t\t \"y\": 2.0,\n\t\t \"angle\": 1.57,\n\t\t \"confidence\": 0.9}\n\tassert d2 == obj\n\n\nif __name__ == '__main__':\n\twhile True:\n\t\tstatus(\"AGV01\",\"robot_status_all1_req\",None)\n\t\tcheckLink(\"AGV01\")\n\t\ttime.sleep(1)\n\t# import utility\n\t# utility.run_tests(__file__)\n\t# test_request()\n\t#while True:\n\t#\tprint (checkLink(\"oulei\"))\n\t#\ttime.sleep(2)\n","sub_path":"branches/driver/seerAgv/agvApi.py","file_name":"agvApi.py","file_ext":"py","file_size_in_byte":13556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"376176014","text":"import os.path\n\nimport pandas\nfrom sklearn import neighbors\nfrom sklearn.externals import joblib\n\nfrom data_result import DataResult, AnyResult\n\n\nclass Restaurant:\n def __init__(self):\n self.restaurants_address = pandas.read_csv('data/restaurants-address.csv')\n self.order_items = pandas.read_csv('data/order-items.csv')\n\n def get_restaurants(self):\n restaurants_address = self.restaurants_address[self.restaurants_address['city'] == 'Yerevan']\n return DataResult(restaurants_address)\n\n def get_restaurant_items(self, restaurant_id):\n order_items = self.order_items[self.order_items['Restaurant'] == restaurant_id]\n return DataResult(order_items)\n\n def get_restaurant_item_cooking_speed(self, restaurant_id, item_number):\n model = self.__load_cooking_speed_model(restaurant_id, item_number)\n x = self.__to_nested_list(range(0, 24 * 60 * 60, 10 * 60))\n y = model.predict(x).tolist()\n\n for idx, val in enumerate(y):\n time = x[idx][0]\n x[idx][0] = '%02d:%02d' % ((time / 60 / 60) % 24, (time / 60) % 60)\n x[idx].append(val / 60)\n\n return AnyResult(x)\n\n def estimate_cooking_time(self, partner_id, restaurant_id, item_number, time, week_day):\n model = self.__load_cooking_speed_model(restaurant_id, item_number)\n return int(model.predict([[time]])[0])\n\n def generate_cooking_speed_data(self):\n orders = pandas.read_csv('data/orders.csv')\n data = pandas.merge(orders, self.order_items, left_on='ID', right_on='OrderNumber')\n\n data['cookingTime'] = data['TimePick'] - data['TimePlace']\n data['time'] = data.TimePlace - data.TransDate\n data = data[data['time'] > 0]\n data = data[data['cookingTime'] > 0]\n data = data[data['time'].notnull()]\n data = data[data['cookingTime'].notnull()]\n\n result = list()\n\n restaurants = data.groupby('Restaurant_x')\n\n for restaurant_id, data_restaurant in restaurants:\n if len(data_restaurant) < 20:\n continue\n items = data_restaurant.groupby('ItemNumber')\n\n item_data = pandas.DataFrame()\n item_data['cookingTime'] = data_restaurant['cookingTime']\n item_data['time'] = data_restaurant['time']\n path = 'data/cooking-speed/%d-0.csv' % (int(restaurant_id))\n item_data.to_csv(path)\n\n for item_number, item_data_all in items:\n if len(item_data_all) < 15:\n continue\n item_data = pandas.DataFrame()\n item_data['cookingTime'] = item_data_all['cookingTime']\n item_data['time'] = item_data_all['time']\n\n path = 'data/cooking-speed/%d-%d.csv' % (int(restaurant_id), int(item_number))\n\n item_data.to_csv(path)\n\n result.append({\n \"restaurantId\": restaurant_id,\n \"itemNumber\": item_number,\n \"itemCounts\": len(item_data),\n \"path\": path,\n })\n\n return AnyResult(result)\n\n def __load_cooking_speed_model(self, restaurant_id, item_number):\n path = 'model/cooking-speed/knn-%d-%d.pkl' % (int(restaurant_id), int(item_number))\n if os.path.isfile(path):\n return joblib.load(path)\n res = self.__train_cooking_speed_model(path, restaurant_id, item_number)\n if not res and item_number:\n return self.__load_cooking_speed_model(restaurant_id, 0)\n return joblib.load(path)\n\n def __load_cooking_speed_data(self, restaurant_id, item_number):\n path = 'data/cooking-speed/%d-%d.csv' % (int(restaurant_id), int(item_number))\n if not os.path.isfile(path):\n return None\n return pandas.read_csv(path)\n\n def __train_cooking_speed_model(self, path, restaurant_id, item_number=0):\n data = self.__load_cooking_speed_data(restaurant_id, item_number)\n if data is None:\n return False\n x = self.__to_nested_list(data['time'])\n y = data['cookingTime'].as_matrix()\n knn = neighbors.KNeighborsRegressor(n_neighbors=len(x) / 6, weights='uniform')\n knn.fit(x, y)\n joblib.dump(knn, path)\n return True\n\n def __to_nested_list(self, arr):\n data = []\n for item in arr:\n data.append([item])\n return data\n","sub_path":"service/restaurant.py","file_name":"restaurant.py","file_ext":"py","file_size_in_byte":4419,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"341959561","text":"#! /usr/bin/python3\n\nimport re\nimport os\n\nscript_path = os.getcwd()\n\npath = \"{}/src\".format(script_path)\n\n\ndef get_files(path):\n files = []\n\n for (dirpath, dirnames, filenames) in os.walk(path):\n for f in filenames:\n files.append(os.path.join(dirpath, f))\n\n for d in dirnames:\n files.extend(get_files(os.path.join(dirpath, d)))\n\n break\n\n return files\n\n\ndef include_gurad(header):\n t = header.replace(path, '').replace('.', '_').replace(\n '/', '_').replace('\\\\', '_').split('_')\n t = list(filter(lambda s: len(s) > 0, t))\n t.insert(0, 'framework')\n return '_'.join(s.upper() for s in t)\n\n\ndef replace_guard(data, guard):\n new_guard = \"#ifndef {0}\\n#define {0}\\n\".format(guard)\n\n data = re.sub(\n r'#ifndef\\s(?P[a-zA-Z_\\\\]*)\\n#define\\s\\1\\n', new_guard, data)\n return data\n\n\nheaders = list(filter(lambda f: f.endswith('.hpp'), get_files(path)))\n\n\nfor header in headers:\n f = open(header, 'r')\n filedata = f.read()\n f.close()\n\n print(header)\n\n guard = include_gurad(header)\n\n newdata = replace_guard(filedata, guard)\n\n f = open(header, 'w')\n f.write(newdata)\n f.close()\n","sub_path":"tools/fix_include_guards.py","file_name":"fix_include_guards.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"365249770","text":"from django.conf.urls import url,include\nfrom django.contrib import admin\nimport views\n\nurlpatterns = [\n url(r'^DashBoard/$', views.DashBoard),\n url(r'^NoticeList/$', views.NoticeList),\n url(r'^MsgList/$', views.MsgList),\n url(r'^MsgList/ViewMsg/$', views.ViewMsg),\n url(r'^MsgList/ViewMsg/MsgRead/$', views.MsgRead),\n url(r'^MemberEdit/$', views.MemberEdit),\n url(r'^MemberEdit1/$', views.MemberEdit1),\n url(r'^MemberEdit/MemberSave/$', views.MemberSave),\n url(r'^MemberEdit1/MemberSave1/$', views.MemberSave1),\n url(r'^MemberEdit/MemberList/$', views.MemberList),\n url(r'^MemberList/$', views.MemberList),\n url(r'^MemberList/ViewMember/$', views.ViewMember),\n url(r'^MemberList/ViewMemberSelf/$', views.ViewMemberSelf),\n url(r'^MemberList/ViewReCome/$', views.ViewReCome),\n url(r'^MemberList/SetAudit/$', views.SetAudit),\n url(r'^MemberList/SetAudit1/$', views.SetAudit1),\n url(r'^MemberList/MemberEdit/$', views.MemberEdit),\n url(r'^MemberOrder/$', views.MemberOrder),\n url(r'^MemberOrder/Deliver/$', views.Deliver),\n url(r'^MemberOrder/Deliver/DeliverSub/$', views.DeliverSub),\n url(r'^UserMap/$', views.UserMap),\n url(r'^UserMap/GetMap/$', views.GetMap),\n url(r'^ComBank/$', views.ComBank),\n url(r'^Promotion/$', views.Promotion),\n url(r'^Promotion/MoneyAudit/$', views.MoneyAudit),\n url(r'^Promotion/MoneySub/$', views.MoneySub),\n url(r'^AdviceList/$', views.AdviceList),\n url(r'^AdviceList/AdviceView/$', views.AdviceView),\n url(r'^AdviceList/AdviceView/AdviceSub/$', views.AdviceSub),\n url(r'^SubService/$', views.SubService),\n url(r'^SubService/SubServiceSave/$', views.SubServiceSave),\n]\n","sub_path":"Services/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"516713121","text":"# coding=utf-8\nimport numpy as np\nfrom scipy import fftpack as fft\n\n\ndef charge_density_deposition(x, dx, x_particles, particle_charge):\n \"\"\"scatters charge from particles to grid\n uses linear interpolation\n x_i | __________p___| x_i+1\n for a particle $p$ in cell $i$ of width $dx$ the location in cell is defined as\n $$X_p = x_p - x_i$$\n then, $F_r = X_p/dx$ is the fraction of charge going to the right side of the cell\n (as $X_p \\to dx$, the particle is closer to the right cell)\n while $F_l = 1-F_r$ is the fraction of charge going to the left\n\n numpy.bincount is used to count particles in cells\n the weights are the fractions for each cell\n\n to change the index for the right going (keeping periodic boundary conditions)\n numpy.roll is used\n \"\"\"\n logical_coordinates = (x_particles / dx).astype(int)\n right_fractions = x_particles / dx - logical_coordinates\n left_fractions = 1 - right_fractions\n charge_to_right = particle_charge * right_fractions\n charge_to_left = particle_charge * left_fractions\n charge_hist_to_right = np.roll(np.bincount(logical_coordinates, charge_to_right, minlength=x.size), +1)\n charge_hist_to_left = np.bincount(logical_coordinates, charge_to_left, minlength=x.size)\n return (charge_hist_to_right + charge_hist_to_left)\n\ndef current_density_deposition(x, dx, x_particles, particle_charge, velocity):\n \"\"\"scatters charge from particles to grid\n uses linear interpolation\n x_i | __________p___| x_i+1\n for a particle $p$ in cell $i$ of width $dx$ the location in cell is defined as\n $$X_p = x_p - x_i$$\n then, $F_r = X_p/dx$ is the fraction of charge going to the right side of the cell\n (as $X_p \\to dx$, the particle is closer to the right cell)\n while $F_l = 1-F_r$ is the fraction of charge going to the left\n\n numpy.bincount is used to count particles in cells\n the weights are the fractions for each cell\n\n to change the index for the right going (keeping periodic boundary conditions)\n numpy.roll is used\n \"\"\"\n current_hist = np.zeros((x.size, 3))\n logical_coordinates = (x_particles / dx).astype(int)\n right_fractions = (x_particles / dx - logical_coordinates).reshape(x_particles.size, 1)\n left_fractions = 1 - right_fractions\n current_to_right = particle_charge * velocity * right_fractions\n current_to_left = particle_charge * velocity * left_fractions\n # OPTIMIZE: vectorise this instead of looping over dimensions\n for dim in range(3):\n current_hist[:,dim] += np.bincount(logical_coordinates, current_to_left[:,dim], minlength=x.size)\n current_hist[:,dim] += np.roll(np.bincount(logical_coordinates, current_to_right[:,dim], minlength=x.size), +1)\n return current_hist\n\n\ndef interpolateField(x_particles, scalar_field, x, dx):\n \"\"\"gathers field from grid to particles\n\n the reverse of the algorithm from charge_density_deposition\n\n there is no need to use numpy.bincount as the map is\n not N (number of particles) to M (grid), but M to N, N >> M\n \"\"\"\n indices_on_grid = (x_particles / dx).astype(int)\n NG = scalar_field.size\n field = (x[indices_on_grid] + dx - x_particles) * scalar_field[indices_on_grid] +\\\n (x_particles - x[indices_on_grid]) * scalar_field[(indices_on_grid + 1) % NG]\n return field / dx\n\n\ndef PoissonSolver(rho, k, NG, epsilon_0=1, neutralize=True):\n \"\"\"solves the Poisson equation spectrally, via FFT\n\n the Poisson equation can be written either as\n (in position space)\n $$\\nabla \\cdot E = \\rho/\\epsilon_0$$\n $$\\nabla^2 V = -\\rho/\\epsilon_0$$\n\n Assuming that all functions in fourier space can be represented as\n $$\\exp{i(kx - \\omega t)}$$\n It is easy to see that upon Fourier transformation $\\nabla \\to ik$, so\n\n (in fourier space)\n $$E = \\rho /(ik \\epsilon_0)$$\n $$V = \\rho / (-k^2 \\epsilon_0)$$\n\n Calculate that, fourier transform back to position space\n and both the field and potential pop out easily\n\n The conceptually problematic part is getting the $k$ wave vector right\n # DOCUMENTATION: finish this description\n \"\"\"\n\n rho_F = fft.fft(rho)\n if neutralize:\n rho_F[0] = 0\n field_F = rho_F / (1j * k * epsilon_0)\n potential_F = field_F / (-1j * k * epsilon_0)\n field = fft.ifft(field_F).real\n # TODO: check for differences with finite difference field gotten from potential\n potential = fft.ifft(potential_F).real\n energy_presum = (rho_F * potential_F.conjugate()).real[:int(NG / 2)] / 2\n return field, potential, energy_presum","sub_path":"algorithms_grid.py","file_name":"algorithms_grid.py","file_ext":"py","file_size_in_byte":4560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"371628137","text":"\"\"\"Callback query handling.\"\"\"\nfrom telegram.ext import run_async\n\nfrom stickerfinder.helper.keyboard import main_keyboard\nfrom stickerfinder.helper.session import hidden_session_wrapper\nfrom stickerfinder.helper.callback import CallbackType, CallbackResult\nfrom stickerfinder.helper.telegram import call_tg_func\nfrom stickerfinder.helper.keyboard import (\n get_nsfw_ban_keyboard,\n get_fix_sticker_tags_keyboard,\n check_user_tags_keyboard,\n get_tag_this_set_keyboard,\n)\nfrom stickerfinder.helper.maintenance import (\n process_task,\n revert_user_changes,\n undo_user_changes_revert,\n distribute_newsfeed_tasks,\n change_language_of_task_changes,\n)\nfrom stickerfinder.helper.tag import (\n handle_next,\n send_tag_messages,\n initialize_set_tagging,\n send_tagged_count_message,\n)\nfrom stickerfinder.models import (\n Chat,\n Task,\n InlineQuery,\n Sticker,\n StickerSet,\n)\n\n\n@run_async\n@hidden_session_wrapper()\ndef handle_callback_query(bot, update, session, user):\n \"\"\"Handle callback queries from inline keyboards.\"\"\"\n query = update.callback_query\n data = query.data\n\n # Extract the callback type, task id\n [callback_type, payload, action] = data.split(':')\n callback_type = int(callback_type)\n action = int(action)\n\n chat = session.query(Chat).get(query.message.chat.id)\n tg_chat = query.message.chat\n\n # Handle task vote ban callbacks\n if CallbackType(callback_type).name == 'task_vote_ban':\n task = session.query(Task).get(payload)\n if CallbackResult(action).name == 'ban':\n task.sticker_set.banned = True\n call_tg_func(query, 'answer', ['Set banned'])\n else:\n task.sticker_set.banned = False\n call_tg_func(query, 'answer', ['Set unbanned'])\n\n if not task.reviewed:\n task.reviewed = True\n process_task(session, tg_chat, chat)\n\n # Handle task vote ban callbacks\n if CallbackType(callback_type).name == 'task_vote_nsfw':\n task = session.query(Task).get(payload)\n if CallbackResult(action).name == 'ban':\n task.sticker_set.nsfw = True\n call_tg_func(query, 'answer', ['Set tagged as nsfw'])\n else:\n task.sticker_set.nsfw = False\n call_tg_func(query, 'answer', ['Set no longer tagged as nsfw'])\n\n if not task.reviewed:\n task.reviewed = True\n process_task(session, tg_chat, chat)\n\n # Handle task user ban callbacks\n elif CallbackType(callback_type).name == 'check_user_tags':\n task = session.query(Task).get(payload)\n # Ban the user\n if CallbackResult(action).name == 'ban':\n task.user.banned = True\n call_tg_func(query, 'answer', ['User banned'])\n message = f'Your tagging activity seemed malicious. You have been banned.'\n call_tg_func(bot, 'send_message', [task.user.id, message], {'reply_markup': main_keyboard})\n elif CallbackResult(action).name == 'unban':\n task.user.banned = False\n call_tg_func(query, 'answer', ['User ban reverted'])\n message = f'Your ban has been lifted.'\n call_tg_func(bot, 'send_message', [task.user.id, message], {'reply_markup': main_keyboard})\n\n # Revert user changes\n elif CallbackResult(action).name == 'revert':\n task.reverted = True\n revert_user_changes(session, task.user)\n message = f'Your tagging activity seemed malicious. All of your tags have been reverted.'\n call_tg_func(bot, 'send_message', [task.user.id, message], {'reply_markup': main_keyboard})\n call_tg_func(query, 'answer', ['All user changes reverted'])\n elif CallbackResult(action).name == 'undo_revert':\n task.reverted = False\n undo_user_changes_revert(session, task.user)\n message = f'All of your tags have been restored.'\n call_tg_func(bot, 'send_message', [task.user.id, message], {'reply_markup': main_keyboard})\n call_tg_func(query, 'answer', ['User changes revert undone'])\n\n # Change the language of all changes of this task.\n elif CallbackResult(action).name == 'change_language':\n is_default_language = task.is_default_language\n change_language_of_task_changes(session, task)\n call_tg_func(query, 'answer', ['Language changed'])\n\n first = 'international' if is_default_language else 'english'\n second = 'english' if is_default_language else 'international'\n command = '/international' if is_default_language else '/english'\n message = f'It appears you have recently tagged stickers in {first}, while being in \"{second}\" mode. '\n message += f'Please use {command} beforehand next time. The tags have been corrected.'\n call_tg_func(bot, 'send_message', [task.user.id, message], {'reply_markup': main_keyboard})\n\n elif CallbackResult(action).name == 'ok':\n if not task.reviewed:\n task.reviewed = True\n process_task(session, tg_chat, chat)\n\n keyboard = check_user_tags_keyboard(task)\n call_tg_func(query.message, 'edit_reply_markup', [], {'reply_markup': keyboard})\n\n # Handle the \"Ban this set\" button\n elif CallbackType(callback_type).name == 'ban_set':\n sticker_set = session.query(StickerSet).get(payload.lower())\n if CallbackResult(action).name == 'ban':\n sticker_set.banned = True\n elif CallbackResult(action).name == 'ok':\n sticker_set.banned = False\n\n keyboard = get_nsfw_ban_keyboard(sticker_set)\n call_tg_func(query.message, 'edit_reply_markup', [], {'reply_markup': keyboard})\n\n # Handle the \"tag as nsfw\" button\n elif CallbackType(callback_type).name == 'nsfw_set':\n sticker_set = session.query(StickerSet).get(payload.lower())\n if CallbackResult(action).name == 'ban':\n sticker_set.nsfw = True\n elif CallbackResult(action).name == 'ok':\n sticker_set.nsfw = False\n\n keyboard = get_nsfw_ban_keyboard(sticker_set)\n call_tg_func(query.message, 'edit_reply_markup', [], {'reply_markup': keyboard})\n\n # Handle the \"tag as furry\" button\n elif CallbackType(callback_type).name == 'fur_set':\n sticker_set = session.query(StickerSet).get(payload.lower())\n if CallbackResult(action).name == 'ok':\n sticker_set.furry = False\n elif CallbackResult(action).name == 'ban':\n sticker_set.furry = True\n\n keyboard = get_nsfw_ban_keyboard(sticker_set)\n call_tg_func(query.message, 'edit_reply_markup', [], {'reply_markup': keyboard})\n\n # Change sticker set language\n elif CallbackType(callback_type).name == 'change_set_language':\n sticker_set = session.query(StickerSet).get(payload.lower())\n if CallbackResult(action).name == 'international':\n sticker_set.is_default_language = False\n elif CallbackResult(action).name == 'default':\n sticker_set.is_default_language = True\n\n keyboard = get_nsfw_ban_keyboard(sticker_set)\n call_tg_func(query.message, 'edit_reply_markup', [], {'reply_markup': keyboard})\n\n # Handle the \"next\" button in the newsfeed chat\n elif CallbackType(callback_type).name == 'newsfeed_next_set':\n sticker_set = session.query(StickerSet).get(payload.lower())\n task = session.query(Task) \\\n .filter(Task.type == Task.SCAN_SET) \\\n .filter(Task.sticker_set == sticker_set) \\\n .one()\n\n task.reviewed = True\n sticker_set.reviewed = True\n\n try:\n task_chat = task.processing_chat[0]\n distribute_newsfeed_tasks(bot, session, [task_chat])\n keyboard = get_nsfw_ban_keyboard(sticker_set)\n call_tg_func(query.message, 'edit_reply_markup', [], {'reply_markup': keyboard})\n except: # noqa\n return\n\n session.commit()\n\n if task_chat is None or task_chat.current_task is None:\n call_tg_func(query, 'answer', ['No new stickers sets'])\n\n try:\n if task.chat and task.chat.type == 'private':\n if sticker_set.banned:\n call_tg_func(bot, 'send_message', [task.chat.id, f'Stickerset {sticker_set.name} has been banned.'])\n\n else:\n keyboard = get_tag_this_set_keyboard(sticker_set.name)\n message = f'Stickerset {sticker_set.name} has been added.'\n if sticker_set.nsfw or sticker_set.furry:\n message += f\"\\n It has been tagged as: {'nsfw' if sticker_set.nsfw else ''} \"\n message += f\"{'furry' if sticker_set.furry else ''}\"\n\n call_tg_func(bot, 'send_message', [task.chat.id, message], {'reply_markup': keyboard})\n return\n except: # noqa\n pass\n\n # Handle the \"Skip this sticker\" button\n elif CallbackType(callback_type).name == 'next':\n current_sticker = chat.current_sticker\n handle_next(session, bot, chat, tg_chat, user)\n if chat.current_sticker is not None:\n keyboard = get_fix_sticker_tags_keyboard(current_sticker.file_id)\n call_tg_func(query.message, 'edit_reply_markup', [], {'reply_markup': keyboard})\n\n # Handle the \"Stop tagging\" button\n elif CallbackType(callback_type).name == 'cancel':\n # Send a message to the user, which shows how many stickers he already tagged,\n # if the user was just tagging some stickers.\n # Otherwise just send the normal cancel success message.\n if not send_tagged_count_message(session, bot, user, chat):\n call_tg_func(query, 'answer', ['All active commands have been canceled'])\n call_tg_func(tg_chat, 'send_message', ['All running commands are canceled'],\n {'reply_markup': main_keyboard})\n\n chat.cancel()\n\n # Handle \"Fix this sticker's tags\"\n elif CallbackType(callback_type).name == 'edit_sticker':\n sticker = session.query(Sticker).get(payload)\n chat.current_sticker = sticker\n if not chat.full_sticker_set and not chat.tagging_random_sticker:\n chat.fix_single_sticker = True\n send_tag_messages(chat, tg_chat, user)\n\n elif CallbackType(callback_type).name == 'tag_set':\n initialize_set_tagging(bot, tg_chat, session, payload, chat, user)\n\n return\n\n\n@run_async\n@hidden_session_wrapper()\ndef handle_chosen_inline_result(bot, update, session, user):\n \"\"\"Save the chosen inline result.\"\"\"\n result = update.chosen_inline_result\n splitted = result.result_id.split(':')\n\n # This is a result from a banned user\n if len(splitted) < 2:\n return\n\n [search_id, file_id] = splitted\n inline_query = session.query(InlineQuery).get(search_id)\n\n # This happens, if the user clicks on a link in sticker set search.\n if inline_query.mode == InlineQuery.SET_MODE:\n sticker = session.query(Sticker).get(file_id)\n if sticker is None:\n return\n\n inline_query.sticker_file_id = file_id\n","sub_path":"stickerfinder/telegram/callback.py","file_name":"callback.py","file_ext":"py","file_size_in_byte":11188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"200779418","text":"from .general_part import GeneralPart\nimport config\nimport time\n\n\nclass ClickPause(GeneralPart):\n\n def __init__(self, driver=None, testing_page=config.MISTOFM_URL):\n super().__init__(driver=driver)\n self.driver = driver\n self.testing_page = testing_page\n\n def __call__(self, *args, **kwargs):\n print('ClickPause')\n self.test_click_pause()\n\n @classmethod\n def __repr__(cls):\n return 'click_pause'\n\n def test_click_pause(self):\n # self.driver.get(self.testing_page)\n print('test_click_pause')\n\n\n # self.click_news_or_mark_or_subs(news_or_mark_or_subs='UserNews')\n\n click_pause = self.driver.find_elements_by_css_selector(\"*[id='pause']\")\n print('click_pause')\n click_pause[0].click()\n time.sleep(3)\n\n self.driver.get(self.testing_page)\n time.sleep(3)\n\n","sub_path":"tests/site_parts/click_pause.py","file_name":"click_pause.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"155299506","text":"import numpy as np\nimport theano.tensor as T\n\nimport lasagne\nfrom lasagne import init\nfrom lasagne import nonlinearities\nfrom lasagne.layers.base import Layer\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\nimport theano\nfrom theano import printing\nimport theano.sparse as sparse\nimport sparse_new\nimport scipy.sparse as sp\n__all__ = [\n \"NewDenseLayer\",\n]\n\nclass SparseDenseLayer(Layer):\n \"\"\"\n lasagne.layers.DenseLayer(incoming, num_units,\n W=lasagne.init.GlorotUniform(), b=lasagne.init.Constant(0.),\n nonlinearity=lasagne.nonlinearities.rectify, **kwargs)\n\n A fully connected layer.\n\n Parameters\n ----------\n incoming : a :class:`Layer` instance or a tuple\n The layer feeding into this layer, or the expected input shape\n\n num_units : int\n The number of units of the layer\n\n W : Theano shared variable, expression, numpy array or callable\n Initial value, expression or initializer for the weights.\n These should be a matrix with shape ``(num_inputs, num_units)``.\n See :func:`lasagne.utils.create_param` for more information.\n\n b : Theano shared variable, expression, numpy array, callable or ``None``\n Initial value, expression or initializer for the biases. If set to\n ``None``, the layer will have no biases. Otherwise, biases should be\n a 1D array with shape ``(num_units,)``.\n See :func:`lasagne.utils.create_param` for more information.\n\n nonlinearity : callable or None\n The nonlinearity that is applied to the layer activations. If None\n is provided, the layer will be linear.\n\n Examples\n --------\n >>> from lasagne.layers import InputLayer, DenseLayer\n >>> l_in = InputLayer((100, 20))\n >>> l1 = DenseLayer(l_in, num_units=50)\n\n Notes\n -----\n If the input to this layer has more than two axes, it will flatten the\n trailing axes. This is useful for when a dense layer follows a\n convolutional layer, for example. It is not necessary to insert a\n :class:`FlattenLayer` in this case.\n \"\"\"\n def __init__(self, incoming, num_units, W=None, R=None,\n b=init.Constant(0.),\n nonlinearity=nonlinearities.rectify,\n **kwargs):\n super(SparseDenseLayer, self).__init__(incoming, **kwargs)\n self.nonlinearity = (nonlinearities.identity if nonlinearity is None\n else nonlinearity)\n self._srng = RandomStreams(lasagne.random.get_rng().randint(1, 2147462579))\n self.num_units = num_units\n\n num_inputs = int(np.prod(self.input_shape[1:]))\n if (W is None):\n W=theano.shared(sp.csc_matrix(np.floatX(np.eye(num_inputs, num_units))))\n self.W = self.add_param(W, (num_inputs, num_units), name=\"W\")\n if (R is None):\n R=theano.shared(sp.csc_matrix(np.floatX(np.eye(num_inputs, num_units))))\n self.R = self.add_param(R, (num_inputs, num_units), name=\"R\")#, trainable=False)\n\n\n if b is None:\n self.b = None\n else:\n self.b = self.add_param(b, (num_units,), name=\"b\",\n regularizable=False)\n\n def get_output_shape_for(self, input_shape):\n return (input_shape[0], self.num_units)\n\n def get_output_for(self, input, **kwargs):\n if input.ndim > 2:\n # if the input has more than two dimensions, flatten it into a\n # batch of feature vectors.\n input = input.flatten(2)\n activation = sparse.new_structured_dot(input, self.W, self.R)\n if self.b is not None:\n activation = activation + self.b.dimshuffle('x', 0)\n return self.nonlinearity(activation)\n\n\n","sub_path":"Compare_new/newdensesparse.py","file_name":"newdensesparse.py","file_ext":"py","file_size_in_byte":3719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"147397653","text":"# encoding: utf-8\n\n\"\"\"\nFootnotes and endnotes part objects\n\"\"\"\n\nfrom __future__ import (\n absolute_import, division, print_function, unicode_literals\n)\n\nimport os\n\nfrom ..opc.constants import CONTENT_TYPE as CT\nfrom ..opc.packuri import PackURI\nfrom ..opc.part import XmlPart\nfrom ..oxml import parse_xml\nfrom ..fntent.fntent import Footnotes, Endnotes\nfrom .story import BaseStoryPart\n\n\nclass FootnotesPart(BaseStoryPart):\n \"\"\"\n Proxy for the footnotes.xml part containing footnote definitions for a document.\n \"\"\"\n @classmethod\n def default(cls, package):\n \"\"\"\n Return a newly created footnote part, containing a default set of\n elements.\n \"\"\"\n partname = PackURI('/word/footnotes.xml')\n content_type = CT.WML_FOOTNOTES\n element = parse_xml(cls._default_footnotes_xml())\n return cls(partname, content_type, element, package)\n\n @property\n def footnotes(self):\n \"\"\"\n The |_Footnotes| instance containing the footnotes ( element\n proxies) for this footnotes part.\n \"\"\"\n return Footnotes(self.element, self)\n\n @classmethod\n def _default_footnotes_xml(cls):\n \"\"\"\n Return a bytestream containing XML for a default footnotes part.\n \"\"\"\n path = os.path.join(\n os.path.split(__file__)[0], '..', 'templates',\n 'default-footnotes.xml'\n )\n with open(path, 'rb') as f:\n xml_bytes = f.read()\n return xml_bytes\n\n\nclass EndnotesPart(BaseStoryPart):\n \"\"\"\n Proxy for the endnotes.xml part containing endnote definitions for a document.\n \"\"\"\n @classmethod\n def default(cls, package):\n \"\"\"\n Return a newly created endnote part, containing a default set of\n elements.\n \"\"\"\n partname = PackURI('/word/endnotes.xml')\n content_type = CT.WML_FOOTNOTES\n element = parse_xml(cls._default_endnotes_xml())\n return cls(partname, content_type, element, package)\n\n @property\n def endnotes(self):\n \"\"\"\n The |_Endnotes| instance containing the endnotes ( element\n proxies) for this endnotes part.\n \"\"\"\n return Endnotes(self.element, self)\n\n @classmethod\n def _default_endnotes_xml(cls):\n \"\"\"\n Return a bytestream containing XML for a default endnotes part.\n \"\"\"\n path = os.path.join(\n os.path.split(__file__)[0], '..', 'templates',\n 'default-endnotes.xml'\n )\n with open(path, 'rb') as f:\n xml_bytes = f.read()\n return xml_bytes\n","sub_path":"docx/parts/fntent.py","file_name":"fntent.py","file_ext":"py","file_size_in_byte":2622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"300359945","text":"class Solution(object):\n def maximumTime(self, time):\n \"\"\"\n :type time: str\n :rtype: str\n \"\"\"\n hour, mins = time.split(\":\")\n ans = \"\"\n\n ans = \"9\" if mins[1] == \"?\" else mins[1]\n ans += \"5\" if mins[0] == \"?\" else mins[0]\n ans += \":\"\n\n if hour[0] == \"?\" and hour[1] == \"?\":\n ans += \"32\"\n elif hour[1] == \"?\":\n if hour[0] == \"2\":\n ans += \"32\"\n else:\n ans += \"9\" + hour[0]\n elif hour[0] == \"?\":\n if hour[1] >= \"4\":\n ans += hour[1] + \"1\"\n else:\n ans += hour[1] + \"2\"\n else:\n ans += hour[1] + hour[0]\n\n return ans[::-1]\n","sub_path":"easy/1736.latest-time-by-replacing-hidden-digits.py","file_name":"1736.latest-time-by-replacing-hidden-digits.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"552775131","text":"import nltk\nimport math \nimport operator\nfrom pickle import dump, load\nfrom bs4 import BeautifulSoup\nfrom nltk.corpus import cess_esp\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import RegexpTokenizer\n\ndef openText():\n f = open('../Corpus/e961024.htm', encoding = 'utf-8')\n text = f.read()\n f.close()\n\n return text\n\ndef openGenerate():\n f = open('../generate.txt',encoding='utf-8')\n lemmas = {}\n for line in f.readlines():\n lemmas[ line.split(' ')[0].replace('#','') ] = line.split(' ')[-1][:-1]\n f.close()\n \n return lemmas\n\ndef normalization(text):\n stop = stopwords.words('spanish')\n t = cleaningText(text)\n t = tokens(t)\n t = [w for w in t if w.lower() not in stop]\n vocabulary = sorted(set(t))\n\n return vocabulary,t\n\ndef lemmatized( token, lemmas):\n tokensLemmatized = []\n for tok in token:\n try:\n tokensLemmatized.append(lemmas[tok])\n except KeyError:\n tokensLemmatized.append(tok)\n\n return tokensLemmatized \n\ndef generateTagger(clean_vocabulary): \n fname = 'combined_taggerP.pkl'\n default_tagger = nltk.DefaultTagger('V')\n patterns=[ (r'.*o$', 'NMS'), # noun masculine singular\n (r'.*os$', 'NMP'), # noun masculine plural\n (r'.*a$', 'NFS'), # noun feminine singular\n (r'.*as$', 'NFP') # noun feminine plural\n ]\n regexp_tagger = nltk.RegexpTagger(patterns, backoff=default_tagger)\n cess_tagged_sents = cess_esp.tagged_sents()\n combined_tagger = nltk.UnigramTagger(cess_tagged_sents, backoff=regexp_tagger)\n \n s_tagged = combined_tagger.tag(clean_vocabulary)\n output = open(fname, 'wb')\n dump(s_tagged, output, -1)\n output.close()\n\n return s_tagged\n\ndef cleanTagger(s_tagged):\n list(s_tagged)\n vocabulary = []\n for i in range(len(s_tagged)):\n vocabulary.append(s_tagged[i][0]+' '+s_tagged[i][1])\n\n return vocabulary\n\ndef getContext(vocabulary,cleanWords):\n contexts = {}\n window = 4\n for termino in vocabulary:\n context = []\n for j,word in enumerate(cleanWords):\n if termino == word :\n context += cleanWords[ 0 if j-window < 0 else j-window : j ] \n context += cleanWords[ j+1 : j+(window+1) if j < len(cleanWords) else len(cleanWords) ] \n contexts[termino] = context\n output = open('contextT.pkl', 'wb')\n dump(contexts, output, -1)\n output.close()\n\n return contexts\n\ndef cleaningText(text):\n soup = BeautifulSoup(text, 'lxml') \n text = soup.get_text()\n \n return text\n\ndef tokens(text):\n words = nltk.word_tokenize(text)\n words=[w.lower() for w in words if w.isalpha()]\n \n return words\n\ndef nounVocabulary( vocabulary ):\n nounVocabulary = [ ]\n for term in vocabulary:\n if term.split(' ')[1][0] == 'n':\n nounVocabulary.append(term)\n\n return nounVocabulary\n\ndef getTopic( vocabulary , tokens ):\n topics = [ ]\n for term in vocabulary:\n frecuency = tokens.count( term )\n topics.append( (term,frecuency) )\n \n return topics\n\ndef getPickle(fileName): \n with open(fileName,'rb') as f:\n return load(f)\n\n\nif __name__ == '__main__':\n \n text = openText()\n vocabulary,tokens = normalization( text )\n lemmas = openGenerate()\n tokenLemma = lemmatized( tokens , lemmas )\n s_tagged = generateTagger( tokenLemma )\n tokensLemmas = cleanTagger( s_tagged )\n \n finalVocabulary = sorted( set(tokensLemmas) )\n finalVocabulary = nounVocabulary( finalVocabulary )\n \n print(len(vocabulary))\n\n topics = getTopic( finalVocabulary , tokensLemmas )\n topics = sorted( topics,key=operator.itemgetter(1),reverse=True )\n\n fv=open('HigherFrecuency.txt','w')\n for t in topics:\n fv.write( '{:30}{:30}\\n'.format(t[0],str(t[1])) )\n fv.close() \n #5172","sub_path":"Practices/SyntagmaticRelations/Practice12/HigherFrecuency.py","file_name":"HigherFrecuency.py","file_ext":"py","file_size_in_byte":3883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"28738630","text":"# -*- coding: utf-8 -*-\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"The Normal (Gaussian) distribution class.\"\"\"\n\nimport edward.models as base_models\nimport numpy as np\nimport inferpy.util\n\nfrom inferpy.util import tf_run_wrapper\nfrom inferpy.models.random_variable import *\nfrom inferpy.replicate import *\nfrom inferpy.util import get_total_dimension\n\n\n\n\n\nclass Normal_old(RandomVariable):\n\n \"\"\" Class implementing the Normal distribution with location `loc`, `scale` and `dim` parameters.\n\n The probability density of the normal distribution is,\n\n .. math::\n\n f(x|\\mu,\\sigma^2)=\\\\frac{1}{{\\\\sigma \\\\sqrt {2\\\\pi}}} e^{-\\\\frac{(x-\\\\mu)^2}{2 \\\\sigma ^2}}\n\n\n where\n\n - :math:`\\mu` is the mean or expectation of the distribution (i.e. `location`),\n - :math:`\\sigma` is the standard deviation (i.e. `scale`), and\n - :math:`\\sigma^{2}` is the variance.\n\n\n\n The Normal distribution is a member of the `location-scale\n family `_.\n\n This class allows the definition of a variable normal distributed of\n any dimension. Each of the dimensions are independent. For example:\n\n .. literalinclude:: ../../examples/normal_dist_definition.py\n\n\n\n \"\"\"\n\n def __init__(self, loc=0, scale=1, dim=None, observed=False, name=\"Normal\"):\n\n \"\"\"Construct Normal distributions\n\n The parameters `loc` and `scale` must be shaped in a way that supports\n broadcasting (e.g. `loc + scale` is a valid operation). If dim is specified,\n it should be consistent with the lengths of `loc` and `scale`\n\n\n Args:\n loc (float): scalar or vector indicating the mean of the distribution at each dimension.\n scale (float): scalar or vector indicating the stddev of the distribution at each dimension.\n dim (int): optional scalar indicating the number of dimensions\n\n Raises\n ValueError: if the parameters are not consistent\n AttributeError: if any of the properties is changed once the object is constructed\n\n \"\"\"\n\n self.__check_params(loc, scale, dim)\n\n\n param_dim = 1\n if dim != None: param_dim = dim\n\n # shape = (batches, dimension)\n self_shape = (replicate.get_total_size(), np.max([get_total_dimension(loc), get_total_dimension(scale), param_dim]))\n\n loc_rep = self.__reshape_param(loc, self_shape)\n scale_rep = self.__reshape_param(scale, self_shape)\n\n # build the distribution\n\n super(Normal, self).__init__(base_models.Normal(loc=loc_rep, scale=scale_rep, name=name), observed=observed)\n\n # getter methods\n\n @property\n @tf_run_wrapper\n def loc(self):\n \"\"\"Distribution parameter for the mean.\"\"\"\n return self.dist.loc\n\n\n @property\n @tf_run_wrapper\n def scale(self):\n \"\"\"Distribution parameter for standard deviation.\"\"\"\n return self.dist.scale\n\n\n\n\n def __check_params(self, loc, scale, dim):\n \"\"\"private method that checks the consistency of the input parameters\"\"\"\n\n\n # loc and scale cannot be multidimensional arrays (by now)\n if np.ndim(loc) > 1 or np.ndim(scale) > 1:\n raise ValueError(\"loc and scale cannot be multidimensional arrays\")\n\n\n dim_loc = get_total_dimension(loc)\n dim_scale = get_total_dimension(scale)\n\n\n # loc and scale lengths must be equal or must be scalars\n if dim_loc > 1 and dim_scale > 1 and dim_loc != dim_scale:\n raise ValueError(\"loc and scale lengths must be equal or must be 1\")\n\n # loc can be a scalar or a vector of length dim\n if dim != None and dim_loc > 1 and dim != dim_loc:\n raise ValueError(\"loc length is not consistent with value in dim\")\n\n if dim != None and dim_scale > 1 and dim != dim_scale:\n raise ValueError(\"scale length is not consistent with value in dim\")\n\n def __compute_shape(self, loc, scale, param_dim):\n\n N = replicate.get_total_size()\n loc_size = np.size(loc)\n loc_scale = np.size(scale)\n\n # if isinstance(loc, RandomVariable):\n\n\n\n self_shape = (N, np.max([loc_size, loc_scale, param_dim]))\n\n\n\n def __reshape_param(self,param, self_shape):\n\n N = self_shape[0]\n D = self_shape[1]\n\n\n # get a D*N unidimensional vector\n\n if np.shape(param) in [(), (1,)] or\\\n (isinstance(param, RandomVariable) and param.dim==1):\n param_vect = np.repeat(param, D * N).tolist()\n else:\n param_vect = np.tile(param, N).tolist()\n\n\n if np.all(list(map(lambda x: np.isscalar(x), param_vect))): # only numerical values\n\n # reshape the list\n if N > 1:\n param_np_mat = np.reshape(np.stack(param_vect), (N, -1))\n else:\n param_np_mat = np.reshape(np.stack(param_vect), (D,))\n\n #transform in tf\n param_tf_mat = tf.constant(param_np_mat, dtype=\"float32\")\n\n else: # with a tensor\n\n # transform the numerical values into tensors\n for i in range(0, len(param_vect)):\n if np.isscalar(param_vect[i]):\n param_vect[i] = [tf.constant(param_vect[i], dtype=\"float32\")]\n elif isinstance(param_vect[i], RandomVariable):\n param_vect[i] = param_vect[i].base_object\n\n # reshape the list\n if N>1:\n param_tf_mat = tf.reshape(tf.stack(param_vect), (N, -1))\n else:\n if D>1:\n param_tf_mat = tf.reshape(tf.stack(param_vect), (D,))\n else:\n param_tf_mat = param_vect[0]\n\n\n return param_tf_mat\n\n PARAMS = [\"loc\", \"scale\"]\n\n def __repr__(self):\n return \"\" % (\n self.name, self.loc, self.scale, self.shape, self.dist.dtype.name)\n\n","sub_path":"inferpy/models/normal.py","file_name":"normal.py","file_ext":"py","file_size_in_byte":6586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"532696334","text":"import random\nimport unittest\nimport readchar\nfrom PIL import Image\nfrom env import DiscreteGridWorld, RenderEnv\nfrom exemplar_env import RandomInitEnv, RandomGoalEnv\n\nfrom py_tools.common import set_seed\nfrom py_tools.common.test import ipdb_on_exception\n\ncontrol_dict={'w': 0, 's': 1, 'a': 2, 'd': 3} # for control\n\nclass TestDiscrete(unittest.TestCase):\n @ipdb_on_exception\n def control(self):\n #env = DiscreteGridWorld('fourroom', (1, 1), (9, 9))\n env = RandomGoalEnv(\n RandomInitEnv(\n DiscreteGridWorld('fourroom', (1, 1), (9, 9)),\n min_dis=7,\n ),\n goal_locs=[(1, 1), (1, 9), (9, 1), (9, 9)],\n )\n env = RenderEnv(env)\n o = env.reset()\n env.render()\n done = False\n while not done:\n c = readchar.readchar()\n if c == 'q':\n break\n elif c == 'r':\n o = env.reset()\n env.render()\n elif c == 'p': # save the observation\n Image.fromarray(env.get_img()).save('observation.jpg')\n elif c in control_dict:\n a = control_dict[c]\n o, r, done, _ = env.step(a)\n env.render()\n print('s={}, r={}'.format(o, r))\n\n\nif __name__ == \"__main__\":\n set_seed(1)\n unittest.main()\n","sub_path":"deep_rl/simple_grid/utest.py","file_name":"utest.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"636691916","text":"# encoding: UTF-8\n\nfrom __future__ import absolute_import\nfrom .vnkorbit import Korbit_TradeApi , Korbit_DataApi\n\napiKey = 'FssV9Jw0aZVCOYbu9YlqVNc2Rhgmh1QpruNgSVhvVmFP1iw1aPfc3APbfz0ZM'\nsecretKey = 'lSMc1TT2AZYX5pyEm01SFqx7PgMjNB18eXWpi8DQKHf0rcYhLiaiRVzJwaVZR'\n\n\ndef testData():\n\td = Korbit_DataApi()\n\n\td.init(0.5 , 1)\n\td.subscribeTick(\"btc_krw\")\n\n\t# d.subscribeTrades(\"btc_krw\")\n\n\t# d.subscribeOrderbooks(\"btc_krw\")\n\n\ndef testTrade():\n\tglobal apiKey , secretKey\n\td = Korbit_TradeApi()\n\td.init(apiKey , secretKey , \"xiaoshuang8921@naver.com\" , \"Wxiaoshuang8921\")\n\n\t# print d.headers\n\td.list_market_orders(currency_pair = 'bch_krw' , offset = 0 , limit = 10)\n\t#print d.buy_currency( coin_amount = 0.01 , price = 10000 , currency_pair = 'bch_krw')\n\n\t#print d.sell_currency( coin_amount = 0.01 , price = 10000000 , currency_pair = 'bch_krw')\n\t# 4441418\n\t#print d.cancel_orders( order_id = 4441418 , currency_pair = 'bch_krw')\n\n# testData()\ntestTrade()","sub_path":"beta/api/korbit/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"370447604","text":"import jax.numpy as np\nimport numpy as onp\nimport jax.scipy as sp\nimport scipy.optimize as optim\nimport pickle\nimport numpy.random as np_rnd\n\nfrom jax import jit, grad, vmap, jacfwd, jacrev\nfrom ngboost.distns import Normal\n\nfrom ngboost.scores import MLE, MLE_SURV, CRPS_SURV, CRPS\nfrom ngboost.learners import default_tree_learner, default_linear_learner\nfrom ngboost.distns.normal import Normal\n\n\nclass NGBoost(object):\n\n def __init__(self, Dist=Normal, Score=MLE(),\n Base=default_tree_learner, natural_gradient=False,\n n_estimators=100, learning_rate=0.1, minibatch_frac=1.0,\n verbose=True, tol=1e-4):\n self.Dist = Dist\n self.Score = Score\n self.Base = Base\n self.natural_gradient = natural_gradient\n self.n_estimators = n_estimators\n self.learning_rate = learning_rate\n self.minibatch_frac = minibatch_frac\n self.verbose = verbose\n self.init_params = None\n self.base_models = []\n self.scalings = []\n self.tol = tol\n self.loss_fn = lambda P, Y: self.Score(self.Dist(P.T), Y).sum()\n self.grad_fn = grad(self.loss_fn)\n #self.grad_fn = jit(vmap(grad(self.loss_fn)))\n self.hessian_fn = jit(vmap(jacrev(grad(self.loss_fn))))\n #self.loss_fn = jit(vmap(self.loss_fn))\n self.Score.setup_distn(self.Dist)\n if isinstance(self.Score, CRPS_SURV):\n self.marginal_score = MLE_SURV()\n elif isinstance(self.Score, CRPS):\n self.marginal_score = MLE()\n else:\n self.marginal_score = self.Score\n self.marginal_loss = lambda P, Y: self.marginal_score(self.Dist(P), Y)\n self.marginal_grad = jit(vmap(grad(self.marginal_loss)))\n self.marginal_loss = jit(vmap(self.marginal_loss))\n self.matmul_inv_fn = jit(vmap(lambda A, b: np.linalg.solve(A, b)))\n\n def pred_param(self, X, max_iter=None):\n m, n = X.shape\n params = np.ones((m, self.Dist.n_params)) * self.init_params\n for i, (models, s) in enumerate(zip(self.base_models, self.scalings)):\n if max_iter and i == max_iter:\n break\n resids = np.array([model.predict(X) for model in models]).T\n params -= self.learning_rate * resids * s\n return params\n\n def sample(self, X, Y, params):\n sample_size = int(self.minibatch_frac * len(Y))\n idxs = np_rnd.choice(np.arange(len(Y)), sample_size, replace=False)\n return idxs, X[idxs,:], Y[idxs], params[idxs, :]\n\n def fit_base(self, X, grads):\n models = [self.Base().fit(X, g) for g in grads.T]\n fitted = np.array([m.predict(X) for m in models]).T\n self.base_models.append(models)\n return fitted\n\n def line_search(self, resids, start, Y, scale_init=1):\n loss_init = self.loss_fn(start, Y).mean()\n scale = scale_init\n while True:\n scaled_resids = resids * scale\n loss = self.loss_fn(start - scaled_resids, Y).mean()\n norm = np.mean(np.linalg.norm(scaled_resids, axis=1))\n if not np.isnan(loss) and (loss < loss_init or norm < self.tol) and\\\n np.linalg.norm(scaled_resids, axis=1).mean() < 5.0:\n break\n scale = scale * 0.5\n self.scalings.append(scale)\n return scale\n\n def fit(self, X, Y, X_val = None, Y_val = None):\n\n loss_list = []\n val_loss_list = []\n self.fit_init_params_to_marginal(Y)\n\n params = self.pred_param(X)\n if X_val is not None and Y_val is not None:\n val_params = self.pred_param(X_val)\n\n for itr in range(self.n_estimators):\n _, X_batch, Y_batch, P_batch = self.sample(X, Y, params)\n\n losses = self.loss_fn(P_batch, Y_batch)\n loss = losses.mean()\n if np.isinf(loss) or np.isnan(loss):\n for i, (p, l, y) in enumerate(zip(P_batch, losses, Y_batch)):\n if np.isinf(l) or np.isnan(l):\n print('[%d] Params=[%.4f,%.4f], loss=%.4f, y=%.4f' % (i, p[0], p[1], l, y))\n print('Loss=%.4f' % self.Dist(p).crps_debug(y))\n breakpoint()\n\n grads = self.grad_fn(P_batch, Y_batch)\n\n if self.natural_gradient:\n grads = self.Score.naturalize(P_batch, grads)\n\n #scale = self.line_search(grads, P_batch, Y_batch, scale_init=1)\n #grads = grads * scale\n\n if np.any(np.isnan(grads)) or np.any(np.isinf(grads)):\n print(grads)\n grads = self.grad_fn(P_batch, Y_batch)\n print('recalculated')\n print(grads)\n print('params')\n print(grads)\n pass\n\n proj_grad = self.fit_base(X_batch, grads)\n scale = self.line_search(proj_grad, P_batch, Y_batch)\n\n loss_list += [loss]\n\n params -= self.learning_rate * scale * np.array([m.predict(X) for m in self.base_models[-1]]).T\n\n val_loss = 0\n if X_val is not None and Y_val is not None:\n val_params -= self.learning_rate * scale * np.array([m.predict(X_val) for m in self.base_models[-1]]).T\n val_loss = self.loss_fn(val_params, Y_val).mean()\n val_loss_list += [val_loss]\n if np.mean(np.array(val_loss_list[-5:])) > \\\n np.mean(np.array(val_loss_list[-10:-5])):\n if self.verbose:\n print(f\"== Quitting at iteration / VAL {itr} (val_loss={val_loss:.4f})\")\n break\n\n if self.verbose:\n grad_norm = np.linalg.norm(grads, axis=1).mean() * scale\n print(f\"[iter {itr}] loss={loss:.4f} val_loss={val_loss:.4f} scale={scale:.4f} \"\n f\"norm={grad_norm:.4f}\")\n\n if np.linalg.norm(proj_grad, axis=1).mean() < self.tol:\n if self.verbose:\n print(f\"== Quitting at iteration / GRAD {itr}\")\n break\n\n return loss_list, val_loss_list\n\n def fit_init_params_to_marginal(self, Y, iters=1000):\n try:\n E = Y['Event']\n T = Y['Time'].reshape((-1, 1))[E == 1]\n except:\n T = Y\n self.init_params = self.Dist.fit(T)\n return\n\n\n def pred_dist(self, X, max_iter=None):\n params = onp.asarray(self.pred_param(X, max_iter))\n dist = self.Dist(params.T)\n return dist\n\n def predict(self, X):\n dist = self.pred_dist(X)\n return list(dist.loc.flatten())\n\n def staged_predict(self, X, max_iter=None):\n predictions = []\n m, n = X.shape\n params = np.ones((m, self.Dist.n_params)) * self.init_params\n for i, (models, s) in enumerate(zip(self.base_models, self.scalings)):\n if max_iter and i == max_iter:\n break\n resids = np.array([model.predict(X) for model in models]).T\n params -= self.learning_rate * resids * s\n dists = self.Dist(onp.asarray(params).T)\n predictions.append(dists.loc.flatten())\n return predictions\n\n def staged_pred_dist(self, X, max_iter=None):\n predictions = []\n m, n = X.shape\n params = np.ones((m, self.Dist.n_params)) * self.init_params\n for i, (models, s) in enumerate(zip(self.base_models, self.scalings)):\n if max_iter and i == max_iter:\n break\n resids = np.array([model.predict(X) for model in models]).T\n params -= self.learning_rate * resids * s\n dists = self.Dist(onp.asarray(params).T)\n predictions.append(dists)\n return predictions\n","sub_path":"ngboost/ngboost.py","file_name":"ngboost.py","file_ext":"py","file_size_in_byte":7744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"57955914","text":"#!/usr/bin/python\n\n## @file\n# avango daemon to initialize tracking and device stations.\n\n# import avango-guacamole libraries\nimport avango.daemon\n\n# import python libraries\nimport os\nimport sys\nimport subprocess\n\n## Initizialies Oculus Rift sensors.\ndef init_oculus():\n _oculus = avango.daemon.Oculus()\n\n _oculus.stations[0] = avango.daemon.Station('oculus-0')\n _oculus.stations[1] = avango.daemon.Station('oculus-1')\n _oculus.stations[2] = avango.daemon.Station('oculus-2')\n\n print(\"Initialized 3 Oculus Rifts\")\n device_list.append(_oculus)\n\n## Initializes AR Track on LCD wall.\ndef init_lcd_wall_tracking():\n\n # create instance of DTrack\n _dtrack = avango.daemon.DTrack()\n _dtrack.port = \"5000\" # ART port at LCD wall\n \n _dtrack.stations[18] = avango.daemon.Station('tracking-oculus-stripe') # oculus rift tracking\n _dtrack.stations[17] = avango.daemon.Station('tracking-oculus-front') # oculus rift tracking\n _dtrack.stations[16] = avango.daemon.Station('tracking-oculus-stag') # oculus rift tracking\n\n _dtrack.stations[3] = avango.daemon.Station('tracking-lcd-glasses-1') # glasses powerwall user one\n _dtrack.stations[4] = avango.daemon.Station('tracking-lcd-glasses-2') # glasses powerwall user two\n\n _dtrack.stations[7] = avango.daemon.Station('tracking-old-spheron') # old spheron device\n\n device_list.append(_dtrack)\n print(\"ART Tracking started at LCD WALL\")\n\n## Initializes AR Track on DLP wall.\ndef init_dlp_wall_tracking():\n\n # create instance of DTrack\n _dtrack = avango.daemon.DTrack()\n _dtrack.port = \"5002\" # ART port at LED wall\n \n # glasses\n _dtrack.stations[1] = avango.daemon.Station('tracking-dlp-glasses-1')\n _dtrack.stations[2] = avango.daemon.Station('tracking-dlp-glasses-2')\n _dtrack.stations[3] = avango.daemon.Station('tracking-dlp-glasses-3')\n _dtrack.stations[4] = avango.daemon.Station('tracking-dlp-glasses-4')\n _dtrack.stations[5] = avango.daemon.Station('tracking-dlp-glasses-5') \n _dtrack.stations[6] = avango.daemon.Station('tracking-dlp-glasses-6')\n\n # devices\n _dtrack.stations[19] = avango.daemon.Station('tracking-new-spheron') # new spheron device\n\n _dtrack.stations[23] = avango.daemon.Station('tracking-dlp-pointer1') # AUGUST1 pointer\n\n _dtrack.stations[26] = avango.daemon.Station('tracking-portal-camera-32') # portal camera 3.2\n _dtrack.stations[25] = avango.daemon.Station('tracking-portal-camera-31') # portal camera 3.1\n\n _dtrack.stations[20] = avango.daemon.Station('tracking-xbox-1') # xbox target \"horse\"\n\n _dtrack.stations[9] = avango.daemon.Station('tracking-dlp-camera-shutter') # camera shutter\n\n #_dtrack.stations[13] = avango.daemon.Station('tracking-dlp-lht-1') # LHT1\n #_dtrack.stations[14] = avango.daemon.Station('tracking-dlp-lht-2') # LHT2\n\n device_list.append(_dtrack)\n print(\"ART Tracking started at DLP WALL\")\n\n\n## Initializes touch input at the table.\ndef init_tuio_input():\n\n _tuio = avango.daemon.TUIOInput()\n _tuio.port = \"3333\" # tuio port\n\n _tuio.stations[0] = avango.daemon.Station('gua-finger0')\n _tuio.stations[1] = avango.daemon.Station('gua-finger1')\n _tuio.stations[2] = avango.daemon.Station('gua-finger2')\n _tuio.stations[3] = avango.daemon.Station('gua-finger3')\n _tuio.stations[4] = avango.daemon.Station('gua-finger4')\n _tuio.stations[5] = avango.daemon.Station('gua-finger5')\n _tuio.stations[6] = avango.daemon.Station('gua-finger6')\n _tuio.stations[7] = avango.daemon.Station('gua-finger7')\n _tuio.stations[8] = avango.daemon.Station('gua-finger8')\n _tuio.stations[9] = avango.daemon.Station('gua-finger9')\n _tuio.stations[10] = avango.daemon.Station('gua-finger10')\n _tuio.stations[11] = avango.daemon.Station('gua-finger11')\n _tuio.stations[12] = avango.daemon.Station('gua-finger12')\n _tuio.stations[13] = avango.daemon.Station('gua-finger13')\n _tuio.stations[14] = avango.daemon.Station('gua-finger14')\n _tuio.stations[15] = avango.daemon.Station('gua-finger15')\n _tuio.stations[16] = avango.daemon.Station('gua-finger16')\n _tuio.stations[17] = avango.daemon.Station('gua-finger17')\n _tuio.stations[18] = avango.daemon.Station('gua-finger18')\n _tuio.stations[19] = avango.daemon.Station('gua-finger19')\n\n device_list.append(_tuio)\n\n\n## Initializes a spacemouse for navigation.\ndef init_spacemouse():\n\n _string = os.popen(\"python find_device.py 1 3Dconnexion SpaceNavigator\").read()\n\n if len(_string) == 0:\n _string = os.popen(\"python find_device.py 1 3Dconnexion SpaceTraveler USB\").read()\n\n _string = _string.split()\n if len(_string) > 0: \n\n _string = _string[0]\n \n # create a station to propagate the input events\n _spacemouse = avango.daemon.HIDInput()\n _spacemouse.station = avango.daemon.Station('device-spacemouse')\n _spacemouse.device = _string\n\n # map incoming spacemouse events to station values\n _spacemouse.values[0] = \"EV_ABS::ABS_X\" # trans X\n _spacemouse.values[1] = \"EV_ABS::ABS_Z\" # trans Y\n _spacemouse.values[2] = \"EV_ABS::ABS_Y\" # trans Z\n _spacemouse.values[3] = \"EV_ABS::ABS_RX\" # rotate X\n _spacemouse.values[4] = \"EV_ABS::ABS_RZ\" # rotate Y\n _spacemouse.values[5] = \"EV_ABS::ABS_RY\" # rotate Z\n\n # buttons\n _spacemouse.buttons[0] = \"EV_KEY::BTN_0\" # left button\n _spacemouse.buttons[1] = \"EV_KEY::BTN_1\" # right button\n\n device_list.append(_spacemouse)\n print(\"SpaceMouse started at:\", _string)\n\n else:\n print(\"SpaceMouse NOT found !\")\n\n## Initializes an old spheron for navigation.\ndef init_old_spheron():\n\n _string = os.popen(\"python find_device.py 1 BUWEIMAR RAPID DEVEL DEVICE\").read()\n _string = _string.split()\n\n if len(_string) > 0:\n \n _string = _string[0]\n\n # create a station to propagate the input events\n _spheron = avango.daemon.HIDInput()\n _spheron.station = avango.daemon.Station(\"device-old-spheron\")\n _spheron.device = _string\n \n # map incoming spheron events to station values\n _spheron.values[0] = \"EV_ABS::ABS_X\" # trans X \n _spheron.values[1] = \"EV_ABS::ABS_Y\" # trans Y\n _spheron.values[2] = \"EV_ABS::ABS_Z\" # trans Z\n _spheron.values[3] = \"EV_ABS::ABS_RX\" # rotate X\n _spheron.values[4] = \"EV_ABS::ABS_RY\" # rotate Y\n _spheron.values[5] = \"EV_ABS::ABS_RZ\" # rotate Z\n \n device_list.append(_spheron)\n \n print('Old Spheron started at:', _string)\n \n else:\n print(\"Old Spheron NOT found !\")\n \n _string = os.popen(\"python find_device.py 1 PIXART USB OPTICAL MOUSE\").read()\n _string = _string.split()\n\n if len(_string) > 0:\n \n _string = _string[0]\n \n # create a station to propagate the input events\n _spheron_buttons = avango.daemon.HIDInput()\n _spheron_buttons.station = avango.daemon.Station(\"device-old-spheron-buttons\") \n _spheron_buttons.device = _string\n \n # map buttons\n _spheron_buttons.buttons[0] = \"EV_KEY::BTN_LEFT\" # left button\n _spheron_buttons.buttons[1] = \"EV_KEY::BTN_MIDDLE\" # middle button \n _spheron_buttons.buttons[2] = \"EV_KEY::BTN_RIGHT\" # right button\n \n device_list.append(_spheron_buttons)\n print('Old Spheron Buttons started at:', _string)\n \n else:\n print(\"Old Spheron ButTons NOT found !\")\n\n## Initializes a new spheron for navigation.\ndef init_new_spheron():\n\n _string_right = os.popen(\"python find_device.py 1 BUW Spheron\").read()\n _string_left = os.popen(\"python find_device.py 2 BUW Spheron\").read()\n\n _string_left = _string_left.split()\n _string_right = _string_right.split()\n\n if len(_string_right) > 0:\n \n _string1 = _string_right[0]\n\n # create a station to propagate the input events\n _spheron1 = avango.daemon.HIDInput()\n _spheron1.station = avango.daemon.Station(\"device-new-spheron-right\")\n _spheron1.device = _string1\n _spheron1.timeout = '30'\n \n # map incoming events to station values\n _spheron1.values[0] = \"EV_ABS::ABS_X\" # joystick trans x\n _spheron1.values[1] = \"EV_ABS::ABS_Y\" # joystick trans y\n _spheron1.values[2] = \"EV_ABS::ABS_Z\" # joystick trans z\n _spheron1.values[6] = \"EV_ABS::ABS_THROTTLE\" # joystick rot y\n \n _spheron1.values[3] = \"EV_REL::REL_RX\" \n _spheron1.values[4] = \"EV_REL::REL_RY\"\n _spheron1.values[5] = \"EV_REL::REL_RZ\"\n \n # buttons\n _spheron1.buttons[0] = \"EV_KEY::BTN_B\" # left button\n _spheron1.buttons[1] = \"EV_KEY::BTN_C\" # middle button\n _spheron1.buttons[2] = \"EV_KEY::BTN_A\" # right button\n \n device_list.append(_spheron1)\n\n print(\"New Spheron (right) found at:\", _string1)\n\n else:\n\n print(\"New Spheron (right) NOT found !\")\n\n if len(_string_left) > 0:\n \n _string2 = _string_left[0]\n\n # create a station to propagate the input events\n _spheron2 = avango.daemon.HIDInput()\n _spheron2.station = avango.daemon.Station(\"device-new-spheron-left\")\n _spheron2.device = _string2\n _spheron2.timeout = '30'\n \n # map incoming events to station values\n _spheron2.values[0] = \"EV_ABS::ABS_X\" # joystick trans x\n _spheron2.values[1] = \"EV_ABS::ABS_Y\" # joystick trans z\n _spheron2.values[2] = \"EV_ABS::ABS_Z\" # joystick trans y\n _spheron2.values[3] = \"EV_ABS::ABS_THROTTLE\" # joystick rot y \n \n device_list.append(_spheron2)\n\n print(\"New Spheron (left) found at:\", _string2)\n \n else:\n print(\"New Spheron (left) NOT found !\")\n\n\n## Initializes a new spheron for navigation.\ndef init_new_globefish():\n\n _string = os.popen(\"python find_device.py 1 BUW Spheron\").read()\n _string = _string.split()\n\n if len(_string) > 0:\n \n _string1 = _string[0]\n\n # create a station to propagate the input events\n _globefish = avango.daemon.HIDInput()\n _globefish.station = avango.daemon.Station(\"device-new-globefish\")\n _globefish.device = _string1\n _globefish.timeout = '30'\n \n # map incoming events to station values\n _globefish.values[0] = \"EV_ABS::ABS_THROTTLE\" # X\n _globefish.values[1] = \"EV_ABS::ABS_Z\" # Y \n _globefish.values[2] = \"EV_ABS::ABS_X\" # Z\n \n _globefish.values[3] = \"EV_REL::REL_RY\" # PITCH\n _globefish.values[4] = \"EV_REL::REL_RX\" # HEAD \n _globefish.values[5] = \"EV_REL::REL_RZ\" # ROLL\n \n # buttons\n # ...\n \n device_list.append(_globefish)\n\n print(\"New Globefish found at:\", _string1)\n\n else:\n print(\"New Globefish NOT found !\")\n \n\n\n## Initalizes a mouse for navigation.\ndef init_mouse():\n\n _string = os.popen(\"python find_device.py 1 Logitech USB\").read()\n _string = _string.split()\n\n if len(_string) > 0:\n \n _string1 = _string[0]\n\n # create a station to propagate the input events\n mouse = avango.daemon.HIDInput()\n mouse.station = avango.daemon.Station('device-mouse')\n mouse.device = _string1\n mouse.timeout = '30'\n\n mouse.values[0] = \"EV_REL::REL_X\"\n mouse.values[1] = \"EV_REL::REL_Y\"\n\n mouse.buttons[0] = \"EV_KEY::BTN_LEFT\"\n mouse.buttons[1] = \"EV_KEY::BTN_MIDDLE\"\n mouse.buttons[2] = \"EV_KEY::BTN_RIGHT\"\n\n device_list.append(mouse)\n\n print(\"Mouse started at:\", _string1)\n\n else:\n print(\"Mouse NOT found !\")\n\n'''\n## Initalizes a mouse for navigation.\ndef init_mouse():\n\n mouse_name = os.popen(\"ls /dev/input/by-id | grep \\\"-event-mouse\\\" | sed -e \\'s/\\\"//g\\' | cut -d\\\" \\\" -f4\").read()\n\n mouse_name = mouse_name.split()\n if len(mouse_name) > 0:\n\n mouse_name = mouse_name[0]\n\n # create a station to propagate the input events\n mouse = avango.daemon.HIDInput()\n mouse.station = avango.daemon.Station('device-mouse')\n mouse.device = \"/dev/input/by-id/\" + mouse_name\n\n mouse.values[0] = \"EV_REL::REL_X\"\n mouse.values[1] = \"EV_REL::REL_Y\"\n\n mouse.buttons[0] = \"EV_KEY::BTN_LEFT\"\n mouse.buttons[1] = \"EV_KEY::BTN_MIDDLE\"\n mouse.buttons[2] = \"EV_KEY::BTN_RIGHT\"\n\n device_list.append(mouse)\n\n print \"Mouse started at:\", mouse_name\n\n else:\n print \"Mouse NOT found !\"\n'''\n\n\n## Initializes a keyboard for navigation.\ndef init_keyboard():\n\n keyboard_name = os.popen(\"ls /dev/input/by-id | grep \\\"-event-kbd\\\" | sed -e \\'s/\\\"//g\\' | cut -d\\\" \\\" -f4\").read()\n\n keyboard_name = keyboard_name.split()\n\n for i, name in enumerate(keyboard_name):\n \n # create a station to propagate the input events\n keyboard = avango.daemon.HIDInput()\n keyboard.station = avango.daemon.Station('device-keyboard' + str(i))\n keyboard.device = \"/dev/input/by-id/\" + name\n\n\n keyboard.buttons[0] = \"EV_KEY::KEY_W\"\n keyboard.buttons[1] = \"EV_KEY::KEY_A\"\n keyboard.buttons[2] = \"EV_KEY::KEY_S\"\n keyboard.buttons[3] = \"EV_KEY::KEY_D\"\n keyboard.buttons[4] = \"EV_KEY::KEY_R\"\n keyboard.buttons[5] = \"EV_KEY::KEY_C\"\n keyboard.buttons[6] = \"EV_KEY::KEY_G\"\n keyboard.buttons[7] = \"EV_KEY::KEY_UP\"\n keyboard.buttons[8] = \"EV_KEY::KEY_DOWN\"\n keyboard.buttons[9] = \"EV_KEY::KEY_0\"\n keyboard.buttons[10] = \"EV_KEY::KEY_1\"\n keyboard.buttons[11] = \"EV_KEY::KEY_2\"\n keyboard.buttons[12] = \"EV_KEY::KEY_3\"\n keyboard.buttons[13] = \"EV_KEY::KEY_4\"\n keyboard.buttons[14] = \"EV_KEY::KEY_5\"\n keyboard.buttons[15] = \"EV_KEY::KEY_6\"\n keyboard.buttons[16] = \"EV_KEY::KEY_7\"\n keyboard.buttons[17] = \"EV_KEY::KEY_8\"\n keyboard.buttons[18] = \"EV_KEY::KEY_9\"\n keyboard.buttons[19] = \"EV_KEY::KEY_F1\"\n keyboard.buttons[20] = \"EV_KEY::KEY_F2\"\n keyboard.buttons[21] = \"EV_KEY::KEY_F3\"\n keyboard.buttons[22] = \"EV_KEY::KEY_F4\"\n keyboard.buttons[23] = \"EV_KEY::KEY_F5\"\n keyboard.buttons[24] = \"EV_KEY::KEY_F6\"\n keyboard.buttons[25] = \"EV_KEY::KEY_F7\"\n keyboard.buttons[26] = \"EV_KEY::KEY_F8\"\n keyboard.buttons[27] = \"EV_KEY::KEY_F9\"\n keyboard.buttons[28] = \"EV_KEY::KEY_F10\"\n keyboard.buttons[29] = \"EV_KEY::KEY_F11\"\n keyboard.buttons[30] = \"EV_KEY::KEY_F12\"\n keyboard.buttons[31] = \"EV_KEY::KEY_HOME\"\n\n\n\n device_list.append(keyboard)\n\n print(\"Keyboard \" + str(i) + \" started at:\", name)\n\n## Initializes a X-Box controller for navigation.\ndef xbox_controller(PLAYER_NUMBER):\n\n _string = os.popen(\"python find_device.py \" + str(PLAYER_NUMBER) + \" Xbox 360 Wireless Receiver\").read()\n _string = _string.split()\n\n if len(_string) > 0:\n _string = _string[0]\n \n # create a station to propagate the input events\n _xbox = avango.daemon.HIDInput()\n _xbox.station = avango.daemon.Station('device-xbox-' + str(PLAYER_NUMBER))\n _xbox.device = _string\n \n _xbox.values[0] = \"EV_ABS::ABS_X\" # left joystick\n _xbox.values[1] = \"EV_ABS::ABS_Y\" # left joystick\n _xbox.values[2] = \"EV_ABS::ABS_RX\" # right joystick\n _xbox.values[3] = \"EV_ABS::ABS_RY\" # right joystick\n\n _xbox.values[4] = \"EV_ABS::ABS_Z\" # left bumper\n _xbox.values[5] = \"EV_ABS::ABS_RZ\" # right bumper\n\n _xbox.buttons[0] = \"EV_KEY::BTN_X\" # Button X\n _xbox.buttons[1] = \"EV_KEY::BTN_B\" # Button B\n _xbox.buttons[2] = \"EV_KEY::BTN_A\" # Button A\n _xbox.buttons[3] = \"EV_KEY::BTN_Y\" # Button Y\n\n _xbox.buttons[4] = \"EV_KEY::BTN_START\" # Start button\n _xbox.buttons[5] = \"EV_KEY::BTN_SELECT\" # Select button\n\n _xbox.buttons[6] = \"EV_KEY::BTN_TL\"\n _xbox.buttons[7] = \"EV_KEY::BTN_TR\"\n\n device_list.append(_xbox)\n \n print(\"XBox Controller \" + str(PLAYER_NUMBER) + \" started at:\", _string)\n \n else:\n print(\"XBox Controller NOT found !\")\n\n\n## Initializes the August pointing device.\ndef init_august_pointer(ID, DEVICE_STATION_STRING):\n\n _string = os.popen(\"python find_device.py 1 MOUSE USB MOUSE\").read()\n _string = _string.split()\n\n if len(_string) > ID:\n \n _string = _string[ID]\n\n _pointer = avango.daemon.HIDInput()\n _pointer.station = avango.daemon.Station(DEVICE_STATION_STRING) # create a station to propagate the input events\n _pointer.device = _string\n #_pointer.timeout = '15'\n\n # map incoming events to station values\n _pointer.buttons[0] = \"EV_KEY::KEY_F5\" # front button\n #_pointer.buttons[0] = \"EV_KEY::KEY_ESC\" # front button\n _pointer.buttons[1] = \"EV_KEY::KEY_PAGEDOWN\" # back button\n _pointer.buttons[2] = \"EV_KEY::KEY_PAGEUP\" # center button\n\n device_list.append(_pointer)\n print('August Pointer found at:', _string)\n \n os.system(\"xinput --set-prop keyboard:'MOUSE USB MOUSE' 'Device Enabled' 0\") # disable X-forwarding of events\n \n else:\n print(\"August Pointer NOT found !\")\n\n## Initializes a portal camera for portal features.\ndef init_portal_camera(VERSION_NUMBER):\n\n _string = os.popen(\"python find_device.py 1 portalCam \" + str(VERSION_NUMBER)).read()\n _string = _string.split()\n\n if len(_string) > 0: \n\n _string = _string[0]\n \n # create a station to propagate the input events\n _portal_camera = avango.daemon.HIDInput()\n _splitted_number = VERSION_NUMBER.split(\".\")\n _portal_camera.station = avango.daemon.Station('device-portal-camera-' + _splitted_number[0] + _splitted_number[1])\n _portal_camera.device = _string\n\n print('device-portal-camera' + _splitted_number[0] + _splitted_number[1])\n\n # map incoming portal camera buttons to station\n _portal_camera.buttons[0] = \"EV_KEY::BTN_START\" # trigger button half step\n _portal_camera.buttons[1] = \"EV_KEY::BTN_MODE\" # trigger button full step\n _portal_camera.buttons[2] = \"EV_KEY::BTN_X\" # top left of trigger\n _portal_camera.buttons[3] = \"EV_KEY::BTN_C\" # top right of trigger\n _portal_camera.buttons[4] = \"EV_KEY::BTN_TL\" # top left button left\n _portal_camera.buttons[5] = \"EV_KEY::BTN_Y\" # top left button right\n _portal_camera.buttons[6] = \"EV_KEY::BTN_Z\" # top left button center\n _portal_camera.buttons[7] = \"EV_KEY::BTN_TR2\" # left thumb left button\n _portal_camera.buttons[8] = \"EV_KEY::BTN_SELECT\" # left thumb right button\n _portal_camera.buttons[9] = \"EV_KEY::BTN_B\" # top right button top\n _portal_camera.buttons[10] = \"EV_KEY::BTN_DEAD\" # top right button bottom\n _portal_camera.buttons[11] = \"EV_KEY::BTN_A\" # top right button center\n _portal_camera.buttons[12] = \"EV_KEY::BTN_THUMBR\"# right thumb left button\n _portal_camera.buttons[13] = \"EV_KEY::BTN_THUMBL\"# right thumb right button\n _portal_camera.buttons[14] = \"EV_KEY::BTN_TL2\" # left and right head button\n _portal_camera.buttons[15] = \"EV_KEY::BTN_TR\" # center head button\n\n\n device_list.append(_portal_camera)\n print(\"Portal Cam \" + VERSION_NUMBER + \" started at:\", _string)\n\n else:\n print(\"Portal Cam \" + VERSION_NUMBER + \" NOT found !\")\n\n## @var device_list\n# List of devices to be handled by daemon.\ndevice_list = []\n\n# init oculus rift sensors\n#init_oculus()\n\n# initialize trackings\ninit_lcd_wall_tracking()\ninit_dlp_wall_tracking()\n\n# initialize x-box controllers\nxbox_controller(1)\n#xbox_controller(2)\n#xbox_controller(3)\n#xbox_controller(4)\n\n# init spherons\ninit_old_spheron()\ninit_new_spheron()\n#init_new_globefish()\n\n# init pointers\ninit_august_pointer(0, \"device-pointer1\")\n\n# init desktop devices\ninit_keyboard()\n#init_mouse()\ninit_spacemouse()\n\n# init portal camera\ninit_portal_camera(\"3.1\")\ninit_portal_camera(\"3.2\")\n\n# init touch input\n#init_tuio_input() # crash ???\n\navango.daemon.run(device_list)\n","sub_path":"lib-server/Daemon.py","file_name":"Daemon.py","file_ext":"py","file_size_in_byte":19149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"505537265","text":"import time\n\nt = time.process_time()\ntot = 0\nrib = 0\nwith open('input.txt') as f:\n for p in f.readlines():\n lwh = p.split('x')\n l = int(lwh[0])\n w = int(lwh[1])\n h = int(lwh[2])\n tot += 2*l*w + 2*w*h + 2*h*l + min(l*w,w*h,h*l)\n rib += 2*(l+w+h) - 2*max(l,w,h) + l*w*h\n\nt = time.process_time() - t\nprint(\"Problem 1: %d\"%tot)\nprint(\"Problem 2: %d\"%rib)\nprint(\"Time elapsed: %d ms\"%int(t * 1000))\n","sub_path":"2015/aoc02/aoc02.py","file_name":"aoc02.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"386656567","text":"import sys\nimport pathlib\n\nimport numpy\nimport h5py\n\n\ndef pack_queries(fname: pathlib.Path):\n with h5py.File(fname, 'r') as f:\n points = numpy.vstack([f[f\"{key}/points\"][()] for key in f.keys()])\n results = numpy.vstack([f[f\"{key}/result\"][()] for key in f.keys()])\n rounded_points = numpy.vstack(\n [f[f\"{key}/shifted/points\"][()] for key in f.keys()])\n rounding_error = numpy.vstack(\n [f[f\"{key}/shifted/error\"][()] for key in f.keys()])\n rounded_results = numpy.vstack(\n [f[f\"{key}/shifted/result\"][()] for key in f.keys()])\n\n fname = fname.parent / (fname.stem + '-packed.hdf5')\n settings = dict(compression=\"gzip\", compression_opts=9)\n with h5py.File(fname, 'w') as f:\n f.create_dataset(\n \"points\", points.shape, dtype=points.dtype, **settings)[...] = points\n f.create_dataset(\n \"result\", results.shape, dtype=results.dtype, **settings)[...] = results\n rounded_grp = f.create_group(f\"rounded\")\n rounded_grp.create_dataset(\n \"points\", rounded_points.shape, dtype=rounded_points.dtype, **settings\n )[...] = rounded_points\n rounded_grp.create_dataset(\n \"result\", rounded_results.shape, dtype=rounded_results.dtype, **settings\n )[...] = rounded_results\n rounded_grp.create_dataset(\n \"error\", rounding_error.shape, dtype=rounding_error.dtype, **settings\n )[...] = rounding_error\n\n\ndef main():\n if sys.argv[1] == \"all\":\n root_dir = pathlib.Path(__file__).parents[1].resolve()\n data_dir = root_dir / \"data\"\n for fname in data_dir.glob(\"**/*.hdf5\"):\n if \"packed\" in str(fname.name):\n continue\n print(fname)\n pack_queries(fname)\n else:\n pack_queries(pathlib.Path(sys.argv[1]))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"tools/pack_collision_queries.py","file_name":"pack_collision_queries.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"284360478","text":"import os\nimport sys\nimport re\n\nfrom myreadline import myreadline\nfrom pipe import pipe\nfrom redirect import redirect\n\n\nwhile True:\n if 'PS1' in os.environ:\n os.write(2, (os.environ['PS1']).encode())\n else:\n os.write(2, \"$ \".encode())\n\n args = myreadline().strip().split(\" \")\n\n if args[0] == \"exit\":\n os.write(2, \"exiting shell...\".encode())\n sys.exit(0)\n \n elif args[0] == \"cd\":\n try:\n os.chdir(args[1])\n except:\n os.write(2, \"No such directory\\n\".encode())\n continue\n\n rc = os.fork()\n \n wait = True\n if '&' in args:\n wait = False\n args.remove('&')\n\n if rc < 0:\n os.write(2, \"fork failed, exiting...\".encode())\n sys.exit(0)\n\n elif rc == 0: # child\n if '|' in args:\n pipe(args)\n continue\n\n if '<' in args:\n redirect(args, '<')\n\n if '>' in args:\n redirect(args, '>')\n\n try:\n os.execve(args[0], args, os.environ)\n except FileNotFoundError:\n pass\n\n for dir in re.split(\":\", os.environ['PATH']): # try each directory in the path\n program = \"%s/%s\" % (dir, args[0])\n\n try:\n os.execve(program, args, os.environ) # try to exec program\n except FileNotFoundError: # ...expected\n pass # ...fail quietly\n\n os.write(2, (\"Could not exec: %s\\n\" % args[0]).encode())\n sys.exit(1)\n \n elif wait:\n childPidCode = os.wait()\n","sub_path":"shell/shell.py","file_name":"shell.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"508156924","text":"import pickle\nimport numpy as np\nfrom PIL import Image\nimport torch as t\n\n_data = None\n\ndef get_all_data():\n global _data\n if _data is not None:\n return _data\n\n file = '/Users/hetelek/Downloads/cifar-10-batches-py/data_batch_1'\n with open(file, 'rb') as fo:\n _data = pickle.load(fo, encoding='bytes')\n return _data\n\ndef load_random_image(force_index=None):\n data = get_all_data()\n\n if force_index:\n i = force_index\n else:\n i = np.random.randint(0, len(data[b'data']))\n\n label = data[b'filenames'][i].decode('utf-8')\n r = data[b'data'][i][:1024]\n g = data[b'data'][i][1024:1024+1024]\n b = data[b'data'][i][1024+1024:]\n\n r = t.tensor(r).reshape((32, 32))\n g = t.tensor(g).reshape((32, 32))\n b = t.tensor(b).reshape((32, 32))\n img = t.stack((r, g, b))\n img = img.type(t.float)\n\n return img, label\n\ndef save_image(tensor, name='img_1.png'):\n np_array = tensor.detach().numpy()\n if len(np_array.shape) == 4:\n np_array = np_array.squeeze()\n assert np_array.shape[0] == 3\n\n np_array = np_array.astype(np.uint8)\n np_array = np.moveaxis(np_array, 0, 2)\n\n img = Image.fromarray(np_array)\n img.save(name)\n\ndef load_disk_image(name):\n img_data = Image.open(name)\n img_arr = np.array(img_data)\n img_arr = t.tensor(img_arr)\n img_arr = img_arr.transpose(0, 2)\n img_arr = img_arr.transpose(1, 2)\n img_arr = img_arr.type(t.float)\n assert img_arr.shape == (3, 32, 32)\n return img_arr\n\ndef load_random_batch(batch_size):\n images = []\n labels = []\n for z in range(0, batch_size):\n i, l = load_random_image(z)\n images.append(i)\n labels.append(l)\n\n batch = t.stack(images)\n return batch, labels\n","sub_path":"src/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"544844808","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 27 17:44:36 2021\n\n@author: vand\n\"\"\"\n\nimport glob\nimport annotator\n\nfoldername = '/Users/vand/Documents/some_example_data/*.png'\n#lenpath = len('/Users/vand/Documents/some_example_data/')\nimages = glob.glob(foldername)\n\nfor image in images:\n print(f'Annotating image {image}') \n app = annotator.PyQt5.QtWidgets.QApplication([]) \n ex = annotator.Annotator.fromFilename(image)\n #ex.annotationsFilename = image[lenpath:-4] + '_annotations.png' \n app.exec_() \n \n \n \n","sub_path":"pycode/annotate_folder_example.py","file_name":"annotate_folder_example.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"327397126","text":"import argparse\n\nfrom management import handlers\nfrom scraping.links_extractor import LinksExtractorError\n\n\nCMD_DICT = {\n 'extract-article-links': handlers.extract_links,\n}\n\n\ndef main():\n main_parser = argparse.ArgumentParser()\n subparsers = main_parser.add_subparsers(title='commands', help='run your command')\n\n extract_links_parser = subparsers.add_parser(\n 'get-links',\n help='extract links from articles and save them to file'\n )\n extract_links_parser.add_argument('-c', '--category', help='specify articles category', default='all')\n extract_links_parser.add_argument('-o', '--output-path', help='specify output path', default='data/raw/links/')\n extract_links_parser.add_argument('-C', '--count', help='specify articles count', default=50)\n extract_links_parser.set_defaults(func=handlers.extract_links)\n\n get_articles_parser = subparsers.add_parser(\n 'get-articles',\n help='downloads articles from links'\n )\n get_articles_parser.add_argument('-o', '--output-path', help='specify output path', default='data/raw/articles')\n get_articles_parser.add_argument('-c', '--category', help='specify articles category')\n get_articles_parser.add_argument('-p', '--input-path', help='specify links path', default='data/raw/links')\n get_articles_parser.set_defaults(func=handlers.get_articles)\n\n json2csv_parser = subparsers.add_parser(\n 'json2csv',\n help='converts articles json to csv'\n )\n json2csv_parser.set_defaults(func=handlers.convert_json_to_csv)\n\n tokenize_parser = subparsers.add_parser(\n 'tokenize',\n help='tokenizes articles'\n )\n tokenize_parser.set_defaults(func=handlers.tokenize_articles)\n\n args = main_parser.parse_args()\n kwargs = dict(args._get_kwargs())\n kwargs.pop('func')\n try:\n args.func(**kwargs)\n except ValueError as err:\n print(str(err))\n except LinksExtractorError:\n print('Bad response, please check your internet conncetion and try again.')\n # except Exception as e:\n # print(e)\n # print('Unknown exception, please try again.')\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"252183547","text":"#!/usr/bin/env python\n\nimport sys\nfrom os.path import dirname\n\n# make sure the current directory is in the python import path\nsys.path.append(dirname(__file__))\n\ntry:\n import paver.doctools\n from paver.easy import options\n \n # default task options\n options(root_dir=dirname(__file__))\n\n # import our tasks\n from task.tests import *\n from task.virtualenv import *\n from task.deploy import *\nexcept:\n pass\n\nfrom paver.setuputils import setup, find_packages\n\n#\n# project dependencies\n#\n\ninstall_requires = [\n 'pyyaml==3.10',\n 'setuptools==0.6c11',\n 'tornado==3.0.1',\n 'toro==0.5',\n 'paramiko'\n]\n\n#\n# Setuptools configuration, used to create python .eggs and such.\n# See: http://bashelton.com/2009/04/setuptools-tutorial/ for a nice\n# setuptools tutorial.\n#\n\nsetup(\n # metadata\n name=\"nova_builder\",\n version=\"0.1\",\n author=\"Anthony Tarola\",\n author_email=\"anthony.tarola@gmail.com\",\n description=\"Bootstrap a Rackspace Cloud server with a script\",\n url=\"https://github.com/atarola/nova_builder\",\n \n # packaging info\n packages=find_packages(exclude=['test', 'test.*', 'task', 'task.*']),\n install_requires=install_requires,\n \n entry_points={\n 'console_scripts': [\n 'nova_builder = nova_builder.util:main'\n ]\n },\n \n zip_safe=False\n)","sub_path":"pavement.py","file_name":"pavement.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"102600894","text":"import os\n\nfrom . import Flask, SocketIO\n\ndef app_settings():\n '''\n Place all configuration options related to flask application including\n secret key, emailing, and other third party implementation.\n '''\n\n template_dir = os.path.abspath('./dist/chatta')\n\n print(template_dir)\n\n app = Flask(__name__, template_folder=template_dir, static_url_path='', static_folder=template_dir)\n app.config['SECRET_KEY'] = 'roel_zkie'\n\n return app\n\n\napp = app_settings()\nsocketio = SocketIO(app)\n","sub_path":"pyserver/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"4099922","text":"from sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import cross_val_score\nimport matplotlib.pyplot as plt\n'''\n交叉验证：\n\n'''\n\niris = load_iris()\nX = iris.data\ny = iris.target\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state= 4)\nknn = KNeighborsClassifier(n_neighbors=4)\nknn.fit(X_train, y_train)\n\ny_pred = knn.predict(X_test)\n\nprint(knn.score(X_test, y_test))\nprint(y_test)\nprint(y_pred)\n\nscores = cross_val_score(knn, X, y, cv=9, scoring='accuracy')\nprint(\"score is \", scores)\nprint(\"score means\", scores.mean())\n\n\nk_range = range(1, 30)\nk_score = []\n\nfor k in k_range:\n knn = KNeighborsClassifier(n_neighbors=k)\n #scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')\n #k_score.append(scores.mean())\n\n loss = -cross_val_score(knn, X, y, cv=10, scoring='mean_squared_error')\n k_score.append(loss.mean())\n\nplt.plot(k_range, k_score)\nplt.xlabel('Value of K for KNN')\nplt.ylabel('Cross-Validated Accuracy')\nplt.show()\n","sub_path":"ml/sklearn/cross_validation.py","file_name":"cross_validation.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"83975840","text":"import os\nimport dropbox\nfrom dropbox.files import WriteMode\nfrom zipfile import ZipFile\nfrom send2trash import send2trash\nfrom config import D_ACCESS_TOKEN\n\n\nclass TransferData:\n def __init__(self, access_token):\n self.access_token = access_token\n\n def upload_file(self, fileOrFolderName, file_to, write_mode):\n dbx = dropbox.Dropbox(self.access_token)\n\n # This should detect individual files\n if fileOrFolderName[-4] == \".\":\n with open(\n fileOrFolderName, \"rb\"\n ) as f: # Deleted leading \"/\" to upload .mp4\n dbx.files_upload(f.read(), file_to, mode=WriteMode(write_mode, None))\n\n # This should upload directories\n else:\n for root, dirs, files in os.walk(fileOrFolderName):\n for filename in files:\n\n localPath = os.path.join(root, filename)\n\n relativePath = os.path.relpath(localPath, fileOrFolderName)\n dropboxPath = os.path.join(\"/\", fileOrFolderName, relativePath)\n\n with open(localPath, \"rb\") as f:\n dbx.files_upload(\n f.read(), dropboxPath, mode=WriteMode(write_mode, None)\n )\n\n def download_file(self, filename, writePath):\n dbx = dropbox.Dropbox(self.access_token)\n\n with open(writePath + filename, \"wb\") as f:\n _, res = dbx.files_download(path=\"/\" + filename)\n f.write(res.content)\n\n def download_folder_zipped(self, folderName, writePath):\n dbx = dropbox.Dropbox(self.access_token)\n\n with open(writePath + folderName + \".zip\", \"wb\") as f:\n _, res = dbx.files_download_zip(path=\"/\" + folderName)\n f.write(res.content)\n\n print(f\"Downloaded the zipped folder {folderName}, now unzipping it.\")\n\n zf = ZipFile(writePath + folderName + \".zip\")\n zf.extractall(writePath)\n zf.close()\n\n print(f\"Deleting the local zip file {folderName}.zip.\")\n send2trash(writePath + folderName + \".zip\")\n print(f\"Downloaded the folder {folderName}, now deleting it from Drobox.\\n\")\n dbx.files_delete_v2(\"/\" + folderName)\n\n def dropboxGetFileDownloadLinks(self):\n dbx = dropbox.Dropbox(self.access_token)\n\n listOfDropboxLinks = []\n response = dbx.files_list_folder(path=\"\")\n for file in response.entries:\n # if \"log\" not in file:\n listOfDropboxLinks.append(\n dbx.sharing_create_shared_link(\"/\" + file.name).url\n )\n return listOfDropboxLinks\n\n def dropboxDeleteFile(self, filename):\n dbx = dropbox.Dropbox(self.access_token)\n\n dbx.files_delete_v2(\"/\" + filename)\n\n\ndef dropboxUploader(fileOrFolderName, write_mode=\"add\"):\n access_token = D_ACCESS_TOKEN\n transferData = TransferData(access_token)\n\n print(f\"{fileOrFolderName} uploading to Dropbox.\\n\")\n\n transferData.upload_file(fileOrFolderName, \"/\" + fileOrFolderName, write_mode)\n\n print(f\"{fileOrFolderName} successfully uploaded to Dropbox\\n\")\n\n\ndef dropboxDownloader(filename, writePath):\n access_token = D_ACCESS_TOKEN\n transferData = TransferData(access_token)\n\n print(\"File downloading from Dropbox.\\n\")\n\n transferData.download_file(filename, writePath)\n\n print(\"File successfully downloaded from Dropbox\\n\")\n\n\ndef dropboxDownloadFolderZipped(folderName, writePath):\n access_token = D_ACCESS_TOKEN\n transferData = TransferData(access_token)\n\n print(f\"\\n{folderName} downloading from Dropbox as a Zip file.\")\n\n transferData.download_folder_zipped(folderName, writePath)\n\n print(f\"{folderName} successfully downloaded from Dropbox.\\n\")\n\n\ndef dropboxGetFileDownloadLinks():\n access_token = D_ACCESS_TOKEN\n transferData = TransferData(access_token)\n\n return transferData.dropboxGetFileDownloadLinks()\n\n\ndef dropboxDeleteFile(filename):\n access_token = D_ACCESS_TOKEN\n transferData = TransferData(access_token)\n\n return transferData.dropboxDeleteFile(filename)\n","sub_path":"dropboxTransfer.py","file_name":"dropboxTransfer.py","file_ext":"py","file_size_in_byte":4073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"355958922","text":"import json\nimport re\nfrom helpers import *\n\ndef topName(listNames):\n maxAmount=0\n maxItem=\"No Information\"\n r=re.compile(\".(?i)*the( |$)\")\n total=float(len(listNames))\n nameDict={}\n for tweetName in listNames:\n for name in tweetName:\n key=name[0]\n if key in nameDict:\n nameDict[key] += 1\n else:\n nameDict[key] = 1\n answer=[]\n for item in nameDict.items():\n if item[1]>= maxAmount:\n maxItem=item[0]\n maxAmount=item[1]\n return maxItem\n\n\n\ndef getWinner (category, tweets, award, run=1):\n winnerList = find_matching_tweets_from_data(\".(?i)*\"+category+\"*\", tweets)\n winListWithoutCategory=[]\n for tweet in winnerList:\n winListWithoutCategory.append(eliminate_common_words(tweet,category,award))\n names=[]\n #print(winnerList)\n names=find_matching_tweets(\"([A-Z|@]['|.|\\w-]*(\\s+[of|A-Z]['|.|\\w-]*)+)\", winListWithoutCategory)\n nameCheck=topName(names)\n #print(\"The winner of \" +category+\" is:\")\n return nameCheck\n\ndef getWinnerHashtag(category, tweets, award):\n award_tweets = find_matching_tweets_from_data(\"(?i).*\" + category + \".*\", tweets)\n award_tweets_without_common_words = [eliminate_common_words(tweet,category,award) for tweet in award_tweets]\n hashtags_in_award_tweets = find_matching_tweet_parts(\"(?i)#\\w+\", award_tweets_without_common_words)\n d = mostCommonD(hashtags_in_award_tweets)\n if \"#s\" in d:\n del d[\"#s\"]\n if \"#Golde\" in d:\n del d[\"#Golde\"]\n if \"#goldenglobes\" in d:\n del d[\"#goldenglobes\"]\n winner_hashtag = max(d, key=d.get) if d else \"No Information\"\n print(winner_hashtag)","sub_path":"twitterlab-master2/winnerFile.py","file_name":"winnerFile.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"457716917","text":"from collections import Counter\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom core.tweet_reader import TweetReader\nimport statistics\nimport operator\n\n\nreader = TweetReader('data/need/full-day-need/09_02.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/08_24.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/08_25.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/08_26.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/08_27.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/08_28.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/08_29.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/08_30.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/08_31.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/09_01.csv', text_column=1, separator='|', encoding='utf8')\n# reader.add_file_content_to_corpus('data/need/full-day-need/09_02.csv', text_column=1, separator='|', encoding='utf8')\n\nwfreq = reader.extract_words_frequency(num_words=None, min_threshold=None, stop_word_file='input/harvey_stopwords.txt', ordered='asc')\n\nneeds = TweetReader('/home/long/TTU-SOURCES/harvey-need/data/daily-need/needs.csv', text_column=0, separator='|', encoding='utf8')\nneed_corpus = needs.get_corpus()\n\nwords_names = []\nwords_count = []\n\nneed_wfreq = dict()\nfrequencies = []\ntotal_frequency = 0\n\nfor (word, freq) in wfreq:\n if word not in need_corpus:\n continue\n need_wfreq[word] = freq\n total_frequency += freq\n frequencies.append(freq)\n\nmedian_freq = statistics.median(frequencies)\n## remove low frequency items\nfinal_wfreq = dict()\nfor word, freq in need_wfreq.items():\n if freq > median_freq:\n final_wfreq[word] = freq\n\nsorted_wfreq = sorted(final_wfreq.items(), key=operator.itemgetter(1))\nfor word, freq in sorted_wfreq:\n words_names.append(word)\n words_count.append(freq)\n\nprint(final_wfreq)\n\nshow_plot = True\n\nif show_plot == True:\n #\n fig, ax = plt.subplots()\n width = 0.56 # the width of the bars\n ind = np.arange(len(words_count)) # the x locations for the groups\n ax.barh(ind, words_count, width, color=\"blue\")\n ax.set_yticks(ind+width/2)\n ax.set_yticklabels(words_names, minor=False)\n plt.title('Word Frequency')\n plt.xlabel('Frequencies')\n plt.ylabel('Words')\n for i, v in enumerate(words_count):\n ax.text(v + 0.2, i - .15, str(v), color='blue', fontweight='bold')\n plt.show()\n\n","sub_path":"code/text_classifier/src/top_words_frequency.py","file_name":"top_words_frequency.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"569272316","text":"#######################################\n#######################################\n## Exercice 9 : signal périodique\n## périodicité spatiale\n#######################################\n#######################################\n\nimport matplotlib.pyplot as plt # on importe matplotlib.pyplot sous l'alias plt\nfrom math import pi # on importe pi de la bibliothèque math\nimport numpy as np # on importe la bibliothèque numpy sous l'alias np\n\n###################\n# question 1\n###################\n# variables de l'énoncé\nI0 = 3\nphi = pi/2\nk = pi\n\n####################\n## question 2\n####################\n# on construit le tableau de toutes les valeurs de x\ndebut = 0 # début de l'ensemble des x\nfin = 5 # fin de l'ensemble des x\npas = 0.01\nx = np.arange(debut, fin+pas, pas)\n\n# on construit le tableau I image de x par la fonction qui nous intéresse\nI = I0*np.cos(k*x+phi)\n\nplt.plot(x,I,'b-', label=\"signal périodique\")# on construit la courbe\n\n# on complète le graphe avec des noms aux axes, un titre, une grille, une légende\nplt.xlabel(\"axe d'espace\")# nom de l'axe selon Ox\nplt.ylabel(\"Intensité\")# de même pour l'ordonnée\nplt.title(\"un signal périodique\")# mettre un titre\nplt.xlim(debut, fin)# faire que le graphe ait des valeurs limites selon Ox\nplt.ylim(-I0,I0)# faire que le graphe ait des valeurs limites selon Oy\nplt.grid()# on affiche une grille\nplt.legend()# on place la légende en utilisant le label de la courbe\nplt.show() # on affiche \n","sub_path":"ondes1.py","file_name":"ondes1.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"272530804","text":"\nn = input()\n\ndef solve(n):\n res = []\n \n def recurse(l, r, n, string):\n if len(string) == n * 2:\n res.append(string)\n return\n if l < n:\n recurse(l + 1, r, n, string + \"(\" )\n if r < l:\n recurse(l, r + 1, n, string + \")\")\n recurse(0, 0, n, \"\")\n return res\nprint(solve(n))\n ","sub_path":"leetcode/generate_paranthesis.py","file_name":"generate_paranthesis.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"53078149","text":"from django.shortcuts import render,redirect\nfrom django.views import View\nfrom servicesapp.models import *\nfrom vehicleapp.models import *\nfrom inventoryapp.models import *\nfrom servicesapp.forms import *\nfrom datetime import date\nfrom functools import wraps\n# Create your views here.\n\n\ndef preinvoice(request):\n preinvoice2 = RoDetails.objects.all().last()\n total_list=[]\n for ropart in preinvoice2.ropartdetails_set.all():\n for part in ropart.ropart_prod.productinventory_set.all():\n total_amt = (ropart.ro_part_qty) * (part.prod_current_selling_price)\n total_list.append(total_amt)\n sub_total = sum(total_list)\n\n return render(request,'invoiceapp/preinvoice.html',{'i':preinvoice2,\"total_list\":total_list,\"sub_total\":sub_total})\n\ndef finalinvoice(request,ro_id):\n \n data = RoDetails.objects.get(ro_id=ro_id)\n\n total_amt = []\n gst_per = []\n final_gst = []\n for i in data.ropartdetails_set.all():\n qty = i.ro_part_qty\n for a in i.ropart_prod.productinventory_set.all():\n amt = a.prod_current_selling_price\n total = amt*qty\n total_amt.append(total)\n sub_total=sum(total_amt)\n\n for i in data.ropartdetails_set.all():\n gst=i.ropart_prod.prod_tax.tax_igst\n gst_per.append(gst)\n\n for amt, gst in zip(total_amt, gst_per):\n tem = (amt*gst)/100\n final_gst.append(tem)\n tem2=sum(final_gst)\n final_total=tem2+sub_total\n grand_total = final_total - (final_total*10/100)\n\n to=data.ro_received_date\n inv=str(to).replace('-','')\n # print(data.ro_id)\n RoDetails.objects.filter(ro_id=ro_id).update(ro_invoice_no=inv)\n return render(request,'invoiceapp/finalinvoice.html',{'gst_per':tem2,'i':data,'sub_total':sub_total,'fianal_toatl':final_total,'grand_total':grand_total})\n\n\n\n\ndef updateprod(request,pk=None):\n\n data = RoPartDetails.objects.get(ro_part_id=pk)\n form = RoPartDetailsForm(instance=data)\n\n return render(request, \"invoiceapp/ropartdetails1.html\",{\"form\":form,\"data\":data,})\n\n\n\n\ndef updateprodinfo(request,pk=None):\n\n qty = request.POST[\"ro_part_qty\"]\n data = RoPartDetails.objects.get(ro_part_id=pk)\n data1 = RoPartDetails.objects.filter(ro_part_id=pk).update(ro_part_qty=qty)\n data2 = RoDetails.objects.get(ropartdetails__ro_part_id=pk)\n\n\n\n return redirect(f'/finalinvoice/{data2.ro_id}/')\n\ndef calci(func):\n def inner(req,pk):\n\n finalinvoice = RoDetails.objects.get(ro_id=pk)\n\n\n for ropart in finalinvoice.ropartdetails_set.all():\n\n for part in ropart.ropart_prod.productinventory_set.all():\n total_qty = part.prod_total_quantity\n used_qty = ropart.ro_part_qty\n\n sold_qty = part.prod_sold_quantity + used_qty\n part.prod_stock_remaining = total_qty-sold_qty\n part.prod_sold_quantity = sold_qty\n part.save()\n\n response = func(req,pk)\n\n return response\n\n\n return wraps(func)(inner)\n\n\n\n\ndef deleteprod(request,pk):\n data = RoPartDetails.objects.get(ro_part_id=pk)\n data2 = RoDetails.objects.get(ropartdetails__ro_part_id=pk)\n\n if request.method==\"POST\":\n data.delete()\n\n return redirect(f'/finalinvoice/{data2.ro_id}/')\n\n\n@calci\ndef print(request,pk):\n data = RoDetails.objects.get(ro_id=pk)\n\n total_amt = []\n gst_per = []\n final_gst = []\n for i in data.ropartdetails_set.all():\n qty = i.ro_part_qty\n for a in i.ropart_prod.productinventory_set.all():\n amt = a.prod_current_selling_price\n total = amt*qty\n total_amt.append(total)\n sub_total=sum(total_amt)\n\n for i in data.ropartdetails_set.all():\n gst=i.ropart_prod.prod_tax.tax_igst\n gst_per.append(gst)\n\n for amt, gst in zip(total_amt, gst_per):\n tem = (amt*gst)/100\n final_gst.append(tem)\n tem2=sum(final_gst)\n final_total=tem2+sub_total\n grand_total = final_total - (final_total*10/100)\n\n to=data.ro_received_date\n inv=str(to).replace('-','')\n # print(data.ro_id)\n RoDetails.objects.filter(ro_id=pk).update(ro_invoice_no=inv)\n return render(request,'invoiceapp/print.html',{'gst_per':tem2,'i':data,'sub_total':sub_total,'fianal_toatl':final_total,'grand_total':grand_total})\n\ndef addnewprod(request,pk):\n finalinvoice = RoDetails.objects.get(ro_id=pk)\n form = RoPartDetailsForm()\n if request.method ==\"POST\":\n form = RoPartDetailsForm(request.POST)\n if form.is_valid():\n form.save()\n return redirect(f'/finalinvoice/{finalinvoice.ro_id}/')\n return render(request,\"invoiceapp/addnewprod.html\",{\"form\":form,\"finalinvoice\":finalinvoice})\n\ndef printpreinvoice(request):\n preinvoice2 = RoDetails.objects.all().last()\n total_list=[]\n for ropart in preinvoice2.ropartdetails_set.all():\n for part in ropart.ropart_prod.productinventory_set.all():\n total_amt = (ropart.ro_part_qty) * (part.prod_current_selling_price)\n total_list.append(total_amt)\n sub_total = sum(total_list)\n\n return render(request,'invoiceapp/printpreinvoice.html',{'i':preinvoice2,\"total_list\":total_list,\"sub_total\":sub_total})\n","sub_path":"RepublicHyundai/invoiceapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"634429627","text":"from pyfirmata import Arduino, util\nimport time, json\n\nimport sys\nsys.path.append('/opt/HomeHub/Library')\nimport system as System\n\n\nCommandFile = (\"/opt/HomeHub/Atmega/communication/AtmegaCmd.json\")\nDataFile = (\"/opt/HomeHub/Atmega/communication/AtmegaData.json\")\nDevice = \"ATMEGA\"\n\nDefaultData = \"{\\\"write\\\":[], \\\"servo\\\":[]}\"\n\ndef CommandAvailable():\n with open(CommandFile, \"r\") as read_file:\n data = read_file.read()\n if not (data == DefaultData):\n return True\n return False\n\ndef ReadCommand():\n with open(CommandFile, \"r\") as read_file:\n data = json.load(read_file)\n return data\n\ndef ResetFile():\n with open(CommandFile, \"w\") as outfile:\n outfile.write(str(DefaultData))\n\ndef UpdateAnalogInputs(board):\n ldr_front_ = board.analog[0].read()\n ldr_baseboard = board.analog[1].read()\n \nSystem.Log(Device, \"- Firmata Test -\")\nSystem.Log(Device, \"Init device\")\n\nboard = Arduino('/dev/ttyS0')\nit = util.Iterator(board)\nit.start()\n\nservo1 = board.get_pin('d:3:s')\nservo2 = board.get_pin('d:5:s')\nservo3 = board.get_pin('d:6:s')\n\nboard.analog[0].enable_reporting()\nboard.analog[1].enable_reporting()\n\nSystem.Log(Device, \"Init complete\")\n\nwhile True:\n try:\n time.sleep(1)\n if(CommandAvailable()):\n data = ReadCommand()\n\n if \"write\" in data:\n if(data[\"write\"] != []):\n for cmd in data[\"write\"]:\n System.Log(Device, F\"Set pin {cmd[0]} to {cmd[1]}\")\n board.digital[cmd[0]].write(cmd[1])\n\n if \"servo\" in data:\n servo = data[\"servo\"]\n if(servo != []):\n for cmd in servo:\n pin, degree = cmd[0], cmd[1]\n System.Log(Device, F\"Set servo {pin} to {degree} degrees\")\n if (pin == 1):\n servo1.write(degree)\n elif (pin == 2):\n servo2.write(degree)\n elif (pin == 3):\n servo3.write(degree)\n\n ResetFile()\n\n except KeyboardInterrupt:\n System.Log(Device, \"Quit\")\n board.exit()\n break\n\n except Exception as e:\n System.Log(Device, (\"[Error]\", e))\n","sub_path":"Install/InstallData/Atmega/atmega-handler.py","file_name":"atmega-handler.py","file_ext":"py","file_size_in_byte":2298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"143948118","text":"from src.pom.home_page import HomePage\nfrom src.tests.base_test_case import BaseTestCase\n\n\nclass HomePageTestCase(BaseTestCase):\n\n def test_title_verification(self):\n home_page_pom = HomePage(self.web_driver)\n home_page_title = home_page_pom.get_title()\n self.assertIsNotNone(home_page_title)\n\n def test_links_functionality(self):\n home_page_pom = HomePage(self.web_driver)\n home_page_pom.click_skill_link()\n current_page_url = home_page_pom.driver.get_current_page_url()\n self.assertEqual(current_page_url, 'https://www.ioet.com/technologiess')\n","sub_path":"src/tests/home_page_test_case.py","file_name":"home_page_test_case.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"631438930","text":"import xml.etree.ElementTree as ET\nfrom collections import OrderedDict\n\ndef get_runinfo(runinfo_xml_file):\n tree = ET.parse(runinfo_xml_file)\n root = tree.getroot()\n runinfo = {\n 'name': root.find('Run').attrib['Id'],\n 'instrument': root.find('Run/Instrument').text,\n 'flowcell': root.find('Run/Flowcell').text\n }\n return runinfo\n\n\ndef get_readinfo_from_runinfo(runinfo_xml_file):\n tree = ET.parse(runinfo_xml_file)\n root = tree.getroot()\n read_dicts=[]\n for child in root.findall('.//Read'):\n read_dicts.append(child.attrib)\n readkey_to_readdata_map=OrderedDict()\n for read_dict in read_dicts:\n number=read_dict.pop('Number')\n readkey_to_readdata_map['read%s' % number] = read_dict\n return readkey_to_readdata_map\n\n","sub_path":"odybcl2fastq/parsers/parse_runinfoxml.py","file_name":"parse_runinfoxml.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"260313332","text":"import json\nimport time\nfrom collections import OrderedDict\n\nfrom .util import script_join\nfrom .data_access import BaseSarDataAccess\n\nexprs = OrderedDict()\n# exprs['rsm.design'] = 'decode.data(rsm.design)'\nexprs['y'] = 'c(334,335, 333, 332, 336, 337, 334, 335, 332, 336, 335, 334, 332, 335, 336, 335)'\nexprs['rsm.design$y'] = 'y'\nexprs['SO.model'] = 'rsm(y ~ SO(x1,x2,x3), data = rsm.design)'\nexprs['rsm.analyze'] = \"summary(SO.model)\"\n\nclass BaseInput:\n\n def get_input(self, input_deal, data, access=None):\n\n raise NotImplemented()\n\n\nclass InputDesignDeal:\n\n def deal(self, data, access=None):\n raise NotImplemented()\n\n\nclass DjangoInput(BaseInput):\n\n def get_input(self, input_deal, data, access=None):\n \"\"\"\n\n :param input_deal: InputDesignDeal\n :param data:\n :return:\n \"\"\"\n if isinstance(input_deal, InputDesignDeal) and isinstance(access, BaseSarDataAccess):\n return input_deal.deal(data, access)\n\n\nclass DjangoInputDesignDeal(InputDesignDeal):\n pass\n\n\nclass CCDInputDesignDeal(DjangoInputDesignDeal):\n\n def __init__(self):\n self.design = {\n \"type\": \"design\",\n \"name\": \"ccd\",\n \"data\": {\n \"design\": {\n \"params\": {\n \"basis\": 1,\n \"n0\": 3,\n \"alpha\": 1.68,\n \"randomize\": \"FALSE\",\n 'inscribed': 'FALSE',\n \"coding\": \"list (x1 ~ (Temp - 150)/10, x2 ~ (Pres - 50)/5, x3 ~ Feedrate - 4)\",\n },\n \"var\": '',\n },\n \"data_access\": {\n 'name': None,\n 'hash_id': None,\n 'data': None,\n 'type': 'design',\n 'access': ['design'],\n 'hash_data': None,\n }\n },\n }\n # self.design = {\n # \"type\": \"design\",\n # \"name\": \"ccd\",\n # \"data\": {\n # \"design\": {\n # \"params\": {\n # \"basis\": 3,\n # \"n0\": 1,\n # \"alpha\": 1.68,\n # \"randomize\": \"FALSE\",\n # \"coding\": \"list (x1 ~ (Temp - 150)/10, x2 ~ (Pres - 50)/5, x3 ~ Feedrate - 4)\",\n # },\n # \"settings\": {\n # 'is_init': 1,\n # 'is_update': 0,\n # }\n # }\n # }\n # }\n\n\n def deal(self, data, access=None):\n # return format\n # {\n # \"type\": \"design\",\n # \"name\": \"ccd\",\n # \"data\": {\n # \"basis\": 3, # int\n # \"n0\": 1, # int\n # \"alpha\": 1.68, # int\n # \"randomize\": \"FALSE\",\n # \"coding\": \"list (x1 ~ (Temp - 150)/10, x2 ~ (Pres - 50)/5, x3 ~ Feedrate - 4)\"\n # ...\n # }\n # }\n self.design['data']['time'] = str(time.time())\n if data['username']:\n self.design['data']['data_access']['name'] = data['username']\n hash_data = hash(json.dumps(data))\n # self.design['data'][\"data_access\"]['name'] = data['cookies']\n if len(data['design_id']):\n self.design['data'][\"data_access\"]['hash_id'] = data['design_id']\n acce = access.access_from(self.design)\n if (acce is not None and (acce['data_access']['hash_data'] == hash_data)) or data.get('history'):\n self.design['data'] = acce\n return self.design\n\n\n self.design['data'][\"data_access\"]['hash_id'] = None\n\n datas = json.loads(data['data'])\n script = {}\n script[\"basis\"] = len(datas)\n code = []\n var = []\n for i, j in enumerate(datas):\n code.append('x{} ~ ({} - {})/{},'.format(str(i+1), j['code'], j['numc'],\n str(float(j['numd'])-float(j['numc']))))\n var.append(j['code'])\n script['coding'] = 'list ({})'.format(''.join(code)[:-1])\n self.design['data'][\"design\"][\"params\"].update(script)\n self.design['data'][\"design\"][\"var\"] = var\n self.design['data'][\"data_access\"]['hash_data'] = hash_data\n access.access_to(self.design)\n return self.design\n\nclass CCDInputDataViewDeal(DjangoInputDesignDeal):\n def __init__(self):\n self.data_view = {\n \"type\": \"data_view\",\n \"name\": \"ccd\",\n \"data\": {\n \"data_view\": {\n 'exprs': exprs,\n 'views': {\n 'view1': 'persp(SO.model, x1 ~ x2, at=xs(SO.model));dev.off();',\n 'view2': 'persp(SO.model, x1 ~ x3, at=xs(SO.model));dev.off();',\n 'view3': 'persp(SO.model, x2 ~ x3, at=xs(SO.model));dev.off();',\n }\n },\n \"data_access\": {\n 'name': None,\n 'hash_id': None,\n 'data': None,\n 'type': 'data_view',\n 'access': ['design', 'analyze'],\n 'hash_data': None,\n }\n },\n 'time': None\n }\n\n def deal(self, data, access=None):\n self.data_view['data']['time'] = str(time.time())\n if data['username']:\n self.data_view['data']['data_access']['name'] = data['username']\n hash_data = hash(json.dumps(data))\n # self.data_view['data']['data_access']['name'] = data['cookies']\n if len(data['data_view_id']):\n self.data_view['data'][\"data_access\"]['hash_id'] = data['data_view_id']\n acce = access.access_from(self.data_view)\n if (acce is not None and (acce['data_access']['hash_data'] == hash_data)) or data.get('history'):\n self.data_view['data'] = acce\n return self.data_view\n self.data_view['data'][\"data_access\"]['hash_id'] = data['analyze_id']\n # return access.access_from(self.analyze['data']['data_access'])\n self.data_view['data']['data_access']['type'] = 'analyze'\n varss = access.access_from(self.data_view)\n self.data_view['data']['data_view']['exprs'] = varss['analyze']['exprs']\n\n self.data_view['data']['data_access']['type'] = 'data_view'\n # var = varss['design']['var']\n # view1 = 'persp(SO.model, {} ~ {}, at=xs(SO.model));dev.off();'.format(var[0], var[1])\n # view2 = 'persp(SO.model, {} ~ {}, at=xs(SO.model));dev.off();'.format(var[0], var[2])\n # view3 = 'persp(SO.model, {} ~ {}, at=xs(SO.model));dev.off();'.format(var[1], var[2])\n # self.data_view['data']['data_view']['views']['view1'] = view1\n # self.data_view['data']['data_view']['views']['view2'] = view2\n # self.data_view['data']['data_view']['views']['view3'] = view3\n self.data_view['data']['design'] = varss['design']\n self.data_view['data']['data_view']['dirname'] = data['dirname']\n self.data_view['data'][\"data_access\"]['hash_data'] = hash_data\n access.access_to(self.data_view)\n return self.data_view\n\nclass CCDInputAnalyzeDeal(DjangoInputDesignDeal):\n\n def __init__(self):\n self.analyze = {\n \"type\": \"analyze\",\n \"name\": \"ccd\",\n \"data\": {\n \"analyze\": {\n 'exprs': exprs,\n },\n \"data_access\": {\n 'name': None,\n 'hash_id': None,\n 'data': None,\n 'type': 'analyze',\n 'access': ['design'],\n }\n }\n }\n # self.analyze = {\n # \"type\": \"analyze\",\n # \"name\": \"ccd\",\n # \"data\": {\n # \"design\": {\n # \"params\": {\n # \"basis\": 3,\n # \"n0\": 1,\n # \"alpha\": 1.68,\n # \"randomize\": \"FALSE\",\n # \"coding\": \"list (x1 ~ (Temp - 150)/10, x2 ~ (Pres - 50)/5, x3 ~ Feedrate - 4)\",\n # },\n # \"settings\": {\n # 'is_init': 1,\n # 'is_update': 0,\n # }\n #\n # },\n # \"analyze\": {\n # 'exprs': exprs,\n # \"settings\": {\n # 'is_init': 1,\n # 'is_update': 0,\n # }\n # }\n # }\n # }\n\n def deal(self, data, access=None):\n self.analyze['data']['time'] = str(time.time())\n if data['username']:\n self.analyze['data']['data_access']['name'] = data['username']\n hash_data = hash(json.dumps(data))\n # self.analyze['data']['data_access']['name'] = data['cookies']\n if len(data['analyze_id']):\n self.analyze['data'][\"data_access\"]['hash_id'] = data['analyze_id']\n # return access.access_from(self.analyze['data']['data_access'])\n acce = access.access_from(self.analyze)\n if (acce is not None and (acce['data_access']['hash_data'] == hash_data)) or data.get('history'):\n self.analyze['data'] = acce\n return self.analyze\n datas = json.loads(data['data'])\n self.analyze['data'][\"data_access\"]['hash_id'] = data['design_id']\n x = self.analyze[\"data\"][\"analyze\"]['exprs']\n script = {}\n y = []\n for _data in datas:\n if _data == '':\n raise Exception\n y.append('{},'.format(_data))\n script['y'] = 'c({})'.format(''.join(y)[:-1])\n x.update(script)\n\n self.analyze['data']['data_access']['type'] = 'design'\n varss = access.access_from(self.analyze)\n # v = script_join('{},', varss['design']['var'])[:-1]\n # s = 'rsm(y ~ SO({}), data = rsm.design)'.format(v)\n # self.analyze['data']['analyze']['exprs']['SO.model'] = s\n self.analyze['data']['data_access']['type'] = 'analyze'\n self.analyze['data']['design'] = varss['design']\n self.analyze['data'][\"data_access\"]['hash_data'] = hash_data\n access.access_to(self.analyze)\n return self.analyze\n\n# import queue\n#\n# class BaseTaskQueue:\n#\n# def get_task(self):\n# \"\"\"\n# get task from queue\n# :return: type dict\n# \"\"\"\n# raise ImportError('not implement get_task')\n#\n# def is_empty(self):\n# \"\"\"\n# queue is empty ?\n# :return:\n# \"\"\"\n# raise ImportError('not implement is_empty')\n#\n# class TestQueue(BaseTaskQueue):\n#\n# def __init__(self):\n# self.queue = queue.Queue()\n#\n# def get_task(self):\n# \"\"\"\n# get task from queue\n# :return: dict\n# \"\"\"\n#\n# return self.queue.get()\n#\n# def is_empty(self):\n#\n# return self.queue.empty()\n#\n#\n\n#\n# \"\"\"\n# q = TestQueue()\n# for i in range(5):\n# q.queue.put(i)\n# print(q.get_task())\n# print(q.get_task())\n# \"\"\"\n","sub_path":"sardesign/sar/input.py","file_name":"input.py","file_ext":"py","file_size_in_byte":11264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"59130738","text":"from ftw.builder import Builder\nfrom ftw.builder import create\nfrom opengever.contact.models import Contact\nfrom opengever.testing import MEMORY_DB_LAYER\nimport unittest2\n\n\nclass TestPerson(unittest2.TestCase):\n\n layer = MEMORY_DB_LAYER\n\n def setUp(self):\n super(TestPerson, self).setUp()\n self.session = self.layer.session\n\n def test_adding(self):\n create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n\n def test_is_contact(self):\n person = create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n\n self.assertTrue(isinstance(person, Contact))\n self.assertEquals('person', person.contact_type)\n\n def test_is_active_by_default(self):\n person = create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n\n self.assertTrue(person.is_active)\n\n def test_person_can_have_multiple_addresses(self):\n peter = create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n\n address1 = create(Builder('address')\n .for_contact(peter)\n .labeled(u'Work')\n .having(street=u'Dammweg 9', zip_code=u'3013',\n city=u'Bern'))\n\n address2 = create(Builder('address')\n .for_contact(peter)\n .labeled(u'Home')\n .having(street=u'Musterstrasse 283',\n zip_code=u'1700',\n city=u'Fribourg'))\n\n self.assertEquals([address1, address2], peter.addresses)\n\n def test_person_can_have_multiple_mail_addresses(self):\n peter = create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n\n home = create(Builder('mailaddress')\n .for_contact(peter)\n .labeled(u'Home')\n .having(address=u'peter@example.com'))\n\n work = create(Builder('mailaddress')\n .for_contact(peter)\n .labeled(u'Work')\n .having(address=u'm.peter@example.com'))\n\n self.assertEquals([home, work], peter.mail_addresses)\n self.assertEquals([u'peter@example.com', u'm.peter@example.com'],\n [mail.address for mail in peter.mail_addresses])\n\n def test_person_can_have_multiple_phonenumbers(self):\n peter = create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n\n home = create(Builder('phonenumber')\n .for_contact(peter)\n .labeled(u'Home')\n .having(phone_number=u'+41791234566'))\n\n work = create(Builder('phonenumber')\n .for_contact(peter)\n .labeled(u'Work')\n .having(phone_number=u'0315110000'))\n\n self.assertEquals([home, work], peter.phonenumbers)\n self.assertEquals([u'+41791234566', u'0315110000'],\n [phone.phone_number for phone in peter.phonenumbers])\n\n def test_person_can_have_multiple_urls(self):\n peter = create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n\n ftw = create(Builder('url')\n .for_contact(peter)\n .labeled(u'Info')\n .having(url=u'http://www.4teamwork.ch'))\n\n gever = create(Builder('url')\n .for_contact(peter)\n .labeled(u'Info')\n .having(url=u'http://www.onegovgever.ch'))\n\n self.assertEquals([ftw, gever], peter.urls)\n self.assertEquals([u'http://www.4teamwork.ch',\n u'http://www.onegovgever.ch'],\n [url.url for url in peter.urls])\n\n def test_fullname_is_lastname_and_firstname_separated_with_a_space(self):\n peter = create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n\n self.assertEquals(u'M\\xfcller Peter', peter.fullname)\n\n def test_title_is_fullname(self):\n peter = create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n self.assertEquals(u'M\\xfcller Peter', peter.get_title())\n\n def test_title_is_extended_with_former_id_in_brackets_when_flag_is_set(self):\n peter = create(Builder('person')\n .having(firstname=u'Peter', lastname=u'M\\xfcller'))\n james = create(Builder('person')\n .having(firstname=u'James',\n lastname=u'M\\xfcller',\n former_contact_id=123456))\n\n self.assertEquals(u'M\\xfcller Peter',\n peter.get_title(with_former_id=True))\n self.assertEquals(u'M\\xfcller James [123456]',\n james.get_title(with_former_id=True))\n","sub_path":"opengever/contact/tests/test_person_unit.py","file_name":"test_person_unit.py","file_ext":"py","file_size_in_byte":5135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"274401006","text":"import warnings\nwarnings.filterwarnings(\"ignore\")\nimport torch\nimport time\nfrom torch.utils.tensorboard import SummaryWriter\nimport os\nimport random\nimport numpy as np\nfrom dataloader import *\nfrom model import *\nfrom utils import get_logger, get_gpu_usage\nimport argparse\nimport pickle\nimport gc\nfrom evaluate_utils import get_val_auc, get_dataset, get_test_auc_scores\n\nparser = argparse.ArgumentParser()\n\n# model related\nparser.add_argument(\"--model\", default='simple', type=str, required=False,\n choices=[\"graphsage\", \"graphsage_relational\",\n \"simple\", \"simple_relational\",\n \"kgbert_va\", \"kgbert_vb\",\n \"kgbertsage_va\", \"kgbertsage_vb\"],\n help=\"choose model\")\nparser.add_argument(\"--encoder\", default='bert', type=str, required=False,\n choices=[\"bert\", \"roberta\", \"bert_large\", \"roberta_large\"],\n help=\"choose encoder\")\nparser.add_argument(\"--num_layers\", default=1, type=int, required=False,\n help=\"number of graphsage layers\")\nparser.add_argument(\"--num_neighbor_samples\", default=4, type=int, required=False,\n help=\"num neighbor samples in GraphSAGE\")\nparser.add_argument(\"--encoding_style\", default='single_cls_trans', type=str, required=False,\n choices=[\"pair_cls_trans\", \"pair_cls_raw\", \"single_cls_trans\", \"single_cls_raw\", \"single_mean\"],\n help=\"the encoding style of classifier (pair_xxx are not available for graphsage)\")\nparser.add_argument(\"--agg_func\", default='MEAN', type=str, required=False,\n choices=[\"MEAN\", \"MAX\", \"ATTENTION\"],\n help=\"the encoding style of classifier (pair_xxx are not available for graphsage)\")\n# train related\nparser.add_argument(\"--gpu\", default='0', type=str, required=False,\n help=\"choose which gpu to use\")\nparser.add_argument(\"--optimizer\", default='SGD', type=str, required=False,\n choices=[\"SGD\", \"ADAM\"],\n help=\"optimizer to be used\")\nparser.add_argument(\"--lr\", default=0.01, type=float, required=False,\n help=\"learning rate\")\nparser.add_argument(\"--lrdecay\", default=0.8, type=float, required=False,\n help=\"learning rate decay every 2000 steps\")\nparser.add_argument(\"--decay_every\", default=500, type=int, required=False,\n help=\"show test result every x steps\")\nparser.add_argument(\"--test_every\", default=250, type=int, required=False,\n help=\"show test result every x steps\")\nparser.add_argument(\"--batch_size\", default=32, type=int, required=False,\n help=\"batch size\")\nparser.add_argument(\"--epochs\", default=3, type=int, required=False,\n help=\"batch size\")\nparser.add_argument(\"--metric\", default='f1', type=str, required=False,\n choices=[\"f1\", \"acc\"],\n help=\"evaluation metric, either f1 or acc\")\nparser.add_argument(\"--save_every_checkpoint\", action=\"store_true\",\n help=\"whether to save every steps.\")\nparser.add_argument(\"--eval_on\", default='link_prediction', type=str, required=False,\n choices=[\"link_prediction\", \"human_annotation\", \"none\"],\n help=\"evaluate on link prediction or the human annotated tuples\")\nparser.add_argument(\"--use_nl_relation\", action=\"store_true\",\n help=\"whether to use natural language to encode relation\")\nparser.add_argument(\"--evaluation_file_path\", \n default=\"data/evaluation_set.csv\", \n type=str, required=False,\n help=\"Path to the evaluation set csv.\")\n\n# data related\nparser.add_argument(\"--aser_rel_format\", default=\"all_aser_rel\",\n choices=[\"all_aser_rel\", \"highest_aser_rel\", \"concate_aser_rel\"],\n help=\"how to make use of ASER edges.\"\n \"all: used in the EMNLP 2021 paper. regard each relation as a sperate edge.\"\n \"highest_aser_rel: select the ASER relation with the highest weight (except for Co_occurence) as the edge for training and testing.\"\n \"concate_aser_rel: concatenate all relations whose weights are larger than a threshold in one string\")\nparser.add_argument(\"--target_relation\", default='all', type=str, required=False,\n help=\"target relation. all or one of the commonsense relations.\")\nparser.add_argument(\"--target_dataset\", default='all', type=str, required=False,\n choices=[\"all\", \"atomic\", \"glucose\", \"cn\"],\n help=\"target dataset. all or one of the commonsense datasets.\")\nparser.add_argument(\"--eval_dataset\", default='all', type=str, required=False,\n choices=[\"all\", \"atomic\", \"glucose\", \"cn\"],\n help=\"evaluation dataset. Only has effect when target_dataset is\"\n \"set to all.\")\nparser.add_argument(\"--load_edge_types\", default='ASER', type=str, required=False,\n choices=[\"CS\", \"ASER\", \"CS+ASER\"],\n help=\"load what edges to data_loader.adj_lists\")\nparser.add_argument(\"--negative_sample\", default='prepared_neg', type=str, required=False,\n choices=[\"prepared_neg\", \"from_all\", \"fix_head\"],\n help=\"nagative sample methods\")\nparser.add_argument(\"--neg_prop\", default=1.0, type=float, required=False,\n help=\"whether to include relation in adj matrix\")\nparser.add_argument(\"--save_tokenized\", action=\"store_true\", \n help=\"whether to tokenize all nodes first and save them.\")\n\n# save paths\nparser.add_argument(\"--graph_cache_path\", default=\"graph_cache\",\n type=str, required=False,\n help=\"path of graph cache\")\nparser.add_argument(\"--file_path\", default='', type=str, required=True,\n help=\"load training graph pickle\")\nparser.add_argument(\"--model_dir\", default='models', type=str, required=False,\n help=\"Where to save models.\")\nparser.add_argument(\"--tensorboard_dir\", default='runs', type=str, required=False,\n help=\"the directory to store tensorboard files.\")\nparser.add_argument(\"--log_name\", default='unnamed', type=str, required=False,\n help=\"special names of log files\")\n# etc\nparser.add_argument(\"--seed\", default=401, type=int, required=False,\n help=\"random seed\")\n\nargs = parser.parse_args()\n# os.environ[\"CUDA_VISIBLE_DEVICES\"] = args.gpu\nlr = args.lr\nshow_step = args.test_every\nbatch_size= args.batch_size\nnum_epochs = args.epochs\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ntest_batch_size = 64\nneg_prop = args.neg_prop\n\nfile_path = args.file_path\n\n# dataloader path\n\n# whether it's a triple classification task (h, r, t)\nrel_in_edge = \"rel_in_edge\" if (\"va\" in args.model or \"relational\" in args.model) else \"\"\n# highest_aser_rel = \"_highest_aser_rel\" if args.highest_aser_rel else \"\"\naser_rel_format = \"_\" + args.aser_rel_format\nuse_nl_rel = \"_use_nl_rel\" if args.use_nl_relation else \"\"\nsave_tokenized = \"_save_token\" if args.save_tokenized else \"\"\n\ngraph_cache = os.path.join(args.graph_cache_path, \"neg_{}_{}_{}_{}_{}.pickle\")\nrelation_string = f\"{args.target_relation}-{args.target_dataset}-relations\"\nif args.target_dataset == \"all\" and args.eval_dataset != \"all\":\n relation_string += f\"-{args.eval_dataset}-eval\"\ngraph_cache = graph_cache.format(f\"{args.negative_sample}-{args.neg_prop}\",\n args.load_edge_types,\n os.path.basename(file_path).rsplit(\".\", 1)[0],\n relation_string,\n rel_in_edge + aser_rel_format + use_nl_rel + save_tokenized)\n\nif not os.path.exists(args.model_dir):\n os.mkdir(args.model_dir)\nmodel_dir = os.path.join(\n args.model_dir,\n f\"{os.path.basename(graph_cache).rsplit('.', 1)[0]}\")\n #f\"_{args.target_relation}_negprop{args.neg_prop}\")\n\nif not os.path.exists(model_dir):\n os.mkdir(model_dir)\n\n# Model\n\nif \"simple\" in args.model:\n model_save_path = os.path.join(model_dir, '{}_{}_best_{}_bs{}_opt_{}_lr{}_decay{}_{}_{}_{}_seed{}.pth'\\\n .format(args.model, args.encoding_style, args.encoder, batch_size, args.optimizer,\n args.lr, args.lrdecay, args.decay_every, args.metric, args.eval_on, args.seed))\n log_path = os.path.join(model_dir, '{}_{}_best_{}_bs{}_opt_{}_lr{}_decay{}_{}_{}_{}_{}_seed{}.log' \\\n .format(args.model, args.encoding_style, args.encoder, batch_size, args.optimizer,\n args.lr, args.lrdecay, args.decay_every, args.metric, args.eval_on, args.log_name, args.seed))\nelif \"sage\" in args.model: # graphsage, kgbertsage\n model_save_path = os.path.join(model_dir, '{}_{}_best_{}_bs{}_opt_{}_lr{}_decay{}_{}_layer{}_neighnum_{}_graph_{}_{}_{}_aggfunc{}_seed{}.pth'\\\n .format(args.model, args.encoding_style, args.encoder, batch_size, args.optimizer, args.lr,\n args.lrdecay, args.decay_every, args.num_layers, \n args.num_neighbor_samples, args.load_edge_types, args.metric, args.eval_on, args.agg_func, args.seed))\n log_path = os.path.join(model_dir, '{}_{}_best_{}_bs{}_opt_{}_lr{}_decay{}_{}_layer{}_neighnum_{}_graph_{}_{}_{}_aggfunc{}_{}_seed{}.log'\\\n .format(args.model, args.encoding_style, args.encoder, batch_size, args.optimizer, args.lr,\n args.lrdecay, args.decay_every, args.num_layers,\n args.num_neighbor_samples, args.load_edge_types, args.metric, args.eval_on, args.agg_func, args.log_name, args.seed))\nelif \"kgbert_\" in args.model:\n model_save_path = os.path.join(model_dir, '{}_{}_best_{}_bs{}_opt_{}_lr{}_decay{}_{}_{}_{}_seed{}.pth'\\\n .format(args.model, args.encoding_style, args.encoder, batch_size, args.optimizer,\n args.lr, args.lrdecay, args.decay_every, args.metric, args.eval_on, args.seed))\n log_path = os.path.join(model_dir, '{}_{}_best_{}_bs{}_opt_{}_lr{}_decay{}_{}_{}_{}_{}_seed{}.log' \\\n .format(args.model, args.encoding_style, args.encoder, batch_size, args.optimizer,\n args.lr, args.lrdecay, args.decay_every, args.metric, args.eval_on, args.log_name, args.seed))\n\ntensorboard_dir = os.path.join(args.tensorboard_dir,\n os.path.basename(model_dir),\n os.path.basename(model_save_path).rsplit(\".\", 1)[0])\nos.makedirs(tensorboard_dir, exist_ok=True)\n\nlogging = get_logger(log_path)\n\nseed = args.seed\nrandom.seed(seed)\nos.environ['PYTHONHASHSEED'] = str(seed)\nnp.random.seed(seed)\ntorch.manual_seed(seed)\ntorch.cuda.manual_seed(seed)\ntorch.cuda.manual_seed_all(seed)\ntorch.backends.cudnn.deterministic = True\nlogging(\"set random seed = %d for random, PYTHONHASHSEED, numpy, torch\" % seed)\n\nlogging(graph_cache)\nif not os.path.exists(graph_cache):\n id2nodestoken_path = os.path.join(args.graph_cache_path,\n \"id2nodestoken_\" + os.path.basename(file_path))\n s = time.time()\n data_loader = MultiGraphDataset(file_path, device, args.encoder,\n node_token_path=id2nodestoken_path,\n target_relation=args.target_relation, \n target_dataset=args.target_dataset,\n eval_dataset=args.eval_dataset,\n edge_include_rel=(\"va\" in args.model or \"relational\" in args.model),\n negative_sample=args.negative_sample, load_edge_types=args.load_edge_types,\n neg_prop=neg_prop,\n aser_rel_format=args.aser_rel_format,\n use_nl_rel=args.use_nl_relation,\n save_tokenized=args.save_tokenized)\n with open(graph_cache, \"wb\") as writer:\n pickle.dump(data_loader,writer,pickle.HIGHEST_PROTOCOL) \n e = time.time()\n logging(f\"after dumping graph cache to {graph_cache}\"\n f\"\\ntime taken: {e - s}\")\nelse:\n s = time.time()\n with open(graph_cache, \"rb\") as reader:\n data_loader = pickle.load(reader)\n e = time.time()\n logging(f\"after loading graph cache from {graph_cache}\"\n f\"\\ntime taken: {e - s}\")\n\nif \"simple\" in args.model:\n model = SimpleClassifier(encoder=args.encoder,\n adj_lists=data_loader.get_adj_list(),\n nodes_tokenized=data_loader.get_nodes_tokenized(),\n nodes_text=data_loader.get_nid2text(),\n device=device,\n enc_style=args.encoding_style,\n num_class=2,\n include_rel=\"relational\" in args.model,\n relation_tokenized=data_loader.get_relations_tokenized(),\n )\nelif 'graphsage' in args.model:\n model = LinkPrediction(encoder=args.encoder,\n adj_lists=data_loader.get_adj_list(),\n nodes_tokenized=data_loader.get_nodes_tokenized(),\n device=device,\n id2node=data_loader.get_nid2text(),\n num_layers=args.num_layers,\n num_neighbor_samples=args.num_neighbor_samples,\n enc_style=args.encoding_style,\n agg_func=args.agg_func,\n num_class=2,\n include_rel=\"relational\" in args.model,\n relation_tokenized=data_loader.get_relations_tokenized(),\n )\nelif 'kgbert' in args.model:\n model = KGBertClassifier(encoder=args.encoder,\n adj_lists=data_loader.get_adj_list() if \"sage\" in args.model else None,\n nodes_tokenized=data_loader.get_nodes_tokenized(),\n relation_tokenized=data_loader.get_relations_tokenized(),\n id2node=data_loader.get_nid2text(),\n enc_style=args.encoding_style,\n agg_func=args.agg_func,\n num_neighbor_samples=args.num_neighbor_samples,\n device=device,\n version=args.model,\n )\n\n# print(model)\ncriterion = torch.nn.CrossEntropyLoss()\nif args.optimizer == \"SGD\":\n optimizer = torch.optim.SGD(model.parameters(), lr=lr)\nelif args.optimizer == \"ADAM\":\n optimizer = torch.optim.Adam(model.parameters(), lr=lr)\noptimizer.zero_grad()\n\nstep = 0\n\nbest_valid_acc = 0\nbest_test_acc = 0 \nbest_valid_f1 = 0\nbest_test_f1 = 0 \nbest_val_auc = 0\nbest_test_auc = 0\n\nmy_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=args.lrdecay)\n\nwriter = SummaryWriter(log_dir=tensorboard_dir)\n\n\n# for eval\ninfer_file = pd.read_csv(args.evaluation_file_path)\neval_dataset = get_dataset(data_loader, infer_file)\ndataset_dev = pd.DataFrame(eval_dataset[\"dev\"])\ndataset_tst = pd.DataFrame(eval_dataset[\"tst\"])\ndataset_tst.insert(len(dataset_tst.columns), \"prediction_value\", np.zeros((len(dataset_tst), 1)))\ndataset_tst.insert(len(dataset_tst.columns), \"final_label\", np.zeros((len(dataset_tst), 1), dtype=np.int64))\n\nfor epoch in range(num_epochs):\n for batch in tqdm(data_loader.get_batch(batch_size=batch_size, mode=\"train\")):\n step += 1\n if step % args.decay_every == 0:\n my_lr_scheduler.step()\n # batch list((node_id1, node_id2))\n edges, labels = batch\n # allocate to right device\n edges = edges.to(device)\n labels = labels.to(device)\n\n logits = model(edges, edges.shape[0])\n loss = criterion(logits, labels)\n\n loss.backward()\n optimizer.step()\n optimizer.zero_grad()\n model.zero_grad()\n\n gpu_usage = get_gpu_usage(int(args.gpu))\n if gpu_usage > 0.9:\n del batch, edges, labels, logits, loss\n gc.collect()\n torch.cuda.empty_cache()\n print(f\"The gpu usage was {gpu_usage} > 0.9, so it was released.\"\n f\"\\nAfter release: {get_gpu_usage(int(args.gpu))}\")\n # evaluate\n if step % show_step == 0:\n if args.save_every_checkpoint:\n torch.save(model.state_dict(), model_save_path + \".step.\" + str(step))\n if args.eval_on == \"link_prediction\":\n val_acc, val_f1 = eval(data_loader, model, test_batch_size, device, \"valid\")\n test_acc, test_f1 = eval(data_loader, model, test_batch_size, device, \"test\")\n \n if args.metric == \"acc\" and val_acc > best_valid_acc:\n best_valid_acc = val_acc\n best_test_acc = test_acc\n best_valid_f1 = val_f1\n best_test_f1 = test_f1\n torch.save(model.state_dict(), model_save_path)\n elif args.metric == \"f1\" and val_f1 > best_valid_f1:\n best_valid_acc = val_acc\n best_test_acc = test_acc\n best_valid_f1 = val_f1\n best_test_f1 = test_f1\n torch.save(model.state_dict(), model_save_path)\n logging(\"epoch {}, step {}, current valid acc: {}, current test acc: {}, current valid f1:{}, current test f1: {}, \".format(epoch, step, val_acc, test_acc, val_f1, test_f1))\n logging(\"current best val acc: {}, test acc: {} current best f1: {}, test f1: {}\".format(best_valid_acc, best_test_acc, best_valid_f1, best_test_f1))\n writer.add_scalars(\"acc\", {\"val\": val_acc, \"test\": test_acc}, step)\n writer.add_scalars(\"f1\", {\"val\": val_f1, \"test\": test_f1}, step)\n elif args.eval_on == \"human_annotation\":\n val_auc = get_val_auc(model, dataset_dev)\n if val_auc >= best_val_auc:\n best_val_auc = val_auc\n test_auc, relation_break_down_auc, main_result_auc = get_test_auc_scores(model, dataset_tst)\n logging(\"epoch {}, step {}, val auc: {}, test auc: {}\".format(epoch, step, best_val_auc, test_auc))\n logging(\"relational break down: \" + relation_break_down_auc)\n logging(\"class break down: \" + main_result_auc)\n torch.save(model.state_dict(), model_save_path)\n elif args.eval_on == \"none\":\n print(\"epoch {}, step {}, no evaluation\".format(epoch, step))\n","sub_path":"model/BertSAGE/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":18691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"154977121","text":"import requests\nimport json\nimport pandas as pd\nimport ast\nfrom utils.utils import Utils\nfrom fetching_data import commit_api\nfrom ml_model.ml_filelevel_model import FileMLModel\n\n\nclass file_predict_comment:\n\n def __init__(self):\n config = Utils().get_config_file('config.ini')\n self.comments_url = config.get('url', 'comments_url', raw=True)\n self.git_username = config.get('GithubCredential', 'user_id', raw=True)\n self.git_password = config.get('GithubCredential', 'password')\n self.requests_commit = config.get('commit_api', 'commit_url')\n\n def predict_file_criticality(self, payload_json):\n branch_name = payload_json['pull_request']['head']['ref']\n branch_url = self.requests_commit + branch_name\n complexity_dict = str(commit_api.main(branch_url))\n complexity_dict = complexity_dict.replace(\"'\", \"\\\"\")\n critical_dict = json.loads(complexity_dict)\n test_data = {}\n columns=['extension_name', 'perc_change_files', 'cy_comp']\n comment_text= ''\n for file in critical_dict.keys():\n file_param = []\n data_line=[1]\n ext = file.split('.')[-1]\n file_param.append(ext)\n percent_files = critical_dict[file][1]\n file_param.append(percent_files)\n cycom = critical_dict[file][2].split(' : ')[-1]\n file_param.append(int(cycom))\n data_line.append(tuple(file_param))\n frame_test = pd.DataFrame([file_param],columns=['extension_name', 'perc_change_files', 'cy_comp'])\n print(frame_test)\n transform_data = FileMLModel.data_transform(frame_test)\n print(transform_data)\n critical_state = str(FileMLModel.test_model(transform_data))\n critical_state = ast.literal_eval(critical_state)\n if \"Not-Critical\" in critical_state[0]:\n user_instruction= 'Please check the file manually'\n else:\n user_instruction= 'No need to check as the prediction declared it critical'\n comment_text = comment_text + \"Prediction for \" + file + \" is \"+ critical_state[0] + \". \" + user_instruction + \"
\"\n # test_data[file] = prediction\n pull_req_project_id = payload_json['pull_request']['number']\n post_comment = \"GIT ASSIST PREDICTION :
%s\" %comment_text\n post_comment_data = '{\\\"body\\\":\\\"%s\\\"}' % post_comment\n print(post_comment_data)\n post_comment_json = json.loads(post_comment_data)\n self.post_comment(pull_req_project_id, post_comment_json)\n return test_data\n \n\n def post_comment(self, pull_req_project_id, comment_data):\n headers = {'Content-Type': 'application/json'}\n myUrl = self.comments_url % pull_req_project_id\n response = requests.post(myUrl, auth=(self.git_username, self.git_password), headers=headers, json=comment_data)\n if response.status_code == 201:\n print(\"Successfully posted a comment\")\n else:\n print(\"Failed to post a response with http status code : \", response.status_code)\n","sub_path":"web_git_prediction/file_predict_comment.py","file_name":"file_predict_comment.py","file_ext":"py","file_size_in_byte":3131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"375317181","text":"\nfrom panda3d.core import BitMask32\n\n\nTAG_OWNER = \"owner\"\n\nMASK_WALLS = BitMask32(1)\nMASK_FLOORS = BitMask32(2)\n\nMASK_INTO_ENEMY = BitMask32(4)\nMASK_INTO_PLAYER = BitMask32(8)\n\nMASK_FROM_PLAYER = BitMask32(16)\nMASK_FROM_ENEMY = BitMask32(32)\n\nMASK_ENEMY_LOCK_SPHERE = BitMask32(64)","sub_path":"CommonValues.py","file_name":"CommonValues.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"71584888","text":"\n\n#calss header\nclass _DOWN():\n\tdef __init__(self,): \n\t\tself.name = \"DOWN\"\n\t\tself.definitions = [u'in or towards a low or lower position, from a higher one: ', u'along: ', u'to: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'prepositions'\n\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/prepositions/_down.py","file_name":"_down.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"507197561","text":"# -*- encoding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom collections import defaultdict\nfrom decimal import Decimal\n\n#\nfrom django.db import models\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.utils import timezone\nfrom django.utils.encoding import python_2_unicode_compatible\n\nfrom currency_history.models import Currency, CurrencyRate\n\nimport time\n\nclass DivManager(models.Manager):\n \"\"\"Dividends manager to return all the years dividends have been received\n \n \"\"\"\n def div_years(self, account_id):\n from django.db import connection\n cursor = connection.cursor()\n cursor.execute(\"\"\"\n select EXTRACT(YEAR FROM date) as year from\n portfolio_transaction where action = 'DIV' and\n account_id=%s group by year order\n by year\"\"\", (account_id) )\n #years = cursor.fetchall()\n desc = cursor.description\n years = [\n dict(zip([col[0] for col in desc], row))\n for row in cursor.fetchall()\n ]\n\n return years\n\n@python_2_unicode_compatible\nclass Account(models.Model):\n name = models.CharField(max_length=50)\n base_currency = models.CharField(verbose_name=_('Base currency,ISO-code'),\n max_length=3, unique=False,\n default='EUR')\n \n positions = {}\n div_years = DivManager()\n # Setting DivManager above causes django to drop default manager. Needs\n # to be added back\n objects = models.Manager()\n\n\n def __init__(self, *args, **kwargs):\n models.Model.__init__(self, *args, **kwargs)\n self.positions = {'$CASH': self.new_position() }\n\n def buySellSecurity(self, security=None, shares=None, date=None,\n price=None, commission=0, action=None, currency=None,\n exchange_rate=None):\n t = Transaction()\n t.account = self\n t.action = action\n t.security = security\n t.shares = Decimal(shares)\n t.date = date\n t.price = Decimal(price)\n t.commission = Decimal(commission)\n t.currency = currency\n t.exchange_rate = exchange_rate\n t.save()\n\n def div(self, security=None, date=None, price=None, commission=0,\n cash_amount=0, currency=None, exchange_rate=None):\n t = Transaction()\n t.account = self\n t.action = 'DIV'\n t.security = security\n t.date = date\n t.price = Decimal(price)\n #t.commission = Decimal(commission)\n t.cash_amount = Decimal(cash_amount)\n t.currency = currency\n t.exchange_rate = exchange_rate\n t.save()\n\n def txnByName(self, security=None):\n print (\"{} {}\".format(security.name, security.id))\n\n # def sell_security(self, security=None, shares=None, date=None,\n # price=None, commission=0, sec_fee=0):\n # t = Transaction()\n # t.account = self\n # t.action = 'SELL'\n # t.security = 1\n # t.shares = Decimal(shares)\n # t.date = date\n # t.price = Decimal(price)\n # t.commission = Decimal(commission)\n # t.sec_fee = Decimal(sec_fee)\n # t.save()\n\n # Price.objects.create(date=date, security=security,\n # price=price)\n\n # def dividend(self, security=None, amount=0.00, date=None):\n # t = Transaction()\n # t.account = self\n # t.action = 'DIV'\n # t.security = security\n # t.date = date\n # t.cash_amount = Decimal(amount)\n # t.save()\n\n def deposit(self, cash_amount=0, date=None, security=None, currency=None):\n t = Transaction()\n t.account = self\n t.action = 'DEP'\n t.cash_amount = Decimal(cash_amount)\n t.date = date\n t.security = security\n t.currency = currency\n t.save()\n\n def withdraw(self, cash_amount=0, date=None, security=None, currency=None):\n t = Transaction()\n t.account = self\n t.action = 'WITH'\n t.cash_amount = Decimal(-cash_amount)\n t.date = date\n t.security = security\n t.currency = currency\n t.save()\n\n # def receive_interest(self, amount=0, date=None):\n # t = Transaction()\n # t.account = self\n # t.action = 'INT'\n # t.cash_amount = Decimal(amount)\n # t.date = date\n # t.save()\n\n # def pay_interest(self, amount=0, date=None):\n # t = Transaction()\n # t.account = self\n # t.action = 'MARGIN'\n # t.cash_amount = Decimal(-amount)\n # t.date = date\n # t.save()\n\n # def stock_split(self, security=None, split_ratio=0, date=None):\n # t = Transaction()\n # t.account = self\n # t.action = 'SS'\n # t.security = security\n # t.split_ratio = split_ratio\n # t.date = date\n # t.save()\n\n def new_position(self):\n return dict(shares=0, price=1, basis=0,\n mktval=0, gain=0, dividends=0, average=0,\n total_return=0, sold=0)\n\n def update_market_value(self, positions, date):\n\n for security in positions:\n p = positions[security]\n if security == '$CASH':\n price = 1.00\n from_currency = self.base_currency\n else:\n latest_price = Price.objects.select_related('currency').filter(\n security__name=security, date__lte=date).latest('date')\n price = latest_price.price\n\n # What is the currency used for this security\n from_currency = latest_price.currency.iso_code\n\n # Store change compared to previous close\n positions[security]['change'] = latest_price.change\n\n # Also change percentage\n positions[security]['change_percentage'] = latest_price.change_percentage\n positions[security]['latest_date'] = latest_price.date\n\n # Prepare for converting currency in Price for the security to\n # base_security\n if from_currency == self.base_currency:\n exchange_rate = 1.0\n else:\n rate = CurrencyRate.objects.get(\n from_currency__iso_code=from_currency,\n to_currency__iso_code=self.base_currency)\n history = rate.history.all()[0]\n exchange_rate = history.value\n\n mktval = p['shares'] * Decimal(price) * Decimal(exchange_rate)\n gain = mktval - p['basis'] + p['sold'] + p['dividends']\n if p['basis']:\n tr = ((mktval + p['sold'] + p['dividends']) / p['basis'] - 1) * 100\n positions[security]['total_return'] = tr\n\n positions[security]['mktval'] = mktval\n positions[security]['gain'] = gain\n positions[security]['price'] = price\n\n folio_mktval = self.mktval()\n for security in positions:\n # skip cash\n if security == '$CASH':\n continue\n p = positions[security]\n positions[security]['folio_percentage'] = (p['mktval'] / folio_mktval ) * 100\n return positions\n\n def get_positions(self, date=None):\n \"\"\"Return a dictionary of all of the positions in this account.\n\n If date is provided, then only include transactions up to (and\n including) that date.\"\"\"\n\n # list of buys of one share\n buyStack = []\n # Dictionary of list containing all buys\n shareTransaction = defaultdict(list)\n # \n buyDict = {}\n if not date:\n date = timezone.now()\n positions = {'$CASH': self.new_position()}\n #positions = {}\n transactions = Transaction.objects.select_related('security').filter(\n account=self, date__lte=date).order_by('date', 'id')\n for t in transactions:\n if t.security.name and t.security.name not in positions:\n positions[t.security.name] = self.new_position()\n # switch based on transaction action\n if t.action in ('DEP', 'WITH'):\n positions['$CASH']['basis'] += t.cash_amount\n positions['$CASH']['shares'] += t.cash_amount\n elif t.action in ('INT', 'MARGIN'):\n positions['$CASH']['shares'] += t.cash_amount\n elif t.action == 'DIV':\n positions['$CASH']['basis'] += t.cash_amount\n positions['$CASH']['shares'] += t.cash_amount\n positions[t.security.name]['dividends'] += t.cash_amount\n elif t.action == 'SS':\n positions[t.security.name]['shares'] *= t.split_ratio\n elif t.action == 'BUY':\n positions[t.security.name]['basis'] += (\n t.shares * t.price * t.exchange_rate + t.commission)\n positions[t.security.name]['shares'] += t.shares\n cost = t.shares * (t.price * t.exchange_rate) + t.commission\n positions['$CASH']['basis'] -= cost\n positions['$CASH']['shares'] -= cost\n\n # Put the bought batch into list, stored in dictionary for\n # this share (Dictionary of list of dictionaries)\n shareTransaction[t.security.name].append({ 'shares' : t.shares, 'price' : t.price })\n elif t.action == 'SELL':\n # Collect list of current holdings: how many is left at the\n # price those were boughtfor\n\n buyList = shareTransaction[t.security.name].pop()\n #if t.security.name == u'Ilkka-Yhtymä II':\n # print \"%s %d \" % (t.security.name, t.shares)\n # print \"Popped\"\n # print shareTransaction[t.security.name]\n # print buyList\n sold = t.shares\n moreToSell = True\n \n # is more being sold that the current bought batch\n if sold >= buyList['shares']:\n while moreToSell:\n # How many needs to be taken from next batch\n sold = sold - buyList['shares']\n\n # Try to prevent floating point precision errors\n # code here\n\n # If stack is empty, ie all shares sold, break out of\n # the loop\n if not shareTransaction[t.security.name]:\n break\n # next item from array\n buyList = shareTransaction[t.security.name].pop()\n if sold < buyList['shares']:\n moreToSell = False\n else: # while condition false\n buyList['shares'] = buyList['shares'] - sold\n if buyList['shares'] > 0:\n shareTransaction[t.security.name].append(buyList)\n else:\n buyList['shares'] = buyList['shares'] - sold\n if buyList['shares'] > 0:\n shareTransaction[t.security.name].append(buyList)\n\n ##\n current_shares = positions[t.security.name]['shares']\n if current_shares:\n old_basis_ps = (positions[t.security.name]['basis'] /\n positions[t.security.name]['shares'])\n positions[t.security.name]['shares'] -= t.shares\n positions[t.security.name]['sold'] += t.shares * (t.price * t.exchange_rate)\n\n else:\n old_basis_ps = t.price\n positions[t.security.name]['basis'] -= old_basis_ps * t.shares\n #positions[t.security.name]['shares'] -= t.shares\n positions['$CASH']['basis'] += t.shares * (t.price * t.exchange_rate) - t.commission\n positions['$CASH']['shares'] += t.shares * (t.price * t.exchange_rate) - t.commission\n\n # Calculate avegare prices per share to be sold\n for key in shareTransaction:\n average = 0\n cost = 0\n count = 0\n for transaction in shareTransaction[key]:\n count += transaction['shares']\n cost += transaction['price'] * transaction['shares']\n if count:\n average = cost / count\n positions[key]['average'] = average\n\n self.positions = positions\n return self.update_market_value(positions, date)\n\n def mktval(self, security=None):\n positions = self.positions\n\n if security:\n return positions[security]['mktval']\n #for p in positions:\n # print \"%s %d \" % (p,positions[p]['mktval'])\n\n #print sum(positions[p]['mktval'] for p in positions)\n #if any(positions):\n return sum(positions[p]['mktval'] for p in positions)\n \n def basis(self, security=None):\n positions = self.positions\n if security:\n return positions[security]['basis']\n return sum(positions[p]['basis'] for p in positions)\n\n def sells(self, security=None):\n positions = self.positions\n if security:\n return positions[security]['sold']\n return sum(positions[p]['sold'] for p in positions)\n \n def gain(self, security=None):\n positions = self.positions\n if security:\n return positions[security]['gain']\n return sum(positions[p]['gain'] for p in positions)\n\n def dividends(self, security=None):\n positions = self.positions\n if security:\n return positions[security]['dividends']\n return sum(positions[p]['dividends'] for p in positions)\n\n def total_return(self, security=None):\n positions = self.positions\n if security:\n return positions[security]['total_return']\n if self.basis():\n return ((self.mktval() + self.dividends() + self.sells()) / self.basis() - 1) * 100\n\n @property\n def cash(self):\n positions = self.positions\n return positions['$CASH']\n\n def __str__(self):\n return self.name\n\n\nclass SecurityManager(models.Manager):\n\n def create_security(self, name, ticker):\n if not name:\n raise ValueError('Security must have name.')\n\n if not ticker:\n raise ValueError('Security must have ticker.')\n\n security = self.model(\n name=name, \n ticker=ticker,\n )\n security.save()\n return security\n\n@python_2_unicode_compatible\nclass PriceTracker(models.Model):\n \"\"\"\n Site to get security prices from\n \"\"\"\n name = models.CharField(max_length=40, blank=False)\n\n def __str__(self):\n return self.name\n\ndef set_default_tracker():\n \"\"\"\n Set default tracker to Kauppalehti for new Securities\n \"\"\" \n tracker, created = PriceTracker.objects.get_or_create(name='Kauppalehti')\n return tracker.pk\n\n@python_2_unicode_compatible\nclass Security(models.Model):\n ticker = models.CharField(max_length = 40, blank=False)\n name = models.CharField(max_length = 40, blank=False)\n price_tracker = models.ForeignKey(PriceTracker, default=set_default_tracker)\n\n objects = SecurityManager()\n\n class Meta:\n ordering = ['name']\n\n def __str__(self):\n return self.name + ' ' + self.ticker\n\n@python_2_unicode_compatible\nclass Price(models.Model):\n date = models.DateField('transaction date')\n security = models.ForeignKey(Security)\n price = models.DecimalField(decimal_places=2, max_digits=10)\n currency = models.ForeignKey(Currency)\n change = models.DecimalField(decimal_places=2, max_digits=6, \n blank=True, null=True)\n change_percentage = models.DecimalField(decimal_places=2, max_digits=6,\n blank=True, null=True)\n\n def __str__(self):\n return self.security.name + ' ' + str(self.date) + ' ' + str(self.price)\n\n\nTRANSACTION_CHOICES = (\n ('BUY', 'Buy'),\n ('SELL', 'Sell'),\n)\n\n@python_2_unicode_compatible\nclass Transaction(models.Model):\n account = models.ForeignKey(Account)\n action = models.CharField(max_length=10, choices=TRANSACTION_CHOICES)\n date = models.DateField('transaction date')\n security = models.ForeignKey(Security)\n shares = models.DecimalField(decimal_places=4, max_digits=10, null=True)\n currency = models.ForeignKey(Currency)\n exchange_rate = models.DecimalField(decimal_places=4, max_digits=10,\n null=False, blank=False, default=Decimal('1.0'))\n price = models.DecimalField(decimal_places=4, max_digits=10, null=True)\n commission = models.DecimalField(decimal_places=2, max_digits=10,\n null=True)\n cash_amount = models.DecimalField(decimal_places=2, max_digits=10,\n null=True)\n sec_fee = models.DecimalField(decimal_places=2, max_digits=10, null=True)\n split_ratio = models.DecimalField(decimal_places=2, max_digits=5,\n null=True)\n\n class Meta:\n ordering = ['date']\n\n def __str__(self):\n return self.action + ' ' + str(self.shares) + ' ' + self.security.name\n\n","sub_path":"portfolio/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":17441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"228942121","text":"\"\"\"Provide worker functions.\"\"\"\nimport logging\nimport traceback\nimport sys\nimport time\nimport uuid\n\nimport socketio\n\nfrom .utils import format_traceback, Registry\n\ntry:\n import queue\nexcept ImportError:\n import Queue as queue\n\n# pylint: disable=unused-argument, redefined-outer-name\n\nJOB_HANDLERS = Registry()\nlogger = logging.getLogger(\"python_client\")\n\n\ndef task_worker(conn, sync_q, logger, abort):\n \"\"\"Implement a task worker.\"\"\"\n while True:\n if abort is not None and abort.is_set():\n break\n try:\n job = sync_q.get()\n except queue.Empty:\n time.sleep(0.1)\n continue\n sync_q.task_done()\n if job is None:\n continue\n handler = JOB_HANDLERS.get(job[\"type\"])\n if handler is None:\n continue\n\n try:\n handler(conn, job, logger)\n except Exception: # pylint: disable=broad-except\n logger.error(\"Error occured in the loop %s\", traceback.format_exc())\n sys.stdout.flush()\n\n\n@JOB_HANDLERS.register(\"getInterface\")\ndef handle_get_interface(conn, job, logger):\n \"\"\"Handle get interface.\"\"\"\n conn.send_interface()\n\n\n@JOB_HANDLERS.register(\"setInterface\")\ndef handle_set_interface(conn, job, logger):\n \"\"\"Handle set interface.\"\"\"\n conn.set_remote(job[\"api\"])\n conn.emit({\"type\": \"interfaceSetAsRemote\"})\n if not conn.init:\n conn.emit({\"type\": \"getInterface\"})\n conn.init = True\n\n\n@JOB_HANDLERS.register(\"interfaceSetAsRemote\")\ndef handle_set_interface_as_remote(conn, job, logger):\n \"\"\"Handle set interface as remote.\"\"\"\n # conn.emit({'type':'getInterface'})\n conn.remote_set = True\n\n\n@JOB_HANDLERS.register(\"execute\")\ndef handle_execute(conn, job, logger):\n \"\"\"Handle execute.\"\"\"\n if not conn.executed:\n try:\n type_ = job[\"code\"][\"type\"]\n if type_ == \"script\":\n content = job[\"code\"][\"content\"]\n exec(content, conn.local) # pylint: disable=exec-used\n conn.executed = True\n elif type_ == \"requirements\":\n pass\n else:\n raise Exception(\"unsupported type\")\n conn.emit({\"type\": \"executeSuccess\"})\n except Exception: # pylint: disable=broad-except\n traceback_error = traceback.format_exc()\n logger.error(\"Error during execution: %s\", traceback_error)\n conn.emit({\"type\": \"executeFailure\", \"error\": traceback_error})\n else:\n logger.info(\"Skip code execution\")\n\n\n@JOB_HANDLERS.register(\"method\")\ndef handle_method(conn, job, logger):\n \"\"\"Handle method.\"\"\"\n interface = conn.interface\n if \"pid\" in job and job[\"pid\"] is not None:\n interface = conn.plugin_interfaces[job[\"pid\"]]\n if job[\"name\"] in interface:\n if \"promise\" in job:\n try:\n resolve, reject = conn.unwrap(job[\"promise\"], False)\n method = interface[job[\"name\"]]\n args = conn.unwrap(job[\"args\"], True)\n # args.append({'id': conn.id})\n result = method(*args)\n resolve(result)\n except Exception: # pylint: disable=broad-except\n traceback_error = traceback.format_exc()\n logger.error(\"Error in method %s: %s\", job[\"name\"], traceback_error)\n reject(Exception(format_traceback(traceback_error)))\n else:\n try:\n method = interface[job[\"name\"]]\n args = conn.unwrap(job[\"args\"], True)\n # args.append({'id': conn.id})\n method(*args)\n except Exception: # pylint: disable=broad-except\n logger.error(\n \"Error in method %s: %s\", job[\"name\"], traceback.format_exc()\n )\n else:\n raise Exception(\"method \" + job[\"name\"] + \" is not found.\")\n\n\n@JOB_HANDLERS.register(\"callback\")\ndef handle_callback(conn, job, logger):\n \"\"\"Handle callback.\"\"\"\n if \"promise\" in job:\n resolve, reject = conn.unwrap(job[\"promise\"], False)\n try:\n method = conn.store.fetch(job[\"num\"])\n if method is None:\n raise Exception(\n \"Callback function can only called once, \"\n \"if you want to call a function for multiple times, \"\n \"please make it as a plugin api function. \"\n \"See https://imjoy.io/docs for more details.\"\n )\n args = conn.unwrap(job[\"args\"], True)\n result = method(*args)\n resolve(result)\n except Exception: # pylint: disable=broad-except\n traceback_error = traceback.format_exc()\n logger.error(\"Error in method %s: %s\", job[\"num\"], traceback_error)\n reject(Exception(format_traceback(traceback_error)))\n else:\n try:\n method = conn.store.fetch(job[\"num\"])\n if method is None:\n raise Exception(\n \"Callback function can only called once, \"\n \"if you want to call a function for multiple times, \"\n \"please make it as a plugin api function. \"\n \"See https://imjoy.io/docs for more details.\"\n )\n args = conn.unwrap(job[\"args\"], True)\n method(*args)\n except Exception: # pylint: disable=broad-except\n logger.error(\"Error in method %s: %s\", job[\"num\"], traceback.format_exc())\n\n\nclass BaseClient(object): # pylint: disable=useless-object-inheritance\n \"\"\"Represent a base socketio client.\"\"\"\n\n queue = None\n _clients = {}\n\n @staticmethod\n def get_client(id):\n return BaseClient._clients.get(id)\n\n def __init__(self, id=None):\n \"\"\"Set up client instance.\"\"\"\n self.id = id or str(uuid.uuid4())\n self.sio = socketio.Client()\n BaseClient._clients[self.id] = self\n\n def setup(self, conn):\n \"\"\"Set up the plugin connection.\"\"\"\n self.sio.connect(conn.opt.server)\n\n def emit(msg):\n \"\"\"Emit a message to the socketio server.\"\"\"\n self.sio.emit(\"from_plugin_\" + conn.secret, msg)\n\n def sio_plugin_message(*args):\n \"\"\"Handle plugin message.\"\"\"\n data = args[0]\n if data[\"type\"] == \"import\":\n emit({\"type\": \"importSuccess\", \"url\": data[\"url\"]})\n elif data[\"type\"] == \"disconnect\":\n conn.abort.set()\n try:\n if \"exit\" in conn.interface and callable(conn.interface[\"exit\"]):\n conn.interface[\"exit\"]()\n except Exception as exc: # pylint: disable=broad-except\n logger.error(\"Error when exiting: %s\", exc)\n\n elif data[\"type\"] == \"execute\":\n if not conn.executed:\n self.queue.put(data)\n else:\n logger.debug(\"Skip execution\")\n emit({\"type\": \"executeSuccess\"})\n elif data[\"type\"] == \"message\":\n _data = data[\"data\"]\n self.queue.put(_data)\n logger.debug(\"Added task to the queue\")\n\n def on_disconnect():\n if not conn.opt.daemon:\n conn.exit(1)\n\n conn.emit = emit\n self.sio.on(\"disconnect\", on_disconnect)\n self.sio.on(\"to_plugin_\" + conn.secret, sio_plugin_message)\n emit({\"type\": \"initialized\", \"dedicatedThread\": True})\n logger.info(\"Plugin %s initialized\", conn.opt.id)\n\n def run_forever(self, conn):\n \"\"\"Run forever.\"\"\"\n raise NotImplementedError\n\n\nclass Client(BaseClient):\n \"\"\"Represent a sync socketio client.\"\"\"\n\n def __init__(self):\n \"\"\"Set up client instance.\"\"\"\n super(Client, self).__init__()\n self.queue = queue.Queue()\n self.task_worker = task_worker\n\n def run_forever(self, conn):\n \"\"\"Run forever.\"\"\"\n self.task_worker(conn, self.queue, logger, conn.abort)\n","sub_path":"imjoy/workers/python_client.py","file_name":"python_client.py","file_ext":"py","file_size_in_byte":8047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"246597113","text":"import random\nclass Vector(object):\n def __init__(self, x, y):\n self.x = x\n self.y = y\n\n def __repr__(self):\n return \"({0}; {1})\".format(self.x, self.y)\n\n def __add__(self, other):\n x = self.x + other.x\n y = self.y + other.y\n return Vector(x, y)\n\n def __sub__(self, other):\n x = self.x - other.x\n y = self.y - other.y\n return Vector(x, y)\n\n def __rmul__(self, other):\n x = self.x * other\n y = self.y * other\n return Vector(x, y)\n\n def __truediv__(self, other):\n x = self.x / other\n y = self.y / other\n return Vector(x, y)\n\n def c(self):\n return (self.x, self.y)\n\n\n\ndef f(point):\n x, y = point\n return 7*(y - 1)*(y - 1) + (x - 2)*(x - 2)\n\ndef nelder_mead(mas, alpha, beta, gamma, e):\n maxiter = 10\n v1 = mas[0]\n v2 = mas[1]\n v3 = mas[2]\n\n while (True):\n adict = {v1: f(v1.c()), v2: f(v2.c()), v3: f(v3.c())}\n points = sorted(adict.items(), key=lambda x: x[1])\n\n b = points[0][0]\n g = points[1][0]\n w = points[2][0]\n\n mid = (g + b) / 2\n\n sum = ((((f(b.c()) - f(w.c())) ** 2) + ((f(g.c()) - f(w.c())) ** 2)) / 3) ** 0.5\n if (sum <= e):\n break\n xr = mid + alpha * (mid - w)\n if f(xr.c()) < f(g.c()):\n w = xr\n else:\n if f(xr.c()) < f(w.c()):\n w = xr\n c = (w + mid) / 2\n if f(c.c()) < f(w.c()):\n w = c\n if f(xr.c()) < f(b.c()):\n xe = mid + gamma * (xr - mid)\n if f(xe.c()) < f(xr.c()):\n w = xe\n else:\n w = xr\n if f(xr.c()) > f(g.c()):\n xc = mid + beta * (w - mid)\n if f(xc.c()) < f(w.c()):\n w = xc\n else:\n w = b + 0.5 * (w - b)\n g = b + 0.5 * (g - b)\n v1 = w\n v2 = g\n v3 = b\n return b\n\n\nmas = list()\nfor i in range(0, 3):\n print(\"Введите координаты \", i+1, \" вершины многоугольника\")\n mas.append(Vector(float(input()), float(input())))\n # mas.append(Vector(random.randint(-100, 100), random.randint(-100, 100)))\n\nalpha = 0\nwhile (alpha <= 0):\n print(\"Введите параметр отражения alpha\")\n alpha = float(input())\nbeta = 0\nwhile (beta <= 0):\n print(\"Введите параметр сжатия beta\")\n beta = float(input())\ngamma = 0\nwhile (gamma <= 0):\n print(\"Введите параметр растяжения gamma\")\n gamma = float(input())\ne = 0\nwhile (e <= 0):\n print(\"Введите параметр e (>0)\")\n e = float(input())\n\nXmin = nelder_mead(mas, alpha, beta, gamma, e)\nprint(\"Fmin\", Xmin, \"= \", f(Xmin.c()))\n# print(mas[0], \", \", mas[1], \", \", mas[2])\n# Xmin = nelder_mead(mas, 1, 0.5, 2, 0.01)\n# print(\"(\", round(Xmin.x, 2), \"; \", round(Xmin.y, 2), \"; \", round(f(Xmin.c()), 4), \")\")\n# Xmin = nelder_mead(mas, 1, 0.6, 3, 0.01)\n# print(\"(\", round(Xmin.x, 2), \"; \", round(Xmin.y, 2), \"; \", round(f(Xmin.c()), 4), \")\")\n# Xmin = nelder_mead(mas, 2, 0.25, 2.5, 0.01)\n# print(\"(\", round(Xmin.x, 2), \"; \", round(Xmin.y, 2), \"; \", round(f(Xmin.c()), 4), \")\")","sub_path":"5 семестр/Методы оптимизации/4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"355234916","text":"import base64\nimport imaplib\n\nfrom bs4 import BeautifulSoup\n\nORG_EMAIL = \"@gmail.com\"\nFROM_EMAIL = \"piemelwaterJr\" + ORG_EMAIL\nFROM_PWD = \"I3u2mB5xfqyy\"\nSMTP_SERVER = \"imap.gmail.com\"\nSMTP_PORT = 993\n\ndef readmail():\n mail = imaplib.IMAP4_SSL(SMTP_SERVER)\n mail.login(FROM_EMAIL, FROM_PWD)\n\n mail.select('inbox')\n\n typ, data = mail.search(None, 'ALL')\n for num in data[0].split():\n typ, data = mail.fetch(b'5', '(RFC822)')\n\n print(data[0][1].decode(\"utf-8\"))\n\n\nreadmail()","sub_path":"DiscordBot/readMails.py","file_name":"readMails.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"171709402","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jul 08 14:18:09 2016\n\n@author: wanbo\n\"\"\"\n\n# Back-Propagation Neural Networks \n# \n\nimport psycopg2 \nfrom numpy import *\nfrom pandas import DataFrame\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets, linear_model\nimport operator\nfrom os import listdir \nfrom datetime import datetime,timedelta\n\nconn = psycopg2.connect(database=\"ml\", user=\"dev\", password=\"mGxGCH38MPZPTa\", host=\"localhost\", port=\"54321\")\ncur = conn.cursor() \ncities = (u\"宁波\",u\"深圳\",u\"北京\",u\"上海\",u\"苏州\",u\"无锡\",u\"天津\",u\"南京\",u\"广州\",u\"杭州\")\n\n#sigmoid function\ndef logsig(x):\n return 1/(1+exp(-x))\n\n#归一化\ndef autoNorm(dataSet):\n minVals = dataSet.min(0)\n maxVals = dataSet.max(0)\n ranges = maxVals - minVals\n normDataSet = zeros(shape(dataSet))\n m = dataSet.shape[0] \n normDataSet = dataSet - tile(minVals, (m,1))\n normDataSet = normDataSet/tile(ranges, (m,1)) \n return normDataSet \n \n#取数据\ndef gettable():\n all_data = {}\n for specity in cities:\n sql = u\"SELECT record_date, city, user_has_coupon, high_value_coupon, push_message, wx_notify, sms_notify, order_num FROM order_predict WHERE city = '{}'\".format(specity)\n cur.execute(sql) \n df = DataFrame(cur.fetchall())\n df.columns = [\"record_date\",\"city\", \"user_has_coupon\",\"high_value_coupon\",\"push_message\",\"wx_notify\",\"sms_notify\",\"order_num\"]\n DATA = df.sort_values([\"record_date\"])\n X =array(DATA[[\"user_has_coupon\",\"high_value_coupon\",\"push_message\",\"wx_notify\",\"sms_notify\"]])\n Y =array(DATA[[\"order_num\"]])\n# samplein = autoNorm(X)\n# sampleout = autoNorm(Y)\n \n samplein = 2*(X-X.min(0))/(X.max(0)-X.min(0))-1\n sampleout = 2*(Y-Y.min(0))/(Y.max(0)-Y.min(0))-1\n# noise =0.03*random.rand(shape(sampleout)[0],shape(sampleout)[1])\n# sampleout += noise\n all_data[specity] = samplein,sampleout \n return X,Y,all_data\n\ndef BPNN(all_data):\n #\n maxepochs = 60000 #training times\n learnrate = 0.003\n errorfinal = 5*10**(-4)\n hiddenunitnum = 8\n errhistory = {} \n conf = {}\n for eachcity in cities:\n samplein1,sampleout1 = all_data[eachcity]\n samnum = shape(samplein1)[0]\n indim = shape(samplein1)[1]\n outdim = shape(sampleout1)[1]\n samplein = samplein1.transpose()\n sampleout = sampleout1.transpose()\n w1 = 0.5*random.rand(hiddenunitnum,indim)-0.1 #weights between input and hidden layer \n b1 = 0.5*random.rand(hiddenunitnum,1)-0.1\n w2 = 0.5*random.rand(outdim,hiddenunitnum)-0.1 #weights between output and hidden layer \n b2 = 0.5*random.rand(outdim,1)-0.1\n errhistory[eachcity] = []\n for i in range(maxepochs):\n hiddenout = logsig((dot(w1,samplein).transpose()+b1.transpose())).transpose()\n networkout = (dot(w2,hiddenout).transpose()+b2.transpose()).transpose()\n err = sampleout - networkout\n sse = sum(sum(err**2))\n \n errhistory[eachcity].append(sse)\n if sse < errorfinal: break\n \n delta2 = err \n delta1 = dot(w2.transpose(),delta2)*hiddenout*(1-hiddenout)\n \n dw2 = dot(delta2,hiddenout.transpose())\n db2 = dot(delta2,ones((samnum,1)))\n \n dw1 = dot(delta1,samplein.transpose())\n db1 = dot(delta1,ones((samnum,1)))\n \n w2 += learnrate*dw2\n b2 += learnrate*db2\n \n w1 += learnrate*dw1\n b1 += learnrate*db1\n conf[eachcity] = [w1,b1,w2,b2]\n return errhistory, conf\n\n# 仿真输出和实际输出对比图\ndef compare(city):\n X,Y,all_data = gettable()\n all_errhistory,all_conf = BPNN(all_data)\n samplein1,sampleout1 = all_data[city]\n samplein = samplein1.transpose()\n sampleout = sampleout1.transpose()\n conf = all_conf[city]\n w1 = conf[0]\n b1 = conf[1]\n w2 = conf[2]\n b2 = conf[3]\n hiddenout = logsig((dot(w1,samplein).transpose()+b1.transpose())).transpose()\n networkout = (dot(w2,hiddenout).transpose()+b2.transpose()).transpose()\n diff = Y.max(0)-Y.min(0)\n networkout2 = (networkout+1)/2\n networkout2 = networkout2*diff + Y.min(0)\n \n return Y,networkout2.transpose()\n ","sub_path":"predict/BP_predict.py","file_name":"BP_predict.py","file_ext":"py","file_size_in_byte":4348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"548422972","text":"import tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport cv2\nimport time\nimport os\nSUMMARY_DIR = \"F:/python/vedio/cnnlog/\"\nIMAGE_SIZE = 128\nNUM_CHANNELS = 3\nNUM_LABELS = 9\nbatch_size=50\n# CONV1_DEEP = 10\nCONV1_DEEP = 10\nCONV1_SIZE = 3\n# CONV2_DEEP = 16\nCONV2_DEEP = 16\nCONV2_SIZE = 3\nLEARNING_RATE_BASE = 0.001\nLEARNING_RATE_DECAY = 0.99\nnum_train = 2001\nMOVING_AVERAGE_DECAY = 0.99\nFC_SIZE = 2048\nMODEL_SAVE_PATH = \"./cnnmodel/\"\nMODEL_NAME = \"model.ckpt\"\ndef variable_summaries(var,name ):\n with tf.name_scope('summaries'):\n tf.summary.histogram(name,var)\n mean = tf.reduce_mean(var)\n tf.summary.scalar('mean/' + name,mean)\n stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))\n tf.summary.scalar('stddev/' + name,stddev)\ndef distort_color(image,color_ordering=0):\n if color_ordering == 0:\n image = tf.image.random_brightness(image, max_delta=32 / 255)\n image = tf.image.random_saturation(image, lower=0.5,upper=1.5)\n image = tf.image.random_hue(image, max_delta=0.2)\n image = tf.image.random_contrast(image, lower = 0.5,upper = 1.5)\n elif color_ordering == 1:\n image = tf.image.random_saturation(image,lower=0.5,upper=1.5)\n image = tf.image.random_brightness(image,max_delta=32/255)\n image = tf.image.random_contrast(image, lower = 0.5,upper = 1.5)\n image = tf.image.random_hue(image,max_delta=0.2)\n elif color_ordering == 2:\n image = tf.image.random_contrast(image, lower=0.5, upper=1.5)\n image = tf.image.random_saturation(image, lower=0.5, upper=1.5)\n image = tf.image.random_brightness(image, max_delta=32 / 255)\n image = tf.image.random_hue(image, max_delta=0.2)\n return tf.clip_by_value(image,0.0,1.0)\ndef preprocess_for_train(image):\n if image.dtype != tf.float32:\n image = tf.image.convert_image_dtype(image,dtype=tf.float32)\n distorted_image = tf.image.random_flip_left_right(image)\n distorted_image = distort_color(distorted_image,np.random.randint(2))\n return distorted_image\ndef read_and_decode(filename,batch_size):\n files = tf.train.match_filenames_once(filename)\n filename_queue = tf.train.string_input_producer(files, shuffle=True)\n reader = tf.TFRecordReader()\n _, serialized_example = reader.read(filename_queue) # 返回文件名和文件\n features = tf.parse_single_example(serialized_example,\n features={\n 'height': tf.FixedLenFeature([], tf.int64),\n 'width': tf.FixedLenFeature([], tf.int64),\n 'channels': tf.FixedLenFeature([], tf.int64),\n 'label': tf.FixedLenFeature([], tf.int64),\n 'img_raw': tf.FixedLenFeature([], tf.string)\n }) # 取出包含image和label的feature对象\n image, label = features['img_raw'], features['label']\n height, width = features['height'], features['width']\n channels = features['channels']\n\n # height = tf.cast(height, tf.uint8)\n # width = tf.cast(width, tf.uint8)\n decoded_image = tf.decode_raw(image, tf.uint8)\n decoded_image = tf.reshape(decoded_image, [IMAGE_SIZE,IMAGE_SIZE,3])\n decoded_image = preprocess_for_train(decoded_image)\n min_after_dequeue = 100\n capacity = 1000 + 3 * batch_size\n image_batch, label_batch = tf.train.shuffle_batch([decoded_image, label], batch_size=batch_size, capacity=capacity,min_after_dequeue=min_after_dequeue)\n image_batch = tf.cast(image_batch, tf.float32)\n return image_batch,label_batch\ndef train(img,label):\n with tf.name_scope('input'):\n tf.summary.image('input',img,batch_size)\n logit = inference(img)\n global_step = tf.Variable(0, trainable=False)\n with tf.name_scope(\"Moving_Average\"):\n variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)\n variable_averages_op = variable_averages.apply(tf.trainable_variables())\n with tf.name_scope(\"loss_function\"):\n # 交叉熵\n cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logit, labels=label)\n cross_entropy_mean = tf.reduce_mean(cross_entropy)\n # 正则L2表达式\n loss = cross_entropy_mean\n tf.summary.scalar('loss', loss)\n with tf.name_scope(\"train_step\"):\n learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step,\n 3500 / batch_size, LEARNING_RATE_DECAY)\n tf.summary.scalar('learning_rate', learning_rate)\n train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=global_step)\n\n with tf.name_scope(\"accuracy\"):\n with tf.name_scope('correct_prediction'):\n correct_prediction = tf.equal(tf.argmax(logit, 1), label)\n with tf.name_scope('accuracy'):\n accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n tf.summary.scalar('acc', accuracy)\n\n with tf.control_dependencies([train_step, variable_averages_op]):\n train_op = tf.no_op(name='train')\n saver = tf.train.Saver()\n merged = tf.summary.merge_all()\n # writer = tf.summary.FileWriter(SUMMARY_DIR, tf.get_default_graph())\n with tf.Session() as sess:\n writer = tf.summary.FileWriter(SUMMARY_DIR, tf.get_default_graph())\n tf.local_variables_initializer().run()\n tf.global_variables_initializer().run()\n coord = tf.train.Coordinator()\n threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n for i in range(num_train):\n summary,_,loss_Value, _, step = sess.run([merged,train_op, loss, train_step, global_step])\n writer.add_summary(summary,i)\n print(\"----------------------\")\n print(\"After %d training step(s),loss on training batch is %g.\" % (step, loss_Value))\n if i%1000 == 0:\n run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n run_metadata = tf.RunMetadata()\n # 将配置信息和记录运行信息的proto传入运行的过程，从而记录运行时每一个节点的时间、空间开销信息\n summary = sess.run(merged, options=run_options, run_metadata=run_metadata)\n # 将节点在运行时的信息写入日志文件\n writer.add_run_metadata(run_metadata, 'step%03d' % i)\n writer.add_summary(summary, i)\n print(\"After %d training step(s),loss on training batch is %g.\" % (step, loss_Value))\n saver.save(sess,os.path.join(MODEL_SAVE_PATH,MODEL_NAME),global_step=global_step)\n print(\"---------------------\")\n coord.request_stop()\n coord.join(threads)\n writer.close()\n\ndef inference(input_img):\n with tf.name_scope('layer1-conv1'):\n with tf.name_scope(\"weight\"):\n weights = tf.Variable(tf.truncated_normal([CONV1_SIZE, CONV1_SIZE, NUM_CHANNELS, CONV1_DEEP], stddev=0.1))\n variable_summaries(weights, 'layer1-conv1' + '/weights')\n with tf.name_scope('biases'):\n biases = tf.Variable(tf.constant(0.0,shape=[CONV1_DEEP]))\n variable_summaries(biases, \"layer1-conv1\" + '/biases')\n with tf.name_scope('plus_b'):\n conv = tf.nn.conv2d(input_img, weights, strides=[1, 1, 1, 1], padding='SAME')\n relu = tf.nn.relu(tf.nn.bias_add(conv, biases ))\n tf.summary.histogram('layer1-conv1' + '/pre_activations',relu)\n with tf.name_scope('layer1-conv2'):\n with tf.name_scope(\"weight\"):\n weights = tf.Variable(tf.truncated_normal([CONV1_SIZE, CONV1_SIZE, CONV1_DEEP, CONV1_DEEP], stddev=0.1))\n variable_summaries(weights, 'layer1-conv2' + '/weights')\n with tf.name_scope('biases'):\n biases = tf.Variable(tf.constant(0.0,shape=[CONV1_DEEP]))\n variable_summaries(biases, \"layer1-conv2\" + '/biases')\n with tf.name_scope('plus_b'):\n conv = tf.nn.conv2d(relu, weights, strides=[1, 1, 1, 1], padding='SAME')\n relu = tf.nn.relu(tf.nn.bias_add(conv, biases ))\n tf.summary.histogram('layer1-conv2' + '/pre_activations',relu)\n with tf.name_scope('layer2-pool1'):\n with tf.name_scope(\"pool1\"):\n pool = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n tf.summary.histogram('layer2-pool1', pool)\n\n with tf.name_scope('layer3-conv2'):\n with tf.name_scope(\"weight\"):\n weights = tf.Variable(tf.truncated_normal([CONV2_SIZE, CONV2_SIZE, CONV1_DEEP, CONV2_DEEP], stddev=0.1))\n variable_summaries(weights, 'layer3-conv2' + '/weights')\n with tf.name_scope('biases'):\n biases = tf.Variable(tf.constant(0.0, shape=[CONV2_DEEP]))\n variable_summaries(biases, \"layer1-conv1\" + '/biases')\n with tf.name_scope('plus_b'):\n conv = tf.nn.conv2d(pool, weights, strides=[1, 1, 1, 1], padding='SAME')\n relu = tf.nn.relu(tf.nn.bias_add(conv, biases))\n tf.summary.histogram('layer3-conv2' , relu)\n print(relu.get_shape())\n with tf.name_scope('layer3-conv3'):\n with tf.name_scope(\"weight\"):\n weights = tf.Variable(tf.truncated_normal([CONV2_SIZE, CONV2_SIZE, CONV2_DEEP, CONV2_DEEP], stddev=0.1))\n variable_summaries(weights, 'layer3-conv3' + '/weights')\n with tf.name_scope('biases'):\n biases = tf.Variable(tf.constant(0.0, shape=[CONV2_DEEP]))\n variable_summaries(biases, \"layer3-conv3\" + '/biases')\n with tf.name_scope('plus_b'):\n conv = tf.nn.conv2d(relu, weights, strides=[1, 1, 1, 1], padding='SAME')\n relu = tf.nn.relu(tf.nn.bias_add(conv, biases))\n tf.summary.histogram('layer3-conv2' , relu)\n with tf.name_scope('layer4-pool2'):\n with tf.name_scope(\"pool2\"):\n pool2 = tf.nn.max_pool(relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n tf.summary.histogram('layer4-pool2', pool2)\n\n pool_shape = pool2.get_shape().as_list()\n nodes = pool_shape[1] * pool_shape[2] * pool_shape[3]\n reshaped = tf.reshape(pool2, [-1, nodes])\n\n with tf.name_scope('layer5-fc1'):\n with tf.name_scope(\"weight3\"):\n weights3 = tf.Variable(tf.truncated_normal([nodes, FC_SIZE], stddev=0.1))\n variable_summaries(weights3, 'layer5-fc1' + '/weights')\n\n with tf.name_scope('biases3'):\n biases3 = tf.Variable(tf.constant(0.1, shape=[FC_SIZE]))\n variable_summaries(biases3, \"layer5-fc1\" + '/biases')\n with tf.name_scope('plus_b'):\n preactivate = tf.matmul(reshaped, weights3) + biases3\n tf.summary.histogram('layer5-fc1' + '/pre_activations', preactivate)\n fc1 = tf.nn.relu(preactivate, name='activation')\n tf.summary.histogram('layer5-fc1' + '/activations', fc1)\n with tf.name_scope('layer6-fc2'):\n with tf.name_scope(\"weight4\"):\n weights4 = tf.Variable(tf.truncated_normal([FC_SIZE, NUM_LABELS], stddev=0.1))\n variable_summaries(weights4, 'layer6-fc2' + '/weights')\n with tf.name_scope('biases4'):\n biases4 = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))\n variable_summaries(biases4,\"layer6-fc2\"+'/biases')\n with tf.name_scope('plus_b'):\n out = tf.matmul(fc1,weights4) + biases4\n tf.summary.histogram('layer6-fc2' + '/pre_activations',out)\n return out\ndef main(argv=None):\n image_batch, label_batch = read_and_decode(\"train.tfrecords\", batch_size=batch_size)\n\n train(image_batch,label_batch)\n\n\nif __name__ == '__main__':\n tf.app.run()\n\n\n\n\n\n","sub_path":"超市异常行为识别/train_cnn.py","file_name":"train_cnn.py","file_ext":"py","file_size_in_byte":11857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"200701040","text":"# Copyright 2016 The TensorFlow Authors All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n\"\"\"Model architecture for predictive model, including CDNA, DNA, and STP.\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\n\nimport tensorflow.contrib.slim as slim\nfrom tensorflow.contrib.layers.python import layers as tf_layers\n\n\n# Amount to use when lower bounding tensors\nRELU_SHIFT = 1e-12\n\n\n\ndef encoder(image_pair, state_pair, conf, reuse= False):\n \"\"\"\n :param image_pair:\n :param state_pair: low dimensional input coordinates\n :return:\n \"\"\"\n with slim.arg_scope([ slim.layers.conv2d, slim.layers.fully_connected,\n tf_layers.layer_norm, slim.layers.conv2d_transpose],\n reuse=reuse):\n\n image_pair = tf.reshape(image_pair, shape=[conf['batch_size'], 64,64,3*2])\n enc0 = slim.layers.conv2d( #32x32x32\n image_pair,\n 32, [5, 5],\n stride=2,\n scope='conv0',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm1'}\n )\n enc1 = slim.layers.conv2d( #16x16x64\n enc0,\n 64, [5, 5],\n stride=2,\n scope='conv1',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n enc2 = slim.layers.conv2d( #8x8x128\n enc1,\n 128, [5, 5],\n stride=2,\n scope='conv2',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n enc3 = slim.layers.conv2d( # 8x8x64\n enc2,\n 64, [5, 5],\n stride=1,\n scope='conv3',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n enc4 = slim.layers.conv2d( # 8x8x16\n enc3,\n 16, [5, 5],\n stride=1,\n scope='conv4',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n enc5 = slim.layers.conv2d( # 8x8x1\n enc4,\n 1, [5, 5],\n stride=1,\n scope='conv5',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n return enc5\n\n\ndef decoder(lt_state, conf, reuse= False):\n \"\"\"\n :param image_pair:\n :param state_pair: lt dimensional input coordinates\n :return:\n \"\"\"\n with slim.arg_scope([slim.layers.conv2d, slim.layers.fully_connected,\n tf_layers.layer_norm, slim.layers.conv2d_transpose],\n reuse=reuse):\n\n dec0 = slim.layers.conv2d_transpose( #8x8x16\n lt_state,\n 16, [5, 5],\n stride=1,\n scope='conv0t',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm1'}\n )\n dec1 = slim.layers.conv2d_transpose( #8x8x64\n dec0,\n 16, [5, 5],\n stride=1,\n scope='conv1t',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n dec2 = slim.layers.conv2d_transpose( #8x8x128\n dec1,\n 64, [5, 5],\n stride=1,\n scope='conv2t',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n dec3 = slim.layers.conv2d_transpose( # 16x16x64\n dec2,\n 64, [5, 5],\n stride=2,\n scope='conv3t',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n dec4 = slim.layers.conv2d_transpose( # 32x32x32\n dec3,\n 32, [5, 5],\n stride=2,\n scope='conv4t',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n dec5 = slim.layers.conv2d_transpose( # 64x64x6\n dec4,\n 6, [5, 5],\n stride=2,\n scope='conv5t',\n # normalizer_fn=tf_layers.batch_norm,\n # normalizer_params={'scope': 'batch_norm2'}\n )\n return dec5\n\n\ndef predictor(lt_dim_state01, actions_01, conf, reuse =False):\n with slim.arg_scope([slim.layers.fully_connected],reuse=reuse):\n with tf.variable_scope('latent_model'):\n actions_01 = tf.reshape(actions_01, shape = [conf['batch_size'], - 1])\n lt_dim_state_flat = tf.reshape(lt_dim_state01,shape = [conf['batch_size'], - 1])\n\n # predicting the next hidden state:\n if 'stopgrad' in conf:\n lt_dim_state_flat = tf.stop_gradient(lt_dim_state_flat)\n\n lt_state_enc0 = tf.concat(1, [actions_01, lt_dim_state_flat])\n\n lt_state_enc1 = slim.layers.fully_connected(\n lt_state_enc0,\n 200,\n scope='hid_state_enc1')\n lt_state_enc2 = slim.layers.fully_connected(\n lt_state_enc1,\n 200,\n scope='hid_state_enc2')\n hid_state_enc3 = slim.layers.fully_connected(\n lt_state_enc2,\n int(lt_dim_state_flat.get_shape()[1]),\n scope='hid_state_enc3',\n activation_fn=None)\n\n hid_state_enc3 = tf.reshape(hid_state_enc3, [conf['batch_size'], 8, 8, 1])\n\n return hid_state_enc3\n\n\ndef construct_model(conf,\n images_01,\n actions_01,\n states_01 = None,\n images_12 = None,\n states_12 = None,\n test = False,\n\n ):\n \"\"\"\n :param conf:\n :param images_01:\n :param actions_01:\n :param states_01:\n :param images_12:\n :param states_12:\n :param test:\n :param lt_state1: used when propagating latent state through latent model\n :return:\n \"\"\"\n\n if test:\n inf_lt_state_01 = encoder(images_01, None, conf) #8x8x1\n pred_lt_state12 = predictor(inf_lt_state_01, actions_01, conf) #8x8x1\n images_23_rec = decoder(pred_lt_state12, conf)\n\n return images_23_rec, pred_lt_state12\n\n else:\n\n inf_lt_state_01 = encoder(images_01, states_01, conf)\n inf_lt_state12 = encoder(images_12, states_12, conf, reuse=True) #8x8x1\n images_01_rec = decoder(inf_lt_state_01, conf)\n pred_lt_state12 = predictor(inf_lt_state_01, actions_01, conf) # 8x8x1\n\n return pred_lt_state12, inf_lt_state12, images_01_rec","sub_path":"python/video_prediction/autoencoder/autoencoder_latentmodel.py","file_name":"autoencoder_latentmodel.py","file_ext":"py","file_size_in_byte":7266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"223404529","text":"import sys\r\nimport os\r\nfrom markov import MarkovMatrix\r\nfrom MarkovAgent import MarkovAgent\r\nfrom PoemUtility import PoemUtility\r\nfrom Poem import Poem\r\n\r\nTITLE = \"\\n\\\r\n**********************************************************\\n\\\r\n* $$$$$$$\\ $$$$$$\\ $$$$$$\\ $$$$$$$$\\ $$\\ $$\\\\ *\\n\\\r\n* $$ __$$\\ $$ __$$\\ $$ __$$\\ $$ _____| $$$\\ $$ | *\\n\\\r\n* $$ | $$ | $$ / $$ | $$ / \\__| $$ | $$$$\\ $$ | *\\n\\\r\n* $$$$$$$ | $$ | $$ | $$ |$$$$\\ $$$$$\\ $$ $$\\$$ | *\\n\\\r\n* $$ ____/ $$ | $$ | $$ |\\_$$ | $$ __| $$ \\$$$$ | *\\n\\\r\n* $$ | $$ | $$ | $$ | $$ | $$ | $$ |\\$$$ | *\\n\\\r\n* $$ | $$$$$$ | \\$$$$$$ | $$$$$$$$\\ $$ | \\$$ | *\\n\\\r\n* \\__| \\______/ \\______/ \\________| \\__| \\__| *\\n\\\r\n**********************************************************\\n\\\r\n* Poetry Generation using Markov Chains *\\n\\\r\n* by: Alex Castro, Alex Li, Ben Guler *\\n\\\r\n**********************************************************\\n\"\r\n\r\ndef pogen(syl, time, genre, sBool):\r\n # Initialize Poem class and generate the poem\r\n category = time + '_' + genre\r\n corpus = PoemUtility.tokenize(category + '.csv')\r\n matrix = MarkovMatrix(corpus, 2)\r\n\r\n poeminstance = Poem(matrix, syl, category, sBool)\r\n print(\"\\nPlease wait a few seconds, PoGen is thinking...\")\r\n poem = poeminstance.generatePoem()\r\n\r\n print(\"\\n***********************************************\\n\")\r\n print(poem)\r\n print(\"***********************************************\\n\")\r\n\r\ndef main():\r\n # Get user import and create poem with user specified parameters\r\n print(TITLE)\r\n print(\"Please tell us what kind of poem you would like to see.\\n(You may type 0 at any point to exit the program)\")\r\n\r\n run = 1\r\n while run != 0:\r\n # Number of lines\r\n lines = -1\r\n while lines < 0 or lines > 50:\r\n lines = input(\"\\nHow many lines does your poem have?\\n[1-50]: \")\r\n try:\r\n lines = int(lines)\r\n except ValueError:\r\n lines = -1\r\n if lines == 0:\r\n return\r\n\r\n # syllables or not\r\n sOption = 'stub'\r\n sBool = False\r\n while True:\r\n if sOption == 'y' or sOption == 'yes':\r\n sBool = True\r\n break\r\n elif sOption == 'n' or sOption == 'no':\r\n sBool = False\r\n break\r\n elif sOption == '0':\r\n return\r\n else:\r\n sOption = str(input(\"\\nDoes your poem follow a syllable scheme?\\n[(Y)es or (N)o]: \"))\r\n sOption = sOption.lower()\r\n\r\n # Syllable scheme\r\n syl = []\r\n if sBool:\r\n while len(syl) != lines:\r\n try:\r\n syl = [int(item) for item in input(\"\\nWhat syllable scheme does your poem have?\\n[eg. 3 5 3]:\").split()]\r\n except ValueError:\r\n syl = []\r\n if len(syl) > 0 and syl[0] == 0:\r\n return\r\n else:\r\n syl = [20] * lines\r\n\r\n # Time Period\r\n time = 'stub'\r\n while True:\r\n if time == 'm' or time == 'modern':\r\n time = 'modern'\r\n break\r\n elif time == 'r' or time == 'renaissance':\r\n time = 'renaissance'\r\n break\r\n elif time == '0':\r\n return\r\n else:\r\n time = str(input(\"\\nWhat time period is your poem?\\n[(M)odern or (R)enaissance)]: \"))\r\n time = time.lower()\r\n\r\n # Genre\r\n genre = 'stub'\r\n while True:\r\n if genre == 'l' or genre == 'love':\r\n genre = 'love'\r\n break\r\n elif genre == 'n' or genre == 'nature':\r\n genre = 'nature'\r\n break\r\n elif genre == 'm' or genre == 'mythology':\r\n genre = 'mythology_folklore'\r\n break\r\n elif genre == '0':\r\n return\r\n else:\r\n genre = str(input(\"\\nWhat genre is your poem?\\n[(L)ove or (N)ature or (M)ythology]: \"))\r\n genre = genre.lower()\r\n\r\n # Poetry Generation\r\n pogen(syl, time, genre, sBool)\r\n\r\n # Generate another poem\r\n repeat = 'stub'\r\n while True:\r\n if repeat == 'y' or repeat == 'yes':\r\n run = 1\r\n break\r\n elif repeat == 'n' or repeat == 'no':\r\n run = 0\r\n break\r\n elif repeat == '0':\r\n return\r\n else:\r\n repeat = str(input(\"\\nWould you like to generate another poem?\\n[(Y)es or (N)o]: \"))\r\n repeat = repeat.lower()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n PoemUtility.classifyPoems('all_200.csv')\r\n main()\r\n print(\"\\nThank you for using PoGen!\\n\")\r\n print(\"You can check out the project at https://github.com/benguler/Pogen-Poetry-Genration-using-Markov-Chains\")\r\n","sub_path":"PoGen.py","file_name":"PoGen.py","file_ext":"py","file_size_in_byte":5033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"278566214","text":"# -*- coding: utf-8 -*-\n\n\"\"\" This module is used to deploy an API to request predictions from the model \"\"\"\n\nimport logging\nfrom pathlib import Path\nimport json\nimport pickle\nfrom flask import Flask, request\nimport numpy as np\nfrom utils_func import get_id_list\nimport os\n\n\napp = Flask(__name__)\n\n\n@app.route(\"/api/\", methods=[\"POST\"])\ndef makecalc():\n \"\"\"USES THE MODEL TO MAKE PREDICTIONS ON THE DATA SENT THROUGH THE API\n\n Returns:\n list: list of the predictions\n \"\"\"\n data = request.get_json()\n data_list = data[\"data\"]\n data_array = np.array(data_list)\n prediction = model.predict(data_array)\n pred_list = prediction.tolist()\n\n return {\"predictions\": pred_list}\n\n\nif __name__ == \"__main__\":\n LOG_FMT = \"%(asctime)s - %(name)s - %(levelname)s - %(message)s\"\n logging.basicConfig(level=logging.INFO, format=LOG_FMT)\n\n # not used in this stub but often useful for finding various files\n project_dir = Path(__file__).resolve().parents[2]\n\n # find .env automagically by walking up directories until it's found, then\n # load up the .env entries as environment variables\n # load_dotenv(find_dotenv())\n\n try :\n with open(str(project_dir) + \"/models/deployment.json\", \"r\") as infile:\n params = json.load(infile)\n\n #model_id = params[\"model_id\"]\n port = params[\"port\"]\n\n modelfile = (\n str(project_dir) + \"/models/models-training/run_\" + model_id + \"/model.pkl\"\n )\n\n print(f\"Using model : {model_id}\")\n\n model = pickle.load(open(modelfile, \"rb\"))\n\n except :\n print('available models : ', get_id_list())\n model_id = get_id_list()[0]\n port = int(os.environ.get('PORT', 33507))\n\n modelfile = (\n str(project_dir) + \"/models/models-training/run_\" + model_id + \"/model.pkl\"\n )\n\n print(f\"Using model : {model_id}\")\n\n model = pickle.load(open(modelfile, \"rb\"))\n\n app.run(debug=True, host=\"0.0.0.0\", port=port)\n","sub_path":"src/models/serve_model.py","file_name":"serve_model.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"501209635","text":"import cv2;\r\nimport numpy as np\r\n\r\nimg = cv2.imread('selm.jpg')\r\nnum_rows, num_cols = img.shape[:2]\r\n\r\n# |1 0 Tx| =T \r\n# |0 1 Ty| =T\r\n# These are translation matrixes\r\ntranslation_matrix = np.float32([ [1,0,2], [0,1,0] ])\r\nimg_translation = cv2.warpAffine(img, translation_matrix, (num_cols, num_rows))\r\n\r\nnew_img= cv2.absdiff(img ,img_translation)\r\ncv2.imshow('Translation', new_img)\r\ncv2.waitKey()\r\ncv2.destroyAllWindows()\r\n","sub_path":"ps1_1_e.py","file_name":"ps1_1_e.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"536179943","text":"import asyncio\nimport pytest\nfrom aionsq.tcp import TCPHandler, NSQReader, NSQWriter\nfrom aionsq.tcp import exceptions\n\n\n@pytest.mark.asyncio\nasync def test_client(event_loop):\n client = TCPHandler('tcp://127.0.0.1:4150', loop=event_loop)\n assert not client.started\n await client.start()\n assert client.started\n client.close()\n await client.wait_closed()\n assert not client.started\n\n\n@pytest.mark.asyncio\nasync def test_publish(event_loop):\n client = NSQWriter('tcp://127.0.0.1:4150', loop=event_loop)\n\n with pytest.raises(exceptions.NotStartedError):\n await client.publish('topic1', 'msg1')\n\n await client.start()\n result = await client.publish('topic1', 'msg1')\n assert result is True\n\n client.close()\n await client.wait_closed()\n\n\n@pytest.mark.asyncio\nasync def test_multi_publish(event_loop):\n client = NSQWriter(':4150', loop=event_loop)\n\n with pytest.raises(exceptions.NotStartedError):\n await client.multi_publish('topic1', ['msg1'])\n\n await client.start()\n result = await client.multi_publish('topic1', ['msg1'])\n assert result is True\n\n client.close()\n await client.wait_closed()\n\n\n@pytest.mark.asyncio\nasync def test_subscribe(event_loop):\n client = NSQReader(':4150', 'topic1', 'chan1', loop=event_loop)\n\n @client.register\n async def listener(msg):\n await msg.success()\n\n await client.start()\n await asyncio.sleep(1)\n\n client.close()\n await client.wait_closed()\n\n\ndef test_consumers_1():\n client = NSQReader(':4150', 'topic1', 'chan1')\n\n assert not client.consumers\n\n @client.register\n async def consumer1(msg):\n return\n\n assert consumer1 in client.consumers\n\n client.unregister(consumer1)\n assert consumer1 not in client.consumers\n assert not client.consumers\n\n client.register(consumer1)\n assert consumer1 in client.consumers\n\n\ndef test_consumers_2():\n client = NSQReader(':4150', 'topic1', 'chan1')\n\n async def consumer1(msg):\n return\n\n assert not client.consumers\n\n client.register(consumer1)\n assert consumer1 in client.consumers\n\n client.unregister(consumer1)\n assert consumer1 not in client.consumers\n assert not client.consumers\n\n client.register(consumer1)\n assert consumer1 in client.consumers\n","sub_path":"tests/test_tcp.py","file_name":"test_tcp.py","file_ext":"py","file_size_in_byte":2294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"241733377","text":"# Copyright (c) 2016 The Chromium Authors. All rights reserved.\n# Use of this source code is governed by a BSD-style license that can be\n# found in the LICENSE file.\n\n\"\"\"Top-level presubmit script for gpu.\n\nSee http://dev.chromium.org/developers/how-tos/depottools/presubmit-scripts\nfor more details about the presubmit API built into depot_tools.\n\"\"\"\n\n\nimport re\n\n\ndef PostUploadHook(cl, change, output_api):\n \"\"\"git cl upload will call this hook after the issue is created/modified.\n\n This hook adds extra try bots list to the CL description in order to run\n extra GPU tests in addition to CQ try bots.\n \"\"\"\n rietveld_obj = cl.RpcServer()\n issue = cl.issue\n description = rietveld_obj.get_description(issue)\n if re.search(r'^CQ_INCLUDE_TRYBOTS=.*', description, re.M | re.I):\n return []\n\n bots = [\n 'master.tryserver.chromium.win:win_optional_gpu_tests_rel',\n 'master.tryserver.chromium.mac:mac_optional_gpu_tests_rel',\n ]\n\n results = []\n new_description = description\n new_description += '\\nCQ_INCLUDE_TRYBOTS=%s' % ';'.join(bots)\n results.append(output_api.PresubmitNotifyResult(\n 'Automatically added optional GPU tests to run on CQ.'))\n\n if new_description != description:\n rietveld_obj.update_description(issue, new_description)\n\n return results\n","sub_path":"third_party/WebKit/Source/modules/webgl/PRESUBMIT.py","file_name":"PRESUBMIT.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"385438223","text":"from tensorflow.keras.applications.vgg16 import VGG16\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions\nimport numpy as np\nimport tensorflow as tf\nimport sys\nsys.path.append('../')\n\nfrom progress.bar import IncrementalBar\nfrom simulate_crossbar.rram_weights import Rram_weights\n\n# This example tests a dog picture\ndef test_dog_picture():\n test_img = './dog.png'\n img2 = image.load_img(test_img)\n resized_images = img2.resize((224, 224))\n print(type(resized_images))\n resized_images.save(\"dog_resized.png\", \"PNG\", optimize=True)\n x = image.img_to_array(resized_images)\n x = np.expand_dims(x, axis=0)\n x = preprocess_input(x)\n features = model.predict(x)\n result = np.argmax(features)\n print(result)\n\ndef iterate_list(input_array, rram_crossbar):\n if (type(input_array) is np.ndarray):\n for index, x in np.ndenumerate(input_array):\n input_array[index] = rram_crossbar.actual_weight(input_array[index])\n else:\n for idx in range(0, len(input_array)):\n iterate_list(input_array[idx], rram_crossbar)\n\ndef main(argv):\n image_path = '../ILSVRC2012_devkit_t12/image/'\n model = VGG16(weights='imagenet', include_top=True)\n #test_dog_picture()\n result_file = open(\"val.txt\", \"r\")\n correct_num = 0\n num_images = 1000\n topk = 5\n filename = tf.placeholder(tf.string, name=\"inputFile\")\n fileContent = tf.read_file(filename, name=\"loadFile\")\n image_file = tf.image.decode_jpeg(fileContent, channels=3, name=\"decodeJpeg\")\n resize_nearest_neighbor = tf.image.resize_images(\n image_file,\n size=[224, 224],\n method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n sess = tf.Session()\n suffix = '%(index)d/%(max)d [%(elapsed)d / %(eta)d / %(eta_td)s]'\n bar = IncrementalBar('Processing', max=num_images, suffix=suffix)\n \n rram_crossbar = Rram_weights(8, 300)\n l_weights = model.get_weights()\n iterate_list(l_weights, rram_crossbar)\n model.set_weights(l_weights)\n\n for i in range(1, num_images + 1):\n img_file = \"{}{}{:0>8d}{}\".format(image_path, \"ILSVRC2012_val_\", i,\n \".JPEG\")\n feed_dict = {filename: img_file}\n with sess.as_default():\n x = resize_nearest_neighbor.eval(feed_dict)\n x = np.expand_dims(x, axis=0)\n x = preprocess_input(x)\n features = model.predict(x)\n #result = np.argmax(features)\n result = np.argsort(features)[0][-topk:]\n result_line = result_file.readline()\n correct_result = int(result_line.split()[1])\n #print(correct_result, result)\n if (correct_result in result): correct_num += 1\n #else: print(img_file)\n bar.next()\n\n bar.finish()\n #resized_images.save(\"picture_resized.jpeg\", \"JPEG\", optimize=True)\n print(\"Accuracy: {0:.2f}%\".format(float(correct_num) / num_images * 100))\n\n\nif __name__ == '__main__':\n main(sys.argv)\n","sub_path":"vgg/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"391104117","text":"# -*- coding: utf-8 -*-\n\nfrom django.template import loader, RequestContext\nfrom django.http import HttpResponse\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\nfrom django.conf import settings\n\nfrom core.manager.baseadmin import AdminManager\nfrom core.manager.system import SystemManager\nfrom core.models import Article, ArticleLanguage, Category, CategoryLanguage\nfrom core.form.article import AdmItemForm, AdmItemLanguageForm\n\nclass SystemObject(SystemManager):\n\n def __init__(self, request, *args, **kwargs):\n super(SystemObject, self).__init__(request, *args, **kwargs)\n self.manager = AdminManager()\n self.manager.fetchOptions = { 'site': int(self.portal.activeSite.id), 'active': self.requester.rData['selectedactivity'], 'activesite': self.requester.rData['activesite'] }\n self.urls.add = 'core.view.articleadmin.add_item'\n self.urls.edit = 'core.view.articleadmin.edit_item'\n self.urls.show_items = 'core.view.articleadmin.show_items'\n self.manager.model = Article()\n self.manager.modelLanguage = ArticleLanguage()\n self.manager.form_class = AdmItemForm().__class__\n self.manager.language_form_class = AdmItemLanguageForm().__class__\n self.manager.order = '-date'\n self.manager.debugger.filename = 'articleadmin.py'\n self.manager.moduleName = '__adm_Articles__'\n self.data.update({ 'filter_activity': reverse('core.view.articleadmin.show_items') })\n self.data.update({ 'savebutton': 1, 'saveaddbutton': 1, 'copybutton': 1, 'addbutton': 1 })\n self.category = AdminManager()\n self.category.model = Category()\n self.category.order = 'parent'\n self.category.fetchOptions = { 'site': self.portal.activeSite.id, 'active': self.requester.rData['selectedactivity'], 'activesite': self.requester.rData['activesite'] }\n self.category.modelLanguage = CategoryLanguage()\n\ndef show_items(request):\n system = SystemObject(request)\n if system.permission.user is None:\n return HttpResponseRedirect(reverse('core.view.userprofileadmin.login'))\n system.show_items(request, admin=True)\n system.template = loader.get_template(system.sheet.get_sheet_file('admin_articles_list'))\n\n c = RequestContext(request, system.get_context())\n return HttpResponse(system.template.render(c))\n\n\ndef edit_item(request, itemId):\n system = SystemObject(request)\n if system.permission.user is None:\n return HttpResponseRedirect(reverse('core.view.userprofileadmin.login'))\n system.manager.fetch_item(itemId)\n system.language.set_non_existent_language_items(system.manager.item, system.manager.modelLanguage.__class__)\n system.manager.set_language(system.language.currentLanguage)\n result = system.edit_item(request, itemId)\n\n if result is not None:\n return result\n\n system.category.fetch_items()\n system.category.set_language(system.language.currentLanguage)\n if system.manager.form is not None:\n system.manager.form.choices(system, 'category')\n\n system.template = loader.get_template(system.sheet.get_sheet_file('admin_articles_edit'))\n c = RequestContext(request, system.get_context())\n return HttpResponse(system.template.render(c))\n\n\ndef add_item(request):\n system = SystemObject(request)\n if system.permission.user is None:\n return HttpResponseRedirect(reverse('core.view.userprofileadmin.login'))\n system.new()\n return HttpResponseRedirect(reverse('core.view.articleadmin.edit_item', args=(system.manager.item.id,)))\n\n\ndef copy_item(request, itemId):\n pass\n\n\ndef change_item(request):\n system = SystemObject(request)\n if system.permission.user is None:\n return HttpResponseRedirect(reverse('core.view.userprofileadmin.login'))\n if system.manager.change_item(request, system.portal.get_active_site()):\n return HttpResponse('1');\n else:\n return HttpResponse('0');\n\n\ndef delete_item(request):\n system = SystemObject(request)\n if system.permission.user is None:\n return HttpResponseRedirect(reverse('core.view.userprofileadmin.login'))\n if system.manager.delete(request):\n return HttpResponse('1');\n else:\n return HttpResponse('0');\n","sub_path":"core/view/articleadmin.py","file_name":"articleadmin.py","file_ext":"py","file_size_in_byte":4277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"77492829","text":"\"\"\"Perform tests against the functionality of albme.py\"\"\"\n\nimport unittest\n\nfrom albme import parse_results_page, parse_detail_page\n\nSEARCH_REPONSE_PAGE = './fixtures/searchResponse.html'\nDETAIL_PAGE = './fixtures/details.html'\n\nclass AlbmeTestCase(unittest.TestCase):\n\t\n\tdef test_parse_results_page(self):\n\t\twith open(SEARCH_REPONSE_PAGE, 'r') as openFile:\n\t\t\tdata = openFile.read()\n\n\t\texpectedResult = [\\\n\t\tu'http://www.albme.org/AlbmeSearchWeb/showLicense?id=130081&typeName=TA']\n\t\tself.assertTrue(expectedResult == parse_results_page(data))\n\n\tdef test_parse_detail_page(self):\n\t\twith open(DETAIL_PAGE, 'r') as openFile:\n\t\t\tdata = openFile.read()\n\n\t\texpectedResult = {'License number:': u'TA.1531', \\\n\t\t'Licensee name': u'Alison Jean Clem ', 'Expiration date:': u'08/20/2003'}\n\t\tself.assertTrue(expectedResult == parse_detail_page(data))\n\nif __name__ == '__main__':\n unittest.main()","sub_path":"albme-py/albme_tests.py","file_name":"albme_tests.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"60615230","text":"import sys\n\nfrom futuremaker import utils\nfrom futuremaker.bitmex.bitmex_ws import BitmexWS\nfrom futuremaker.bot import Bot\nfrom futuremaker.algo import Algo\n\n\nclass AlertGo(Algo):\n\n def update_candle(self, df, candle):\n print('update_candle %s > ', df.index[-1], df.iloc[-1], candle)\n\n\nif __name__ == '__main__':\n params = utils.parse_param_map(sys.argv[1:])\n api = utils.ccxt_exchange('bitmex', api_key='05y_4aALSt-BrVgnNhSfhppP', api_secret=params['api_secret'],\n is_async=False, testnet=True)\n # api.private_post_position_leverage({'symbol': symbol, 'leverage': leverage})\n ws = BitmexWS('XBT/USD', candle_period='1m',\n api_key='05y_4aALSt-BrVgnNhSfhppP', api_secret=params['api_secret'],\n testnet=True)\n\n bot = Bot(api, ws, symbol='XBT/USD', candle_limit=20,\n candle_period='1m', testnet=True,\n api_key='05y_4aALSt-BrVgnNhSfhppP', api_secret=params['api_secret'],\n # leverage=1,\n # dry_run=dry_run, telegram_bot_token=telegram_bot_token,\n # telegram_chat_id=telegram_chat_id, http_port=http_port, backtest=backtest, test_start=test_start,\n # test_end=test_end\n )\n\n algo = AlertGo()\n bot.run(algo)\n","sub_path":"bots/sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":1287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"332219795","text":"\"\"\"\nGiven a string s, partition s such that every substring of the partition is a palindrome.\nReturn all possible palindrome partitioning of s.\nFor example, given s = \"aab\",\nReturn\n[\n [\"aa\",\"b\"],\n [\"a\",\"a\",\"b\"]\n]\n\"\"\"\n\n#http://www.jianshu.com/p/0f1ee51f400c\n#https://discuss.leetcode.com/topic/52156/easy-to-understand-neat-and-modular-java-solution-comments-included\nclass Solution(object):\n def partition(self, s):\n \"\"\"\n :type s: str\n :rtype: List[List[str]]\n \"\"\"\n return self.dfs(s)\n\n def dfs(self, s):\n res = []\n if len(s) == 0:\n return [ [] ]\n for i in range(len(s)):\n #if self.isPalindrome(s[:i+1]):\n if s[:i+1] == s[i::-1]:\n get = self.dfs(s[i+1:])\n for ele in get:\n res.append([s[:i+1]]+ele)\n return res\n \n def isPalindrome(self, s):\n length = len(s)\n for i in range(length/2):\n if s[i]!= s[length-1-i]:\n return False\n return True\n","sub_path":"131_Palindrome_Partitioning_intelligent.py","file_name":"131_Palindrome_Partitioning_intelligent.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"29001290","text":"from artiq.experiment import*\n\nclass tutorial_LEDrealTime(EnvExperiment):\n \"\"\"Tutuorial: LED real time\"\"\"\n def build(self): #Adds the device drivers as attributes and adds the keys to the kernel invarients\n self.setattr_device(\"core\")\n self.setattr_device(\"led0\")\n self.setattr_device(\"led1\")\n @kernel #Tells the system to run the following on the core device\n def run(self):\n self.core.reset()\n for i in range(10): \n for i in range(5):\n delay(300*ms)\n with parallel: #These events happen simultaneously\n self.led0.pulse(300*ms)\n self.led1.pulse(300*ms)\n \n for i in range(2): #These events happen sequentially\n self.led0.pulse(300*ms)\n self.led1.pulse(300*ms)","sub_path":"tutorial_ledPulse.py","file_name":"tutorial_ledPulse.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"23008910","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom tkinter import scrolledtext\n\nsrc_code=''' \nuint_8 v1 = 0;\n\nfor(uint8_t i = 0 ; i < 6 ; i++)\n{\n v1 += passwd[i] ^ (49 + i);\n}'''\ndata = open(\"log.txt\",\"r\").read()\nindex = data.find('''[*]---- Start vmCode ----''')\nmipsel_code = data[:index]\nsecCode = data[index:]\n\nwin = tk.Tk()\nwin.resizable(False,False)#大小固定\nwin.title(\"虚拟机保护演示\") # 添加标题\n\n# 创建一个容器,\nmonty = ttk.LabelFrame(win,text=\"Demo\") # 创建一个容器，其父容器为win\nmonty.grid(column=0, row=0, padx=10, pady=10) # padx pady 该容器外围需要留出的空余空间\nttk.Label(monty, text=\"===>\").grid(column=1, row=1) # 添加一个标签，并将其列设置为1，行设置为0\n\n# 静态文本框\nTextFrame = ttk.LabelFrame(monty,text=\"C语言\")\nTextFrame.grid(column=0,row=1,padx=10, pady=10)\nscrolW = 50 # 设置文本框的长度\nscrolH = 10 # 设置文本框的高度\nscr = tk.Text(TextFrame, width=scrolW, height=scrolH, wrap=tk.WORD) # wrap=tk.WORD 这个值表示在行的末尾如果有一个单词跨行，会将该单词放到下一行显示,比如输入hello，he在第一行的行尾,llo在第二行的行首, 这时如果wrap=tk.WORD，则表示会将 hello 这个单词挪到下一行行首显示, wrap默认的值为tk.CHAR\nscr.insert(1.0,src_code)\nscr.grid() \n\n# frame two\nsecond = ttk.LabelFrame(monty,text=\"MIPS指令\")\nsecond.grid(column=2, row=1, padx=10, pady=10)\nchange_one = scrolledtext.ScrolledText(second, width=70, height=20, wrap=tk.WORD)\nchange_one.grid(row = 0)\n\n#frame three\nthird = ttk.LabelFrame(second,text=\"虚拟机指令\")\nthird.grid(row=1, padx=10, pady=10)\nchange_two = scrolledtext.ScrolledText(third, width=70, height=20, wrap=tk.WORD) \nchange_two.grid(row = 0)\n\n# button被点击之后会被执行\ndef clickMe(): # 当acction被点击时,该函数则生效\n change_one.insert(1.0,mipsel_code)\n change_two.insert(1.0,secCode)\n\n# 按钮\naction = ttk.Button(monty, text=\"转换\", command=clickMe,style=\"C.TButton\",compound = \"text\") # 创建一个按钮, text：显示按钮上面显示的文字, command：当这个按钮被点击之后会调用command函数\naction.grid(column=1, row=3) # 设置其在界面中出现的位置 column代表列 row 代表行\naction.focus()\n\nwin.mainloop()","sub_path":"myVm/badvm-3.0/showDemo.py","file_name":"showDemo.py","file_ext":"py","file_size_in_byte":2333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"290261135","text":"d={\"name\": \"Armen\", \"age\": 15, \"grades\": [10, 8, 8, 4, 6, 7]}\n\nsum=0\n\nfor x in d[\"grades\"]:\n sum+=x\n \nif sum/len(d[\"grades\"])>7:\n print(\"Good job\")\nelse:\n print(\"You need to work more\")","sub_path":"Lecture4/Homework4/problem2.py","file_name":"problem2.py","file_ext":"py","file_size_in_byte":197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"271909480","text":"import cv2\nimport h5py \nimport scipy\nfrom scipy import ndimage\nimport hashlib\nimport numpy as np\nimport os\nimport re\nimport sys\nimport time\nimport tifffile\nfrom skimage import transform\nfrom skimage import feature\nimport random\n\ndef find_majority_element_in_list(k):\n myMap = {}\n maximum = ( '', 0 ) # (occurring element, occurrences)\n for n in k:\n if n in myMap: myMap[n] += 1\n else: myMap[n] = 1\n\n # Keep track of maximum on the go\n if myMap[n] > maximum[1]: maximum = (n,myMap[n])\n\n return maximum[0]\n\ndef _hist_match(source, template):\n \n \"\"\"\n Adjust the pixel values of a grayscale image such that its histogram\n matches that of a target image\n\n Arguments:\n -----------\n source: np.ndarray\n Image to transform; the histogram is computed over the flattened\n array\n template: np.ndarray\n Template image; can have different dimensions to source\n Returns:\n -----------\n matched: np.ndarray\n The transformed output image\n \"\"\"\n\n oldshape = source.shape\n source = source.ravel()\n template = template.ravel()\n\n # get the set of unique pixel values and their corresponding indices and\n # counts\n s_values, bin_idx, s_counts = np.unique(source, return_inverse=True,\n return_counts=True)\n t_values, t_counts = np.unique(template, return_counts=True)\n\n # take the cumsum of the counts and normalize by the number of pixels to\n # get the empirical cumulative distribution functions for the source and\n # template images (maps pixel value --> quantile)\n s_quantiles = np.cumsum(s_counts).astype(np.float64)\n s_quantiles /= s_quantiles[-1]\n t_quantiles = np.cumsum(t_counts).astype(np.float64)\n t_quantiles /= t_quantiles[-1]\n\n # interpolate linearly to find the pixel values in the template image\n # that correspond most closely to the quantiles in the source image\n interp_t_values = np.interp(s_quantiles, t_quantiles, t_values)\n\n return interp_t_values[bin_idx].reshape(oldshape)\n\n\ndef img_flip(im, flipH, flipV):\n \n \"\"\"\n Flip image.\n \"\"\"\n \n imFlip = im.copy()\n if flipH:\n imFlip = np.flip(imFlip, axis=1)\n if flipV:\n imFlip = np.flip(imFlip, axis=0)\n return(imFlip)\n \ndef is_number(s):\n \n \"\"\"\n Check if a string is a number.\n \"\"\"\n\n try:\n float(s)\n return True\n except ValueError:\n pass\n \n try:\n import unicodedata\n unicodedata.numeric(s)\n return True\n except (TypeError, ValueError):\n pass\n \n return False\n\n# Define a matlab like gaussian 2D filter\ndef matlab_style_gauss2D(shape=(7,7),sigma=1):\n\n \"\"\"\n 2D gaussian filter - should give the same result as:\n MATLAB's fspecial('gaussian',[shape],[sigma])\n \"\"\"\n m,n = [(ss-1.)/2. for ss in shape]\n y,x = np.ogrid[-m:m+1,-n:n+1]\n h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )\n h.astype(dtype=K.floatx())\n h[ h < np.finfo(h.dtype).eps*h.max() ] = 0\n sumh = h.sum()\n if sumh != 0:\n h /= sumh\n h = h*2.0\n h = h.astype('float32')\n return h\n\ndef print_checkpoint(message):\n \"\"\"\n Print message and current time\n \"\"\"\n print(message)\n tabs = message.count(\"\\t\")\n print((\"\\t\" * tabs) + time.asctime(time.localtime(time.time())) + \"\\n\")\n sys.stdout.flush()\n \ndef print_warning(error_message=\"\"):\n sys.stderr.write(\"Warning:\\n\" + error_message)\n\n","sub_path":"merfishdecoder/util/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":3505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"110072549","text":"import Sorting\n# Array class that holds integers\n# Performance is bad because I resize array after every operation\n# Meaning that length is always equal to the true count of values\nclass Array:\n \n def __init__(self, length):\n self.length = length\n # python's arrays are already dynamic so the method \n # below to create a static sized list is unnecessary\n # I'm only doing it so I can think about it as a static array\n self.contents = [None] * length\n\n # Linear time complexity. Have to shift elements\n def remove(self, num):\n value_removed = False\n last_index = 0\n for i in range(0, self.length, 1):\n if self.contents[i] == num:\n self.contents[i] = None\n value_removed = True\n last_index = i\n break\n if value_removed:\n for i in range(last_index, self.length-1, 1):\n self.contents[i] = self.contents[i+1]\n self.length -= 1\n self.contents[self.length] = None\n new_contents = [None] * self.length\n for i in range(0, self.length, 1):\n new_contents[i] = self.contents[i]\n self.contents = new_contents\n else:\n print(\"Could not find value in contents\")\n return\n \n # Linear time complexity. Have to shift elements\n def insert(self, index, num):\n if index > self.length:\n print(\"Can't insert to index greater than length\")\n return\n if index < 0:\n print(\"Can't insert to index less than zero\")\n return\n new_contents = [None] * (self.length + 1)\n \n for i in range(0, index, 1):\n new_contents[i] = self.contents[i]\n new_contents[index] = num\n for j in range(index+1, self.length+1, 1):\n new_contents[j] = self.contents[j-1]\n self.length += 1\n self.contents = new_contents\n\n def find(self, num):\n for i in range(0, self.length, 1):\n if self.contents[i] == num:\n return i\n return None\n\n \ndef main():\n array = Array(5)\n i = 0\n while i < array.length:\n array.contents[i] = i\n i += 1\n print(array.contents)\n\n array.remove(0)\n print(array.contents)\n print(array.length)\n\n array.remove(3)\n print(array.contents)\n print(array.length)\n\n array.insert(0, 0)\n print(array.contents)\n print(array.length)\n\n array.insert(3, 3)\n print(array.contents)\n print(array.length)\n\n array.insert(5, 5)\n print(array.contents)\n print(array.length)\n\n array.insert(10, 1)\n array.remove(69)\n\n array.contents = [4, 2, 1, 6, 3, 7, 5]\n sort = Sorting.Sorting()\n sort.selection_sort(array.contents)\n print(array.contents)\n\n\nif __name__ == \"__main__\":\n main()","sub_path":"Array.py","file_name":"Array.py","file_ext":"py","file_size_in_byte":2860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"529631832","text":"import calendar\nimport datetime\nimport time\n\nfrom flask import Flask, render_template, request, Markup\n\nfrom shinesite.dbclass import DbClass\n\ntime.sleep(15)\n\napp = Flask(__name__)\ndb = DbClass()\n\ndef printresults():\n print\n\n@app.route('/')\ndef onboarding():\n return render_template(\"onboarding.html\")\n\n\n@app.route('/about')\ndef about():\n return render_template(\"about.html\")\n\n\n@app.route('/home')\ndef home():\n result = db.getAlarmToday()\n alarm = result[0]\n wakeup = result[1]\n return render_template(\"home.html\", alarm=alarm, wakeup=wakeup)\n\n\n@app.route('/alarms')\ndef alarms():\n result = db.getAlarmToday()\n alarm = result[0]\n wakeup = result[1]\n month = \"

\" + datetime.datetime.now().strftime(\"%B\") + \"

\"\n html = \"\"\n kalender = \"\"\n for week in calendar.monthcalendar(2017, 3):\n kalender += \"\"\n for day in week:\n if day == datetime.date.today().day:\n kalender += \"\"\n kalender += \"\"\n html += kalender\n html += \"
\"\n elif day == 0:\n kalender += \"\"\n else:\n kalender += \"\"\n if day == 0:\n kalender += \" \"\n else:\n kalender += str(day)\n kalender += \"
\"\n return render_template(\"alarms.html\", alarm=alarm, wakeup=wakeup, month=month, kalender=Markup(html))\n\n\n@app.route('/alarms/add', methods=['GET', 'POST'])\ndef addalarm():\n musiclist = db.getSounds()\n if request.method == 'POST':\n if request.form['sunrise']: sunrise = 1\n else: sunrise = 0\n if request.form['snooze']: snooze = 1\n else: snooze = 0\n if request.form['name']: name = request.form['name']\n else: name = 0\n if request.form['date']: date = request.form['date']\n else: date = 0\n if request.form['monday']: mon = 1\n else: mon = 0\n if request.form['tuesday']: tue = 1\n else: tue = 0\n if request.form['wednesday']: wen = 1\n else: wen = 0\n if request.form['thursday']: thu = 1\n else: thu = 0\n if request.form['friday']: fri = 1\n else: fri = 0\n if request.form['saturday']: sat = 1\n else: sat = 0\n if request.form['sunday']: sun = 1\n else: sun = 0\n print(sunrise, snooze, name, date, mon, tue, wen, thu, fri, sat, sun)\n return render_template(\"addalarm.html\", musiclist=musiclist)\n\n\n@app.route('/nights')\ndef nights():\n return render_template(\"nights.html\")\n\n\n@app.route('/nights/add')\ndef addnight():\n return render_template(\"addalarm.html\")\n\n\n@app.route('/sleepcharts')\ndef sleepcharts():\n return render_template(\"sleepcharts.html\")\n\n\n@app.route('/settings')\ndef settings():\n return render_template(\"settings.html\")\n\n\n@app.errorhandler(404)\ndef pagenotfound(error):\n return render_template(\"error.html\", error=error)\n\n\nif __name__ == '__main__':\n app.run(host=\"0.0.0.0\",\n debug=True)","sub_path":"shinesite/project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"647954396","text":"# =============================================================\n# Author: http://aleskrejci.cz\n# Version: 15.07.2016\n# =============================================================\n\nimport re\nimport subprocess\n\n\n# wmic process get Name, ProcessId, ParentProcessId, Description /format:list\narg_param = ['Name', 'ProcessId', 'Description']\n\n\ndef extract_data(arg_list, sep='='):\n\tdict_data = {}\n\tfor arg in arg_list:\n\t\tsplit_array = arg.split(sep)\n\t\tif len(split_array) >= 2:\n\t\t\tdict_data[split_array[0]] = sep.join(split_array[1:])\n\treturn dict_data\n\n\ncmd = 'wmic process get ' + ','.join(arg_param) + ' /format:list'\nproc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)\noutput = proc.stdout.read().decode(u'utf8') # Dekodovani z bytove formy\n\nparam_array = []\nfor line in re.split('\\n\\s*\\n', output.strip()):\n\targ_list = re.split('[~\\r\\n]+', line.strip())\n\tparam_array.append(extract_data(arg_list))\n\nprint(param_array)\n","sub_path":"python/wmic_winproc.py","file_name":"wmic_winproc.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"419756030","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\"\"\"\r\nThis is an adapted version of rwinslow's surface script which can be found \r\non https://github.com/rwinslow/surface. The adapted version was converted \r\ninto a two-dimensional line evaluator instead of a three-dimensional surface\r\nevaluator. Additionally, new metrics were implemented. Apart from this,\r\nmost of the code is unchanged.\r\n\"\"\"\r\n\r\n\r\nclass Surface():\r\n\r\n def __init__(self, raw_data, cutoff=80, sample_width=643):\r\n self.cutoff = cutoff\r\n self.sample_width = sample_width\r\n self.primary = raw_data\r\n self.npr = len(self.primary)\r\n self.parse_waviness()\r\n self.parse_roughness()\r\n self.calculate_metrics()\r\n\r\n def __str__(self):\r\n return F\"{self.metrics}\"\r\n\r\n def parse_waviness(self):\r\n \"\"\" Parse waviness from each row of the primary profile\r\n\r\n Computes the FFT of the primary profile line-by-line.\r\n\r\n To prevent non-zero values at the boundaries, the primary profile is\r\n extended at the beginning and end by a flipped version of itself.\r\n\r\n The dataset is all real valued, so the FFT is symmetric. Thus, the\r\n signal strength must be doubled to fit the data correctly.\r\n\r\n For waviness, a low-pass filter is used (allows low frequencies/long\r\n wavelength signals) to allow the wavelengths longer than the cutoff\r\n wavelength to contribute to the final waviness profile. All values\r\n outside the range of allowed values are set to zero.\r\n\r\n \"\"\"\r\n\r\n self.waviness = []\r\n\r\n for i in range(1):\r\n row = self.primary\r\n profile = []\r\n flipped = row[::-1]\r\n\r\n profile.extend(flipped)\r\n profile.extend(row)\r\n profile.extend(flipped)\r\n\r\n f = np.array(np.fft.fft(profile))\r\n f[1:-1] = f[1:-1] * 2\r\n self.wavelengths = []\r\n for j in range(1, len(self.primary)):\r\n wavelength = 2 * (3 * self.sample_width) / j\r\n self.wavelengths.extend([wavelength])\r\n # print(wavelength)\r\n\r\n if (wavelength <= self.cutoff):\r\n stop_index = j\r\n break\r\n\r\n filtered = f\r\n filtered[stop_index:-1] = 0\r\n\r\n self.waviness.append(np.real(np.fft.ifft(filtered))\r\n [self.npr:2 * self.npr].tolist())\r\n\r\n def parse_roughness(self):\r\n \"\"\" Parse roughness from primary and waviness profiles\r\n\r\n Runs through each row in primary and waviness profiles and finds the\r\n difference between them to get the roughness\r\n\r\n \"\"\"\r\n self.roughness = []\r\n for i in range(self.npr):\r\n self.roughness.append(self.primary[i] - self.waviness[0][i])\r\n\r\n def calculate_metrics(self):\r\n \"\"\" Calculate metrics for each row of waviness and roughness\r\n\r\n Calculates:\r\n Wa = Average waviness\r\n Ra = Average roughness\r\n\r\n \"\"\"\r\n # calculate overall amplitude t\r\n pt = Surface.calculate_amplitude(self.primary)\r\n wt = Surface.calculate_amplitude(self.waviness[0])\r\n rt = Surface.calculate_amplitude(self.roughness)\r\n\r\n # calculate averages\r\n pa = Surface.calculate_average(self.primary)\r\n wa = Surface.calculate_average(self.waviness[0])\r\n ra = Surface.calculate_average(self.roughness)\r\n\r\n # calculate roughness values\r\n rz = Surface.calculate_rz(self.roughness)[\"Rz\"]\r\n rz1max = Surface.calculate_rz(self.roughness)[\"Rz1max\"]\r\n\r\n self.metrics = {\r\n 'Pt': pt,\r\n 'Pa': pa,\r\n 'Wt': wt,\r\n 'Wa': wa,\r\n 'Rt': rt,\r\n 'Ra': ra,\r\n 'Rz': rz,\r\n 'Rz1max': rz1max,\r\n }\r\n\r\n def plot(self):\r\n \"\"\" plot primary, waviness and roughness data\r\n \r\n \"\"\"\r\n plt.subplot(3, 1, 1)\r\n plt.plot(self.primary)\r\n plt.title('Roughness analysis')\r\n plt.ylabel('Primary data')\r\n\r\n plt.subplot(3, 1, 2)\r\n plt.plot(self.waviness[0])\r\n plt.ylabel('waviness')\r\n\r\n plt.subplot(3, 1, 3)\r\n plt.plot(self.roughness)\r\n plt.xlabel('distance s')\r\n plt.ylabel('roughness')\r\n\r\n plt.show()\r\n\r\n @staticmethod\r\n def calculate_amplitude(arr):\r\n arr_min = min(arr)\r\n arr_max = max(arr)\r\n if (arr_min < 0 and arr_max < 0) or (arr_min > 0 and arr_max > 0):\r\n return max(arr) - min(arr)\r\n else:\r\n return max(arr) + np.abs(min(arr))\r\n\r\n @staticmethod\r\n def calculate_average(arr):\r\n res = 0\r\n for e in arr:\r\n res += np.abs(e)\r\n return res / len(arr)\r\n\r\n @staticmethod\r\n def calculate_rz(arr):\r\n segment_length = int(len(arr) / 5)\r\n first = 0\r\n last = segment_length\r\n res = []\r\n for i in range(5):\r\n res.append(Surface.calculate_amplitude(arr[first:last]))\r\n first += segment_length\r\n last += segment_length\r\n\r\n return {\r\n 'Rz': Surface.calculate_average(res),\r\n 'Rz1max': max(res)\r\n }\r\n\r\n\r\nif __name__ == \"__main__\":\r\n pass\r\n","sub_path":"03_uniformity_stability/surface2D.py","file_name":"surface2D.py","file_ext":"py","file_size_in_byte":5303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"379113617","text":"import logging\n\nimport pandas as pd\nimport numpy as np\n\nfrom cnd_generator.utils.io.dict.dictdiffer import DictDiffer\nfrom cnd_generator.utils.pd.series import Contain\nfrom cnd_generator.preprocessing.load_config import report\n\ndef get_failed_index(bool):\n\t\"\"\"\n\tfind all False in bool array\n\n\t:type bool: pandas.core.frame.DataFrame\n\t:return:\n\t\"\"\"\n\tmat_bool = bool.astype(int).as_matrix()\n\trow, col = mat_bool.nonzero()\n\treturn row, col\n\ndef get_ith(df, col, cond):\n\trow, col = get_failed_index(df[col].isin(cond))\n\treturn row\n\n\nclass DfUtils():\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n report.debug('testing')\n\n def df_differ(self, df1, df2, cols):\n diff = {}\n invalid = set()\n df1, df2 = self._df_differ_condition(df1, df2)\n left = df1[cols].to_dict()\n right = df2[cols].to_dict()\n for col in cols:\n invalid_set = set()\n diff_check = DictDiffer(left[col], right[col])\n changed_ind = diff_check.changed(safe=True)\n if changed_ind:\n diff[col] = {}\n for ind in changed_ind:\n str_list = list(map(str, [left[col][ind], right[col][ind]]))\n str_diff = \"|\".join(str_list)\n diff[col][ind] = str_diff\n invalid = invalid.union(diff_check.added())\n invalid = invalid.union(diff_check.removed())\n # print('got here')\n # invalid.union(diff_check.added())\n # invalid.union(diff_check.removed())\n return diff, invalid\n\n def _df_differ_condition(self, df1, df2):\n # df1.index = df1.index.astype(str)\n # df2.index = df2.index.astype(str)\n return df1, df2\n\n def df_cmp_report(self, df1, df2, index, cols, bool_report=True):\n \"\"\"\n\t\tThis method is used to compare two die files.\n\t\tDefault bool_report = False\n\t\tInput: any two dataframes\n\t\tOutput: if bool_report = True, report in boolean where the matches are\n\t\tif bool_report = False, report in text\n\t\t\"\"\"\n def compare_bool(x):\n if x.__len__() is 1:\n return\n else:\n return False if x.unique().tolist().__len__() > 1 else True\n\n def compare_report(x):\n if x.__len__() is 1:\n # str_list = x.astype(str).tolist()\n return \"nf\"\n else:\n if x.unique().tolist().__len__() > 1:\n str_list = x.astype(str).tolist()\n return \"|\".join(str_list)\n else:\n return np.nan\n\n df1.reset_index(inplace=True)\n df2.reset_index(inplace=True)\n ind = index\n # group the two dataframe together\n df = pd.concat([df1, df2])\n grouped = df.groupby(ind)\n\n check_out = pd.DataFrame(index=grouped.groups.keys())\n\n # rpocess through each 'grouped' ind.. if there exists in both dataframe, len of group should be more than 1\n if bool_report:\n for col in cols:\n check = grouped.agg({col: lambda x: compare_bool(x)}).astype(bool)\n check_out[col] = check\n else:\n for col in cols:\n check = grouped.agg({col: lambda x: compare_report(x)})\n check_out[col] = check\n\n return check_out\n\n def get_index_col(self):\n val = self.df.index.values\n dup_ser = pd.Series(val, index=self.df.index)\n return dup_ser\n\n def drop_rows_by(self, invalid_ind, by='index', inplace=True):\n \"\"\"\n :param invalid_ind: provided a list of rows items [item1, item2] Note, cannot drop nan\n :param by: provide a column header to drop\n :param inplace: if inplace is true, will update the dataframe\n :return:\n \"\"\"\n ind_bad = Contain(invalid_ind, by=by)\n\n if ind_bad.items.__len__() > 0:\n report.error('Dropped invalid row(s) from table: {}'.format(ind_bad.items))\n\n if by is 'index':\n cleaned_df = self.df[self.df.index != ind_bad]\n cleaned_df_log = self.df_log[self.df_log.index != ind_bad]\n cleaned_df_style = self.df_style[self.df_style.index != ind_bad]\n else:\n cleaned_df = self.df[self.df[by] != ind_bad]\n cleaned_df_log = self.df_log[self.df_log[by] != ind_bad]\n cleaned_df_style = self.df_style[self.df_style[by] != ind_bad]\n\n if inplace:\n self.df = cleaned_df\n self.df_log = cleaned_df_log\n self.df_style = cleaned_df_style\n\n return cleaned_df\n\n def is_duplicated(self, col, dropna=True, report=True):\n \"\"\"\n report back if there are duplicate in series\n :param col: column name to run test\n :return: False - no duplicate, True - for duplicated detected\n \"\"\"\n # # drop nan if needed\n # if dropna:\n # self.df[col].dropna(inplace=True)\n #\n if col is 'index':\n work_col = self._index()\n else:\n work_col = self.df[col]\n\n # drop nan if needed\n # if dropna:\n # \tself.df[col].dropna(inplace=True)\n\n #TODO: need to implement drop nan for index\n bool_out = work_col.duplicated()\n dup_ind = work_col[bool_out] if report else work_col[-bool_out] # report all ind that are not unique\n bool_out = True if dup_ind.__len__() > 0 else False\n\n return bool_out, dup_ind\n\n def is_empty(self, col, report=True):\n \"\"\"\n use to find empty rows in columns\n :param col:\n :return: False - there is no empty, True - if there are empty rows\n \"\"\"\n if col is 'index':\n work_col = self._index()\n else:\n work_col = self.df[col]\n\n bool_out = work_col.isnull()\n empty_ind = work_col[bool_out] if report else work_col[-bool_out] # report all ind that are empty\n bool_out = True if empty_ind.__len__() > 0 else False\n return bool_out, empty_ind\n\n def is_str(self, col, dropna=True, report=True):\n if col is 'index':\n work_col = self._index()\n else:\n work_col = self.df[col]\n\n # drop nan if needed\n if dropna:\n work_col.dropna(inplace=True)\n\n bool_out = work_col.apply(lambda x: isinstance(x, str))\n str_ind = work_col[bool_out] if report else work_col[-bool_out] # report all ind that are empty\n bool_out = str_ind.__len__() == bool_out.__len__()\n return bool_out, str_ind\n\n def is_numeric(self, col, dropna=True, report=True):\n # drop nan if needed\n if col is 'index':\n work_col = self._index()\n else:\n work_col = self.df[col]\n\n if dropna:\n work_col.dropna(inplace=True)\n\n number_type = (int, float, np.int64, np.float64)\n\n bool_out = work_col.apply(lambda x: isinstance(x, number_type))\n num_ind = work_col[bool_out] # report all ind that are empty\n\n bool_out_report = num_ind.__len__() == bool_out.__len__()\n num_ind_report = work_col[bool_out] if report else work_col[-bool_out] # report all ind that are empty\n return bool_out_report, num_ind_report\n\n def is_regex(self, col, pattern_ex, dropna=True, report=True):\n #TODO: try to decorate these checks into Dataframe\n if col is 'index':\n work_col = self._index()\n else:\n work_col = self.df[col]\n\n if dropna:\n work_col.dropna(inplace=True)\n\n # some example for REGEX\n # '^[0-9]+$' - '^' start match at beginning\n # '+' allow for ONE or MORE matches\n # '*' allows ZERO or MORE matches\n # ' ' take out for exactly one match\n # '$' to avoid partial match, half-way matching\n\n bool_out = work_col.str.contains(pattern_ex)\n bool_out.fillna(value=False, inplace=True) # for all that are empty, mark as false\n match_ind = work_col[bool_out] # report all ind that are empty\n\n bool_out_report = match_ind.__len__() == bool_out.__len__()\n match_ind_report = work_col[bool_out] if report else work_col[-bool_out] # report all ind that are empty\n return bool_out_report, match_ind_report\n\n def is_in(self, col, match_to, dropna=True, report=True):\n if col is 'index':\n work_col = self._index()\n else:\n work_col = self.df[col]\n\n if dropna:\n work_col.dropna(inplace=True)\n\n bool_out = work_col.isin(match_to)\n match_ind = work_col[bool_out] # report all ind that are empty\n\n match_ind_report = work_col[bool_out] if report else work_col[-bool_out] # report all ind that are empty\n bool_out_report = match_ind.__len__() == bool_out.__len__()\n return bool_out_report, match_ind_report\n\n def is_(self, type, col, dropna=True, report=True):\n # drop nan if needed\n if col is 'index':\n work_col = self._index()\n else:\n work_col = self.df[col]\n\n if dropna:\n work_col.dropna(inplace=True)\n\n # number_type = (int, float, np.int64, np.float64)\n\n bool_out = work_col.apply(lambda x: isinstance(x, type))\n num_ind = work_col[bool_out] # report all ind that are empty\n\n bool_out_report = num_ind.__len__() == bool_out.__len__()\n num_ind_report = work_col[bool_out] if report else work_col[-bool_out] # report all ind that are empty\n return bool_out_report, num_ind_report\n\n def _index(self):\n val = self.df.index.values\n dup_ser = pd.Series(val, index=self.df.index)\n return dup_ser\n\n def _get_col(self, col):\n if self.df.index.name == col:\n out = self.get_index_col()\n elif sum(self.df.columns.isin([col])):\n out = self.df[col]\n else:\n raise NameError(\"Invalid '{}' as a column\".format(col))\n return out\n\nif __name__ == '__main__':\n from cnd_generator.tools.log.setup_log import log_setup\n from cnd_generator.preprocessing.load_config import *\n\n log_setup()\n\n df1 = pd.DataFrame({'A': ('1', '2', '3', '4'),\n 'C': pd.Series(1, index=list(range(4)), dtype='float32'),\n 'D': np.array([3] * 4, dtype='int32'),\n 'E': pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n 'F': 4,})\n df2 = pd.DataFrame({'A': ('1', '2', '3', '4'),\n 'C': pd.Series(1, index=list(range(1, 5)), dtype='float32'),\n 'D': np.array([3] * 4, dtype='int64'),\n 'E': pd.Categorical([\"test\", \"train\", \"test1\", \"train2\"]),\n 'F': 4.0,})\n\n df1 = df1.set_index('A')\n df2 = df2.set_index('A')\n\n my_diff = DfUtils()\n out, invalid = my_diff.df_differ(df1, df2, cols=['D', 'E', 'F'])\n print(out)\n print(invalid)\n","sub_path":"cnd_generator/utils/pd/dataframe.py","file_name":"dataframe.py","file_ext":"py","file_size_in_byte":10976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"542656925","text":"class ParkingSystem:\n\n def __init__(self, big: int, medium: int, small: int):\n self.big = big\n self.medium = medium\n self.small = small\n\n def addCar(self, carType: int) -> bool:\n if carType == 1:\n if self.big < 1:\n return False\n else:\n self.big -= 1\n return True\n if carType == 2:\n if self.medium < 1:\n return False\n else:\n self.medium -= 1\n return True\n if carType == 3:\n if self.small < 1:\n return False\n else:\n self.small -= 1\n return True\n\n # Your ParkingSystem object will be instantiated and called as such:\nobj = ParkingSystem(1, 1, 0)\nparam_1 = obj.addCar(1)\nparam_2 = obj.addCar(2)\nparam_3 = obj.addCar(3)\nparam_4 = obj.addCar(1)\n\nprint(param_1, param_2, param_3, param_4)\n","sub_path":"python/1603-Design-Parking-System.py","file_name":"1603-Design-Parking-System.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"436681553","text":"#0) Input - MNIST dataset => 1) Convolutional and Max-Pooling => 2) Convolutional and Max-Pooling\n#3) Fully Connected Layer => 4) Processing - Dropout => 5) Readout layer - Fully Connected\n#6) Outputs - Classified digits\n\n#source ~/tensorflow/bin/activate ,start tensorflow in terminal\n#tensorboard --logdir=/tmp/convolutional_network/1 , start tensorborad\n\n#To avoid few warning about GPU computation\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2' \n#Importe Mnist data\nfrom tensorflow.examples.tutorials.mnist import input_data\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n\nimport tensorflow as tf\n\n#Repository where is save logdata for tensorboard\nLOGDIR = \"/tmp/convolutional_network/1\"\n\n#################################################################################################\n################################## Implementing the Regression ##################################\n#################################################################################################\nwidth = 28 # width of the image in pixels \nheight = 28 # height of the image in pixels\nflat = width * height # number of pixels in one image \nclass_output = 10 # number of possible classifications for the problem\n\nx = tf.placeholder(tf.float32, shape=[None, flat], name=\"x\")\ny_ = tf.placeholder(tf.float32, shape=[None, class_output],name=\"label\")\n\nx_image = tf.reshape(x, [-1,28,28,1])\n\n#Convolutional Layer 1\n#Defining kernel weight and bias\n#We define a kernle here. The Size of the filter/kernel is 5x5; \n#Input channels is 1 (greyscale); and we need 32 different feature maps (here, 32 feature maps means 32 different filters are applied on each image. \n#So, the output of convolution layer would be 28x28x32). In this step, we create a filter/kernel tensor of shape [filter_height, filter_width, in_channels, out_channels]\nwith tf.name_scope(\"conv1\"):\n\tW_conv1 = tf.Variable(tf.truncated_normal([5, 5, 1, 32], stddev=0.1), name=\"W\")\n\tb_conv1 = tf.Variable(tf.constant(0.1, shape=[32]), name=\"B\") #32 biases for 32 outputs because \n\n\tconvolve1 = tf.nn.conv2d(x_image, W_conv1, strides=[1, 1, 1, 1], padding='SAME') + b_conv1\n\th_conv1 = tf.nn.relu(convolve1)\t#ReLU activation Function swap all negative numbers in 0.\n\tconv1 = tf.nn.max_pool(h_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #max_pool_2x2: recupère la valeur max dans cette fenetre\n\ttf.summary.histogram(\"weights\", W_conv1)\n\ttf.summary.histogram(\"biases\", b_conv1)\n\ttf.summary.histogram(\"activations\", h_conv1)\n\n#Convolutional Layer 2\nwith tf.name_scope(\"conv2\"):\n\tW_conv2 = tf.Variable(tf.truncated_normal([5, 5, 32, 64], stddev=0.1), name=\"W\")\n\tb_conv2 = tf.Variable(tf.constant(0.1, shape=[64]), name=\"B\") #64 biases for 64 outputs = 32 from the first layers + 32 from the second layer\n\n\tconvolve2= tf.nn.conv2d(conv1, W_conv2, strides=[1, 1, 1, 1], padding='SAME') + b_conv2\n\th_conv2 = tf.nn.relu(convolve2) #ReLU activation Function swap all negative numbers in 0.\n\tconv2 = tf.nn.max_pool(h_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #max_pool_2x2\n\t#The output is 64 matrix of [7x7]\n\ttf.summary.histogram(\"weights\", W_conv2)\n\ttf.summary.histogram(\"biases\", b_conv2)\n\ttf.summary.histogram(\"activations\", h_conv2)\n\n#Layer 3 Fully connected layer\nwith tf.name_scope(\"Fully_connected_layer\"):\n\tlayer2_matrix = tf.reshape(conv2, [-1, 7*7*64])# flattening of output layer 2\n\tW_fc1 = tf.Variable(tf.truncated_normal([7 * 7 * 64, 1024], stddev=0.1), name=\"W\")\n\tb_fc1 = tf.Variable(tf.constant(0.1, shape=[1024]), name=\"B\") # 1024 biases for 1024 outputs car 10 label 2^10 \n\t\n\tfcl=tf.matmul(layer2_matrix, W_fc1) + b_fc1\n\th_fc1 = tf.nn.relu(fcl)\n\ttf.summary.histogram(\"weights\", W_fc1)\n\ttf.summary.histogram(\"biases\", b_fc1)\n\ttf.summary.histogram(\"activations\", fcl)\n\n#Reduce overfiting : after lot of training test the accuracy diverge\nkeep_prob = tf.placeholder(tf.float32)\nlayer_drop = tf.nn.dropout(h_fc1, keep_prob)\n\n#Readout Layer\n#In last layer, CNN takes the high-level filtered images and translate them into votes using softmax. \n#Input channels: 1024 (neurons from the 3rd Layer) and 10 output labels\nwith tf.name_scope(\"OUT\"):\n\tW_fc2 = tf.Variable(tf.truncated_normal([1024, 10], stddev=0.1), name=\"W\") #1024 neurons\n\tb_fc2 = tf.Variable(tf.constant(0.1, shape=[10]), name=\"B\") # 10 possibilities for digits [0,1,2,3,4,5,6,7,8,9]\n\n\tylogits=tf.matmul(layer_drop, W_fc2) + b_fc2\n\ty_CNN= tf.nn.softmax(ylogits)\n\ttf.summary.histogram(\"weights\", W_fc2)\n\ttf.summary.histogram(\"biases\", b_fc2)\n\ttf.summary.histogram(\"activations\", ylogits)\n\n##############################################################################\n################################## Training ##################################\n##############################################################################\nwith tf.name_scope(\"total\"):\n\tcross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=ylogits, labels=y_)\n\t#cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_CNN), reduction_indices=[1]))\n\tcross_entropy = tf.reduce_mean(cross_entropy)\ntf.summary.scalar('cross_entropy', cross_entropy)\n\nwith tf.name_scope(\"accuracy\"):\n\tcorrect_prediction = tf.equal(tf.argmax(y_CNN,1), tf.argmax(y_,1))\n\taccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\ntf.summary.scalar('accuracy', accuracy)\n\nwith tf.name_scope(\"entrainement\"):\n\ttrain_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n\n###################################################################################\n################################## Start Session ##################################\n###################################################################################\nsumm = tf.summary.merge_all()\nsess = tf.InteractiveSession()\nsess.run(tf.global_variables_initializer())\n\nwriter = tf.summary.FileWriter(LOGDIR)\nwriter.add_graph(sess.graph)\n\nfor i in range(2001):\n\tbatch = mnist.train.next_batch(100)\n\tif i%100 == 0:\n\t\t[train_accuracy, s] = sess.run([accuracy, summ], feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})\n\t\twriter.add_summary(s, i)\n\t\t#train_accuracy = accuracy.eval(feed_dict={x:batch[0], y_: batch[1], keep_prob: 1.0})\n\t\tprint(\"step %d\"%(i))\n\ttrain_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})\n\n# Visualization \nprint(\"test accuracy %g\"%accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))\n\nsess.close() #finish the session","sub_path":"Neural_Network/CNN_tensorboard.py","file_name":"CNN_tensorboard.py","file_ext":"py","file_size_in_byte":6393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"493542675","text":"import pygame\r\n\r\n\r\nclass cube:\r\n rows,width = 20,500\r\n def __init__(self,start,dirnx=1,dirny=0,color=(255,0,0)):\r\n self.pos = start\r\n self.dirnx = dirnx\r\n self.dirny = dirny\r\n self.color = color\r\n\r\n def move(self):\r\n # for event in pygame.event.get():\r\n keys = pygame.key.get_pressed()\r\n # for key in keys:\r\n if keys[pygame.K_LEFT]:\r\n self.dirnx, self.dirny = (-1,0)\r\n elif keys[pygame.K_RIGHT]:\r\n self.dirnx, self.dirny = (1,0)\r\n elif keys[pygame.K_UP]:\r\n self.dirnx, self.dirny = (0,-1)\r\n elif keys[pygame.K_DOWN]:\r\n self.dirnx, self.dirny = (0,1)\r\n else:\r\n self.dirnx, self.dirny = self.pos\r\n\r\n self.pos = (self.pos[0]+self.dirnx,self.pos[1]+self.dirny)\r\n\r\n # self.pos\r\n\r\n def draw(self,surface):\r\n dis = self.width // self.rows\r\n pygame.draw.rect(surface,self.color,(self.pos[0]*dis,self.pos[1]*dis,dis,dis))\r\n print(self.pos)\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef drawGrid(width,rows,surface):\r\n sizeBtwn = width // rows\r\n x,y=0,0\r\n\r\n for l in range(rows):\r\n x = x+sizeBtwn\r\n y= y + sizeBtwn\r\n\r\n pygame.draw.line(surface,(255,255,255),(x,0),(x,width))\r\n pygame.draw.line(surface,(255,255,255),(0,y),(width,y))\r\n pygame.display.update()\r\n\r\n\r\n\r\ndef redrawWindow(surface):\r\n global rows, width, Mycube\r\n surface.fill((0,0,0))\r\n Mycube.draw(surface)\r\n drawGrid(width,rows,surface)\r\n\r\n\r\n\r\n\r\ndef main():\r\n\r\n global rows, width, Mycube\r\n rows, width = 20,500\r\n pygame.init()\r\n win = pygame.display.set_mode((width,width))\r\n green = (0,255,0)\r\n run = True\r\n clock = pygame.time.Clock()\r\n\r\n while run:\r\n clock.tick(15)\r\n Mycube = cube((1,0))\r\n Mycube.move()\r\n redrawWindow(win)\r\n pygame.display.update()\r\n\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n run = False\r\n\r\n pygame.quit()\r\n\r\n#\r\n\r\n\r\nmain()\r\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"260216011","text":"#!/usr/bin/python3\n# -*- coding:utf-8 -*-\n# author:Fillico\n# date:2019/6/23\n# description:投标页面的测试用例\nimport logging\nimport time\nimport unittest\n\nimport pytest\nfrom ddt import ddt, data\nfrom selenium import webdriver\nfrom FUTURE_WEB_UI.PageObject.index_page import IndexPage\nfrom FUTURE_WEB_UI.PageObject.login_page import LoginPage\nfrom FUTURE_WEB_UI.PageObject.user_page import UserPage\nfrom FUTURE_WEB_UI.TestDatas import common_datas as CD\nfrom FUTURE_WEB_UI.TestDatas import invest_datas as ID\nfrom FUTURE_WEB_UI.TestDatas import login_datas as LD\n\nfrom FUTURE_WEB_UI.PageObject.bid_page import BidPage\n\n\ndef test_demo():\n print(\"类之外的测试用例\")\n\n@ddt\nclass TestInvestPage:\n\n @classmethod\n def setUpClass(cls):\n logging.info(\"======用例前置：初始化浏览器会话，登录前程贷系统======\")\n cls.driver = webdriver.Chrome()\n cls.driver.maximize_window()\n cls.driver.get(CD.web_login_url)\n # LoginPage(cls.driver).login(LD.success_data[\"user\"], LD.success_data[\"password\"])\n LoginPage(cls.driver).login(LD.success_data[\"user\"], LD.success_data[\"password\"])\n # 调用首页的一个函数,实现从首页任意选择一个标,点击抢投标按钮\n IndexPage(cls.driver).click_invest_btn()\n cls.bid_page = BidPage(cls.driver)\n # cls.user_page = UserPage(cls.driver)\n # 登录\n\n def tearDown(self):\n logging.info(\"======每一个用例后置：刷新当前页面======\")\n self.driver.refresh()\n time.sleep(0.5)\n\n @classmethod\n def tearDownClass(cls):\n logging.info(\"======用例后置类：关闭浏览器会话，清理环境======\")\n cls.driver.quit()\n\n @data(*ID.invest_fail_data)\n def test_invest_failed(self, data):\n logging.info(\"======投资用例：异常场景-投资金额为非100的整数倍======\")\n # userMoney_beforeInvest = self.user_page.get_user_left_money() # 投资前获取余额\n userMoney_beforeInvest = self.bid_page.get_user_left_money() # 投资前获取余额\n self.bid_page.invest(data[\"invest_money\"]) # 投资\n userMoney_afterInvest = self.bid_page.get_user_left_money() # 投资后获取余额\n # 断言:投资前和投资后的金额是否相等\n self.assertEqual(userMoney_beforeInvest, userMoney_afterInvest)\n # 投资失败的弹出框文字\n self.assertEqual(self.bid_page.get_fail_alert_text(), data[\"check\"])\n\n @data(*ID.invest_wrong_formater)\n def test_invest_failed_wrong_format(self, data):\n logging.info(\"======投资用例：异常场景-投资金额为错误的格式等======\")\n # before_invest = self.bid_page.get_user_left_money()\n\n # self.bid_page.invest(data[\"invest_money\"])\n btn_msg = self.bid_page.get_btn_text(data[\"invest_money\"])\n # self.bid_page.get_element_text()\n # after_invest = self.user_page.get_user_left_money()\n # self.assertEqual(before_invest, after_invest)\n # 投资金额不是10的倍数,校验按钮上面的文字\n self.assertEqual(data[\"check\"], btn_msg)\n\n @pytest.mark.smoke\n def test_invest_success(self):\n logging.info(\"======投资用例：正常场景-投资成功\")\n before_invest = self.bid_page.get_user_left_money()\n self.bid_page.invest(ID.invest_success_data[\"invest_money\"])\n self.bid_page.click_activeButton_on_success_popup() # 点击查看并激活按钮\n # 投资成功,校验投资后的金额等于投资前的金额减去投资金额\n # self.assertEqual(int(float(UserPage(self.driver).get_user_left_money())),\n # (int(before_invest) - ID.invest_success_data[\"invest_money\"]))\n self.assertEqual(ID.invest_success_data[\"invest_money\"] , int(int(before_invest)-float(UserPage(self.driver).get_user_left_money())))\n","sub_path":"FUTURE_WEB_UI/TestCases/unittest_test_invest.py","file_name":"unittest_test_invest.py","file_ext":"py","file_size_in_byte":3898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"384874928","text":"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n#\r\n# pl.py\r\n# \r\n# Copyright 2017 Dominique Revuz \r\n# \r\n\r\n\r\nclass ErrorPL(Exception):\r\n \"\"\"\r\n Class d'exception du projet pour la séparée des exception standard\r\n \"\"\"\t\r\n pass\r\n\r\nimport collections\r\n\r\nclass PlSyntaxError(Exception):\r\n \"\"\" Raised if an error occured during parsing a PL \"\"\"\r\n\r\n def __init__(self, line, number_line, message=\"Syntax error\"):\r\n self.line = line\r\n self.message = message\r\n self.number_line = number_line\r\n \r\n def __str__(self):\r\n return self.message+' (l.'+self.number_line+\"): '\"+self.line+\"'\"\r\n\r\nclass PlMultilineError(Exception):\r\n \"\"\" Raised if a multiline value is never closed \"\"\"\r\n \r\n def __init__(self, key, number_line, message=\"Value on multiple line is never closed (Can't find '==')\"):\r\n self.message = message\r\n self.key = key\r\n self.number_line = number_line\r\n \r\n def __str__(self):\r\n return 'Key: '+self.key+' - '+self.message+\" - line \"+self.number_line\r\n \r\nclass InvalidExtensionError(Exception):\r\n \"\"\" Raised when trying to parse a file with an unknown extension \"\"\"\r\n \r\n def __init__(self, filename):\r\n self.filename = filename\r\n \r\n def __str__(self):\r\n return \"The file '\"+filename+\"' has an invalide extension, should be .pl, .pls or .pltp\"\r\n\r\ndef dicfusion(dst,src):\r\n \"\"\"\r\n >>> dst={'A':'A','a':'a','b':{'f1':'ff1','f2':'pas bo'},'c':'c',}\r\n >>> src={'a':'not a','b':{'f2':'ff2','f3':'ff3'},'d':'d',}\r\n >>> dicfusion(dst,src)\r\n {'d': 'd', 'a': 'not a', 'b': {'f1': 'ff1', 'f3': 'ff3', 'f2': 'ff2'}, 'A': 'A', 'c': 'c'}\r\n \"\"\"\r\n dres=dict()\r\n\r\n for k,v in src.items():\r\n if not k in dst or type(v)==str :\r\n dst[k]=v\r\n elif type(v) != type(dst[k]):\r\n raise ErrorPL(\"Erreur de fusion\")\r\n elif type(v) == list:\r\n dst[k].extend(v)\r\n else:\r\n dst[k].update(v)\r\n return dst\r\n\r\n\r\nif __name__ == '__main__':\r\n raise ErrorPL(\"Not CLI\")\r\n\r\n","sub_path":"home/lib/pysrc/attic/serverpl_pysrc/pl.py","file_name":"pl.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"64551025","text":"import git\nimport json\nimport os\nimport time\nimport subprocess\n\ndef gitget(server, user, reponame):\n\t'''Загрузка содержимого репозитория.\n\n\tСоздаёт папку и клонирует в неё\n\tрепозиторий по URL-у.\n\tВозвращает путь до папки'''\n\n\tif not os.path.exists('build'):\n\t\tos.makedirs('build')\n\trepodir = os.path.join(os.getcwd(), 'build', reponame + time.strftime(\"%Y%m%d%H%M%S\"))\n\trepo = git.Repo.init(repodir)\n\torigin = repo.create_remote('origin', 'git@' + server + ':' + user + '/' +reponame)\n\torigin.fetch()\n\trepo.create_head('master', origin.refs.master).set_tracking_branch(origin.refs.master).checkout()\n\torigin.pull()\n\tprint(\"git got\", server, user, reponame)\n\treturn repodir # путь до папки\n\ndef check(repodir):\n\t'''Проверка репозитория.\n\n\tВовзвращает результат проверки'''\n\tprint(\"checking\", repodir)\n\tcflags = ['--std=c89', '-Wall', '-Werror']\n\tdirs = set(next(os.walk(repodir))[1])\n\ttry:\n\t\tusername = (dirs - {'.git'}).pop()\n\texcept KeyError:\t\n\t\treturn error('username folder not found')\n\n\twith open(os.path.join(repodir, username, 'build.json')) as data_file: # проверяем наличие build.json\n\t\tif data_file: \n\t\t\tdata = json.load(data_file)\n\t\telse:\n\t\t\treturn error('automatic check is not supported by this repo')\n\n\tlang = data[\"lang\"] # определяем язык сборки\n\tif lang == \"lang_C\":\n\t\tgcc = 'gcc'\n\telif lang == \"lang_C++\":\n\t\tgcc = 'g++'\n\telse:\n\t\treturn error('language is not supported')\n\n\tflags = data[\"flags\"]\n\tfiles = data[\"files\"]\n\tformatversion = data[\"format-version\"]\n\tappversion = data[\"app-version\"]\n\tappbuild = data[\"app-build\"]\n\t\n\tfor f in next(os.walk(os.path.join(repodir, username)))[1]: # ищем вызовы system() в коде\n\t\twith open(f, 'r') as source:\n\t\t\tif re.match(\"system\\d*\\(.*\\)*\", source):\n\t\t\t\treturn error('repo not compatible')\n\n\treturn {'ok': 'built'}, data, gcc, flags, flags, repodir, username, files\n\nasync def build(gcc, cflags, flags, repodir, username, files):\n\t'''Сборка файлов с исходным кодом.\n\n\tВозвращает результат сборки'''\n\tresult = list()\n\tfor f in files:\n\t\tfilename = os.path.join(repodir, username, f)\n\t\tproc = subprocess.Popen( \\\n [gcc, *cflags, *flags, \"-o\", os.path.join(repodir, \"binaries\", f.split(\".\")[0]+\".o\"), filename], \\\n stderr=subprocess.PIPE)\t\n\t\toutput = proc.stderr.read().decode()\n\t\tresult.append({\"filename\":f, \"output\":output})\n\tprint(\"built\", username)\n\treturn result\n\ndef error(text):\n\t'''Возвращает ошибку.\n\n\tДля внутреннего использования'''\n\treturn ({'error':text}, None, None, None, None, None, None)\n","sub_path":"proveryalka.py","file_name":"proveryalka.py","file_ext":"py","file_size_in_byte":2794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"255889177","text":"import re\n\nfrom sys import argv\n\npattern = r\"^.{1,3}ll$\"\npattern = r\"(en\\.wikipedia\\.org/.{40})\"\nresults = []\n\n\ntext = open(argv[1],encoding='utf-8').read()\n\nmatches = re.findall(pattern, text)\nfor m in matches:\n print(m)\n\n# for a in results: print(a)\nprint(' '.join(results))\nprint(len(results))","sub_path":"_projlab/cheatsheet-code/search_text-regex.py","file_name":"search_text-regex.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"397000353","text":"import pandas as pd\nimport logging\nfrom simpletransformers.classification import ClassificationModel\nimport sys\nfrom pathlib import Path\nimport sklearn\nimport json\nimport torch\n\ndef main():\n if len(sys.argv) != 5:\n print('Usage: python train_downstream.py ', file=sys.stderr)\n sys.exit(1)\n logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',\n datefmt='%m/%d/%Y %H:%M:%S',\n level=logging.INFO)\n logger = logging.getLogger(__name__)\n model_type = sys.argv[1]\n model_path = sys.argv[2]\n data_directory = Path(sys.argv[3])\n args = json.load(open(sys.argv[4], \"r\"))\n train_path = data_directory / \"train.tsv\"\n test_path = data_directory / \"test.tsv\"\n val_path = data_directory / \"val.tsv\"\n classes = data_directory / \"classes.txt\"\n with open(classes, encoding=\"utf-8\") as f:\n num_labels = sum(1 for line in f if line.rstrip())\n # Train and Evaluation data needs to be in a Pandas Dataframe of two columns. The first column is the text with type str, and the second column is the label with type int.\n train_data = pd.read_csv(train_path, sep=\"\\t\", header=None)\n train_df = pd.DataFrame(train_data)\n train_df.dropna(subset=[0], inplace=True)\n train_df = train_df.sample(frac=1).reset_index(drop=True)\n logger.info(dict(train_df.dtypes))\n eval_data = pd.read_csv(test_path, sep=\"\\t\", header=None)\n eval_df = pd.DataFrame(eval_data)\n eval_df.dropna(subset=[0], inplace=True)\n val_data = pd.read_csv(val_path, sep=\"\\t\", header=None)\n val_df = pd.DataFrame(val_data)\n val_df.dropna(subset=[0], inplace=True)\n # Create a ClassificationModel\n model_pre = ClassificationModel(model_type, model_path, use_cuda=True)\n if model_type == \"bert\":\n model_downstream = ClassificationModel(model_type, \"bert-base-multilingual-cased\", num_labels=num_labels, use_cuda=True, args=args)\n model_downstream.model.bert = model_pre.model.bert\n elif model_type == \"xlmroberta\":\n model_downstream = ClassificationModel(\"xlmroberta\", \"xlm-roberta-base\", num_labels=num_labels, use_cuda=True, args=args)\n model_downstream.model.roberta = model_pre.model.roberta\n else:\n logger.info(\"model_type is wrong.\")\n del model_pre\n logger.info(\"This is a\")\n logger.info(model_type)\n logger.info(\"model from:\")\n logger.info(model_path)\n logger.info(args)\n logger.info(\"The train, test and val data was obtained from:\")\n logger.info(data_directory)\n logger.info(\"Total number of labels:\")\n logger.info(num_labels)\n logger.info(\"Start training now.\")\n # Train the model\n model_downstream.train_model(train_df, eval_df=val_df, acc=sklearn.metrics.accuracy_score, f1=sklearn.metrics.f1_score)\n logger.info(\"Training concluded. Start with evaluation...\")\n # Evaluate the model\n result, model_outputs, wrong_predictions = model_downstream.eval_model(eval_df, acc=sklearn.metrics.accuracy_score, f1=sklearn.metrics.f1_score)\n logger.info(result)\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"train_downstream.py","file_name":"train_downstream.py","file_ext":"py","file_size_in_byte":3167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"33524005","text":"smth= [\n {\n \"name\": \"Bob\",\n \"age\": 20,\n \"marks\": {\n \"Math\": 98,\n \"Python\":100\n }\n },\n {\n \"name\": \"Boba\",\n \"age\": 19,\n \"marks\": {\n \"Physic\": 98,\n }\n },\n {\n \"name\": \"Boban\",\n \"age\": 22,\n \"marks\": {\n }\n }\n\n]\n\ndef max(x):# Допоміжна функція яка шукає найбліьший елемент списку\n for i in range(len(x)):\n max=x[0]\n if x[i]>max:\n max=x[i]\n return max\n else:\n max=x[0]\n return max\ndef max_age(smth,x):\n if len(smth)==0:\n return max(x)\n x.append(smth[0].get(\"age\"))\n return max_age(smth[1:],x)\nx=[]\nprint(max_age(smth,x))\ndef get_names(smth,x):\n if len(smth)==0:\n return x\n x.append(smth[0].get(\"name\"))\n return get_names(smth[1:],x)\na=[]\nprint(get_names(smth,a))","sub_path":"km-82/Nesterchuk_Artem/workshop2/homework/task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"514624876","text":"from app.Model.Model import Model\n\nclass Image(Model):\n def __init__(self, json):\n self.id = 0\n self.fileName = None\n self.smallImageLocation = None\n self.mediumImageLocation = 0\n self.largeImageLocation = None\n self.threeDModelLocation = None\n self.is3DModelType = 'N'\n # Foreign Key for product.\n self.productId = None\n super().__init__(json)\n","sub_path":"app/Model/Image.py","file_name":"Image.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"457428140","text":"from sklearn.linear_model import Lasso\nfrom scipy.fftpack import dct, idct\nfrom scipy.sparse import coo_matrix\nfrom matplotlib.pyplot import plot, show, figure, title\nimport numpy as np\n\nN = 5000\nFS = 4e4\nM = 500\nf1, f2 = 697, 1336 # Pick any two touchtone frequencies\nduration = 1./8\nt = np.linspace(0, duration, duration*FS)\nf = np.sin(2*np.pi*f1*t) + np.sin(2*np.pi*f2*t)\nf = np.reshape(f, (len(f),1))\n\n# Displaying the test signal\nplot(t,f)\ntitle('Original Signal')\nshow()\n\n# Randomly sampling the test signal\nk = np.random.randint(0,N,(M,))\nk = np.sort(k) # making sure the random samples are monotonic\nb = f[k]\nplot(t,f,'b', t[k],b,'r.')\ntitle('Original Signal with Random Samples')\nshow()\n\nD = dct(np.eye(N))\nA = D[k,:]\n\nlasso = Lasso(alpha=0.001)\nlasso.fit(A,b.reshape((M,)))\n\n# Plotting the reconstructed coefficients and the signal\nplot(lasso.coef_)\ntitle('IDCT of the Reconstructed Signal')\nrecons = dct(lasso.coef_.reshape((N,1)),axis=0)\nfigure()\nplot(t,recons)\ntitle('Reconstucted Signal')\nshow()\n\nrecons_sparse = coo_matrix(lasso.coef_)\nsparsity = 1 - float(recons_sparse.getnnz())/len(lasso.coef_)\nprint (sparsity)\n","sub_path":"signal_reconstruction.py","file_name":"signal_reconstruction.py","file_ext":"py","file_size_in_byte":1130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"3672600","text":"fin = open('A-large.in')\nfout = open('output.txt', 'w')\nT = int(fin.readline())\nfor test in range(T):\n fout.write('Case #{0}: '.format(test + 1))\n\n val = int(fin.readline())\n if val == 0:\n fout.write('INSOMNIA\\n')\n else:\n s = set()\n n = 0\n while len(s) < 10:\n n += val\n for d in str(n):\n s.add(d)\n fout.write(str(n) + '\\n')\n\n","sub_path":"codes/CodeJamCrawler/16_0_1/NikolayKochetov/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"409765246","text":"from django.contrib import admin\nfrom django.contrib.auth.models import Group\nfrom django.contrib.auth.admin import UserAdmin\nfrom django.utils.translation import ugettext as _\n\nfrom photos.models import Photo\nfrom .models import Advertiser, Follower, MyUser\nfrom .forms import UserChangeForm, UserCreationForm\n\n\nclass MyUserAdmin(UserAdmin):\n form = UserChangeForm\n add_form = UserCreationForm\n\n list_display = ('username', 'is_superuser', 'is_admin', 'is_verified',\n 'date_joined', 'times_flagged')\n list_filter = ('is_active', 'is_admin', 'is_superuser', 'is_verified')\n readonly_fields = ['date_joined', 'last_login', 'modified',\n 'times_flagged']\n fieldsets = (\n (None,\n {'fields': ('username', 'email', 'password',)}),\n ('Basic information',\n {'fields': ('full_name', 'edu_email', 'gender', 'bio', 'website',\n 'profile_picture',)}),\n ('Points',\n {'fields': ('available_points', 'total_points',)}),\n ('Permissions',\n {'fields': ('is_active', 'is_admin', 'is_superuser',\n 'is_verified', 'user_permissions')}),\n (_('Dates'),\n {'fields': ('date_joined', 'last_login', 'modified',)}),\n (_('Flags'),\n {'fields': ('times_flagged',)}),\n )\n\n add_fieldsets = (\n (None,\n {'classes': ('wide',),\n 'fields': ('username', 'email', 'password1', 'password2',)}),\n )\n search_fields = ('email', 'username', 'full_name',)\n ordering = ('username',)\n filter_horizontal = ('user_permissions',)\n actions = ('activate', 'disable', 'verified',)\n\n def activate(self, request, queryset):\n queryset.update(is_active=True)\n Photo.objects.filter(creator=queryset).update(is_active=True)\n activate.short_description = _(\"Activate selected users\")\n\n def disable(self, request, queryset):\n queryset.update(is_active=False)\n Photo.objects.filter(creator=queryset).update(is_active=False)\n disable.short_description = _(\"Disable selected users\")\n\n def verified(self, request, queryset):\n queryset.update(is_verified=True)\n oby_verified = MyUser.objects.get(username=\"verified\")\n follower, created = Follower.objects.get_or_create(\n user=oby_verified)\n followed, created = Follower.objects.get_or_create(user=queryset)\n followed.followers.add(follower)\n verified.short_description = _(\"Verify selected users\")\n\nadmin.site.register(MyUser, MyUserAdmin)\n\n\nclass AdvertiserAdmin(admin.ModelAdmin):\n list_display = ('__unicode__', 'user_status', 'company_name',\n 'is_active', 'creations_allowed',)\n list_filter = ('is_active', 'user_status',)\n fields = ('user_status', 'user', 'company_name', 'description',\n 'company_website', 'twitter', 'instagram', 'is_active',\n 'creations_allowed', 'created', 'modified',)\n readonly_fields = ('created', 'modified',)\n\n class Meta:\n model = Advertiser\n\n # add actions for changing the state of the advertiser\n\nadmin.site.register(Advertiser, AdvertiserAdmin)\nadmin.site.unregister(Group)\n","sub_path":"accounts/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":3216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"373875807","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport pickle\nimport os\nimport numpy as np\nfrom tqdm import tqdm\nfrom scipy.io import wavfile\nfrom python_speech_features import mfcc\nfrom keras.models import load_model\nimport pandas as pd\nfrom sklearn.metrics import accuracy_score\n\n\n# In[108]:\n\n\ndef build_predictions(audio):\n #clean audio dir\n y_pred = []\n fn_prob={}\n\n print('Extracting features from audio')\n# print(os.listdir(audio))\n for fn in tqdm(os.listdir(audio)):\n rate, wav = wavfile.read(os.path.join(audio, fn))\n #label = fn2class[fn]\n #c = classes.index(label)\n y_prob = []\n \n# p_path = os.path.join('pickles', 'conv.p')\n# with open(p_path, 'rb') as handle:\n# config = pickle.load(handle)\n \n# model = load_model(config.model_path)\n \n for i in range (0, wav.shape[0] - config.step, config.step):\n sample = wav[i:i+config.step]\n x = mfcc(sample, rate, numcep=config.nfeat, nfilt=config.nfilt, nfft=config.nfft)\n x = (x - config.min) / (config.max - config.min)\n\n if config.mode =='conv':\n x = x.reshape(1, x.shape[0], x.shape[1],1)\n elif config.mode == 'time':\n x = np.expand_dims(x, axis=0)\n y_hat = model.predict(x)\n y_prob.append(y_hat)\n y_pred.append(np.argmax(y_hat))\n #y_true.append(c)\n\n fn_prob[fn] = np.mean(y_prob, axis = 0).flatten()\n\n return y_pred, fn_prob\n\n\n# In[109]:\n\n\n\nfrom PyQt5.QtWidgets import QWidget,QScrollArea, QTableWidget, QVBoxLayout,QTableWidgetItem\nimport pandas as pd\ndef dispdf(df):\n win = QWidget()\n scroll = QScrollArea()\n layout = QVBoxLayout()\n table = QTableWidget()\n scroll.setWidget(table)\n layout.addWidget(table)\n win.setLayout(layout) \n\n\n \n table.setColumnCount(len(df.columns))\n table.setRowCount(len(df.index))\n for i in range(len(df.index)):\n for j in range(len(df.columns)):\n table.setItem(i,j,QTableWidgetItem(str(df.iloc[i, j])))\n\n win.show()\n\n\n# In[114]:\n\n\ndef loadMods():\n directory = 'realTestClean/'\n #create directory if not exist for clean files\n if not os.path.exists(directory):\n os.makedirs(directory)\n \n #directory for clean files\n #clean the audio and put it in a specific dir\n CleanAudio(entry_text.get()+\"/\")\n \n \n data = [f for f in os.listdir(directory)]\n\n df = pd.DataFrame(data, columns = ['fname']) \n\n classes = ['cel', 'cla', 'flu', 'gac', 'gel', 'org', 'pia', 'sax', 'tru', 'vio', 'voi']\n\n\n #fn2class = dict(df.fname)\n\n# p_path = os.path.join('pickles', 'conv.p')\n\n# with open(p_path, 'rb') as handle:\n# config = pickle.load(handle)\n\n# model = load_model(config.model_path)\n\n y_pred, fn_prob = build_predictions(directory)\n\n # acc_score = accuracy_score(y_true=y_true, y_pred=y_pred)\n\n\n\n y_probs = []\n #modify the dataframe and fill it up with the associated class probability from al of the agrogated results\n #every tehnrh of a second, and store that probability\n for i, row in df.iterrows():\n y_prob = fn_prob[row.fname]\n y_probs.append(y_prob)\n for c, p in zip(classes, y_prob):\n df.at[i, c] = p\n\n y_pred = [classes[np.argmax(y)] for y in y_probs]\n df['y_pred'] = y_pred\n df.to_csv('predictions.csv', index = False)\n\n\n txt.delete('1.0', END)\n\n for i in range(len(df)):\n print(df.iloc[i][0],\" \",df.iloc[i][12])\n txt.insert(INSERT, (df.iloc[i][0] +\"-------->\"+ (df.iloc[i][12]) + '\\n'))\n #txt.insert(INSERT, )\n #4txt.insert(INSERT, (df.iloc[i][0],\" \",df.iloc[i][12],'\\n'))\n \n\n\n# In[ ]:\n\n\n\n\n\n# In[115]:\n\n\ndef set_text(text):\n self.entryText.set(text)\n return\n\ndef clicked():\n res = \"Welcome to \" + txt.get()\n lbl.configure(text= res)\n \ndef browsefunc():\n filename = filedialog.askdirectory() #choose file\n #lbl.config(text=filename)\n # define new text (you can modify this to your needs!)\n new_text = filename\n # delete content from position 0 to end\n entry.delete(0, tk.END)\n # insert new_text at position 0\n entry.insert(0, new_text)\n \ndef classify():\n lbl.config(text='change the value')\n\n\n# In[116]:\n\n\nfrom tkinter import *\nfrom tkinter.ttk import Frame, Button, Style\nfrom tkinter import filedialog\nimport tkinter as tk\nimport os\nimport librosa\nfrom tqdm import tqdm\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.io import wavfile\nfrom python_speech_features import mfcc, logfbank\nfrom cleanData import CleanAudio\n# from testPredOnNewFile import Predictions\n# class Example(Frame):\n\n# def __init__(self):\n# super().__init__()\n\n# self.initUI()\n\n\n# def initUI(self):\n\n# self.master.title(\"Buttons\")\n# self.style = Style()\n# self.style.theme_use(\"default\")\n\n \n \n# frame = Frame(self, relief=RAISED, borderwidth=1)\n# frame.pack(fill=BOTH, expand=True)\n \n# self.pack(fill=BOTH, expand=True)\n\n\n# entry1 = tk.Entry(frame)\n# entry1.pack(fill=X, padx=50, expand=True)\n \n# browsButton = Button(frame,text=\"Browse\",command=browsefunc)\n# browsButton.pack(side=RIGHT,padx=5, pady=5)\n \n# clfButton = Button(frame,text=\"Classify\")\n# clfButton.pack(side=RIGHT)\n \n \n# closeButton = Button(self, text=\"Close\")\n# closeButton.pack(side=RIGHT, padx=5, pady=5)\n# okButton = Button(self, text=\"OK\")\n# okButton.pack(side=RIGHT)\n\n\n# In[124]:\n\n\nimport tkinter as tk\nfrom tkinter import scrolledtext\n\n\nroot = tk.Tk()\nroot.geometry(\"400x300+400+400\")\n\n\nentry_text = tk.StringVar()\nentry = tk.Entry(root, textvariable=entry_text)\n\nbutton_1 = tk.Button(root, text=\"Browse\", command=browsefunc)\nbutton_2 = tk.Button(root, text=\"Classify\", command=loadMods)\n\nlbl = Label(root, text=\"Instrument\")\nlbl.pack()\n\n\ntxt = scrolledtext.ScrolledText(root, width=50,height=20)\ntxt.pack(side=TOP)\n\n\n\n#load model before loading gui\n\np_path = os.path.join('pickles', 'conv.p')\n\nwith open(p_path, 'rb') as handle:\n config = pickle.load(handle)\n\nmodel = load_model(config.model_path)\n\n# CleanAudio('realTest/')\n\n# # build_predictions('realTestClean')\n# loadMods('realTestClean')\n\n\n\n\nentry.pack(fill=X, padx=50, expand=True, ipady=3)\nbutton_1.pack(side=RIGHT)\nbutton_2.pack(side=RIGHT)\nroot.mainloop()\n# CleanAudio(entry_text.get())\nprint(entry_text.get())\n\n\n# In[99]:\n\n\n# root = Tk()\n# root.geometry(\"300x200+300+300\")\n# app = Example()\n# root.mainloop()\n\n# # lbl = Label(root, text=\"Hello\")\n\n# # lbl.grid(column=0, row=2)\n\n# # txt = Entry(root,width=10)\n\n# # txt.grid(column=0, row=1)\n\n\n\n\n\n# # btn = Button(root, text=\"Click Me\", command=browsefunc)\n\n\n# # btn.grid(column=0, row=0)\n\n# # root.mainloop()\n\n\n# In[125]:\n\n\n# from tkinter import *\n\n# class StylizedButton(Button):\n# def __init__(self, *args, **kwargs):\n# '''\n# Buttons don't do so well getting borders, so we are going to \n# do some black magic here to put the button into a frame\n# whilst retaining the reference to the button.\n# Warning: this is dumb.\n# '''\n# f = Frame( args[0]\n# ,highlightbackground=\"green\"\n# ,highlightcolor=\"green\"\n# ,highlightthickness=1)\n# super().__init__(f, *args[1:], **kwargs)\n# self.configure( activebackground='black'\n# ,activeforeground='green'\n# ,background='black'\n# ,foreground='lime'\n# ,relief='solid')\n# f.pack()\n \n# class App(Tk):\n# def __init__(self, *args, **kwargs):\n# super().__init__(*args, **kwargs)\n# self.configure(bg='black',)\n# self.geometry('100x100')\n# StylizedButton( self, text='this button is SEXY'\n# ,command=lambda: print('hi')).pack()\n \n# # App().mainloop()\n\n\n# In[55]:\n\n\n# def skata(df):\n \n# for i in range(len(df.index)):\n# for j in range(len(df.columns)):\n# table.setItem(i,j,QTableWidgetItem(str(df.iloc[i, j])))\n\n# win.show()\n\ndf = pd.read_csv('predictions.csv')\n# win = QWidget()\n# scroll = QScrollArea()\n# layout = QVBoxLayout()\n# table = QTableWidget()\n# scroll.setWidget(table)\n# layout.addWidget(table)\n# win.setLayout(layout) \n\n# cols = ['filename','cel', 'cla', 'flu', 'gac', 'gel', 'org', 'pia', 'sax', 'tru', 'vio', 'voi']\n\n# table.setColumnCount(len(df.columns))\n# table.setRowCount(len(df.index))\n# skata(df)\nfor i in range(len(df)):\n print(df.iloc[i][0],\" \",df.iloc[i][12])\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":8775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"49407254","text":"class Solution:\n def modifyString(self, s: str) -> str:\n n, res=len(s), []\n for i, v in enumerate(s):\n if v!='?': ch=v\n else:\n ch='a'\n if i>0 and res[-1]==ch: ch='b'\n if i= len(self.__text)-1:\n break\n\n return self.__curpos + 1\n\n def State(self):\n BeginTextInx = self.__curpos\n\n self.StateName()\n self.Modifier()\n\n #отдельно обрабатываем пустое состояние\n if self.__text[self.__curpos:self.__curpos+2] == '{}':\n self.__tokens.append({'id': self.__AvailableTokens['bracket'],'data':'{'})\n self.__tokens.append({'id': self.__AvailableTokens['bracket'],'data':'}'})\n self.__curpos += 2\n\n return self.__curpos - BeginTextInx\n\n\n if self.__text[self.__curpos] == '{':\n self.__tokens.append({'id': self.__AvailableTokens['bracket'],'data':'{'})\n self.__curpos += 1\n else:\n raise Exception(\"Syntax error. Token: State. Text: \"+self.__text[self.__curpos:] )\n\n self.Transitions()\n\n if self.__text[self.__curpos] == '}':\n self.__tokens.append({'id': self.__AvailableTokens['bracket'],'data':'}'})\n self.__curpos += 1\n else:\n raise Exception(\"Syntax error. Token: State. Text: \"+self.__text[self.__curpos:] )\n\n return self.__curpos - BeginTextInx\n\n\n def StateName(self):\n BeginTextInx = self.__curpos\n\n token_text = re.match( '\\$[a-zA-Z0-9_]+', self.__text[self.__curpos:] )\n\n if token_text and len(token_text.group()) != 0:\n token_text = token_text.group()\n\n self.__tokens.append({'id': self.__AvailableTokens['identifier'], 'data':token_text})\n self.__curpos += len(token_text)\n else:\n raise Exception(\"Syntax error. Token: StateName. Text: \" + self.__text[self.__curpos:])\n\n\n return self.__curpos - BeginTextInx\n\n\n def Transitions(self):\n BeginTextInx = self.__curpos\n\n while True:\n\n self.Transition()\n if self.__text[self.__curpos] == '}':\n break\n\n\n return self.__curpos - BeginTextInx\n\n\n def Transition(self):\n BeginTextInx = self.__curpos\n\n self.Symbol()\n\n self.__curpos += 1#съедаем ':'\n self.StateNames()\n\n if self.__text[self.__curpos] == ';':\n self.__tokens.append({'id': self.__AvailableTokens['split'], 'data':';'})\n self.__curpos += 1\n\n return self.__curpos - BeginTextInx\n\n def StateNames(self):\n\n BeginTextInx = self.__curpos\n\n while True:\n\n self.StateName()\n\n if self.__text[self.__curpos] == ',':\n self.__curpos += 1\n else:\n break\n\n return self.__curpos - BeginTextInx\n\n\n def Symbol(self):\n BeginTextInx = self.__curpos\n\n #отдельно учитываем epsilon-переходы\n if self.__text[ self.__curpos : self.__curpos+3 ] == 'eps':\n self.__tokens.append({'id': self.__AvailableTokens['symbol'], 'data':'eps'})\n self.__curpos += len('eps')\n\n return self.__curpos - BeginTextInx\n\n token_text = re.match( '[^:,{}$]{1}', self.__text[self.__curpos] )\n\n if token_text != None and len(token_text.group()) != 0:\n token_text = token_text.group()\n\n self.__tokens.append({'id': self.__AvailableTokens['symbol'], 'data':token_text})\n self.__curpos += len(token_text)\n else:\n raise Exception(\"Syntax error. Token: Symbol. Text: \" + self.__text[self.__curpos:])\n\n return self.__curpos - BeginTextInx\n\n\n def Modifier(self):\n BeginTextInx = self.__curpos\n\n token_text = re.match( '[a-zA-Z:]*', self.__text[self.__curpos:] )\n\n if token_text != None and len(token_text.group()) != 0:\n token_text = token_text.group()\n\n self.__tokens.append({'id': self.__AvailableTokens['modifier'], 'data':token_text})\n self.__curpos += len(token_text)\n\n return self.__curpos - BeginTextInx\n","sub_path":"NFAL.py","file_name":"NFAL.py","file_ext":"py","file_size_in_byte":7757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"488511582","text":"from asyncio import iscoroutine\n\nimport attr\nfrom pyee import TwistedEventEmitter as EventEmitter\nfrom twisted.internet.defer import Deferred\nfrom txdbus.objects import DBusObject, DBusProperty\n\nimport korbenware.dbus.path as path\nfrom korbenware.dbus.tree import insert_into_tree\nfrom korbenware.twisted.util import returns_deferred\n\n\nclass Object(EventEmitter):\n def __init__(self, service_obj, dbus_obj=None):\n super().__init__()\n self.service_obj = service_obj\n self.dbus_obj = dbus_obj\n\n def emit(self, name, data):\n if name not in self.service_obj.signals:\n return\n\n xform = self.service_obj.signals[name]\n self.dbus_obj.emitSignal(name, xform.dump(data))\n\n super().emit(self, name, data)\n\n def on(self, *args, **kwargs):\n raise NotImplementedError(\n 'Signals can only be emitted by the server, not received'\n )\n\n\n@attr.s\nclass Server:\n connection = attr.ib()\n service = attr.ib()\n bus_names = attr.ib()\n dbus_obj_cls = attr.ib()\n dbus_obj = attr.ib()\n\n @classmethod\n async def create(server_cls, connection, service):\n bus_names = []\n attrs = dict()\n objects = dict()\n\n for obj_path, service_obj in service.objects.items():\n obj = Object(service_obj)\n\n # Attach our object ot the server cls\n insert_into_tree(objects, path.split(obj_path), obj)\n\n # Generate and add the interface\n iface = service_obj.iface\n attrs['iface'] = iface\n attrs['dbusInterfaces'] = [iface]\n\n def bind(args_xform, returns_xform, fn):\n @returns_deferred\n async def proxy_fn(remote_object, *args):\n xformed_args = args_xform.load(args)\n maybe_coro = fn(*xformed_args)\n\n if (\n iscoroutine(maybe_coro)\n or isinstance(maybe_coro, Deferred)\n ):\n ret = await maybe_coro\n else:\n ret = maybe_coro\n\n return returns_xform.dump(ret)\n\n return proxy_fn\n\n # Add the dbus method callbacks\n for (\n method_name,\n (args_xform, returns_xform, fn)\n ) in service_obj.methods.items():\n\n key = f'dbus_{method_name}'\n\n proxy_fn = bind(args_xform, returns_xform, fn)\n attrs[key] = proxy_fn\n proxy_fn.__name__ = key\n\n defaults = dict()\n\n # Add dbus properties\n for (\n prop_name, (xform, default, kwarg)\n ) in service_obj.properties.items():\n attrs[prop_name] = DBusProperty(prop_name)\n defaults[prop_name] = default\n\n dbus_obj_cls = type(\n path.basename(obj_path),\n (DBusObject,),\n attrs\n )\n dbus_obj = dbus_obj_cls(obj_path)\n\n for attr_name, default in defaults.items():\n setattr(dbus_obj, attr_name, default)\n\n obj.dbus_obj = dbus_obj\n\n connection.exportObject(dbus_obj)\n\n bus_names.append(\n await connection.requestBusName(service.namespace)\n )\n\n server = server_cls(\n connection,\n service,\n bus_names,\n dbus_obj_cls,\n dbus_obj\n )\n\n for attr_, obj in objects.items():\n setattr(server, attr_, obj)\n\n return server\n","sub_path":"korbenware/dbus/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":3622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"43715895","text":"import os\nimport unittest\nimport json\nfrom flask_sqlalchemy import SQLAlchemy\n\nfrom flaskr import create_app\nfrom models import setup_db, Question, Category\n\n\nclass TriviaTestCase(unittest.TestCase):\n \"\"\"This class represents the trivia test case\"\"\"\n\n def setUp(self):\n \"\"\"Define test variables and initialize app.\"\"\"\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n self.new_question = {\n 'question': 'What is the most common programming language?',\n 'answer': 'Javascript',\n 'category': 5,\n 'difficulty': 2\n }\n self.new_question_empty = {\n 'question': '',\n 'answer': '',\n 'category': 1,\n 'difficulty': 1\n }\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()\n \n def tearDown(self):\n \"\"\"Executed after reach test\"\"\"\n pass\n\n def test_get_all_categories(self):\n '''\n Test to get all categories\n return 'success': True, 'categories': categories\n :pass\n '''\n res = self.client().get('/categories')\n data = json.loads(res.data)\n # check if the status code = 200\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertTrue(data['categories'])\n self.assertEqual(len(data['categories']), 6)\n \n def test_get_paginate_questions(self):\n '''\n Test to get 10 questions\n return 'success': True, get maximum 10 questions\n :pass\n '''\n res = self.client().get('/questions')\n data = json.loads(res.data)\n # check if the status code = 200\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertTrue(data['categories'])\n self.assertTrue(data['totalQuestions'])\n self.assertTrue(data['questions'])\n self.assertEqual(len(data['questions']), 10)\n\n def test_error_paginate_questions(self):\n '''\n Test the unreasonable number set in pages\n return 'success': False, 404, and resource not found\n :pass\n '''\n res = self.client().get('/questions?page=10000000')\n data = json.loads(res.data)\n # confirm the status response code is 404\n self.assertEqual(res.status_code, 404)\n self.assertEqual(data['success'], False)\n # confirm response has element equal \"message\": \"resource not found\"\n self.assertEqual(data['message'], 'Resource not found')\n\n def test_sucsse_delete_question(self):\n '''\n Test the delete the question exit in db\n :pass\n '''\n res = self.client().delete('/questions/14')\n data = json.loads(res.data)\n self.assertEqual(data['success'], True)\n self.assertEqual(data['message'], \"Question successfully deleted\")\n self.assertTrue(data['delete_id'])\n\n def test_delete_question_not_exit(self):\n '''\n Test the delete the question not exit in db\n becesed cannot delete record not exit in db\n :pass\n '''\n res = self.client().delete('/questions/62334')\n data = json.loads(res.data)\n # confirm the status response code is 422\n self.assertEqual(res.status_code, 422)\n self.assertEqual(data['success'], False)\n self.assertEqual(data['message'], \"Unprocessable\")\n\n def test_create_new_question(self):\n '''\n Test insert question with data in db\n :pass\n '''\n res = self.client().post('/questions', json=self.new_question)\n data = json.loads(res.data)\n # check if the status code = 200\n self.assertEqual(res.status_code, 200)\n self.assertIsNotNone(data['question'])\n\n def test_404_if_questions_creation_no_data(self):\n '''\n Test insert question without data in db\n :pass\n '''\n res = self.client().post('/questions', json=self.new_question_empty)\n data = json.loads(res.data)\n # confirm the status response code is 422\n self.assertEqual(res.status_code, 422)\n self.assertEqual(data['success'], False)\n self.assertEqual(data['message'], 'Unprocessable')\n\n def test_search_in_questions(self):\n '''\n Test search question with data in db\n :pass\n '''\n data_json = {\n 'searchTerm': 'Cassius Clay'\n }\n res = self.client().post('/questions/search', json=data_json)\n data = json.loads(res.data)\n # check if the status code = 200\n self.assertEqual(res.status_code, 200)\n self.assertIsNotNone(data['questions'])\n\n def test_search_in_questions_no_data_in_db(self):\n '''\n Test insert question with data not in db\n :pass\n '''\n res = self.client().post('/questions/search',json={'searchTerm': 'xxxyz'})\n data = json.loads(res.data)\n # confirm the status response code is 404\n self.assertEqual(res.status_code, 404)\n\n def test_get_questions_on_category(self):\n '''\n Test get question on category with data in db\n :pass\n '''\n res = self.client().get('/categories/{}/questions'.format(4))\n data = json.loads(res.data)\n # check if the status code = 200\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertEqual(data['category'], 'History')\n self.assertTrue(data['category'])\n self.assertTrue(data['totalQuestions'])\n self.assertTrue(data['questions'])\n\n def test_get_error_questions_on_category(self):\n '''\n Test get question on category with data not exit in db\n :pass\n '''\n res = self.client().get('/categories/{}/questions'.format(19))\n data = json.loads(res.data)\n # confirm the status response code is 404\n self.assertEqual(res.status_code, 404)\n self.assertEqual(data['success'], False)\n\n def test_get_all_quizzes(self):\n '''\n Test play quizzes on all categores with data in db\n :pass\n '''\n data_json = {\n \"previous_questions\": [3, 5, 10, 11, 12, 4],\n \"quiz_category\": {\"type\": \"click\", \"id\": 0}\n }\n res = self.client().post('/quizzes', json=data_json)\n data = json.loads(res.data)\n # check if the status code = 200\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertIsNotNone(data['question'])\n self.assertNotEqual(data['question']['id'], 3)\n self.assertNotEqual(data['question']['id'], 12)\n\n def test_get_quizzes_in_category(self):\n '''\n Test play quizzes on category with data in db\n :pass\n '''\n data_json = {\n \"previous_questions\": [3, 4, 10, 12, 11, 5],\n \"quiz_category\": {\"type\": \"Art\", \"id\": 2}\n }\n res = self.client().post('/quizzes', json=data_json)\n data = json.loads(res.data)\n # check if the status code = 200\n self.assertEqual(res.status_code, 200)\n self.assertEqual(data['success'], True)\n self.assertIsNotNone(data['question'])\n self.assertNotEqual(data['question']['id'], 3)\n self.assertNotEqual(data['question']['id'], 12)\n\n def test_error_quiz_category_not_found_quizzes(self):\n '''\n Test play quizzes on none (no data) category with data in db\n :pass\n '''\n data_json = {\n \"previous_questions\": [3, 4, 10, 12, 11, 5],\n \"quiz_category\": None\n }\n res = self.client().post('/quizzes', json=data_json)\n data = json.loads(res.data)\n self.assertEqual(res.status_code, 422)\n self.assertEqual(data['success'], False)\n\n\n# Make the tests conveniently executable\nif __name__ == \"__main__\":\n unittest.main()","sub_path":"backend/test_flaskr.py","file_name":"test_flaskr.py","file_ext":"py","file_size_in_byte":8277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"280431221","text":"from os import getenv\nimport pyodbc\nimport json\nimport mssqlify\nimport sys\nimport io\n\nimport logging\nlogging.basicConfig(level=logging.INFO)\n\nimport urllib\n\ndef init_mssql_connection(server, username, password, database):\n conn = pyodbc.connect('DRIVER={ODBC Driver 13 for SQL Server};SERVER='+server+';DATABASE='+database+';UID='+username+';PWD='+ password)\n return conn\n cursor = conn.cursor()\n cursor.execute(query)\n table = cursor.fetchall()\n return table\n\ndef init_mssql_cursor(conn):\n return conn.cursor()\n\ndef execute_mssql_query(conn, cursor, query):\n cursor.execute(query)\n conn.commit()\n\ndef fetch_from_mssql_query(cursor, query):\n cursor.execute(query)\n return cursor.fetchall()\n\ndef get_category(value):\n if value in ('Q022'):\n return 'QC001'\n elif value in ('Q003'):\n return 'QC002'\n elif value in ('Q019-1', 'Q019-2', 'Q019-3', 'Q019-4', 'Q019-5', 'Q020', 'Q023'):\n return 'QC004'\n else:\n return 'QC003'\n\ndef check_if_exists(dict, key):\n try:\n dict[key]\n return True\n except:\n return False\n\ndef read_json_file(filename):\n queries = []\n with open(filename) as f:\n data = json.load(f)\n DBOResult = mssqlify.MSQueryBuilder(\n \"dbo.result\",\n [\n \"resultDate\", \"week\", \"productId\", \"websiteId\", \"categoryId\",\n \"questionId\", \"value\", \"score\", \"recordId\", \"year\"\n ]\n )\n '''\n DBOWebshot = mssqlify.MSQueryBuilder(\n \"dbo.webshot\",\n [\n \"recordId\", \"url\"\n ]\n )\n '''\n keys = {}\n for row in data[\"rows\"]:\n row = row[\"value\"]\n try:\n # I'll just create a hash table that contains all unique keys and determine from there\n # if the row is duplicate, which if it is, then it will not insert as a new rows of data\n key = row[\"product_id\"] + \"-\" + row[\"website_id\"] + \"-\" + str(row[\"week\"]).encode(\"utf8\")\n '''\n if (row[\"webshots\"] == \"\"):\n DBOWebshot.addData(\n {\n \"recordId\": row[\"product_id\"] + \"-\" + row[\"website_id\"] + \"-\" + row[\"_id\"].split(\"@\")[1],\n \"url\": row[\"webshots\"]\n }\n )\n '''\n if not check_if_exists(keys, key):\n for answer in row[\"answers\"]:\n try:\n DBOResult.addData(\n {\n \"resultDate\": row[\"_id\"].split(\"@\")[1],\n \"week\": row[\"week\"],\n \"productId\": row[\"product_id\"],\n \"websiteId\": row[\"website_id\"],\n \"categoryId\": get_category(answer[\"question_id\"]),\n \"questionId\": answer[\"question_id\"],\n \"value\": \"\",\n \"score\": answer[\"score\"],\n \"recordId\": row[\"product_id\"] + \"-\" + row[\"website_id\"] + \"-\" + row[\"_id\"].split(\"@\")[1],\n \"year\": row[\"year\"]\n }\n )\n except:\n logging.info(\"Imported data with no value: \" + answer[\"question_id\"])\n keys[key] = \"\"\n else:\n logging.info(\"Duplicate found at \" + key)\n except Exception as e:\n pass\n return DBOResult.makeQuery()\n\ndef read_from_rows(rows):\n DBOResult = mssqlify.MSQueryBuilder(\n \"dbo.result\",\n [\n \"resultDate\", \"week\", \"productId\", \"websiteId\", \"categoryId\",\n \"questionId\", \"value\", \"score\", \"recordId\", \"year\"\n ]\n )\n\n for row in rows:\n DBOResult.addData(\n {\n \"resultDate\": str(row[1]),\n \"week\": row[2],\n \"productId\": row[3],\n \"websiteId\": row[4],\n \"categoryId\": row[5],\n \"questionId\": row[6],\n \"value\": row[7],\n \"score\": row[8],\n \"recordId\": row[9],\n \"year\": row[10]\n }\n )\n return DBOResult.makeQuery()\n\ndef download_from_couch(week, year):\n w = str(week)\n y = str(year)\n filename = \"week_\" + w + \"_year_\" + y + \".json\"\n url = \"http://172.20.33.12:5984/product-baseline/_design/details/_view/by_week_and_year?key=[\" + w + \",\" + y + \"]\"\n urllib.urlretrieve(url, filename)\n return filename\n\ndef write_to_file(queries, filename):\n f = open(filename, \"w\")\n for query in queries:\n f.write(query + \";\\n\")\n\ndef _make_gen(reader):\n b = reader(1024 * 1024)\n while b:\n yield b\n b = reader(1024*1024)\n\ndef rawgencount(filename):\n f = open(filename, 'rb')\n f_gen = _make_gen(f.read)\n return sum( buf.count(b'\\n') for buf in f_gen )\n\ndef fileExists(path):\n from pathlib import Path\n f = Path(path)\n if f.is_file():\n return True\n return False\n\nif __name__ == \"__main__\":\n server = 'tcp:msdetail.database.windows.net'\n username = 'detail'\n password = 'AxosDAQCWv3KcBJL'\n database = 'msdetail'\n querystore = \"pahabol.sql\"\n totallines = rawgencount(querystore)\n conn = init_mssql_connection(server, username, password, database)\n cursor = init_mssql_cursor(conn)\n with io.open(querystore, \"+r\", encoding=\"utf-8\") as fp:\n count = 1\n for line in fp:\n logging.info(\"Progress: \" + str(count) + \"/\" + str(totallines))\n execute_mssql_query(conn, cursor, line)\n count += 1\n","sub_path":"sqlrunner.py","file_name":"sqlrunner.py","file_ext":"py","file_size_in_byte":5885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"545710454","text":"import httplib2\nimport pprint\nfrom apiclient.discovery import build\nfrom apiclient.http import MediaFileUpload\nfrom oauth2client.client import OAuth2WebServerFlow\nfrom oauth2client.file import Storage\nimport argparse\nfrom oauth2client import tools\nfrom apiclient.discovery import build\n\ndef authorizeUser():\n\n\tparser = argparse.ArgumentParser(parents=[tools.argparser])\n\tflags = parser.parse_args()\n\n\n\t\"\"\"\n\t\tUncomment the following lines and add your client ID and secret for the program to run properly\n\t\"\"\"\n\t# CLIENT_ID = 'Your client ID'\n\t# CLIENT_SECRET = 'Your client secret'\n\n\t# Check https://developers.google.com/drive/scopes for all available scopes\n\tOAUTH_SCOPE = 'https://www.googleapis.com/auth/drive'\n\n\n\t# Check https://developers.google.com/drive/scopes for all available scopes\n\tOAUTH_SCOPE = 'https://www.googleapis.com/auth/drive'\n\n\t# Redirect URI for installed apps\n\tREDIRECT_URI = 'urn:ietf:wg:oauth:2.0:oob'\n\n\t# Path to the file to upload\n\tFILENAME = 'test.txt'\n\tflow = OAuth2WebServerFlow(CLIENT_ID, CLIENT_SECRET, OAUTH_SCOPE, redirect_uri=REDIRECT_URI)\n\n\t#see if credentials exist\n\tstorage = Storage('cred_file')\n\tcredentials = storage.get()\n\n\t#if credentials not set, then authenticate then Run through the OAuth flow and retrieve credentials\n\tif credentials is None or credentials.invalid:\n\t\tcredentials = tools.run_flow(flow, storage, flags)\n\t\tstorage.put(credentials)\n\n\t# Create an httplib2.Http object and authorize it with our credentials\n\thttp = httplib2.Http()\n\thttp = credentials.authorize(http)\n\n\tdrive_service = build_service(credentials)\n\n\treturn drive_service\n\n\ndef build_service(credentials):\n \"\"\"Build a Drive service object.\n\n Args:\n credentials: OAuth 2.0 credentials.\n\n Returns:\n Drive service object.\n \"\"\"\n http = httplib2.Http()\n http = credentials.authorize(http)\n return build('drive', 'v2', http=http)","sub_path":"authorize.py","file_name":"authorize.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"206049294","text":"from sklearn.ensemble import IsolationForest\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import confusion_matrix, roc_auc_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import roc_curve, auc\nimport struct\n\nimport seaborn as sns\nimport pandas as pd\nimport numpy as np\nimport matplotlib\n\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport tensorflow\nimport sys\nfrom ctypes import *\n\n##### value n for calculation outliers bounds #####\nn = 2\nLower_bound = 0\nUpper_bound = 0\n\ndef iqr_bounds(scores,k=1.5):\n print(type(scores))\n q1 = np.quantile(scores,0.25)\n q3 = np.quantile(scores,0.75)\n iqr = q3 - q1\n lower_bound=(q1 - k * iqr)\n upper_bound=(q3 + k * iqr)\n print(\"Lower bound:{} \\nUpper bound:{}\".format(lower_bound,upper_bound))\n return lower_bound,upper_bound\n\n##### Predict Function #####\ndef predict(x,y,n,lower_bound,upper_bound,is_test = False):\n anomaly_scores = isolation_forest.decision_function(x)\n\n if is_test == False:\n arr = np.zeros(len(y))\n for i in range(0, len(y)):\n if (anomaly_scores[i] < lower_bound) | (anomaly_scores[i] > upper_bound):\n arr[i] = 1\n else:\n arr[i] = 0\n y = arr\n y = y == 1\n y_pred = isolation_forest.predict(x)\n y_pred = y_pred == -1\n ### calculate confusion matrix between y vs y_pred for train and for validating sets ###\n\n cf_matrix = confusion_matrix(y, y_pred)\n print(cf_matrix)\n report = classification_report(y, y_pred)\n print(report)\n auc = roc_auc_score(y, y_pred)\n print(\"AUC= \", auc)\n\n group_names = ['True Neg', 'False Pos', 'False Neg', 'True Pos']\n group_counts = ['{0:0.0f}'.format(value) for value in\n cf_matrix.flatten()]\n group_percentages = ['{0:.2%}'.format(value) for value in\n cf_matrix.flatten() / np.sum(cf_matrix)]\n labels = [f'{v1}\\n{v2}\\n{v3}' for v1, v2, v3 in\n zip(group_names, group_counts, group_percentages)]\n labels = np.asarray(labels).reshape(2, 2)\n plt.figure(figsize=(15, 10))\n sns.heatmap(cf_matrix, annot=labels, fmt='', cmap='Blues')\n plt.show()\n\n if is_test:\n x['predicted'] = y_pred\n x.to_csv(r'x_test_with_predictions.csv', index=False, header=True)\n plt.figure(figsize=(15, 10))\n plt.hist(anomaly_scores, bins=100)\n plt.xlabel(\"Anomaly Scores where outlier bounds = [\" + str(\"{:.5}\".format(round(lower_bound, 5))) + ',' + str(\n \"{:.5}\".format(round(upper_bound, 5))) + \"]\", fontsize=14)\n plt.ylabel('Number of Data Points', fontsize=14)\n plt.show()\ndf = pd.read_csv('data.csv')\n\n##### use lableEncoder to encode categorical data #####\n\nprint(df.dtypes)\nencs = dict()\ndata = df.copy() #.sample(frac=1)\nfor col in data.columns:\n if data[col].dtype == \"object\" or data[col].dtype == \"bool\":\n encs[col] = LabelEncoder()\n encs[col].fit(df[col])\n data[col] = encs[col].transform(data[col])\nprint(data.dtypes)\n\n##### Shaffle Dataset #####\n\ndf_len = len(data)\nfor f in range(0,3):\n data=data.iloc[np.random.permutation(df_len)]\nfig,ax = plt.subplots(figsize=(12,7))\nsns.heatmap(data.corr(),fmt=\"\",linewidths=0.3,ax=ax)\nplt.show()\ndata['Class'] = 0\nY = data['Class']\nX = data.drop(['Class'],axis=1)\n\nx_train,x_test,y_train, y_test = train_test_split(X,Y,test_size=0.2,random_state=42)\nx_train, x_val,y_train,y_val = train_test_split(x_train,y_train,test_size=0.2,random_state=42)\n############################### Create Model #################################\nisolation_forest = IsolationForest(n_estimators=21, max_samples='auto',contamination = float(0.03306),bootstrap=False,random_state = 42,behaviour='new',n_jobs=-1)\nisolation_forest.fit(x_train)\n\n### Calculate anomaly in training set based on threshold\nanomaly_scores_train = isolation_forest.decision_function(x_train)\nlower_bound, upper_bound = iqr_bounds(anomaly_scores_train, k=n)\n\npredict(x_train,y_train,n,lower_bound, upper_bound)\npredict(x_val,y_val,n,lower_bound, upper_bound)\npredict(x_test,y_test,n,lower_bound, upper_bound, True)\n\nprint(\"End\")\n\n\n\n","sub_path":"shift5/isolated_forest_anomaly_detection.py","file_name":"isolated_forest_anomaly_detection.py","file_ext":"py","file_size_in_byte":4572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"531072345","text":"#!/usr/bin/python\n# -*- coding:utf8 -*-\nfrom enum import Enum\nimport time\n\n# 定義常量\nPizzaProgress = Enum('PizzaProgress', 'queued preparation baking ready')\nPizzaDough = Enum('PizzaDough', 'thin thick')\nPizzaSauce = Enum('PizzaSauce', 'tomato creme_fraiche')\nPizzaTopping = Enum(\n 'PizzaTopping',\n 'mozzarella double_mozzarella bacon ham mushrooms red_onion oregano')\nSTEP_DELAY = 3\n\n\nclass Pizza:\n def __init__(self, name):\n self.name = name\n self.dough = None\n self.sauce = None\n self.topping = []\n\n def __str__(self):\n return self.name\n\n def prepare_dough(self, dough):\n self.dough = dough\n print('preparing the {} dough of your {}...'.format(\n self.dough.name, self))\n time.sleep(STEP_DELAY)\n print('done with the {} dough'.format(self.dough.name))\n\n\nclass MargaritaBuilder:\n def __init__(self):\n self.pizza = Pizza('margarita')\n self.progress = PizzaProgress.queued\n self.baking_time = 5\n\n def prepare_dough(self):\n self.progress = PizzaProgress.preparation\n self.pizza.prepare_dough(PizzaDough.thin)\n\n def add_sauce(self):\n print('adding the tomato sauce to your margarita...')\n self.pizza.sauce = PizzaSauce.tomato\n time.sleep(STEP_DELAY)\n print('done with the tomato sauce')\n","sub_path":"builder/builder_pizza.py","file_name":"builder_pizza.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"330550782","text":"# This program demonstrates how different objects can be rendered in the same time,\n# and how attributes like size and color can be changed\n\n# load VTK\nfrom vtk import *\n\n# Create a Cylinder, giving size, resolution and color\ncylinder = vtkCylinderSource()\ncylinder.SetResolution(80)\ncylinder.SetHeight(4)\ncylinder.SetRadius(4)\n\ncylinderMapper = vtkPolyDataMapper()\ncylinderMapper.SetInput(cylinder.GetOutput())\n\ncylinderActor = vtkActor()\ncylinderActor.SetMapper(cylinderMapper)\ncylinderActor.GetProperty().SetColor(0.0,1.0,1.0)\n\n# Create a Cone, giving size, resolution, position and color\ncone = vtkConeSource()\ncone.SetResolution(120)\ncone.SetHeight(12)\ncone.SetRadius(3)\ncone.SetCenter(5,0,0)\n\nconeMapper = vtkPolyDataMapper()\nconeMapper.SetInputConnection(cone.GetOutputPort())\n\nconeActor = vtkActor()\nconeActor.SetMapper(coneMapper)\nconeActor.GetProperty().SetColor(1.0,0.0,1.0)\n\n\n#prop=vtkProperty()\nprop=coneActor.GetProperty()\nprop.SetDiffuse(0.7)\nprop.SetSpecular(0.4)\nprop.SetSpecularPower(20)\n\nconeActor.SetProperty(prop)\n \n#coneActor.GetProperty().SetDiffuse(0.7)\n#coneActor.GetProperty().SetSpecular(0.4)\n#coneActor.GetProperty().SetSpecularPower(20)\n \ncylinderActor.GetProperty().SetDiffuse(0.7)\ncylinderActor.GetProperty().SetSpecular(0.4)\ncylinderActor.GetProperty().SetSpecularPower(80)\n\n\n# Create a renderer and assign the actors to the renderer\nren = vtkRenderer()\nren.AddActor(cylinderActor)\nren.AddActor(coneActor)\nren.SetBackground(0.6, 0.6, 0.7)\n\n# Create the window and set the name and size of the window\nrenWin = vtkRenderWindow()\nrenWin.AddRenderer(ren)\nrenWin.SetWindowName(\"Cone & Cylinder\")\nrenWin.SetSize(500,500)\n\n# Make sure that we can interact with the application \niren = vtkRenderWindowInteractor()\niren.SetRenderWindow(renWin)\n\n# Initialze and start the application\niren.Initialize()\niren.Start()\n\n\n","sub_path":"exercises/Shading.py","file_name":"Shading.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"472908995","text":"# Functions that preprocess (NOT FETCH) data should be put in here\nimport numpy as np\nimport tensorflow as tf\nfrom fetch_hf import get_image_data\nimport os\nimport matplotlib.pyplot as plt\nimport cv2\n\nsess = tf.InteractiveSession()\nnormal_train = \"/Users/frank/Documents/GitHub/alexnet-sbs/dataSet/train/NORMAL\"\nilled_train = \"/Users/frank/Documents/GitHub/alexnet-sbs/dataSet/train/PNEUMONIA\"\nnormal_test = \"/Users/frank/Documents/GitHub/alexnet-sbs/dataSet/test/NORMAL\"\nilled_test = \"/Users/frank/Documents/GitHub/alexnet-sbs/dataSet/test/PNEUMONIA\"\n\ndef largest_image_size(img_array): #Tom: possible np.amax() faster execution\n max_row = float('-inf')\n max_column = float('-inf')\n for i in range(1, len(img_array), 2):\n array = img_array[i]\n if len(array) > max_row:\n max_row = len(array)\n if len(array[0]) > max_column:\n max_column = len(array[0])\n largest_size = (max_row, max_column)\n return largest_size\n\n\ndef smallest_image_size(img_array): #Tom: possible np.amax() faster execution\n min_row = float('inf')\n min_column = float('inf')\n for i in range(1, len(img_array), 2):\n array = img_array[i]\n if len(array) < min_row:\n min_row = len(array)\n if len(array[0]) < min_column:\n min_column = len(array)\n largest_size = (min_row, min_column)\n return largest_size\n\n\ndef resize_img(img, shape):\n resized = cv2.resize(img, shape, interpolation=cv2.INTER_AREA)\n return resized\n\n\ndef div_and_save(lis, path, batch_size = 100):\n max_len = len(lis)\n for i in range(0, len(lis), batch_size):\n temp = lis[i:min(i+100, max_len)]\n file = path + \"/\" + \"batch\" + str(int(i/batch_size)) + \".npy\"\n print(file)\n np.save(file, temp)\n\n\nprint(\"loading\")\nlabel_list_normal_train, img_list_normal_train = get_image_data(normal_train)\nlabel_list_illed_train, img_list_illed_train = get_image_data(illed_train)\nlabel_list_normal_test, img_list_normal_test = get_image_data(normal_test)\nlabel_list_illed_test, img_list_illed_test = get_image_data(illed_test)\n\nprint(\"resizing\")\nresized_normal_train = []\nfor img in img_list_normal_train:\n resized_normal_train.append(resize_img(img, shape = (227, 227)))\n\nresized_illed_train = []\nfor img in img_list_illed_train:\n resized_illed_train.append(resize_img(img, shape = (227, 227)))\n\nresized_normal_test = []\nfor img in img_list_normal_test:\n resized_normal_test.append(resize_img(img, shape = (227, 227)))\n\nresized_illed_test = []\nfor img in img_list_illed_test:\n resized_illed_test.append(resize_img(img, shape = (227, 227)))\n\nprint(\"zipping\")\nnormal_train = np.asarray(list(zip(label_list_normal_train, resized_normal_train)))\nilled_train = np.asarray(list(zip(label_list_illed_train, resized_illed_train)))\n\nnormal_test = np.asarray(list(zip(label_list_normal_test, resized_normal_test)))\nilled_test = np.asarray(list(zip(label_list_illed_test, resized_illed_test)))\n\ncombined_train = np.concatenate((normal_train, illed_train))\ncombined_test = np.concatenate((normal_test, illed_test))\nnp.random.shuffle(combined_train)\nnp.random.shuffle(combined_test)\n\ndiv_and_save(combined_train, \"/Users/frank/Documents/GitHub/alexnet-sbs/dataSet/parsed_train\")\n\ndiv_and_save(combined_test, \"/Users/frank/Documents/GitHub/alexnet-sbs/dataSet/parsed_test\", batch_size = 500)\n","sub_path":"src/chest_xray_tf/data_preprocessing_hf.py","file_name":"data_preprocessing_hf.py","file_ext":"py","file_size_in_byte":3358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"211254164","text":"from django.urls import path\n\nfrom . import views\n\n\nurlpatterns = [\n path('', views.HomePageView.as_view(), name='home'),\n path('about/', views.AboutPageView.as_view(), name='about'),\n path('sites/', views.SiteListView.as_view(), name='sites'),\n path('sites//', views.SiteDetailView.as_view(), name='site_detail'),\n path('countries/',views.CountryAreaListView.as_view(), name='country_area'),\n path('countries/', views.CountryAreaDetailView.as_view(),name='country_area_detail'),\n ]\n","sub_path":"heritagesites/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"631783347","text":"import logging\nfrom typing import Dict, Union, List\nfrom pgdrive.utils.constans import Decoration\nimport numpy\nfrom panda3d.bullet import BulletBoxShape, BulletRigidBodyNode\nfrom panda3d.core import Vec3, LQuaternionf, BitMask32, Vec4, CardMaker, TextureStage, RigidBodyCombiner, \\\n TransparencyAttrib, SamplerState, NodePath\n\nfrom pgdrive.pg_config.body_name import BodyName\nfrom pgdrive.pg_config.cam_mask import CamMask\nfrom pgdrive.scene_creator.blocks.constants import BlockDefault\nfrom pgdrive.scene_creator.lanes.circular_lane import CircularLane\nfrom pgdrive.scene_creator.lanes.lane import AbstractLane, LineType, LaneNode\nfrom pgdrive.scene_creator.lanes.straight_lane import StraightLane\nfrom pgdrive.scene_creator.road.road import Road\nfrom pgdrive.scene_creator.road.road_network import RoadNetwork\nfrom pgdrive.utils.asset_loader import AssetLoader\nfrom pgdrive.utils.coordinates_shift import panda_position\nfrom pgdrive.utils.element import Element\nfrom pgdrive.utils.math_utils import norm\nfrom pgdrive.world.pg_physics_world import PGPhysicsWorld\n\n\nclass BlockSocket:\n \"\"\"\n A pair of roads in reverse direction\n Positive_road is right road, and Negative road is left road on which cars drive in reverse direction\n BlockSocket is a part of block used to connect other blocks\n \"\"\"\n def __init__(self, positive_road: Road, negative_road: Road = None):\n self.positive_road = positive_road\n self.negative_road = negative_road if negative_road else None\n self.index = None\n\n\nclass Block(Element, BlockDefault):\n \"\"\"\n Abstract class of Block,\n BlockSocket: a part of previous block connecting this block\n\n <----------------------------------------------\n road_2_end <---------------------- road_2_start\n <~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~>\n road_1_start ----------------------> road_1_end\n ---------------------------------------------->\n BlockSocket = tuple(road_1, road_2)\n\n When single-direction block created, road_2 in block socket is useless.\n But it's helpful when a town is created.\n \"\"\"\n def __init__(self, block_index: int, pre_block_socket: BlockSocket, global_network: RoadNetwork, random_seed):\n super(Block, self).__init__(random_seed)\n # block information\n assert self.ID is not None, \"Each Block must has its unique ID When define Block\"\n assert self.SOCKET_NUM is not None, \"The number of Socket should be specified when define a new block\"\n if block_index == 0:\n from pgdrive.scene_creator.blocks import FirstBlock\n assert isinstance(self, FirstBlock), \"only first block can use block index 0\"\n elif block_index < 0:\n logging.debug(\"It is recommended that block index should > 1\")\n self._block_name = str(block_index) + self.ID\n self.block_index = block_index\n self.number_of_sample_trial = 0\n\n # each block contains its own road network and a global network\n self._global_network = global_network\n self.block_network = RoadNetwork()\n\n # used to spawn npc\n self._reborn_roads = []\n\n # own sockets, one block derives from a socket, but will have more sockets to connect other blocks\n self._sockets = []\n\n # used to connect previous blocks, save its info here\n self._pre_block_socket = pre_block_socket\n self.pre_block_socket_index = pre_block_socket.index\n\n # a bounding box used to improve efficiency x_min, x_max, y_min, y_max\n self.bounding_box = None\n\n # used to create this block, but for first block it is nonsense\n if block_index != 0:\n self.positive_lanes = self._pre_block_socket.positive_road.get_lanes(self._global_network)\n self.negative_lanes = self._pre_block_socket.negative_road.get_lanes(self._global_network)\n self.positive_lane_num = len(self.positive_lanes)\n self.negative_lane_num = len(self.negative_lanes)\n self.positive_basic_lane = self.positive_lanes[-1] # most right or outside lane is the basic lane\n self.negative_basic_lane = self.negative_lanes[-1] # most right or outside lane is the basic lane\n self.lane_width = self.positive_basic_lane.width_at(0)\n\n if self.render:\n # render pre-load\n self.road_texture = self.loader.loadTexture(AssetLoader.file_path(\"textures\", \"sci\", \"color.jpg\"))\n self.road_texture.setMinfilter(SamplerState.FT_linear_mipmap_linear)\n self.road_texture.setAnisotropicDegree(8)\n self.road_normal = self.loader.loadTexture(AssetLoader.file_path(\"textures\", \"sci\", \"normal.jpg\"))\n self.ts_color = TextureStage(\"color\")\n self.ts_normal = TextureStage(\"normal\")\n self.side_texture = self.loader.loadTexture(AssetLoader.file_path(\"textures\", \"side_walk\", \"color.png\"))\n self.side_texture.setMinfilter(SamplerState.FT_linear_mipmap_linear)\n self.side_texture.setAnisotropicDegree(8)\n self.side_normal = self.loader.loadTexture(AssetLoader.file_path(\"textures\", \"side_walk\", \"normal.png\"))\n self.side_walk = self.loader.loadModel(AssetLoader.file_path(\"models\", \"box.bam\"))\n\n def construct_block(self, root_render_np: NodePath, pg_physics_world: PGPhysicsWorld) -> bool:\n \"\"\"\n Randomly Construct a block, if overlap return False\n \"\"\"\n self.set_config(self.PARAMETER_SPACE.sample())\n success = self._sample_topology()\n self._create_in_world()\n self.attach_to_pg_world(root_render_np, pg_physics_world)\n return success\n\n def destruct_block(self, pg_physics_world: PGPhysicsWorld):\n self._clear_topology()\n self.detach_from_pg_world(pg_physics_world)\n self.node_path.removeNode()\n self.dynamic_nodes.clear()\n self.static_nodes.clear()\n\n def _sample_topology(self) -> bool:\n \"\"\"\n Sample a new topology, clear the previous settings at first\n \"\"\"\n self.number_of_sample_trial += 1\n self._clear_topology()\n no_cross = self._try_plug_into_previous_block()\n for i, s in enumerate(self._sockets):\n s.index = i\n self._global_network += self.block_network\n return no_cross\n\n def construct_from_config(self, config: Dict, root_render_np: NodePath, pg_physics_world: PGPhysicsWorld):\n assert set(config.keys()) == self.PARAMETER_SPACE.parameters, \\\n \"Make sure the parameters' name are as same as what defined in parameter_space.py\"\n self.set_config(config)\n success = self._sample_topology()\n self._create_in_world()\n self.attach_to_pg_world(root_render_np, pg_physics_world)\n return success\n\n def get_socket(self, index: int) -> BlockSocket:\n \"\"\"\n Get i th socket\n \"\"\"\n if index < 0 or index >= len(self._sockets):\n raise ValueError(\"Socket of {}: index out of range\".format(self.class_name))\n return self._sockets[index]\n\n def add_reborn_roads(self, reborn_roads: Union[List[Road], Road]):\n \"\"\"\n Use this to add spawn roads instead of modifying the list directly\n \"\"\"\n if isinstance(reborn_roads, List):\n for road in reborn_roads:\n self._add_one_reborn_road(road)\n elif isinstance(reborn_roads, Road):\n self._add_one_reborn_road(reborn_roads)\n else:\n raise ValueError(\"Only accept List[Road] or Road in this func\")\n\n def get_reborn_roads(self):\n return self._reborn_roads\n\n def get_reborn_lanes(self):\n \"\"\"\n return a 2-dim array [[]] to keep the lane index\n \"\"\"\n ret = []\n for road in self._reborn_roads:\n lanes = road.get_lanes(self.block_network)\n ret.append(lanes)\n return ret\n\n def add_sockets(self, sockets: Union[List[BlockSocket], BlockSocket]):\n \"\"\"\n Use this to add sockets instead of modifying the list directly\n \"\"\"\n if isinstance(sockets, BlockSocket):\n self._add_one_socket(sockets)\n elif isinstance(sockets, List):\n for socket in sockets:\n self._add_one_socket(socket)\n\n def set_part_idx(self, x):\n \"\"\"\n It is necessary to divide block to some parts in complex block and give them unique id according to part idx\n \"\"\"\n self.PART_IDX = x\n self.ROAD_IDX = 0 # clear the road idx when create new part\n\n def add_road_node(self):\n \"\"\"\n Call me to get a new node name of this block.\n It is more accurate and recommended to use road_node() to get a node name\n \"\"\"\n self.ROAD_IDX += 1\n return self.road_node(self.PART_IDX, self.ROAD_IDX - 1)\n\n def road_node(self, part_idx: int, road_idx: int) -> str:\n \"\"\"\n return standard road node name\n \"\"\"\n return self._block_name + str(part_idx) + self.DASH + str(road_idx) + self.DASH\n\n def _add_one_socket(self, socket: BlockSocket):\n assert isinstance(socket, BlockSocket), \"Socket list only accept BlockSocket Type\"\n self._sockets.append(socket)\n\n def _add_one_reborn_road(self, reborn_road: Road):\n assert isinstance(reborn_road, Road), \"Spawn roads list only accept Road Type\"\n self._reborn_roads.append(reborn_road)\n\n def _clear_topology(self):\n self._global_network -= self.block_network\n self.block_network.graph.clear()\n self.PART_IDX = 0\n self.ROAD_IDX = 0\n self._reborn_roads.clear()\n self._sockets.clear()\n\n def _try_plug_into_previous_block(self) -> bool:\n \"\"\"\n Try to plug this Block to previous block's socket, return True for success, False for road cross\n \"\"\"\n raise NotImplementedError\n\n \"\"\"------------------------------------- For Render and Physics Calculation ---------------------------------- \"\"\"\n\n def _create_in_world(self):\n \"\"\"\n Create NodePath and Geom node to perform both collision detection and render\n \"\"\"\n self.lane_line_node_path = NodePath(RigidBodyCombiner(self._block_name + \"_lane_line\"))\n self.side_walk_node_path = NodePath(RigidBodyCombiner(self._block_name + \"_side_walk\"))\n self.lane_node_path = NodePath(RigidBodyCombiner(self._block_name + \"_lane\"))\n self.lane_vis_node_path = NodePath(RigidBodyCombiner(self._block_name + \"_lane_vis\"))\n graph = self.block_network.graph\n for _from, to_dict in graph.items():\n for _to, lanes in to_dict.items():\n self._add_lane_surface(_from, _to, lanes)\n for _id, l in enumerate(lanes):\n line_color = l.line_color\n self._add_lane(l, _id, line_color)\n self.lane_line_node_path.flattenStrong()\n self.lane_line_node_path.node().collect()\n\n self.side_walk_node_path.flattenStrong()\n self.side_walk_node_path.node().collect()\n self.side_walk_node_path.hide(CamMask.ScreenshotCam)\n\n # only bodies reparent to this node\n self.lane_node_path.flattenStrong()\n self.lane_node_path.node().collect()\n\n self.lane_vis_node_path.flattenStrong()\n self.lane_vis_node_path.node().collect()\n self.lane_vis_node_path.hide(CamMask.DepthCam | CamMask.ScreenshotCam)\n\n self.node_path = NodePath(self._block_name)\n self.node_path.hide(CamMask.Shadow)\n\n self.side_walk_node_path.reparentTo(self.node_path)\n self.lane_line_node_path.reparentTo(self.node_path)\n self.lane_node_path.reparentTo(self.node_path)\n self.lane_vis_node_path.reparentTo(self.node_path)\n\n self.bounding_box = self.block_network.get_bounding_box()\n\n def _add_lane(self, lane: AbstractLane, lane_id: int, colors: List[Vec4]):\n parent_np = self.lane_line_node_path\n lane_width = lane.width_at(0)\n for k, i in enumerate([-1, 1]):\n line_color = colors[k]\n if lane.line_types[k] == LineType.NONE or (lane_id != 0 and k == 0):\n if isinstance(lane, StraightLane):\n continue\n elif isinstance(lane, CircularLane) and lane.radius != lane_width / 2:\n # for ramp render\n continue\n if lane.line_types[k] == LineType.CONTINUOUS or lane.line_types[k] == LineType.SIDE:\n if isinstance(lane, StraightLane):\n lane_start = lane.position(0, i * lane_width / 2)\n lane_end = lane.position(lane.length, i * lane_width / 2)\n middle = lane.position(lane.length / 2, i * lane_width / 2)\n self._add_lane_line2bullet(lane_start, lane_end, middle, parent_np, line_color, lane.line_types[k])\n elif isinstance(lane, CircularLane):\n segment_num = int(lane.length / Block.CIRCULAR_SEGMENT_LENGTH)\n for segment in range(segment_num):\n lane_start = lane.position(segment * Block.CIRCULAR_SEGMENT_LENGTH, i * lane_width / 2)\n lane_end = lane.position((segment + 1) * Block.CIRCULAR_SEGMENT_LENGTH, i * lane_width / 2)\n middle = (lane_start + lane_end) / 2\n\n self._add_lane_line2bullet(\n lane_start, lane_end, middle, parent_np, line_color, lane.line_types[k]\n )\n # for last part\n lane_start = lane.position(segment_num * Block.CIRCULAR_SEGMENT_LENGTH, i * lane_width / 2)\n lane_end = lane.position(lane.length, i * lane_width / 2)\n middle = (lane_start + lane_end) / 2\n self._add_lane_line2bullet(lane_start, lane_end, middle, parent_np, line_color, lane.line_types[k])\n\n if lane.line_types[k] == LineType.SIDE:\n radius = lane.radius if isinstance(lane, CircularLane) else 0.0\n segment_num = int(lane.length / Block.SIDE_WALK_LENGTH)\n for segment in range(segment_num):\n lane_start = lane.position(segment * Block.SIDE_WALK_LENGTH, i * lane_width / 2)\n lane_end = lane.position((segment + 1) * Block.SIDE_WALK_LENGTH, i * lane_width / 2)\n middle = (lane_start + lane_end) / 2\n self._add_side_walk2bullet(lane_start, lane_end, middle, radius, lane.direction)\n # for last part\n lane_start = lane.position(segment_num * Block.SIDE_WALK_LENGTH, i * lane_width / 2)\n lane_end = lane.position(lane.length, i * lane_width / 2)\n middle = (lane_start + lane_end) / 2\n if norm(lane_start[0] - lane_end[0], lane_start[1] - lane_end[1]) > 1e-1:\n self._add_side_walk2bullet(lane_start, lane_end, middle, radius, lane.direction)\n\n elif lane.line_types[k] == LineType.STRIPED:\n straight = True if isinstance(lane, StraightLane) else False\n segment_num = int(lane.length / (2 * Block.STRIPE_LENGTH))\n for segment in range(segment_num):\n lane_start = lane.position(segment * Block.STRIPE_LENGTH * 2, i * lane_width / 2)\n lane_end = lane.position(\n segment * Block.STRIPE_LENGTH * 2 + Block.STRIPE_LENGTH, i * lane_width / 2\n )\n middle = lane.position(\n segment * Block.STRIPE_LENGTH * 2 + Block.STRIPE_LENGTH / 2, i * lane_width / 2\n )\n\n self._add_lane_line2bullet(\n lane_start, lane_end, middle, parent_np, line_color, lane.line_types[k], straight\n )\n\n if straight:\n lane_start = lane.position(0, i * lane_width / 2)\n lane_end = lane.position(lane.length, i * lane_width / 2)\n middle = lane.position(lane.length / 2, i * lane_width / 2)\n self._add_box_body(lane_start, lane_end, middle, parent_np, lane.line_types[k])\n\n def _add_box_body(self, lane_start, lane_end, middle, parent_np: NodePath, line_type):\n length = norm(lane_end[0] - lane_start[0], lane_end[1] - lane_start[1])\n if LineType.prohibit(line_type):\n node_name = BodyName.Continuous_line\n else:\n node_name = BodyName.Stripped_line\n body_node = BulletRigidBodyNode(node_name)\n body_node.setActive(False)\n body_node.setKinematic(False)\n body_node.setStatic(True)\n body_np = parent_np.attachNewNode(body_node)\n shape = BulletBoxShape(Vec3(length / 2, Block.LANE_LINE_WIDTH / 2, Block.LANE_LINE_THICKNESS))\n body_np.node().addShape(shape)\n body_np.node().setIntoCollideMask(BitMask32.bit(Block.LANE_LINE_COLLISION_MASK))\n self.dynamic_nodes.append(body_np.node())\n\n body_np.setPos(panda_position(middle, 0))\n direction_v = lane_end - lane_start\n theta = -numpy.arctan2(direction_v[1], direction_v[0])\n body_np.setQuat(LQuaternionf(numpy.cos(theta / 2), 0, 0, numpy.sin(theta / 2)))\n\n def _add_lane_line2bullet(\n self,\n lane_start,\n lane_end,\n middle,\n parent_np: NodePath,\n color: Vec4,\n line_type: LineType,\n straight_stripe=False\n ):\n length = norm(lane_end[0] - lane_start[0], lane_end[1] - lane_start[1])\n if length <= 0:\n return\n if LineType.prohibit(line_type):\n node_name = BodyName.Continuous_line\n else:\n node_name = BodyName.Stripped_line\n\n # add bullet body for it\n if straight_stripe:\n body_np = parent_np.attachNewNode(node_name)\n else:\n scale = 2 if line_type == LineType.STRIPED else 1\n body_node = BulletRigidBodyNode(node_name)\n body_node.setActive(False)\n body_node.setKinematic(False)\n body_node.setStatic(True)\n body_np = parent_np.attachNewNode(body_node)\n shape = BulletBoxShape(Vec3(scale / 2, Block.LANE_LINE_WIDTH / 2, Block.LANE_LINE_THICKNESS))\n body_np.node().addShape(shape)\n body_np.node().setIntoCollideMask(BitMask32.bit(Block.LANE_LINE_COLLISION_MASK))\n self.dynamic_nodes.append(body_np.node())\n\n # position and heading\n body_np.setPos(panda_position(middle, 0))\n direction_v = lane_end - lane_start\n theta = -numpy.arctan2(direction_v[1], direction_v[0])\n body_np.setQuat(LQuaternionf(numpy.cos(theta / 2), 0, 0, numpy.sin(theta / 2)))\n\n if self.render:\n # For visualization\n lane_line = self.loader.loadModel(AssetLoader.file_path(\"models\", \"box.bam\"))\n lane_line.getChildren().reparentTo(body_np)\n body_np.setScale(length, Block.LANE_LINE_WIDTH, Block.LANE_LINE_THICKNESS)\n body_np.set_color(color)\n\n def _add_side_walk2bullet(self, lane_start, lane_end, middle, radius=0.0, direction=0):\n length = norm(lane_end[0] - lane_start[0], lane_end[1] - lane_start[1])\n body_node = BulletRigidBodyNode(BodyName.Side_walk)\n body_node.setActive(False)\n body_node.setKinematic(False)\n body_node.setStatic(True)\n side_np = self.side_walk_node_path.attachNewNode(body_node)\n shape = BulletBoxShape(Vec3(1 / 2, 1 / 2, 1 / 2))\n body_node.addShape(shape)\n body_node.setIntoCollideMask(BitMask32.bit(Block.LANE_LINE_COLLISION_MASK))\n self.dynamic_nodes.append(body_node)\n\n if radius == 0:\n factor = 1\n else:\n if direction == 1:\n factor = (1 - self.SIDE_WALK_LINE_DIST / radius)\n else:\n factor = (1 + self.SIDE_WALK_WIDTH / radius) * (1 + self.SIDE_WALK_LINE_DIST / radius)\n direction_v = lane_end - lane_start\n vertical_v = (-direction_v[1], direction_v[0]) / numpy.linalg.norm(direction_v)\n middle += vertical_v * (self.SIDE_WALK_WIDTH / 2 + self.SIDE_WALK_LINE_DIST)\n side_np.setPos(panda_position(middle, 0))\n theta = -numpy.arctan2(direction_v[1], direction_v[0])\n side_np.setQuat(LQuaternionf(numpy.cos(theta / 2), 0, 0, numpy.sin(theta / 2)))\n side_np.setScale(\n length * factor, self.SIDE_WALK_WIDTH, self.SIDE_WALK_THICKNESS * (1 + 0.1 * numpy.random.rand())\n )\n if self.render:\n side_np.setTexture(self.ts_color, self.side_texture)\n self.side_walk.instanceTo(side_np)\n\n def _add_lane_surface(self, from_: str, to_: str, lanes: List):\n \"\"\"\n Add the land surface to world, this surface will record the lane information, like index\n :param from_: From node\n :param to_: To Node\n :param lanes: All lanes of this road\n :return: None\n \"\"\"\n\n # decoration only has vis properties\n need_body = False if (from_, to_) == (Decoration.start, Decoration.end) else True\n if isinstance(lanes[0], StraightLane):\n for index, lane in enumerate(lanes):\n middle = lane.position(lane.length / 2, 0)\n end = lane.position(lane.length, 0)\n direction_v = end - middle\n theta = -numpy.arctan2(direction_v[1], direction_v[0])\n width = lane.width_at(0) + self.SIDE_WALK_LINE_DIST * 2\n length = lane.length\n self._add_lane2bullet(middle, width, length, theta, lane, (from_, to_, index))\n else:\n for index, lane in enumerate(lanes):\n segment_num = int(lane.length / self.CIRCULAR_SEGMENT_LENGTH)\n for i in range(segment_num):\n middle = lane.position(lane.length * (i + .5) / segment_num, 0)\n end = lane.position(lane.length * (i + 1) / segment_num, 0)\n direction_v = end - middle\n theta = -numpy.arctan2(direction_v[1], direction_v[0])\n width = lane.width_at(0) + self.SIDE_WALK_LINE_DIST * 2\n length = lane.length\n self._add_lane2bullet(middle, width, length * 1.3 / segment_num, theta, lane, (from_, to_, index))\n\n def _add_lane2bullet(self, middle, width, length, theta, lane: Union[StraightLane, CircularLane], lane_index):\n \"\"\"\n Add lane visualization and body for it\n :param middle: Middle point\n :param width: Lane width\n :param length: Segment length\n :param theta: Rotate theta\n :param lane: Lane info\n :return: None\n \"\"\"\n segment_np = NodePath(LaneNode(BodyName.Lane, lane, lane_index))\n segment_node = segment_np.node()\n segment_node.setActive(False)\n segment_node.setKinematic(False)\n segment_node.setStatic(True)\n shape = BulletBoxShape(Vec3(length / 2, 0.1, width / 2))\n segment_node.addShape(shape)\n self.static_nodes.append(segment_node)\n segment_np.setPos(panda_position(middle, -0.1))\n segment_np.setQuat(\n LQuaternionf(\n numpy.cos(theta / 2) * numpy.cos(-numpy.pi / 4),\n numpy.cos(theta / 2) * numpy.sin(-numpy.pi / 4), -numpy.sin(theta / 2) * numpy.cos(-numpy.pi / 4),\n numpy.sin(theta / 2) * numpy.cos(-numpy.pi / 4)\n )\n )\n segment_np.reparentTo(self.lane_node_path)\n if self.render:\n cm = CardMaker('card')\n cm.setFrame(-length / 2, length / 2, -width / 2, width / 2)\n cm.setHasNormals(True)\n cm.setUvRange((0, 0), (length / 20, width / 10))\n card = self.lane_vis_node_path.attachNewNode(cm.generate())\n card.setPos(panda_position(middle, numpy.random.rand() * 0.01 - 0.01))\n\n card.setQuat(\n LQuaternionf(\n numpy.cos(theta / 2) * numpy.cos(-numpy.pi / 4),\n numpy.cos(theta / 2) * numpy.sin(-numpy.pi / 4), -numpy.sin(theta / 2) * numpy.cos(-numpy.pi / 4),\n numpy.sin(theta / 2) * numpy.cos(-numpy.pi / 4)\n )\n )\n card.setTransparency(TransparencyAttrib.MMultisample)\n card.setTexture(self.ts_color, self.road_texture)\n\n @staticmethod\n def create_socket_from_positive_road(road: Road) -> BlockSocket:\n \"\"\"\n We usually create road from positive road, thus this func can get socket easily.\n Note: it is not recommended to generate socket from negative road\n \"\"\"\n assert road.start_node[0] != Road.NEGATIVE_DIR and road.end_node[0] != Road.NEGATIVE_DIR, \\\n \"Socket can only be created from positive road\"\n positive_road = Road(road.start_node, road.end_node)\n return BlockSocket(positive_road, -positive_road)\n","sub_path":"pgdrive/scene_creator/blocks/block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":25048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"49242395","text":"import numpy as np\r\nfrom sklearn.neighbors import NearestNeighbors\r\n\r\n\r\nclass KNNClassifier:\r\n def __init__(self, k, strategy, metric, weights, test_block_size=None):\r\n self.strategy = strategy\r\n self.metric = metric\r\n self.k = k\r\n self.weights = weights \r\n self.teach_x = None\r\n self.teach_y = None\r\n self.test_block_size = test_block_size\r\n \r\n def fit(self, X, y=None):\r\n self.teach_x = X\r\n self.teach_y = y.astype(np.int)\r\n if self.strategy != 'my_own':\r\n self.neigh = NearestNeighbors(n_neighbors=self.k, algorithm=self.strategy, metric=self.metric)\r\n self.neigh.fit(X, y) \r\n return self\r\n \r\n def dist(self, X, Y):\r\n x_s = np.sum(X ** 2, axis=1)\r\n y_s = np.sum(Y ** 2, axis=1)\r\n if self.metric == 'euclidean':\r\n Matrix = np.sqrt(np.fabs(x_s[:, np.newaxis] + y_s[np.newaxis, :] - 2 * np.dot(X, Y.T)))\r\n else:\r\n x_s[x_s == 0] = 1\r\n y_s[y_s == 0] = 1\r\n one = np.ones((X.shape[0], Y.shape[0]))\r\n Matrix = one - (np.dot(X, Y.T) / (np.sqrt(x_s)[:, np.newaxis] * np.sqrt(y_s)[np.newaxis, :]))\r\n return Matrix\r\n \r\n def find_kneighbors(self, X, return_distance=True):\r\n if self.test_block_size is None:\r\n if self.strategy != 'my_own':\r\n return self.neigh.kneighbors(X, self.k, return_distance)\r\n else:\r\n M = self.dist(X, self.teach_x)\r\n M_ind = np.argpartition(M, range(self.k))[..., : self.k]\r\n if return_distance:\r\n M_dist = np.partition(M, range(self.k))[..., : self.k]\r\n return tuple((M_dist, M_ind)) \r\n return M_ind\r\n else:\r\n i = 0\r\n j = X.shape[0]\r\n k = 0\r\n s = 0\r\n M_ind = np.zeros((X.shape[0], self.k), dtype=np.int)\r\n if return_distance:\r\n M_dist = np.zeros((X.shape[0], self.k))\r\n while k * self.test_block_size < X.shape[0]:\r\n k += 1\r\n if k * self.test_block_size < j:\r\n s = k * self.test_block_size\r\n else:\r\n s = j\r\n \r\n if self.strategy != 'my_own':\r\n if return_distance:\r\n M_dist[i: s, ...], M_ind[i: s, ...] = self.neigh.kneighbors(X[i: s, ...], self.k, return_distance)\r\n else:\r\n M_ind[i: s, ...] = self.neigh.kneighbors(X[i: s, ...], self.k, return_distance)\r\n i = s\r\n else:\r\n mas = self.dist(X[i: s, ...], self.teach_x)\r\n M_ind[i: s, ...] = np.argpartition(mas, range(self.k))[..., : self.k]\r\n if return_distance:\r\n M_dist[i: s, ...] = np.partition(mas, range(self.k))[..., : self.k]\r\n i = s\r\n \r\n if return_distance:\r\n return tuple((M_dist, M_ind)) \r\n return M_ind\r\n \r\n def predict(self, X):\r\n eps = 1e-05\r\n y = np.arange(X.shape[0], dtype=int)\r\n M_dist, M_ind = self.find_kneighbors(X)\r\n for i in np.arange(X.shape[0]):\r\n if self.weights:\r\n w = 1 / (eps + M_dist[i])\r\n else:\r\n w = None\r\n y[i] = np.argmax(np.bincount(self.teach_y[M_ind[i].astype(np.int)].astype(np.int), weights=w))\r\n return y\r\n","sub_path":"nearest_neighbors.py","file_name":"nearest_neighbors.py","file_ext":"py","file_size_in_byte":3567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"269774049","text":"import pytest\nfrom unittest.mock import patch, PropertyMock\nfrom pytest_splunk_addon.standard_lib.addon_parser.tags_parser import (\n TagsParser,\n)\n\n\noutput_to_build = {\n \"eventtype=fiction_for_tags_positive\": {\n \"tags_positive_event\": \"enabled\",\n \"tags_disabled_event\": \"disabled\",\n },\n \"source=%2Fopt%2Fsplunk%2Fvar%2Flog%2Fsplunk%2Fsplunkd.log\": {\n \"tags_positive_event\": \"enabled\",\n \"tags_disabled_event\": \"disabled\",\n },\n}\n\n\ndef test_tags_can_be_parsed_and_extracted(parser_instance):\n assert list(parser_instance.tags.sects.keys()) == [\n \"eventtype=fiction_for_tags_positive\",\n \"source=%2Fopt%2Fsplunk%2Fvar%2Flog%2Fsplunk%2Fsplunkd.log\",\n ], \"tags can not be called or does not have sects attribute\"\n\n\ndef test_tags_can_be_parsed_and_returned(parser_instance):\n expected_outputs = [\n {\n \"stanza\": 'eventtype=\"fiction_for_tags_positive\"',\n \"tag\": \"tags_positive_event\",\n \"enabled\": True,\n },\n {\n \"stanza\": 'eventtype=\"fiction_for_tags_positive\"',\n \"tag\": \"tags_disabled_event\",\n \"enabled\": False,\n },\n {\n \"stanza\": 'source=\"/opt/splunk/var/log/splunk/splunkd.log\"',\n \"tag\": \"tags_positive_event\",\n \"enabled\": True,\n },\n {\n \"stanza\": 'source=\"/opt/splunk/var/log/splunk/splunkd.log\"',\n \"tag\": \"tags_disabled_event\",\n \"enabled\": False,\n },\n ]\n for i, event in enumerate(parser_instance.get_tags()):\n assert event == expected_outputs[i], \"expeceted event {} not found\".format(\n expected_outputs[i]\n )\n\n\ndef test_get_tags_calls_app_get_config(parser_instance):\n for _ in parser_instance.get_tags():\n pass\n parser_instance.app.get_config.assert_called_once_with(\"tags.conf\")\n\n\ndef test_no_tags_config_file(parser_instance):\n parser_instance.app.get_config.side_effect = OSError\n assert parser_instance.tags is None, \"tags created when no config file exists\"\n\n\ndef test_nothing_returned_when_no_tags_config_file(parser):\n with patch.object(TagsParser, \"tags\", new_callable=PropertyMock) as tags_mock:\n tags_mock.return_value = None\n parser_instance = parser(TagsParser, \"get_config\", {})\n output = [tag for tag in parser_instance.get_tags() if tag]\n assert output == [], \"tags returned when no config file exists\"\n\n\n@pytest.fixture(scope=\"module\")\ndef parsed_output(build_parsed_output):\n return build_parsed_output(output_to_build)\n\n\n@pytest.fixture()\ndef parser_instance(parsed_output, parser):\n return parser(TagsParser, \"get_config\", parsed_output)\n","sub_path":"tests/unit/tests_standard_lib/test_addon_parser/test_tags_parser.py","file_name":"test_tags_parser.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"506081815","text":"#!/usr/bin/env python3\n\n#####################\n# A template script to lunch several suhmo chanelizing runs with different space resolution\n# in order to evaluate the convergence order.\n# User should be in the CONV_ANA folder\n# Usage:\n# python scripts/multi_runs.py \n#####################\nimport sys\nimport os\nimport shutil\nimport csv\nimport numpy as np\n\n# Paths to root and executable definition\nconvergence_dir = os.getcwd()\nroot_dir = os.getcwd() + \"/../\" \nfor f in os.listdir(root_dir):\n if ( f.startswith(\"Suhmo2d\") and f.endswith(\".ex\")):\n executable = f\n# Go into root \nos.chdir(root_dir)\n\n# Cases we want to run\npatterns = [\"1lev\", \"2lev\", \"3lev\", \"4lev\", \"5lev\", \"6lev\", \"7lev\"]\n# Process each runs -- run test case AND PostProc\nfor i, pat in enumerate(patterns):\n print(\"Running case \", pat)\n exec_dir = root_dir + \"/{}/\".format(pat)\n os.chdir(exec_dir)\n os.system(\"mpirun -n 4 ../{} {}\".format(executable, \"input.hydro\"))\n shutil.move(exec_dir+\"pout.0\", exec_dir+\"{}\".format(\"run.output\") )\n print(\".. running pp too \")\n os.system(\"../{} {}\".format(executable, \"input.hydro_pp\"))\n shutil.move(exec_dir+\"pout.0\", exec_dir+\"{}\".format(\"postproc.output\") )\n os.system(\"rm pout.1 pout.2 pout.3 plot000000.2d.hdf5 plot003000.2d.hdf5 plot001500.2d.hdf5\")\n","sub_path":"exec/0_convergence_channelized/CONV_ANA/scripts/multi_runs.py","file_name":"multi_runs.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"164913044","text":"import re\nimport pymongo\nclint = pymongo.MongoClient('localhost', 27017)#mogoDB数据库\nwords = clint['words']\nurls = clint['new_urls']\nyd_words = words['yd']#need chanage\nall_urls=urls['all_urls']#链接结果\nlog=urls['log']\nmid_words = words['mid']#need chanage\n\n\n\n\nurl='url'\n_id='1'\nid='001'\nname='test'\ntype='8'\npv='55'\ncode='test-code'\nnot_null_data='''\n集赞88个\n'''\ndef RM(url,_id,id,name,type,pv,code,not_null_data):\n num=0\n sum=0\n for i in mid_words.find():\n regx=i['word']\n cheack=re.search(regx,str(not_null_data),flags=0)\n if cheack is None:\n sum=sum+1\n if sum==mid_words.count():\n #push_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n safe_data={\n\n '_id': _id,\n 'id': id,\n 'name': name,\n 'type': type,\n 'url': url,\n 'pv': pv,\n 'code': code,\n #'time': push_time,\n 'state': '安全'\n }\n #urls_result.save(safe_data)\n print(' ' + str(id) + '记录安全')\n else:\n print(' ' + str(id) + '传入复审提交')\n #recheack_post(url, _id, id, name, type, pv, code, not_null_data)\n\ndef RP(url,_id,id,name,type,pv,code,not_null_data):\n num=0\n sum=0\n for i in yd_words.find():\n regx=i['word']\n cheack=re.search(regx,str(not_null_data),flags=0)\n if cheack is None:\n sum=sum+1\n if sum==yd_words.count():\n RM(url, _id, id, name, type, pv, code, not_null_data)\n print(' ' + str(id) + '传入医疗判断')\n else:\n #flase_post(url, _id, id, name, type, pv, code, not_null_data)\n log_data={\n\n '_id': _id,\n 'id': id,\n 'name': name,\n 'type': type,\n 'url': url,\n 'pv': pv,\n 'code': code,\n # 'time': push_time,\n 'state': '安全'\n }\n print('waring!!!')\n #log.insert_one(log_data)\n rm_data={\n '_id':_id\n }\n log.remove(rm_data)\nRP(url,_id,id,name,type,pv,code,not_null_data)\n","sub_path":"re_test.py","file_name":"re_test.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"235314509","text":"from flask import Flask\nfrom flask import jsonify\nfrom flask import session, request, render_template, redirect\n\nfrom werkzeug.security import gen_salt\n\nfrom flask_sqlalchemy import SQLAlchemy\n\n\napp = Flask(__name__, template_folder='templates')\napp.debug = True\napp.secret_key = 'secret'\napp.config.update({\n 'SQLALCHEMY_DATABASE_URI': 'sqlite:///db.sqlite',\n})\ndb = SQLAlchemy(app)\n\n\nclass User(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n username = db.Column(db.String(40), unique=True)\n\nclass Client(db.Model):\n client_id = db.Column(db.String(40), primary_key=True)\n client_secret = db.Column(db.String(55), index=True, nullable=False)\n\n # creator of the client\n user_id = db.Column(db.ForeignKey('user.id'))\n user = db.relationship('User')\n _realms = db.Column(db.Text)\n _redirect_uris = db.Column(db.Text)\n\n @property\n def redirect_uris(self):\n if self._redirect_uris:\n return self._redirect_uris.split()\n return []\n\n @property\n def default_redirect_uri(self):\n return self.redirect_uris[0]\n\n @property\n def default_realms(self):\n if self._realms:\n return self._realms.split()\n return []\n\n\nclass Grant(db.Model):\n\tid = db.Column(db.Integer, primary_key=True)\n\tuser_id = db.Column(\n\t\tdb.Integer, db.ForeignKey('user.id', ondelete='CASCADE')\n\t\t)\n\tuser = db.relationship('User')\n\tclient_id = db.Column(\n\t\tdb.String(40), db.ForeignKey('client.client_id'),\n\t\tnullable=False,\n\t\t)\n\tclient = db.relationship('Client')\n\tcode = db.Column(db.String(255), index=True, nullable=False)\n\tredirect_uri = db.Column(db.String(255))\n\texpires = db.Column(db.DateTime)\n\t_scopes = db.Column(db.Text)\n\n\tdef delete(self):\n\t\tdb.session.delete(self)\n\t\tdb.session.commit()\n\t\treturn self\n\n\t@property\n\tdef scopes(self):\n\t\tif self._scopes:\n\t\t\treturn self._scopes.split()\n\t\treturn []\n\nclass Token(db.Model):\n\tid = db.Column(db.Integer, primary_key=True)\n\tclient_id = db.Column(\n\t\tdb.String(40), db.ForeignKey('client.client_id'),\n\t\tnullable=False,\n\t)\n\tclient = db.relationship('Client')\n\n\tuser_id = db.Column(\n\t\tdb.Integer, db.ForeignKey('user.id')\n\t)\n\n\tuser = db.relationship('User')\n\t# currently only bearer is supported\n\ttoken_type = db.Column(db.String(40))\n\taccess_token = db.Column(db.String(255), unique=True)\n\trefresh_token = db.Column(db.String(255), unique=True)\n\texpires = db.Column(db.DateTime)\n\t_scopes = db.Column(db.Text)\n\n\t@property\n\tdef scopes(self):\n\t\tif self._scopes:\n\t\t\treturn self._scopes.split()\n\t\treturn []\t\t\t\n\ndef current_user():\n if 'id' in session:\n uid = session['id']\n return User.query.get(uid)\n return None\n\n\n@app.route('/client')\ndef client():\n user = current_user()\n if not user:\n return redirect('/')\n item = Client(\n client_id=gen_salt(40),\n client_secret=gen_salt(50),\n user_id=user.id,\n _scopes=\"access\"\n )\n db.session.add(item)\n db.session.commit()\n return jsonify(\n client_id=item.client_key,\n client_secret=item.client_secret\n )\n\n@app.route('/', methods=('GET', 'POST'))\ndef home():\n if request.method == 'POST':\n username = request.form.get('username')\n user = User.query.filter_by(username=username).first()\n if not user:\n user = User(username=username)\n db.session.add(user)\n db.session.commit()\n session['id'] = user.id\n return redirect('/')\n user = current_user()\n return render_template('home.html', user=user)\n\n\n\n\nfrom flask_oauthlib.provider import OAuth2Provider\n\n#ACCEPT INSECURE HTTP CONNECTIONS\nimport os\nos.environ['OAUTHLIB_INSECURE_TRANSPORT'] = 'true'\n\noauth = OAuth2Provider(app)\n\n@oauth.clientgetter\ndef load_client(client_id):\n\treturn Client.query.filter_by(client_id=client_id).first()\n\n@oauth.grantgetter\ndef load_grant(client_id, code):\n\treturn Grant.query.filter_by(client_id=client_id, code=code).first()\n@oauth.grantsetter\ndef save_grant(client_id, code, request, *args, **kwargs):\n\t# decide the expires time yourself\n\texpires = datetime.utcnow() + timedelta(seconds=100)\n\tgrant = Grant(\n\t\tclient_id=client_id,\n\t\tcode=code['code'],\n\t\tredirect_uri=request.redirect_uri,\n\t\t_scopes=' '.join(request.scopes),\n\t\tuser=current_user(),\n\t\texpires=expires\n\t)\n\tdb.session.add(grant)\n\tdb.session.commit()\n\treturn grant\n\n\n@oauth.tokengetter\ndef load_token(access_token=None, refresh_token=None):\n\tif access_token:\n\t\treturn Token.query.filter_by(access_token=access_token).first()\n\telif refresh_token:\n\t\treturn Token.query.filter_by(refresh_token=refresh_token).first()\n\n\n@oauth.tokensetter\ndef save_token(token, request, *args, **kwargs):\n\ttoks = Token.query.filter_by(\n\tclient_id=request.client.client_id,\n\t\tuser_id=request.user.id\n\t).all()\n\t# make sure that every client has only one token connected to a user\n\tdb.session.delete(toks)\n\texpires_in = token.pop('expires_in')\n\texpires = datetime.utcnow() + timedelta(seconds=expires_in)\n\ttok = Token(**token)\n\ttok.expires = expires\n\ttok.client_id = request.client.client_id\n\ttok.user_id = request.user.id\n\tdb.session.add(tok)\n\tdb.session.commit()\n\treturn tok\n\n\n\n@app.route('/oauth/token')\n@oauth.token_handler\ndef access_token():\n return None\n\n@app.route('/api/me')\n@oauth.require_oauth()\ndef me():\n return jsonify(username=request.oauth.user.username)\n\n\nif __name__ == '__main__':\n db.create_all()\n app.run()","sub_path":"auth-server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"415903580","text":"'''\ntranscribe.py\n\nOverview of how to implement various transcriptions for offline or\nonline applications.\n\nNote some of these transcription methods require environment variables\nto be setup (e.g. Google). \n'''\nimport os, json, time, datetime \nimport speech_recognition as sr_audio\n\ndef sync_record(filename, duration, fs, channels):\n print('recording')\n myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=channels)\n sd.wait()\n sf.write(filename, myrecording, fs)\n print('done recording')\n \ndef convert_audio(file):\n # convert to proper format with FFmpeg shell script \n filename=file[0:-4]+'_temp.wav'\n command='ffmpeg -i %s -acodec pcm_s16le -ac 1 -ar 16000 %s'%(file,filename)\n os.system(command)\n return filename\n\n\ndef transcribe_google(file):\n # transcribe with google speech API, $0.024/minute \n r=sr_audio.Recognizer()\n with sr_audio.AudioFile(file) as source:\n audio = r.record(source) \n transcript=r.recognize_google_cloud(audio)\n print('google transcript: '+transcript)\n\n return transcript \n \n# transcribe with pocketsphinx (open-source)\ndef transcribe_sphinx(file):\n r=sr_audio.Recognizer()\n with sr_audio.AudioFile(file) as source:\n audio = r.record(source) \n transcript=r.recognize_sphinx(audio)\n print('sphinx transcript: '+transcript)\n \n return transcript \n \n# transcribe with deepspeech (open-source, but can be CPU-intensive)\ndef transcribe_deepspeech(file):\n # get the deepspeech model installed if you don't already have it (1.6 GB model)\n # can be computationally-intensive, so make sure it works on your CPU\n if 'models' not in os.listdir():\n os.system('brew install wget')\n os.system('pip3 install deepspeech')\n os.system('wget https://github.com/mozilla/DeepSpeech/releases/download/v0.1.1/deepspeech-0.1.1-models.tar.gz')\n os.system('tar -xvzf deepspeech-0.1.1-models.tar.gz')\n # make intermediate text file and fetch transcript \n textfile=file[0:-4]+'.txt'\n command='deepspeech models/output_graph.pb %s models/alphabet.txt models/lm.binary models/trie >> %s'%(file,textfile)\n os.system(command)\n transcript=open(textfile).read()\n print('deepspeech transcript: '+transcript)\n # remove text file \n os.remove(textfile)\n \n return transcript \n\ndef transcribe_all(file):\n # get transcripts from all methods and store in .json file \n filename=convert_audio(file)\n\n try:\n google_transcript=transcribe_google(filename)\n except:\n google_transcript=''\n try:\n sphinx_transcript=transcribe_sphinx(filename)\n except:\n sphinx_transcript=''\n try:\n deepspeech_transcript=transcribe_deepspeech(filename)\n except:\n deepspeech_transcript=''\n \n os.remove(filename)\n\n # write to .json\n jsonfilename=file[0:-4]+'.json'\n jsonfile=open(jsonfilename,'w')\n data={\n 'filename': file,\n 'date': str(datetime.datetime.now()), \n 'transcripts': {\n 'google':google_transcript,\n 'sphinx':sphinx_transcript,\n 'deepspeech':deepspeech_transcript}\n }\n json.dump(data,jsonfile)\n\n return jsonfilename\n","sub_path":"chapter_3_featurization/transcribe.py","file_name":"transcribe.py","file_ext":"py","file_size_in_byte":3211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"533231596","text":"from microbit import *\nfrom radio import *\n\nradio.on()\nradio.config(channel=19, power=7)\n\nmy_message = \"Be nice to yu turkeys dis christmas, Cos' turkeys just wanna hav fun, Turkeys are cool, turkeys are wicked, An every turkey has a Mum.\"\n\n# Event loop.\nwhile True:\n radio.send(my_message)\n incoming = radio.receive()\n \n if incoming is not None:\n display.show(incoming)\n print(incoming)\n \n sleep(500)","sub_path":"radio.py","file_name":"radio.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"317272947","text":"# https://leetcode.com/problems/people-whose-list-of-favorite-companies-is-not-a-subset-of-another-list/\n\n# Given the array favoriteCompanies where favoriteCompanies[i] is the list of favorites\n# companies for the ith person (indexed from 0).\n#\n# Return the indices of people whose list of favorite companies is not a subset of any\n# other list of favorites companies. You must return the indices in increasing order.\n#\n# Example 1:\n# Input: favoriteCompanies = [\n# [\"leetcode\",\"google\",\"facebook\"],[\"google\",\"microsoft\"],[\"google\",\"facebook\"],[\"google\"],[\"amazon\"]\n# ]\n# Output: [0,1,4]\n# Explanation:\n# Person with index=2 has favoriteCompanies[2]=[\"google\",\"facebook\"] which is a subset of\n# favoriteCompanies[0]=[\"leetcode\",\"google\",\"facebook\"] corresponding to the person with index 0.\n# Person with index=3 has favoriteCompanies[3]=[\"google\"] which is a subset of\n# favoriteCompanies[0]=[\"leetcode\",\"google\",\"facebook\"] and favoriteCompanies[1]=[\"google\",\"microsoft\"].\n# Other lists of favorite companies are not a subset of another list, therefore, the answer is [0,1,4].\n#\n# Example 2:\n# Input: favoriteCompanies = [[\"leetcode\",\"google\",\"facebook\"],[\"leetcode\",\"amazon\"],[\"facebook\",\"google\"]]\n# Output: [0,1]\n# Explanation: In this case favoriteCompanies[2]=[\"facebook\",\"google\"] is a subset of\n# favoriteCompanies[0]=[\"leetcode\",\"google\",\"facebook\"], therefore, the answer is [0,1].\n#\n# Example 3:\n# Input: favoriteCompanies = [[\"leetcode\"],[\"google\"],[\"facebook\"],[\"amazon\"]]\n# Output: [0,1,2,3]\n\n# Constraints:\n#\n# 1 <= favoriteCompanies.length <= 100\n# 1 <= favoriteCompanies[i].length <= 500\n# 1 <= favoriteCompanies[i][j].length <= 20\n# All strings in favoriteCompanies[i] are distinct.\n# All lists of favorite companies are distinct, that is,\n# If we sort alphabetically each list then favoriteCompanies[i] != favoriteCompanies[j].\n# All strings consist of lowercase English letters only.\n\nclass Solution(object):\n def peopleIndexes(self, favoriteCompanies):\n \"\"\"\n :type favoriteCompanies: List[List[str]]\n :rtype: List[int]\n \"\"\"\n result = []\n for i in range(len(favoriteCompanies)):\n if not any(\n [\n all(company in other for company in favoriteCompanies[i]) and i != idx\n for idx, other in enumerate(favoriteCompanies)\n ]):\n result.append(i)\n\n return result","sub_path":"leetcode/list_is_not_subset_of_other_lists.py","file_name":"list_is_not_subset_of_other_lists.py","file_ext":"py","file_size_in_byte":2425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"367024497","text":"from PyQt5.QtCore import Qt, pyqtSlot\nfrom PyQt5.QtGui import QPixmap, QImage, QPainterPath, QCloseEvent\nfrom PyQt5.QtWidgets import (QWidget, QVBoxLayout, QLabel, QApplication,\n QSlider, QHBoxLayout, QPushButton, QCheckBox, QLineEdit)\nfrom Generic.pyqt5_widgets import CheckedSlider\nfrom Generic.filedialogs import open_directory\n\nimport sys\n\nclass DMDGui:\n def __init__(self):\n self.init_ui()\n\n def init_ui(self):\n # Create window and layout\n app = QApplication(sys.argv)\n self.win = QWidget()\n self.vbox = QVBoxLayout(self.win)\n\n led_chooser = LEDselector(self.win)\n display_chooser = DisplaySelector(self.win)\n rate_chooser = PatternRateSelector(self.win)\n file_chooser = FileSelector(self.win)\n\n self.vbox.addWidget(led_chooser)\n self.vbox.addWidget(display_chooser)\n self.vbox.addWidget(rate_chooser)\n self.vbox.addWidget(file_chooser)\n\n\n\n # Finalise window\n self.win.setWindowTitle('DMD Control Gui')\n self.win.setLayout(self.vbox)\n self.win.show()\n sys.exit(app.exec_())\n\n def upload_button(self):\n self.upload_filename_lblB = QLabel()\n self.upload_filename_lblB.setText(self.filename)\n self.upload_images = QPushButton(\"Upload Images\")\n self.upload_images.clicked.connect(self.upload_callback)\n\n def upload_callback(self):\n pass\n\n def display_chooser_callback(self, display_off, display_on, display_cycle):\n print(display_off, display_on, display_cycle)\n\n\n\n def image_framerate_textbox(self):\n self.image_framerate = QLineEdit()\n self.image_framerate.setText(\"Framerate\")\n self.image_framerate.textChanged[str].connect(self.image_framerate_callback)\n\n def image_framerate_callback(self):\n self.framerate = int(self.image_framerate.text())\n\n\n\n\n def add_button(self):\n # Add Save Button\n widget = QWidget()\n hbox = QHBoxLayout()\n self.saveButton = QPushButton(\"Save\")\n self.saveButton.clicked.connect(self.on_click)\n hbox.addWidget(self.saveButton)\n widget.setLayout(hbox)\n self.vbox.addWidget(widget)\n\nclass LEDselector(QWidget):\n\n def __init__(self, parent):\n self.redval = 0\n self.greenval = 0\n self.blueval = 0\n\n QWidget.__init__(self, parent)\n self.setLayout(QVBoxLayout())\n\n self.red_led = CheckedSlider(parent,\"red\", self.red_led_val, start=0, end=100, dpi=1, initial=self.redval)\n self.green_led = CheckedSlider(parent,\"green\", self.green_led_val, start=0, end=100, dpi=1, initial=self.greenval)\n self.blue_led = CheckedSlider(parent,\"blue\", self.blue_led_val, start=0, end=100, dpi=1, initial=self.blueval)\n\n self.layout().addWidget(self.red_led)\n self.layout().addWidget(self.green_led)\n self.layout().addWidget(self.blue_led)\n\n def red_led_val(self, redval):\n if self.red_led.check:\n self.red_led.slider.setEnabled(True)\n self.redval = redval\n else:\n self.red_led.slider.setEnabled(False)\n\n def green_led_val(self, greenval):\n if self.green_led.check:\n self.green_led.slider.setEnabled(True)\n self.greenval = greenval\n else:\n self.green_led.slider.setEnabled(False)\n\n def blue_led_val(self, blueval):\n if self.blue_led.check:\n self.blue_led.slider.setEnabled(True)\n self.blueval = blueval\n else:\n self.blue_led.slider.setEnabled(False)\n\n\n\n\nclass DisplaySelector(QWidget):\n\n def __init__(self, parent):\n self.off = True\n self.on = False\n self.cycle = False\n\n QWidget.__init__(self, parent)\n self.setLayout(QHBoxLayout())\n\n self.display_off = QPushButton(\"off\")\n self.display_off.setCheckable(True)\n self.display_off.clicked[bool].connect(self.display_off_callback)\n\n self.display_on = QPushButton(\"on\")\n self.display_on.setCheckable(True)\n self.display_on.clicked[bool].connect(self.display_on_callback)\n\n self.display_cycle = QPushButton(\"cycle\")\n self.display_cycle.setCheckable(True)\n self.display_cycle.clicked[bool].connect(self.display_cycle_callback)\n\n self.call_function()\n\n self.layout().addWidget(self.display_off)\n self.layout().addWidget(self.display_on)\n self.layout().addWidget(self.display_cycle)\n\n def display_off_callback(self, state):\n self.off = True\n self.on = False\n self.cycle = False\n self.call_function()\n\n def display_on_callback(self, state):\n self.on = True\n self.off = False\n self.cycle = False\n self.call_function()\n\n def display_cycle_callback(self, state):\n self.cycle = True\n self.off = False\n self.on = False\n self.call_function()\n\n def call_function(self):\n self.display_off.setChecked(self.off)\n self.display_on.setChecked(self.on)\n self.display_cycle.setChecked(self.cycle)\n #self.function(self.off, self.on, self.cycle)\n\nclass PatternRateSelector(QWidget):\n def __init__(self, parent, rate=100, exposure=10, triggered=False):\n QWidget.__init__(self, parent)\n self.rate_val=str(rate)\n self.exposure_val=str(exposure)\n self.triggered_val=triggered\n\n self.directory = 'None Selected'\n self.parent = parent\n hbox = QHBoxLayout()\n\n self.rate = QLineEdit()\n self.rate.setText(self.rate_val)\n self.rate.returnPressed.connect(self.rate_callback)\n self.rate_lbl = QLabel('Rate (Hz)')\n self.exposure = QLineEdit()\n self.exposure.setText(self.exposure_val)\n self.exposure.returnPressed.connect(self.exposure_callback)\n self.exposure_lbl = QLabel('Exposure (us)')\n self.triggered = QCheckBox()\n self.triggered.setChecked(self.triggered_val)\n self.triggered.stateChanged.connect(self.triggered_callback)\n self.triggered_lbl = QLabel('Triggered')\n\n layout_rate = QVBoxLayout()\n layout_exposure = QVBoxLayout()\n layout_triggered = QVBoxLayout()\n hbox.addLayout(layout_rate)\n hbox.addLayout(layout_exposure)\n hbox.addLayout(layout_triggered)\n\n layout_rate.addWidget(self.rate_lbl)\n layout_rate.addWidget(self.rate)\n layout_exposure.addWidget(self.exposure_lbl)\n layout_exposure.addWidget(self.exposure)\n layout_triggered.addWidget(self.triggered_lbl)\n layout_triggered.addWidget(self.triggered)\n\n self.setLayout(hbox)\n\n def rate_callback(self):\n value = self.rate.text()\n\n\n def exposure_callback(self):\n value = self.exposure.text()\n\n\n def triggered_callback(self, state):\n self.triggered_val = bool(self.triggered.checkState())\n print(self.triggered_val)\n if self.triggered_val:\n self.rate.setEnabled(False)\n else:\n self.rate.setEnabled(True)\n\n \n\nclass FileSelector(QWidget):\n def __init__(self, parent):\n QWidget.__init__(self, parent)\n self.directory = 'None Selected'\n self.parent = parent\n self.setLayout(QHBoxLayout())\n\n self.load_file_button = QPushButton(\"Load Patterns\")\n self.load_file_button.clicked[bool].connect(self.load_file_callback)\n self.load_file_label = QLabel(self)\n self.load_file_label.setText(self.directory)\n\n self.layout().addWidget(self.load_file_button)\n self.layout().addWidget(self.load_file_label)\n\n def load_file_callback(self):\n directory = open_directory(caption='Select images', directory='/opt/Microscope/DMD/', parent=self.parent)\n self.directory = directory\n print(self.directory)\n self.load_file_label.setText(self.directory)\n\n\n\n\nif __name__ == '__main__':\n dmd = DMDGui()","sub_path":"dmd2.py","file_name":"dmd2.py","file_ext":"py","file_size_in_byte":7950,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"579806052","text":"from admiral.envs.predator_prey import PredatorPrey_v1\n\nfrom getkey import getkey, keys\n\nkey_action_mapping = {\n keys.DOWN: 0,\n keys.LEFT: 1,\n keys.UP: 2,\n keys.RIGHT: 3,\n keys.ENTER: 4,\n keys.ESC: -1,\n keys.Q: -1\n}\n\nenv = PredatorPrey_v1.build({'view': 4})\n\nobs = env.reset()\nenv.render()\nwhile True:\n action = key_action_mapping.get(getkey(), None)\n if action is not None:\n if action == -1:\n break\n next_obs, _, done, _ = env.step(action)\n env.render()\n obs = next_obs\n if done:\n break","sub_path":"examples/arrowcontrol.py","file_name":"arrowcontrol.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"33119305","text":"# -*- coding: utf-8 -*-\n\n\ndef sumdigits(n):\n sumatory = 0\n for i in str(n):\n sumatory += int(i)\n return sumatory\n\n\ndef gen_pos_result(N, E):\n powers = []\n for i in range(2, N):\n for j in range(2, E):\n power = i ** j\n if sumdigits(power) == i:\n powers.append(power)\n return powers\n\n\ndef result():\n TH = 30\n # L = 10000\n # N = 10000\n\n N = 100\n E = 100\n\n powers = []\n\n powers = gen_pos_result(N, E)\n powers.sort()\n\n # print \"Resultado de 0123: \", powers[TH - 1]\n return powers[TH - 1]\n","sub_path":"projecteuler/problems/d0100/p0119/r0119.py","file_name":"r0119.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"46163212","text":"import pymysql\n\n\n#player hitting\ndef playerHitting(db, *args):\n\n # prepare a cursor object using cursor() method\n cursor = db.cursor()\n mylist = list(args)\n\n #id = inicial + apellido + equipo\n #ej. KHendricksCHC\n id = mylist[1].replace(\" \", \"\")\n id = id + mylist[2]\n\n print(\n id,\n mylist[0],\n mylist[1],\n mylist[2],\n mylist[3],\n mylist[4],\n mylist[5],\n mylist[6],\n mylist[7],\n mylist[8],\n mylist[9],\n mylist[10],\n mylist[11],\n mylist[12],\n mylist[13],\n )\n insertPlayer = \"INSERT INTO player VALUES (\\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", %s, %s, \\\"%s\\\", \\\"%s\\\", %s, \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", %s, NULL, NULL);\" % (\n id,\n mylist[0],\n mylist[1],\n mylist[2],\n mylist[3],\n mylist[4],\n mylist[5],\n mylist[6],\n mylist[7],\n mylist[8],\n mylist[9],\n mylist[10],\n mylist[11],\n mylist[12],\n mylist[13],\n )\n\n print(insertPlayer)\n\n insertPlayerStats = \"INSERT INTO playerstats VALUES (\\\"%s\\\", %s, %s, %s, %s, %s, %s, NULL, NULL, NULL, NULL, NULL, NULL, NULL);\" % (\n id,\n mylist[14],\n mylist[15],\n mylist[16],\n mylist[17],\n mylist[18],\n mylist[19],\n )\n\n try:\n cursor.execute(insertPlayer)\n db.commit()\n cursor.execute(insertPlayerStats)\n db.commit()\n except:\n print(\"Error: Unable to insert hitting data\")\n\n\n#player pitching\ndef playerPitching(db, *args):\n\n # prepare a cursor object using cursor() method\n cursor = db.cursor()\n mylist = list(args)\n\n #id = inicial + apellido + equipo\n #ej. JBeeksBOS\n id = mylist[1].replace(\" \", \"\").split()\n id = id + mylist[2]\n\n insertPlayer = \"INSERT INTO player VALUES (\\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", %s, %s, \\\"%s\\\", \\\"%s\\\", %s, \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", NULL, NULL, %s, NULL);\" % (\n id,\n mylist[0], #foto\n mylist[1], #nombre\n mylist[2], #numero\n mylist[3], #altura\n mylist[4], #edad\n mylist[5], #apodo\n mylist[6], #nacimiento DATE\n mylist[7], #draft year\n mylist[8], #draft team\n mylist[9], #draft round\n mylist[10], #debut DATE\n mylist[11], #equipo\n mylist[12], #rank\n )\n\n insertPlayerStats = \"INSERT INTO playerstats VALUES (\\\"%s\\\", %s, %s, NULL, %s, %s, %s, %s, %s, NULL, NULL, NULL, NULL, NULL);\" % (\n id,\n mylist[13],\n mylist[14],\n mylist[15],\n mylist[16],\n mylist[17],\n mylist[18],\n mylist[19],\n )\n\n try:\n cursor.execute(insertPlayer)\n db.commit()\n cursor.execute(insertPlayerStats)\n db.commit()\n except:\n print(\"Error: Unable to insert pitching data\")\n\n\n#player fielding\ndef playerFielding(db, *args):\n\n # prepare a cursor object using cursor() method\n cursor = db.cursor()\n mylist = list(args)\n\n #id = inicial + apellido + equipo\n #ej. JBeeksBOS\n id = mylist[0].replace(\" \", \"\")\n id = id + mylist[1]\n\n insertPlayer = \"UPDATE player SET POSICION = \\\"%s\\\", RANKF = %s WHERE ID = \\\"%s\\\";\" % (\n mylist[2],\n mylist[3],\n id,\n )\n\n insertPlayerStats = \"UPDATE playerstats SET OPORTUNIDADES = %s, PUTOUT = %s, ASISTENCIAS = %s, ERRORES = %s, AVGERRORES = %s WHERE ID = \\\"%s\\\";\" % (\n mylist[4],\n mylist[5],\n mylist[6],\n mylist[7],\n mylist[8],\n id,\n )\n #print(insertPlayer)\n #print(insertPlayerStats)\n try:\n cursor.execute(insertPlayer)\n db.commit()\n cursor.execute(insertPlayerStats)\n db.commit()\n except:\n print(\"Error: Unable to insert fielding data\")\n\n\ndef teamHitting(db, *args):\n\n # prepare a cursor object using cursor() method\n cursor = db.cursor()\n mylist = list(args)\n\n teamNames = [\n \"Arizona Diamondbacks\", \"Atlanta Braves\", \"Baltimore Orioles\",\n \"Boston Red Sox\", \"Chicago Cubs\", \"Chicago White Sox\",\n \"Cincinnati Reds\", \"Cleveland Indians\", \"Colorado Rockies\",\n \"Detroit Tigers\", \"Houston Astros\", \"Kansas City Royals\",\n \"Los Angeles Angels\", \"Los Angeles Dodgers\", \"Miami Marlins\",\n \"Milwaukee Brewers\", \"Minnesota Twins\", \"New York Mets\",\n \"New York Yankees\", \"Oakland Athletics\", \"Philadelphia Phillies\",\n \"Pittsburgh Pirates\", \"San Diego Padres\", \"San Francisco Giants\",\n \"Seattle Mariners\", \"St. Louis Cardinals\", \"Tampa Bay Rays\",\n \"Texas Rangers\", \"Toronto Blue Jays\", \"Washington Nationals\"\n ]\n\n teamAbbrev = [\n \"ARI\", \"ATL\", \"BAL\", \"BOS\", \"CHC\", \"CWS\", \"CIN\", \"CLE\", \"COL\", \"DET\",\n \"HOU\", \"KC\", \"LAA\", \"LAD\", \"MIA\", \"MIL\", \"MIN\", \"NYM\", \"NYY\", \"OAK\",\n \"PHI\", \"PIT\", \"SD\", \"SF\", \"SEA\", \"STL\", \"TB\", \"TEX\", \"TOR\", \"WSH\"\n ]\n i = 0\n for team in teamNames:\n if (teamNames[i] == mylist[0]):\n abbrev = teamAbbrev[i]\n i += 1\n\n insertTeam = \"INSERT INTO team VALUES (\\\"%s\\\", \\\"%s\\\", \\\"%s\\\", %s, NULL, NULL);\" % (\n mylist[0],\n abbrev,\n mylist[1],\n mylist[9],\n )\n\n insertTeamStats = \"INSERT INTO teamstats VALUES (\\\"%s\\\", %s, %s, %s, %s, %s, %s, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL);\" % (\n mylist[0],\n mylist[3],\n mylist[4],\n mylist[5],\n mylist[6],\n mylist[7],\n mylist[8],\n )\n\n try:\n #print(insertTeam)\n #print(insertTeamStats)\n cursor.execute(insertTeam)\n db.commit()\n cursor.execute(insertTeamStats)\n db.commit()\n except:\n print(\"Error: Unable to insert data\")\n\n\n#\ndef teamPitching(db, *args):\n\n # prepare a cursor object using cursor() method\n cursor = db.cursor()\n mylist = list(args)\n\n #id = inicial + apellido + equipo\n #ej. JBeeksBOS\n\n insertTeam = \"UPDATE team SET ERANKP = %s WHERE ENOMBRE = \\\"%s\\\" ;\" % (\n mylist[7],\n mylist[0],\n )\n\n insertTeamStats = \"UPDATE teamstats SET ERUNSALLOWED = %s, EHITSALLOWED = %s, EHRALLOWED = %s, EAVGHITSA = %s, EWINS = %s , ELOSSES = %s WHERE ENOMBRE = \\\"%s\\\" ;\" % (\n mylist[1],\n mylist[2],\n mylist[3],\n mylist[4],\n mylist[5],\n mylist[6],\n mylist[0],\n )\n\n try:\n # print(insertTeam)\n #print(insertTeamStats)\n cursor.execute(insertTeam)\n db.commit()\n cursor.execute(insertTeamStats)\n db.commit()\n except:\n print(\"Error: Unable to insert data\")\n\n\n#\ndef teamFielding(db, *args):\n\n # prepare a cursor object using cursor() method\n cursor = db.cursor()\n mylist = list(args)\n\n #id = inicial + apellido + equipo\n #ej. JBeeksBOS\n\n insertTeam = \"UPDATE team SET ERANKF = %s WHERE ENOMBRE = \\\"%s\\\" ;\" % (\n mylist[6],\n mylist[0],\n )\n\n insertTeamStats = \"UPDATE teamstats SET EOPORTUNIDADES = %s, EPUTOUT = %s, EASISTENCIAS = %s, EERRORES = %s, EAVGERRORES = %s WHERE ENOMBRE = \\\"%s\\\" ;\" % (\n mylist[1], mylist[2], mylist[3], mylist[4], mylist[5], mylist[0])\n\n try:\n cursor.execute(insertTeam)\n db.commit()\n #print(insertTeamStats)\n cursor.execute(insertTeamStats)\n db.commit()\n except:\n print(\"Error: Unable to insert data\")\n\ndef teamInfo(db, *args):\n\n # prepare a cursor object using cursor() method\n cursor = db.cursor()\n mylist = list(args)\n\n #id = inicial + apellido + equipo\n #ej. JBeeksBOS\n\n insertTeam = \"UPDATE team SET LOGO = \\\"%s\\\", WEB = \\\"%s\\\", ESTADIO = \\\"%s\\\" WHERE ENOMBRE = \\\"%s\\\" ;\" % (\n mylist[0],\n mylist[1],\n mylist[2],\n mylist[3],\n )\n\n try:\n cursor.execute(insertTeam)\n db.commit()\n except:\n print(\"Error: Unable to insert data\")","sub_path":"insert.py","file_name":"insert.py","file_ext":"py","file_size_in_byte":7887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"182490296","text":"# -*- coding: utf-8 -*-\r\nimport jieba\r\nimport jieba.analyse\r\nimport jieba.posseg\r\nfrom bs4 import BeautifulSoup\r\nfrom bs4 import Comment\r\nfrom snownlp import SnowNLP\r\n\r\nfrom nlp_toolkit import NLPToolkit\r\nfrom nlp_toolkit import project_path\r\nfrom smurfs.util.common import is_linux\r\n\r\n\r\nclass TextUtil(object):\r\n nlp = NLPToolkit()\r\n\r\n def __int__(self):\r\n stop_words_file = ('%s/stanford_nlp/dict/stop_words.txt' % project_path)\r\n dict_words_file = ('%s/stanford_nlp/dict/dict.txt.big.txt' % project_path)\r\n jieba.set_dictionary(dict_words_file)\r\n jieba.analyse.set_stop_words(stop_words_file)\r\n jieba.initialize()\r\n if is_linux():\r\n jieba.enable_parallel(10)\r\n\r\n def extract_tags(self, text):\r\n tags_list = jieba.analyse.extract_tags(text, topK=20, allowPOS=('ns', 'n', 'vn', 'v', 'nr', 'nt'))\r\n tags = \",\".join(tags_list)\r\n return tags\r\n\r\n def extract_entity(self, text):\r\n entity = list([])\r\n words = self.nlp.getStanfordSegmenter().segment(text.decode(\"utf-8\"))\r\n tags_list = jieba.analyse.extract_tags(text, topK=20, allowPOS=('ns', 'n', 'vn', 'v', 'nr', 'nt'))\r\n tags_str = \" \".join(tags_list)\r\n\r\n res = self.nlp.getStanfordNERTagger().tag(words.split())\r\n res2 = self.nlp.getStanfordNERTagger().tag(tags_str.split())\r\n res.extend(res2)\r\n for word, tag in res:\r\n tag = tag.lower()\r\n if tag == \"location\" or tag == \"person\" or (tag == \"organization\" and len(word) > 1) or (\r\n tag == \"misc\" and len(word) > 1):\r\n entity.append((\"%s:%s\" % (word, tag)))\r\n return entity\r\n\r\n def extract_desc(self, text, len):\r\n return text[0: len] + \"...\"\r\n\r\n def format_html(self, content):\r\n soup = BeautifulSoup(str(content).replace(\"\\r\", \"\").replace(\"\\n\", \"\").strip(), \"lxml\")\r\n tags = soup.find_all(True)\r\n for tag in tags:\r\n del tag.attrs\r\n if type(tag.string) is Comment:\r\n tag.decompose()\r\n if str(tag.name).lower() == \"script\":\r\n tag.decompose()\r\n if str(tag.name).lower() == \"iframe\":\r\n tag.decompose()\r\n html = str(soup).strip()\r\n return html\r\n\r\n def format_text(self, html):\r\n soup = BeautifulSoup(html, \"lxml\")\r\n return soup.get_text().strip()\r\n\r\n def extract_sentiments(self, text):\r\n s = SnowNLP(text)\r\n\r\n sentiment = float(s.sentiments)\r\n if sentiment > 0.6:\r\n sentiment = \"正\"\r\n elif sentiment > 0.45:\r\n sentiment = \"正\"\r\n else:\r\n sentiment = \"正\"\r\n return sentiment\r\n","sub_path":"public-sentiment/intelligence-system-robot/intelligence-system-robot/smurfs/util/text_util.py","file_name":"text_util.py","file_ext":"py","file_size_in_byte":2720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"493733848","text":"\"\"\"B - Shiritori\nhttps://atcoder.jp/contests/abc109/tasks/abc109_b\nN\nW_1\nW_2\n:\nW_N\n\n>>> main(4, [\"hoge\", \"english\", \"hoge\", \"enigma\"])\nNo\n>>> main(9, [\"basic\", \"c\", \"cpp\", \"php\", \"python\", \"nadesico\", \"ocaml\", \"lua\",\\\n \"assembly\"])\nYes\n>>> main(8, [\"a\", \"aa\", \"aaa\", \"aaaa\", \"aaaaa\", \"aaaaaa\", \"aaa\", \"aaaaaaa\"])\nNo\n>>> main(3, [\"abc\", \"arc\", \"agc\"])\nNo\n\"\"\"\n\n\ndef main(n: int, w: list) -> None:\n if len(set(w)) != n:\n print(\"No\")\n return\n\n last_letter = \"\"\n\n for w_n in w:\n if last_letter == \"\":\n last_letter = w_n[-1]\n continue\n\n if last_letter != w_n[0]:\n print(\"No\")\n return\n\n last_letter = w_n[-1]\n\n print(\"Yes\")\n\n\nif __name__ == \"__main__\":\n n = int(input())\n w = [input() for _ in range(n)]\n\n main(n, w)\n","sub_path":"abc/abc109/abc109_b.py","file_name":"abc109_b.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"235445632","text":"# Loads the ROOT environment and style\nimport ROOT as r\nfrom Analysis.JMEDAS.pileupCorr import *\n\n# Disable pop-up windows for smoother running over ssh\nr.gROOT.SetBatch(True)\n\ninf = r.TFile(\"PileupHistograms.root\",\"READ\")\nc = MakeCanvas(filename=\"PileupHistograms.root\")\nc.Draw()\nc.Print('pileup_reweighting.pdf','pdf')\n","sub_path":"section2/pileup_reweighting.py","file_name":"pileup_reweighting.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"442878742","text":"import numpy as np\nfrom scipy import optimize as opt\nimport matplotlib.pyplot as plt\n\nfrom function.himmelblau import himmelblau\nfrom function.himmelblau import himmelblau_grad_in_point\nfrom function.himmelblau import himmelblau_hess_in_point\nfrom custom_opt.newton import newton\n\nplt.style.use('ggplot')\n\nstack_trace = []\n\ndef callback(x):\n stack_trace.append([x[0], x[1]])\n\n\nx0 = [5, 5]\nstack_trace.append(x0)\nmin = opt.minimize(fun=himmelblau, x0=x0, callback=callback, method=newton, hess=himmelblau_hess_in_point,\n options={\"grad\": himmelblau_grad_in_point,\n \"is_modified\": False})\nprint(\"{}\".format(min.x))\n\nx = np.linspace(-6, 6, 500)\ny = np.linspace(-6, 6, 500)\nxx, yy = np.meshgrid(x, y)\nz = himmelblau((xx, yy))\nlevels = [5, 10, 20, 40, 80, 100, 150, 200, 300, 400]\nplt.contour(xx, yy, z, levels)\nstack_trace_x = []\nstack_trace_y = []\nfor i in range(len(stack_trace)):\n stack_trace_x.append(stack_trace[i][0])\n stack_trace_y.append(stack_trace[i][1])\nplt.plot(stack_trace_x, stack_trace_y, '-o')\nplt.show()\nprint(stack_trace)\n","sub_path":"lab_4/report/newton/himmelblau/report.py","file_name":"report.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"229447797","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\nimport can\n\nfrom canopen_301_402.constants import *\nfrom canopen_301_402.canopen_msgs.msg import CanOpenMessage\nfrom canopen_301_402.canopen_msgs.cob import CanOpenId\n\nclass CanOpenMessageSdoReadResponse(CanOpenMessage):\n \"\"\"docstring for CanOpenMessageSdoReadResponse\"\"\"\n def __init__(self, canopen, node_id, index, subindex, data, original_can_msg=None):\n\n self.canopen = canopen\n\n self.connection_set = self.canopen.connection_set\n service = CanOpenService.sdo_tx\n function_code = self.connection_set.determine_function_code(service)\n\n self._read_data = data\n\n len_read_data = len(self._read_data)\n\n # encode number of data bytes to be written\n sdo_upload_response = ((4-len_read_data)<<2) | CanData.sdo_upload_response\n\n\n data = ([sdo_upload_response, # specifies, that we want to read value from object dictionary\n (index & 0xff), # index low byte\n (index >> 8), # index high byte\n subindex] + # 8 bit subindex\n list(self._read_data))\n\n # initialize CanOpenMessage\n super(CanOpenMessageSdoReadResponse, self).__init__(function_code, node_id, service, data, original_can_msg = original_can_msg)\n \n # set sdo read request message fields\n self._index = index\n self._subindex = subindex\n\n @property\n def index(self):\n return self._index\n \n @property\n def subindex(self):\n return self._subindex\n \n @property\n def read_data(self):\n return self._read_data\n \n \n @classmethod\n def try_from_canopen_msg(cls, msg, canopen):\n '''\n @summary: try to convert from canopen msg\n @param cls: CanOpenMessageSdoReadResponse\n @param msg: CanOpenMessage\n @param canopen: CanOpen\n @result: None, if not possible, CanOpenMessageSdoReadResponse instance\n '''\n\n if ((msg.service == CanOpenService.sdo_tx) and\n (msg.node_id > 0) and \n (len(msg.data) >= 4) and\n ((msg.data[0] & CanData.sdo_upload_response) == CanData.sdo_upload_response)):\n\n index = msg.data[1] + (msg.data[2] << 8)\n subindex = msg.data[3]\n len_response_data = 4-((msg.data[0] >> 2) & 0b11)\n data = msg.data[4:4+len_response_data]\n\n return CanOpenMessageSdoReadResponse(canopen, msg.node_id, index, subindex, data, original_can_msg = msg)\n\n \n else:\n return None\n","sub_path":"src/canopen_301_402/canopen_msgs/msg_sdo_read_response.py","file_name":"msg_sdo_read_response.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"241034447","text":"from goods.models import GoodsChannel\n\ndef get_categories():\n \"\"\"\n\n {\n key-组号：value 这一组下面的所有一二三级\n '1':{\n 'channels当前这一组中所有的一级数据':[组1-cat1,组1-cat2...],\n 'sub_cats':当前这一组里面的所有二级数据\n 'sub_cats':[{id:cat2.id,name:cat2.name,sub_cats:[cat3,cat3]},{}]\n cat2.id,cat2.name,cat2.sub_cats:[cat3,cat3]记录它里面的所有三级\n }\n\n '2':{'channel':[],\n 'sub_cats':[],\n }\n\n }\n \"\"\"\n\n # 定义一个字典变量来包装所有商品类型数据\n\n categories = {}\n # 查询出所有的商品频道数据并前按照组号和列号进行排序\n good_channels_qs = GoodsChannel.objects.order_by('group_id','sequence')\n # 遍历商品频道查询集\n\n for channel in good_channels_qs:\n # 获取当前的组号\n\n group_id = channel.group_id\n # 判断当前组号在大字典中是否存在\n if group_id not in categories:\n categories[group_id] = {'channels':[],'sub_cats':[]}\n # 通过频道获取当前它对应的一级类别模型\n cat1 = channel.category\n # 把频道中的url赋值给对应的一级类别模型\n cat1.url = channel.url\n # 把一级类别数据，添加到指定组channels列表中\n categories[group_id]['channels'].append(cat1)\n\n # 获取当前一级下的所有二级\n cat2_qs = cat1.subs.all()\n # 遍历二级类型的查询集\n for cat2 in cat2_qs:\n # 通过指定的二级获取它下面的所有三级\n cat3_qs = cat2.subs.all()\n # 将当前二级下的所有三级查询集保存到二级的sub_cats属性上\n cat2.sub_cats = cat3_qs\n # 把当前这一组下面的所有二级添加到sub_cats中\n categories[group_id]['sub_cats'].append(cat2)\n return categories","sub_path":"meiduo_mall/apps/contents/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"132708999","text":"from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.db.models.signals import post_save\nfrom django.dispatch import receiver\n# Create your models here.\n\nclass UserProfile(models.Model):\n first_name = models.CharField(max_length=14, verbose_name='Имя')\n last_name = models.CharField(max_length=20, verbose_name='Фамилия')\n user = models.OneToOneField(User, verbose_name='Пользователь', related_name='profile')\n vk_url = models.URLField(verbose_name='Профиль в VK', null=True,)\n twitter_url = models.URLField(verbose_name='Профиль в Twitter', null=True,)\n ok_url = models.URLField(verbose_name='Профиль в Ok', null=True,)\n fb_url = models.URLField(verbose_name='Профиль в FaceBook', null=True,)\n about = models.CharField(max_length=140, null=True, verbose_name='О себе')\n\n def __str__(self):\n return self.first_name + self.last_name\n\n @receiver(post_save, sender=User)\n def create_user_profile(sender, instance, created, **kwargs):\n if created:\n UserProfile.objects.create(user=instance)\n\n @receiver(post_save, sender=User)\n def save_user_profile(sender, instance, **kwargs):\n instance.profile.save()","sub_path":"user_profile/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"434053664","text":"def ft_percent_lower_uppercase(x):\r\n a = 'qwertyuioplkjhgfdsazxcvbnm'\r\n A = 'QWERTYUIOPLKJHGFDSAZXCVBNM'\r\n b = 'ёйцукенгшщзхъэждлорпавыфячсмитьбю'\r\n B = 'ЁЙЦУКЕНГШЩЗХЪЭЖДЛОРПАВЫФЯЧСМИТЬБЮ'\r\n s = 0\r\n h = 0\r\n c = 0\r\n for i in x:\r\n if i in a or i in b:\r\n s += 1\r\n if i in A or i in B:\r\n h += 1\r\n c += 1\r\n if s / h == 1:\r\n return 1\r\n else:\r\n return s / h * 100\r\n","sub_path":"ft_percent_lower_uppercase.py","file_name":"ft_percent_lower_uppercase.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"47166260","text":"import requests\nimport re\nimport reply\n\ndef process(sender, message):\n if message == 'nct:':\n reply.send(sender, \"[Cú pháp] nct: \")\n else:\n parameter = message.replace(\"nct: \", \"\")\n url_valid = re.match(\"https?:\\/\\/www\\.nhaccuatui\\.com\\/bai-hat\\/[-.a-z0-9A-Z]+\\.html\", parameter)\n if url_valid:\n content = requests.get(parameter).text\n xml = re.search(\"https?:\\/\\/www\\.nhaccuatui\\.com\\/flash\\/xml\\?key1=[0-9a-z]{30,40}\", content).group(0)\n headers = {'content-type': 'application/xml'}\n xmlcontent = requests.get(xml, headers=headers).text\n link = re.search(\"https?:\\/\\/[^\\/]+\\.nixcdn\\.com\\/[-_\\/a-zA-Z0-9]+\\.mp3\", xmlcontent).group(0)\n reply.send(sender, \"[Download] \"+link)\n else:\n reply.send(sender, \"Bạn đã nhập sai rồi 😡\")\n\n","sub_path":"modules/nct.py","file_name":"nct.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"581290392","text":"\nimport tweepy\nimport csv\nimport ssl\nimport time\nimport datetime\nfrom requests.exceptions import Timeout, ConnectionError\nfrom requests.packages.urllib3.exceptions import ReadTimeoutError\n\n# Add your Twitter API credentials\nconsumer_key = \"consumer_key\"\nconsumer_secret = \"consumer_secret_key\"\naccess_key = \"Access_key\"\naccess_secret = \"Secret_key\"\n\n# Handling authentication with Twitter\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_key, access_secret)\n\n# Create a wrapper for the API provided by Twitter\napi = tweepy.API(auth)\n\n# Setting up the keywords, hashtag or mentions we want to listen\nkeywords = [\"disaster\", \"wildfire\", \"earthquake\", \"shootout\", \"flood\", \"#forestfire\", \"Cyclone\", \"wildfire\", \"tsunami\", \"typhoon\",\n \"strom\", \"volcanic eruptions\", \"landslide\", \"aftershock\", \"accident\",\"ablaze\", \"airplane accident\",\"ambulance\", \"annihilated\",\n \"armageddon\", \"arson\", \"arsonist\", \"attack\", \"attacked\", \"battle\", \"bioterror\", \"blood\", \"blown up\", \"body bagging\", \"bomb\",\n \"bombed\", \"bombing\", \"bridge collapse\", \"buildings burning\", \"burned\", \"bush fires\", \"casualties\", \"catastrophe\", \"chemical emergency\",\n \"collapse\", \"collision\", \"crash\", \"damage\", \"danger\", \"dead\", \"deaths\", \"debris\", \"demolish\", \"demolition\", \"derail\", \"destroy\", \"destruction\",\n \"detonate\", \"detonation\", \"devastated\", \"drought\", \"dust storm\", \"emergency\", \"emergency plan\", \"engulfed\", \"evacuate\", \"evacuation\",\n \"explode\", \"exploded\", \"explosion\", \"famine\", \"fatal\", \"fatalities\", \"fear\", \"fire\", \"fire truck\", \"flames\", \"flooding\", \"hail\",\n \"hailstorm\", \"hazard\", \"hazardous\", \"heat wave\", \"hellfire\", \"hijack\", \"hijacker\", \"hostage\", \"hurricane\", \"injured\", \"lava\",\n \"lightning\", \"loud bang\", \"mass murder\", \"massacre\", \"mayhem\", \"meltdown\", \"military\", \"mudslide\", \"natural disaster\", \n \"nuclear disaster\", \"obiliterate\", \"oil spill\", \"outbreak\", \"panic\", \"police\", \"radiation emergency\", \"rainstorm\", \"razed\",\n \"refugees\", \"rescue\", \"rescued\", \"riot\", \"sandstorm\", \"screaming\", \"seismic\", \"sinkhole\", \"siren\", \"smoke\", \"snowstorm\", \"stretcher\",\n \"structural failure\", \"suicide bomb\", \"sunk\", \"survive\", \"terrorism\", \"terrorist\", \"threat\", \"thunder\", \"thunderstorm\", \"tornado\",\n \"tragedy\", \"trapped\", \"upheaval\", \"violent storm\", \"volcano\", \"war zone\", \"weapon\", \"whirlwind\", \"wild fire\",\"windstorm\",\n \"wounded\", \"wreck\"]\n\n# Set the name for CSV file where the tweets will be saved\nfilename = \"tweets\"\n\n# We need to implement StreamListener to use Tweepy to listen to Twitter\nclass StreamListener(tweepy.StreamListener):\n\n def on_status(self, status):\n\n try:\n # saves the tweet object\n tweet_object = status\n\n # Checks if its a extended tweet (>140 characters)\n if 'extended_tweet' in tweet_object._json:\n tweet = tweet_object.extended_tweet['full_text']\n else:\n tweet = tweet_object.text\n\n '''Convert all named and numeric character references\n (e.g. >, >, >) in the string s to the\n corresponding Unicode characters'''\n tweet = (tweet.replace('&', '&').replace('<', '<')\n .replace('>', '>').replace('"', '\"')\n .replace(''', \"'\").replace(';', \" \")\n .replace(r'\\u', \" \"))\n\n # Save the keyword that matches the stream\n keyword_matches = []\n for word in keywords:\n if word.lower() in tweet.lower():\n keyword_matches.extend([word])\n\n keywords_strings = \", \".join(str(x) for x in keyword_matches)\n\n # Save other information from the tweet\n user = status.author.screen_name\n location = status.user.location \n timeTweet = status.created_at\n tweetId = status.id\n tweetUrl = \"https://twitter.com/statuses/\" + str(tweetId)\n\n # Exclude retweets, too many mentions and too many hashtags\n if not any((('RT @' in tweet, 'RT' in tweet,\n tweet.count('@') >= 2, tweet.count('#') >= 4))):\n\n # Saves the tweet information in a new row of the CSV file\n writer.writerow([user, location, tweet, keywords_strings, timeTweet,\n tweetUrl])\n\n except Exception as e:\n print('Encountered Exception:', e)\n pass\n\ndef work():\n\n # Opening a CSV file to save the gathered tweets\n with open(filename+\".csv\", 'w') as file:\n global writer\n writer = csv.writer(file)\n\n # Add a header row to the CSV\n writer.writerow([\"User\",\"Location\", \"Tweet\", \"Matched Keywords\", \"Date\", \n \"Tweet URL\"])\n\n # Initializing the twitter streap Stream\n try:\n streamingAPI = tweepy.streaming.Stream(auth, StreamListener())\n streamingAPI.filter(track=keywords)\n\n # Stop temporarily when hitting Twitter rate Limit\n except tweepy.RateLimitError:\n print(\"RateLimitError...waiting ~15 minutes to continue\")\n time.sleep(1001)\n streamingAPI = tweepy.streaming.Stream(auth, StreamListener())\n streamingAPI.filter(track=[keywords])\n\n # Stop temporarily when getting a timeout or connection error\n except (Timeout, ssl.SSLError, ReadTimeoutError,\n ConnectionError) as exc:\n print(\"Timeout/connection error...waiting ~15 minutes to continue\")\n time.sleep(1001)\n streamingAPI = tweepy.streaming.Stream(auth, StreamListener())\n streamingAPI.filter(track=[keywords])\n\n # Stop temporarily when getting other errors\n except tweepy.TweepError as e:\n if 'Failed to send request:' in e.reason:\n print(\"Time out error caught.\")\n time.sleep(1001)\n streamingAPI = tweepy.streaming.Stream(auth, StreamListener())\n streamingAPI.filter(track=[keywords])\n else:\n print(\"Other error with this user...passing\")\n pass\n\nif __name__ == '__main__':\n\n work()\n\nimport pandas as pd\n\nds_today=pd.read_csv(filename + datetime.datetime.now().strftime(\"%Y-%m-%d-%H\")+\".csv\")\nprint(ds_today[\"Tweet\"])\n\ndef clean_data(name):\n # Replace email addresses with 'email'\n processed = name.str.replace(r'^.+@[^\\.].*\\.[a-z]{2,}$',\n 'emailaddress')\n\n # Replace URLs with 'webaddress'\n processed = processed.str.replace(r'^http\\://[a-zA-Z0-9\\-\\.]+\\.[a-zA-Z]{2,3}(/\\S*)?$',\n 'webaddress')\n\n # Replace money symbols with 'moneysymb' (£ can by typed with ALT key + 156)\n processed = processed.str.replace(r'£|\\$', 'moneysymb')\n\n # Replace 10 digit phone numbers (formats include paranthesis, spaces, no spaces, dashes) with 'phonenumber'\n processed = processed.str.replace(r'^\\(?[\\d]{3}\\)?[\\s-]?[\\d]{3}[\\s-]?[\\d]{4}$',\n 'phonenumbr')\n\n # Replace numbers with 'numbr'\n processed = processed.str.replace(r'\\d+(\\.\\d+)?', 'numbr')\n\n # Remove punctuation\n processed = processed.str.replace(r'[^\\w\\d\\s]', ' ')\n\n # Replace whitespace between terms with a single space\n processed = processed.str.replace(r'\\s+', ' ')\n\n # Remove leading and trailing whitespace\n processed = processed.str.replace(r'^\\s+|\\s+?$', '')\n\n # change words to lower case - Hello, HELLO, hello are all the same word\n processed = processed.str.lower()\n \n return processed\n\n\nds_today[\"Tweet\"] = clean_data(ds_today[\"Tweet\"])\nprint(ds_today[\"Tweet\"])\n\nimport nltk\nnltk.download('stopwords')\nnltk.download('wordnet')\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\n\nstop_words = set(stopwords.words(\"english\"))\n\nds_today[\"Tweet\"] = ds_today[\"Tweet\"].apply(lambda x:\" \".join(term for term in x.split() if term not in stop_words))\nps = PorterStemmer()\n\nds_today[\"Tweet\"] = ds_today[\"Tweet\"].apply(lambda x:\" \".join([ps.stem(word) for word in x.split()]))\n\n\nwl = WordNetLemmatizer()\n\nds_today[\"Tweet\"] = ds_today[\"Tweet\"].apply(lambda x:\" \".join([wl.lemmatize(word) for word in x.split()]))\nprint(ds_today[\"Tweet\"])\n\n","sub_path":"tweet_collection/api_implementation.py","file_name":"api_implementation.py","file_ext":"py","file_size_in_byte":8444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"374709427","text":"from PyQt5.QtWidgets import QApplication, QVBoxLayout, QHBoxLayout, QMainWindow, QLabel, QFrame\nfrom PyQt5 import QtCore\n\n\ndef MousePosition(MediaPlayer, position):\n if position.y() > QApplication.desktop().screenGeometry().height()-50 or position.y() < 50: # The cursor is up or down\n Set_visible(MediaPlayer, True)\n else: # The cursor is in the middle\n Set_visible(MediaPlayer, False)\n MediaPlayer.sch_listWidget.setVisible(False)\n\n\ndef fullscreen(MediaPlayer):\n if MediaPlayer.firstTime_fullscreen: # For first time must be craete some labels to fill gaps and frame\n MediaPlayer.Label_temp1 = QLabel(\"\", MediaPlayer)\n MediaPlayer.Label_temp2 = QLabel(\"\", MediaPlayer)\n MediaPlayer.Label_temp3 = QLabel(\"\", MediaPlayer)\n MediaPlayer.Label_temp4 = QLabel(\"\", parent=MediaPlayer)\n MediaPlayer.Label_temp5 = QLabel(\"\", parent=MediaPlayer)\n MediaPlayer.Label_temp7 = QLabel(\"\", parent=MediaPlayer)\n MediaPlayer.frame = QFrame(parent=MediaPlayer)\n MediaPlayer.lay = QHBoxLayout()\n MediaPlayer.frame.setLayout(MediaPlayer.lay)\n\n MediaPlayer.firstTime_fullscreen = False\n\n Set_visible(MediaPlayer, MediaPlayer.isFullScreen())\n MediaPlayer.menubar.setVisible(MediaPlayer.isFullScreen())\n if not MediaPlayer.isFullScreen():\n Remove_from_layout(MediaPlayer)\n MediaPlayer.showFullScreen()\n\n else:\n Add_to_layout(MediaPlayer)\n MediaPlayer.showNormal()\n\n\ndef Remove_from_layout(MediaPlayer):\n \"\"\"To remove everything from their layout\"\"\"\n screenWidth = QApplication.desktop().screenGeometry().width()\n screenHeight = QApplication.desktop().screenGeometry().height()\n # Remove widget of video from its layout and resize , move it\n MediaPlayer.verticalLayout.removeWidget(MediaPlayer.videowidget)\n MediaPlayer.videowidget.resize(screenWidth, screenHeight)\n MediaPlayer.videowidget.move(0, 0)\n\n # Remove Slider and Time_Label and Duration_label from their layout and resize , move it\n MediaPlayer.horizontalLayout_4.removeWidget(MediaPlayer.label_Duration)\n MediaPlayer.horizontalLayout_4.removeWidget(MediaPlayer.Slider_Play)\n MediaPlayer.horizontalLayout_4.removeWidget(MediaPlayer.label_Time)\n\n MediaPlayer.Label_temp4.move(0, screenHeight-50)\n MediaPlayer.Label_temp4.resize(15, 20)\n\n MediaPlayer.label_Time.move(15, screenHeight-50)\n MediaPlayer.label_Time.resize(70, 20)\n\n MediaPlayer.Slider_Play.move(85, screenHeight-50)\n MediaPlayer.Slider_Play.resize(screenWidth-170, 20)\n MediaPlayer.Label_temp5.move(screenWidth-85, screenHeight-50)\n MediaPlayer.Label_temp5.resize(15, 20)\n\n MediaPlayer.label_Duration.move(screenWidth-70, screenHeight-50)\n MediaPlayer.label_Duration.resize(70, 20)\n\n # Remove some pushButton from their layout and resize , move it\n MediaPlayer.horizontalLayout.removeWidget(MediaPlayer.pushButton_Start)\n MediaPlayer.horizontalLayout.removeWidget(MediaPlayer.pushButton_stop)\n MediaPlayer.horizontalLayout.removeWidget(MediaPlayer.pushButton_next)\n MediaPlayer.horizontalLayout.removeWidget(MediaPlayer.pushButton_previous)\n MediaPlayer.horizontalLayout.removeWidget(MediaPlayer.pushButton_open)\n\n MediaPlayer.pushButton_Start.move(screenWidth/2-15, screenHeight-30)\n MediaPlayer.pushButton_stop.move(screenWidth/2+45, screenHeight-30)\n MediaPlayer.pushButton_next.move(screenWidth/2+15, screenHeight-30)\n MediaPlayer.pushButton_previous.move(screenWidth/2-45, screenHeight-30)\n MediaPlayer.pushButton_open.move(screenWidth/2-75, screenHeight-30)\n\n # Remove some pushButton and volume_Slider from their layout and resize , move it\n MediaPlayer.horizontalLayout_5.removeWidget(MediaPlayer.pushButton_Setting)\n MediaPlayer.horizontalLayout_5.removeWidget(\n MediaPlayer.pushButton_Playlist)\n MediaPlayer.horizontalLayout_5.removeWidget(\n MediaPlayer.pushButton_Tag_of_file)\n MediaPlayer.horizontalLayout_3.removeWidget(MediaPlayer.pushButton_volume)\n MediaPlayer.horizontalLayout_3.removeWidget(MediaPlayer.Slider_Volume)\n\n MediaPlayer.pushButton_Setting.move(0, screenHeight-30)\n MediaPlayer.pushButton_Playlist.move(30, screenHeight-30)\n MediaPlayer.pushButton_Tag_of_file.move(screenWidth-158, screenHeight-30)\n MediaPlayer.pushButton_volume.move(screenWidth-129, screenHeight-30)\n MediaPlayer.Slider_Volume.move(screenWidth-100, screenHeight-30)\n MediaPlayer.Slider_Volume.resize(85, 30)\n MediaPlayer.Label_temp7.move(screenWidth-15, screenHeight-30)\n MediaPlayer.Label_temp7.resize(15, 30)\n\n # Move and resize temporary Label to fill gaps\n MediaPlayer.Label_temp1.move(60, screenHeight-30)\n MediaPlayer.Label_temp1.resize(screenWidth/2-75-60, 30)\n\n MediaPlayer.Label_temp2.move(screenWidth/2+75, screenHeight-30)\n MediaPlayer.Label_temp2.resize(screenWidth-158-screenWidth/2-75, 30)\n # Remove search and Bookmark pushButton from their layout\n MediaPlayer.horizontalLayout_6.removeWidget(MediaPlayer.pushButton_Search)\n MediaPlayer.horizontalLayout_6.removeWidget(MediaPlayer.search_lineEdit)\n MediaPlayer.horizontalLayout_2.removeWidget(\n MediaPlayer.pushButton_BookMark)\n MediaPlayer.horizontalLayout_2.removeWidget(MediaPlayer.lineEdit_Bookmark)\n\n # Add search and Bookmark pushButton to the layout and add this layout to frame\n MediaPlayer.lay.addWidget(MediaPlayer.pushButton_BookMark, 0)\n MediaPlayer.lay.addWidget(MediaPlayer.lineEdit_Bookmark, 1)\n MediaPlayer.lay.addWidget(MediaPlayer.Label_temp3, 1)\n\n MediaPlayer.lay.addWidget(MediaPlayer.search_lineEdit, 2)\n MediaPlayer.lay.addWidget(MediaPlayer.pushButton_Search, 3)\n MediaPlayer.frame.resize(screenWidth, 40)\n\n MediaPlayer.frame.move(0, 0)\n\n\ndef Add_to_layout(MediaPlayer):\n \"\"\"To Add everything To their layout\"\"\"\n MediaPlayer.horizontalLayout_5.removeItem(MediaPlayer.horizontalLayout)\n MediaPlayer.horizontalLayout_5.removeItem(MediaPlayer.horizontalLayout_3)\n MediaPlayer.verticalLayout.addWidget(MediaPlayer.videowidget)\n MediaPlayer.verticalLayout.removeItem(MediaPlayer.horizontalLayout_4)\n MediaPlayer.verticalLayout.addLayout(MediaPlayer.horizontalLayout_4)\n MediaPlayer.horizontalLayout_4.addWidget(MediaPlayer.label_Time)\n MediaPlayer.horizontalLayout_4.addWidget(MediaPlayer.Slider_Play)\n MediaPlayer.horizontalLayout_4.addWidget(MediaPlayer.label_Duration)\n MediaPlayer.horizontalLayout_5.addWidget(MediaPlayer.pushButton_Setting)\n MediaPlayer.horizontalLayout_5.addWidget(\n MediaPlayer.pushButton_Playlist, 12, alignment=QtCore.Qt.AlignLeft)\n\n MediaPlayer.horizontalLayout.addWidget(MediaPlayer.pushButton_open)\n MediaPlayer.horizontalLayout.addWidget(MediaPlayer.pushButton_previous)\n MediaPlayer.horizontalLayout.addWidget(MediaPlayer.pushButton_Start)\n MediaPlayer.horizontalLayout.addWidget(MediaPlayer.pushButton_next)\n MediaPlayer.horizontalLayout.addWidget(MediaPlayer.pushButton_stop)\n\n MediaPlayer.horizontalLayout_5.addLayout(MediaPlayer.horizontalLayout)\n\n MediaPlayer.horizontalLayout_5.addWidget(\n MediaPlayer.pushButton_Tag_of_file, 15, QtCore.Qt.AlignRight)\n MediaPlayer.horizontalLayout_3.addWidget(MediaPlayer.pushButton_volume)\n MediaPlayer.horizontalLayout_3.addWidget(MediaPlayer.Slider_Volume)\n MediaPlayer.horizontalLayout_5.addLayout(MediaPlayer.horizontalLayout_3)\n\n MediaPlayer.lay.removeWidget(MediaPlayer.pushButton_Search)\n MediaPlayer.lay.removeWidget(MediaPlayer.pushButton_BookMark)\n MediaPlayer.lay.removeWidget(MediaPlayer.lineEdit_Bookmark)\n MediaPlayer.lay.removeWidget(MediaPlayer.search_lineEdit)\n\n MediaPlayer.horizontalLayout_2.addWidget(MediaPlayer.pushButton_BookMark)\n MediaPlayer.horizontalLayout_2.addWidget(MediaPlayer.lineEdit_Bookmark)\n\n MediaPlayer.horizontalLayout_6.addWidget(MediaPlayer.search_lineEdit)\n MediaPlayer.horizontalLayout_6.addWidget(MediaPlayer.pushButton_Search)\n # Temporary label and frame must be hide when mainwindow is in Normal screen\n MediaPlayer.frame.setVisible(False)\n MediaPlayer.Label_temp1.setVisible(False)\n MediaPlayer.Label_temp2.setVisible(False)\n MediaPlayer.Label_temp4.setVisible(False)\n MediaPlayer.Label_temp5.setVisible(False)\n MediaPlayer.Label_temp7.setVisible(False)\n\n\ndef Set_visible(MediaPlayer, Bool):\n # To SetVisible all widget True or False\n MediaPlayer.Label_temp1.setVisible(Bool)\n MediaPlayer.Label_temp2.setVisible(Bool)\n MediaPlayer.Label_temp4.setVisible(Bool)\n MediaPlayer.Label_temp5.setVisible(Bool)\n MediaPlayer.Label_temp7.setVisible(Bool)\n MediaPlayer.Slider_Play.setVisible(Bool)\n MediaPlayer.label_Time.setVisible(Bool)\n MediaPlayer.label_Duration.setVisible(Bool)\n MediaPlayer.pushButton_stop.setVisible(Bool)\n MediaPlayer.pushButton_previous.setVisible(Bool)\n MediaPlayer.pushButton_open.setVisible(Bool)\n MediaPlayer.pushButton_next.setVisible(Bool)\n MediaPlayer.pushButton_Setting.setVisible(Bool)\n MediaPlayer.pushButton_volume.setVisible(Bool)\n MediaPlayer.Slider_Volume.setVisible(Bool)\n MediaPlayer.pushButton_Playlist.setVisible(Bool)\n MediaPlayer.pushButton_Tag_of_file.setVisible(Bool)\n MediaPlayer.pushButton_Search.setVisible(Bool)\n MediaPlayer.pushButton_BookMark.setVisible(Bool)\n MediaPlayer.frame.setVisible(Bool)\n # For focuse Start button must be last\n MediaPlayer.pushButton_Start.setVisible(Bool)\n","sub_path":"FullScreen/Fullscreen.py","file_name":"Fullscreen.py","file_ext":"py","file_size_in_byte":9472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"177142302","text":"import baostock as bs\nimport pandas as pd\n\nlg = bs.login()\nprint('login respond error_code:'+lg.error_code)\nprint('login respond error_msg:'+lg.error_msg)\n\nrs = bs.query_all_stock(day=\"2021-06-15\")\nprint('query_all_stock respond error_code:'+rs.error_code)\nprint('query_all_stock respond error_msg:'+rs.error_msg)\n\ndata_list = []\nwhile (rs.error_code == '0') & rs.next():\n data_list.append(rs.get_row_data())\nresult = pd.DataFrame(data_list, columns=rs.fields)\n\ncsv = result.to_csv(\"all_stock.csv\", encoding=\"gbk\", index=False)\nprint(result)\nbs.logout()","sub_path":"bao_test.py","file_name":"bao_test.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"171298774","text":"# -*- coding: utf-8 -*-\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import Select\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.common.exceptions import NoAlertPresentException\nimport unittest, time, re\n\nclass UntitledTestCase(unittest.TestCase):\n def setUp(self):\n self.driver = webdriver.Chrome()\n self.driver.implicitly_wait(30)\n self.base_url = \"https://www.katalon.com/\"\n self.verificationErrors = []\n self.accept_next_alert = True\n \n def test_untitled_test_case(self):\n driver = self.driver\n driver.get(\"http://hh.yologoer.com/roomideas.shtml?bcid=bb243cb8-87c5-4418-8b07-245bd8cd3a4b\")\n driver.maximize_window()\n time.sleep(3)\n driver.find_element_by_link_text(u\"所有商品\").click()\n time.sleep(3)\n a=driver.find_element_by_xpath(\"/html/body/div[8]/img[1]\")\n driver.execute_script(\"$(arguments[0]).click()\", a)\n time.sleep(3)\n\n b=driver.find_element_by_xpath(\n u\"(.//*[normalize-space(text()) and normalize-space(.)='卧室织物'])[1]/following::div[1]\")\n\n # driver.find_element_by_xpath(\n # u\"(.//*[normalize-space(text()) and normalize-space(.)='卧室织物'])[1]/following::div[1]\")\n driver.execute_script(\"$(arguments[0]).click()\", b)\n time.sleep(3)\n\n driver.find_element_by_xpath(\"/html/body/div[7]/div/div/div[4]/div/ul/li/a\").click()\n time.sleep(3)\n\n\n driver.find_element_by_xpath(\"//ul[@id='goods_list']/li[12]/div/i\").click()\n # driver.find_element_by_xpath(\"/html/body/div[7]/div[1]/div/ul/li[12]/div/i\").click()\n # c=driver.find_element_by_xpath(\"/html/body/div[7]/div[1]/div/ul/li[3]/a/img\")\n # driver.execute_script(\"$(arguments[0]).click()\", c)\n time.sleep(5)\n\n\n def is_element_present(self, how, what):\n try: self.driver.find_element(by=how, value=what)\n except NoSuchElementException as e: return False\n return True\n \n def is_alert_present(self):\n try: self.driver.switch_to_alert()\n except NoAlertPresentException as e: return False\n return True\n \n def close_alert_and_get_its_text(self):\n try:\n alert = self.driver.switch_to_alert()\n alert_text = alert.text\n if self.accept_next_alert:\n alert.accept()\n else:\n alert.dismiss()\n return alert_text\n finally: self.accept_next_alert = True\n \n def tearDown(self):\n self.driver.quit()\n self.assertEqual([], self.verificationErrors)\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"testaddcart.py","file_name":"testaddcart.py","file_ext":"py","file_size_in_byte":2779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"628702766","text":"import torch.cuda\nimport torch\nfrom torch import nn\n\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n## cuda -> gpu 사용\nprint(\"Using {} devise\".format(device))\n\nclass NeuralNetwork(nn.Module):\n def __init__(self):\n super(NeuralNetwork, self).__init__()\n self.flatten = nn.Flatten()\n self.fc1 = nn.Linear(28*28, 512)\n self.linear_relu_stack = nn.Sequential(\n nn.Linear(28*28, 512),\n nn.ReLU(),\n nn.Linear(512, 512),\n nn.ReLU(),\n nn.Linear(512, 10),\n nn.ReLU()\n )\n\n def forward(self, x):\n x = self.flatten(x)\n # x1 = self.fc1(x)\n # x1 = nn.ReLU()(x1)\n # x2 = x1 + x\n logits = self.linear_relu_stack(x)\n return logits\n\nmodel = NeuralNetwork().to(device)\nprint(model)","sub_path":"python2.py","file_name":"python2.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"199208136","text":"from btcrpc.utils.rpc_calls.bitcoin_cash_rpc import BitcoinCashRpc\nfrom btcrpc.utils.rpc_calls.bitcoin_rpc import BitcoinRpc\nfrom btcrpc.utils.rpc_calls.litecoin_rpc import LitecoinRpc\nfrom btcrpc.utils.rpc_calls.python_bitcoinrpc import PythonBitcoinRpc\n\nclass RpcGenerator(object):\n @staticmethod\n def get_rpc_instance(wallet, currency):\n if currency == 'btc':\n return BitcoinRpc(wallet, currency)\n elif currency == 'bch':\n return BitcoinCashRpc(wallet, currency)\n elif currency == 'ltc':\n return LitecoinRpc(wallet, currency)\n else:\n return PythonBitcoinRpc(wallet, currency)\n\n #Removed code that creates an instance of every rpc instance.\n # return {\n # 'btc': BitcoinRpc(wallet,currency),\n # 'bch': BitcoinCashRpc(wallet,currency),\n # 'ltc': LitecoinRpc(wallet,currency)\n # }.get(currency, PythonBitcoinRpc(wallet,currency))\n","sub_path":"btcxblockchainapi/btcrpc/utils/rpc_calls/rpc_instance_generator.py","file_name":"rpc_instance_generator.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"423549093","text":"'''\n\nWritten by Josh Sobel\njoshsobel89@gmail.com\nhttp://www.joshsobelrigs.com\n\nSelect 1 vertex and run with:\n\nimport js_animPolish.js_stickyMod as js_stickyMod\nreload (js_stickyMod)\njs_stickyMod.run ()\n\n'''\n\n\n\nimport maya.cmds as cmds\nimport maya.mel as mel\nimport sys\n\n\n\n##############\n### Basics ###\n##############\n\n\n\ndef rivet_sel (loc = 0):\n\n\tsel = cmds.ls (sl = 1, fl = 1)\n\tif len(sel) == 1:\n\t\tif '.vtx[' in sel[0]:\n\t\t\tsurf = cmds.ls (sl = 1, o = 1)[0]\n\t\t\triv = rivet (sel[0], surf, loc = loc)\n\t\t\treturn riv\n\t\telse:\n\t\t\tcmds.warning ('Select 1 vertex.')\n\telse:\n\t\tcmds.warning ('Select 1 vertex.')\n\ndef rivet (vtx, surf, outGeo = '', loc = 0):\n\n\t# Get geo\n\tgeo = cmds.listRelatives (surf, p = 1)[0]\n\n\t# Get vertex ID\n\tvtxId = vtx.split ('[')[1]\n\tvtxId = vtxId.split (']')[0]\n\n\t# Get adjacent edge\n\tedges1 = cmds.polyInfo (vtx, vertexToEdge = 1)[0]\n\tedges1 = edges1.split (':')[1]\n\tedges1 = edges1.split (' ')\n\tedges = []\n\tfor i in edges1:\n\t\tif i:\n\t\t\tif '\\n' not in i:\n\t\t\t\tedges.append (int(i))\n\n\t# Create a live curve on edge\n\tcme = cmds.createNode ('curveFromMeshEdge', n = '{}_edge_{}_cry'.format (surf,edges[1]))\n\tcmds.setAttr ('{}.edgeIndex[0]'.format (cme), edges[1])\n\tif outGeo:\n\t\tcmds.connectAttr (outGeo, '{}.inputMesh'.format (cme))\n\telse:\n\t\tcmds.connectAttr ('{}.worldMesh'.format (surf), '{}.inputMesh'.format (cme))\n\n\t# Create a curve info node for edge curve\n\tpoc = cmds.createNode ('pointOnCurveInfo', n = '{}_e{}_v{} _poc'.format (surf,edges[1],vtxId))\n\tcmds.setAttr ('{}.turnOnPercentage'.format (poc), 1)\n\tcmds.connectAttr ('{}.outputCurve'.format (cme), '{}.inputCurve'.format (poc))\n\n\t# Create rivet\n\tif loc == 1:\n\t\triv = cmds.spaceLocator (n = '{}_RIV'.format (geo))[0]\n\telse:\n\t\triv = cmds.createNode ('transform', n = '{}_RIV'.format (geo))\n\tcmds.connectAttr ('{}.result.position'.format (poc), '{}.translate'.format (riv))\n\n\t# Check if at the correct end of the curve\n\tchk1 = cmds.xform (riv, q = 1, ws = 1, t = 1)\n\tchk2 = cmds.xform (vtx, q = 1, ws = 1, t = 1)\n\tif chk1 != chk2 :\n\t\tcmds.setAttr ('{}.parameter'.format (poc), 1)\n\n\t# Create a normal constraint to orient\n\tcnst = cmds.normalConstraint (surf, riv, n = '{}_nrmlCnst'.format (riv), aim = [0,1,0], u = [1,0,0])[0]\n\tif outGeo:\n\t\tcon = cmds.listConnections ('{}.target[0].targetGeometry'.format (cnst), d = 0, p = 1)[0]\n\t\tcmds.disconnectAttr (con, '{}.target[0].targetGeometry'.format (cnst))\n\t\tcmds.connectAttr (outGeo, '{}.target[0].targetGeometry'.format (cnst))\n\tcmds.connectAttr ('{}.tangent'.format (poc), '{}.worldUpVector'.format (cnst))\n\tcmds.setAttr ('{}.hiddenInOutliner'.format (cnst), 1)\n\n\treturn riv\n\n\n\ndef stickyMod (surf, falloff = 1):\n\n\tsm_dfrm,sm_hdl = cmds.softMod (surf)\n\tsm_shp = cmds.listRelatives (sm_hdl, c = 1)[0]\n\tcmds.setAttr ('{}.visibility'.format (sm_shp), 0)\n\tcmds.setAttr ('{}.visibility'.format (sm_hdl), l = 1, k = 0)\n\tcmds.setAttr ('{}.rp'.format (sm_hdl), 0, 0, 0)\n\tcmds.setAttr ('{}.sp'.format (sm_hdl), 0, 0, 0)\n\tcmds.setAttr('{}.falloffAroundSelection'.format (sm_dfrm), 0)\n\tcmds.addAttr (sm_hdl, ln = 'envelope', at = 'float', dv = 1, min = 0, max = 1, k = 1)\n\tcmds.connectAttr ('{}.envelope'.format (sm_hdl), '{}.envelope'.format (sm_dfrm))\n\tcmds.addAttr (sm_hdl, ln = 'falloffRadius', at = 'float', min = .001, dv = falloff, k = 1)\n\tcmds.connectAttr ('{}.falloffRadius'.format (sm_hdl), '{}.falloffRadius'.format (sm_dfrm))\n\tcmds.setAttr ('{}.displayHandle'.format (sm_hdl), 1)\n\tcmds.connectAttr ('{}.worldMatrix'.format (surf), '{}.geomMatrix[0]'.format (sm_dfrm))\n\toutGeo = cmds.listConnections ('{}.input[0].inputGeometry'.format (sm_dfrm), plugs = 1)[0]\n\n\trslt = sm_dfrm, sm_hdl, outGeo\n\treturn rslt\n\n\n\n###########\n### Run ###\n###########\n\n\n\ndef run_sel (falloff = 1, color = 22, sphVis = 1, smooth = 3, lra = 1, mode = 1, scale = 1):\n\n\ttsl = cmds.selectPref (q = 1, tso = 1)\n\tif tsl != 1:\n\t\tcmds.selectPref (tso = 1)\n\n\tsel = cmds.ls (sl = 1, o = 1)\n\tif sel:\n\t\tsurf = sel[0]\n\t\tcomps = cmds.ls (os = 1, fl = 1)\n\t\tbads = []\n\t\tfor c in comps:\n\t\t\tif '.vtx' not in c and '.cv' not in c and '.u' not in c:\n\t\t\t\tbads.append (c)\n\t\tif not bads:\n\t\t\tcomp = comps[-1]\n\t\t\tif '.vtx' in comp:\n\t\t\t\tsm = run_vtx (comp, surf, falloff = falloff, color = color, sphVis = sphVis, lra = lra, mode = mode, scale = scale)\n\t\t\t\tif len(comps) >= 2:\n\t\t\t\t\tcmds.setAttr ('{}.falloffRadius'.format (sm[1]), 100)\n\t\t\t\t\tcmds.setAttr ('{}.sphere'.format (sm[1]), 0)\n\t\t\t\t\tfloodReplace (comps, sm[0])\n\t\t\t\t\tif smooth >= 1:\n\t\t\t\t\t\tfloodSmooth (comps, sm[0], smooth = smooth)\n\t\t\t\t\tsys.stdout.write ('Sticky Mod created in multi-vertex mode. Map has been flooded, Falloff Radius set to 100, and sphere visibility disabled.')\n\t\t\t\telse:\n\t\t\t\t\tsys.stdout.write ('Sticky Mod created!')\n\t\t\t\tcmds.select (sm[1])\n\t\t\telif '.u' in comp:\n\t\t\t\tif len(comps) == 1:\n\t\t\t\t\tsm = run_cp (comp, surf, falloff = falloff, color = color, sphVis = sphVis, lra = lra, mode = mode, scale = scale)\n\t\t\t\t\tsys.stdout.write ('Sticky Mod created!')\n\t\t\t\telse:\n\t\t\t\t\tcmds.warning ('Select at least 1 vertex on 1 mesh, or 1 curve point on 1 curve.')\n\t\t\telif '.cv' in comp:\n\t\t\t\tcmds.warning ('Select at least 1 vertex on 1 mesh, or 1 curve point on 1 curve.')\n\t\t\telse:\n\t\t\t\tcmds.warning ('Select at least 1 vertex on 1 mesh, or 1 curve point on 1 curve.')\n\t\telse:\n\t\t\tcmds.warning ('Select at least 1 vertex on 1 mesh, or 1 curve point on 1 curve.')\n\telse:\n\t\tcmds.warning ('Select at least 1 vertex on 1 mesh, or 1 curve point on 1 curve.')\n\n\ndef run_vtx (vtx, surf, falloff = 1, color = 22, sphVis = 1, lra = 1, mode = 1, scale = 1):\n\n\t# Create soft mod\n\tsm = stickyMod (surf, falloff = falloff)\n\tsm_dfrm, sm_hdl, outGeo = sm\n\n\t# Create rivet\n\triv = rivet (vtx, surf, outGeo = outGeo)\n\n\t# Create offset to drive falloff center\n\toff = cmds.circle (n = '{}_OFF'.format (sm_dfrm))[0]\n\tcmds.setAttr ('{}.sx'.format (off), .75)\n\tcmds.setAttr ('{}.sy'.format (off), .75)\n\tcmds.setAttr ('{}.sz'.format (off), .75)\n\tcmds.makeIdentity (off, a = 1, s = 1)\n\tcnst = cmds.parentConstraint (riv, off)\n\tcmds.delete (cnst)\n\tcmds.parent (off, riv)\n\tcmds.makeIdentity (off, a = 1, t = 1, r = 1, s = 1)\n\tinf = cmds.createNode ('transform', n = '{}_INF'.format (sm_hdl), p = riv)\n\tcmds.setAttr ('{}.hiddenInOutliner'.format (inf), 1)\n\tcmds.setAttr ('{}.inheritsTransform'.format (inf), 0)\n\tcmds.pointConstraint (off, inf)\n\tcmds.connectAttr ('{}.t'.format (inf), '{}.falloffCenter'.format (sm_dfrm), force = 1)\n\n\tcmds.setAttr ('{}.sx'.format (off), l = 1, k = 0)\n\tcmds.setAttr ('{}.sy'.format (off), l = 1, k = 0)\n\tcmds.setAttr ('{}.sz'.format (off), l = 1, k = 0)\n\tcmds.setAttr ('{}.v'.format (off), l = 1, k = 0)\n\n\t# Parent soft mod under rivet\n\tcmds.parent (sm_hdl, off, r = 1)\n\tcmds.connectAttr ('{}.pim'.format (sm_hdl), '{}.bindPreMatrix'.format (sm_dfrm), force = 1)\n\n\t# Finalize\n\tsm = [sm_dfrm, sm_hdl]\n\tsm = finalize (sm, surf, color = color, sphVis = sphVis, lra = lra, mode = mode, scale = scale)\n\n\t# Return result\n\treturn sm\n\n\n\ndef run_cp (cp, surf, falloff = 1, color = 22, sphVis = 1, lra = 1, mode = 1, scale = 1):\n\n\t# Get geo\n\tgeo = cmds.listRelatives (surf, p = 1)[0]\n\n\t# Create soft mod\n\tsm = stickyMod (surf, falloff = falloff)\n\tsm_dfrm, sm_hdl, outGeo = sm\n\n\t# Create pointOnCurveInfo\n\tpoc = cmds.createNode ('pointOnCurveInfo', n = '{}_{}_POC'.format (surf,sm_hdl))\n\tparam = cp.split ('[')[1]\n\tparam = float(param.split (']')[0])\n\tcmds.setAttr ('{}.parameter'.format (poc), param)\n\tcmds.connectAttr (outGeo, '{}.inputCurve'.format (poc))\n\n\t# Create rivet\n\triv = cmds.createNode ('transform', n = '{}_RIV'.format (geo))\n\tcmds.connectAttr ('{}.position'.format (poc), '{}.translate'.format (riv))\n\n\t# Create offset to drive falloff center\n\toff = cmds.circle (n = '{}_OFF'.format (sm_dfrm))[0]\n\tcmds.setAttr ('{}.sx'.format (off), .75)\n\tcmds.setAttr ('{}.sy'.format (off), .75)\n\tcmds.setAttr ('{}.sz'.format (off), .75)\n\tcmds.makeIdentity (off, a = 1, s = 1)\n\tcnst = cmds.parentConstraint (riv, off)\n\tcmds.delete (cnst)\n\tcmds.parent (off, riv)\n\tcmds.makeIdentity (off, a = 1, t = 1, r = 1, s = 1)\n\tinf = cmds.createNode ('transform', n = '{}_INF'.format (sm_hdl), p = riv)\n\tcmds.setAttr ('{}.hiddenInOutliner'.format (inf), 1)\n\tcmds.setAttr ('{}.inheritsTransform'.format (inf), 0)\n\tcmds.pointConstraint (off, inf)\n\tcmds.connectAttr ('{}.t'.format (inf), '{}.falloffCenter'.format (sm_dfrm), force = 1)\n\n\tcmds.setAttr ('{}.sx'.format (off), l = 1, k = 0)\n\tcmds.setAttr ('{}.sy'.format (off), l = 1, k = 0)\n\tcmds.setAttr ('{}.sz'.format (off), l = 1, k = 0)\n\tcmds.setAttr ('{}.v'.format (off), l = 1, k = 0)\n\n\t# Parent soft mod under rivet\n\tcmds.parent (sm_hdl, off, r = 1)\n\tcmds.connectAttr ('{}.pim'.format (sm_hdl), '{}.bindPreMatrix'.format (sm_dfrm), force = 1)\n\n\t# Finalize\n\tsm = [sm_dfrm, sm_hdl]\n\tsm = finalize (sm, surf, color = color, sphVis = sphVis, lra = lra, mode = mode, scale = scale)\n\n\t# Return result\n\treturn sm\n\n\n\ndef finalize (sm, surf, color = 22, sphVis = 1, lra = 1, mode = 1, scale = 1):\n\n\t# Get names\n\tsm_dfrm = sm[0]\n\tsm_hdl = sm[1]\n\toff = cmds.listRelatives (sm_hdl, p = 1)[0]\n\triv = cmds.listRelatives (off, p = 1)[0]\n\tgeo = cmds.listRelatives (surf, p = 1)[0]\n\tif ':' in geo:\n\t\tgeo = geo.split (':')[1]\n\n\t# Falloff\n\tcmds.addAttr (sm_hdl, ln = 'falloffMode_keyable', nn = 'Falloff Mode', at = 'enum', en = 'Volume:Surface', k = 1)\n\tcmds.connectAttr ('{}.falloffMode_keyable'.format (sm_hdl), '{}.falloffMode'.format (sm_dfrm))\n\n\t# Falloff curve\n\tcmds.addAttr (sm_hdl, ln = 'falloffCurve', at = 'enum', en = 'None:Linear:Smooth:Spline', k = 1, dv = 2)\n\tcmds.connectAttr ('{}.falloffCurve'.format (sm_hdl), '{}.falloffCurve[0].falloffCurve_Interp'.format (sm_dfrm))\n\n\t# Sphere\n\tshp = cmds.listRelatives (off, s = 1)[0]\n\tcrv2, make2 = cmds.circle (n = shp.replace ('Shape','2'), nr = [0,1,0])\n\tshp2 = cmds.listRelatives (crv2, s = 1)[0]\n\tcmds.setAttr ('{}.sx'.format (crv2), .75)\n\tcmds.setAttr ('{}.sy'.format (crv2), .75)\n\tcmds.setAttr ('{}.sz'.format (crv2), .75)\n\t#cmds.setAttr ('{}.ihi'.format (make2), 0)\n\tcmds.makeIdentity (crv2, a = 1, s = 1)\n\tcrv3, make3 = cmds.circle (n = shp.replace ('Shape','3'), nr = [1,0,0])\n\tshp3 = cmds.listRelatives (crv3, s = 1)[0]\n\tcmds.setAttr ('{}.sx'.format (crv3), .75)\n\tcmds.setAttr ('{}.sy'.format (crv3), .75)\n\tcmds.setAttr ('{}.sz'.format (crv3), .75)\n\t#cmds.setAttr ('{}.ihi'.format (make3), 0)\n\tcmds.makeIdentity (crv3, a = 1, s = 1)\n\tcmds.parent (shp2, off, add = 1, s = 1)\n\tcmds.parent (shp3, off, add = 1, s = 1)\n\n\thist = cmds.listHistory (shp)\n\tfor h in hist:\n\t\tif 'makeNurbCircle' in h:\n\t\t\tmake = h\n\t\t\tcmds.setAttr ('{}.ihi'.format (h), 0)\n\t\tif 'transformGeometry' in h:\n\t\t\tcmds.setAttr ('{}.ihi'.format (h), 0)\n\n\thist = cmds.listHistory (shp2)\n\tfor h in hist:\n\t\tif 'makeNurbCircle' in h:\n\t\t\tmake2 = h\n\t\t\tcmds.setAttr ('{}.ihi'.format (h), 0)\n\t\tif 'transformGeometry' in h:\n\t\t\tcmds.setAttr ('{}.ihi'.format (h), 0)\n\n\thist = cmds.listHistory (shp3)\n\tfor h in hist:\n\t\tif 'makeNurbCircle' in h:\n\t\t\tmake3 = h\n\t\t\tcmds.setAttr ('{}.ihi'.format (h), 0)\n\t\tif 'transformGeometry' in h:\n\t\t\tcmds.setAttr ('{}.ihi'.format (h), 0)\n\n\tcmds.connectAttr ('{}.falloffRadius'.format (sm_hdl), '{}.radius'.format (make))\n\tcmds.connectAttr ('{}.falloffRadius'.format (sm_hdl), '{}.radius'.format (make2))\n\tcmds.connectAttr ('{}.falloffRadius'.format (sm_hdl), '{}.radius'.format (make3))\n\tcmds.delete (crv2, crv3)\n\tcmds.addAttr (sm_hdl, ln = 'sphere', at = 'bool', dv = sphVis)\n\tcmds.setAttr ('{}.sphere'.format (sm_hdl), cb = 1)\n\tcmds.connectAttr ('{}.sphere'.format (sm_hdl), '{}.v'.format (shp))\n\tcmds.connectAttr ('{}.sphere'.format (sm_hdl), '{}.v'.format (shp2))\n\tcmds.connectAttr ('{}.sphere'.format (sm_hdl), '{}.v'.format (shp3))\n\n\t# LRA\n\tcmds.addAttr (sm_hdl, ln = 'localRotationAxis', at = 'bool', dv = lra)\n\tcmds.setAttr ('{}.localRotationAxis'.format (sm_hdl), cb = 1)\n\tcmds.connectAttr ('{}.localRotationAxis'.format (sm_hdl), '{}.displayLocalAxis'.format (sm_hdl))\n\n\t# Nurbs Curve\n\tcrv = cmds.curve (d = 1, p = [(-0.5,0.5,0.5), (0.5,0.5,0.5), (0.5,0.5,-0.5), (-0.5,0.5,-0.5), (-0.5,0.5,0.5), (-0.5,-0.5,0.5), (-0.5,-0.5,-0.5), (0.5,-0.5,-0.5), (0.5,-0.5,0.5), (-0.5,-0.5,0.5), (0.5,-0.5,0.5), (0.5,0.5,0.5), (0.5,0.5,-0.5), (0.5,-0.5,-0.5), (-0.5,-0.5,-0.5), (-0.5,0.5,-0.5)])\n\tcrv_shp = cmds.listRelatives (crv, s = 1)[0]\n\tfor a in ['sx','sy','sz']:\n\t\tcmds.setAttr ('{}.{}'.format (crv,a), .4)\n\tcmds.makeIdentity (crv, a = 1, s = 1)\n\tfor a in ['sx','sy','sz']:\n\t\tcmds.setAttr ('{}.{}'.format (crv,a), scale)\n\tcmds.makeIdentity (crv, a = 1, s = 1)\n\tcmds.parent (crv, off)\n\tfor a in ['tx','ty','tz','rx','ry','rz','sx','sy','sz']:\n\t\tif 't' in a or 'r' in a:\n\t\t\tcmds.setAttr ('{}.{}'.format (crv,a), 0)\n\t\telif 's' in a:\n\t\t\tcmds.setAttr ('{}.{}'.format (crv,a), 1)\n\tcmds.parent (crv_shp, sm_hdl, add = 1, s = 1)\n\tcmds.delete (crv)\n\tshps = cmds.listRelatives (sm_hdl, s = 1)\n\tfor shp1 in shps:\n\t\tif 'curve' in shp1:\n\t\t\tcrv_shp2 = shp1\n\tif mode == 1:\n\t\tvis_dv = 0\n\telif mode == 2:\n\t\tvis_dv = 1\n\tcmds.addAttr (sm_hdl, ln = 'visualizationMode', at = 'enum', en = 'Handle:Nurbs Curve', dv = vis_dv)\n\tcmds.setAttr ('{}.visualizationMode'.format (sm_hdl), cb = 1)\n\tcmds.connectAttr ('{}.visualizationMode'.format (sm_hdl), '{}.v'.format (crv_shp2))\n\trev = cmds.createNode ('reverse', n = '{}_REV'.format (sm_hdl))\n\tcmds.setAttr ('{}.ihi'.format (rev), 0)\n\tcmds.connectAttr ('{}.visualizationMode'.format (sm_hdl), '{}.inputX'.format (rev))\n\tcmds.connectAttr ('{}.outputX'.format (rev), '{}.displayHandle'.format (sm_hdl))\n\n\t# Color\n\tcmds.setAttr ('{}.overrideEnabled'.format (sm_hdl), 1)\n\tcmds.setAttr ('{}.overrideColor'.format (sm_hdl), color)\n\n\t# Group\n\tgrp = 'stickyMods_GRP'\n\tif not cmds.objExists (grp):\n\t\tcmds.group (n = grp, em = 1, w = 1)\n\tgeoGrp = '{}_MOD_GRP'.format (geo)\n\tif not cmds.objExists (geoGrp):\n\t\tcmds.group (n = geoGrp, em = 1, w = 1)\n\t\tcmds.parent (geoGrp, grp)\n\tcmds.parent (riv, geoGrp)\n\n\t# Layer\n\tdl = 'stickyMods_LAY'\n\tif not cmds.objExists (dl):\n\t\tcmds.createDisplayLayer (n = dl, e = 1)\n\t\tcmds.editDisplayLayerMembers (dl, grp)\n\t\tcmds.setAttr ('{}.color'.format (dl), color)\n\ttry:\n\t\tcmds.disconnectAttr ('stickyMods_LAY.drawInfo', '{}.drawOverride'.format (shp))\n\t\tcmds.disconnectAttr ('stickyMods_LAY.drawInfo', '{}.drawOverride'.format (shp2))\n\t\tcmds.disconnectAttr ('stickyMods_LAY.drawInfo', '{}.drawOverride'.format (shp3))\n\texcept:\n\t\tpass\n\tcmds.setAttr ('{}.overrideEnabled'.format (shp), 1)\n\tcmds.setAttr ('{}.overrideColor'.format (shp), 29)\n\tcmds.setAttr ('{}.overrideEnabled'.format (shp2), 1)\n\tcmds.setAttr ('{}.overrideColor'.format (shp2), 29)\n\tcmds.setAttr ('{}.overrideEnabled'.format (shp3), 1)\n\tcmds.setAttr ('{}.overrideColor'.format (shp3), 29)\n\n\t# Cleanup\n\tsms = cmds.ls ('{}_*_MOD'.format (geo))\n\tif sms:\n\t\tcnt = len(sms) + 1\n\telse:\n\t\tcnt = 1\n\tsm_hdl = cmds.rename (sm_hdl, '{}_{}_MOD'.format (geo,cnt))\n\tcmds.rename (crv_shp2, '{}_{}_CRVShape'.format (geo,cnt))\n\tsm_dfrm = '{}SoftMod'.format (sm_hdl)\n\tcmds.rename (riv, '{}_{}_MOD_RIV'.format (geo,cnt))\n\tcmds.rename (off, '{}_{}_MOD_OFF'.format (geo,cnt))\n\tsm_dfrm = cmds.rename (sm_dfrm, '{}_{}_MOD_DFRM'.format (geo,cnt))\n\tcmds.select (sm_hdl)\n\tsm = [sm_dfrm, sm_hdl]\n\n\treturn sm\n\n\n\n#####################\n### Utility Funcs ###\n#####################\n\n\n\ndef floodReplace (comps, sm_dfrm):\n\n\tobj = str(comps[0]).split ('.')[0]\n\tcmds.hilite (obj, replace = 1)\n\tcmds.select (comps)\n\n\tmel.eval ('artSetToolAndSelectAttr( \"artAttrCtx\", \"softMod.{}.weights\" );'.format (sm_dfrm))\n\tmel.eval ('artAttrInitPaintableAttr;')\n\tmel.eval ('artAttrValues artAttrContext;')\n\tmel.eval ('artAttrPaintOperation artAttrCtx Replace;')\n\tmel.eval ('artAttrCtx -e -opacity 1 `currentCtx`;')\n\tmel.eval ('artAttrCtx -e -value 0 `currentCtx`;')\n\tcmds.select (comps)\n\tmel.eval ('invertSelection;')\n\tmel.eval ('artAttrCtx -e -clear `currentCtx`;')\n\n\tcmds.select (obj)\n\tmel.eval ('SelectTool;')\n\n\n\ndef floodSmooth (comps, sm_dfrm, smooth = 3):\n\n\tobj = str(comps[0]).split ('.')[0]\n\tcmds.select (obj)\n\tcmds.hilite (obj, replace = 1)\n\tcmds.select ('{}.vtx[*]'.format (obj))\n\n\tmel.eval ('artSetToolAndSelectAttr( \"artAttrCtx\", \"softMod.{}.weights\" );'.format (sm_dfrm))\n\tmel.eval ('artAttrInitPaintableAttr;')\n\tmel.eval ('artAttrValues artAttrContext;')\n\tmel.eval ('artAttrPaintOperation artAttrCtx Smooth;')\n\tmel.eval ('artAttrCtx -e -opacity 1 `currentCtx`;')\n\tcmds.select ('{}.vtx[*]'.format (obj))\n\tfor i in range (smooth):\n\t\tmel.eval ('artAttrCtx -e -clear `currentCtx`;')\n\n\tcmds.select (obj)\n\tmel.eval ('SelectTool;')\n\n\n\ndef addGeo_sel ():\n\n\tsel = cmds.ls (sl = 1)\n\tadded = 0\n\tif len(sel) >= 2:\n\t\tsm = ''\n\t\tfor i in sel:\n\t\t\tif i.endswith ('_MOD'):\n\t\t\t\tsm_hdl = i\n\t\t\t\tcons = cmds.listConnections (sm_hdl)\n\t\t\t\tfor c in cons:\n\t\t\t\t\tif i in c and (cmds.nodeType (c) == 'softMod'):\n\t\t\t\t\t\tsm = c\n\t\tif sm:\n\t\t\tfor geo in sel:\n\t\t\t\tif geo != sm_hdl:\n\t\t\t\t\ttry:\n\t\t\t\t\t\taddGeo (sm, geo)\n\t\t\t\t\t\tadded = 1\n\t\t\t\t\texcept:\n\t\t\t\t\t\tpass\n\t\telse:\n\t\t\tcmds.warning (\"No stickyMod in selection.\")\n\n\tif added == 1 and len(sel) >= 3:\n\t\tsys.stdout.write (\"Added multiple meshes to '{}'.\".format (sm))\n\n\n\ndef addGeo (sm, geo):\n\n\tsurf = cmds.listRelatives (geo, s = 1)[0]\n\tcnt = 0\n\tyes = 0\n\twhile yes == 0:\n\t\tif cnt <= 100:\n\t\t\ttry:\n\t\t\t\tcmds.deformer (sm, e = 1, g = geo)\n\t\t\t\tif not cmds.isConnected ('{}.worldMatrix'.format (surf), '{}.geomMatrix[{}]'.format (sm,cnt)):\n\t\t\t\t\tcmds.connectAttr ('{}.worldMatrix'.format (surf), '{}.geomMatrix[{}]'.format (sm,cnt))\n\t\t\t\t\tsys.stdout.write (\"Added '{}' to '{}'.\".format (geo,sm))\n\t\t\t\tyes = 1\n\t\t\texcept:\n\t\t\t\tcnt += 1\n\t\telse:\n\t\t\tcmds.warning (\"Couldn't add '{}' to '{}'.\".format (geo,sm))\n\t\t\tbreak\n\n\n\ndef removeGeo_sel ():\n\n\tsel = cmds.ls (sl = 1)\n\tremoved = 0\n\tif len(sel) >= 2:\n\t\tsm = ''\n\t\tfor i in sel:\n\t\t\tif i.endswith ('_MOD'):\n\t\t\t\tsm_hdl = i\n\t\t\t\tcons = cmds.listConnections (sm_hdl)\n\t\t\t\tfor c in cons:\n\t\t\t\t\tif i in c and (cmds.nodeType (c) == 'softMod'):\n\t\t\t\t\t\tsm = c\n\t\tif sm:\n\t\t\tfor geo in sel:\n\t\t\t\tif geo != sm_hdl:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tremoveGeo (sm, geo)\n\t\t\t\t\t\tremoved = 1\n\t\t\t\t\texcept:\n\t\t\t\t\t\tpass\n\t\telse:\n\t\t\tcmds.warning (\"No stickyMod in selection.\")\n\n\tif removed == 1 and len(sel) >= 3:\n\t\tsys.stdout.write (\"Removed multiple meshes from '{}'.\".format (sm))\n\n\n\ndef removeGeo (sm, geo):\n\n\tsurf = cmds.listRelatives (geo, s = 1)[0]\n\tcnt = 0\n\tyes = 0\n\twhile yes == 0:\n\t\tif cnt <= 100:\n\t\t\ttry:\n\t\t\t\tcmds.deformer (sm, e = 1, rm = 1, g = geo)\n\t\t\t\tcmds.disconnectAttr ('{}.worldMatrix'.format (surf), '{}.geomMatrix[{}]'.format (sm,cnt))\n\t\t\t\tyes = 1\n\t\t\t\tsys.stdout.write (\"Removed '{}' from '{}'.\".format (geo,sm))\n\t\t\texcept:\n\t\t\t\tcnt += 1\n\t\telse:\n\t\t\tcmds.warning (\"Couldn't remove '{}' from '{}'.\".format (geo,sm))\n\t\t\tbreak\n\n\n\ndef breakRot_sel ():\n\n\tsel = cmds.ls (sl = 1)\n\tyes = 0\n\tif sel:\n\t\tfor i in sel:\n\t\t\ttry:\n\t\t\t\tbreakRot (i)\n\t\t\t\tyes = 1\n\t\t\texcept:\n\t\t\t\tpass\n\tif yes == 0:\n\t\tcmds.warning (\"No stickyMods in selection.\")\n\n\n\ndef breakRot (sm):\n\n\toff = cmds.listRelatives (sm, p = 1)[0]\n\triv = cmds.listRelatives (off, p = 1)[0]\n\n\tcons = []\n\tcon = cmds.listConnections ('{}.rx'.format (riv), d = 0, p = 1)\n\tif con:\n\t\tcons.append (con[0])\n\t\tcmds.disconnectAttr (con[0], '{}.rx'.format (riv))\n\tcon = cmds.listConnections ('{}.ry'.format (riv), d = 0, p = 1)\n\tif con:\n\t\tcons.append (con[0])\n\t\tcmds.disconnectAttr (con[0], '{}.ry'.format (riv))\n\tcon = cmds.listConnections ('{}.rz'.format (riv), d = 0, p = 1)\n\tif con:\n\t\tcons.append (con[0])\n\t\tcmds.disconnectAttr (con[0], '{}.rz'.format (riv))\n\n\tsys.stdout.write (\"Broke rotations on the rivet for '{}'.\".format (sm))\n\n\treturn cons\n\n\n\ndef oriToWorld_sel ():\n\n\tsel = cmds.ls (sl = 1)\n\tyes = 0\n\tif sel:\n\t\tfor i in sel:\n\t\t\tif i.endswith ('_MOD'):\n\t\t\t\toriToWorld (i)\n\t\t\t\tyes = 1\n\t\tcmds.select (sel)\n\tif yes == 0:\n\t\tcmds.warning (\"No stickyMods in selection.\")\n\n\n\ndef oriToWorld (sm):\n\n\toff = cmds.listRelatives (sm, p = 1)[0]\n\triv = cmds.listRelatives (off, p = 1)[0]\n\n\tloc = cmds.spaceLocator ()[0]\n\tcnst = cmds.orientConstraint (loc, off)\n\tcmds.delete (cnst,loc)\n\n\n\ndef aimAtObj_sel ():\n\n\tsel = cmds.ls (sl = 1)\n\tif sel:\n\t\tif len(sel) == 2:\n\t\t\tsm = ''\n\t\t\tfor i in sel:\n\t\t\t\tif i.endswith ('_MOD'):\n\t\t\t\t\tsm = i\n\t\t\t\telse:\n\t\t\t\t\tobj = i\n\t\t\tif sm and obj:\n\t\t\t\taimAtObj (obj, sm)\n\t\t\tcmds.select (sm)\n\t\telse:\n\t\t\tcmds.warning (\"Select an object and a stickyMod.\")\n\telse:\n\t\tcmds.warning (\"Select an object and a stickyMod.\")\n\n\n\ndef aimAtObj (obj, sm):\n\n\toff = cmds.listRelatives (sm, p = 1)[0]\n\triv = cmds.listRelatives (off, p = 1)[0]\n\n\tcnst = cmds.aimConstraint (obj, off, aim = [0,0,1], u = [0,1,0], wut = 'scene')\n\tcmds.delete (cnst)","sub_path":"js_stickyMod.py","file_name":"js_stickyMod.py","file_ext":"py","file_size_in_byte":20262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"297722328","text":"#grabs and cleans data. Also adds it together\nimport pickle\n\ndef clean(filename):\n\t#Import the file \n\tinput_file = open(filename,'r') \n\trawText = pickle.load(input_file)\n\n\t#Truncate the header and footer\n\tfor i in range(0,len(rawText)):\n\t\tif rawText[i:i+6]==\"ACT I.\":\n\t\t\tstart=i+6\n\t\t\tbreak\n\treverseText=rawText[::-1]\n\tfor i in range(0,len(reverseText)):\n\t\t# print reverseText[i:i+8]\n\t\tif reverseText[i:i+7]==\"DNE EHT\":\n\t\t\tend=i+7\n\t\t\tbreak\n\ttruncText=rawText[start:-end-1]\n\n\t#Wipe text of newline and return indicators, parse into individual words, and then delete all extra spaces\n\tcleanerText=truncText.replace(\"\\r\",\"\")\n\tcleanText=cleanerText.replace(\"\\n\",\"\")\n\twordList=cleanText.split(\" \")\n\twhile '' in wordList:\n\t\twordList.remove('')\n\treturn wordList","sub_path":"project3/cleandata.py","file_name":"cleandata.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"483337741","text":"from nltk.corpus import framenet\nfrom lxml import etree\nimport datetime\nimport json\n\nimport verbnet\nimport config\n\nVN_LOC = config.VN_RESOURCE_PATH\n\nvn = verbnet.VerbNetParser(directory=VN_LOC)\npossible_classes = {\"-\".join(c.split(\"-\")[1:]): [m.name for m in vn.verb_classes_dict[c].members] for c in\n vn.verb_classes_dict}\n\npossible_frames = {}\nfor lu in framenet.lus():\n if lu.frame.name not in possible_frames:\n possible_frames[lu.frame.name] = [lu.lexemes[0].name]\n else:\n possible_frames[lu.frame.name].append(lu.lexemes[0].name)\n\n\nclass Mapping():\n def __init__(self, member, vn_class, fn_frame):\n self.member = member\n self.vn_class = vn_class\n self.fn_frame = fn_frame\n self.errors = self.verify()\n\n def __str__(self):\n return self.member + \" \" + self.vn_class + \" \" + self.fn_frame\n\n def __eq__(self, other):\n return self.member == other.member and self.vn_class == other.vn_class and self.fn_frame == other.fn_frame\n\n def __lt__(self, other):\n if self.vn_class == other.vn_class:\n return self.member < other.member\n return self.vn_class < other.vn_class\n\n def __gt__(self, other):\n if self.vn_class == other.vn_class:\n return self.member > other.member\n return self.vn_class > other.vn_class\n\n def __hash__(self):\n return hash(self.member) * hash(self.vn_class) * hash(self.fn_frame)\n\n def as_xml(self):\n out_node = etree.Element(\"vncls\", attrib={\"class\":self.vn_class, \"fnframe\":self.fn_frame, \"vnmember\":self.member})\n return out_node\n\n def verify(self):\n res = []\n if self.vn_class not in possible_classes.keys():\n res.append(\"class doesn't exits\")\n elif self.member not in possible_classes[self.vn_class]:\n res.append(\"verb not in class\")\n if self.fn_frame not in possible_frames.keys():\n res.append(\"frame doesn't exist\")\n elif self.member not in possible_frames[self.fn_frame]:\n res.append(\"verb not in frame\")\n return res\n\n\nclass ElementMapping(Mapping):\n def __init__(self, element):\n super().__init__(\"\", element.attrib[\"class\"], element.attrib[\"fnframe\"])\n self.role_dict = {}\n\n for role in element.getchildren()[0].getchildren():\n self.role_dict[role.attrib[\"vnrole\"].lower()] = role.attrib[\"fnrole\"]\n\n\ndef load_mappings(mapping_file, as_dict=True):\n mappings = set()\n tree = etree.parse(open(mapping_file, encoding=\"utf-8\"))\n root = tree.getroot()\n\n for e in root:\n mappings.add(Mapping(e.attrib[\"vnmember\"], e.attrib[\"class\"], e.attrib[\"fnframe\"]))\n\n if as_dict:\n d = {}\n for m in mappings:\n k = m.vn_class + \"-\" + m.member\n if k not in d:\n d[k] = []\n d[k].append(m.fn_frame)\n return d\n return mappings\n\n\ndef load_element_mappings(mapping_file, to_dict=True):\n mappings = set()\n tree = etree.parse(open(mapping_file, encoding=\"utf-8\"))\n root = tree.getroot()\n\n for e in root:\n mappings.add(ElementMapping(e))\n\n if to_dict:\n d = {}\n for m in mappings:\n k = m.vn_class + \";\" + m.fn_frame\n d[k] = m.role_dict\n return d\n\n return mappings\n\n\ndef write_mappings(mappings, output_file, version=\"sl2\", out_format=\"json\"):\n if out_format == \"json\":\n json.dump(mappings, open(output_file, \"w\"))\n else:\n root = etree.Element('verbnet-framenet_MappingData', attrib={\"date\": str(datetime.datetime.now()), \"versionID\":version})\n\n for m in sorted(list(mappings)):\n print (m)\n root.append(m.as_xml())\n out_str = etree.tostring(root, pretty_print=True)\n with open(output_file, 'wb') as output:\n output.write(out_str)\n\n\ndef combine_old_and_fixed(output_file):\n old_maps = load_mappings(config.OLD_VN2FN_PATH)\n new_maps = load_mappings(config.FIXED_VN2FN_PATH)\n old_maps = {m for m in old_maps if not m.errors}\n for m in new_maps:\n if m in old_maps:\n old_maps.remove(m)\n old_maps.add(m)\n write_mappings(old_maps, output_file)\n\n\ndef test():\n m = load_mappings(config.VN2FN_PATH, as_dict=True)\n write_mappings(m, \"../instances/vn-fn2.json\", \"json\")\n\nif __name__ == \"__main__\":\n test()","sub_path":"tools/vnfn.py","file_name":"vnfn.py","file_ext":"py","file_size_in_byte":4367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"52261725","text":"import json\nimport os\nimport random\nimport requests\nimport string\n\nfrom functools import wraps\n\nfrom flask import Flask\nfrom flask import flash\nfrom flask import g\nfrom flask import jsonify\nfrom flask import render_template\nfrom flask import redirect\nfrom flask import request\nfrom flask import session\nfrom flask import url_for\n\nfrom flask.json import jsonify\n\nfrom forms import CategoryForm\nfrom forms import ItemForm\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom models import Base\nfrom models import User\nfrom models import Category\nfrom models import Item\n\n\n#Change config here\napp = Flask(__name__)\napp.config.from_object('settings.DevelopmentConfig')\n\n\n#Database config\nengine = create_engine(app.config.get('DATABASE_URI'))\nBase.metadata.bind = engine\nDB_Session = sessionmaker(bind=engine)\ndb_session = DB_Session()\n\n\n@app.context_processor\ndef injectUser():\n \"\"\"\n\n Injects the username, if it exists, into templates.\n\n NOTE!\n Sort of redundent as templates can access g.uid, thus can just set\n g.user = session.get('username', None).\n\n ASLO NOTE!\n Can be used to pass functions to templates!\n\n Args:\n None\n\n Returns:\n dict like object\n\n \"\"\"\n return {'username': session.get('username', None)}\n\n\ndef get_categories(function):\n \"\"\"\n\n Gets all the categories from the db and stores them in g.\n\n Args:\n None\n\n Returns:\n function\n\n \"\"\"\n @wraps(function)\n def wrapper(*args, **kwargs):\n\n g.categories = db_session.query(Category).all()\n\n return function(*args, **kwargs)\n\n return wrapper\n\n\ndef get_items_by_cid(function):\n \"\"\"\n\n Gets all items from db with item.cid = cid and stores them in g.\n\n Args:\n (int) cid\n\n Returns:\n function\n\n \"\"\"\n @wraps(function)\n def wrapper(cid=None, *args, **kwargs):\n\n g.items = db_session.query(Item).filter_by(cid=cid).all()\n\n return function(cid, *args, **kwargs)\n\n return wrapper\n\n\ndef login_required(function):\n \"\"\"\n\n Checks if a user is logged in.\n Redirects to index if not logged in and stores user id, uid, in g if user\n is logged in.\n\n Args:\n None\n\n Returns:\n function if uid in session else redirect to index\n\n \"\"\"\n @wraps(function)\n def wrapper(*args, **kwargs):\n\n g.uid = session.get('uid', None)\n\n if not g.uid:\n\n flash('Login required')\n return redirect(url_for('index'))\n\n return function(*args, **kwargs)\n\n return wrapper\n\n\ndef get_category_by_id(function):\n \"\"\"\n\n Gets a specific category from the db. Stores category in g if it exists,\n redirects to index if category doesn't exist.\n\n Args:\n (int) cid\n\n Returns:\n function if category else redirect to index\n\n \"\"\"\n @wraps(function)\n def wrapper(cid=None, *args, **kwargs):\n\n g.category = db_session.query(Category).filter_by(id=cid).first()\n\n if not g.category:\n\n flash('Category not found')\n return redirect(url_for('index'))\n\n return function(cid, *args, **kwargs)\n\n return wrapper\n\n\ndef user_owns_category(function):\n \"\"\"\n\n Checks to see if g.uid, the user id in g, is equal to g.category.uid, the\n user id of a category also stored in g.\n\n Args:\n None\n\n Returns:\n function if g.uid is g.category.uid else redirect to index\n\n \"\"\"\n @wraps(function)\n def wrapper(*args, **kwargs):\n\n if g.uid != g.category.uid:\n\n flash('Permission denied')\n return redirect(url_for('index'))\n\n return function(*args, **kwargs)\n\n return wrapper\n\n\ndef get_item_by_id(function):\n \"\"\"\n\n Gets a specific item from db by id. Stores item in g if it exsists,\n redirects to the expected parent category if the item doesn't exist.\n\n Args:\n (int) cid\n (int) iid\n\n Returns:\n function if category else redirect to categoryRead(cid)\n\n \"\"\"\n @wraps(function)\n def wrapper(cid=None, iid=None, *args, **kwargs):\n\n g.item = db_session.query(Item).filter_by(id=iid).first()\n\n if not g.item:\n\n flash('Item not found')\n return redirect(url_for('categoryRead', cid=cid))\n\n return function(cid, iid, *args, **kwargs)\n\n return wrapper\n\n\ndef user_owns_item(function):\n \"\"\"\n\n Checks to see if the user id in g, g.uid, is equal to g.item.uid, the\n user id of an item also stored in g. If the user doesn't own the item,\n redirects to the category of the item.\n\n Args:\n cid (int), default = None.\n iid (int), default = None.\n\n Returns:\n function if g.uid is g.item.uid else redirect to category(cid)\n\n \"\"\"\n @wraps(function)\n def wrapper(cid=None, iid=None, *args, **kwargs):\n\n if g.uid != g.item.uid:\n\n flash('Permission denied')\n return redirect(url_for('index'))\n\n return function(cid, iid, *args, **kwargs)\n\n return wrapper\n\n\n@app.route('/')\n@app.route('//')\n@get_categories\ndef index(JSON=None):\n\n if JSON and JSON.upper() == 'JSON':\n return jsonify([category.serialize for category in g.categories])\n\n return render_template('index.html',\n categories=g.categories)\n\n\n@app.route('/category/create/', methods=['GET', 'POST'])\n@login_required\ndef categoryCreate():\n\n form = CategoryForm()\n\n if form.validate_on_submit():\n\n category = Category(\n name=form.name.data,\n uid=g.uid\n )\n\n db_session.add(category)\n db_session.commit()\n\n flash('{category} created'.format(category=category.name))\n return redirect(url_for('categoryRead', cid=category.id))\n\n return render_template('categoryform.html',\n form=form)\n\n\n@app.route('/category//read/')\n@app.route('/category//read//')\n@get_category_by_id\n@get_items_by_cid\ndef categoryRead(cid, JSON=None):\n\n if JSON and JSON.upper() == 'JSON':\n return jsonify([item.serialize for item in g.items])\n\n else:\n return render_template('categoryread.html',\n category=g.category,\n items=g.items)\n\n\n@app.route('/category//update/', methods=['GET', 'POST'])\n@login_required\n@get_category_by_id\n@user_owns_category\ndef categoryUpdate(cid):\n\n form = CategoryForm(obj=g.category)\n\n if form.validate_on_submit():\n\n data = {\n 'name': form.name.data\n }\n\n db_session.query(Category).filter_by(id=cid).update(data)\n db_session.commit()\n\n flash('{category} updated'.format(category=category.name))\n return redirect(url_for('categoryRead', cid=cid))\n\n return render_template('categoryform.html',\n form=form)\n\n\n@app.route('/category//delete/')\n@login_required\n@get_category_by_id\n@user_owns_category\ndef categoryDelete(cid):\n\n db_session.delete(g.category)\n db_session.commit()\n\n flash('{category} deleted'.format(category=g.category.name))\n return redirect(url_for('index'))\n\n\n@app.route('/category//item//read/')\n@app.route('/category//item//read//')\n@get_category_by_id\n@get_item_by_id\ndef itemRead(cid, iid, JSON=None):\n\n if JSON and JSON.upper() == 'JSON':\n return jsonify(g.item.serialize)\n\n return render_template('itemread.html',\n category=g.category,\n item=g.item)\n\n\n@app.route('/category//item/create/', methods=['GET', 'POST'])\n@login_required\n@get_category_by_id\ndef itemCreate(cid):\n\n form = ItemForm()\n\n if form.validate_on_submit():\n\n item = Item(\n name=form.name.data,\n description=form.description.data,\n uid=g.uid,\n cid=cid\n )\n\n db_session.add(item)\n db_session.commit()\n\n flash('{item} created'.format(item=item.name))\n return redirect(url_for('categoryRead', cid=cid))\n\n return render_template('itemform.html',\n category=g.category,\n form=form)\n\n\n@app.route('/category//item//update/', methods=['GET', 'POST'])\n@login_required\n@get_category_by_id\n@get_item_by_id\n@user_owns_item\ndef itemUpdate(cid, iid):\n\n form = ItemForm(obj=g.item)\n\n if form.validate_on_submit():\n\n data = {\n 'name': form.name.data,\n 'description': form.description.data\n }\n\n db_session.query(Item).filter_by(id=g.item.id).update(data)\n db_session.commit()\n\n flash('{item} updated'.format(item=g.item.name))\n return redirect(url_for('itemRead', cid=cid, iid=iid))\n\n return render_template('itemform.html',\n category=g.category,\n item=g.item,\n form=form)\n\n\n@app.route('/category//item//delete/')\n@login_required\n@get_item_by_id\n@user_owns_item\ndef itemDelete(cid, iid):\n\n db_session.delete(g.item)\n db_session.commit()\n\n flash('{name} deleted'.format(name=g.item.name))\n return redirect(url_for('categoryRead', cid=cid))\n\n\n@app.route('/auth/login/')\ndef login():\n \"\"\"\n\n Construct the url for google sign in and redirect to the url.\n\n \"\"\"\n char_space = (string.lowercase + string.uppercase + string.digits)\n session['state'] = ''.join(random.choice(char_space) for x in xrange(32))\n\n url = app.config.get('AUTH_URI')\n payload = {\n 'client_id': app.config.get('CLIENT_ID'),\n 'response_type': 'code',\n 'scope': 'openid+email+profile',\n 'redirect_uri': app.config.get('REDIRECT_URI'),\n 'state': session['state'],\n 'prompt': 'consent'\n }\n\n for count, (key, value) in enumerate(payload.iteritems(), 0):\n url += '?' if count is 0 else '&'\n url += '{key}={value}'.format(key=key, value=value)\n\n return redirect(url)\n\n\n@app.route('/auth/callback/')\ndef callbackHandling():\n \"\"\"\n\n Checks the state token created in login and errors from google. Exchanges\n the one time code for an access token, which is stored in session, then\n redirects to exchangeToken.\n\n \"\"\"\n if 'state' not in session:\n\n flash('Nice try, FBI!')\n return redirect(url_for('index'))\n\n elif request.args.get('state') != session['state']:\n\n flash('State token did not match')\n return redirect(url_for('index'))\n\n elif request.args.get('error'):\n\n flash('Permission to login denied by user')\n return redirect(url_for('index'))\n\n else:\n\n url = app.config.get('TOKEN_URI')\n headers = {\n 'content-type': 'application/x-www-form-urlencoded'\n }\n payload = {\n 'code': request.args.get('code'),\n 'client_id' : app.config.get('CLIENT_ID'),\n 'client_secret': app.config.get('CLIENT_SECRET'),\n 'redirect_uri': app.config.get('REDIRECT_URI'),\n 'grant_type': 'authorization_code'\n }\n\n info = requests.post(url=url, data=payload, headers=headers)\n\n if info.status_code == requests.codes.ok:\n\n info = info.json()\n\n session['access_token'] = info['access_token']\n\n return redirect(url_for('exchangeToken'))\n\n else:\n\n flash('There was an unexpected error when loggin in')\n return redirect(url_for('index'))\n\n\n@app.route('/auth/exchangetoken/')\ndef exchangeToken():\n \"\"\"\n\n Use the access token to request user information from google, storing the\n information in session and redirecting to index.\n\n \"\"\"\n url = app.config.get('EXCHANGE_URI')\n headers = {\n 'Authorization': 'Bearer {token}'.format(token=session['access_token'])\n }\n\n info = requests.get(url=url, headers=headers)\n\n if info.status_code == requests.codes.ok:\n\n info = info.json()\n\n session['email'] = info['email']\n session['username'] = info['name']\n\n user = db_session.query(User).filter_by(email=session['email']).first()\n\n if not user:\n\n user = User(\n email=session['email']\n )\n\n db_session.add(user)\n db_session.commit()\n\n session['uid'] = user.id\n\n flash('Welcome, {username}'.format(username=session['username']))\n\n else:\n\n flash('There was an unexpected error exchanging your token')\n\n return redirect(url_for('index'))\n\n\n@app.route('/auth/logout/')\ndef logout():\n \"\"\"\n\n Send a get request to revoke permission for access token and redirect to\n index.\n\n \"\"\"\n if 'access_token' in session:\n\n url = app.config.get('REVOKE_URI')\n payload = {\n 'token': session['access_token']\n }\n\n r = requests.get(url=url, params=payload)\n\n if r.status_code == requests.codes.ok:\n\n flash('Goodbye, {username}'.format(username=session['username']))\n session.clear()\n\n else:\n\n flash('There was an unexpected error when logging out')\n\n return redirect(url_for('index'))\n\n\nif __name__ == '__main__':\n #Google requires HTTPS for OAuth2, set to = '0' or delete in production\n os.environ['DEBUG'] = '1'\n app.secret_key = os.urandom(24)\n app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":13421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"545062102","text":"import sys\nfrom PySide.QtGui import *\n\nfrom Base.login import login_UI\n\nclass Login_Application(QMainWindow):\n def __init__(self):\n #Main UI set up\n QMainWindow.__init__(self, None)\n self.setMinimumSize(1280, 720)\n self.setWindowTitle(\"HMS version 1.0.1\")\n\n # Init main Widget\n\n self.central_widget = QStackedWidget()\n self.setCentralWidget(self.central_widget)\n login_widget = login_UI.Login_UI(self)\n self.central_widget.addWidget(login_widget)\n\n # Init state attributes\n self.state = 'Not Login'\n\n # Add widget\n self.login_widget = login_UI.Login_UI(self)\n self.central_widget.addWidget(self.login_widget)\n\n\n\n # Change page signal (send from log in UI page)\n def changePageLoginSection(self, signal = None, user = None):\n if (signal == \"login\" and user != None):\n print('login pressed')\n print(type(user))\n self.state = \"Login\"\n\n else:\n ### create pop-up\n print('Wrong login')\n\n\ndef main():\n app = QApplication(sys.argv)\n ui = Login_Application()\n ui.show()\n app.exec_()\n\nif __name__ == \"__main__\":\n main()","sub_path":"Base/login/Login_Application.py","file_name":"Login_Application.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"484474349","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\nfrom nose.tools import raises, eq_, ok_\n\nfrom werkzeug.test import Client\nfrom werkzeug.wrappers import BaseResponse\n\nfrom clastic import Application, Route, render_basic\nfrom clastic.errors import BadGateway\n\n\ndef render_error_basic(_error):\n return _error\n\n\ndef render_error_broken(_error):\n return 1/0\n\n\ndef accum_render_error(_error, request, error_list):\n if 'reraise' in request.path:\n raise _error\n if 'badgateway' in request.path:\n _error = BadGateway()\n error_list.append(_error)\n return _error\n\n\ndef odd_endpoint(number):\n if number % 2:\n return 'True'\n raise ValueError('not in my house')\n\n\ndef test_app_error_render():\n rt = Route('/', odd_endpoint, render_basic)\n yield eq_, rt._render_error, None\n\n app = Application([rt], render_error=render_error_basic)\n yield eq_, rt._render_error, render_error_basic\n\n cl = Client(app, BaseResponse)\n yield eq_, cl.get('/1').status_code, 200\n\n err_resp = cl.get('/2')\n yield eq_, err_resp.status_code, 500\n yield ok_, 'not in my house' in err_resp.data\n\n err_resp = cl.get('/non-int')\n yield eq_, err_resp.status_code, 404\n\n\n@raises(NameError)\ndef test_unresolved_error_render():\n rt = Route('/', odd_endpoint, render_basic)\n Application([rt], render_error=lambda nopenope: False)\n\n\ndef test_broken_error_render():\n rt = Route('/', odd_endpoint, render_basic)\n app = Application([rt], render_error=render_error_broken)\n cl = Client(app, BaseResponse)\n err_resp = cl.get('/2')\n yield eq_, err_resp.status_code, 500\n yield ok_, 'not in my house' in err_resp.data\n\n\ndef test_error_render_count():\n rt = Route('//', odd_endpoint, render_basic)\n error_list = []\n rsrc = {'error_list': error_list}\n app = Application([rt], rsrc, render_error=accum_render_error)\n cl = Client(app, BaseResponse)\n\n err_resp = cl.get('/39')\n yield eq_, err_resp.status_code, 200\n err_resp = cl.get('/2')\n yield eq_, err_resp.status_code, 500\n yield eq_, len(error_list), 1\n\n # reraising means the error will be handled by the default\n # handler, so no length change should occur\n err_resp = cl.get('/4/reraise')\n yield eq_, err_resp.status_code, 500\n yield eq_, len(error_list), 1\n\n err_resp = cl.get('/6/badgateway')\n yield eq_, err_resp.status_code, 502\n yield eq_, len(error_list), 2\n","sub_path":"clastic/tests/test_render_error.py","file_name":"test_render_error.py","file_ext":"py","file_size_in_byte":2491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"348060341","text":"from django.utils import translation\nfrom django.urls import reverse_lazy\n\nfrom directory_constants import urls\n\nfrom core import context_processors\n\n\ndef test_footer_contact_link_processor_flag_on_settings(settings):\n settings.FEATURE_FLAGS = {\n **settings.FEATURE_FLAGS,\n 'INTERNATIONAL_CONTACT_LINK_ON': True,\n }\n\n actual = context_processors.footer_contact_us_link(None)\n\n expected = urls.build_great_url('international/contact/')\n assert actual['footer_contact_us_link'] == expected\n\n\ndef test_footer_contact_link_processor_flag(settings):\n settings.FEATURE_FLAGS = {\n **settings.FEATURE_FLAGS,\n 'INTERNATIONAL_CONTACT_LINK_ON': False,\n }\n\n actual = context_processors.footer_contact_us_link(None)\n\n assert actual['footer_contact_us_link'] == urls.CONTACT_US\n\n\ndef test_directory_components_html_lang_attribute(settings):\n\n with translation.override('fr'):\n actual = context_processors.directory_components_html_lang_attribute(None) # noqa\n\n assert actual[\n 'directory_components_html_lang_attribute'\n ] == translation.get_language()\n\n with translation.override('de'):\n actual = context_processors.directory_components_html_lang_attribute(None) # noqa\n\n assert actual[\n 'directory_components_html_lang_attribute'\n ] == translation.get_language()\n\n\ndef test_site_home_link():\n actual = context_processors.site_home_link(None)\n assert actual == {\n 'site_home_link': {\n 'label': 'Great.gov.uk International',\n 'url': reverse_lazy('index')\n }\n }\n","sub_path":"core/tests/test_context_processors.py","file_name":"test_context_processors.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"502983684","text":"#!/usr/bin/env python\n\nimport sys, os, re\nimport numpy as np\n\n# Let file1 be the first argument\nfile1 = sys.argv[1]\nprint(\"file1: \" + file1)\n\n# Read the file as a numpy matrix of floats\ndata = np.loadtxt(file1, dtype='float', delimiter=' ')\n\n# print data dimensions\nprint(\"data.shape: \" + str(data.shape))\n\n# Find the min and max of first column\nminx = np.min(data[:,0])\nmaxx = np.max(data[:,0])\nprint(\"minx: \" + str(minx))\nprint(\"maxx: \" + str(maxx))\n\n# same for y\nminy = np.min(data[:,1])\nmaxy = np.max(data[:,1])\nprint(\"miny: \" + str(miny))\nprint(\"maxy: \" + str(maxy))\n\n#shift_x = 364368.55011\n#shift_y = 4305710.65095\n#shift_z = 1.01000\n\n# The x axis in DART is actually pointing toward south, and y axis of DART is pointing toward east. Therefore, when you consider the traditional coordinate system: x points to the east, and y points to the north. The DART coordinate system is 90 clockwise rotation of the horizontal coordinate frame. \n\nx_out = data[:, 0] - (maxx - minx)/2\ny_out = data[:, 1] - (maxy - miny)/2\nz_out = data[:, 2]\n\n# Save this to a text file with double precision as 3 columns\noutFile = \"dtm.txt\"\nprint(\"Will save file: \" + outFile)\nnp.savetxt(outFile, np.c_[x_out, y_out, z_out], fmt='%.17g', \n delimiter=' ', newline='\\n', header='', footer='', comments='# ')\n\n\n\n","sub_path":"bin/daret_shift.py","file_name":"daret_shift.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"480347892","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport cv2\nimport numpy as np\n\ndef easy_vis(img, bboxes, lmks, scale=(1,1), threshold=0.9, save_path=None):\n #- Nothing to display\n if len(bboxes) == 0:\n return img\n\n #- remove bboxes cls_prob. < threshold\n conf_idx = np.asarray(np.where(bboxes[:,4] > threshold)).reshape(-1)\n bboxes = bboxes[conf_idx]\n\n #- x,y sclale\n bboxes[:,0] = bboxes[:,0] * scale[0]\n bboxes[:,1] = bboxes[:,1] * scale[1]\n bboxes[:,2] = bboxes[:,2] * scale[0]\n bboxes[:,3] = bboxes[:,3] * scale[1]\n\n #- visualize all faces\n for box in bboxes:\n cv2.rectangle(img, \n (box[0].astype(int), box[1].astype(int)),\n (box[2].astype(int), box[3].astype(int)), \n (0, 255, 255), 2)\n\n cv2.putText(img, '{:.2f}'.format(box[4]),\n org=(int(box[0]), int(box[1]-10)),\n fontFace=cv2.FONT_HERSHEY_SIMPLEX,\n fontScale=1,\n color=(0,0,255),\n thickness=2)\n\n #- visualize all landmarks\n if lmks is not None:\n lmks = lmks.reshape(-1, 6, 2)\n lmks = lmks[conf_idx]\n\n lmks[:,:,0] = lmks[:,:,0] * scale[0]\n lmks[:,:,1] = lmks[:,:,1] * scale[1]\n lmks = lmks.astype('int')\n\n for lmk in lmks:\n cv2.circle(img, (lmk[0,0], lmk[0,1]), radius=3, thickness=2, color=(0, 0, 255))\n cv2.circle(img, (lmk[1,0], lmk[1,1]), radius=3, thickness=2, color=(0, 0, 255))\n cv2.circle(img, (lmk[2,0], lmk[2,1]), radius=3, thickness=2, color=(0, 0, 255))\n cv2.circle(img, (lmk[3,0], lmk[3,1]), radius=3, thickness=2, color=(0, 0, 255))\n cv2.circle(img, (lmk[4,0], lmk[4,1]), radius=3, thickness=2, color=(0, 0, 255))\n cv2.circle(img, (lmk[5,0], lmk[5,1]), radius=3, thickness=2, color=(0, 0, 255))\n\n if save_path is not None:\n cv2.imwrite(save_path, img)\n\n return img\n\n\n","sub_path":"detlib/detector/vision.py","file_name":"vision.py","file_ext":"py","file_size_in_byte":1980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"56249862","text":"import typing\n\nimport pytest\nimport sniffio\n\n\n@pytest.mark.anyio\nasync def test_quickstart(capsys: typing.Any) -> None:\n import aiodine\n\n async def moo() -> str:\n print(\"What does the cow say?\")\n return \"moo!\"\n\n async def cowsay(what: str = aiodine.depends(moo)) -> None:\n print(f\"Going to say {what!r}...\")\n print(f\"Cow says {what}\")\n\n await aiodine.call_resolved(cowsay)\n\n captured = capsys.readouterr()\n assert captured.out.rstrip(\"\\n\").split(\"\\n\") == [\n \"What does the cow say?\",\n \"Going to say 'moo!'...\",\n \"Cow says moo!\",\n ]\n\n\n@pytest.mark.anyio\nasync def test_usage_with_context_managers(capsys: typing.Any) -> None:\n if sniffio.current_async_library() == \"curio\":\n pytest.xfail(\n \"curio currently disallows async code in 'finally' block \"\n \"of vanilla async generator\"\n )\n\n import aiodine\n\n # On 3.7+, use `from contextlib import asynccontextmanager`.\n from aiodine.compat import asynccontextmanager\n\n class Database:\n def __init__(self, url: str) -> None:\n self.url = url\n\n async def connect(self) -> None:\n print(f\"Connecting to {self.url!r}\")\n\n async def fetchall(self) -> typing.List[dict]:\n print(\"Fetching data...\")\n return [{\"id\": 1}]\n\n async def disconnect(self) -> None:\n print(f\"Releasing connection to {self.url!r}\")\n\n @asynccontextmanager\n async def get_db() -> typing.AsyncIterator[Database]:\n db = Database(url=\"sqlite://:memory:\")\n await db.connect()\n try:\n yield db\n finally:\n await db.disconnect()\n\n async def main(db: Database = aiodine.depends(get_db)) -> None:\n rows = await db.fetchall()\n print(\"Rows:\", rows)\n\n await aiodine.call_resolved(main)\n\n captured = capsys.readouterr()\n assert captured.out.rstrip(\"\\n\").split(\"\\n\") == [\n \"Connecting to 'sqlite://:memory:'\",\n \"Fetching data...\",\n \"Rows: [{'id': 1}]\",\n \"Releasing connection to 'sqlite://:memory:'\",\n ]\n","sub_path":"tests/test_readme.py","file_name":"test_readme.py","file_ext":"py","file_size_in_byte":2109,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"537223479","text":"\"\"\"\nUVA 112 - Tree Summing\nSearching strategy: DFS\n\"\"\"\n\ntrue, false = True, False\nspace, parenthesis_start, parenthesis_close = ' ', '(', ')'\ngraph = {}\ninput_line = ''\ni = 0\n\n\ndef read_char():\n global i, input_line\n while i is not len(input_line):\n input_line = input()\n i = 0\n char = input_line[i]\n i += 1\n while char.isspace():\n char = input_line[i]\n i += 1\n\n return char\n\n\ndef build_tree_recursive():\n char = read_char()\n if char is parenthesis_close:\n return []\n root = ''\n while char is not parenthesis_start:\n root += char\n char = read_char()\n\n root = int(root)\n graph[root] = build_tree_recursive()\n\n\ndef build_tree_from_console():\n char = read_char()\n target_sum = ''\n while char is not ' ':\n target_sum += char\n char = read_char()\n target_sum = int(target_sum)\n\n return build_tree_recursive(), target_sum\n\n\ndef main():\n root, target_sum = build_tree_from_console()\n\n\nmain()\n","sub_path":"test/112 - Tree Summing.py","file_name":"112 - Tree Summing.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"149058556","text":"import naming\nimport PLvimeo\nimport PLconfluence\nimport json\nimport os\nimport yaml\n\ndef main():\n name = naming.naming('./etc/config.yaml')\n name.loadConfig()\n i=0\n name.get_filepath(i) \n name.collect_date()\n name.collect_event()\n name.collect_name() \n name.collect_speaker()\n name.create_title() \n name.create_filename() \n name.rename_file() \n name.convert_video()\n name.archive_video()\n name.add_to_box()\n vimeo = PLvimeo.PLvimeo('./etc/config.yaml')\n vimeo.loadConfig()\n vimeo.authenticate() \n vimeo.upload(name.filepath)\n vimeo.title(name.title)\n print(\"Beginning confluence upload.\")\n confluence = PLconfluence.PLconfluence('./etc/config.yaml')\n confluence.loadConfig()\n confluence.getPage(name.event)\n confluence.createPayload(name.title, vimeo.location)\n confluence.createPage() \n\nmain()\n","sub_path":"videouploader/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"493173992","text":"from sqlalchemy import BigInteger, Column, DateTime, ForeignKey, Integer, String\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy_utils import EncryptedType\n\nfrom config import CONFIG\nfrom models.models import Base, auto_str\n\n__all__ = [\"MessageDiff\", \"LoggedMessage\"]\n\n\n@auto_str\nclass MessageDiff(Base):\n __tablename__ = \"message_edits\"\n\n id = Column(Integer, primary_key=True, nullable=False)\n original_message = Column(Integer, ForeignKey(\"messages.id\"), nullable=False)\n new_content = Column(\n EncryptedType(type_in=String, key=CONFIG.BOT_SECRET_KEY), nullable=False\n )\n created_at = Column(DateTime, nullable=False)\n\n original = relationship(\"LoggedMessage\", back_populates=\"edits\")\n\n\n@auto_str\nclass LoggedMessage(Base):\n __tablename__ = \"messages\"\n\n id = Column(Integer, primary_key=True, nullable=False)\n message_uid = Column(BigInteger, nullable=False)\n message_content = Column(\n EncryptedType(type_in=String, key=CONFIG.BOT_SECRET_KEY), nullable=False\n )\n author = Column(Integer, ForeignKey(\"users.id\"), nullable=False)\n created_at = Column(DateTime, nullable=False)\n deleted_at = Column(DateTime, nullable=True)\n channel_name = Column(\n EncryptedType(type_in=String, key=CONFIG.BOT_SECRET_KEY), nullable=False\n )\n\n user = relationship(\"User\", back_populates=\"messages\")\n edits = relationship(\n \"MessageDiff\", back_populates=\"original\", order_by=MessageDiff.created_at\n )\n karma = relationship(\"KarmaChange\", back_populates=\"message\")\n","sub_path":"models/messages.py","file_name":"messages.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"18459363","text":"import os\nimport re\nimport time\nimport urllib3\nfrom threading import Thread\n\n\n\ndef image_request(url,item,image_path):\n # 对图片进行请求\n print('正在请求{}'.format(url))\n image_content = req.request('GET', url) # 网络io请求\n # 文件写入图片\n print('开始写入图片')\n with open('{}/{}'.format(image_path, item[0].split('/')[-1]), 'wb') as f:\n f.write(image_content.data)\n\n\ndef save_images(items):\n t_list = []\n for item in items:\n if not item[0]:\n continue\n # 构造图片存储路径\n image_path = './images/{}'.format(item[1])\n # 判断文件夹是否存在，不存在就创建\n if not os.path.exists(image_path):\n os.mkdir(image_path)\n # 判断图片url地址是否包含域名\n image_url = item[0]\n if not 'http' in item[0]:\n image_url = '{}{}'.format('http://www.weimeitupian.com',item[0])\n\n # 创建线程实例\n t = Thread(target=image_request,args=(image_url,item,image_path))\n # 启动线程\n # # 单线程\n t.start()\n t.join()\n # #多任务\n # t_list.append(t)\n #\n # for t in t_list:\n # t.start()\n # for t in t_list:\n # t.join()\n\n\nif __name__ == '__main__':\n req = urllib3.PoolManager()\n start_time = time.time()\n for page in range(1,4):\n print('正在下载第{}页的图片数据...'.format(page))\n data = req.request('GET','http://www.weimeitupian.com/page/{}'.format(page))\n # 通过正则表达式 匹配标题和图片地址\n items = re.findall(r'-->.*?\"(.*?)\"',data.data.decode(),re.S)\n save_images(items)\n print('耗时:{}秒'.format(time.time()-start_time))","sub_path":"35爬虫/作业/第二次作业/urllib3爬取图片.py","file_name":"urllib3爬取图片.py","file_ext":"py","file_size_in_byte":1795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"312714327","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Oct 28 15:32:57 2017\r\n\r\n@author: tmald\r\n\"\"\"\r\n\r\ndef decod(pfile, codice):\r\n ind=open(pfile)\r\n a=ind.readline()\r\n risposta=set()\r\n while a!='':\r\n a=a.rstrip('\\n')\r\n if len(a)==len(codice):\r\n conversione=codice\r\n con=0\r\n for n in codice:\r\n conversione=conversione.replace(n,a[con])\r\n if len(set(conversione))==len(set(codice)) and conversione==a:\r\n risposta.add(a)\r\n con+=1\r\n \r\n a=ind.readline()\r\n ind.close()\r\n return risposta\r\n","sub_path":"students/1809290/homework02/program03.py","file_name":"program03.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"448723707","text":"from urllib import request\nimport json\nfrom urllib import parse\nimport urllib\nimport time\nimport json\nclass WJY:\n def __init__(self,macid):\n self.__MACID=macid\n self.__URL=\"\"\n\n self.__SIGNID=[]\n def is_json(self,file):\n '''\n 判断是不是json\n '''\n try:\n json.loads(file)\n except:\n return False\n return True\n def str_json(self,data):\n data=json.loads(data)\n return data\n def get(self,url):\n '''\n get请求,不带参数\n '''\n try:\n response=request.urlopen(url) \n #print(\"查看 response 响应信息类型: \",type(response))\n page=response.read()\n page=page.decode('utf-8')\n #url解码\n data=parse.unquote(page)\n #print(\"data@@@@@@@@@@@@@@@@@@@@@@@@\",data)\n if self.is_json(data):\n data=self.str_json(data)\n return data\n except:\n return 'error'\n \n def gets(self,url,data):\n '''\n get请求,带参数\n '''\n try:\n # data = json.dumps(data)\n # print(data)\n data = bytes(urllib.parse.urlencode(data), encoding='utf8')\n response=request.urlopen(url=url,data=data) \n #print(\"查看 response 响应信息类型: \",type(response))\n page=response.read()\n page=page.decode('utf-8')\n #url解码\n data=parse.unquote(page)\n #print(\"data@@@@@@@@@@@@@@@@@@@@@@@@\",data)\n if self.is_json(data):\n data=self.str_json(data)\n return data\n except:\n return 'error'\n def post(self,url,data):\n '''\n post请求\n '''\n try: \n data_string=urllib.parse.urlencode(data) \n last_data=bytes(data_string,encoding='utf-8')\n response=urllib.request.urlopen(url,data=last_data)\n data=response.read().decode('utf-8')\n #print(response.read().decode('utf-8'))\n if self.is_json(data):\n data=self.str_json(data)\n return data\n except:\n print(\"接口有问题\")\n def leave_url(self):\n data=self.get(self.__URL) \n data=self.get(data[\"login\"])\n url=data['data'][\"LEAVE_URL\"]+'&macid='+self.__MACID\n return url\n def respon(self):\n \n # if data==0:\n # pass\n # if data==1:\n pass\n def stu_code(self):\n '''\n ''' \n cc={}\n data=self.get(self.__URL)\n data1=self.get(data[\"login\"])\n # print(data1)\n # print(type(data1))\n data2=data1[\"data\"][\"classInfo\"]\n # print(data2)\n # print(type(data2))\n for i in data2:\n tmp={\n \"classId\":i[\"classId\"]\n }\n data3=self.gets(data[\"class\"],tmp)\n for j in data3:\n data4=data3[\"data\"][\"childs\"]\n \n for k in data4: \n \n if k[\"signId\"]:\n if '#' in k[\"signId\"]:\n kahao=k[\"signId\"].split('#') \n for m in kahao:\n if m:\n self.__SIGNID.append(m) \n \n if '#' not in k[\"signId\"]:\n cc[\"signId\"]=k[\"signId\"]\n cc[\"name\"]=k[\"name\"]\n cc[\"\"]\n self.__SIGNID.append(k[\"signId\"])\n\n\n \n\n print(self.__SIGNID)\n \n \n \n#设备参数\nzbh=WJY('ZBH-9843EE')\nzbh.stu_code()","sub_path":"MQ1/wjy/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"436501040","text":"from django.shortcuts import render\nfrom .models import SearchQuery\nfrom board.models import BoardPost\n\ndef search_view(request):\n query = request.GET.get('q', None)\n user = None\n if request.user.is_authenticated:\n user = request.user\n context = {'query': query}\n if query is not None:\n SearchQuery.objects.create(user=user, query=query)\n board_list = BoardPost.objects.search(query)\n context['board_list'] = board_list\n return render(request, 'search/view.html', context)\n","sub_path":"src/search/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"83880926","text":"import asyncio\nimport logging\n\nfrom app.settings import Settings\nfrom app.services import rclone_service, sas_service, transfer_service\n\nlogging.basicConfig(level=logging.INFO, format='%(name)s | %(levelname)s | %(message)s')\nlogging.getLogger('uamqp').setLevel(logging.WARNING)\nlogger = logging.getLogger(__name__)\n\n\nasync def run() -> None:\n settings = Settings()\n _log_startup_info(settings)\n\n client = await sas_service.get_sas_generator_client()\n\n try:\n azure_remote, s3_remote = await rclone_service.setup_rclone_remotes(client)\n transfer_service.transfer_data(azure_remote, s3_remote)\n transfer_service.transfer_logs(azure_remote, s3_remote)\n except SystemExit as error:\n raise SystemExit(f\"SystemExit raised with error: {error}\")\n\n\ndef _log_startup_info(settings: Settings) -> None:\n logger.info(\n f\"Running transfer-file with the following environment variables: \"\n f\"AZURE_BASE_URL={settings.azure_base_url}, \"\n f\"AZURE_CONTAINER={settings.azure_container}, \"\n f\"BUCKET_NAME={settings.bucket_name}, \"\n f\"DATASETT_ID={settings.datasett_id}, \"\n f\"IAM_ACCESS_KEY_ID={settings.iam_access_key_id}, \"\n f\"IAM_SECRET_ACCESS_KEY={settings.iam_secret_access_key}, \"\n f\"S3_ENDPOINT_URL={settings.s3_endpoint_url}, \"\n f\"S3_PATH={settings.s3_path}, \"\n f\"S3_LOGFILES_PATH={settings.s3_logfiles_path}, \"\n f\"SAS_GENERATOR_BASE_URL={settings.sas_generator_base_url}, \"\n f\"OBJEKT_ID={settings.objekt_id}\",\n )\n\n\nif __name__ == '__main__':\n asyncio.run(run())\n","sub_path":"transfer-archive/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"497941393","text":"import smartsheet # Import all needed packages\r\nimport os\r\nimport time\r\nfrom time import sleep\r\nimport PySimpleGUI as sg\r\n\r\nform1 = sg.FlexForm('Smart Sheet Attachments API Token') # Create separate forms depending on what the window needs to display\r\nform2 = sg.FlexForm('Sheet Information') # Form for second window\r\nlayout01 = [ # Create layout for first window\r\n [sg.Text('Please enter your API Token')], # API Token Window Header\r\n [sg.Text('API Token', size=(15, 1)), sg.InputText('')], # Input for API Token as text\r\n [sg.Submit(), sg.Cancel()] # Buttons for Submit and Cancel\r\n ] \r\n\r\n\r\nbutton, values = form1.Layout(layout01).Read() # Present form 1 and get read information\r\nform1.Close() # Once form is submitted, close window\r\nAPI_Token = values[0] # Store value taken from window to API_Token variable\r\nsmart = smartsheet.Smartsheet(API_Token) # Initialize smartsheet API using token\r\n\r\nresponse = smart.Sheets.list_sheets(API_Token) # Get a list of all sheets\r\nsheets = response.data # Store list of sheet data in variable sheets\r\n\r\n\r\nsheetNames = [] # Create empty array to store sheet names\r\nfor x in range(0,len(sheets)): # Iterate through all sheets\r\n print(sheets[x].name) # Print function to determine funtionality\r\n sheetNames.append(sheets[x].name) # Append all sheet names to sheetNames array\r\n\r\nlayout02 = [ # Create layout for second window\r\n [sg.Text('Please select your Sheet, Image Folder, and Starting Row.')], # Header for window\r\n [sg.InputCombo(sheetNames)], # Drop down list containing options for every sheet name\r\n [sg.Text('Image Folder', size=(15, 1)), sg.InputText(''), sg.FolderBrowse()], # Browse to image folder destination\r\n [sg.Text('Start Row', size=(15, 1)), sg.InputText('')], # Input for start row as string\r\n [sg.Submit(), sg.Cancel()] # Buttons for Submit and Cancel\r\n ]\r\n\r\n\r\n\r\n\r\nbutton, values = form2.Layout(layout02).Read() # Present second window and read information\r\nform2.Close() # When information is submitted, close window\r\nsheetName = values[0] # Store selected sheet name to variable sheetName\r\nprint(sheetName) # Print to check value of sheetName variable\r\nindex = 0 # Create new index to save value of index to match name to ID\r\nfor x in range(0,len(sheets)): # For loop for every sheet name\r\n if sheetName == sheets[x].name: # Iterate through names to match up and then save index\r\n index = x # Store index needed to index variable for later use\r\nprint(sheets[index].id_) # Print to check if that the ID matches the sheet name\r\nsheet_id = sheets[index].id_ # Store sheet ID fpr later use\r\nsheet = smart.Sheets.get_sheet(sheet_id) # Initialize sheet using sheet ID\r\nbasepath = values[1] # Store path from GUI window to basepath variable\r\nstartRow = int(values[2]) # Store start row value, convert from string to integer\r\n\r\n\r\n \r\nfiles = [] # Create array to store file names from directory\r\n \r\nfor entry in os.listdir(basepath): # Loop through every file in the folder and append each one to the new files array\r\n print(entry) # Print function used to check entries in directory, and then commented out\r\n files.append(entry) # Add entries to files array\r\n print(files) # Print function used to check contents of new array being built from directory files, then commented out\r\n\r\n \r\nfor x in range(0,len(files)): # Loop through and attach each file to its specific row (Sequential after starting row)\r\n #print(sheet.rows[x].id_) # Print function used to verify that the loop iterates through each row ID in order, then commented out\r\n #sleep(5) # Sleep function is used to force the program to wait to upload a new attachment. This avoids hitting the upload rate limit\r\n sg.OneLineProgressMeter('Progress',x+1,len(files),'key')\r\n smart.Attachments.attach_file_to_row( # Function to attach a file to a row. Need to give the Sheet ID, Row ID, File Name, and FULL File Path\r\n sheet_id, # Sheet ID passed from GUI\r\n sheet.rows[startRow + x - 1].id_, # Loops through the row IDs for the sheet\r\n (str(files[x]), # Loops through and names the file properly\r\n open(str(basepath)+'//'+ str(files[x]), 'rb'), # add base path and file name together to get the file's full path and to get the right file\r\n 'application/msword') # application/msword does not seem to do anything because this is uploading JPG images and nothing bad happens\r\n)","sub_path":"Scripts/Archive/01_SmartSheetAttachments.py","file_name":"01_SmartSheetAttachments.py","file_ext":"py","file_size_in_byte":6586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"229109468","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport pytest\n\nfrom mock import patch\n\nfrom night_scheduler.framework.sun.sun import Sun\n\n\nclass TestSun(object):\n FAKE_LATITUDE = \"00\"\n FAKE_LONGITUDE = \"11\"\n FAKE_DATE = \"YYYY-MM-DD\"\n FAKE_SUNSET = \"99:88:77 PM\"\n FAKE_SUNRISE_SUNSERT_ORG_ANSWER = {\n \"results\": {\n \"sunrise\": \"4:26:42 AM\",\n \"sunset\": \"99:88:77 PM\",\n \"solar_noon\": \"11:50:51 AM\",\n \"day_length\": \"14:48:18\",\n \"civil_twilight_begin\": \"3:54:08 AM\",\n \"civil_twilight_end\": \"7:47:34 PM\",\n \"nautical_twilight_begin\": \"3:12:59 AM\",\n \"nautical_twilight_end\": \"8:28:43 PM\",\n \"astronomical_twilight_begin\": \"2:25:39 AM\",\n \"astronomical_twilight_end\": \"9:16:04 PM\"\n },\n \"status\": \"OK\"\n }\n\n @classmethod\n def setup_method(self, method):\n self.patcher_requests_get = patch('requests.get')\n\n self.mock_requests_get = self.patcher_requests_get.start()\n self.mock_requests_get.return_value = TestSun.FAKE_SUNRISE_SUNSERT_ORG_ANSWER\n\n self.sun = Sun(latitude=TestSun.FAKE_LATITUDE,\n longitude=TestSun.FAKE_LONGITUDE,\n date=TestSun.FAKE_DATE)\n\n @classmethod\n def teardown_method(self, method):\n self.mock_requests_get = self.patcher_requests_get.stop()\n\n # ##############################################################################################\n def test__get_sunset__no_params__calou_and_today_called(self):\n self.sun.get_sunset()\n\n self.mock_requests_get.assert_called_once_with(url=\"{}/json?lat={}&lng={}&date={}\".format(\n Sun.URL,\n TestSun.FAKE_LATITUDE,\n TestSun.FAKE_LONGITUDE,\n TestSun.FAKE_DATE\n ))\n\n def test__get_sunset__no_params__retuns_sunset_hour(self):\n sunset = self.sun.get_sunset()\n\n assert sunset == TestSun.FAKE_SUNSET","sub_path":"tests/framework/test_sun.py","file_name":"test_sun.py","file_ext":"py","file_size_in_byte":2072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"389640015","text":"\n\n#calss header\nclass _CONDITIONAL():\n\tdef __init__(self,): \n\t\tself.name = \"CONDITIONAL\"\n\t\tself.definitions = [u'(relating to) a sentence, often starting with \"if\" or \"unless\", in which one half expresses something which depends on the other half: ', u'(a form of a verb) expressing the idea that one thing depends on another thing: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_conditional.py","file_name":"_conditional.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"509055946","text":"import sys, argparse\nimport random\nimport time\n\nimport pyspark\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.functions import *\nfrom pyspark.ml.recommendation import ALS, ALSModel\nfrom pyspark.mllib.evaluation import RankingMetrics\n\nfrom annoy import AnnoyIndex\nfrom tqdm import tqdm\nfrom pyspark.sql.types import *\n\ndef querying(spark, model_path, tree_path, query_df, rank):\n start = time.time()\n query_df = spark.read.parquet(query_df)\n model = ALSModel.load(model_path)\n \n def get_nn(u):\n from annoy import AnnoyIndex\n tree = AnnoyIndex(int(rank), \"dot\")\n tree.load(tree_path)\n return tree.get_nns_by_vector(u, n = 500)\n\n user_factors = model.userFactors\n\n get_nn_udf = udf(get_nn, returnType=(ArrayType(IntegerType())))\n\n query_users = [row['user'] for row in query_df.select('user').distinct().collect()]\n user_factors = user_factors.filter(col(\"id\").isin(query_users))\n \n print(\"Annoy query begins...\")\n annoy_prediction = user_factors.withColumn(\"track\", get_nn_udf(col(\"features\")))\n predictions = annoy_prediction.select(col(\"id\").alias(\"user\"), \"track\").repartition(\"user\")\n \n ground_truth = query_df.groupby(\"user\").agg(collect_list('track').alias(\"ground_truth\")).repartition(\"user\")\n \n df_result = predictions.join(broadcast(ground_truth), on = \"user\", how = \"inner\")\n predictionAndLabels = df_result.rdd.map(lambda row: (row['track'], row['ground_truth']))\n \n metrics = RankingMetrics(predictionAndLabels)\n MAP = metrics.meanAveragePrecision\n print(\"MAP (annoy)\", MAP)\n \n prec = metrics.precisionAt(500)\n print(\"Precision @ 500(annoy)\", prec)\n \n end = time.time()\n print(\"Annoy query ends\")\n print(\"Annoy query_time: \", end - start)\n\n spark.stop()\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--model_path\", help = \"path of ALS model\")\n parser.add_argument(\"--tree_path\", help = \"path to save annoy trees\")\n parser.add_argument(\"--rank\", help = \"rank\")\n parser.add_argument(\"--query_df\", help = \"path to the query file\")\n \n spark = SparkSession.builder.appName(\"query\")\\\n .config(\"spark.executor.memory\", \"16g\")\\\n .config(\"spark.driver.memory\", \"16g\")\\\n .config(\"spark.sql.shuffle.partitions\", \"50\")\\\n .getOrCreate()\n \n args = parser.parse_args()\n \n model_path = args.model_path\n rank = args.rank\n tree_path = args.tree_path\n query_df = args.query_df\n \n querying(spark, model_path, tree_path, query_df, rank)","sub_path":"Final-Project/annoy/annoy_query.py","file_name":"annoy_query.py","file_ext":"py","file_size_in_byte":2549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"181917458","text":"def verify(isbn):\n try:\n arr = convert_to_array(isbn)\n except Exception as e:\n print(e)\n return False\n\n sum = 0\n mult = 10\n for elem in arr:\n sum += (mult * elem)\n mult -= 1\n return sum % 11 == 0\n\n\ndef convert_to_array(isbn):\n arr = []\n for c in isbn[:-1]:\n if c != '-':\n arr.append(ord(c) - 48)\n if isbn[-1] == 'X':\n arr.append(10)\n else:\n arr.append(ord(isbn[-1]) - 48)\n if len(arr) != 10:\n raise Exception(\"incorrect length\")\n else:\n return arr","sub_path":"python/isbn-verifier/isbn_verifier.py","file_name":"isbn_verifier.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"481812005","text":"import ccxt\nimport pandas as pd\nimport time, datetime\nfrom function.Signals import signal_bolling\nfrom function.Trade import next_run_time\nfrom function.Trade import get_bitfinex_candle_data\nfrom function.Trade import auto_send_email\npd.set_option('expand_frame_repr', False) # 当列太多时不换行\npd.set_option('display.max_rows', 2000)\n\n\"\"\"\n自动交易主要流程\n\n# 通过while语句，不断的循环\n\n# 每次循环中需要做的操作步骤\n 1. 更新账户信息\n 2. 获取实时数据\n 3. 根据最新数据计算买卖信号 \n 4. 根据目前仓位、买卖信息，结束本次循环，或者进行交易\n 5. 交易\n\"\"\"\n\n# =====参数\ntime_interval = '15m' # 间隔运行时间，不能低于5min\ncount = 1000\nleverage_rate = 2.8\n# 设置翻墙代理\nexchange = ccxt.bitfinex({\n 'proxies': {\n 'http': 'http://127.0.0.1:1087',\n 'https': 'http://127.0.0.1:1087',\n },\n})\n\nexchange.apiKey = \"uSuQ8FMuuk1BJgQAmyQVbLtIHm4wGn0nuIL00cvIjW8\"\nexchange.secret = \"0myTvqw9Hu8amQ4TgqZdv4HEVthohQAM0558qpi3olR\"\n\nsymbol = \"ETH/USDT\"\nbase_coin = symbol.split('/')[-1]\ntrade_coin = symbol.split('/')[0]\n\npara = [100, 3] # 策略参数\n\nn = 0\n\n# =====主程序\nwhile True:\n # ===监控邮件内容\n email_title = '策略报表'\n email_content = ''\n n += 1\n #定义margin交易参数\n params = {'type': 'market'}\n print(\"======= 第%s次交易开始，交易开始时间为%s =======\" % (n, datetime.datetime.now()))\n\n # ===从服务器更新账户balance信息, {'type': 'trading'} 为margin账号\n balance = exchange.fetch_balance({'type': 'trading'})['total']\n base_coin_amount = float(balance[base_coin])\n trade_coin_amount = float(balance[trade_coin])\n\n #定义下单(包含多单，空单)的数量，通过杠杆下单，\n order_margin_amount = trade_coin_amount * leverage_rate\n\n print('margin account:', base_coin, base_coin_amount, trade_coin, trade_coin_amount)\n print('order_margin_amount:', order_margin_amount)\n # # ===sleep直到运行时间\n run_time = next_run_time(time_interval)\n time.sleep(max(0, (run_time - datetime.datetime.now()).seconds))\n\n while True: # 在靠近目标时间时\n if datetime.datetime.now() < run_time:\n continue\n else:\n break\n #===获取最新数据\n while True:\n # 获取数据\n try:\n df = get_bitfinex_candle_data(exchange, symbol, time_interval, limit=count) #此部分需要加入异常处理（网络异常），加日志\n # 判断是否包含最新的数据\n _temp = df[df['candle_begin_time'] == (run_time - datetime.timedelta(minutes=int(time_interval.strip('m'))))]\n if _temp.empty:\n print('获取数据不包含最新的数据，重新获取')\n continue\n else:\n break\n except Exception as e:\n print(\"连接bfx异常\", e)\n continue\n\n df = df[df['candle_begin_time'] < pd.to_datetime(run_time)] # 去除target_time周期的数据\n df = signal_bolling(df, para=para)\n signal = df.iloc[-1]['signal']\n print(df)\n print('\\n交易信号', signal)\n exit()\n # 获取现有账号margin 仓位\n margin_pos = exchange.private_post_positions()\n try:\n #如果有margin仓位不为空，并且为卖出信号\n if len(margin_pos) != 0 and signal == 0:\n print('\\n卖出')\n margin_pos_amount = float(exchange.private_post_positions()[0]['amount'])\n margin_profit = float(exchange.private_post_positions()[0][\"pl\"])\n #如果是做多单的话，则需要通过sell 来卖掉这些多单\n if margin_pos_amount > 0:\n order_info = exchange.create_market_sell_order(symbol=symbol, amount=margin_pos_amount, params=params)\n price = order_info['info']['price']\n #如果是做多单的话，则需要通过buy 来卖掉这些空单\n elif margin_pos_amount < 0:\n order_info = exchange.create_market_buy_order(symbol=symbol, amount=margin_pos_amount, params=params)\n price = order_info['info']['price']\n\n # 邮件标题\n email_title += '_卖出_' + trade_coin\n # 邮件内容\n email_content += '卖出信息：\\n'\n email_content += '卖出数量：' + str(margin_pos_amount) + '\\n'\n email_content += '卖出价格：' + str(price) + '\\n'\n email_content += '卖出获利：' + str(margin_profit) + '\\n'\n\n\n # 买入多单\n if len(margin_pos) == 0 and signal == 1:\n print('\\n买入')\n # 买入多单\n order_info = exchange.create_market_buy_order(symbol=symbol, amount=order_margin_amount, params=params)\n price = order_info['info']['price']\n # 邮件标题\n email_title += '_买入多单_' + trade_coin\n # 邮件内容\n email_content += '买入信息：\\n'\n email_content += '买入数量：' + str(order_margin_amount) + '\\n'\n email_content += '买入价格：' + str(price) + '\\n'\n\n # 买入空单\n if len(margin_pos) == 0 and signal == -1:\n print('\\n买入')\n # 买入多单\n order_info = exchange.create_market_sell_order(symbol=symbol, amount=order_margin_amount, params=params)\n price = order_info['info']['price']\n # 邮件标题\n email_title += '_买入空单_' + trade_coin\n # 邮件内容\n email_content += '买入信息：\\n'\n email_content += '买入数量：' + str(order_margin_amount) + '\\n'\n email_content += '买入价格：' + str(price) + '\\n'\n except Exception as e:\n print(\"交易异常：\", e)\n\n # =====发送邮件\n # 每个半小时发送邮件\n if run_time.minute % 30 == 0:\n # 发送邮件\n auto_send_email('clv89@mail.yst.com.cn', email_title, email_content)\n # =====本次交易结束\n print(email_title)\n print(email_content)\n print('=====本次运行完毕\\n')\n time.sleep(6 * 1)\n","sub_path":"bfx_trade.py","file_name":"bfx_trade.py","file_ext":"py","file_size_in_byte":6193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"6030382","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom __future__ import unicode_literals\r\n\r\nfrom django.db import models\r\nfrom django.utils.text import slugify\r\n\r\n\r\nclass Redirect(models.Model):\r\n \r\n name = models.CharField(\r\n verbose_name='Nazwa',\r\n max_length=64,\r\n )\r\n \r\n slug = models.SlugField()\r\n \r\n target_url = models.URLField(\r\n verbose_name='Adres docelowy',\r\n max_length=2048,\r\n blank = True,\r\n null = True\r\n )\r\n \r\n class Meta:\r\n verbose_name = 'Przekierowanie'\r\n verbose_name_plural = 'Przekierowania'\r\n \r\n def __unicode__(self):\r\n return self.name\r\n \r\n def save(self, *args, **kwargs):\r\n self.slug = slugify(self.name)\r\n super(Redirect, self).save(*args, **kwargs)","sub_path":"src/apro/allegro_tools/models/redirects.py","file_name":"redirects.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"425569666","text":"import tkinter\nfrom PIL import ImageGrab,ImageTk\nimport time\nimport ctypes\nroot=tkinter.Tk()\nscreenWidth=root.winfo_screenwidth()\nscreenHeight=root.winfo_screenheight()\nroot.geometry(str(screenWidth)+'x'+str(screenHeight)+'+0+0')\nroot.overrideredirect(True)\n\nroot.resizable(False,False)\n\ncanvas=tkinter.Canvas(root,bg='white',width=screenWidth,height=screenHeight)\nimage=ImageTk.PhotoImage(ImageGrab.grab())\ncanvas.create_image(screenWidth//2,screenHeight//2,image=image)\n\ndef onMouseRightClick(event):\n global root\n root.destroy()\n#canvas.bind('',onMouseRightClick)\n\nradius=20\ndef onMouseMove(event):\n global canvas\n ctypes\n x=event.x\n y=event.y\n print(x,y)\n subIm=ImageGrab.grab((x-radius,y-radius,x+radius,y+radius))\n subIm=subIm.resize((radius*3,radius*3))\n subIm=ImageTk.PhotoImage(subIm)\n #mag=tkinter.Canvas(root,bg='white',width=radius*3,height=radius*3)\n #mag.pack()\n canvas.create_image(x-70,y-70,image=subIm)\n canvas.update()\n time.sleep(0.5)\n\ncanvas.bind('',onMouseMove)\n\ncanvas.pack(fill=tkinter.BOTH,expand=tkinter.YES)\n\nroot.mainloop()","sub_path":"testmagnifier.py","file_name":"testmagnifier.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"162202732","text":"from flask import Flask, redirect, url_for, request,render_template\r\nimport pickle\r\nimport numpy as np\r\nimport sklearn\r\napp = Flask(__name__)\r\n\r\n\r\ndef ValuePredictor(to_predict_list):\r\n X = np.array([to_predict_list]).reshape(1,9)\r\n pkl = open('model.pkl', 'rb')\r\n model1=pickle.load(pkl)\r\n result=model1.predict(X)\r\n return result\r\n\r\n\r\n\r\n@app.route(\"/\")\r\ndef index():\r\n return render_template(\"index.html\");\r\n@app.route('/result', methods =['POST'])\r\ndef result():\r\n if request.method == \"POST\":\r\n to_predict_list = request.form.values()\r\n to_predict_list = list(map(float,to_predict_list))\r\n print(\"******************************************\",to_predict_list)\r\n\r\n result=ValuePredictor(to_predict_list)\r\n print(\"result value:\", result)\r\n if result == 0:\r\n prediction='NOT EXIT'\r\n\r\n else:\r\n prediction='EXIT'\r\n\r\n return render_template(\"result.html\",prediction=prediction)\r\n\r\n\r\nif __name__ == '__main__':\r\n app.run(debug = True)\r\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"153422179","text":"import subprocess\nimport glob\n\nusername = '...' # <--- CHANGE HERE\n\nsubprocess.run([\"scp\", \"-pr\", \"./call_gpu_copy.py\", username + \"@gpucluster.doc.ic.ac.uk:Documents/rl-medical\"])\n\nfiles = glob.glob(\"./examples/LandmarkDetection/DQN/*.py\")\nfor file in files:\n subprocess.run([\"scp\", \"-pr\", file, username + \"@gpucluster.doc.ic.ac.uk:Documents/rl-medical/examples/LandmarkDetection/DQN\"])\n\n\n\n\n\nsshProcess = subprocess.Popen(['ssh',\n username + \"@gpucluster.doc.ic.ac.uk\"],\n stdin=subprocess.PIPE,\n stdout = subprocess.PIPE,\n universal_newlines=True,\n bufsize=0)\n\nsshProcess.stdin.write(f\"python3 Documents/rl-medical/call_gpu_copy.py\\n\")\nsshProcess.stdin.close()\n","sub_path":"transfer.py","file_name":"transfer.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"609306494","text":"# Standard Library\nfrom datetime import datetime\n\n\n# External Libraries, sqlalchemy\nfrom sqlalchemy import create_engine\nfrom sqlalchemy import ForeignKey\nfrom sqlalchemy import Column, UnicodeText, DateTime, String, Integer, Unicode\nfrom sqlalchemy.orm import relationship, backref\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.ext.associationproxy import association_proxy\n\n\n# Base\nBase = declarative_base()\n\n\nclass User(Base):\n __tablename__ = 'users'\n\n user_id = Column(Integer(), primary_key=True)\n username = Column(String(255), index=True, nullable=False, unique=True)\n\n\nclass Tag(Base):\n __tablename__ = 'tags'\n\n tag_id = Column(Integer(), primary_key=True)\n name = Column(UnicodeText(), index=True, unique=True)\n\n def __init__(self, name):\n self.name = name\n\n\nclass Posts_Tags(Base):\n __tablename__ = 'posts_tags'\n\n post_id = Column(Integer(), ForeignKey('posts.post_id'), primary_key=True)\n tag_id = Column(Integer(), ForeignKey('tags.tag_id'), primary_key=True)\n\n\nclass Post(Base):\n __tablename__ = 'posts'\n\n post_id = Column(UnicodeText(), primary_key=True)\n title = Column(Unicode(), index=True)\n link = Column(UnicodeText())\n description = Column(UnicodeText())\n user_id = Column(Integer(), ForeignKey('users.user_id'))\n publication_date = Column(DateTime())\n last_retrieved_date = Column(DateTime(), default=datetime.now())\n updated_date = Column(DateTime(), default=datetime.now(), onupdate=datetime.now())\n\n # Relationships\n user = relationship('User', backref=backref('users'), order_by=post_id)\n tag = relationship('Tag', secondary='posts_tags')\n\n tag_names = association_proxy('tag', 'name')\n\n\ndef create_models():\n # Engine: SQLite, in memory\n # engine = create_engine('sqlite:///:memory:')\n\n # Engine: SQLite, file\n engine = create_engine('sqlite:///rss_habr.db')\n\n # Persisting the Schema\n Base.metadata.create_all(engine)\n\n # Session\n Session = sessionmaker(bind=engine)\n session = Session()\n\n return session\n\n\ndef main():\n create_models()\n\n","sub_path":"demo_project/models_habr.py","file_name":"models_habr.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"319934467","text":"import pygame\r\nimport random\r\nfrom math import sqrt, pow\r\n\r\n# Инициализирует pygame\r\npygame.init()\r\n\r\n# Создает новый экран\r\nwidth, height = 800, 600\r\nscreen = pygame.display.set_mode((width, height))\r\n\r\n# Title and logo\r\npygame.display.set_caption(\"Space aggressors\")\r\nicon = pygame.image.load('icon.png')\r\npygame.display.set_icon(icon)\r\n\r\n# Background\r\nbgcolor = (50, 50, 50)\r\nbgpicture = pygame.image.load(\"clouds-space-purple.jpg\")\r\n\r\n# Player\r\nplayerImage = pygame.image.load(\"player.png\")\r\nplayerX = width / 2 - 30 # 370\r\nplayerY = height / 3 * 2 + 80 # 480\r\nplayerX_d = 0 # Скорость движение по горизонтали\r\n\r\n# Enemy\r\n# Делаем несколько врагов и помещаем их данные в отдельные массивы\r\nnum_of_enemy = 6\r\nenemyImage = []\r\nenemyX = []\r\nenemyY = []\r\nenemyX_d = []\r\nenemyY_d = []\r\n\r\nfor i in range(num_of_enemy):\r\n enemyImage.append(pygame.image.load(\"enemy.png\"))\r\n enemyX.append(random.randint(1, 740)) # Спаунится в рандомном месте по горизонтали\r\n enemyY.append(10) # 480\r\n enemyX_d.append(random.choice([-0.2, 0.2]))\r\n enemyY_d.append(0.1)\r\n\r\n# Bullet\r\n# Ready - пулю не видно; Fire - пуля на экране\r\nbulletImage = pygame.image.load(\"bullet.png\")\r\nbulletX = 0\r\nbulletY = 480\r\nbulletX_d = 0\r\nbulletY_d = 0.7\r\nbullet_state = \"ready\"\r\n\r\n# Score\r\nscore = 0\r\nfont = pygame.font.Font('freesansbold.ttf', 24)\r\n\r\n################\r\n###-Functions-##\r\n################\r\n\r\n# FPS\r\nclock = pygame.time.Clock()\r\n\r\n\r\ndef player():\r\n screen.blit(playerImage, (playerX, playerY))\r\n\r\n\r\ndef enemy(x, y, i):\r\n screen.blit(enemyImage[i], (x, y))\r\n\r\n\r\ndef fire_bullet(x, y):\r\n global bullet_state\r\n # global bulletImage\r\n bullet_state = 'fire'\r\n screen.blit(bulletImage, (x + 16, y))\r\n\r\n\r\n# Функция проверки коллизии пули и врага\r\ndef is_collision(enemyX, enemyY, bulletX, bulletY):\r\n # Формула расчета дистанции между точками\r\n distance = sqrt((pow(enemyX - bulletX, 2)) + (pow(enemyY - bulletY, 2)))\r\n if distance < 30:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\ndef update_fps():\r\n fps = str(int(clock.get_fps()))\r\n fps_text = font.render(fps, True, pygame.Color(\"coral\"))\r\n return fps_text\r\n\r\ndef score_render():\r\n score_text = font.render(\"Score: \" + str(score), True, [255, 255, 255])\r\n return score_text\r\n\r\n\r\n# Game Loop\r\nrunning = True\r\nfps_enable = False\r\nwhile running:\r\n screen.fill(bgcolor) # Устанавливаем цвет заливки экрана\r\n screen.blit(bgpicture, (0, 0))\r\n screen.blit(score_render(), (10, 10))\r\n if fps_enable:\r\n screen.blit(update_fps(), (750, 10))\r\n\r\n for event in pygame.event.get(): # Создаем цикл, перебирающий все происходящие эвенты\r\n # Если нажимается крестик на окне => игра закрывается\r\n if event.type == pygame.QUIT:\r\n running = False\r\n\r\n # Обработка нажатий клавиш\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_LEFT:\r\n playerX_d = -0.3\r\n if event.key == pygame.K_RIGHT:\r\n playerX_d = 0.3\r\n if event.key == pygame.K_SPACE:\r\n if bullet_state == \"ready\":\r\n bulletX = playerX\r\n fire_bullet(bulletX, bulletY)\r\n\r\n if event.type == pygame.KEYUP:\r\n if event.key == pygame.K_RIGHT or pygame.K_LEFT:\r\n playerX_d = 0\r\n\r\n # Передвижение игрока\r\n playerX += playerX_d # Реализация перемещения с помощью стрелок клавиатуры\r\n if playerX <= 0: # Барьеры слева и справа\r\n playerX = 0\r\n elif playerX >= 736:\r\n playerX = 736\r\n\r\n # Передвижение врага\r\n for i in range(num_of_enemy):\r\n enemyX[i] += enemyX_d[i]\r\n if enemyX[i] <= 0: # Барьеры слева и справа\r\n enemyX_d[i] = 0.2\r\n enemyY[i] += 50\r\n elif enemyX[i] >= 738:\r\n enemyX_d[i] = -0.2\r\n enemyY[i] += 50\r\n\r\n # Коллизия пули и врага\r\n collision = is_collision(enemyX[i], enemyY[i], bulletX, bulletY)\r\n if collision:\r\n bulletY = 480\r\n bullet_state = \"ready\"\r\n enemyX[i] = random.randint(1, 740)\r\n enemyY[i] = 10\r\n score += 1\r\n # Отображаем всех врагов\r\n enemy(enemyX[i], enemyY[i], i)\r\n\r\n # Исчезновение пули, когда она вверху\r\n if bulletY <= 0:\r\n bulletY = 480\r\n bullet_state = \"ready\"\r\n\r\n # Передвижение пули\r\n if bullet_state == \"fire\":\r\n fire_bullet(bulletX, bulletY)\r\n bulletY -= bulletY_d\r\n\r\n player()\r\n # clock.tick(60)\r\n pygame.display.update() # Обновляем экран\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"383152084","text":"import torch\r\nfrom torch.autograd import Variable\r\nfrom tqdm.autonotebook import tqdm\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nimport torch.optim.lr_scheduler\r\nimport time\r\nfrom torch.utils.data import DataLoader, TensorDataset\r\nimport numpy as np\r\n\r\nfrom sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\r\nimport inspect\r\nfrom sklearn.datasets import load_digits\r\nfrom sklearn import datasets, model_selection\r\n\r\nimport pandas as pd\r\nfrom matplotlib import pyplot as plt\r\nfrom matplotlib import cm\r\n\r\nclass AlexNet(nn.Module):\r\n\r\n def __init__(self):\r\n super(AlexNet, self).__init__()\r\n self.conv1 = nn.Conv2d(1, 96, kernel_size = 5, stride =1 , padding = 2, bias = True)\r\n self.conv2 = nn.Conv2d(96, 256, kernel_size = 5, stride = 1, padding = 2, bias = True)\r\n self.conv3 = nn.Conv2d(256, 384, kernel_size = 3, stride = 1, padding = 1, bias = True)\r\n self.conv4 = nn.Conv2d(384, 384, kernel_size = 3, stride = 1, padding = 1, bias = True)\r\n self.conv5 = nn.Conv2d(384, 256, kernel_size = 3, stride = 1, padding = 1, bias = True)\r\n self.fc1 = nn.Linear(1024,2304)\r\n self.fc2 = nn.Linear(2304, 10)\r\n self.fc3 = nn.Linear(10, 10)\r\n self.norm = nn.LocalResponseNorm(size= 5)\r\n\r\n def forward(self, x):\r\n x = F.max_pool2d(self.norm(F.relu(self.conv1(x))), kernel_size = 3, stride = 2)\r\n x = F.max_pool2d(self.norm(F.relu(self.conv2(x))), kernel_size = 3, stride = 2)\r\n x = F.relu(self.conv3(x))\r\n x = F.relu(self.conv4(x))\r\n x = F.max_pool2d(F.relu(self.conv5(x)), kernel_size = 3, stride = 2)\r\n x = x.view(-1, 1024)\r\n x = F.dropout(F.relu(self.fc1(x)), 0.5)\r\n x = F.dropout(self.fc2(x), 0.5)\r\n return self.fc3(x)\r\n\r\n\r\ndef calculate_metric(metric_fn, true_y, pred_y):\r\n if \"average\" in inspect.getfullargspec(metric_fn).args:\r\n return metric_fn(true_y, pred_y, average=\"macro\")\r\n else:\r\n return metric_fn(true_y, pred_y)\r\n \r\ndef print_scores(p, r, f1, a, batch_size):\r\n for name, scores in zip((\"precision\", \"recall\", \"F1\", \"accuracy\"), (p, r, f1, a)):\r\n print(f\"\\t{name.rjust(14, ' ')}: {sum(scores)/batch_size:.4f}\")\r\n\r\n\r\nstart_ts = time.time()\r\n\r\nmnist = datasets.fetch_openml('mnist_784', data_home=\"mnist_784\")\r\nmnist_label = mnist.target\r\nmnist_data = mnist.data / 255\r\n\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n\r\ntrain_size = 50000\r\nval_size = 10000\r\ntest_size = 10000\r\n\r\n\r\ntrain_X, test_X, train_Y, test_Y = model_selection.train_test_split(mnist_data, mnist_label, train_size = 60000, test_size = test_size)\r\ntrain_X, val_X, train_Y, val_Y = model_selection.train_test_split(train_X, train_Y, train_size = train_size, test_size = val_size)\r\n\r\nval_X = val_X.reshape((len(val_X),1,28,28))\r\ntrain_X = train_X.reshape((len(train_X),1,28,28))\r\ntest_X = test_X.reshape((len(test_X),1,28,28))\r\n\r\nval_X = torch.tensor(val_X, dtype=torch.float)\r\nval_Y = torch.tensor([int(x) for x in val_Y])\r\ntrain_X = torch.tensor(train_X, dtype=torch.float)\r\ntrain_Y = torch.tensor([int(x) for x in train_Y])\r\ntest_X = torch.tensor(test_X, dtype=torch.float)\r\ntest_Y = torch.tensor([int(x) for x in test_Y])\r\n\r\nif torch.cuda.is_available() : \r\n train_X = train_X.to(device)\r\n train_Y = train_Y.to(device)\r\n test_X = test_X.to(device)\r\n test_Y = test_Y.to(device)\r\n val_X = val_X.to(device)\r\n val_Y = val_Y.to(device)\r\n\r\n# data is already loaded to gpu, so they warped with DataLoader in gpu\r\ntrain = TensorDataset(train_X, train_Y)\r\nval = TensorDataset(val_X, val_Y)\r\ntrain_loader = DataLoader(train, batch_size = 100, shuffle = True)\r\nval_loader = DataLoader(val, batch_size = 100, shuffle = True)\r\n\r\nmodel = AlexNet()\r\n\r\n# Parameter configuration\r\nmodel = model.to(device)\r\ncriterion = nn.CrossEntropyLoss()\r\nlearning_rate = 0.001\r\n#optimizer = optim.Adadelta(model.parameters())\r\noptimizer = optim.Adam(model.parameters(), lr = learning_rate)\r\nscheduler = optim.lr_scheduler.LambdaLR(optimizer=optimizer, lr_lambda = lambda epoch : 0.9 ** epoch)\r\ntrain_loss_list = []\r\nval_loss_list = []\r\nbatches = len(train_loader)\r\nval_batches = len(val_loader)\r\nepochs = 12\r\n\r\n# Learning mode starts\r\nfor epoch in range(epochs):\r\n total_loss = 0.0\r\n scheduler.step()\r\n progress = tqdm(enumerate(train_loader), total = batches)\r\n precision, recall, f1, accuracy = [], [], [], []\r\n model.train()\r\n\r\n for i, data in progress:\r\n train_x, train_y = data\r\n optimizer.zero_grad()\r\n output = model(train_x)\r\n loss = criterion(output, train_y)\r\n loss.backward()\r\n optimizer.step()\r\n total_loss += loss.item()\r\n #progress.set_description(\"Loss : {:.4f}\".format(total_loss/(i+1)))\r\n\r\n if(i+1) % 100 == 0 :\r\n model.eval()\r\n with torch.no_grad():\r\n val_loss = 0.0\r\n for j, val in enumerate(val_loader):\r\n val_x, val_y = val\r\n val_output = model(val_x)\r\n v_loss = criterion(val_output, val_y)\r\n val_loss += v_loss.item()\r\n predicted_classes = torch.max(val_output, 1)[1]\r\n for acc, metric in zip((precision, recall, f1, accuracy), \r\n (precision_score, recall_score, f1_score, accuracy_score)):\r\n acc.append(\r\n calculate_metric(metric, val_y.cpu(), predicted_classes.cpu())\r\n )\r\n train_loss_list.append(total_loss / 100)\r\n val_loss_list.append(val_loss / len(val_loader))\r\n temp_loss = total_loss\r\n total_loss = 0\r\n\r\n torch.cuda.empty_cache()\r\n for param_group in optimizer.param_groups:\r\n print(\"Current Learning rate is : {}\".format(param_group['lr']))\r\n print(f\"Epoch {epoch+1}/{epochs}, training loss : {temp_loss / 100}, validation loss : {val_loss / val_batches}\")\r\n print_scores(precision, recall, f1, accuracy, batches)\r\n\r\nprint(f\"Training time: {time.time()-start_ts}s\")\r\n \r\n#Evaluation\r\nnum = int(epochs * batches / 100)\r\niters = range(0, num)\r\nplt.plot(iters, train_loss_list, 'g', label='Training loss')\r\nplt.plot(iters, val_loss_list, 'b', label = 'Validation loss')\r\nplt.title('Training and Validation Loss')\r\nplt.xlabel('iters')\r\nplt.ylabel('Loss')\r\nplt.legend()\r\nplt.show()\r\n\r\nplt.savefig(\"MNIST_AlexNet_LrCurve.png\")\r\n\r\n\r\ntest = TensorDataset(test_X, test_Y)\r\ntest_loader = DataLoader(test, batch_size = 1000, shuffle = False)\r\ntrain_loader = DataLoader(train, batch_size = 1000, shuffle = False)\r\n\r\nprecision2, recall2, f12, accuracy2 = [], [], [], []\r\nprecision3, recall3, f13, accuracy3 = [], [], [], []\r\nresult2 = []\r\nresult3 = []\r\n\r\ntest_loss = 0\r\nwith torch.no_grad():\r\n for k, data in enumerate(test_loader):\r\n test_x, test_y = data\r\n test_output = model(test_x)\r\n t_loss = criterion(test_output, test_y)\r\n test_loss += t_loss.item()\r\n predicted_classes2 = torch.max(test_output, 1)[1]\r\n result2.append(predicted_classes2)\r\n for acc2, metric2 in zip((precision2, recall2, f12, accuracy2), \r\n (precision_score, recall_score, f1_score, accuracy_score)):\r\n acc2.append(\r\n calculate_metric(metric2, test_y.cpu(), predicted_classes2.cpu())\r\n )\r\nprint(\"test_score\")\r\nprint_scores(precision2, recall2, f12, accuracy2, len(test_loader))\r\n\r\ntrain_loss = 0\r\nwith torch.no_grad():\r\n for h, data in enumerate(train_loader):\r\n train_x, train_y = data\r\n train_output = model(train_x)\r\n train_loss = criterion(train_output, train_y)\r\n train_loss += t_loss.item()\r\n predicted_classes3 = torch.max(train_output, 1)[1]\r\n result3.append(predicted_classes3)\r\n for acc3, metric3 in zip((precision3, recall3, f13, accuracy3), \r\n (precision_score, recall_score, f1_score, accuracy_score)):\r\n acc3.append(\r\n calculate_metric(metric3, train_y.cpu(), predicted_classes3.cpu())\r\n )\r\n\r\nprint(\"train_score\")\r\nprint_scores(precision3, recall3, f13, accuracy3, len(train_loader))\r\n\r\n\r\n## Precision Check\r\naccumulated_result1 = []\r\naccumulated_result2 = []\r\nfor i in range(0, len(result2)):\r\n accumulated_result1 = np.concatenate((accumulated_result1, result2[i].cpu().numpy()), axis = None)\r\n\r\nfor i in range(0, len(result3)):\r\n accumulated_result2 = np.concatenate((accumulated_result2, result3[i].cpu().numpy()), axis = None)\r\n\r\n\r\nprint(\"test precision score : \", precision_score(test_Y.cpu().numpy(), accumulated_result1, average = None))\r\nprint(\"train precision score : \", precision_score(train_Y.cpu().numpy(), accumulated_result2, average = None))","sub_path":"source_code/MNIST_AlexNet.py","file_name":"MNIST_AlexNet.py","file_ext":"py","file_size_in_byte":8879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"92630976","text":"# command processor \n\nimport Aurora.commandsDictionary as chatbox_commands_dictionary\nimport Aurora.commands as chatbox_commands\nfrom Aurora.utils import *\n\nimport sys\nimport json\nimport re\n\ndef process_command(command, args):\n errors = check_command_and_args_errors(command, args)\n if len(errors) > 0:\n result = None\n beautified_result = '
'.join(errors)\n else:\n \targs_dict = json.loads(args)\n \tresult = getattr(chatbox_commands, command)(**args_dict)\n \tbeautified_result = beautitian.beautify_result(result)\n \n return {\n 'result': result,\n 'beautified_result': beautified_result,\n }\n\ndef get_command_name_list():\n command_args_dictionary = chatbox_commands_dictionary.COMMAND_ARGS_DICTIONARY\n command_name_list = []\n\n for command, args in command_args_dictionary.items():\n command_name_list.append(command)\n\n return command_name_list\n\ndef get_command_list_for_view():\n command_args_dictionary = chatbox_commands_dictionary.COMMAND_ARGS_DICTIONARY\n command_list_for_view = []\n\n for command_name, args in command_args_dictionary.items():\n command_display = ''+command_name+' : { '\n command_default = ''+command_name+' : { '\n command_desc = 'Required arguments: [ '\n \n for arg_name, arg_properties in args.items():\n command_display += '\"'+arg_name+'\": '+arg_properties['type']+', '\n command_default += '\"'+arg_name+'\": '+arg_properties['default']+', '\n if arg_properties['required']:\n command_desc += arg_name+', '\n \n command_display = command_display[:-2]+' }'\n command_default = command_default[:-2]+' }'\n command_desc = command_desc[:-2]+' ]' if len(command_desc) > 2 else command_desc+']'\n\n command_list_for_view.append({\n 'command_name' : command_name,\n 'command_display': command_display,\n 'command_default': command_default,\n 'command_desc' : command_desc,\n })\n\n return command_list_for_view\n\ndef check_command_and_args_errors(command_name, args_str):\n errors = []\n command_args_dictionary = chatbox_commands_dictionary.COMMAND_ARGS_DICTIONARY\n commands_names = command_args_dictionary.keys()\n\n if not _is_command_exists(command_name):\n errors.append('Command does not exist')\n return errors\n\n if not _is_valid_JSON(args_str):\n errors.append(\"Your given argument is not valid JSON string\")\n return errors\n\n given_args_name_value_dict = json.loads(args_str)\n command_args_name_info_dict = command_args_dictionary[command_name]\n\n _check_required_args(given_args_name_value_dict, command_args_name_info_dict, errors)\n _check_valid_args(given_args_name_value_dict, command_args_name_info_dict, errors)\n _check_args_attributes(given_args_name_value_dict, command_args_name_info_dict, errors)\n _check_minimum_maximum_dependency(given_args_name_value_dict, command_args_name_info_dict, errors)\n\n return errors\n\ndef _is_command_exists(command_name):\n commands_names = chatbox_commands_dictionary.COMMAND_ARGS_DICTIONARY.keys()\n if command_name in commands_names:\n return True\n return False\n\ndef _is_valid_JSON(json_str):\n try:\n json.loads(json_str)\n return True\n except ValueError:\n return False\n\ndef _check_required_args(given_args, command_args, errors):\n given_args_names = given_args.keys()\n required_args_names = [ key for key, value in command_args.items() if value['required'] ] \n absent_args_names = list(set(required_args_names) - set(given_args_names))\n if len(absent_args_names) > 0:\n errors.append('Arguments ['+','.join(absent_args_names)+'] are not defined')\n return\n\ndef _check_valid_args(given_args, command_args, errors):\n given_args_names = given_args.keys()\n command_args_names = command_args.keys()\n unnecessary_args_names = list(set(given_args_names) - set(command_args_names))\n if len(unnecessary_args_names) > 0:\n errors.append('Arguments ['+','.join(unnecessary_args_names)+'] are not valid')\n return\n\ndef _check_args_attributes(given_args, command_args, errors):\n given_args_names = given_args.keys()\n command_args_names = command_args.keys()\n common_args_names = list(set(given_args_names).intersection(command_args_names))\n\n for arg_name in common_args_names:\n command_arg_info = command_args[arg_name]\n given_arg_value = given_args[arg_name]\n\n dynamic_method_arg_dict = {\n 'arg_name': arg_name,\n 'given_arg_value': given_arg_value,\n 'command_arg_info': command_arg_info,\n 'errors': errors\n }\n \n for attr_name, attr_value in command_args[arg_name].items():\n dynamic_method_name = '__check_arg_attribute_'+attr_name\n getattr(sys.modules[__name__], dynamic_method_name)(**dynamic_method_arg_dict)\n\n return\n\ndef _check_minimum_maximum_dependency(given_args, command_args, errors):\n if all (k in command_args for k in ('minimum','maximum')):\n minimum_value = given_args['minimum'] if 'minimum' in given_args else command_args['minimum']['default']\n maximum_value = given_args['maximum'] if 'maximum' in given_args else command_args['maximum']['default']\n if minimum_value >= maximum_value:\n errors.append('Value of argument [maximum] must be bigger than [minimum]')\n return\n\ndef __check_arg_attribute_type(arg_name, given_arg_value, command_arg_info, errors):\n if given_arg_value is None:\n return\n\n given_arg_type = type(given_arg_value).__name__\n command_arg_type = command_arg_info['type']\n if command_arg_type == 'number':\n if given_arg_type != 'int' and given_arg_type != 'float':\n errors.append('Type of argument ['+arg_name+'] should be '+command_arg_type)\n return\n elif command_arg_type == 'text':\n if given_arg_type != 'str':\n errors.append('Type of argument ['+arg_name+'] should be '+command_arg_type)\n return\n elif command_arg_type == 'email':\n if given_arg_type != 'str':\n errors.append('Type of argument ['+arg_name+'] should be '+command_arg_type+' in Text format')\n elif not re.match(r\"[^@]+@[^@]+\\.[^@]+\", given_arg_value):\n errors.append('Value of argument ['+arg_name+'] should be a valid '+command_arg_type)\n return\n elif command_arg_type == 'password':\n if given_arg_type != 'str':\n errors.append('Type of argument ['+arg_name+'] should be '+command_arg_type+' in Text format')\n elif not re.match(r\"^(?=.*[a-z])(?=.*[A-Z])(?=.*\\d).+$\", given_arg_value):\n errors.append('Value of argument ['+arg_name+'] should contain one uppercase character, one lowercase character and one digit')\n return\n elif given_arg_type != command_arg_type:\n errors.append('Type of argument ['+arg_name+'] should be '+command_arg_type)\n return\n return\n\ndef __check_arg_attribute_required(arg_name, given_arg_value, command_arg_info, errors):\n if command_arg_info['required'] and given_arg_value is None:\n errors.append('Value of argument ['+arg_name+'] is required')\n return\n \ndef __check_arg_attribute_minimum(arg_name, given_arg_value, command_arg_info, errors):\n given_arg_type = type(given_arg_value).__name__\n if not ( given_arg_type == 'int' or given_arg_type == 'float' ):\n return\n minimum_value = command_arg_info['minimum']\n if given_arg_value < minimum_value:\n errors.append('Value of argument ['+arg_name+'] should be bigger than '+str(minimum_value))\n return\n\ndef __check_arg_attribute_maximum(arg_name, given_arg_value, command_arg_info, errors):\n given_arg_type = type(given_arg_value).__name__\n if not ( given_arg_type == 'int' or given_arg_type == 'float' ):\n return\n maximum_value = command_arg_info['maximum']\n if given_arg_value > maximum_value:\n errors.append('Value of argument ['+arg_name+'] should be smaller than '+str(maximum_value))\n return\n\ndef __check_arg_attribute_default(arg_name, given_arg_value, command_arg_info, errors):\n return\n\n","sub_path":"Aurora/commandProcessor.py","file_name":"commandProcessor.py","file_ext":"py","file_size_in_byte":8267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"239541730","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Dec 21 21:27:54 2019\r\n\r\n@author: chris\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nfrom random import randrange\r\nimport timeit\r\n\r\n#findMin for O(n**2)\r\ndef findMin(alist):\r\n \r\n start = timeit.default_timer()\r\n \r\n overallmin = alist[0]\r\n for i in alist:\r\n issmallest = True\r\n for j in alist:\r\n if i > j:\r\n issmallest = False\r\n if issmallest:\r\n overallmin = i\r\n \r\n duration = timeit.default_timer()-start\r\n \r\n return overallmin,duration\r\n\r\nnumbers=[]\r\nfor i in range(1,1001):\r\n numbers.append(i)\r\n \r\n \r\ndef findMin2 (alist):\r\n \r\n start = timeit.default_timer()\r\n \r\n minSoFar = alist[-1]\r\n for i in range(len(alist)):\r\n if alist[i] < minSoFar:\r\n minSoFar = alist[i]\r\n \r\n end = timeit.default_timer()-start\r\n \r\n return minSoFar, end\r\n \r\n\r\nyListSize=[]\r\nxDuration=[]\r\nyListSize1=[]\r\nxDuration1=[]\r\n\r\n#creating lists for O(n)\r\nfor listSize in range(1000,10001,1000):\r\n alist = [randrange(100000) for x in range(listSize)]\r\n start = timeit.default_timer()\r\n print(findMin2(alist))\r\n end = timeit.default_timer()-start\r\n \r\n yListSize.append(listSize)\r\n xDuration.append(end)\r\n \r\n# print(\"size: %d time %f\" % (listSize, end))\r\n \r\n\r\n#creating lists for O(n**2)\r\nfor listSize in range(1000,10001,1000):\r\n alist = [randrange(100000) for x in range(listSize)]\r\n start = timeit.default_timer()\r\n print(findMin(alist))\r\n end = timeit.default_timer()-start\r\n \r\n yListSize1.append(listSize)\r\n xDuration1.append(end)\r\n \r\n# print(\"size: %d time %f\" % (listSize, end))\r\n\r\n#sorting lists\r\nxDuration.sort()\r\nyListSize.sort()\r\nxDuration1.sort()\r\nyListSize1.sort()\r\n\r\n#plotting data O(n**2)\r\nplt.title(\"O(n**2) & O(n) notation\")\r\nplt.xlabel(\"Duration\")\r\nplt.ylabel(\"List size\")\r\nplt.plot(xDuration,yListSize)","sub_path":"n^2_minNumber.py","file_name":"n^2_minNumber.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"189213377","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Author: Labstep \n\nimport labstep\nimport os\nimport labstep.generic.entity.repository as entityRepository\nfrom labstep.entities.experimentProtocol.model import ExperimentProtocol\nfrom labstep.entities.protocolVersion.model import ProtocolVersion\n\n\ndef getParent():\n \"\"\"\n Get Parent based on Jupyter environment variables.\n\n \"\"\"\n if ('LABSTEP_API_KEY' not in os.environ.keys()):\n raise Exception(\"Not in jupyter\")\n\n user = labstep.authenticate()\n\n if ('LABSTEP_JUPYTER_EXPERIMENT_GUID' in os.environ.keys()):\n experimentGuid = os.environ['LABSTEP_JUPYTER_EXPERIMENT_GUID']\n if experimentGuid:\n return entityRepository.getEntity(user, ExperimentProtocol, experimentGuid, useGuid=True)\n\n if('LABSTEP_JUPYTER_PROTOCOL_GUID' in os.environ.keys()):\n protocolGuid = os.environ['LABSTEP_JUPYTER_PROTOCOL_GUID']\n\n if protocolGuid:\n return entityRepository.getEntity(user, ProtocolVersion, protocolGuid, useGuid=True)\n\n raise Exception(\"No Jupyter Parent Found\")\n","sub_path":"labstep/service/jupyter.py","file_name":"jupyter.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"375729223","text":"from telethon import events\nimport subprocess\nimport asyncio\nimport time\n\n\n@command(pattern=\"^.cmds\", outgoing=True)\nasync def install(event):\n if event.fwd_from:\n return\n cmd = \"ls userbot/plugins\"\n process = await asyncio.create_subprocess_shell(\n cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE\n )\n stdout, stderr = await process.communicate()\n o = stdout.decode()\n _o = o.split(\"\\n\")\n o = \"\\n\".join(_o)\n OUTPUT = f\"**List of Plugins:**\\n{o}\\n\\n\"\n await event.edit(OUTPUT)\n","sub_path":"userbot/plugins/command_list.py","file_name":"command_list.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"401216536","text":"class Node(object):\n def __init__(self,data):\n self.data=data\n self.next=None\nclass Stack(object):\n def __init__(self):\n self.node=Node(None)\n self.head=self.node\n self.size=0\n def is_empty(self):\n return self.size==0\n def get_size(self):\n return self.size\n def push(self,data):\n node=Node(data)\n node.next=self.head.next\n self.head.next=node\n self.size+=1\n def pop(self):\n if not self.is_empty():\n current_node=self.head.next\n if self.get_size()==1:\n self.head.next=None\n self.size-=1\n else:\n self.head.next=self.head.next.next\n self.size-=1\n return current_node.data\n else:\n print(\"栈为空\")\n def top(self):\n if not self.is_empty():\n return self.head.next.data\n else:\n print(\"栈为空\")\nclass Test():\n def BracktMatch(self,strl):\n ls=Stack()\n i=0\n while i power:\n size /= power\n n += 1\n size = round(size, 2)\n size = str(size) + \" \" + powers[n] + 'B'\n return size\n else:\n return \"\"\n\n\ndef readable_time(time):\n if time < 0:\n return \"\"\n else:\n days = time // 86400\n hours = (time - days * 86400) // 3600\n minutes = (time - days * 86400 - hours * 3600) // 60\n seconds = round((time - days * 86400 - hours * 3600 - minutes * 60), 2)\n time = (\"{}d:\".format(days) if days else \"\") + \\\n (\"{}h:\".format(hours) if hours else \"\") + \\\n (\"{}m:\".format(minutes) if minutes else \"\") + \\\n (\"{}s\".format(seconds) if seconds else \"\")\n return time\n\n\ndef is_device(device):\n return isinstance(device, (type, myjdapi.Jddevice))\n\n\ndef longest_substr(data):\n substr = ''\n if len(data) > 1 and len(data[0]) > 0:\n for i in range(len(data[0])):\n for j in range(len(data[0]) - i + 1):\n if j > len(substr) and all(data[0][i:i + j] in x for x in data):\n substr = data[0][i:i + j]\n return substr\n\n\ndef check_hoster(to_check, configfile):\n hosters = RssConfig(\"Hosters\", configfile).get_section()\n for hoster in hosters:\n if hosters[hoster] == \"True\":\n if hoster in to_check.lower():\n return True\n return False\n","sub_path":"rsscrawler/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":5861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"584690719","text":"import matplotlib.pylab as plt\nimport networkx as nx\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics.pairwise import distance_metrics\n\n\ndef handle_2d_plot(\n embedding,\n kind,\n color=None,\n xlabel=None,\n ylabel=None,\n show_operations=False,\n annot=False,\n axis_option=None,\n):\n \"\"\"\n Handles the logic to perform a 2d plot in matplotlib.\n\n **Input**\n\n - embedding.md: a `whatlies.Embedding` object to plot\n - kind: what kind of plot to make, can be `scatter`, `arrow` or `text`\n - color: the color to apply, only works for `scatter` and `arrow`\n - xlabel: manually override the xlabel\n - ylabel: manually override the ylabel\n - show_operations: setting to also show the applied operations, only works for `text`\n - axis_option: a string which is passed to `matplotlib.pyplot.axis` function.\n \"\"\"\n name = embedding.name if show_operations else embedding.orig\n if kind == \"scatter\":\n if color is None:\n color = \"steelblue\"\n plt.scatter([embedding.vector[0]], [embedding.vector[1]], c=color)\n if kind == \"arrow\":\n plt.scatter([embedding.vector[0]], [embedding.vector[1]], c=\"white\", s=0.01)\n plt.quiver(\n [0],\n [0],\n [embedding.vector[0]],\n [embedding.vector[1]],\n color=color,\n angles=\"xy\",\n scale_units=\"xy\",\n scale=1,\n )\n plt.text(embedding.vector[0] + 0.01, embedding.vector[1], name)\n if (kind == \"text\") or annot:\n plt.text(embedding.vector[0] + 0.01, embedding.vector[1], name)\n\n plt.xlabel(\"x\" if not xlabel else xlabel)\n plt.ylabel(\"y\" if not ylabel else ylabel)\n if axis_option is not None:\n plt.axis(axis_option)\n\n\ndef plot_graph_layout(embedding_set, kind=\"cosine\", **kwargs):\n \"\"\"\n Handles the plotting of a layout graph using the embeddings in an embeddingset as input.\n\n **Input**\n\n - embeddings: a set of `whatlies.Embedding` objects to plot\n - kind: distance metric options: 'cityblock', 'cosine', 'euclidean', 'l2', 'l1', 'manhattan',\n \"\"\"\n\n vectors = [token.vector for k, token in embedding_set.items()]\n label_dict = {i: w for i, (w, _) in enumerate(embedding_set.items())}\n dist_fnc = distance_metrics()[kind]\n dist = dist_fnc(np.array(vectors), np.array(vectors))\n # Greate graph\n graph = nx.from_numpy_matrix(dist)\n distance = pd.DataFrame(dist).to_dict()\n # Chhange layout positions of the graph\n pos = nx.kamada_kawai_layout(graph, dist=distance)\n # Draw nodes and labels\n nx.draw_networkx_nodes(graph, pos, node_color=\"b\", alpha=0.5)\n nx.draw_networkx_labels(graph, pos, labels=label_dict, **kwargs)\n","sub_path":"whatlies/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":2721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"175473667","text":"#!/usr/bin/env python3\n\nfrom io import StringIO\nfrom parser import Parser\nfrom assembler import Assembler\n\nimport os\nimport subprocess\nimport unittest\n\ndef run_builtin_assembler(file):\n cmd = [\"../../tools/Assembler.sh\", file]\n subprocess.check_call(cmd)\n\ndef read_builtin_output(file):\n file = file.replace(\".asm\", \".hack\")\n with open(file) as resource:\n content = resource.read()\n return content.strip()\n\nclass AssemblerTest(unittest.TestCase):\n def setUp(self):\n self.prog_dirs = [\"add\", \"max\", \"rect\", \"pong\"]\n\n def test_programs(self):\n # dir is keyword so lets avoid overriding\n for _dir in self.prog_dirs:\n test_files = [file for file in os.listdir(_dir) if file.endswith(\".asm\")]\n\n for file in test_files:\n path_to_file = f\"{_dir}/{file}\"\n\n\n assembler = Assembler()\n with open(path_to_file, \"r\") as program:\n actual = assembler.assemble(program)\n\n run_builtin_assembler(path_to_file)\n expected = read_builtin_output(path_to_file)\n\n actual_lines = actual.split(\"\\n\")\n expected_lines = expected.split(\"\\n\")\n\n a, e = len(actual_lines), len(expected_lines)\n self.assertEqual(a, e, f\"lines should match {a} != {e}\")\n\n matches = list(zip(expected_lines, actual_lines))\n\n for i in range(len(matches)):\n (expected, actual) = matches[i]\n self.assertEqual(expected, actual, f\"should match line {(i+1)} of {(file)}\")\n\n def tearDown(self):\n # dir is keyword so lets avoid overriding\n for _dir in self.prog_dirs:\n files = [file for file in os.listdir(_dir) if file.endswith(\".hack\")]\n for file in files:\n os.remove(f\"{_dir}/{file}\")\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"projects/06/src/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"619212269","text":"import joblib\nimport cv2\nimport numpy as np\nimport os\n\n\nsift=cv2.xfeatures2d.SIFT_create()\nknn = joblib.load('./knn_model.m')\nsvm = joblib.load('./svm_model.m')\n\ndef predict(img):\n # pre-process\n gray= img\n gray=cv2.resize(gray, (400, 400), interpolation=cv2.INTER_AREA)\n gray=cv2.equalizeHist(gray)\n keypoints, des=sift.detectAndCompute(gray, None)\n\n # predict\n img_clustered_word = knn.predict(des)\n img_bow_hist = np.array(np.bincount(img_clustered_word, minlength=252))\n X = img_bow_hist\n\n pred = svm.predict([X])\n return pred\n\n\n\nif __name__ == '__main__':\n filepath = './Test/pros/'\n filelist = os.listdir(filepath)\n for filename in filelist:\n print(filename)\n if filename == \".gitkeep\":\n continue\n # print(os.path.join(filepath, filename))\n img=cv2.imread(os.path.join(filepath, filename),0)\n # cv2.imshow(\"img\", img)\n # cv2.waitKey(0)\n result = predict(img)\n if result == 0:\n print(\"fan\")\n if result == 1:\n print(\"zheng\")","sub_path":"bottle-caps-recognition-ui/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"300656466","text":"import tensorflow as tf\n\n# create a variable whose original value is 2.\na = tf.Variable(2, name=\"scalar\")\n\n# assign a * 2 to a and call that op a_times_two\na_times_two = a.assign(a * 2)\n\ninit = tf.global_variables_initializer()\n\nwith tf.Session() as session:\n session.run(init)\n writer = tf.summary.FileWriter('./graphs', session.graph)\n print(session.run(a_times_two))\n print(session.run(a_times_two))\n print(session.run(a_times_two))\n\nwriter.close()\n\n","sub_path":"tensorflow-ex27.py","file_name":"tensorflow-ex27.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"65247533","text":"from distutils.core import setup\n\nversion = '0.2.0'\n\nsetup(\n name=\"django-colorfield\",\n version=version,\n keywords=[\"django\", \"color\"],\n author='Sean Marlow',\n author_email='kodemaven@gmail.com',\n license='MIT',\n long_description=\"A small app providing a colorpicker field for django\",\n description=\"A small app providing a colorpicker field for django\",\n classifiers=[\n \"License :: OSI Approved :: MIT License\",\n ],\n packages=['colorfield'],\n package_data={\n 'colorfield': ['static/colorfield/colorpicker/*', 'templates/colorfield/*'],\n },\n install_requires=['django>=1.7'],\n requires=['django (>=1.7)'],\n)\n\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"381265247","text":"\nfrom .models import Animal,Cage\nfrom .serializers import AnimalSerializer,CageSerializer, UserSerializer\nfrom rest_framework.decorators import detail_route\nfrom rest_framework.response import Response\nfrom django.contrib.auth.models import User\nfrom rest_framework import permissions, renderers, viewsets\nfrom .permissions import IsOwnerOrReadOnly\nfrom rest_framework.decorators import api_view\nfrom rest_framework.reverse import reverse\nfrom django.http import Http404\n\n@api_view(['GET'])\ndef api_root(request,format=None):\n return Response({\n 'Animal': reverse('animal-list', request=request, format=format),\n 'Cage': reverse('cage-list', request=request, format=format),})\n\n\n\nclass AnimalViewSet(viewsets.ModelViewSet):\n \"\"\" YOU CAN'T PUT LIONS AND FLAMINGOS IN THE SAME CAGE! \"\"\"\n\n queryset = Animal.objects.all()\n serializer_class = AnimalSerializer\n permission_classes = (\n permissions.IsAuthenticatedOrReadOnly,\n IsOwnerOrReadOnly,)\n\n def perform_update(self, serializer):\n\n\n last_cage = self.request.POST['cid'].replace(\"/\",\"\").replace(\"http:127.0.0.1:8000apicage\",\"\") # the cage id we want to put animal in it\n c = Cage.objects.filter(id=last_cage)[0].animals.all()\n\n if c: # if there are other animals in the cage\n newSizeOfCage = len(c) + 1 # looking for the new size\n size = Cage.objects.filter(id=last_cage)[0].size # the old size of the cage\n\n value = 0\n if newSizeOfCage <= size: # if there is a empty space\n\n if self.request.POST['type'] == 'Lion': # if the animal is lion\n for ty in Cage.objects.filter(name=last_cage)[0].animals.all(): # look for is there any flamingo in the cage\n if ty.type == 'Flamingo':\n value += 1\n if value == 0: # there is no flamingo\n serializer.save(owner=self.request.user) # save it\n else: # so there is flamingo\n raise Http404(\"There is flamingo in the cage. Pick another cage.\")\n\n if self.request.POST['type'] == 'Flamingo': # if the animal is flamingo\n for ty in c: # look for is there any lion in the cage\n if ty.type == 'Lion':\n value += 1\n if value == 0: # there is no lion\n serializer.save(owner=self.request.user) # save it\n else: # so there is lion\n raise Http404(\"There is lion in the cage. Pick another cage.\")\n else: # for other animals, just save it\n serializer.save(owner=self.request.user)\n\n else: # if there is no emty space\n raise Http404(\"The cage is full. Pick an another cage\")\n elif Cage.objects.filter(id=last_cage)[0].size >= 1: # there is no other animal and there is space for an animal\n serializer.save(owner=self.request.user)\n else:\n raise Http404(\"Pick another cage.\")\n\n\n\n\n def perform_create(self, serializer):\n\n cage = self.request.POST['cid'].replace(\"/\",\"\").replace(\"http:127.0.0.1:8000apicage\",\"\") #cage's id\n\n c = Cage.objects.filter(id=cage)[0].animals.all()\n if c: # if there are other animals in the cage\n newSizeOfCage = len(c) + 1 # looking for the new size of the cage\n size = Cage.objects.filter(id=cage)[0].size # the old size of the cage\n value = 0\n if newSizeOfCage <= size: # if there is a empty space\n if self.request.POST['type'] == 'Lion': # if the animal is lion\n for ty in c: # look for is there any flamingo in the cage\n if ty.type == 'Flamingo':\n value += 1\n if value==0: # there is no flamingo\n serializer.save(owner=self.request.user) # save it\n else: # so there is flamingo\n raise Http404(\"There is flamingo in the cage. Pick another cage.\")\n\n if self.request.POST['type'] == 'Flamingo': # if the animal is flamingo\n for ty in c: # look for is there any lion in the cage\n if ty.type == 'Lion':\n value += 1\n if value == 0: # there is no lion\n serializer.save(owner=self.request.user) # save it\n else: # so there is lion\n raise Http404(\"There is lion in the cage. Pick another cage.\")\n\n else: # for other animals, just save it\n serializer.save(owner=self.request.user)\n\n else: # if there is no empty space\n raise Http404(\"The cage is full. Pick an another cage\")\n elif Cage.objects.filter(id=cage)[0].size>=1: # there is no other animal and there is space for an animal\n serializer.save(owner=self.request.user)\n else:\n raise Http404(\"Pick another cage.\")\n\n \n\nclass CageViewSet(viewsets.ModelViewSet):\n\n queryset = Cage.objects.all()\n serializer_class = CageSerializer\n permission_classes = (\n permissions.IsAuthenticatedOrReadOnly,\n IsOwnerOrReadOnly,)\n\n @detail_route(renderer_classes=[renderers.StaticHTMLRenderer])\n def highlight(self, request, *args, **kwargs):\n Cage = self.get_object()\n return Response(Cage.highlighted)\n\n def perform_create(self, serializer):\n serializer.save(owner=self.request.user)\n\nclass UserViewSet(viewsets.ReadOnlyModelViewSet):\n\n queryset = User.objects.all()\n serializer_class = UserSerializer\n","sub_path":"zoo/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"503981832","text":"from django.conf.urls import patterns, include, url\nfrom django.http import HttpResponseRedirect\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n \n (r'^$', lambda r : HttpResponseRedirect('/blog')),\n url(r'^admin/', include(admin.site.urls)),\n url(r'^haiku/', include('haikuba.urls')),\n url(r'^', 'haikuba.views.home')\n\n)\n","sub_path":"haiku/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"545104295","text":"# Autor: Mónica Monserrat Palacios Rodríguez, A01375137\n# UTF-8\n# Tarea 5\n\nimport pygame\nimport math\nfrom random import randint\n\n#Se establecen las variables constantes\nANCHO = 800\nALTO = 800\n\nNEGRO = (0,0,0)\nBLANCO = (255, 255, 255)\nopcion = 1\n\n#Primera función, dibuja cuadros y círculos crecientes\ndef dibujarCuadrosYCirculos():\n pygame.init () #Se inicia pygame\n ventana = pygame.display.set_mode((ANCHO, ALTO))\n reloj = pygame.time.Clock()\n termina = False\n\n while not termina: #While para que no se cierre hasta que se indique\n for evento in pygame.event.get():\n if evento.type == pygame.QUIT:\n termina = True\n\n ventana.fill(BLANCO)\n v1 = 400 #se establecen estas variables para las coordenadas\n v2 = 0\n\n for i in range(0, 40, 1):\n v1 = v1-10 #Se acumula el valor en las variables i veces\n v2 = v2+(2*10)\n pygame.draw.rect(ventana, NEGRO, (v1, v1, v2, v2), 1) #Se dibuja el cuadrado con las variables que usamos\n\n for i in range(10, 400, 10):\n pygame.draw.circle(ventana, NEGRO, (ANCHO // 2, ALTO // 2), i, 1) #Se dibuja el círculo\n pygame.display.flip()\n reloj.tick(40)\n\n pygame.quit()\n\n#Segunda función, se dibujan líneas para cada cuadrante con un for y crea una parábola\ndef dibujarParabola():\n pygame.init() #Se inicia Pygame\n ventana = pygame.display.set_mode((ANCHO, ALTO))\n reloj = pygame.time.Clock()\n termina = False\n\n while not termina: #Ciclo while para que siga corriendo hasta que se indique lo contrario\n\n for evento in pygame.event.get():\n if evento.type == pygame.QUIT:\n termina = True\n\n ventana.fill(BLANCO)\n\n for i in range (0, 400, 10): #Se dibujan las líneas para cada cuadrante\n random = (randint(0, 255), randint(0, 255), randint(0, 255))\n pygame.draw.line(ventana, random, (ANCHO // 2 + i, ALTO // 2), (ALTO // 2, i), 1)\n pygame.draw.line(ventana, random, (ANCHO // 2 - i, ALTO // 2), (ALTO // 2, i), 1)\n pygame.draw.line(ventana, random, (ANCHO // 2, ALTO - i), (ANCHO // 2 - i, ALTO // 2), 1)\n pygame.draw.line(ventana, random, (ANCHO // 2, ALTO - i), (ANCHO // 2 + i, ALTO // 2), 1)\n\n pygame.display.flip()\n reloj.tick(40)\n\n pygame.quit()\n\n#Tercer función, se dibuja un espiral con líneas divididas\ndef dibujarEspiral():\n pygame.init()\n ventana = pygame.display.set_mode((ANCHO, ALTO))\n reloj = pygame.time.Clock()\n termina = False\n\n while not termina:\n\n for evento in pygame.event.get():\n if evento.type == pygame.QUIT:\n termina=True\n\n ventana.fill(BLANCO)\n FINDX = 400\n FINDY = 400\n\n for contador in range (0,40,1):\n contadorA = 5+(contador*20)\n contadorB = 10+(contador*20)\n contadorC = 15+(contador*20)\n contadorD = 20+(contador*20)\n\n INICIOAX = FINDX\n INICIOAY = FINDY\n FINAX = FINDX + contadorA\n FINAY = FINDY\n pygame.draw.line(ventana, NEGRO, (INICIOAX, INICIOAY), (FINAX, FINAY), 1)\n\n INICIOBX = FINAX\n INICIOBY = FINAY\n FINBX = FINAX\n FINBY = FINAY-contadorB\n pygame.draw.line(ventana, NEGRO, (INICIOBX, INICIOBY), (FINBX, FINBY), 1)\n\n INICIOCX = FINBX\n INICIOCY = FINBY\n FINCX = FINBX-contadorC\n FINCY = FINBY\n pygame.draw.line(ventana, NEGRO, (INICIOCX, INICIOCY), (FINCX, FINCY), 1)\n\n INICIODX = FINCX\n INICIODY = FINCY\n FINDX = FINCX\n FINDY = FINCY + contadorD\n pygame.draw.line(ventana, NEGRO, (INICIODX, INICIODY), (FINDX, FINDY), 1)\n\n pygame.display.flip()\n reloj.tick(40)\n\n pygame.quit()\n\n# Cuarta función, dibuja 12 círculos a partir de un for para teta\ndef dibujarCirculos():\n pygame.init() #Se inicia pygame\n ventana = pygame.display.set_mode((ALTO, ANCHO))\n reloj = pygame.time.Clock()\n termina = False\n\n while not termina: #Ciclo while para que siga corriendo hasta que se indique lo contrario\n for evento in pygame.event.get():\n if evento.type == pygame.QUIT:\n termina = True\n\n ventana.fill(BLANCO)\n\n\n r = 150 #Radio\n\n for teta in range(0, 360, 30):\n anguloRadianes = teta*(math.pi/180) #de grados a radianes\n x = int(r * math.cos(anguloRadianes))\n y = int(r * math.sin(anguloRadianes))\n\n pygame.draw.circle(ventana, NEGRO, (400 + x, 400 + y), r, 1) #Se dibujan los círculos\n\n pygame.display.flip()\n reloj.tick(40)\n pygame.quit()\n\n#Quinta función, calcula la aproximación de Pi\ndef aproximarPi(n):\n suma = 0 #Se establece la suma\n for i in range(1, n + 1):\n suma = suma + (1 / i ** 2)\n pi = (6 * (suma)) ** (1 / 2)\n\n return pi #regresa pi\n\n#Sexta función, calcula cuántos números de 4 dígitos hay divisibles entre 19\ndef calcularNumerosDivisibles():\n num_divisibles = 0\n for i in range(1000, 10000): #Para los cuatro dígitos\n if (i % 19) == 0:\n num_divisibles += 1\n\n return num_divisibles\n\n#Séptima función, realiza una pirámide con números a partir de un for\ndef calcularPiramides():\n piramide = \"\\n\"\n incremento = 0\n resultado = 0\n\n for i in range(1, 10, 1):\n incremento = int(\"1\" * i) + incremento\n resultado = (resultado * 8) + i\n piramide = piramide + (\"%d * 8 + %d = %d\\n\" % (incremento, i, resultado))\n\n\n for i in range(1, 10, 1):\n incremento = int(\"1\" * i)\n resultado = incremento * incremento\n piramide = piramide + (\"%d * %d = %d\\n\" % (incremento, incremento, resultado))\n\n return piramide #Regresa las pirámides\n\n\ndef main(): #Menú\n print (\"\"\"\n \n Tarea 5. Seleccione qué quiere hacer.\n 1. Dibujar cuadros y círculos\n 2. Dibujar espiral\n 3. Dibujar círculos\n 4. Dibujar parábolas\n 5. Aproximar Pi\n 6. Contar divisibles entre 19\n 7. Imprimir pirámides de números\n 0. Salir\n \"\"\")\n #While para que siga preguntando las opciones\n opcion = 1\n while opcion!= 0:\n print(\" \")\n opcion = int(input(\"¿Qué desea hacer? : \"), )\n if opcion < 0 or opcion > 7:\n print(\"El número %d no es una opción válida. \" % opcion)\n elif opcion == 0:\n print(\"¡Adiós!\")\n else:\n if opcion == 1:\n dibujarCuadrosYCirculos()\n if opcion == 2:\n dibujarEspiral()\n if opcion == 3:\n dibujarCirculos()\n if opcion == 4:\n dibujarParabola()\n if opcion == 5:\n num = aproximarPi(int(input(\"Ingresa un número : \")))\n print(\"La aproximación es: %.8f\" % num)\n if opcion == 6:\n num = calcularNumerosDivisibles()\n print(\"Hay %d números de 4 dígitos divisibles entre 19.\" % num)\n if opcion == 7:\n piramides = calcularPiramides()\n print(piramides)\n if opcion == 0:\n print(\"¡Adiós!\")\n\nmain()","sub_path":"Tarea5.py","file_name":"Tarea5.py","file_ext":"py","file_size_in_byte":7268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"388316296","text":"from delivery.models.asset import Asset\nfrom delivery.models.multiple_choice_option import MultipleChoiceOption\nfrom delivery.models.taxonomy_element import TaxonomyElement\nfrom delivery.models.taxonomy_term import TaxonomyTerm\nimport json\nimport sys\n\nclass ContentItem:\n def __init__(self, delivery_item_response, custom_inline_resolver=None, custom_link_resolver=None, use_inline_item_resolver='False', modular_content=None):\n content_item = delivery_item_response\n self.system = content_item['system'] \n self.id = self.system['id']\n self.name = self.system['name']\n self.codename = self.system['codename'] \n self.language = self.system['language']\n self.type = self.system['type'] \n self.elements = content_item['elements']\n self.modular_content = modular_content\n self.use_inline_item_resolver = use_inline_item_resolver\n self.custom_inline_resolver = custom_inline_resolver\n self.custom_link_resolver = custom_link_resolver\n \n\n def get_element(self, codename):\n try:\n if codename:\n return self.elements[codename]\n except Exception:\n print(f'Element with codename: {codename} does not exist.')\n sys.exit(1)\n\n def get_element_value(self, element_value_codename):\n try:\n if element_value_codename:\n element_value = self.elements[element_value_codename]['value']\n if self.use_inline_item_resolver =='True' and self.elements[element_value_codename]['type'] == 'rich_text':\n try: \n element_value = self.custom_inline_resolver.resolve(element_value, self.modular_content)\n element_value = self.custom_link_resolver.resolve(element_value, self.elements[element_value_codename]['links'])\n except:\n print(f'Custom resolver required when \"use_inline_item_resolver = True\" in the config.ini.')\n sys.exit(1)\n return element_value\n except Exception as err:\n print(err)\n print(f'Element does not have a value with codename: {element_value_codename} .')\n sys.exit(1)\n \n\n def get_assets(self, element_codename): \n try:\n if element_codename:\n assets = []\n asset_element = self.get_element(element_codename) \n for asset in asset_element['value']:\n strongly_typed_asset = Asset(asset) \n assets.append(strongly_typed_asset) \n return assets \n except Exception:\n print(f'Asset with codename: {element_codename} does not exist.')\n sys.exit(1)\n\n def get_asset(self, element_codename, asset_name): \n try:\n if element_codename:\n asset_element = self.get_element(element_codename)\n assets = self.get_assets(element_codename) \n for asset in assets: \n if asset_name in asset.name: \n return asset\n else:\n return f'Asset with name: {asset_name} does not exist in the {element_codename} element'\n except Exception:\n print(f'Asset with codename: {element_codename} does not exist.')\n sys.exit(1)\n\n def get_options(self, element_codename):\n element = self.get_element_value(element_codename)\n multiple_choice_options = []\n\n try:\n for option in element:\n strongly_typed_option = MultipleChoiceOption(option)\n multiple_choice_options.append(strongly_typed_option)\n return multiple_choice_options\n except Exception:\n print(f'No options selected for {element_codename} element.')\n sys.exit(1) \n\n def get_taxonomy_terms(self, element_codename):\n element = self.get_element_value(element_codename)\n taxonomy_terms = []\n\n try:\n for term in element:\n strongly_typed_term = TaxonomyTerm(term)\n taxonomy_terms.append(strongly_typed_term)\n return taxonomy_terms\n except Exception:\n print(f'No taxonomy terms for {element_codename} element.')\n sys.exit(1)\n \n def get_linked_items(self, element_codename):\n element = self.get_element_value(element_codename) \n linked_items = [] \n\n try: \n for linked_item_codename in element:\n linked_item = ContentItem(\n self.modular_content[linked_item_codename], \n self.custom_inline_resolver, \n self.custom_link_resolver, \n self.use_inline_item_resolver,\n self.modular_content \n )\n linked_items.append(linked_item)\n return linked_items\n except Exception:\n print(f'Linked Element with codename: {element_codename} does not exist.')\n sys.exit(1)\n \n\n\n\n\n","sub_path":"delivery/models/content_item.py","file_name":"content_item.py","file_ext":"py","file_size_in_byte":5409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"498854913","text":"from urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\nfrom time import sleep\nimport winsound as ws\nimport threading\n\ndef crawler(url):\n res = urlopen(url)\n soup = BeautifulSoup(res,\"html.parser\")\n items = soup.select('.prd-btns')\n fail = items \n while True:\n res = urlopen(url)\n soup = BeautifulSoup(res,\"html.parser\")\n items = soup.select('.prd-btns') \n print(items)\n if items != fail:\n freq = 2000\n dur = 5000\n ws.Beep(freq,dur) # winsound.Beep(frequency,duration)\n break\n sleep(1)\n\nsmartBlack = \"http://welkeepsmall.com/shop/shopdetail.html?branduid=1007193&xcode=023&mcode=002&scode=&type=X&sort=manual&cur_code=023002&GfDT=bm10W1w%3D\"\nnewSmart1 = \"http://www.welkeepsmall.com/shop/shopdetail.html?branduid=1000798&xcode=023&mcode=002&scode=&type=X&sort=manual&cur_code=023002&GfDT=bmp%2FW11G\"\nnewSmart2 = \"http://www.welkeepsmall.com/shop/shopdetail.html?branduid=1000801&xcode=023&mcode=002&scode=&type=X&sort=manual&cur_code=023002&GfDT=a213WA%3D%3D\"\nrealBlack = \"http://www.welkeepsmall.com/shop/shopdetail.html?branduid=1001308&xcode=023&mcode=003&scode=&type=X&sort=regdate&cur_code=023003&GfDT=bmp7W10%3D\"\npremium1 = \"http://www.welkeepsmall.com/shop/shopdetail.html?branduid=922816&xcode=023&mcode=001&scode=&type=X&sort=regdate&cur_code=023001&GfDT=bm54W14%3D\"\npremium2 = \"https://imnews.imbc.com/replay/2020/nwtoday/article/5664456_32531.html\"\nnow = datetime.now()\n\ncrawler(smartBlack)\n","sub_path":"maskCrawler/restockCrawler.py","file_name":"restockCrawler.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"397449579","text":"from operator import itemgetter\n\ndef takeSecond(elem):\n return elem[1]\n\nif __name__ == '__main__':\n\n l = []\n s = set()\n T = int(input())\n for _ in range(T):\n name = input()\n score = float(input())\n l.append([name, score])\n s.add(score)\n \n l.sort(key=itemgetter(1,0))\n ss = list(s)\n ss.sort(key=float)\n last = ss[1]\n i = 0\n if T > 2:\n i = 1\n\n for sub in l[i:]:\n if sub[1] == last: \n print(sub[0])\n","sub_path":"Python/Basic Data Types/NestedLists.py","file_name":"NestedLists.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"547621449","text":"#juego ahorcado\n#importar bibliotecas\nimport random\n\n#declarar variables\nTABLERO=[''' \n ___\n | | \n |\n |\n |\n ___|___''', '''\n \n ___\n | |\n O |\n |\n |\n ___|___''' , '''\n \n ___\n | |\n O |\n | |\n |\n ___|___ ''', '''\n \n ___\n | |\n O |\n | |\n / |\n ___|___ ''', '''\n \n ___\n | |\n O |\n | |\n / \\ |\n ___|___''' , '''\n \n ___\n | |\n O |\n /| |\n / \\ |\n ___|___ ''', '''\n\n ___\n | |\n O |\n /|\\ |\n / \\ |\n ___|___ ''' ]\n \n\nPALABRAS='perro gato pajaro cabra vaca cienpies python tigre leon gorila mono dinosaurio'.split()\n\n#declarar funciones\ndef fpalabra_secreta(PALABRAS):\n indice_palabra=random.randint(0,len(PALABRAS)-1)\n sec_palabra=PALABRAS[indice_palabra]\n return sec_palabra\n\ndef fmostrar_Tablero(TABLERO, letras_no, letras_ok, palabra_secreta):\n print(TABLERO[len(letras_no)])\n print()\n print(\"Letras Descartadas: \", end='')\n for letra in letras_no:\n print(letra, end=' ')\n print()\n espacios_vacios = '_' * len(palabra_secreta)\n for i in range(len(palabra_secreta)):\n if palabra_secreta[i] in letras_ok:\n espacios_vacios = espacios_vacios[:i] + palabra_secreta[i] + espacios_vacios[i+1:]\n\n for letra in espacios_vacios:\n print(letra, end=' ')\n print()\n\ndef fobtener_intento(letrasprobadas):\n while True:\n print('Adivina una letra: ')\n intento= input()\n intento=intento.lower()\n if len(intento)!=1:\n print(\"Por favor, introduce solo una letra: \")\n elif intento in letrasprobadas:\n print(\"Ya has probado esa letra\")\n elif intento not in 'abcdefghijklmnopqrstuvwxyzñç':\n print(\"Por favor, ingrese una LETRA: \")\n else:\n return intento\n\ndef fjugar_de_nuevo():\n print(\"Quieres jugar de nuevo?: \")\n return input().lower().startswith('s')\n\n\n#bloque principal\n\nprint(\"***********AHORCADO************\")\nletras_no=''\nletras_ok=''\npalabra_secreta= fpalabra_secreta(PALABRAS)\njuego_terminado=False\n\nwhile True:\n fmostrar_Tablero(TABLERO, letras_no, letras_ok, palabra_secreta)\n intento = fobtener_intento(letras_no + letras_ok)\n\n if intento in palabra_secreta:\n letras_ok = letras_ok + intento\n encontrado_todas=True\n\n for i in range(len(palabra_secreta)):\n if palabra_secreta[i] not in letras_ok:\n encontrado_todas = False\n break\n\n if encontrado_todas:\n print(\"Si, la palabra secreta es \", palabra_secreta, \"Has ganado!!\")\n juego_terminado = True\n\n else:\n letras_no = letras_no + intento\n if len(letras_no)==len(TABLERO)-1:\n fmostrar_Tablero(TABLERO, letras_ok, letras_no, palabra_secreta)\n print(\"Te has quedado sin intentos despues de \"+ str(len(letras_no)))\n juego_terminado = True\n\n if juego_terminado:\n if fjugar_de_nuevo():\n letras_no = ''\n letras_ok = ''\n juego_terminado = False\n palabra_secreta = fpalabra_secreta(PALABRAS)\n else:\n break\n\n\n\n","sub_path":"def_ahorcado_completo.py","file_name":"def_ahorcado_completo.py","file_ext":"py","file_size_in_byte":3222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"276547879","text":"import cv2\nfrom PIL import Image\nimport numpy\nfrom random import randint,choice\n\ndef build_mask(percentageWidth,percentageHeight, shape,target):\n pixelWidth = int(shape[1] * percentageWidth)\n pixelHeight = int(shape[0] * percentageHeight)\n r_x = randint(1, abs(shape[1] - pixelWidth))\n r_y = randint(1, abs(shape[0] - pixelHeight))\n\n mask = numpy.zeros((shape[0], shape[1]))\n mask[r_y:r_y + pixelHeight, r_x:r_x + pixelWidth] = 255\n Image.fromarray(mask.astype('uint8')).save(target)\n\n trial_boxes = [\n [0, 0, r_x, shape[0] - pixelHeight],\n [0, 0, shape[1] - pixelWidth, r_y],\n [r_x + pixelWidth, 0, shape[1]-pixelWidth , shape[0]-pixelHeight],\n [0, r_y + pixelHeight, shape[1]-pixelWidth, shape[0]-pixelHeight]\n ]\n\n boxes = [box for box in trial_boxes \\\n if (box[2] - box[0]) > 0 and (box[3] - box[1]) > 0]\n maxbox = 0\n maxboxid = 0\n pos = 0\n for box in trial_boxes:\n area = (box[2] - box[0]) * (box[3] - box[1])\n if area > maxbox:\n maxbox = area\n maxboxid=pos\n pos+=1\n\n box = choice(boxes) if len(boxes) > 0 else trial_boxes[maxboxid]\n\n new_position_x = randint(box[0], box[2])\n new_position_y = randint(box[1], box[3])\n return (new_position_x,new_position_y)\n\ndef transform(img,source,target,**kwargs):\n percentageWidth = float(kwargs['percentage_width'])\n percentageHeight = float(kwargs['percentage_height'])\n cv_image = numpy.array(img)\n new_position_x,new_position_y= build_mask(percentageWidth,percentageHeight,cv_image.shape,target)\n return {'paste_x': new_position_x, 'paste_y': new_position_y},None\n\ndef operation():\n return {\n 'category': 'Select',\n 'name': 'SelectRegion',\n 'description':'Mask Selector: ',\n 'software':'OpenCV',\n 'version':cv2.__version__,\n 'arguments':{'percentage_width': {'type': \"float[0.01:0.9]\", 'description':'percentage of width to crop'},\n 'percentage_height': {'type': \"float[0.01:0.9]\", 'description':'percentage of width to crop'}},\n 'transitions': [\n 'image.image'\n ]\n }\n\ndef suffix():\n return '.png'\n","sub_path":"plugins/MaskSelector/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"87566255","text":"f = open(\"12.txt\", \"r\")\n\nmap = {}\nlines = f.readlines()\ni = 0\nwhile True:\n line = lines[i]\n if line.startswith(\"cpy\"):\n x,y = line[4:].split()\n if not map.has_key(y):\n map[y] = 0\n if x.isdigit():\n map[y] = int(x)\n else:\n map[y] = map[x]\n elif line.startswith(\"inc\"):\n x = line[4:].split()[0]\n if not map.has_key(x):\n map[x] = 0\n map[x] += 1\n elif line.startswith(\"dec\"):\n x = line[4:].split()[0]\n if not map.has_key(x):\n map[x] = 0\n map[x] -= 1\n else:\n x,y = line[4:].split()\n if x.isalpha() and not map.has_key(x):\n map[x] = 0\n if not((x.isdigit() and int(x) == 0) or (not x.isdigit() and map[x] == 0)):\n i += int(y)\n continue\n i += 1\n print(map)\n\nprint(map)\n","sub_path":"2016/12.py","file_name":"12.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"627768681","text":"from parlai.agents.programr.parser.exceptions import ParserException\nfrom parlai.agents.programr.parser.pattern.matcher import EqualsMatch\nfrom parlai.agents.programr.parser.pattern.nodes.base import PatternNode\n# from parlai.agents.programr.utils.logging.ylogger import YLogger\nimport parlai.utils.logging as logging\n\nDEBUG = False\n\nclass PatternBotNode(PatternNode):\n\n def __init__(self, attribs, text, userid='*'):\n PatternNode.__init__(self, userid)\n if 'name' in attribs:\n self._property = attribs['name']\n elif 'property' in attribs:\n self._property = attribs['property']\n elif text:\n self._property = text\n else:\n raise ParserException(\"Invalid bot node, neither name or property specified as attribute or text\")\n\n def is_bot(self):\n return True\n\n @property\n def property(self):\n return self._property\n\n def to_xml(self, client_context, include_user=False):\n string = \"\"\n if include_user:\n string += '\\n'%(self.userid, self.property)\n else:\n string += '\\n' % self.property\n string += super(PatternBotNode, self).to_xml(client_context)\n string += \"\"\n return string\n\n def to_string(self, verbose=True):\n if verbose:\n return \"BOT [%s] [%s] property=[%s]\" % (self.userid, self._child_count(verbose), self.property)\n return \"BOT property=[%s]\" % (self.property)\n\n def equivalent(self, other):\n if other.is_bot():\n if self.userid == other.userid:\n if self.property == other.property:\n return True\n return False\n\n def equals(self, brain, words, word_no):\n word = words.word(word_no)\n\n if self.userid != '*':\n if self.userid != brain.userid:\n return EqualsMatch(False, word_no)\n\n if brain.properties.has_property(self.property):\n if word == brain.properties.property(self.property):\n if DEBUG:\n # YLogger.debug(brain, \"Found word [%s] as bot property\", word)\n logging.debug(f\"Found word {word} as bot property\")\n return EqualsMatch(True, word_no, word)\n\n return EqualsMatch(False, word_no)\n","sub_path":"parlai/agents/programr/parser/pattern/nodes/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":2343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"641549873","text":"from tkinter import *\nfrom tkinter import messagebox\ndef malemessage():\n messagebox.showinfo(\"Success\", \"You have selected male as an option\")\ndef femalemessage():\n messagebox.showinfo(\"Success\", \"You have selected female as an option\")\ndef click_me():\n if i.get()==1:\n malemessage()\n else:\n femalemessage()\n\nroot = Tk()\n\ni=IntVar()\nr1 = Radiobutton(root,text=\"Male\",variable=i,value=1)\nr2 = Radiobutton(root, text=\"Female\", variable=i, value=2)\nr1.pack()\nr2.pack()\nbutton=Button(root, text=\"Click Me\", command=click_me)\nbutton.pack()\n\nroot.geometry(\"300x300+120+120\")\nroot.mainloop()","sub_path":"untitled text.py","file_name":"untitled text.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"647632471","text":"import re\nimport sys\nfrom pathlib import Path\n\nfrom setuptools import find_packages, setup\n\nhere = Path(sys.argv[0] if __name__ == \"__main__\" else __file__).resolve().parent\n\n\ndef read_reqs(reqs_path: Path):\n return re.findall(\n r\"(^[^#\\n-][\\w\\[,\\]]+[-~>=<.\\w]*)\", reqs_path.read_text(), re.MULTILINE\n )\n\n\ninstall_requirements = read_reqs(\n here / \"requirements\" / \"_base.in\"\n) # WEAK requirements\n\ntest_requirements = read_reqs(\n here / \"requirements\" / \"_test.txt\"\n) # STRONG requirements\n\nreadme = Path(here / \"README.md\").read_text()\n\nsetup(\n name=\"simcore-models-library\",\n version=\"0.1.0\",\n author=\"Sylvain Anderegg (sanderegg)\",\n description=\"Core service library for simcore pydantic models\",\n python_requires=\"~=3.8\",\n classifiers=[\n \"Development Status :: 2 - Pre-Alpha\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: MIT License\",\n \"Natural Language :: English\",\n \"Programming Language :: Python :: 3.8\",\n ],\n long_description=readme,\n license=\"MIT license\",\n install_requires=install_requirements,\n packages=find_packages(where=\"src\"),\n package_dir={\"\": \"src\"},\n include_package_data=True,\n test_suite=\"tests\",\n tests_require=test_requirements,\n extras_require={\"test\": test_requirements},\n zip_safe=False,\n)\n","sub_path":"packages/models-library/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"305550456","text":"import xlsxwriter\nimport argparse\n\nfrom slackclient import SlackClient\nfrom resources.credentials import SLACK_API_TOKEN\nfrom resources.config import FIELDS_LIST\nfrom typing import List\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-f\", \"--filename\", help=\"File name for output spreadsheet\", type=str)\n\n\ndef add_to_workbook(workbook: xlsxwriter.Workbook, members: List):\n row = 1\n column = 0\n bold = workbook.add_format({'bold': 1})\n worksheet = workbook.add_worksheet('Slack')\n for key_value in members[0].keys():\n worksheet.write(0, column, key_value, bold)\n column += 1\n column = 0\n for member in members:\n for k in member:\n worksheet.write_string(row, column, member.get(k) if member.get(k) != '' else 'N/A')\n column += 1\n row += 1\n column = 0\n\n\ndef main(filename: str):\n sc = SlackClient(SLACK_API_TOKEN)\n users = sc.api_call(\"users.list\")\n members = users.get('members')\n parsed_members = []\n for member in members:\n if member.get('id') != 'USLACKBOT' and not member.get('is_bot'):\n fields = {f: v for f, v in member.items() if f in FIELDS_LIST and f != 'profile'}\n if 'profile' in FIELDS_LIST:\n fields = {**fields, **{f: v for f, v in member.get('profile').items() if f in FIELDS_LIST}}\n else:\n continue\n if fields:\n parsed_members.append(fields)\n add_to_workbook(workbook=xlsxwriter.Workbook(filename+\".xlsx\"), members=parsed_members)\n\n\nif __name__ == '__main__':\n args = parser.parse_args()\n if args.filename:\n main(args.filename)\n else:\n main('slack-userlist')\n","sub_path":"slack-userlist.py","file_name":"slack-userlist.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"6"} +{"seq_id":"464272379","text":"from typing import List\n\nfrom test_framework import generic_test\n\n\ndef buy_and_sell_stock_once(prices: List[float]) -> float:\n min_buy = float('inf')\n max_profit = float('-inf')\n\n # At any price, we\n # 1. Update the minimum price thus-far\n # 2. Update the maximum possible profit\n for i in range(len(prices)):\n min_buy = min(min_buy, prices[i])\n max_profit = max(max_profit, prices[i] - min_buy)\n\n return max_profit\n\n\nif __name__ == '__main__':\n exit(\n generic_test.generic_test_main('buy_and_sell_stock.py',\n 'buy_and_sell_stock.tsv',\n buy_and_sell_stock_once))\n","sub_path":"epi_judge_python/buy_and_sell_stock.py","file_name":"buy_and_sell_stock.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"641374283","text":"\r\nfrom __future__ import print_function\r\nimport ast\r\nimport tensorflow as tf\r\nfrom tensorflow.contrib import rnn\r\nimport csv\r\nimport pandas as pd\r\nimport numpy as np\r\nimport tools_new\r\nfrom sklearn.metrics import accuracy_score\r\nwith open('day.csv') as csvfile:\r\n readCSV = csv.reader(csvfile, delimiter=',')\r\n temps = []\r\n atemps = []\r\n hums = []\r\n windspeeds = []\r\n for row in readCSV:\r\n temp = ast.literal_eval(row[5])\r\n atemp = ast.literal_eval(row[6])\r\n hum = ast.literal_eval(row[7])\r\n windspeed = ast.literal_eval(row[8])\r\n temps.append([temp])\r\n hums.append([hum])\r\n atemps.append([hum])\r\n windspeeds.append([windspeed])\r\n\r\ndata = np.concatenate((np.asarray(temps),np.asarray(atemps),np.asarray(windspeeds)), axis=1)\r\ndata = np.asarray(data).reshape(731,3,1)\r\nlabel = np.asarray(hums)\r\ndata_train = data[:700]\r\nlabel_train = label[:700]\r\ndata_test = data[700:]\r\nlabel_test = label[700:]\r\n\r\n\r\n# Training Parameters\r\nepoch = 3500\r\nbatch_size = 700\r\ntotal = label_train.shape[0]\r\nSet = np.arange(total, dtype='i')\r\nBatch = int(total/batch_size)\r\n\r\nX = tf.placeholder(\"float\", [None, 3, 1])\r\nY = tf.placeholder(\"float\", [None, 1])\r\n\r\nweights = {'out': tf.Variable(tf.random_normal([100, 1]))}\r\nbiases = {'out': tf.Variable(tf.random_normal([1]))}\r\n\r\n\r\ndef RNN(x, weights, biases):\r\n x = tf.unstack(x, 3, 1)\r\n gru_cell = tf.contrib.rnn.GRUBlockCellV2(100)\r\n outputs, states = rnn.static_rnn(gru_cell, x, dtype=tf.float32)\r\n return tf.matmul(outputs[-1], weights['out']) + biases['out']\r\n\r\nlogits = RNN(X, weights, biases)\r\n\r\nloss_op = tf.reduce_mean(tf.reduce_sum(tf.square(logits - Y), 1)) \r\noptimizer = tf.train.AdamOptimizer(learning_rate=0.001)\r\ntrain_op = optimizer.minimize(loss_op)\r\n\r\n\r\nwith tf.Session() as sess:\r\n sess.run(tf.global_variables_initializer())\r\n\r\n for step in range(1, epoch+1):\r\n np.random.shuffle(Set)\r\n for b in range(0,Batch):\r\n batch_set = Set[b*batch_size:(b+1)*batch_size]\r\n batch_data, batch_label = tools_new.next_batch(data_train,label_train,batch_set)\r\n sess.run(train_op, feed_dict={X: batch_data, Y: batch_label})\r\n\r\n if step % 10 == 0 or step == 1:\r\n loss, pre= sess.run([loss_op,logits], feed_dict={X: batch_data, Y: batch_label})\r\n acc = 1-(np.square(np.abs(pre - batch_label)) ).mean()\r\n print ('Step:%d Batch:%d Minibatch Loss: %.8f Accuracy: %.8f ' % (step,b+1,loss,acc))\r\n print (\"Test!\")\r\n loss, pre= sess.run([loss_op,logits], feed_dict={X: data_test, Y: label_test})\r\n acc = 1-(np.square(np.abs(pre - label_test)) ).mean()\r\n print ('Loss: %.8f Accuracy: %.8f ' % (loss,acc))","sub_path":"GRU.py","file_name":"GRU.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"19"} +{"seq_id":"79679346","text":"#!/usr/bin/python3\n# encoding: utf-8 \n# @Time : 2018/1/26 0026 9:15\n# @author : zza\n# @Email : 740713651@qq.com\nimport json\nimport re\nimport time\nfrom pprint import pprint\n\nimport os\nimport requests\nfrom requests import RequestException, ReadTimeout, ConnectTimeout, HTTPError\n\n\nclass logger:\n\n @classmethod\n def info(cls, param):\n logger.show_info(\"info\", param)\n\n @classmethod\n def error(cls, err):\n logger.show_info(\"error\", err)\n\n @classmethod\n def critical(cls, param):\n logger.show_info(\"critical\", param)\n\n @classmethod\n def show_info(cls, tag, param):\n print(tag, param)\n\n\nclass RequestsHelper:\n \"\"\"对requests请求进行简单封装, 请求时遇到浏览器验证自动处理cookies\n\n :from model import get_html: 一般调用可以直接这样用\n 对requests的请求进行请求前的准备, 和请求后的异常处理\n \"\"\"\n\n def __init__(self):\n self.user_agent = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/62.0.3202.75 Safari/537.36\",\n }\n self.cookies = None\n\n def set_cookies_with_selenium(self, url, css_selector='form'):\n \"\"\" 用selenium访问传进来的url, 获取cookies, 目的是为了破解浏览器验证\n\n 服务器上跑这段代码时可能会因为phantomjs出错, 可以按照README里面的方法安装phantomjs\n 这里没有处理状态码不合格的情况\n :params url: url\n :params css_selector: css选择器, 当此此element不存在时才会认为通过了浏览器验证, 默认为from, 因为目前浏览器验证都有from\n :return: cookies\n \"\"\"\n # 需要时才导入\n logger.info(\"get cookies with selenium\")\n from selenium import webdriver\n from selenium.webdriver.common.desired_capabilities import DesiredCapabilities\n from selenium.webdriver.support import expected_conditions as EC\n from selenium.webdriver.support.ui import WebDriverWait\n from selenium.webdriver.common.by import By\n from selenium.common.exceptions import TimeoutException\n\n # 设置UA\n dcap = dict(DesiredCapabilities.PHANTOMJS)\n dcap[\"phantomjs.page.settings.userAgent\"] = self.user_agent['User-Agent']\n try:\n # 这里的service_args存在疑问\n driver = webdriver.PhantomJS(desired_capabilities=dcap, service_args=['--ignore-ssl-errors=true'])\n if not (url.startswith('http://') or url.startswith('https://')):\n # 这个判断必须放在driver赋值之后\n raise ValueError('用于获取cookies的url格式不正确')\n driver.maximize_window()\n driver.get(url)\n\n time.sleep(10)\n # 等待, 直到 css_selector定位到的元素不存在\n res = WebDriverWait(driver, 10).until(EC.invisibility_of_element_located((By.CSS_SELECTOR, css_selector)))\n if res:\n self.cookies = {item['name']: item['value'] for item in driver.get_cookies()}\n else:\n raise ValueError('此url不能通过浏览器验证')\n except ValueError:\n raise ValueError\n except TimeoutException:\n # 网络超时, 重新请求\n self.set_cookies_with_selenium(url, css_selector=css_selector)\n except Exception as err:\n logger.error(err)\n\n def get_html(self, url, retry=10, timeout=60, method='get', **kwargs):\n \"\"\" 获取网页\n\n :params url: url\n :params retry: 发生网络错误重新执行的次数 默认10\n :params timeout: 超时秒数, 默认60\n :params method: 请求方法, 默认get\n :params args: 直接传入requests.request方法\n :params kwargs: 直接传入requests.request方法\n :return: `Response`; `url` If HTTPError ; `False` If retry time > retry ;\n \"\"\"\n if retry < 0:\n return False\n try:\n if 'headers' in kwargs:\n self.user_agent.update(kwargs.pop(\"headers\"))\n resp = requests.request(url=url, headers=self.user_agent, timeout=timeout, method=method,\n cookies=self.cookies, **kwargs)\n resp.raise_for_status()\n # resp.encoding = resp.apparent_encoding\n\n if not hasattr(self, 'need_cookies'):\n # 第一次可以成功获得页面, 则不需要cookies\n self.need_cookies = False\n if resp is not None:\n return resp\n else:\n return self.get_html(url, retry - 1)\n except (ReadTimeout, ConnectTimeout, ConnectionError) as rcc:\n # 网络异常, 重新请求\n logger.error(\"网络出现异常, 重新请求: {}\\r\\n{}\".format(url, rcc))\n return self.get_html(url, retry - 1)\n except HTTPError as err:\n if not hasattr(self, 'need_cookies'):\n # 第一次访问不可以成功获得页面, 默认为需要cookies\n self.need_cookies = True\n\n logger.critical(\"状态码错误: {}\\r\\n{}\".format(url, err))\n\n if self.need_cookies:\n # 需要cookies, 重新请求\n self.temp_cookies = self.cookies\n self.set_cookies_with_selenium(url)\n\n # 当selenium请求页面之后, cookies没有改变, 说明不是cookies导致的状态码错误, 直接返回\n if self.cookies is not self.temp_cookies:\n return self.get_html(url, retry - 1)\n\n # 状态码错误, 放弃该时间循环的请求\n return url\n\n except RequestException:\n # 不能处理的异常, 抛出\n logger.error('获取页面错误')\n raise RequestException\n\n\nbase_uri = \"https://etherscan.io\"\n\n\ndef get_test(url):\n res = RequestsHelper().get_html(url).text\n have_contract = re.findall(\n \"
([\\s\\S]*)